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 is 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

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. Two of the most important basic problems in MIMO are to efficiently exploit partial channel state information (CSI) at the transmitter, and to design near-optimal, low-complexity receiver architectures that are suitable for hardware implementation. With regard to exploiting CSI at the transmitter our recent work has concerned schemes that can make use of combined long-term (statistical) CSI and of instantaneous, but heavily quantized CSI feedback [1]. In the area of MIMO receivers, we have developed fully parallelizable soft-detection algorithms that run in deterministic time [2].

• [1] T. Kim, M. Bengtsson, E. G. Larsson, M. Skoglund, "Exploiting combined long-term and short-term channel state information in MIMO links", IEEE Transactions on Wireless Communications, vol. 7, no. 7, pp. 2409-2414, Jul. 2008.
• [2] E. G. Larsson and J. Jalden, "Soft MIMO detection at fixed complexity via partial marginalization", IEEE Transactions on Signal Processing, vol. 56, no. 8, pp. 3397-3407, Aug. 2008.

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 a cornerstone 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 [1,2], and optimal mappings at the relay [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] M. N. Khormuji and E. G. Larsson, "Finite-SNR analysis and optimization of decode-and-forward relaying over slow fading channels", IEEE Transactions on Vehicular Technology, vol. 58, pp. 4292-4305, Oct. 2009.
• [3] M. N. Khormuji and E. G. Larsson, "Rate-optimized constellation rearrangement for the relay channel", IEEE Communication Letters, vol. 12, no. 9, pp. 618-620, Sep. 2008.

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 fundamental aspects of spectrum sensing [2]. Our work also concerns how to deal with unknown parameters, such as noise power and signal power [3]. 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 unkown parameters.

• [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. G. Larsson and G. Regnoli, "Detection of weak signals for cognitive radio: Does small-scale fading help?", IEEE Communications Letters, Oct. 2007.
• [3] E. Axell and E. G. Larsson, "A Bayesian Approach to Spectrum Sensing, Denoising and Anomaly Detection", in Proc. of IEEE ICASSP, Apr. 2009.

Resource allocation in wireless networks

This topic refers to optimum allocation of radio resources (power, time, bandwidth) to users in a multiuser system. Fundamentally, there is a conflict situation inherent in the resource allocation problem since different users have conflicting objectives. Our most recent work has mainly focused on resource allocation for multiple-antenna systems [1,2]. One important tool to analyze the problem and devise allocation strategies has been game theory.

• [1] E. G. Larsson and E. Jorswieck, "Competition and collaboration on the MISO inteference channel", Proc. Allerton, 2007.
• [2] E. Jorsweick and E. G. Larsson, "The MISO interference channel from a game-theoretic perspective: A combination of selfishness and altruism achieves Pareto optimality", Proc. IEEE ICASSP 2008, to appear.

Representation and transmission of scheduling information in multi-user OFDM systems

This topic refers to the study of radio resource efficient schemes for transmission of multi-user scheduling information in OFDM systems. 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. However, the transmission of scheduling information to the different users competes with payload data for channel resources. The amount of scheduling data is highly dependent of the scheduling method used, both in respect to scheduling granularity and basic modus operandi. The goal of the scheduler is typically - given some secondary constraint e.g. fairness among users - to utilize muklti-user diversity to maximize system throughput. The gain from this approach must of course be set in constrast to the increased overhead caused by the control signaling of potentially intricate and information heavy scheduling desicsions over the time/frequency domain. This research topic aims at studying the fundamental limitations of this problem [1], as well as suggesting practical guidelines and solutions for the joint design of scheduling methods and scheduling transmission principles.

• [1] J. Eriksson, R. Moosavi, E. G. Larsson, N. Wiberg, P. Frenger and F. Gunnarsson, "On Coding of Scheduling Information in OFDM", in Proc. of IEEE VTC, Apr. 2009.

Signal processing for communications

Signal processing is one of the core functions of a communications receiver. Our work deals with all aspects of optimum detection, multuser detection, interference suppression, channel estimation, and pilot design.

Coding theory; effective algorithms for list decoding

List decoding of error-correcting block codes is a generalization of the normal unique decoding scheme employed for such codes. It has the attractive advantage that it enables utilization of more of the true error correcting capabilities of the code. The basic idea is a decoder which given a received word, produces as output a list of all codewords within a given Hamming radius from the received word. The interesting case is when this radius is chosen larger than half the minimum distance of the code thus enabling recovery from high weight error patterns. The concept of list decoding was conceived as early as in the 1950's by P. Elias and independently by J. M. Wozencraft, but was not really considered for practical applications since the construction of efficient algorithms for performing the list decoding proved to be a hard task. However, in 1998 an efficient algorithm for list decoding of the much used Reed-Solomon codes was presented by M. Sudan, and met with great excitement by the coding theoretic community. Refining the Sudan algorithm and extending its basic ideas to other codes and decoding schemes has since its introduction been the subject of considerable research interest. The refinement efforts strives towards lowering the computational complexity of the algorithm further, although it is efficient by nature. One way of attacking this problem is to impose restrictions on the list size which reduces the complexity, but also limits the performance. The first natural relaxation of the normal, unique decoding strategy is to use a list decoder with an allowed list size of two. We have in our work [1,2] studied the performance of list decoding schemes under these strict limitations.

• [1] Jonas Eriksson. "Aspects of List-of-Two Decoding". PhD thesis, Department of Electrical Engineering, Linköpings Universitet, 2006.
• [2] Jonas Eriksson. "A weight-Based Characterization of the Set of Correctable Error patterns Under List-of-2 Decoding". Advances in Mathematics of Communications (AMC), 1(3):331-356, Aug. 2007.