Research at Communication Systems

Our research activities are broad and span many aspects of communications and wireless communications in particular. Our general interests and expertise are described below. For a list of specific, funded research projects and publication lists, see the links in the menu to the left.

Signal Processing for MIMO Wireless Communications Receivers

MIMO refers to the use of multiple antennas at the transmitter and receiver in a wireless communications link. Essentially MIMO provides spatial diversity (multiple signal paths between the transmitter and the receiver) which translates into increased throughput and/or increased link reliability at a given transmit power. One of the most important basic problem in MIMO is to design near-optimal, low-complexity receiver architectures that are suitable for hardware implementation. Recent contributions from our group in this field include:

1. M. Čirkić, D. Persson, and E. G. Larsson, "Allocation of Computational Resources for Soft MIMO Detection", IEEE Journal of Selected Topics in Signal Processing, 2011.
2. D. Persson and E. G. Larsson, "Partial Marginalization Soft MIMO Detection with Higher Order Constellations", IEEE Transactions on Signal Processing, vol. 59, no. 1, pp. 453-458, Jan. 2011.
3. Mirsad Čirkić, Erik G. Larsson, Near-Optimal Soft-Output Fixed-Complexity MIMO Detection via Subspace Marginalization and Interference Suppression", IEEE International Conference on Acoustics,Speech and Signal Processing, 2012.

Cooperative Communications and Relaying

The basic idea with cooperative communications is to enable a data packet to be transmitted over multiple paths in a wireless network. For example, if a source S wants to send data to a destination D but encounters a "bad" channel (e.g. due to fading), it may transmit the packet to a relay node R who then forwards it to D. The current interest in cooperative communications is fueled by the fact that many contemporary communication systems use simple forms of relaying to increase coverage, especially in tunnels and remote rural areas. Relaying is also an enabling technology in future (4G) personal communication systems, and for ad hoc and wireless sensor networks. Our recent contributions to the field relate to two fundamental challenges in cooperative communications: the design of spectrally efficient transmission schemes and resource (power, bandwidth) allocation for cooperative links [1], schemes that combine relaying and ARQ [2], and beamforming methods [3].

1. M. N. Khormuji and E. G. Larsson, "Cooperative Transmission Based on Decode-and-Forward Relaying with Partial Repetition Coding", IEEE Transactions on Wireless Communications, vol. 8, no. 4, pp. 1716-1725, Apr. 2009.
2. T. V. K. Chaitanya and E. G. Larsson, "Superposition Modulation Based Symmetric Relaying with Hybrid ARQ: Analysis and Optimization", IEEE Transactions on Vehicular Technology, 2011.
3. Ebrahim Avazkonandeh Gharavol, Erik G. Larsson, The Sign-Definiteness Lemma and Its Applications to Robust Transceiver Optimization for Multiuser MIMO Systems", IEEE Trans. Signal Processing, Jan. 2013.

Cognitive Radio and Dynamic Spectrum Access

The idea of cognitive radio is that radio spectrum licensed to primary users may be used in an unlicensed fashion by secondary users, provided that these secondary users do not create harmful interference for the primary users. For example, frequencies used for cellular telephony or TV broadcasting may be locally reused for a local sensor network provided that the latter transmits with low enough power. Cognitive radio has received much attention during the last few years. Much of this interest is sparked by recent measurements which show that radio spectrum by large is vastly under-utilized. One of the fundamental challenges of cognitive radio is that before an unlicensed user can begin transmitting, she must ensure that nobody else is using the carrier in question. This requires reliable detection of very weak signals. Our current work concerns system aspects [1] and the design of detection algorithms. All communication signals have some structure, introduced for example by modulation, channel coding and to facilitate synchronization. The focus of our work is to derive detectors that exploit the known structure of the signal, to obtain better performance or to circumvent the problem of unknown parameters [2,3,4]. We also have experimental activities in this area [5].

1. E. G. Larsson and M. Skoglund, "Cognitive Radio in a Frequency Planned Environment: Some Basic Limits", IEEE Transactions on Wireless Communications, vol. 7, no. 12, pp. 4800-4806, Dec. 2008.
2. E. Axell and E. G. Larsson, "Optimal and Sub-Optimal Spectrum Sensing of OFDM Signals in Known and Unknown Noise Variance", IEEE Journal on Selected Areas in Communications, vol. 29, no. 2, pp. 290-304, Feb. 2011.
3. D. Danev, E. Axell, and E. G. Larsson, "Spectrum Sensing Methods for Detection of DVB-T Signals in AWGN and Fading Channels", in Proc. IEEE PIMRC, Sep. 2010.
4. Erik Axell, Erik G. Larsson, Multiantenna Spectrum Sensing of a Second-Order Cyclostationary Signal",Proceedings of the 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011.
5. Anton Blad, Erik Axell, Erik G. Larsson, Spectrum Sensing of OFDM Signals in the Presence of CFO: New Algorithms and Empirical Evaluation Using USRP, Proceedings of the 13th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2012.

