Superposition coded symmetric relaying is a bandwidth-efficient cooperative scheme where each source node simultaneously transmits both its own "local" packet and "relay" packet that originated at its partner by adding the modulated local and relay signals in Euclidean space. This paper investigates the power allocation and outage probability of a superposition coded symmetric relaying system with finite-constellation signaling. We first derive the mutual information (MI) metrics for the system. The derived MI metrics consist of two parts: one represents the MI conveyed by the modulated signal corresponding to its own data, and the other represents the MI conveyed by the modulated signal corresponding to its partners data. Using MI based effective signal-to-noise ratio mapping technique, we attain expressions for the outage probability. Furthermore, we discuss power allocation policies that minimize the outage probability. Simulation results are presented to verify the correctness of the outage probability analysis and the benefits of the power allocation.

This paper derives a novel multiple symbols soft-decision metrics for frequency-shift keying signals which are affected by additive symmetric alpha-stable (S alpha S) noise and fading. The approximate metric, which is for the case where channel state information (phases, amplitudes, and noise dispersion parameter) is unknown is obtained based on a generalized-likelihood ratio (GLR) approach. The metric is obtained in closed form and proved to be effective. The performances of the multiple symbols soft-decision metrics are compared numerically for a turbo-coded system. The proposed multiple symbols metric provides substantial improvement over earlier single-symbol metrics.

We propose multiple-input multiple-output (MIMO)transmitter power allocation which takes dissipation in the power amplifiers into account. We derive the equations of the generalproblem with full channel state information (CSI), discuss its challenges, and supply solutions in two special cases, namely fora multiple-input single-output channel with a linear beamformer constraint, and for a parallel MIMO channel. The proposed algorithms show substantial gains in terms of rate and total consumed power compared to previous state of the art over awide range of parameter settings, and have low computational complexities.

We consider the optimization of Chase combining (CC)-based hybrid-automatic repeat request (HARQ) schemes with a limit on the maximum number of retransmissions. We formulate two optimization problems: (i) minimizing the packet drop probability (PDP) under a total average transmit power constraint, and (ii) minimizing the average transmit power under a fixed PDP constraint. Towards solving these equivalent optimization problems, we provide a closed-form expression for the outage probability of a CC-HARQ scheme. We then show that solving the optimization problems using an exact expression of the outage probability becomes complex with an increase in the maximum number of retransmissions. We propose an alternative approach in which we approximate the optimization problems by using an approximate outage probability expression and formulate the two optimization problems as two equivalent geometric programming problems (GPPs), which can be solved efficiently even for a large limit on the maximum number of retransmissions.

The results show that the optimal power allocation solution provides significant gains over the equal power allocation solution. For PDP values below 10^-3 , the optimal solution provided by the GPP approach has a performance close to that of the solution provided by solving the optimization problem exactly using nonlinear optimization techniques.

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Non-parametric belief propagation (NBP) is a well-known message passing method for cooperative localization in wireless networks. However, due to the over-counting problem in the networks with loops, NBP's convergence is not guaranteed, and its estimates are typically less accurate. One solution for this problem is non-parametric generalized belief propagation based on junction tree. However, this method is intractable in large-scale networks due to the high-complexity of the junction tree formation, and the high-dimensionality of the particles. Therefore, in this article, we propose the non-parametric generalized belief propagation based on pseudo-junction tree (NGBP-PJT). The main difference comparing with the standard method is the formation of pseudo-junction tree, which represents the approximated junction tree based on thin graph. In addition, in order to decrease the number of high-dimensional particles, we use more informative importance density function, and reduce the dimensionality of the messages. As by-product, we also propose NBP based on thin graph (NBP-TG), a cheaper variant of NBP, which runs on the same graph as NGBP-PJT. According to our simulation and experimental results, NGBP-PJT method outperforms NBP and NBP-TG in terms of accuracy, computational, and communication cost in reasonably sized networks.

We formally generalize the sign-definiteness lemma to the case of complex-valued matrices and multiple norm-bounded uncertainties. This lemma has found many applications in the study of the stability of control systems, and in the design and optimization of robust transceivers in communications. We then present three different novel applications of this lemma in the area of multi-user multiple-input multiple-output (MIMO) robust transceiver optimization. Specifically, the scenarios of interest are: (i) robust linear beamforming in an interfering adhoc network, (ii) robust design of a general relay network, including the two-way relay channel as a special case, and (iii) a half-duplex one-way relay system with multiple relays. For these networks, we formulate the design problems of minimizing the (sum) MSE of the symbol detection subject to different average power budget constraints. We show that these design problems are non-convex (with bilinear or trilinear constraints) and semiinfinite in multiple independent uncertainty matrix-valued variables. We propose a two-stage solution where in the first step the semi-infinite constraints are converted to linear matrix inequalities using the generalized signdefiniteness lemma, and in the second step, we use an iterative algorithm based on alternating convex search (ACS). Via simulations we evaluate the performance of the proposed scheme.

A fundamental aspect in performance engineering of wireless networks is optimizing the set of links that can be concurrently activated to meet given signal-to-interference-and-noise ratio (SINR) thresholds. The solution of this combinatorial problem is the key element in scheduling and cross-layer resource management. In this paper, we assume multiuser decoding receivers, which can cancel strongly interfering signals. As a result, in contrast to classical spatial reuse, links being close to each other are more likely to be active concurrently. Our focus is to gauge the gain of successive interference cancellation (SIC), as well as the simpler, yet instructive, case of parallel interference cancellation (PIC), in the context of optimal link activation. We show that both problems are NP-hard and develop compact integer linear programming formulations that enable to approach global optimality. We provide an extensive numerical performance evaluation, indicating that for low to medium SINR thresholds the improvement is quite substantial, especially with SIC, whereas for high SINR thresholds the improvement diminishes and both schemes perform equally well.

Multiple-input multiple-output (MIMO) technology is maturing and is being incorporated into emerging wireless broadband standards like long-term evolution (LTE) [1]. For example, the LTE standard allows for up to eight antenna ports at the base station. Basically, the more antennas the transmitter/receiver is equipped with, and the more degrees of freedom that the propagation channel can provide, the better the performance in terms of data rate or link reliability. More precisely, on a quasi static channel where a code word spans across only one time and frequency coherence interval, the reliability of a point-to-point MIMO link scales according to Prob(link outage) ` SNR-ntnr where nt and nr are the numbers of transmit and receive antennas, respectively, and signal-to-noise ratio is denoted by SNR. On a channel that varies rapidly as a function of time and frequency, and where circumstances permit coding across many channel coherence intervals, the achievable rate scales as min(nt, nr) log(1 + SNR). The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time [2].

We present approximations of the LLR distribution for a class of fixed-complexity soft-output MIMO detectors, such as the optimal soft detector and the soft-output via partial marginalization detector. More specifically, in a MIMO AWGN setting, we approximate the LLR distribution conditioned on the transmitted signal and the channel matrix with a Gaussian mixture model (GMM). Our main results consist of an analytical expression of the GMM model (including the number of modes and their corresponding parameters) and a proof that, in the limit of high SNR, this LLR distribution converges in probability towards a unique Gaussian distribution.

