Soft-Information Assisted Near-Optimum Nonlinear Detection for BLAST-type Space Division Multiplexing OFDM Systems

In this contribution, a nonlinear hybrid detection scheme based on a novel soft-information assisted Genetic Algorithm (GA) is proposed for a Turbo Convolutional (TC) coded Space Division Multiplexing (SDM) aided Orthogonal Frequency Division Multiplexing (OFDM) system. Our numerical results show that the performance of the currently known GA-assisted system can be improved by about 2dB with the aid of the GA’s population-based soft solution, approaching the optimum performance of the soft-information assisted Maximum Likelihood (ML) detection, while exhibiting a lower complexity, especially in high-throughput scenarios. Furthermore, the proposed scheme is capable of achieving a good performance even in the so-called overloaded systems, where the number of transmit antennas is higher than the number of receiver antennas. Index Terms—Genetic algorithm, orthogonal frequency division multiplexing, soft information, space division multiplexing

Weighting Particle Swarm Optimization SIMO MC-CDMA Multiuser Detectors

Abstract-This paper analyzes the performance of two heuristic approaches applied to a synchronous multicarrier multiuser detection (MUD) of multiple receive antennas code division multiple access (SIMO MC-CDMA) system. The particle swarm optimization (PSO) with weighting particle position based on combining multi-fitness functions (woPSO) is proposed and compared with the conventional PSO SIMO MC-CDMA. The woPSO strategy deal with the multi-objective dilemma imposed by the spatial diversity that results in independent likelihood function for each receive antenna. Additionally, the computational complexity of these algorithms was taken into account in order to show which one has the best trade-off in terms of performance and implementation complexity aspects. Index Terms-MC-CDMA, multiuser detection, particle swarm optimization, single/multiple-objective optimization

Iterative Joint Channel Estimation and Multi-User Detection for Multiple-Antenna Aided OFDM Systems

Multiple-Input-Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems have recently attracted substantial research interest. However, compared to Single-Input-Single-Output (SISO) systems, channel estimation in the MIMO scenario becomes more challenging, owing to the increased number of independent transmitter-receiver links to be estimated. In the context of the Bell LAyered Space-Time architecture (BLAST) or Space Division Multiple Access (SDMA) multi-user MIMO OFDM systems, none of the known channel estimation techniques allows the number of users to be higher than the number of receiver antennas, which is often referred to as a “rank-deficient” scenario, owing to the constraint imposed by the rank of the MIMO channel matrix. Against this background, in this paper we propose a new Genetic Algorithm (GA) assisted iterative Joint Channel Estimation and Multi-User Detection (GA-JCEMUD) approach for multi-user MIMO SDMA-OFDM systems, which provides an effective solution to the multi-user MIMO channel estimation problem in the above-mentioned rank-deficient scenario. Furthermore, the GAs invoked in the data detection literature can only provide a hard-decision output for the Forward Error Correction (FEC) or channel decoder, which inevitably limits the system’s achievable performance. By contrast, our proposed GA is capable of providing “soft” outputs and hence it becomes capable of achieving an improved performance with the aid of FEC decoders. A range of simulation results are provided to demonstrate the superiority of the proposed scheme. Index Terms—Channel estimation, genetic algorithm, multiple-input-multiple-output, multi-user detection, orthogonal frequency division multiplexing, space division multiple access

Fixed-complexity quantum-assisted multi-user detection for CDMA and SDMA

In a system supporting numerous users the complexity of the optimal Maximum Likelihood Multi-User Detector (ML MUD) becomes excessive. Based on the superimposed constellations of K users, the ML MUD outputs the specific multilevel K-user symbol that minimizes the Euclidean distance with respect to the faded and noise-contaminated received multi-level symbol. Explicitly, the Euclidean distance is considered as the Cost Function (CF). In a system supporting K users employing M-ary modulation, the ML MUD uses MK CF evaluations (CFE) per time slot. In this contribution we propose an Early Stopping-aided Durr-Høyer algorithm-based Quantum-assisted MUD (ES-DHA QMUD) based on two techniques for achieving optimal ML detection at a low complexity. Our solution is also capable of flexibly adjusting the QMUD's performance and complexity trade-off, depending on the computing power available at the base station. We conclude by proposing a general design methodology for the ES-DHA QMUD in the context of both CDMA and SDMA systems

Antenna-Diversity-Assisted Genetic-Algorithm-Based Multiuser Detection Schemes for Synchronous CDMA Systems

A spatial diversity reception assisted multiuser code-division multiple-access detector based on genetic algorithms (GAs) is proposed. Two different GA-based individual-selection strategies are considered. In our first approach, the so-called individuals of the GA are selected for further exploitation, based purely on the sum of their corresponding figures of merit evaluated for the individual antennas. According to our second strategy, the GA’s individuals are selected based on the concept of the so-called Pareto optimality, which uses the information from the individual antennas independently. Computer simulations showed that the GAs employing the latter strategy achieve a lower bit-error rate as compared to the former strategy. For a 15-user GA-assisted system employing a spreading factor of 31, a complexity reduction factor of 81 was achieved at a performance identical to that of the optimum multiuser detector using full search. Index Terms—Antenna diversity, genetic algorithms, multiuser detection, synchronous code-division multiple access (CDMA)