Multiple-Input Multiple-Output (MIMO) Systems

The advent of MIMO systems has raised prospects for achieving large increases in system throughput. The theoretical performance gains are very large, but considerable work remains to investigate the performance of MIMO systems in practical scenarios and to find receiver structures which offer an acceptable trade-off between complexity and performance. The use of multiple antennas at both transmitter and receiver has the potential to raise throughput and lower the probability of error. Unfortunately, optimum detection is very complex (particularly for high-level modulation schemes) and so the search is on to find receiver structures which offer an acceptable trade-off between complexity and performance. A number of projects are underway in the MIMO area including, space-time coding and spatial multiplexing, iterative decoding, decoder complexity reduction, MIMO antenna selection and transmitter precoding. Investigations are also underway in the related areas of multiple user detection (MUD) and interference cancellation. An important area being studied at the moment is that of cross-layer design which permits a number of the ideas mentioned previously to be applied in an optimum manner in a multi-user network. A concept closely related to MIMO is that of collaborative networks. In this case it is assumed that each node has only one antenna, but is able to work with other nodes to form a so called virtual antenna array. A project in this area is investigating the optimisation of node transmit power and the application of space-time coding. Another project in this area is looking at cooperative space-time coding algorithms to enhance QoS in low power sensor networks.

MIMO antenna selection

MIMO systems employing multiple antennas can offer significant capacity gains over traditional single-input single-output (SISO) systems. However, multiple antennas require multiple RF chains which consist of amplifiers, analog to digital converters, mixers, etc., that are typically very expensive. An approach for reducing the cost while maintaining the high potential data rate of a MIMO system is to employ a reduced number of RF chains at the receiver (or transmitter) and attempt to optimally allocate each chain to one of a larger number of receive (transmit) antennas which are usually cheaper elements. In this way, only the best set of antennas is used, while the remaining antennas are not employed, thus reducing the number of RF chains required.

Existing antenna selection algorithms assume perfect channel knowledge and optimise criteria such as Shannon capacity or various bounds on the error rate. In this project we begin by examining MIMO antenna selection algorithms where the set of possible solutions is large and only a noisy estimate of the channel is available. Using an approach similar to that employed by traditional adaptive filtering algorithms, we propose a new framework based on simulation based discrete stochastic optimisation algorithms to adaptively select a better antenna subset using criteria for example maximum mutual information, bounds on error rate, etc. These discrete stochastic approximation algorithms are ideally suited to minimize the error rate since computing a closed form expression for the error rate is intractable. We also consider time-varying channels for which the antenna selection algorithms can track the time-varying optimal antenna configuration. We present several numerical examples to show the convergence of these algorithms under various performance criteria, and also demonstrate their tracking capabilities. We later propose various new antenna selection criteria and also fast antenna selection algorithms.

Design of space-time codes

Wireless communication using multiple antennas can increase the multiplexing gain (i.e., throughput) and diversity gain (i.e., robustness) of a communication system in fading channels. It has been shown that for any given number of antennas there is a fundamental trade-off between these two gains. Pioneering works on space-time architectures have focused on maximizing either the diversity gain or the multiplexing gain. However, recent works have proposed space-time architectures that simultaneously achieve good diversity and multiplexing performance. In this project a family of lattice space-time (LAST) codes is considered that can achieve the optimum diversity-multiplexing trade-off in delay-limited MIMO channels. Using stochastic optimisation techniques we design LAST codes that can optimise the error rate.

Low complexity detectors for MIMO receivers

Optimum detection in MIMO systems is in general computationally demanding. Consequently sub-optimum detection techniques offering an acceptable trade-off between complexity and performance are of practical interest. In this work, the performance and complexity of lattice-reduction aided detectors has been investigated for various MIMO schemes. Sphere like decoding techniques have also been investigated. Firstly, a new decoder, namely the automatic sphere decoder, is able to perform sphere decoding without the need to define a search radius, thereby eliminating any sensitivity to this parameter. Second, a pre-processing stage that dramatically improves the efficiency of sphere decoding has been proposed. The advantage offered is particularly large at low SNRs and when modulations having high spectral efficiency (e.g., 64QAM) are employed, two important operating regimes where current sphere decoding algorithms are prohibitively complex.