Energy-efficient multiuser SIMO: achieving probabilistic robustness with Gaussian channel uncertainty

  • Authors:

  • Gan Zheng;Kai-Kit Wong;Tung-Sang Ng

  • Affiliations:

  • Department of Electronic and Electrical Engineering, University College London, UK;Department of Electronic and Electrical Engineering, University College London, UK;Electrical and Electronic Engineering Department, The University of Hong Kong

  • Venue:
  • IEEE Transactions on Communications
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper addresses the joint optimization of power control and receive beamforming vectors for a multiuser single-input multiple-output (SIMO) antenna system in the uplink in which mobile users are single-antenna transmitters and the base station receiver has multiple antennas. Channel state information at the receiver (CSIR) is exploited but the CSIR is imperfect with its uncertainty being modeled as a random Gaussian matrix. Our objective is to devise an energy-efficient solution to minimize the individual users' transmit power while meeting the users' signal-to-interference plus noise ratio (SINR) constraints, under the consideration of CSIR and its error characteristics. This is achieved by solving a sum-power minimization problem, subject to a collection of users' outage probability constraints on their target SINRs. Regarding the signal power minus the sum of inter-user interferences (SMI) power as Gaussian, an iterative and convergent algorithm which is proved to reach the global optimum for the joint power allocation and receive beamforming solution, is proposed, though the optimization problem is indeed non-convex. A systematic scheme to detect feasibility and find a feasible initial solution, if there exists any, is also devised. Simulation results verify the use of Gaussian approximation and robustness of the proposed algorithm in terms of users' probability constraints, and indicate a significant performance gain as compared to the zero-forcing (ZF) and minimum mean-square-error (MMSE) beamforming systems.