Iterative signal detection for large scale GSM-MIMO systems

Generalized spatial modulations (GSM) represent a novel multiple input multiple output (MIMO) scheme which can be regarded as a compromise between spatial multiplexing MIMO and conventional spatial modulations (SM), achieving both spectral efficiency (SE) and energy efficiency (EE). Due to the high computational complexity of the maximum likelihood detector (MLD) in large antenna settings and symbol constellations, in this paper we propose a lower complexity iterative suboptimal detector. The derived algorithm comprises a sequence of simple processing steps, namely an unconstrained Euclidean distance minimization problem, an element wise projection over the signal constellation and a projection over the set of valid active antenna combinations. To deal with scenarios where the number of possible active antenna combinations is large, an alternative version of the algorithm which adopts a simpler cardinality projection is also presented. Simulation results show that, compared with other existing approaches, both versions of the proposed algorithm are effective in challenging underdetermined scenarios where the number of receiver antennas is lower than the number of transmitter antennas.info:eu-repo/semantics/acceptedVersio

Multi-input multi-output (MIMO) detection by a colony of ants

The traditional mobile radio channel has always suffered from the detrimental effects of multipath fading. The use of multiple antennae at both ends of the wireless channel has proven to be very effective in combatting fading and enhancing the channel's spectral efficiency. To exploit the benefits offered by Multi-Input Multi-Output (MIMO) systems, both the transmitter and the receiver have to be optimally designed. In this thesis, we are concerned with the problem of receiver design for MIMO systems in a spatial multiplexing scheme. The MIMO detection problem is an NP-hard combinatorial optimization problem. Solving this problem to optimality requires an exponential search over the space of all possible transmitted symbols in order to find the closest point in a Euclidean sense to the received symbols; a procedure that is infeasible for large systems. We introduce a new heuristic algorithm for the detection of a MIMO wireless system based on the Ant Colony Optimization (ACO) metaheuristic. The new algorithm, AntMIMO, has a simple architecture and achieves near maximum likelihood performance in polynomial time

Optimization Algorithms in Wireless and Quantum Communications

Since the first communication systems were developed, the scientific community has been witnessing attempts to increase the amount of information that can be transmitted. In the last 10--15 years there has been a tremendous amount of research towards developing multi-antenna systems which would hopefully provide high-data-rate transmissions. However, increasing the overall amount of transmitted information increases the complexity of the necessary signal processing. A large portion of this thesis deals with several important issues in signal processing of multi-antenna systems. In almost every particular case the goal is to develop a technique/algorithm so that the overall complexity of the signal processing is significantly decreased. In the first part of the thesis a very important problem of signal detection in MIMO (multiple-input multiple-output) systems is considered. The problem is analyzed in two different scenarios: when the transmission medium (channel) 1) is known and 2) is unknown at the receiver. The former case is often called coherent and the later non-coherent MIMO detection. Both cases usually amount to solving highly complex NP-hard combinatorial optimization problems. For the coherent case we develop a significant improvement of the traditional sphere decoder algorithm commonly used for this type of detection. An interesting connection between the new improved algorithm and the H-infinity estimation theory is established, and the performance improvement over the standard sphere decoder is demonstrated. For the non-coherent case we develop a counterpart to the standard sphere decoder, the so-called out-sphere decoder. The complexity of the algorithm is viewed as a random variable; its expected value is analyzed and shown to be significantly smaller than the one of the overall exhaustive search. In the non-coherent case, in addition to the complexity analysis of the exact out-sphere decoder, we analyze the performance loss of a suboptimal technique. We show that only a moderate loss of a few dbs in power required at the transmitter will occur if a polynomial algorithm based on the semi-definite relaxation is used in place of any exact technique (which of course is not known to be polynomial). In the second part of the thesis we consider a few problems that arise in wireless broadcast channels. Namely, we consider the problem of the information symbol vector design at the transmitter. A polynomial linear precoding technique is constructed. It enables achieving data rates very close to the ones achieved with DPC (dirty paper coding) technique. Additionally, for another suboptimal polynomial scheme (the so-called nulling and cancelling), we show that it asymptotically achieves the same data rate as the optimal, exponentially complex, DPC. In the last part of the thesis we consider a quantum counterpart of the signal detection from classical communication. In quantum systems the signals are quantum states and the quantum detection problem amounts to designing measurement operators which have to satisfy certain quantum mechanics laws. A specific type of quantum detection called unambiguous detection, which has numerous applications including quantum filtering, has recently attracted a lot of attention in the research community. We develop a general framework for numerically solving this problem using the tools from the convex optimization theory. Furthermore, in the special case where the two quantum states are of rank 2, we construct an explicit analytical solution for the measurement operators. At the end we would like to emphasize that the contribution of this thesis goes beyond the specific problems mentioned here. Most algorithmic optimization techniques developed in this paper are generally applicable. While it is a fact that our results were originally motivated by wireless and quantum communications applications, we believe that the developed techniques will find applications in many different areas where similar optimization problems appear.</p

Convex Optimisation for Communication Systems

In this thesis new robust methods for the efficient sharing of the radio spectrum for underlay cognitive radio (CR) systems are developed. These methods provide robustness against uncertainties in the channel state information (CSI) that is available to the cognitive radios. A stochastic approach is taken and the robust spectrum sharing methods are formulated as convex optimisation problems. Three efficient spectrum sharing methods; power control, cooperative beamforming and conventional beamforming are studied in detail. The CR power control problem is formulated as a sum rate maximisation problem and transformed into a convex optimisation problem. A robust power control method under the assumption of partial CSI is developed and also transformed into a convex optimisation problem. A novel method of detecting and removing infeasible constraints from the power allocation problem is presented that results in considerably improved performance. The performance of the proposed methods in Rayleigh fading channels is analysed by simulations. The concept of cooperative beamforming for spectrum sharing is applied to an underlay CR relay network. Distributed single antenna relay nodes are utilised to form a virtual antenna array that provides increased gains in capacity through cooperative beamforming. It is shown that the cooperative beamforming problems can be transformed into convex optimisation problems. New robust cooperative beamformers under the assumption of partial and imperfect CSI are developed and also transformed into convex optimisation problems. The performance of the proposed methods in Rayleigh fading channels is analysed by simulations. Conventional beamforming to allow efficient spectrum sharing in an underlay CR system is studied. The beamforming problems are formulated and transformed into convex optimisation problems. New robust beamformers under the assumption of partial and imperfect CSI are developed and also transformed into convex optimisation problems. The performance of the proposed methods in Rayleigh fading channels is analysed by simulations

Semidefinite relaxations for MIMO transmissions with high-order QAM constellations

In this letter, simple semidefinite relaxations are proposed for the detection of high-order quadrature amplitude modulations in multiple-input–multiple-output systems. The detector is based on the addition of several convex and concave quadratic inequality constraints into the maximum-likelihood detector before relaxing the problem into a convex one. Combined with a randomized sampling procedure, it is shown that the performance and worst-case computational complexity of the proposed approach makes it very competitive against existing detectors in large problem sizes.info:eu-repo/semantics/publishedVersio