Network utility maximization in uplink multiuser wireless LANs

Multiuser Multiple-Input Multiple-Output (MU-MIMO) enables several simultaneous transmission from the stations toward a multiple antennas access point of a WLAN and thereby boosts the network throughput. However, this throughput enhancement greatly depends on which stations are transmitting concurrently, which in turn necessitates the design of an efficient scheduling algorithm. In this paper, we model uplink MU-MIMO scheduling as a network utility maximization problem that is a non-convex problem due to the wideband channel aware transmission rate function and scheduling constraints. Relying on the zero duality gap, a stochastic learning algorithm is designed to solve the dual of that problem. Determination of stochastic subgradient in the stochastic learning algorithm involves an NP- complete problem, hence imperialist competitive algorithm is exploited to solve this problem. The simulation results substantiate significant throughput improvement and airtime fairness superiority of this scheme compared to the existing ones. The results also show the proposed scheme is not able to guarantee the optimal throughput due to employing imperialist competitive algorithm.