www.tjprc.org editor@tjprc.org International Journal of Electrical and Electronics Engineering Research (IJEEER) ISSN(P): 2250-155X; ISSN(E): 2278-943X Vol. 4, Issue 2, Apr 2014, 71-82 © TJPRC Pvt. Ltd. MAXIMUM LIKELIHOOD WITH HEURISTIC DETECTORS IN LARGE MIMO SYSTEMS FOR EFFECTIVELY RECEIVING BITS D. YOGANANDAN 1 , S. CHANDRAMOHAN 2 & P. VENKATESAN 3 1 Lecturer, AAIST, Chennai, Tamil Nadu, India 2 Assistant Professor, SCSVMV University, Kanchipuram, Tamil Nadu, India 3 Senior Assistant Professor, SCSVMV University, Kanchipuram, Tamil Nadu, India ABSTRACT We propose low-complexity detectors for large MIMO systems with QAM constellations. These detectors work at the bit level and consist of three stages. In the first stage, ML decisions on certain bits are made in an efficient way. In the second stage, soft values for the remaining bits are calculated with multi iteration concept. In the third stage, these remaining bits are detected by means of a heuristic programming method for high-dimensional optimization that uses the soft values (“soft -heuristic” algorithm). We propose two soft-heuristic algorithms with different performance and complexity. We also consider a feedback of the results of the third stage for computing improved soft values in the second stage. KEYWORDS: Genetic Algorithm, Heuristic Programming, ICI Mitigation, Large MIMO Systems, MIMO Detection, Multiple-Input Multiple-Output Systems, OFDM 1. INTRODUCTION (MIMO) MULTIPLE-INPUT/MULTIPLE-OUTPUT (MIMO) systems for wireless communications have received considerable interest. A MIMO system with input dimension N t and output dimension N r can be described by the input-output relation y = Hs+n (1) Where s ϵ S Nt is the transmit symbol vector (here, S denotes a finite symbol alphabet y ϵ C Nr is the received vector H ϵ C Nr x Nt , is the channel matrix, and n ϵ C Nr is a noise vector. The MIMO model is relevant to multi antenna wireless systems, orthogonal frequency-division multiplexing (OFDM) systems and code-division multiple access (CDMA) systems. Here, we consider the detection of s fro m y under the frequently used assumptions that the channel matrix H is known and the noise is n independent and identically distributed (iid) circularly symmetric complex Gaussian, n ~ ƇƝ(0,ᶆI Nr ) where ᶆ is the noise Variance I Nr and is the Nr x Nt identity matrix. State of the Art The result of maximum-likelihood (ML) detection, which minimizes the error probability for equally likely transmit Vectors, s ϵ S Nt is given by [1] Ŝ ML (y) = arg min sϵS Nt ||y - Hs|| (2)