LAUSR

Mobile data offloading allows alternative network systems such as Wi-Fi hotspot access points (APs) to offload the traffic that is originally targeted for the cellular network operators (CNOs). It can alleviate the cellular network congestions and enhance users’ quality-of-service. One of the main challenges of the mobile data offloading is to develop simple and distributed traffic allocation strategies that can optimally allocate the cellular data traffic from multiple CNOs to the APs. In this work, we propose a distributed optimization framework with decomposition-coordination to solve this problem. In our framework, the utility maximization problem of the cellular network system with mobile data offloading is formulated as a nonsmooth convex optimization problem. We then divide this problem into a set of subproblems, and each of them is solved by a CNO or an AP using its local information. All the subproblems are coordinated with each other by a virtual data offloading coordinator (VDOC), which can collect the intermediate calculation values from CNOs and APs. The VDOC will then feedback the coordination results calculated from the collected values to the corresponding CNOs and APs. We propose two distributed algorithms that can achieve the global optimization solution under different scenarios. The first one is the multiblock proximal Jacobi alternating direction method of multipliers (ProxJ-ADMM). In this algorithm, the communication between the VDOC and CNOs or APs is assumed to be fully synchronized, and the VDOC will only feedback the coordination result after successfully receiving all the values from APs and CNOs. We relax this assumption by introducing the second algorithm referred to as the distributed asynchronized ADMM (Async-ADMM) algorithm. In this algorithm, the coordination between the VDOC and APs or CNOs does not need to be perfectly synchronized and the VDOC will feedback a coordination result whenever it receives a value from at least one AP or CNO. We prove that both algorithm can achieve the global optimal solution. We present the numerical results to verify the performance of our proposed approaches under various network settings and conditions.