@inproceedings{ab1d20308be14615953485eff1077cdb,
title = "Efficient implementation of a quasi-maximum-likelihood detector based on semi-definite relaxation",
abstract = "Existing approaches to the Maximum-Likelihood (ML) detection problem in digital communications either suffer from exponential complexity (e.g. Sphere Decoder and its variants) or exhibit significant Bit-Error-Rate (BER) degradation (e.g. LMMSE Detector). In this paper we present an efficient implementation of a semi-definite relaxation-based detector (SDR Detector) which can achieve nearoptimal BER performance with worst-case polynomial complexity. This implementation (available online) can be 100 times faster than an off-the-shelf SeDuMi-based implementation, outperforms Sphere Decoder in low Signal-to-Noise Ratio (SNR) or high dimension regimes, and matches the speed of Sphere Decoder in the high SNR regime. The core of the detector is an optimized dual-scaling interiorpoint method (implemented in C) for the relaxed semi-definite program. SNR-sensitive improvements are achieved by a dimension reduction strategy and a warm start technique based on a truncated version of the Sphere Decoding algorithm. Extensive numerical simulations show that the BER performance and the running time of SDR Detector compare favorably to that of other near-optimal detection strategies.",
keywords = "Duality, Interior-point methods, MIMO systems, Maximum likelihood detection, Semi-definite relaxation",
author = "Mikalai Kisialiou and Luo, {Zhi Quan}",
year = "2007",
doi = "10.1109/ICASSP.2007.367323",
language = "English (US)",
isbn = "1424407281",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "IV1329--IV1332",
booktitle = "2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07",
note = "2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 ; Conference date: 15-04-2007 Through 20-04-2007",
}