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

Iterative Near-Maximum-Likelihood Detection in Rank-Deficient Downlink SDMA Systems

Abstract—In this paper, a precoded and iteratively detected downlink multiuser system is proposed, which is capable of operating in rankdeficient scenarios, when the number of transmitters exceeds the number of receivers. The literature of uplink space division multiple access (SDMA) systems is rich, but at the time of writing there is a paucity of information on the employment of SDMA techniques in the downlink. Hence, we propose a novel precoded downlink SDMA (DL-SDMA) multiuser communication system, which invokes a low-complexity nearmaximum-likelihood sphere decoder and is particularly suitable for the aforementioned rank-deficient scenario. Powerful iterative decoding is carried out by exchanging extrinsic information between the precoder’s decoder and the outer channel decoder. Furthermore, we demonstrate with the aid of extrinsic information transfer charts that our proposed precoded DL-SDMA system has a better convergence behavior than its nonprecoded DL-SDMA counterpart. Quantitatively, the proposed system having a normalized system load of Ls = 1.333, i.e., 1.333 times higher effective throughput facilitated by having 1.333 times more DL-SDMA transmitters than receivers, exhibits a “turbo cliff” at an Eb/N0 of 5 dB and hence results in an infinitesimally low bit error rate (BER). By contrast, at Eb/N0 = 5 dB, the equivalent system dispensing with precoding exhibits a BER in excess of 10%. Index Terms—Iterative decoding, maximum likelihood detection, space division multiple access (SDMA) downlink, sphere decoding

Iterative Joint Channel Estimation and Symbol Detection for Multi-User MIMO OFDM

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 literature, no channel estimation technique allows the number of users to be higher than the number of receiver antennas, which is often referred to as an “overloaded” scenario. In this contribution we propose a new Genetic Algorithm (GA) assisted iterative joint channel estimation and multiuser detection approach for MIMO SDMA-OFDM systems, which exhibits a robust performance in the above-mentioned overloaded scenario. Furthermore, GA-aided Multi-User Detection (MUD) techniques found in the literature can only provide a hard-decision output, while the proposed GA is capable of providing “soft” outputs, hence achieving an improved performance with the aid of channel decoders. Finally, a range of simulation results are provided to demonstrate the superiority of the proposed scheme

Luby Transform Coding Aided Iterative Detection for Downlink SDMA Systems

A Luby Transform (LT) coded downlink Spatial Division Multiple Access (SDMA) system using iterative detection is proposed, which invokes a low-complexity near-Maximum-Likelihood (ML) Sphere Decoder (SD). The Ethernet-based Internet section of the transmission chain inflicts random packet erasures, which is modelled by the Binary Erasure Channel (BEC), which the wireless downlink imposes both fading and noise. A novel log-Likelihood Ratio based packet reliability metric is used for identifying the channel-decoded packets, which are likely to be error-infested. Packets having residual errors must not be passed on to the KT decoder for the sake of avoiding LT-decoding –induced error propagation. The proposed scheme is capable of maintaining an infinitesimally low packet error ratio in the downlink of the wireless Internet for Eb/n0 values in excess of about 3dB

Reduced complexity single-carrier maximum-likelihood detection for decision feedback assisted space-time equalization

A novel Decision-Feedback (DF) aided reduced complexity Maximum Likelihood (ML) Space-Time Equalizer (STE) designed for single-carrier multiple antenna assisted receivers is introduced. The proposed receiver structure is based on a recursive tree search, which is capable of achieving ML performance at a moderate computational cost and substantially outperforms the linear benchmarker based on the Minimum Mean-Squared Error (MMSE) criterion. Additionally a further complexity reduction scheme is proposed, which exploits the specific characteristics of both the wide-band channel and the proposed DF-STE

Luby Transform Coding Aided Iterative Detection for Downlink SDMA Systems

A Luby Transform (LT) coded downlink Spatial Division Multiple Access (SDMA) system using iterative detection is proposed, which invokes a low-complexity near-Maximum-Likelihood (ML) Sphere Decoder (SD). The Ethernet-based Internet section of the transmission chain inflicts random packet erasures, which is modelled by the Binary Erasure Channel (BEC), which the wireless downlink imposes both fading and noise. A novel log-Likelihood Ratio based packet reliability metric is used for identifying the channel-decoded packets, which are likely to be error-infested. Packets having residual errors must not be passed on to the KT decoder for the sake of avoiding LT-decoding –induced error propagation. The proposed scheme is capable of maintaining an infinitesimally low packet error ratio in the downlink of the wireless Internet for Eb/n0 values in excess of about 3dB