Fair resource allocation for multiuser MIMO communications network

Abstract

This paper studies the fairness optimization of dynamic multiuser multicarrier allocation in the cellular downlink of MIMO orthogonal frequency division multiple access (OFDMA) systems. The varying capacity demands of different users motivate the fairness problem. In the resource allocation approaches that maximizing the sum rate or minimizing the total power often leads to poor fairness among users. The allocation is prone to starvation situation for the users with deep fading subchannels. Hence, this work considers the fairness issue and proposes to maximize the minimum rate surplus, where the rate surplus is defined as the difference between the demand data rate and the resulting allocated data rate. The fairness is inverse proportional to the gap of the maximum rate surplus to the minimum rate surplus among all users. In this work, the design of the precoding and decoding matrices for the MIMO structure is also developed. To solve the optimization problem, an iterative algorithm is proposed to optimize the subcarrier assignment with low complexity. Simulation results on multiuser MIMO environment show that the proposed algorithm strikes the balance between sum rate and fairness. Comparing with the stateof-the-art works, the proposed algorithm shows an advantage in keeping the sum rate while the fairness is significantly improved.

Multi-Objective Optimization of Wireless Information and Power Transfer in Multiuser OFDMA Systems

“…The reformulation is based on a weighted aggregation of the multiple objectives [10], [12], or a transformation of some objectives into constraints [10]- [15]. This approach has several limitations: 1) the conversion into SOO requires prior knowledge about the relative importance of each objective; even so, the accurate and precise selection on the weights could be quite difficult in practice, 2) the formulation of SOO may lose some perspectives of tradeoff among different objectives, and 3) the transformation of some objectives into constraints may produce improper (unattainable) constraints, which could result in an infeasible problem [16]- [18].…”

Interference Alignment and Fairness Algorithms for MIMO Cognitive Radio Systems

“…In [16], the max-min fairness has been considered in a multipair two-way relay system. In [17,18], the authors have achieved fairness by allocating the resource in MIMO networks. In cognitive radio networks, the max-min fairness has also been considered in [19].…”