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B. K. Praveen and S. Das, “Neural Network Based Multiuser Detection Techniques in SDMA-OFDM System,” Proceedings of Annual IEEE India Conference, Hyderabad, 16-18 December 2011, pp. 1-4. doi:10.1109/INDCON.2011.6139436

has been cited by the following article:

• TITLE: Performance of GA and PSO aided SDMA/OFDM Over-Loaded System in a Near-Realistic Fading Environment

  AUTHORS: K. V. Shahnaz, C. K. Ali

  KEYWORDS: Space Division Multiple Access (SDMA); Orthogonal Frequency Division Multiplexing (OFDM); GA; PSO; TGn Channel Model

  JOURNAL NAME: Wireless Engineering and Technology, Vol.3 No.4, November 1, 2012

  ABSTRACT: In this work, two popular evolutionary algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) based SDMA-OFDM multi user detection (MUD) have been presented which overcome the limitations of classical detectors. They are simple to implement and their complexity in terms of decision-metric evaluations is very less compared to maximum likelihood detection (MLD). These techniques are shown to provide a high performance as compared to the other detectors especially in a rank-deficient scenario where numbers of users are high as compared to the base station (BS) antennas. In this scenario, Zero forcing (ZF) and minimum mean square error (MMSE) based MUDs exhibit severe performance degradation. To investigate almost realistic performance of a wireless communication system, it is important to use a proper channel model. Since the simulation parameters in this work are based on IEEE 802.11n wireless local area network (WLAN) standard, TGn is the channel model used.