Statistical QoS-Driven Resource Allocation and Source Adaptation for D2D Communications Underlaying OFDMA-Based Cellular Networks

TL;DR: This paper proposes an effective resource allocation and source adaptation policy to maximize the system throughput while satisfying each user’s delay QoS requirement and proposes effective algorithms based on an alternating optimization method, successive convex approximation method, and outer approximation method.

Abstract: Device-to-device (D2D) communication has become a key technology in the fifth-generation cellular networks. Resource allocation is critical to ensure satisfactory performance. However, most existing resource allocation policies focus on delay-unaware performance metrics, such as throughput and power consumption, thus being effective only in delay-insensitive scenarios. To overcome this problem, in this paper, we consider an orthogonal frequency division multiple access-based cellular network, where multiple cellular users and D2D pairs along with delay quality-of-service (QoS) requirements coexist to share multiple sub-channels. We propose an effective resource allocation and source adaptation policy to maximize the system throughput while satisfying each user’s delay QoS requirement. Specifically, we formulate a constraint optimization problem and solve it using the Lagrangian approach. In the dual domain, the key problem is solving for the dual function under given dual variable. This is a mixed integer non-linear programming problem with non-concave function and is non-linearly coupled over layers, thus being very difficult to solve. In response, we propose effective algorithms based on an alternating optimization method, successive convex approximation method, and outer approximation method. Analysis on the convergence and optimality of the proposed algorithms is given. Simulation results show that our proposed policy can improve the QoS-guaranteed system throughput significantly compared with the baselines.