Resource allocation for D2D-assisted haptic communications

Abstract

Haptic communications is recognized as a promising enabler of extensive services by enabling real-time haptic control and feedback in remote environments, e.g., teleoperation and autonomous driving. Considering the strict transmission requirements on reliability and latency, Device-to-Device (D2D) communications is introduced to assist haptic communications. In particular, the teleoperators with poor channel quality are assisted by auxiliaries, and each auxiliary and its corresponding teleoperator constitute a D2D pair. However, the haptic interaction and the scarcity of radio resources pose severe challenges to the resource allocation, especially facing the sporadic packet arrivals. First, the contention-based access scheme is applied to achieve low-latency transmission, where the resource scheduling latency is omitted and users can directly access available resources. In this context, we derive the reliability index of D2D pairs under the contention-based access scheme, i.e., closed-loop packet error probability. Then, the reliability performance is guaranteed by bidirectional power control, which aims to minimize the sum packet error probability of all D2D pairs. Potential game theory is introduced to solve the problem with low complexity. Accordingly, a distributed power control algorithm based on synchronous log-linear learning is proposed to converge to the optimal Nash Equilibrium. Experimental results demonstrate the superiority of the proposed learning algorithm.