Large antenna arrays at the transmitter (TX) have recently been shown to achieve remarkable intra-cell interference suppression at low complexity. However, building large arrays in practice, would require the use of power-efficient RF amplifiers, which generally have poor linearity characteristics and hence would require the use of input signals with a very small peak-to-average power ratio (PAPR). In this paper, we consider the single-user Multiple-Input Single-Output (MISO) channel for the case where the TX antennas are constrained to transmit signals having constant envelope (CE). We show that, with per-antenna CE transmission the effective channel seen by the receiver is a SISO AWGN channel with its input constrained to lie in a doughnut-shaped region. For a broad class of fading channels, analysis of the effective doughnut channel shows that under a per-antenna CE input constraint, i) compared to an average-only total TX power constrained MISO channel, the extra total TX power required to achieve a desired information rate is small and bounded, ii) with N TX antennas an O(N) array power gain is achievable, and iii) for a desired information rate, using power-efficient amplifiers with CE inputs would require significantly less total TX power when compared to using highly linear (power-inefficient) amplifiers with high PAPR inputs.

Modern underground mines require reliable wireless communication for transmitting voice data, operating surveillance cameras, and monitoring mining equipment such as heavy vehicles. The electromagnetic characteristics of mines therefore have to be considered when determining the type of wireless technology for such critical applications. In this reported work, measurements of radio interference levels, path loss, and multipath propagation are performed in the world's now largest iron ore mine, situated in Sweden, to determine a suitable wireless technology for this mine.

We provide a coding scheme, by which an additional lonely bit can be piggybacked on a payload data packet encoded with a linear channel code, at no essential extra cost in power or bandwidth. The underlying principle is to use the additional bit to select which of two linear codes that should be used for encoding the payload packet, this way effectively creating a nonlinear code. We give a fast algorithm for detecting the additional bit, without decoding the data packet. Applications include control signaling, for example, transmission of ACK/NACK bits

The impulsiveness correction factor (ICF) has earlier been proposed as a simple measure for estimating the effect from pulsed interference on binary phase shift keying modulated digital radio systems. The advantage is that with the ICF, large errors introduced by the additive white Gaussian noise interference approximation can be reduced significantly, whereas the simplicity in interference analyses can be maintained. In this study, the ICF is further developed for other modulation schemes, such as M-phase shift keying and M -quadrature amplitude modulation and for a larger set of interference types. The results show that for one pulsed interference signal the ICF is suitable for all investigated modulation schemes. If the interference is a mixtureof modulated and pulsed signals or several pulsed signals the authors suggest and  valuate ways to extend the ICF.

In this paper, we address the design of codes which achieve modulation diversity in block fading single-input single-output (SISO) channels with signal quantization at the receiver. With an unquantized receiver, coding based on algebraic rotations is known to achieve maximum modulation coding diversity. On the other hand, with a quantized receiver, algebraic rotations may not guarantee gains in diversity. Through analysis, we propose specific rotations which result in the codewords having equidistant component-wise projections. We show that the proposed coding scheme achieves maximum modulation diversity with a low-complexity minimum distance decoder and perfect channel knowledge. Relaxing the perfect channel knowledge assumption we propose a novel channel training/estimation technique to estimate the channel. We show that our coding/training/estimation scheme and minimum distance decoding achieves an error probability performance similar to that achieved with perfect channel knowledge.

We investigate the problem of broadcasting analog sources to several users using short codes,employing several antennas at both the transmitter and the receiver, and channel-optimized quantization.Our main objective is to minimize the sum mean square error distortion. A joint multi-user encoder, aswell as a structured encoder with separate encoders for the different users, are proposed. The first encoderoutperforms the latter, which in turn offers large improvements compared to state-of-the-art, over a widerange of channel signal-to-noise ratios. Our proposed methods handle bandwidth expansion, i.e., usageof more channel than source dimensions, automatically. We also derive a lower bound on the distortion.

We compute the random coding error exponent for linear multihop amplify-and-forward (AF) relay channels. Instead of considering only the achievable rate or the error probability as a performance measure separately, the error exponent results can give us insight into the fundamental tradeoff between the information rate and communication reliability in these channels. This measure enables us to determine what codeword length that is required to achieve a given level of communication reliability at a rate below the channel capacity. We first derive a general formula for the random coding exponent of general multihop AF relay channels. Then we present a closed-form expression of a tight upper bound on the random coding error exponent for the case of Rayleigh fading. From the exponent expression, the capacity of these channels is also deduced. The effect of the number of hops on the performance of linear multihop AF relay channels from theerror exponent point of view is studied. As an application of the random coding error exponent analysis, we then find the optimal number of hops which maximizes the communication reliability (i.e., the random coding error exponent) for a given data rate. Numerical results verify our analysis, and show the tightness of the proposed bound.

We consider soft MIMO detection for the case of block fading. That is, the transmitted codeword spans over several independent channel realizations and several instances of the detection problem must be solved for each such realization. We develop methods that adaptively allocate computational resources to the detection problems of each channel realization, under a total per-codeword complexity constraint. Our main results are a formulation of the problem as a mathematical optimization problem with a well-defined objective function and constraints, and algorithms that solve this optimization problem efficiently computationally.

We present a retransmission scheme based on superposition modulation for the symmetric relaying scenario when the number of retransmissions for a data packet is limited. We consider both diversity combining based as well as code combining based retransmission schemes. Under the assumption that the receiver implements a mechanism that can use all accumulated received mutual information when decoding the message, we derive the packet error probability (PEP) expressions for the proposed retransmission scheme for the case when only one retransmission is allowed. Based on the PEP expressions derived, we provide a closed-form solution for the optimal superposition ratio (the fraction of power used for the relaying operation). Simulation results show that the proposed retransmission scheme offers significant gains compared to a retransmission scheme based on classical decode-and-forward (DF) relaying.

In this paper a family of constacyclic ternary quasi-perfect linear block codes is presented. This family extends the result presented in a previous work by the first two authors, where the existence of codes with the presented parameters was stated as an open question. The codes have a minimum distance 5 and covering radius 3.

In this article, we consider practical approaches to Costa precoding (also known as dirty paper coding). Specifically, we propose a symbol-by-symbol scheme for cancellation of interference known at the transmitter in a relay-aided downlink channel. For finite-alphabet signaling and interference, we derive the optimal (in terms of maximum mutual information) modulator under a given power constraint. A sub-optimal modulator is also proposed by formulating an optimization problem that maximizes the minimum distance of the signal constellation, and this non-convex optimization problem is approximately solved by semi-definite relaxation. For the case of binary signaling with binary interference, we obtain a closed-form solution for the sub-optimal modulator, which only suffers little performance degradation compared to the optimal modulator in the region of interest. For more general signal constellations and more general interference distributions, we propose an optimized Tomlinson-Harashima precoder (THP), which uniformly outperforms conventional THP with heuristic parameters. Bit-level simulation shows that the optimal and sub-optimal modulators can achieve significant gains over the THP benchmark as well as over non-Costa reference schemes, especially when the power of the interference is larger than the power of the noise.

