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

Abstract

Abstract-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 transmitterreceiver 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.

GA Based Sensing of Sparse Multipath Channels with Superimposed Training Sequence

“…Moreover, for the case of sparse multipath channels, this estimation method do not exploit the available prior knowledge of channel's sparsity, thus fails to correctly estimate the zero valued channel taps. Therefore, an extension of this method is proposed in [14] for the case of sparse multipath channels, which propose the following solution to obtain the channel's estimate Dantzig Selector (DS) has been casted to optimize the estimate in (13). This method performs betters than (11) for the cases of sparse multipath channels [14].…”

“…The gradient free nature of GA (with sufficient chromosomes) make them very robust against getting stuck in the local optimum solutions. GAs have also been employed, for solving the channel estimation problem, in the literature [8]- [13]. However, no GA based channel estimation technique with ST sequence exist in the literature which also exploits the available prior knowledge of channel's sparsity for accurate estimation of channel.…”

Comparative Analysis of GA-MMSE and PSO-MMSE Based MUD Technique Aided SDMA/OFDM System

“…As shown in the upper half of Figure, the received signal is the noise-contaminated superposition transmitted signals is OFDM-demodulated at the P receiver antenna elements and forwarded to the iterative GA-MMSE and PSO-MMSE MUD [1,6]. Then the detected soft bitsˆ b(l) s are generated, which are forwarded to the L independent FEC decoders for channel decoding.…”

Stochastic Optimization Assisted Joint Channel Estimation and Multi-User Detection for OFDM/SDMA

“…Intensive research efforts have been devoted to developing efficient approaches for Channel Estimation (CE) in multi-user OFDM/SDMA systems [1,[4][5][6]. In order to achieve a near-optimal performance, joint CE and signal detection schemes have recently received significant research attention [7][8][9]. The optimal solutions of CE and/or MUD, namely Maximum-likelihood (ML) CE and ML-MUD, are naturally desired.…”

“…The most popular algorithms 1 include Genetic Algorithms (GA) [12], Repeated Weighted Boosting Search (RWBS) [13], Particle Swarm Optimization (PSO) [14] and Differential Evolution (DE) [15]. More specifically, significant advances have been made in the development of these stochastic optimization algorithms, including single-user joint channel and data estimation [13,16], CE and/or MUD in the multi-user Code Division Multiple Access (CDMA) UpLink (UL) [17][18][19][20], in the SDMA aided OFDM UL [1,7,9], in MUD assisted Space-Time Block Coding (STBC) [21,22], in CE for Multiple Input Multiple Output (MIMO) systems [23], in the MultiUser Transmission (MUT) aided DownLink (DL) [24,25], in channel allocation [26,27] as well as in a diverse range of other applications.…”