Logo image
The equivalence of semidefinite relaxation MIMO detectors for higher-order QAM
Journal article   Peer reviewed

The equivalence of semidefinite relaxation MIMO detectors for higher-order QAM

Wing-Kin Ma, Chao-Cheng Su, Joakim Jalden, Tsung-Hui Chang and Chong-Yung Chi
IEEE Journal on Selected Topics in Signal Processing, Vol.3(6), pp.1038-1052
12/2009

Abstract

Convex optimization Multi-input multi-output (MIMO) detection Semidefinite programming (SDP) Semidefinite relaxation (SDR)
In multi-input multi-output (MIMO) detection, semidefinite relaxation (SDR) has been shown to be an efficient high-performance approach. For BPSK and QPSK, it has been found that SDR can provide near-optimal bit error probability performance. This has stimulated a number of recent research endeavors that aim to apply SDR to the high-order QAM cases. These independently developed SDRs are different in concept, structure and complexity, and presently no serious analysis has been given to compare these methods. This paper analyzes the relationship of three such SDR methods, namely the polynomial-inspired SDR (PI-SDR) by Wiesel , the bound-constrained SDR (BC-SDR) by Sidiropoulos and Luo, and the virtually-antipodal SDR (VA-SDR) by Mao Rather unexpectedly, we prove that the three SDRs are equivalent in the following sense: The three SDRs yield the same optimal objective values, and their optimal solutions have strong correspondences. Specifically, we establish this solution equivalence between BC-SDR and VA-SDR for any 4 q -QAM constellations, and that between BC-SDR and PI-SDR for 16-QAM and 64-QAM. Moreover, the equivalence result holds for any channel, problem size, and signal-to-noise ratio. Our theoretical findings are confirmed by simulations, where the three SDRs offer identical symbol error probabilities. Additional simulation results are also provided to demonstrate the effectiveness of SDR compared to some other MIMO detectors, in terms of complexity and symbol error performance. © 2009 IEEE.

Metrics

1 Record Views

Details

Logo image