We consider a slow fading n_t x n_r multiple-input multiple-output (MIMO) system with channel state information (CSI) at both the transmitter and receiver. Since communication in such scenarios is subject to block fading, reception reliability, quantified in terms of the achievable diversity gain, is of importance. A simple and well known precoding scheme is based upon the singular value decomposition (SVD) of the channel matrix, which transforms the MIMO channel into parallel subchannels. Despite having low maximum likelihood decoding (MLD) complexity, this SVD based precoding scheme provides a diversity gain which is limited by the diversity gain of the weakest subchannel. We therefore propose X- and YCodes, which improve the diversity gain of the SVD precoding scheme, by jointly coding information across a pair of subchannels (i.e., pairing subchannels). In particular, subchannels with high diversity gain are paired with those having low diversitygain. A pair of subchannels is jointly encoded using a 2 x 2 real matrix, which is fixed a priori and does not change with each channel realization. For X-Codes, these matrices are 2-dimensional rotation matrices parameterized by a single angle, while for Y-Codes, these matrices are 2-dimensional upper left triangular matrices. Also, since joint coding is performed only across a pair of subchannels, the joint MLD complexity remains low. In particular, the MLD complexity of Y-Codes is even lower than that of X-Codes, and is equivalent to symbol by symbol detection. Moreover, we propose X-, Y-Precoders with the same structure as X-, Y-Codes, but with encoding matrices adapted to each channel realization. With respect to the error probability performance, the optimal encoding matrices for X-, YCodes/ Precoders are derived analytically and numerically. Whencompared to other precoding schemes reported in the literature, it is observed that X-Codes/Precoders perform better in wellconditioned channels, while Y-Codes/Precoders perform better in ill-conditioned channels.

A new method for multiple-input multiple-output (MIMO) detection with soft-output, the partial marginalization (PM) algorithm, was recently proposed. Advantages of the method are that it is straightforward to parallelize, and that it offers a fully predictable runtime. PM trades performance for computational complexity via a user-defined parameter. In the limit of high computational complexity, the algorithm becomes the MAP demodulator. The PM algorithm also works with soft-input, but until now it has been unclear how to apply it for other modulation formats than binary phase-shift keying (BPSK) per real dimension. In this paper, we explain how to extend PM with soft-input to general signaling constellations, while maintaining the low complexity advantage of the original algorithm.

This paper proposes a new algorithm for downlink scheduling in OFDMA systems. The method maximizes the throughput, taking into account the amount of signaling needed to transmit scheduling maps to the users. A combinatorial problem is formulated and solved via a dynamic programming approach reminiscent of the Viterbi algorithm. The total computational complexity of the algorithm is upper boundedby O(K^4N) where K is the number of users that are being considered for scheduling in a frame and N is the number of resource blocks per frame.

This paper considers transmission of scheduling information in  OFDMA-based cellular communication systems such as 3GPP long-term  evolution (LTE). These systems provide efficient usage of radio  resources by allowing users to be scheduled dynamically in both  frequency and time. This requires considerable amounts of scheduling  information to be sent to the users.  The paper compares two basic transmission strategies: transmitting a  separate scheduling message to each user versus broadcasting a joint  scheduling message to all users. Different scheduling granularities  are considered, as well as different scheduling algorithms. The  schemes are evaluated in the context of the LTE downlink using  multiuser system simulations, assuming a full-buffer situation.  The results show that separate transmission of the scheduling  information requires a slightly lower overhead than joint  broadcasting, when proportional fair scheduling is employed and the  users are spread out over the cell area. The results also indicate  that the scheduling granularity standardized for LTE provides a good  trade-off between scheduling granularity and overhead.

Tensor modeling and algorithms for computing various tensor decompositions (the Tucker/HOSVD and CP decompositions, as discussed here, most notably) constitute a very active research area in mathematics. Most of this research has been driven by applications. There is also much software available, including MATLAB toolboxes [4]. The objective of this lecture has been to provide an accessible introduction to state of the art in the field, written for a signal processing audience. We believe that there is good potential to find further applications of tensor modeling techniques in the signal processing field.

We study the achievable rate region of the multiple-input  single-output (MISO) interference channel (IFC), under the  assumption that all receivers treat the interference as additive  Gaussian noise. We assume the case of two users, and that the  channel state information (CSI) is only partially known at the  transmitters. Our main result is a characterization of  Pareto-optimal transmit strategies, for channel matrices that  satisfy a certain technical condition. Numerical examples are  provided to illustrate the theoretical results.

The maximum-likelihood multiple-symbol differential detector (ML-MSDD) has better bit-error-rate performance than many other detectors for differential modulation. Unfortunately, the computational complexity of ML-MSDD quickly becomes prohibitive as the observation window size grows. While low-complexity MSDD algorithms for the time-invariant Rayleigh fading channel have been considered before, there is a need for low-complexity MSDD algorithms for general time-varying Rayleigh fading channels. A polynomial-time complexity approach called semi-definite relaxation (SDR) is employed to achieve differential detection with near maximum-likelihood (ML) performance. The proposed SDR quasi-maximum-likelihood (QML) multiple-symbol differential detection (SDR-QML-MSDD) is efficient in that its complexity is polynomial in the observation window size, even in the worst case, while it exhibits almost the same performance as ML-MSDD does.

The goal of this lecture has been to provide an overview of approaches, in the communications receiver context. Which method is the best in practice? This depends much on the purpose of solving : what error rate can be tolerated, what is the ultimate measure of performance (e.g., frame-error-rate, worst-case complexity, or average complexity), and what computational platform is used. Additionally, the bits in s may be part of a larger code word and different s vectors in that code word may either see the same H (slow fading) or many different realizations of H (fast fading). This complicates the picture, because notions that are important in slow fading (such as spatial diversity) are less important in fast fading, where diversity is provided anyway by time variations. Detection for MIMO has been an active field for more than ten years, and this research will probably continue for some time.

Cognitive radio is a new concept of reusing a licensed spectrum in an unlicensed manner. The motivation for cognitive radio is various measurements of spectrum utilization, that generally show unused resources in frequency, time and space. These "spectrum holes" could be exploited by cognitive radios. Some studies suggest that the spectrum is extremely underutilized, and that these spectrum holes could provide ten times the capacity of all existing wireless devices together. The spectrum could be reused either during time periods where the primary system is not active, or in geographical positions where the primary system is not operating. In this paper, we deal primarily with the concept of geographical reuse, in a frequency-planned primary network. We perform an analysis of the potential for communication in a geographical spectrum hole, and in particular the achievable sum-rate for a secondary network, to some order of magnitude. Simulation results show that a substantial sum-rate could be achieved if the secondary users communicate over small distances. For a small number of secondary links, the sum-rate increases linearly with the number of links. However, the spectrum hole gets saturated quite fast, due to interference caused by the secondary users. A spectrum hole may look large, but it disappears as soon as someone starts using it.

Maximum-likelihood detection in MIMO communications amounts to solving a least-squares problem with a constellation (alphabet) constraint. One popular method that can be used to solve this problem is sphere decoding. We show in this letter that by employing a simple stopping criterion, it is possible to significantly reduce the complexity of sphere decoding over a wide range of SNRs, without a noticeable performance degradation. Specifically, simulation results demonstrate that a 10%-90% reduction of the average complexity could be achieved.

This paper derives new soft-decision metrics for coded orthogonal signaling in impulsive noise, more specifically symmetric-stable noise. For the case of a known channel amplitude and known noise dispersion, exact metrics are derived both for Cauchy and Gaussian noise. For the case that the channel amplitude or the dispersion is unknown, approximate metrics are obtained in closed-form based on a generalized-likelihood ratio approach. The performance of the new metrics is compared numerically for a turbo-coded system, and the sensitivity to side information of the optimum receiver for Cauchy noise is considered. The gain that can be achieved by using a properly chosen decoding metric is quantified, and it is shown that this gain is significant. The application of the results to frequency hopping ad hoc networks is also discussed.

