MG-Local: a multivariable control framework for optimal wireless resource management

Competition for finite resources causes severe congestion and collisions in wireless networks. Without effective management, the network can become unstable, and users may experience very long delay, significant packet loss and poor throughput. In this paper, we propose a multivariable globalized-local (MG-Local) framework of resource management to find a balance between fair allocation and efficient utilization. This framework uses adaptive multivariable control to improve control effectiveness. Our design combines the advantages of both global and local optimization methods, and drives the system toward a global optimum by intelligently exploiting local information, without message passing. We demonstrate the effectiveness of this generic resource-management framework by applying it at the medium access control layer, which is the major performance bottleneck in wireless network [1]. Our experimental results show that our method significantly outperforms four other approaches in terms of throughput, packet loss rate, delay, and fairness.