Som, P and Datta, T and Chockalingam, A and Rajan, BS (2010) Improved large-MIMO detection based on damped belief propagation. In: Information Theory Workshop (ITW), 2010 IEEE , 6-8 Jan. 2010, Cairo.
PDF
Improved_Large.pdf - Published Version Restricted to Registered users only Download (1MB) | Request a copy |
Abstract
In this paper, we consider the application of belief propagation (BP) to achieve near-optimal signal detection in large multiple-input multiple-output (MIMO) systems at low complexities. Large-MIMO architectures based on spatial multiplexing (V-BLAST) as well as non-orthogonal space-time block codes(STBC) from cyclic division algebra (CDA) are considered. We adopt graphical models based on Markov random fields (MRF) and factor graphs (FG). In the MRF based approach, we use pairwise compatibility functions although the graphical models of MIMO systems are fully/densely connected. In the FG approach, we employ a Gaussian approximation (GA) of the multi-antenna interference, which significantly reduces the complexity while achieving very good performance for large dimensions. We show that i) both MRF and FG based BP approaches exhibit large-system behavior, where increasingly closer to optimal performance is achieved with increasing number of dimensions, and ii) damping of messages/beliefs significantly improves the bit error performance.
Item Type: | Conference Paper |
---|---|
Publisher: | IEEE |
Additional Information: | Copyright 2010 IEEE. Personal use of this material is permitted.However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. |
Department/Centre: | Division of Electrical Sciences > Electrical Communication Engineering |
Date Deposited: | 20 Dec 2011 06:17 |
Last Modified: | 20 Dec 2011 06:17 |
URI: | http://eprints.iisc.ac.in/id/eprint/39056 |
Actions (login required)
View Item |