A simple structure to exploit both long-term and partial short-term channel state information at the transmitter (CSIT) over a family of correlated multiple-antenna channels is proposed. Partial short-term CSIT in the form of a weighting matrix is combined with a unitary transformation based on the long-term channel statistics. The heavily quantized feedback link is directly optimized to maximize the expected achievable rate under different power constraints, using vector quantization and convex optimization techniques on a sample channel distribution. Robustness against errors in the feedback link is also pursued with tools in channel optimized vector quantization. Simulations indicate the benefits of the proposed scheme.

We consider the problem of coordinating two competingmultiple-antenna wireless systems (operators) that operatein the same spectral band. We formulate a rate region whichis achievable by scalar coding followed by power allocationand beamforming. We show that all interesting points on thePareto boundary correspond to transmit strategies where bothsystems use the maximum available power. We then argue thatthere is a fundamental need for base station cooperation whenperforming spectrum sharing with multiple transmit antennas.More precisely, we show that if the systems do not cooperate,there is a unique Nash equilibrium which is inefficient in the sensethat the achievable rate is bounded by a constant, regardless ofthe available transmit power. An extension of this result to thecase where the receivers use successive interference cancellation(SIC) is also provided.Next we model the problem of agreeing on beamformingvectors as a non-transferable utility (NTU) cooperative gametheoreticproblem, with the two operators as players. Specificallywe compute numerically the Nash bargaining solution, which isa likely resolution of the resource conflict assuming that theplayers are rational. Numerical experiments indicate that selfishbut cooperating operators may achieve a performance which isclose to the maximum-sum-rate bound.

We present a symbol-by-symbol approach to the problem of canceling known interference at the transmitter in a communication system. In the envisioned system, the modulator maps an information symbol (taken from a finite alphabet) and an interference symbol (from the complex field) onto a transmitted constellation point. Our scheme is based on joint optimization of a modulator and demodulator, subject to a constraint on the average transmit power. The demodulator picks the information symbol (as a function of the received symbol) that minimizes the average error probability. We emphasize that our focus is on transmission in a single (complex) dimension, and hence the proposed technique is a “modulation” rather than a “coding” scheme. We illustrate that the new scheme outperforms Tomlinson–Harashima precoding, which is a classical but suboptimal solution to the one-dimensional known-interference precoding problem. In our simulations, the new approach is able to perform close to the no-interference bound.

In this paper we present a family of ternary quasi-perfect BCH codes. These codes are of minimum distance 5 and covering radius 3. The first member of this family is the ternary quadratic-residue code of length 13.

List decoding of block codes is an alternative approach to the decoding problem with appealing qualities. The fairly recent development of efficient algorithms for list decoding of Reed-Solomon codes spur new fuel to the study of this decoding strategy. In this paper we give a weight-based characterization of the set of correctable error patterns under list-of-2 decoding of (tau, 2)-list-decodable linear codes with known weight distribution. We apply our characterization of the set of correctable error patterns to a few codes in a family of low-rate list-of-2 decodable Reed-Solomon codes. We study the increase in error-correction performance obtained in a symmetric AWGN channel by using list-of-2 decoding instead of traditional decoding for these codes. Some simulation results for list-of-2 decoding on QAM channels using the Guruswami-Sudan algorithm for decoding of Reed-Solomon codes are also presented.

We derive the optimal receiver for RAKE diversity combining on channels with a sparse impulse response. The receiver is based on the Bayesian philosophy and thus it requires the knowledge of certain a priori parameters. However, we also derive an empirical Bayesian version of our receiver, which does not require any a priori knowledge, nor the choice of any user parameters. We show that both versions of our detector can outperform a classical training-based maximum-ratio-combining detector.

Target tracking in wireless sensor networks is traditionally achieved by localization and tracking (LAT), where the sensors are first localized, and in a later stage the target is tracked. This approach is sub-optimal since the sensor-target observations are not used to refine the position estimates of the sensors. In contrast, simultaneous localization and tracking (SLAT) uses these observations to track the target while simultaneously localizing the sensors. In this paper, we propose a novel centralized SLAT method based on real-time nonparametric belief propagation, which has nearly the same complexity and the same communication cost as LAT, and can provide both sensors' and target's estimated distributions in non-Gaussian form.

Low density parity check (LDPC) codes can be decoded with a variety of decoding algorithms, offering a trade-off in terms of complexity, latency, and performance. We describe seven distinct LDPC decoders and provide a performance comparison for a practical regular LDPC code. Our simulations indicate that the best performance/latency trade-off is achieved by one version ofthe reweighted max-product decoder. When latency is not an issue, the traditional sum-product decoder yields the best performance.

We propose improvements for the physical uplink control channel (PUCCH) format 2 control signaling in 3GPP-LTE systems. These improvements can be useful for low-cost UE design and optimization planned for the future LTE releases. In the proposed method, instead of repeating a single QPSK symbol across the 12 subcarriers in each OFDM symbol of a resource block as done in the current release of the standard, we pick two QPSK symbols from two independent resource blocks and repeat them across 6 subcarriers each. The proposed method has a performance gain of about 5.5 dB and 1.85 dB over the conventional method with one and two receiving antennas at the base station, respectively. These gains can be achieved without the use of any additional transmission power, time-frequency resources or receiver complexity.

The cell-specific QPSK sequences specified in the standard for PUCCH transmission are chosen according to the conventional repetition across the 12 subcarriers, hence we suggest new cell-specific QPSK sequences which minimize the peak-to-average-power ratio (PAPR) of the PUCCH signal with the proposed method.

We consider the uplink of multicell multiuser MIMO(MU-MIMO) systems with very large antenna arrays at the base station (BS). We assume that the BS estimates the channel through uplink training, and then uses this channel estimate to detect the signals transmitted from a multiplicity of autonomous users in its cell. By taking the correlation between the channel estimate and the interference from other cells into account, we propose an optimal linear receiver (OLR) which maximizes the received signal-to-interference-plus-noise (SINR). Analytical approximations of the exact and lower bound on the achievable rate are then derived. The bound is very tight, especially at large number of BS antennas. We show that at low SINR, maximal-ratio combing (MRC) receiver performs as well as OLR, however at high SINR, OLR outperforms MRC. Compared with the typical minimum mean-square error receiver, our proposed OLR improves systematically the system performance, especially when the interference is large.

In this paper, we study ACK/NACK messages detection in the LTE physical uplink control channel (PUCCH) with multiple receive antennas. The LTE PUCCH is typically characterized by high interference variability due to severe inter-user interferences and slot-level frequency hopping. We present detection methods applicable for the cases when the noise variances at the receiver are known and unknown. Noise here may comprise both thermal noise and interference. The proposed detection technique is based on the generalized likelihood-ratio test (GLRT) paradigm. Simulation results show that GLRT-based detector offers a significant gain over the training-based maximum-likelihood detector when the noise variances in two slots are different and unknown. For the case when the noise variances at the receiver are known, the GLRT-based detector has nearly the same performance as the training-based maximum-likelihood detector.

