On the empirical distribution of eigenvalues of a class of large dimensional random matrices
Journal of Multivariate Analysis
Multiuser Detection
Random matrix theory and wireless communications
Communications and Information Theory
Space Time Coding for Broadband Wireless Communications
Space Time Coding for Broadband Wireless Communications
Approximation Bounds for Quadratic Optimization with Homogeneous Quadratic Constraints
SIAM Journal on Optimization
Efficient implementation of quasi-maximum-likelihood detection based on semidefinite relaxation
IEEE Transactions on Signal Processing
Semidefinite relaxation based multiuser detection for M-ary PSK multiuser systems
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Signal Processing
Linear multiuser receivers: effective interference, effective bandwidth and user capacity
IEEE Transactions on Information Theory
A universal lattice code decoder for fading channels
IEEE Transactions on Information Theory
A statistical-mechanics approach to large-system analysis of CDMA multiuser detectors
IEEE Transactions on Information Theory
On maximum-likelihood detection and the search for the closest lattice point
IEEE Transactions on Information Theory
Randomly spread CDMA: asymptotics via statistical physics
IEEE Transactions on Information Theory
A Near-Maximum-Likelihood Decoding Algorithm for MIMO Systems Based on Semi-Definite Programming
IEEE Transactions on Information Theory
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We consider semidefinite programming relaxation (SDR) of a binary quadratic minimization problem. This NP-hard problem arises naturally in the maximum-likelihood detection of discrete signals for digital communications. We analyze the average performance of the SDR algorithm for a class of randomly generated binary quadratic minimization problems. Although the SDR worst-case approximation ratio is unbounded for this NP-hard problem, our analysis shows that SDR can provide in polynomial time a provably near-optimal solution, achieving a constant factor approximation of the optimal objective value in probability. Moreover, this constant factor remains bounded with increasing problem size. Our proof is based on an asymptotic analysis of Karush-Kuhn-Tucker optimality conditions using random matrix theory.