Linear detectors such as zero-forcing (ZF) and minimum mean square error (MMSE) require only a small fraction of computational complexity compared to maximum likelihood (ML) detector. However, linear detections suffer...
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ISBN:
(纸本)9781424492688
Linear detectors such as zero-forcing (ZF) and minimum mean square error (MMSE) require only a small fraction of computational complexity compared to maximum likelihood (ML) detector. However, linear detections suffer from severe performance degradation. In this paper, we propose a novel detection scheme which obtains the initial symbol detection by MMSE detector and then perform symbol ordering by signal-to-interference-and-noise ratio (SINR). The MMSE detected symbols with higher SINR are retained as part of final solution and cancelled from the original received signals. The remaining symbols with lower SINR are detected by K-best algorithm, which selects K best nodes in each layer of the partial tree search. The small value of K is sufficient to achieve good performances, and therefore the extra computational complexity is minimal. Simulation results show the performance superiority of the proposed method compared to the conventional MMSE detection. Moreover, at the similar symbol error rates, the total number of nodes visited in the proposed approach is much smaller than the conventional K-best detection scheme.
We consider soft multiple-input multiple-output (mimo) detection for the case of block fading. That is, the transmitted codeword spans over several independent channel realizations and several instances of the detecti...
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We consider soft multiple-input multiple-output (mimo) detection for the case of block fading. That is, the transmitted codeword spans over several independent channel realizations and several instances of the detection problem must be solved for each such realization. We develop methods that adaptively allocate computational resources to the detection problems of each channel realization, under a total per-codeword complexity constraint. Our main results are a formulation of the problem as a mathematical optimization problem with a well-defined objective function and constraints, and algorithms that solve this optimization problem efficiently computationally.
This paper considers a low-complexity Gaussian message passing iterative detection (GMPID) algorithm for a massive multiuser multiple-input multiple-output (MU-mimo) system, in which a base station with M antennas ser...
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This paper considers a low-complexity Gaussian message passing iterative detection (GMPID) algorithm for a massive multiuser multiple-input multiple-output (MU-mimo) system, in which a base station with M antennas serves K Gaussian sources simultaneously. Both K and M are very large numbers, and we consider the cases that K < M. The GMPID is a message passing algorithm operating on a fully connected loopy graph, which is well understood to be non-convergent in some cases. As it is hard to analyze the GMPID directly, the large-scale property of the massive MU-mimo is used to simplify the analysis. First, we prove that the variances of the GMPID definitely converge to the mean square error of minimum mean square error (mmse) detection. Second, we derive two sufficient conditions that make the means of the GMPID converge to those of the mmse detection. However, the means of GMPID may not converge when K/M >= (root 2- 1)(2). Therefore, a modified GMPID called scale-and-add GMPID, which converges to the mmse detection in mean and variance for any K < M, and has a faster convergence speed than the GMPID, but has no higher complexity than the GMPID, is proposed. Finally, numerical results are provided to verify the validity and accuracy of the theoretical results.
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