In this paper, we address the rate control problem in a multi-hop random access wireless network, with the objective of achieving proportional fairness amongst the end-to-end sessions. The problem is considered in the framework of nonlinear optimization. Compared to its counterpart in a wired network where link capacities are assumed to be fixed, rate control in a multi-hop random access network is much more complex and requires joint optimization at both the transport layer and the link layer. This is due to the fact that the attainable throughput on each link in the network is `elastic' and is typically a non-convex and non-separable function of the transmission attempt rates. Two cross-layer algorithms, a dual based algorithm and a primal based algorithm, are proposed in this paper to solve the rate control problem in a multi-hop random access network. Both algorithms can be implemented in a distributed manner, and work at the link layer to adjust link attempt probabilities and at the transport layer to adjust session rates. We prove rigorously that the two proposed algorithms converge to the globally optimal solutions. Simulation results are provided to support our conclusions.
['Xin Wang', 'Koushik Kar']
Cross-layer rate control for end-to-end proportional fairness in wireless networks with random access