Resource Allocation in Wireless Networks

This topic refers to optimum allocation of radio resources (power and spectrum) to users in multiuser systems. Spectrum in particular is a scarce resource which has to be efficiently used to accomodate the exponentially increasing traffic. Modern wireless systems are already using aggressive frequency reuse factors and emerging ones may even need to allow spectrum sharing between different operators. The main impairement that needs to be tackled is the interference created by simultaneous use of the spectral resources in adjacent areas. Our recent work has mainly focused on resource allocation for multiple-antenna systems, where interference can be mitigated by transmit beamforming and power control techniques. The scenario of interest is modelled by the MISO or MIMO interference channel. Fundamentally, there is a conflict situation inherent in the resource allocation problem since different users have conflicting objectives. One important tool to analyze the problem and devise allocation strategies has been game theory [1]. A challenging research problem is to find the achievable rate regions and the set of strategies that yield the optimal rates, under various practically-relevant scenarios, e.g. for partial CSI [2] or multiuser decoding receivers [3,4]. The ultimate goal is to understand in which cases spectrum sharing enhances the performance of wireles systems [5] and to propose efficient cross-layer techinques to achieve the maximum spectrum sharing gain [6].

1. E. G. Larsson, E. A. Jorswieck, J. Lindblom, and R. Mochaourab, "Game Theory and the Flat-Fading Gaussian Interference Channel: Analyzing Resource Conflicts in Wireless Networks", IEEE Signal Processing Magazine, vol. 26, no. 5, pp. 18-27, Sep. 2009.
2. J. Lindblom, E. G. Larsson, E. A. Jorswieck, "Parameterization of the MISO IFC Rate Region: The Case of Partial Channel State Information", IEEE Transactions on Wireless Communications, vol. 9, no. 2, pp. 500-504, Feb. 2010.
3. J. Lindblom, E. Karipidis, and E. G. Larsson, "Efficient Computation of the Pareto Boundary for the Two-user MISO Interference Channel with Multi-user Decoding Capable Receivers", in Proceedings of 4th IEEE Internationla Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011, pp. 241-244, invited.
4. E. Karipidis, D. Yuan, and E. G. Larsson, "Mixed-integer Linear Programming Framework for Max-min Power Control with Single-stage Interference Cancellation", in Proceedings of 36th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 3448-3451.
5. J. Lindblom and E. G. Larsson, "Does Non-Orthogonal Spectrum Sharing in the Same Cell Improve the Sum-Rate of Wireless Operators?", in Proceedings of 13th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2012, pp. 6-10, invited.
6. L. Yu, E. Karipidis, and E. G. Larsson, "Coordinated Scheduling and Beamforming for Multicell Spectrum Sharing Networks Using Branch & Bound", in Proceedings of 20th European Signal Processing Conference (EUSIPCO), 2012, pp. 819-823, invited.

Representation and Transmission of Control Information in Multi-User OFDM Systems

This topic refers to the study of radio resource efficient schemes for transmission of control information in multi-user OFDM systems. Control information plays a vital part of the operation of today's complex multi-user communication systems, and competes with payload data for channel resources. The amount of data to be transmitted is typically not that large but needs to be transmitted in a relatively robust fashion. One type of control information is scheduling information. The conveying of user scheduling information is vital for high throughput operation of many modern wireless OFDM access systems (e.g. 3G Long Term Evolution, WiMAX), since it allows for exploitation of multi-user diversity. The amount of scheduling data is highly dependent of the scheduling method used. The goal of the scheduler is typically - given some secondary constraint e.g. fairness among users - to utilize multi-user diversity to maximize system throughput. The gain from this approach must of course be set in contrast to the increased overhead caused by the control signaling of potentially intricate and information heavy scheduling decisions over the time/frequency domain. This research topic aims at studying the fundamental limitations of this problem [1]. We also work on blind detection schemes for control information [2]. Another research direction in this topic is the design of improved schemes for error protection of control signaling data [3].

1. R. Moosavi, J. Eriksson, E. G. Larsson, N. Wiberg, P. Frenger, and F. Gunnarsson, "Comparison of Strategies for Signaling of Scheduling Assignments in Wireless OFDMA", IEEE Transactions on Vehicular Technology, vol. 59, no. 9, pp. 4527-4542, Sep. 2010.
2. R. Moosavi and E. G. Larsson, "A Fast Scheme for Blind Identification of Channel Codes", in Proc. IEEE GLOBECOM, Dec. 2011.
3. T. V. K. Chaitanya and E. G. Larsson, "Improving 3GPP-LTE Uplink Control Signaling Performance Using Complex-Field Coding", IEEE Transactions on Vehicular Technology, To appear.

Coding and Signal Processing for Large Multi-User MIMO Systems

Visit the large-scale MIMO homepage here

Information theoretic results on multi-user MIMO suggest the achievability of unlimited and very high spectral efficiency and reliability with increasing spatial dimensions. This is the basic premise behind the upcoming Large MIMO technology for future high throughput and reliable cellular wireless communications [1]. We envisage that a typical large multi-user MIMO system, would consist of a base station having a very large antenna array (possibly tens to hundreds of antennas) communicating with multiple users on the same time-frequency resource.

