Constrained least squares detector for OFDM/SDMA-based wireless networks

Abstract

Abstract-The two major obstacles toward high-capacity indoor wireless networks are distortion due to the indoor channel and the limited bandwidth which necessitates a high spectral efficiency. A combined orthogonal frequency division multiplexing (OFDM) / spatial division multiple access (SDMA) approach can efficiently tackle both obstacles and paves the way for cheap, high-capacity wireless indoor networks [27], [26]. The channel distortion due to multipath propagation is efficiently mitigated with OFDM while the bandwidth efficiency can be increased with the use of SDMA. However, to keep the cost of an indoor wireless network comparable to its wired counterpart's cost, low-complexity SDMA processors with good performance are of special interest. In this paper, we propose a new multiuser SDMA detector which is designed for constant modulus signals. This constrained least squares (CLS) receiver, which deterministically exploits the constant modulus nature of the subcarrier modulation to achieve better separation, is compared in terms of performance and complexity with the zero forcing (ZF) and the minimum mean square error (MMSE) receiver. Additionally, since the CLS detector relies on reliable channel knowledge at the receiver, we propose a strategy for estimating the multiple input multiple output (MIMO) channels. Simulations for a Hiperlan II-based [13] case-study show that the CLS detector significantly outperforms the ZF detector and comes close to the performance of the MMSE detector for QPSK. For higher order M-PSK, the CLS detector outperforms the MMSE detector. Furthermore, the estimation complexity for the CLS detector is substantially lower than that for the MMSE detector which additionally requires estimation of the noise power.Index Terms-Adaptive antennas, antenna diversity, channel estimation, orthogonal frequency division multiplexing (OFDM), space division multiple access (SDMA).

Differential Evolution Algorithm Aided Minimum Symbol Error Rate Multi-User Detection for Multi-User OFDM/SDMA Systems

“…As one of the most popular linear SDMA-receiver design strategy, Minimum Mean Square Error (MMSE) MUD [3] strikes a tradeoff between the achievable MultiUser Interference (MUI) rejection and noise amplification. In [4], a Acknowledgments: The financial support of the China Scholarship Council (CSC) and the EPSRC under the auspices of the China-UK Science Bridge as well as of the RC-UK under the India-UK Advanced Technology centre initiative is gratefully acknowledged.…”

“…This CLS-MUD outperformed the MMSE MUD, despite its lower computational complexity. In general, the MMSE-MUD [3] and the CLS-MUD [4] may be viewed as linear MUDs, which minimize the Mean Square Error (MSE) at the MUD's output. However, it is not the MSE, but the BER or Symbol Error Ratio (SER) that really matters in most communications system.…”

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

“…Naturally, for near-single-user MUD the CIRs have to be accurately estimated [1,3]. 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].…”

Turbo Multi-User Detection for OFDM/SDMA Systems Relying on Differential Evolution Aided Iterative Channel Estimation

“…Naturally, for a MUD to achieve near-single-user performance, the CIRs have to be accurately estimated [1,4]. Intensive research efforts have been devoted to developing efficient approaches for channel estimation (CE) in multi-user OFDM/SDMA systems [1,5,6]. In order to achieve a nearoptimal performance, joint CE and signal detection schemes have recently received significant research attention [2,7,8].…”

“…As one of the most popular linear SDMA-receiver design strategy, minimum mean square error (MMSE) MUD [3,4] strikes a tradeoff between the achievable multi-user interference (MUI) rejection and noise amplification. In [6], a constrained least squares (CLS) detector was designed for constant modulus signals, which exploited the constant modulus nature of the subcarrier modulation. This CLS-MUD outperformed the MMSE MUD, despite its lower computational complexity.…”