S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on Single-Objective Optimization

Abstract

This paper analyzes the complexity-performance trade-off of several heuristic near-optimum multiuser detection (MuD) approaches applied to the uplink of synchronous single/multiple-input multiple-output multicarrier code division multiple access (S/MIMO MC-CDMA) systems. Genetic algorithm (GA), short term tabu search (STTS) and reactive tabu search (RTS), simulated annealing (SA), particle swarm optimization (PSO), and 1-opt local search (1-LS) heuristic multiuser detection algorithms (Heur-MuDs) are analyzed in details, using a single-objective antenna-diversity-aided optimization approach. MonteCarlo simulations show that, after convergence, the performances reached by all nearoptimum Heur-MuDs are similar. However, the computational complexities may differ substantially, depending on the system operation conditions. Their complexities are carefully analyzed in order to obtain a general complexity-performance framework comparison and to show that unitary Hamming distance search MuD (uH-ds) approaches (1-LS, SA, RTS and STTS) reach the best convergence rates, and among them, the 1-LS-MuD provides the best trade-off between implementation complexity and bit error rate (BER) performance.

Physics-inspired heuristics for soft MIMO detection in 5G new radio and beyond

“…Heuristics for MIMO Detection. Heuristic approaches inspired by Biology or Combinatorial Optimization methods such as genetic algorithms, reactive tabu search, and particle swarm optimization for MIMO detection exist [2,29,66], but significant performance gains are not observed. Some studies have used analog quantum hardware platforms [38,39], but these are specialized platforms that not yet generally available.…”

Parameter Optimization in ACO-MuD DS/CDMA

“…For BPSK signaling, uplink receiver side and just one antenna at the base-station (BS) receiver and each of K users' transmitter, i.e., from the interest user receiver viewpoint at BS, we have a single-input-single output (SISO) communication system, with K − 1 interfering users. So, the multiuser detection problem at the BS receiver side is constituted by 2 K possible candidate solutions in (3). This solutions are seen by the algorithm as all the possibles vector-candidates (or trails) that the ants can travel.…”

“…In the last decade, proposals based on heuristic methods have been reported to solve the MuD problem, getting performance close to the ML performance with polynomial computational complexity [2], [3]. The use of heuristic search algorithms is motivated by the fact that optimization problems related to wireless communication systems results in non-polynomial (NP-hard) problems, e.g, MuD optimization problem [4].…”

Particle-Swarm-Optimization-Based Multiuser Detector for Multicarrier CDMA Communications

“…Two quite different, suboptimal approaches for MUD emerged, and they had been proved to have much lower complexity than the optimum multi-user detector: interference cancellation (IC) and adaptive filtering. The minimum mean square error (MMSE) multi-user detection is described in [5], [6], while an interference cancellation based MUD has been proposed in [7], [8], [9]. The IC techniques can be broadly broken into serial and parallel schemes for canceling MAI.…”

“…Many evolutionary algorithms, such as Genetic algorithm (GA) [9], [11], [12], ant colony optimization (ACO) [13], simulated annealing (SA) [9], [15] , Short Term Taub Search (STTS) [9], Reactive Taub Search (RTS) [9] and particle swarm optimization (PSO) [9], [15], [16], [18], [19], [20], have been proposed.…”