On Achieving Optimal Congestion Control for Elastic Traffic in Cognitive Radio Ad Hoc Networks under Predictable Contact

In this paper, we investigate the congestion control problem for elastic traffic over cognitive radio ad hoc networks under predictable contact. In particular, we develop a differential game based framework achieving optimal congestion control, aiming to adjust the rate of flows from upstream secondary users (SUs) to bottleneck SU. The proposed framework can balance the predictable contact, noncooperation, and rate based congestion control among SUs while utilizing the spectrum opportunities vacated by primary users. Further, we also devise an optimal rate regulation algorithm, which guarantees the constraint of the saturation value of the buffer of bottleneck SU. The simulation results demonstrate the effectiveness and feasibility of our proposed congestion control scheme.