Wireless communication is expected to improve the safety and the productivity in underground tunnels for industrial use. However the multiple shapes and structures of tunnels affect wireless communication characteristics in terms of signal propagation which is significantly different from terrestrial environments. This paper presents comprehensive broadband measurements and simulations of multipath propagation and path loss in two underground environments. The results can be used in the development of new communication systems in tunnels that provide industrial services.

To evaluate the performance degradation of a digital communication system, the Middleton’s Class A noise model has shown to be a useful model for real interference sources. By varying the model parameter A and Г, interference from pure Gaussian to a highly impulsive interference can be represented. The digital communication system performance is often determined by using the well-known Gaussian approximation (GA) to determine the bit error probability (BEP) of the system. The GA-approximation has been shown to severely underestimate the BEP for impulsive interference and therefore an earlier proposal, based on a so-called Impulsiveness Correction Factor (ICF) is used to correct the GA-approximation. In earlier work, the ICF has been extended to the case in which the interference signal consists of multiple interference signals. However, a limiting factor in that case is that one of the interference signals has to be dominant in power. In this paper, a simple but useful method is proposed to derive the ICF for a sum of equally distributed Class A-interference signals when none of the signals are dominant in power. This method is a complement to the earlier method and extends the usefulness of the ICF in interference analyses to a wider area of interference signals.

The effect of oscillator phase noise on the sumrate performance of a frequency selective multi-user multipleinputmultiple-output (MU-MIMO) uplink channel is studiedunder imperfect channel state information. A maximum ratiocombining detection strategy is employed by the base station(BS) (having a large antenna array of M elements), and ananalytical expression of a lower bound on the sum capacity ofthe system is derived. It is shown that an array power gainof O(pM) is achievable. It is also observed that phase noiseeffectively limits the fraction of the time used for informationtransmission and the number of users in the system. Finally itis concluded that, phase noise degrades the performance butdoes not eliminate the fundamental gains of a Large ScaleAntenna System (LSAS), i.e., power efficiency and high sumrate performance with low complexity receiver processing.

We consider downlink cellular multi-user communication betweena base station (BS) having $N$ antennas and $M$ single-antenna users, i.e.,an $N /times M$ Gaussian Broadcast Channel (GBC).Under an average only total transmit power constraint (APC), large antenna arrays at the BS (having tens to a few hundred antennas) have been recently shown to achieve remarkablemulti-user interference (MUI) suppression with simple precoding techniques.However, building large arrays in practice, would require cheap/power-efficient Radio-Frequency(RF) electronic components. The type of transmitted signal that facilitates the use ofmost power-efficient RF components is a constant envelope (CE) signal (i.e.,the amplitude of the signal transmitted from each antenna is constant for every channel useand every channel realization).Under certain mild channel conditions (including i.i.d. fading), we analytically show that,even under the stringent per-antenna CE transmission constraint (compared to APC),MUI suppression can still be achieved with large antenna arrays. Our analysis also reveals that,with a fixed $M$ and increasing $N$, the total transmitted power can be reduced while maintaining a constant signal-to-interference-noise-ratio (SINR) level at each user.We also propose a novel low-complexity CE precoding scheme, using which,we confirm our analytical observations for the i.i.d. Rayleigh fading channel, through Monte-Carlo simulations.Simulation of the information sum-rate under the per-antenna CE constraint, shows that, for a fixed $M$ and a fixed desired sum-rate, the required total transmit powerdecreases linearly with increasing $N$, i.e.,an $O(N)$ array power gain.Also, in terms of the total transmit power required to achieve a fixed desired information sum-rate,despite the stringent per-antenna CE constraint, the proposed CE precoding scheme performs close to the GBC sum-capacity (under APC) achieving scheme.

This paper presents some results from of the FP7 ICT-LOLA (achieving LOw LAtency in wireless communications) project on the design of clusterized wireless mesh network based on 3GPP LTE. First, we focus on the general MAC/PHY structure of the clusterized mesh network based on 3GPP LTE. Then, the concept of virtual link is presented for inter-cluster communications combining MAC layer forwarding, hybrid automatic repeat request (HARQ) and cooperative communications with Decode and Forward (DF). The goal of a virtual link is to enable low latency data transfer in inter-cluster communications. The virtual link solution is studied by simulations thanks to OpenAirInterface which integrates LTE MAC and PHY layer procedures, as well as adaptations needed for the LOLA wireless mesh network. Simulation results show that the proposed distributed solution smoothly adapts to the link conditions. A loss in throughput efficiency is the price to be paid in certain configurations for the distributed operation of the virtual link. Nevertheless, the technique helps in reducing the average number of transmissions thus contributing to improve the latency of the system.

We study non-orthogonal spectrum sharing to determine under what circumstances operators can gain by such sharing. To model the spectrum sharing, we use the multiple-input single-output (MISO) interference channel (IC) assuming that the operators transmit in the same band. For the baseline scenario of no sharing, we use the MISO broadcast channel (BC) assuming that the operators transmit in disjunct bands. For both the IC and BC, we give achievable (lower) and upper bounds on the maximum sum-rate. While these bounds are well-known we also propose a new fast algorithm for finding a lower bound on the sum-rate of the BC using linear beamforming.

We use the bounds to numerically evaluate the potential gain of non-orthogonal spectrum sharing. In this study we assume that the operators efficiently utilize all their spatial degrees of freedom. We will see that the gains from spectrum sharing under these circumstances are limited.

The distributed Alamouti space-time code in two-way fixed gain amplify-and-forward (AF) relay is proposed in this paper. In particular, closed-form expressions for approximated ergodic sum-rate and exact pairwise error probability (PWEP) are derived for Nakagami-m fading channels. To reveal further insights into array and diversity gains, an asymptotic PWEPis also obtained. Finally, numerical results are provided to corroborate the proposed theoretical analysis.

We consider the joint allocation of receiver, bit, and power to subcarriers in the downlink of multicell orthogonal frequency-division multiple-access (OFDMA) networks. Assuming that the cells share the entire bandwidth and that the rates are discrete, we formulate the joint allocation problem as a nonlinear mixed integer program (MIP), which however has exponential worst-case complexity. We capitalize on the capability of the receivers to measure the interference-plus-noise on every subcarrier and decompose the joint problem into a set of smaller-scale linear MIPs solved by individual base stations. Accordingly, we propose a distributed algorithm with linear complexity, in which the base stations participate in the problem solution in a round-robin manner. Simulation results demonstrate the effectiveness of the proposed algorithm in comparison with the iterative waterfilling algorithm and the successive optimal solution, by means of standard branch-and-cut solvers, of the individual MIPs.

We consider the uplink of a multicell multiuser MIMO system. The data detection is done by using the zero-forcing (ZF) technique, assuming the base station has perfect channel state information. We derive an exact closed-form expression for the uplink rate per user. We further study the asymptotic performance of the system. We show that, at high signal-to-noise ratio, the system is interference-limited and hence, we cannot improve the system performance by increasing the transmit power at each user. Instead, by increasing the number of base station antennas, the effects of interference and noise can be reduced, thereby improving the system performance. In particular, we show that, with very large antenna arrays at the base station, the transmit power of each user can be made inversely proportional to the number of base station antennas while maintaining a desired quality-of-service. Numerical results verify our analysis.

