Stochastic optimization assisted joint channel estimation and multi-user detection for OFDM/SDMA

Zhang, Jiankang, Chen, Sheng, Mu, Xiaomin and Hanzo, Lajos (2012) Stochastic optimization assisted joint channel estimation and multi-user detection for OFDM/SDMA. In, 76th Vehicular Technology Conference (VTC 2012 Fall), Québec, Canada, 03 - 06 Sep 2012. 5pp.


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Stochastic optimization assisted joint Channel Estimation (CE) and Multi-User Detection (MUD) were conceived and compared in the context of multi-user Multiple-Input Multiple-Output (MIMO) aided Orthogonal Frequency-Division Multiplexing/Space Division Multiple Access (OFDM/SDMA) systems. The development of stochastic optimization algorithms, such as Genetic Algorithms (GA), Repeated Weighted Boosting Search (RWBS), Particle Swarm Optimization (PSO) and Differential Evolution (DE) has stimulated wide interests in the signal processing and communication research community. However, the quantitative performance versus complexity comparison of GA, RWBS, PSO and DE techniques applied to joint CE and MUD is a challenging open issue at the time of writing, which has to consider both the continuous-valued CE optimization problem and the discretevalued MUD optimization problem. In this study we fill this gap in the open literature. Our simulation results demonstrated that stochastic optimization assisted joint CE and MUD is capable of approaching both the Cramer-Rao Lower Bound (CRLB) and the Bit Error Ratio (BER) performance of the optimal ML-MUD, respectively, despite the fact that its computational complexity is only a fraction of the optimal ML complexity

Item Type: Conference or Workshop Item (Paper)
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Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Southampton Wireless Group
ePrint ID: 342648
Accepted Date and Publication Date:
September 2012Delivered
Date Deposited: 12 Sep 2012 09:35
Last Modified: 31 Mar 2016 14:33
Further Information:Google Scholar

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