A reinforcement learning approach for cost- and energy-aware mobile data offloading

With rapid increases in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying WiFi hotspots to offload their mobile traffic. However, these network-centric methods usually do not fulfill interests of mobile users (MUs). MUs consider many problems to decide whether to offload their traffic to a complementary WiFi network. In this paper, we study the WiFi offloading problem from MU's perspective by considering delay-tolerance of traffic, monetary cost, energy consumption as well as the availability of MU's mobility pattern. We first formulate the WiFi offloading problem as a finite-horizon discrete-time Markov decision process (FDTMDP) with known MU's mobility pattern and propose a dynamic programming based offloading algorithm. Since MU's mobility pattern may not be known in advance, we then propose a reinforcement learning based offloading algorithm, which can work well with unknown MU's mobility pattern. Extensive simulations are conducted to validate our proposed offloading algorithms.