In this paper, we deal with the problem of joint optimization of the source precoder, the relay beamformer and the destination equalizer in a nonregenerative relay network with only a partial knowledge of the Channel State Information (CSI). We model the partial CSI using a deterministic norm bounded error model, and we use a system-wide mean square error performance measure which is constrained based on the transmit power regulations for both source and relay nodes. Most conventional designs employ the average performance optimization, however, we solve this problem from a worst-case design perspective. The original problem formulation is a semi-infinite trilinear optimization problem which is not convex. To solve this problem we extend the existing theories to deal with the constraints which are semi-infinite in different independent complex matrix variables. We show that the equivalent approximate problem is a set of linear matrix inequalities, that can be solved iteratively. Finally simulation results assess the performance of the proposed scheme.

In this paper, we deal with the problem of joint optimization of the source precoder, the relay beamformer and the destination equalizer in a nonregenerative relay network with only a partial knowledge of the Channel State Information (CSI).

We model the partial CSI using a deterministic norm bounded error model, and we use a system-wide mean square error performance measure which is constrained based on the transmit power regulations for both source and relay nodes.

Most conventional designs employ the average performance optimization, however, we solve this problem from a worst-case design perspective.

The original problem formulation is a semi-infinite trilinear optimization problem which is not convex.

To solve this problem we extend the existing theories to deal with the constraints which are semi-infinite in different independent complex matrix variables.

We show that the equivalent approximate problem is a set of linear matrix inequalities, that can be solved iteratively.

Finally simulation results assess the performance of the proposed scheme.

We study the two-user multiple-input single-output (MISO) interference channel for the scenario where the transmitters have perfect channel state information and employ single-stream beamforming. We assume that the receivers are able of decoding the data from both transmitters. Hence, the signal from the interfering transmitter might be decoded, treating the desired signal as noise, and subtracted from the received signal. We propose an efficient method for finding the Pareto boundary of the corresponding achievable rate region. This method has a complexity which is constant in the number of transmit antennas.

A multiplicity of autonomous terminals simultaneously transmits data streams to a compact array of antennas. The array uses imperfect channel-state information derived from transmitted pilots to extract the individual data streams. The power radiated by the terminals can be made inversely proportional to the square-root of the number of base station antennas with no reduction in performance. In contrast if perfect channel-state information were available the power could be made inversely proportional to the number of antennas. A maximum-ratio combining receiver normally performs worse than a zero-forcing receiver. However as power levels are reduced, the cross-talk introduced by the inferior maximum-ratio receiver eventually falls below the noise level and this simple receiver becomes a viable option.

In this paper, we study an achievable rate region of the two-user multiple-input single-output (MISO) interference channel. We find the transmit beamforming vectors that achieve Pareto-optimal points. We do so, by deriving a sufficient condition for Pareto optimality. Given the beamforming vector of one transmitter, this condition enables us to determine the beamforming vector of the other transmitter that forms a Pareto-optimal pair. The latter can be done in closed form by solving a cubic equation. The result is validated against state-of-the-art methods via numerical illustrations.

We study the nonorthogonal spectrum sharing scenario, in which coexisting transmitters of different operators concurrently utilize the same frequency band. The transmitters (and possibly the receivers) are equipped with multiple antennas. In EU FP7 project SAPHYRE, we propose various transmit beamforming techniques to manage the interference in the physical layer. We show that operating points that are more efficient than orthogonal spectrum sharing can be reached, when the operators cooperatively design their beamforming vectors to minimize the interference.

Dynamic OFDMA has been recognized as a promising technique forimproving the performance of future wireless cellularsystems. However, this potential performance improvement comes at thecost of additional signaling overhead, which can have a non-negligibleeffect on the system efficiency.  In this paper, we propose a newmethod for optimizing the frame length for the downlink in OFDMAsystems. The method maximizes the system efficiency by taking intoaccount both the channel conditions and the amount of signalingoverhead needed to deliver scheduling maps to the users. We formulatethe frame length optimization problem mathematically. By exploitingthe structure of this problem, we develop an algorithm that solves asequence of dynamic programming problems. Simulation results revealsome insight into fundamental limitations as well as provide guidelinesfor the design of dynamic OFDMA systems.

We derive a Gaussian approximation of the LLR distribution  conditioned on the transmitted signal and the channel matrix for the  soft-output via partial marginalization MIMO detector. This detector  performs exact ML as a special case. Our main results consist of  discussing the operational meaning of this approximation and a proof  that, in the limit of high SNR, the LLR distribution of interest  converges in probability towards a Gaussian distribution.

In this paper, we create a unified framework for spectrum sensing of signals which have covariance matrices with known eigenvalue multiplicities. We derive the generalized likelihood-ratio test (GLRT) for this problem, with arbitrary eigenvalue multiplicities under both hypotheses. We also show a number of applications to spectrum sensing for cognitive radio and show that the GLRT for these applications, of which some are already known, are special cases of the general result.

The use of relays is a promising way to extend coverage and increase rates in Long Term Evolution Advanced (LTE Advanced) networks. Performing a transmission in several hops (BS-relay & relay-user) requires more radio resources than a single hop transmission. This paper analyses how the available radio resource should be allocated between relays and users in order to maximize throughput and/or fairness. Time and frequency multiplexed backhaul is investigated under some basic assumptions on the division of the radio resource. It is shown that the system will typically be backhaul limited and that the two ways of multiplexing will perform equally when fairness is taken into account. The analysis results in throughput/fairness suboptimal solutions, dependant on the number of relays per cell, spectral efficiencies and fraction of users served by relays. The analysis is verified by simulations, performed using a MATLAB based network simulator.

Physical resource sharing between wireless operators and service providers is necessary in order to support efficient, competitive, and innovative wireless communication markets. By sharing resources, such as spectrum or infrastructure, which are usually exclusively allocated interference is created on the physical layer. Therefore, the economic gains, regulatory overhead, and engineering efforts need to be addressed by a consolidated cross-layer approach. This paper describes briefly the approach taken by the EU FP7 project SAPHYRE.

We consider the problem of sum rate maximization with joint resource allocation and interference mitigation by multiantenna processing in wireless networks. The denominators in the users' signal-to-interference-plus-noise expressions are assumed to be representable in the form of matrix-based, concave interference functions. It is shown that the problem of interest for this system model can be readily rewritten as a minimization of a difference of convex functions. Based on this representation, an iterative algorithm with guaranteed convergence is employed to calculate possibly suboptimal solutions of the main problem, which is known to be NP-hard. The proposed technique enables achieving a large portion of the globally optimal sum rate. It is also very efficient and rather general in terms of allowing interesting extensions, compared with the related results from the literature.

This paper considers the control signaling on the downlink in wireless multiple access systems, with focus on the part of the control signaling that carries information on the user's time/frequency scheduling assignments. A new idea is presented to reduce the amount of channel resources needed for this signaling. The idea is to exploit the fact that provided that only one single user is scheduled on each channel resource, then the different users' scheduling assignments are correlated. This correlation can be exploited by encoding the scheduling information differentially. In order to recover the scheduling information, a user must then decode the scheduling information of some of the others. This is possible, because on the downlink, all users can hear the transmission by the base station so that users with a high SNR may decode the control signaling sent to users with a lower SNR. We present a practical scheme to exploit this idea. Both analytical analysis and numerical examples illustrate that the proposed technique can provide a substantial reduction in signaling traffic.

