Cross-layer rate optimization for proportional fairness in multihop wireless networks with random access

In this paper, we address the rate control problem in a multihop 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 with its counterpart in a wired network where link capacities are fixed, rate control in a multihop random access network is much more complex and requires joint optimization at both the transport and link layers. This is due to the fact that the attainable throughput on each link in the network is "elastic" and is typically a nonconvex and nonseparable function of the transmission attempt rates. Two cross-layer algorithms, a dual-based algorithm and a penalty-based algorithm, are proposed in this paper to solve the rate control problem in a multihop 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 in support of our conclusions.