A Stochastic Optimization Framework for Channel Bonding in Wireless LANs Under Demand Uncertainty

Channel bonding is one promising approach to cope with rising WLAN data demand, given scarce spectrum resources. An access point (AP) can aggregate multiple contiguous channels to satisfy demand. We discuss how to optimally utilize available frequency bands under uncertainty in AP demands using two stochastic optimization frameworks: a static scheme which minimizes the total occupied bandwidth while satisfying the demand of each AP with probability at least β, and an adaptive scheme that allows adaptability of the bandwidth allocation in response to the AP demand variations. Given its complexity, we propose a novel framework to solve the adaptive stochastic optimization problem efficiently. The proposed framework exploits the special structure of the problem through decomposition into two subproblems. A particle swarm optimization (PSO)-based algorithm is tailored to the first-stage problem in order to obtain good solutions. The second-stage problem is further decomposed into several subproblems that can be solved independently in parallel. Our numerical results (i) demonstrate the advantages of stochastic compared to deterministic allocation, (ii) illustrate that the proposed framework reaches the optimal solution for the two-stage problem in few iterations, and (iii) explain the bandwidth-user satisfaction trade-off provided by the adaptive allocation approach.