We study the uplink control signaling in 3GPP-Long Term Evolution (LTE) systems. Specifically, we propose a precoding method that uses complex-field coding (CFC) to improve the performance of the PUCCH format 2 control signaling. In the case of perfect channel state information (CSI) at the receiver and with a single receive antenna, the proposed method offers significant gains compared to the coding currently used in 3GPP-LTE. However the gains are marginal with two receive antennas. In order to examine the impact of channel estimation errors, we also derive the optimal detector for the case of imperfect receiver CSI, both for conventional coding and for the proposed CFC method.

In this paper, we study retransmission strategies for the symmetric relaying scenario when the number of retransmissions for a data packet is limited. We propose a retransmission scheme based on superposition modulation and give the exact packet drop probability expression for it when only one retransmission is allowed. We compare the performance of the proposed retransmission scheme with retransmission schemes based on classical decode-and-forward (DF) relaying and non-cooperative transmission. When only two retransmissions are allowed, the proposed scheme has a performance gain of 3 dB in SNR over the retransmission scheme based on classical DF relaying. Through simulations, we also study the effect of varying the superposition ratio for the retransmissions.

We present a survey of state-of-the-art algorithms for spectrum  sensing in cognitive radio. The algorithms discussed range from  energy detection to sophisticated feature detectors. The feature  detectors that we present all have in common that they exploit some  known structure of the transmitted signal.  In particular we treat  detectors that exploit cyclostationarity properties of the signal,  and detectors that exploit a known eigenvalue structure of the  signal covariance matrix.  We also consider cooperative  detection. Specifically we present data fusion rules for soft and  hard combining, and discuss the energy efficiency of several  different sensing, sleeping and censoring schemes in detail.

The computational complexity of optimum decoding for an orthogonal space-time block code is quantified. Four equivalent techniques of optimum decoding which have the same computational complexity are specified. Modifications to the basic formulation in special cases are calculated and illustrated by means of examples.

We derive detection metrics for soft-output diversity combining for  the case of imperfect channel state information at the receiver.  We  treat in particular the case when the noise variance at the receiver  is unknown.  We contrast conventional training-based methods to a  detector based on the generalized likelihood-ratio (GLR) test  paradigm.  We study the performance of the detectors via EXIT chart  analysis and via simulations of LDPC coded transmission over a fast  Rayleigh fading channel.  The results show that the GLR receivers  can significantly outperform the conventional detectors.

We consider soft MIMO detection for the case of block fading. That is, the transmitted codeword spans over several independent channel realizations and several instances of the detection problem must be solved for each such realization. We develop methods that adaptively allocate the computational resources to the detection problems of each channel realization, under a total per-codeword complexity constraint. Our main results are a formulation of the problem as a mathematical optimization problem and a greedy algorithm to approximate it in a computationally feasible fashion.

This paper deals with the problem of discriminating samples that contain only noise from samples that contain a signal embedded in noise. The focus is on the case when the variance of the noise is unknown. We derive the optimal soft decision detector using a Bayesian approach. The complexity of this optimal detector grows exponentially with the number of observations and as a remedy, we propose a number of approximations to it. The problem under study is a fundamental one and it has applications in signal denoising, anomaly detection, and spectrum sensing for cognitive radio. We illustrate the results in the context of the latter.

We consider the two-user multiple-input single output (MISO) interference channel (IFC) and assume that the receivers treat the interference as additive Gaussian noise. We study the rates that can be achieved in a slow-fading scenario, allowing an outage probability. We introduce three definitions for the outage region of the IFC. The definitions differ on whether the rates are declared in outage jointly or individually and whether there is perfect or statistical information about the channels. Even for the broadcast and the multiple-access channels, which are special cases of the IFC, there exist several definitions of the outage rate regions. We provide interpretations of the definitions and compare the corresponding regions via numerical simulations. Also, we discuss methods for finding the regions. This includes a characterization of the beamforming strategies, which are optimal in the sense that achieve rate pairs on the Pareto boundary of the outage rate region.

We consider the problem of finding Pareto-optimal (PO) operating points for the multiple-input single-output (MISO) interference channel when the transmitters have statistical (covariance) channel knowledge. We devise a computationally efficient algorithm, based on semidefinite relaxation, to compute the PO rates and the enabling beamforming vectors. We illustrate the effectiveness of our algorithm by a numerical example.

Control signaling strategies for scheduling informationin cellular OFDM systems are studied. A single-cell multiusersystem model is formulated that provides system capacityestimates accounting for the signaling overhead. Differentscheduling granularities are considered, including the one usedin the specifications for the 3G Long Term Evolution (LTE).A greedy scheduling method is assumed, where each resourceis assigned to the user for which it can support the highestnumber of bits. The simulation results indicate that the costof control signaling does not outweigh the scheduling gain, whencompared with a simple round-robin scheme that does not needsignaling of scheduling information. Furthermore, in the studiedscenario, joint coding and signaling of scheduling informationover all selected users is found to be superior to separate codingand signaling for each user. The results also indicate that thescheduling granularity used for LTE provides better performancethan the full granularity.

This paper studies the achievable rate region of the two-use rmultiple antenna broadcast channel with linear precoding. It consists of two parts: In the first part, the set of beamformingvectors which achieve points on the Pareto boundary are characterized by a single real valued parameter per user. It is shown that a certain linear combination of the zero-forcing (ZF) beamforming vector and the maximum ratio-transmission (MRT) beamforming vector can achieve all Pareto boundary points of the rate region. In the second part, an iterative algorithm is proposed to compute the maximum sum-rate. Based on the characterization of the Pareto boundary, we develop an outer approximation algorithm for monotonic optimization using polyblocks. Numerical examples provide comparisons between rate and capacity regions as well as between the maximum sum-rate and maximum capacity operating points.

In a multi-user MIMO downlink where the base station has only estimates of the channels of the users, the sum-rate of multi-user beamforming saturates at high SNR. However, this is not the case for single-user beamforming. We propose a low-complexity user scheduling algorithm that selects the number of active users based on a closed-form approximation of the average sum-rate, and in particular does not add multiple users in the single-user optimality range. In order to develop this algorithm we derive the expected value of the rate of zero-forcing beamforming and MMSE beamforming with estimated channels and modify the greedy user selection accordingly. The gain of the proposed method is shown in numerical simulations.

We study the achievable rate region of the multipleinputsingle-output (MISO) interference channel (IFC), under theassumption that all receivers treat the interference as additiveGaussian noise. The main result is a parameterization of thePareto boundary for the case where there are two users, thetransmitters use beamforming, and the channel state information(CSI) is only partially known at the transmitters. The result isillustrated by two numerical results.

Cognitive radio describes a promising concept for improving the utilization of radio spectrum. It allows secondary users to access licensed radio spectrum in an opportunistic fashion, given that the quality of service for primary users is maintained. In this scenario, the detection of unused radio spectrum is the main obstacle: it requires the recognition of lowpower signals in a noisy environment. Therefore, we consider the case where cognitive users are supported by a wireless sensor network (WSN) providing beliefs on the event that the primary user is active. The beliefs provided by the WSN nodes are then combined by the cognitive radio in order to make a final decision. Due to limitations in complexity, power, and bandwidth, the WSN nodes apply the estimate-and-forward protocol to communicate their beliefs and to relay the beliefs of adjacent nodes. For the case where the WSN is subject to noisy communication links, we derive and analyze the optimal receiver which provides the cognitive radio with log-likelihood ratios for the estimated and forwarded hypotheses. The performance of the optimal receiver is compared to the performance of sub-optimal receivers.

