A Near Optimal Linear Precoding for Multiuser MIMO Throughput Maximization 


Vol. 34,  No. 4, pp. 414-423, Apr.  2009


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  Abstract

This paper considers a linear precoding scheme that achieves near optimal sum rate. While the minimum mean square error (MMSE) precoding provides the better MSE performance at all signal-to-noise ratio (SNR) than the zero forcing (ZF) precoding, its sum rate shows superior performance to ZF precoding at low SNR but inferior performance to ZF precoding at high SNR, From this observation, we first propose a near optimal linear precoding scheme in terms of sum rate. The resulting precoding scheme regularizes ZF precoding to maximize the sum rate, resulting in better sum rate performance than both ZF precoding and MMSE precoding at all SNR ranges. To find regularization parameters, we propose a simple algorithm such that locally maximal sum rate is achieved. As a low complexity alternative, we also propose a simple power re-allocation scheme in the conventional regularized channel inversion scheme. Finally, the proposed scheme is tested under the presence of channel estimation error. By simulation, we show that the proposed scheme can maintain the performance gain in the presence of channel estimation error and is robust to the channel estimation error.

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  Cite this article

[IEEE Style]

S. Jang, J. Yang, K. Jang, D. K. Kim, "A Near Optimal Linear Precoding for Multiuser MIMO Throughput Maximization," The Journal of Korean Institute of Communications and Information Sciences, vol. 34, no. 4, pp. 414-423, 2009. DOI: .

[ACM Style]

Seunghun Jang, Janghoon Yang, Kyuhwan Jang, and Dong Ku Kim. 2009. A Near Optimal Linear Precoding for Multiuser MIMO Throughput Maximization. The Journal of Korean Institute of Communications and Information Sciences, 34, 4, (2009), 414-423. DOI: .

[KICS Style]

Seunghun Jang, Janghoon Yang, Kyuhwan Jang, Dong Ku Kim, "A Near Optimal Linear Precoding for Multiuser MIMO Throughput Maximization," The Journal of Korean Institute of Communications and Information Sciences, vol. 34, no. 4, pp. 414-423, 4. 2009.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880