Practically realizing such large MIMO systems is a challenge. There are many issues which have to be resolved, for large MIMO systems to become a reality. One of the most important issue is that of the complexity of multi-user detection in the uplink and precoding in the downlink. It is known that optimal multi-user detection has a complexity which is exponential in the number of users, and a similar fact is true for optimal downlink precoding. Nevertheless, our recent results have demonstrated the existence of low-complexity near-optimal multi-user detection algorithms [2].

Similarly, in the downlink it has been recently shown that a large MIMO system with fixed number of users and unlimited number of base station antennas can achieve high spectral efficiency even with imperfect channel knowledge and low-complexity precoding. Even though this simple precoding scheme can achieve high spectral efficiency, it still performs far from the optimal schemes. We are currently involved in finding low-complexity precoding schemes which are near-optimal. One recent contributions is the per-antenna constant-envelope precoder [3].

Another issue is that of characterizing the achievable information rates under more realistic assumptions on the channel model. In practice, it is unlikely that the the rank of the channel matrix would increase unbounded with increasing number of users and base station antennas. This is primarily due to the limited number of scatterers in real-world MIMO channels. We treated some aspects of this problem in [4].

1. F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, and F. Tufvesson, Scaling up MIMO: Opportunities and Challenges with Very Large Arrays, IEEE Signal Proces. Mag., to appear, 2012.
2. K. V. Vardhan, S. K. Mohammed, A. Chockalingam, and B. Sundar Rajan, "A Low-Complexity Detector for Large MIMO Systems and Multicarrier CDMA Systems,'' IEEE Journal on Selected Areas in Communications, vol. 26, no.3, pp. 473-485, Apr. 2008.
3. Saif Khan Mohammed, Erik G Larsson, Per-antenna Constant Envelope Precoding for Large Multi-User MIMO Systems", To appear in IEEE Transactions on Communications, 2013.
4. H. Q. Ngo, T. L. Marzetta, and E. G. Larsson, "Analysis of the Pilot Contamination Effect in Very Large Multicell Multiuser MIMO Systems for Physical Channel Models", in Proc. IEEE ICASSP, May 2011.

Cooperative Localization

A number of applications in wireless sensor networks (WSN) require sensor nodes to obtain their absolute or relative positions. Equipping every sensor with a GPS receiver may be expensive, energy prohibitive and limited to outdoor applications. Therefore, we consider the problem in which some small number of sensors, called anchor nodes, obtain their coordinates via GPS or by installing them at points with known coordinates, and the rest, unknown nodes, must determine their own coordinates using the anchor nodes and measured inter-sensor distances. If unknown nodes were capable of high-power transmission, they would be able to make measurements with all anchor nodes. This represents single-hop localization. However, we prefer to use energy-conserving devices without energy necessary for long-range communication. In this case, each unknown node has available only the noisy measurements of the distance to several neighboring nodes (not necessarily anchor nodes). In other words, we still allow unknown nodes to make measurements with anchor nodes (if possible), but now we additionally allow unknown nodes to make measurements with other unknown nodes. It is still necessary that there is minimum of three (for 2D) or four (for 3D) anchor nodes in the network, but not necessarily directly connected to all unknown nodes. This technique is known as cooperative (or multi-hop) localization (CL).

CL typically consists of two phases: the measurement phase, and the localization phase. In the first phase, the inter-sensor distances are estimated using time-of-arrival (TOA), time-difference-of-arrival (TDOA), or received-signal-strength (RSS). It is also possible to estimate the angle of arrival (AOA), but this approach is rarely used due to the high equipment cost. Then, these estimates are used as input for a localization algorithm which provides the estimates of all unknown sensor positions and eventually associated uncertainties. CL may enable many of applications such as search-and-rescue, equipment monitoring and control, intrusion detection, target tracking, road traffic monitoring, health monitoring, and surveillance. There are many open issues in this field, such as increased complexity, high communication overhead, and decreased performance caused by non-rigid or loopy graphs. Our recent contributions include:

1. V. Savic and S. Zazo, "Reducing communication overhead for cooperative localization using nonparametric belief propagation," in IEEE Wireless Communications Letters, vol. 1, no. 4, pp. 308-311, Aug. 2012.
2. V. Savic, H. Wymeersch and S. Zazo, "Distributed target tracking based on belief propagation consensus," in IEEE Proc. of the 20th European Signal Processing Conference (EUSIPCO) , Aug. 2012.
3. H. Wymeersch, F. Penna, and V. Savic, "Uniformly reweighted belief propagation for estimation and detection in wireless networks," in IEEE Transactions on Wireless Communications, vol. 11, no. 4, pp. 1587-1595, April 2012.
4. V. Savic and S. Zazo, "Belief propagation techniques for cooperative localization in wireless sensor networks," in Position Location - Theory, Practice and Advances: A Handbook for Engineers and Academics, Wiley, 2011.

Sidansvarig: Erik G. Larsson
Senast uppdaterad: 2013 05 19   23:59