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

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

LDPC code-based bandwidth efficient coding schemes for wireless communications

This dissertation deals with the design of bandwidth-efficient coding schemes with Low-Density Parity-Check (LDPC) for reliable wireless communications. Code design for wireless channels roughly falls into three categories: (1) when channel state information (CSI) is known only to the receiver (2) more practical case of partial CSI at the receiver when the channel has to be estimated (3) when CSI is known to the receiver as well as the transmitter. We consider coding schemes for all the above categories. For the first scenario, we describe a bandwidth efficient scheme which uses highorder constellations such as QAM over both AWGN as well as fading channels. We propose a simple design with LDPC codes which combines the good properties of Multi-level Coding (MLC) and bit-interleaved coded-modulation (BICM) schemes. Through simulations, we show that the proposed scheme performs better than MLC for short-medium lengths on AWGN and block-fading channels. For the first case, we also characterize the rate-diversity tradeoff of MIMO-OFDM and SISO-OFDM systems. We design optimal coding schemes which achieve this tradeoff when transmission is from a constrained constellation. Through simulations, we show that with a sub-optimal iterative decoder, the performance of this coding scheme is very close to the optimal limit for MIMO (flat quasi-static fading), MIMO-OFDM and SISO OFDM systems. For the second case, we design non-systematic Irregular Repeat Accumulate (IRA) codes, which are a special class of LDPC codes, for Inter-Symbol Interference (ISI) fading channels when CSI is estimated at the receiver. We use Orthogonal Frequency Division Multiplexing (OFDM) to convert the ISI fading channel into parallel flat fading subchannels. We use a simple receiver structure that performs iterative channel estimation and decoding and use non-systematic IRA codes that are optimized for this receiver. This combination is shown to perform very close to a receiver with perfect CSI and is also shown to be robust to change in the number of channel taps and Doppler. For the third case, we look at bandwidth efficient schemes for fading channels that perform close to capacity when the channel state information is known at the transmitter as well as the receiver. Schemes that achieve capacity with a Gaussian codebook for the above system are already known but not for constrained constellations. We derive the near-optimum scheme to achieve capacity with constrained constellations and then propose coding schemes which perform close to capacity. Through linear transformations, a MIMO system can be converted into non-interfering parallel subchannels and we further extend the proposed coding schemes to the MIMO case too

Low-power Secret-key Agreement over OFDM

Information-theoretic secret-key agreement is perhaps the most practically feasible mechanism that provides unconditional security at the physical layer to date. In this paper, we consider the problem of secret-key agreement by sharing randomness at low power over an orthogonal frequency division multiplexing (OFDM) link, in the presence of an eavesdropper. The low power assumption greatly simplifies the design of the randomness sharing scheme, even in a fading channel scenario. We assess the performance of the proposed system in terms of secrecy key rate and show that a practical approach to key sharing is obtained by using low-density parity check (LDPC) codes for information reconciliation. Numerical results confirm the merits of the proposed approach as a feasible and practical solution. Moreover, the outage formulation allows to implement secret-key agreement even when only statistical knowledge of the eavesdropper channel is available.Comment: 9 pages, 4 figures; this is the authors prepared version of the paper with the same name accepted for HotWiSec 2013, the Second ACM Workshop on Hot Topics on Wireless Network Security and Privacy, Budapest, Hungary 17-19 April 201

Power allocation and linear precoding for wireless communications with finite-alphabet inputs

This dissertation proposes a new approach to maximizing data rate/throughput of practical communication system/networks through linear precoding and power allocation. First, the mutual information or capacity region is derived for finite-alphabet inputs such as phase-shift keying (PSK), pulse-amplitude modulation (PAM), and quadrature amplitude modulation (QAM) signals. This approach, without the commonly used Gaussian input assumptions, complicates the mutual information analysis and precoder design but improves performance when the designed precoders are applied to practical systems and networks. Second, several numerical optimization methods are developed for multiple-input multiple-output (MIMO) multiple access channels, dual-hop relay networks, and point-to-point MIMO systems. In MIMO multiple access channels, an iterative weighted sum rate maximization algorithm is proposed which utilizes an alternating optimization strategy and gradient descent update. In dual-hop relay networks, the structure of the optimal precoder is exploited to develop a two-step iterative algorithm based on convex optimization and optimization on the Stiefel manifold. The proposed algorithm is insensitive to initial point selection and able to achieve a near global optimal precoder solution. The gradient descent method is also used to obtain the optimal power allocation scheme which maximizes the mutual information between the source node and destination node in dual-hop relay networks. For point-to-point MIMO systems, a low complexity precoding design method is proposed, which maximizes the lower bound of the mutual information with discretized power allocation vector in a non-iterative fashion, thus reducing complexity. Finally, performances of the proposed power allocation and linear precoding schemes are evaluated in terms of both mutual information and bit error rate (BER). Numerical results show that at the same target mutual information or sum rate, the proposed approaches achieve 3-10dB gains compared to the existing methods in the medium signal-to-noise ratio region. Such significant gains are also indicated in the coded BER systems --Abstract, page iv-v

Code optimization and analysis for multiple-input and multiple-output communication systems

Design and analysis of random-like codes for various multiple-input and multiple-output communication systems are addressed in this work. Random-like codes have drawn significant interest because they offer capacity-achieving performance. We first consider the analysis and design of low-density parity-check (LDPC) codes for turbo multiuser detection in multipath CDMA channels. We develop techniques for computing the probability density function (pdf) of the extrinsic messages at the output of the soft-input soft-output (SISO) multiuser detectors as a function of the pdf of input extrinsic messages, user spreading codes, channel impulse responses, and signal-to-noise ratios. Using these techniques, we are able to accurately compute the thresholds for LDPC codes and design good irregular LDPC codes. We then apply the tools of density evolution with mixture Gaussian approximations to optimize irregular LDPC codes and to compute minimum operational signal-to-noise ratios for ergodic MIMO OFDM channels. In particular, the optimization is done for various MIMO OFDM system configurations which include different number of antennas, different channel models and different demodulation schemes. We also study the coding-spreading tradeoff in LDPC coded CDMA systems employing multiuser joint decoding. We solve the coding-spreading optimization based on the extrinsic information SNR evolution curves for the SISO multiuser detectors and the SISO LDPC decoders. Both single-cell and multi-cell scenarios will be considered. For each of these cases, we will characterize the extrinsic information for both finite-size systems and the so-called large systems where asymptotic performance results must be evoked. Finally, we consider the design optimization of irregular repeat accumulate (IRA) codes for MIMO communication systems employing iterative receivers. We present the density evolution-based procedure with Gaussian approximation for optimizing the IRA code ensemble. We adopt an approximation method based on linear programming to design an IRA code with the extrinsic information transfer (EXIT) chart matched to that of the soft MIMO demodulator