Wireless Communications Systems: Advanced Techniques for Signal Reception
Wireless Communications Systems: Advanced Techniques for Signal Reception
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Implementation of a Markov chain Monte Carlo based multiuser/MIMO detector
IEEE Transactions on Circuits and Systems Part I: Regular Papers
On the complexity of sphere decoding in digital communications
IEEE Transactions on Signal Processing
Convergence analyses and comparisons of Markov chain Monte Carloalgorithms in digital communications
IEEE Transactions on Signal Processing
On the sphere-decoding algorithm I. Expected complexity
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Signal Processing - Part I
Markov chain Monte Carlo algorithms for CDMA and MIMO communication systems
IEEE Transactions on Signal Processing
Closest point search in lattices
IEEE Transactions on Information Theory
On maximum-likelihood detection and the search for the closest lattice point
IEEE Transactions on Information Theory
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In this paper we study a Markov Chain Monte Carlo (MCMC) Gibbs sampler for solving the integer leastsquares problem. In digital communication the problem is equivalent to performing Maximum Likelihood (ML) detection in Multiple-Input Multiple-Output (MIMO) systems. While the use of MCMC methods for such problems has already been proposed, our method is novel in that we optimize the "temperature" parameter so that in steady state, i.e. after the Markov chain has mixed, there is only polynomially (rather than exponentially) small probability of encountering the optimal solution. More precisely, we obtain the largest value of the temperature parameter for this to occur, since the higher the temperature, the faster the mixing. This is in contrast to simulated annealing techniques where, rather than being held fixed, the temperature parameter is tended to zero. Simulations suggest that the resulting Gibbs sampler provides a computationally efficient way of achieving approximative ML detection in MIMO systems having a huge number of transmit and receive dimensions. In fact, they further suggest that the Markov chain is rapidly mixing. Thus, it has been observed that even in cases were ML detection using, e.g. sphere decoding becomes infeasible, the Gibbs sampler can still offer a near-optimal solution using much less computations.