Hybrid ZF-ML detection for N × N spatial multiplexing systems

Hong Wei Shieh, Yinman Lee, Sok Ian Sou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Both zero-forcing (ZF) and maximum-likelihood (ML) criteria are widely used for signal detection in multipleinput multiple-output (MIMO) spatial multiplexing systems. In this paper, we propose a special formulation to combine these two detection algorithms for the N × N scenario, which can provide a good compromise between the error-rate performance and computational complexity. Specifically, the detection is decomposed into some ZF stages, and at each of these stages a fraction of the data streams is nulled to zero. After that, an ML stage is performed utilizing the idea of ML with reduced dimension. We show that the resultant bit-error rate (BER) performance of this hybrid ZF-ML detection can be much better than that of the ZF detection, and the total number of floating-point operations (flops) can be comparable with that of the conventional ZF calculation.

Original languageEnglish
Title of host publication2011 IEEE 7th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob'2011
Pages327-332
Number of pages6
DOIs
Publication statusPublished - 2011 Dec 28
Event2011 IEEE 7th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob'2011 - Shanghai, China
Duration: 2011 Oct 102011 Oct 12

Publication series

NameInternational Conference on Wireless and Mobile Computing, Networking and Communications
ISSN (Print)2161-9646
ISSN (Electronic)2161-9654

Other

Other2011 IEEE 7th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob'2011
Country/TerritoryChina
CityShanghai
Period11-10-1011-10-12

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

Fingerprint

Dive into the research topics of 'Hybrid ZF-ML detection for N × N spatial multiplexing systems'. Together they form a unique fingerprint.

Cite this