This paper presents a new approach to soft demodulation for MIMO channels. The proposed method is an approximation to the exact a posteriori probability-per-bit computer. The main idea is to marginalize the posterior density for the received data exactly over the subset of the transmitted bits that are received with the lowest signal-to-noise-ratio, and then marginalize this density approximately over the remaining bits. Unlike the exact demodulator, whose complexity is huge due to the need for enumerating all possible combinations of transmitted constellation points, the proposed method has very low complexity. Additionally, its complexity is fixed, which makes it suitable for pipelined implementation. Numerical examples illustrate its performance on slow fading 4times4 and 6times6 complex MIMO channels.

Cognitive radio is a new concept of reusing spectrum in an opportunistic manner. Cognitive radio is motivated by recent measurements of spectrum utilization, showing unused resources in frequency, time and space. Introducing cognitive radios in a primary network inevitably creates increased interference to the primary users. Secondary users must sense the spectrum and detect primary users' signals at very low SNR, to avoid causing too much interference.This dissertation studies this detection problem, known as spectrum sensing.

The fundamental problem of spectrum sensing is to discriminate an observation that contains only noise from an observation that contains a very weak signal embedded in noise. In this work, detectors are derived that exploit known properties of the second-order moments of the signal. In particular, known structures of the signal covariance are exploited to circumvent the problem of unknown parameters, such as noise and signal powers or channel coefficients.

The dissertation is comprised of six papers, all in different ways related to spectrum sensing based on second-order statistics. In the first paper, we considerspectrum sensing of orthogonal frequency-division multiplexed (OFDM) signals in an additive white Gaussian noise channel. For the case of completely known noise and signal powers, we set up a vector-matrix model for an OFDM signal with a cyclic prefix and derive the optimal Neyman-Pearson detector from first principles. For the case of completely unknown noise and signal powers, we derive a generalized likelihood ratio test (GLRT) based on empirical second-order statistics of the received data. The proposed GLRT detector exploits the non-stationary correlation structure of the OFDM signal and does not require any knowledge of the noise or signal powers.

In the second paper, we create a unified framework for spectrum sensing of signals which have covariance matrices with known eigenvalue multiplicities. We derive the GLRT for this problem, with arbitrary eigenvalue multiplicities under both hypotheses. We also show a number of applications to spectrum sensing for cognitive radio.

The general result of the second paper is used as a building block, in the third and fourth papers, for spectrum sensing of second-order cyclostationary signals received at multiple antennas and orthogonal space-time block coded (OSTBC) signals respectively. The proposed detector of the third paper exploits both the spatial and the temporal correlation of the received signal, from knowledge of the fundamental period of the cyclostationary signal and the eigenvalue multiplicities of the temporal covariance matrix.

In the fourth paper, we consider spectrum sensing of signals encoded with an OSTBC. We show how knowledge of the eigenvalue multiplicities of the covariance matrix are inherent owing to the OSTBC, and propose an algorithm that exploits that knowledge for detection. We also derive theoretical bounds on the performance of the proposed detector. In addition, we show that the proposed detector is robust to a carrier frequency offset, and propose another detector that deals with timing synchronization using the detector for the synchronized case as a building block.

A slightly different approach to covariance matrix estmation is taken in the fifth paper. We consider spectrum sensing of Gaussian signals with structured covariance matrices, and propose to estimate the unknown parameters of the covariance matrices using covariance matching estimation techniques (COMET). We also derive the optimal detector based on a Gaussian approximation of the sample covariance matrix, and show that this is closely connected to COMET.

The last paper deals with the problem of discriminating samples that containonly noise from samples that contain a signal embedded in noise, when the variance of the noise is unknown. We derive the optimal soft decision detector using a Bayesian approach. The complexity of this optimal detector grows exponentially with the number of observations and as a remedy, we propose a number of approximations to it. The problem under study is a fundamental one andit has applications in signal denoising, anomaly detection, and spectrum sensing for cognitive radio.

The last three decades have seen significant advances in the wireless communication field. As the data rates of wireless systems are increasing, the demand for mobile services also is increasing rapidly. Like other natural resources, radio spectrum suitable for mobile wireless communications is also limited. In order to keep up with this ever increasing demand, there is a requirement of new signal processing algorithms.

Diversity is a technique used in wireless systems to combat the effects of fading and thereby improve reliability of data transfer. There are many ways in which algorithms can exploit diversity in wireless channels. Hybrid-automatic repeat request (H-ARQ) schemes and relaying mechanisms are two such diversity extracting techniques. Even though these diversity achieving techniques have been well understood in theory, there are many ways in which one can optimize these techniques for specific application scenarios.In this thesis, we focus on improving the performance of retransmission schemes and relaying systems.

In the first part of the thesis, we improve the performance of H-ARQ schemes in the 3GPP- long term evolution (LTE) system by improving the performance of feedback signaling. We employ complex-field coding to extract the inherent frequency diversity available in the resources. Next, we provide a sub-optimal solution to the outage-optimal power allocation problem in incremental redundancy based H-ARQ system, whose performance is practically the same as that of the optimal solution.

In the later part of the thesis, we propose a retransmission scheme based on superposition coding (SPC) for the symmetric relaying scenario. We provide packet error probability (PEP) expressions and solutions for the optimal fraction of power allocated for the partners' data. Finally, we study the optimal bits-to-symbol mappings for SPC and its effect on an H-ARQ scheme and the symmetric relaying scenario using SPC.

The need for wireless communications has increased during the last decades. To increase the data rates of the communication links there is a need of allocating larger frequency bands. These bands are strictly regulated and the majority of the frequencies are allocated to licensed systems. The splitting of the bandwidth is orthogonal, which mean that the different systems are not interfering each other. But, orthogonal splitting is inefficient since it does not exploit all degrees of freedom in the wireless channels.

There are also unlicensed bands where different systems co-exist and operate simultaneously in a non-orthogonal manner and interfere each other. This interference degrades the performance of each system. This motivates the use of so-called spectrum sharing techniques for interference management.

The spectrum sharing can be modeled via the so-called interference channel (IFC). This consists of at least two transmitter (TX)-receiver (RX) pairs. These pairs can share resources such as frequency, time, power, code, or space. Here, the focus is on the sharing of spatial resources. By employing multiple antennas at the TXs, spatial diversity is obtained and it is possible to steer the power in any spatial direction. Assuming a single antenna at each RX we get the so-called multiple-input single-output (MISO) IFC.

There is a conflict inherent in the IFC since the TX-RX pairs optimize conflicting objectives, e.g., the data rates. To analyze this conflict we use game-theoretic concepts. In general, the situation where the TXs transmit in the directions which are optimal for their objective is inefficient. That is, it is possible increase all rates of some (or all) TX-RX pairs without decreasing the rate of any of the pairs. To do so, the TXs change their strategies such that interference is decreased.

We define several rate regions, which depend on the channel model and channelstate information at the transmitters. Also, some of the most important game-theoretic operating points are described.

We point out an error in a derivation in the recent paper [1], and provide a correct and much shorter calculation of the result in question. In passing, we also connect the results in [1] to the literature on array signal processing and on principal component analysis, and show that the main findings of [1] follow as special cases of standard results in these fields.