A low complexity user scheduling algorithm for uplink multiuser MIMO systems

A low complexity user scheduling algorithm based on a novel adaptive Markov chain Monte Carlo (AMCMC) method is proposed to achieve the maximal sum capacity in an uplink multiple-input multiple-output (MIMO) multiuser system. Compared with the existing scheduling algorithms, our algorithm is not only more efficient but also converges to within 99% of the optimal capacity obtained by exhaustive search. We demonstrate the convergence of the proposed scheduling algorithm and study the tradeoff between its complexity and performance.

[1]  Tim Hesterberg,et al.  Monte Carlo Strategies in Scientific Computing , 2002, Technometrics.

[2]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[3]  Jeffrey S. Rosenthal,et al.  Coupling and Ergodicity of Adaptive MCMC , 2007 .

[4]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[5]  Jeffrey G. Andrews,et al.  Low Complexity User Selection Algorithms for Multiuser MIMO Systems with Block Diagonalization , 2006, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[6]  L Tierney,et al.  Some adaptive monte carlo methods for Bayesian inference. , 1999, Statistics in medicine.

[7]  Huaiyu Zhu.  On Information and Sufficiency , 1997 .

[8]  Emre Telatar,et al.  Capacity of Multi-antenna Gaussian Channels , 1999, Eur. Trans. Telecommun..

[9]  Nando de Freitas,et al.  An Introduction to MCMC for Machine Learning , 2004, Machine Learning.

[10]  Kin-Nang Lau.  Analytical framework for multiuser uplink MIMO space-time scheduling design with convex utility functions , 2004, IEEE Transactions on Wireless Communications.

[11]  Andrea J. Goldsmith,et al.  Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels , 2003, IEEE Trans. Inf. Theory.

[12]  H. Robbins.  A Stochastic Approximation Method , 1951 .

[13]  J. Rosenthal,et al.  Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms , 2007, Journal of Applied Probability.