Resource Management for Cellular-Assisted Device-to-Device (D2D) Communications

Device-to-Device (D2D) communication has become a promising candidate for future wireless communication systems to improve the system spectral efficiency, while reducing the latency and energy consumption of individual communication. With the assistance of cellular network, D2D communications can greatly reduce the transmit distance by utilizing the spatial dispersive nature of ever increasing user devices. Further, substantial spectrum reuse gain can be achieved due to the short transmit distance of D2D communication. It, however, significantly complicates the resource management and performance analysis of D2D communication underlaid cellular networks. Despite an increasing amount of academic attention and industrial interests, how to evaluate the system performance advantages of D2D communications with resource management remains largely unknown. On account of the proximity requirement of D2D communication, the resource management of D2D communication generally consists of admission access control and resource allocation. Resource allocation of cellular assisted D2D communications is very challenging when frequency reuse is considered among multiple D2D pairs within a cell, as intense inter D2D interference is difficult to tackle and generally causes extremely large amount of signaling overheads for channel state information (CSI) acquisition. Hence, the first part of this thesis is devoted to the resource allocation of cellular assisted D2D communication and the performance analysis. A novel resource allocation scheme for cellular assisted D2D communication is developed with low signaling overhead, while maintaining high spectral efficiency. By utilizing the spatial dispersive nature of D2D pairs, a geography-based sub-cell division strategy is proposed to group the D2D pairs into multiple disjoint clusters, and sub-cell resource allocation is performed independently for the D2D pairs within each sub-cell without the need of any prior knowledge of inter D2D interference. Under the proposed resource allocation scheme, tractable approximation for the inter D2D interference modeling is obtained and a computationally efficient expression for the average ergodic sum capacity of the cell is derived. The expression further allows us to obtain the optimal number of sub-cells that maximizes the average ergodic sum capacity of the cell. It is shown that with small CSI feedback, the system capacity/spectral efficiency can be improved significantly by adopting the proposed resource allocation scheme, especially in dense D2D deployment scenario. The investigation of use cases for cellular assisted D2D communication is another important topic which has direct effect on the performance evaluation of D2D communication. Thanks to the spatial dispersive nature of devices, D2D communication can be utilized to harvest the vast amount of the idle computation power and storage space distributed at the devices, which yields sufficient capacities for performing computation-intensive and latency-critical tasks. Therefore, the second part of this thesis focuses on the D2D communication assisted Mobile Edge Computing (MEC) network. The admission access control of D2D communication is determined by both disciplines of mobile computing and wireless communications. Specifically, the energy minimization problem in D2D assisted MEC networks is addressed with the latency constraint of each individual task and the computing resource constraint of each computing entity. The energy minimization problem is formed as a two-stage optimization problem. At the first stage, an initial feasibility problem is formed to maximize the number of executed tasks, and the global energy minimization problem is tackled in the second stage while maintaining the maximum number of executed tasks. Both of the optimization problems in two stages are NP-hard, therefore a low-complexity algorithm is developed for the initial feasibility problem with a supplementary algorithm further proposed for energy minimization. Simulation results demonstrate the near-optimal performance of the proposed algorithms and the fact that the number of executed tasks is greatly increased and the energy consumption per executed task is significantly reduced with the assistance of D2D communication in MEC networks, especially in dense user scenario