Iterative Energy-Efficient Stable Matching Approach for Context-Aware Resource Allocation in D2D Communications

Energy efficiency (EE) is critical to fully achieve the huge potentials of device-to-device (D2D) communications with limited battery capacity. In this paper, we consider the two-stage EE optimization problem, which consists of a joint spectrum and power allocation problem in the first stage, and a context-aware D2D peer selection problem in the second stage. We provide a general tractable framework for solving the combinatorial problem, which is NP-hard due to the binary and continuous optimization variables. In each stage, user equipments (UEs) from two finite and disjoint sets are matched in a two-sided stable way based on the mutual preferences. First, the preferences of UEs are defined as the maximum achievable EE. An iterative power allocation algorithm is proposed to optimize EE under a specific match, which is developed by exploiting nonlinear fractional programming and Lagrange dual decomposition. Second, we propose an iterative matching algorithm, which first produces a stable match based on the fixed preferences, and then dynamically updates the preferences according to the latest matching results in each iteration. Finally, the properties of the proposed algorithm, including stability, optimality, complexity, and scalability, are analyzed in detail. Numerical results validate the efficiency and superiority of the proposed algorithm under various simulation scenarios

Matching Theory for Future Wireless Networks: Fundamentals and Applications

The emergence of novel wireless networking paradigms such as small cell and cognitive radio networks has forever transformed the way in which wireless systems are operated. In particular, the need for self-organizing solutions to manage the scarce spectral resources has become a prevalent theme in many emerging wireless systems. In this paper, the first comprehensive tutorial on the use of matching theory, a Nobelprize winning framework, for resource management in wireless networks is developed. To cater for the unique features of emerging wireless networks, a novel, wireless-oriented classification of matching theory is proposed. Then, the key solution concepts and algorithmic implementations of this framework are exposed. Then, the developed concepts are applied in three important wireless networking areas in order to demonstrate the usefulness of this analytical tool. Results show how matching theory can effectively improve the performance of resource allocation in all three applications discussed

Game-theoretic Resource Allocation Methods for Device-to-Device (D2D) Communication

Device-to-device (D2D) communication underlaying cellular networks allows mobile devices such as smartphones and tablets to use the licensed spectrum allocated to cellular services for direct peer-to-peer transmission. D2D communication can use either one-hop transmission (i.e., in D2D direct communication) or multi-hop cluster-based transmission (i.e., in D2D local area networks). The D2D devices can compete or cooperate with each other to reuse the radio resources in D2D networks. Therefore, resource allocation and access for D2D communication can be treated as games. The theories behind these games provide a variety of mathematical tools to effectively model and analyze the individual or group behaviors of D2D users. In addition, game models can provide distributed solutions to the resource allocation problems for D2D communication. The aim of this article is to demonstrate the applications of game-theoretic models to study the radio resource allocation issues in D2D communication. The article also outlines several key open research directions.Comment: Accepted. IEEE Wireless Comms Mag. 201

Joint relay selection and resource allocation for energy-efficient D2D cooperative communications using matching theory

Device-to-device (D2D) cooperative relay can improve network coverage and throughput by assisting users with inferior channel conditions to implement multi-hop transmissions. Due to the limited battery capacity of handheld equipment, energy efficiency is an important issue to be optimized. Considering the two-hop D2D relay communication scenario, this paper focuses on how to maximize the energy efficiency while guaranteeing the quality of service (QoS) requirements of both cellular and D2D links by jointly optimizing relay selection, spectrum allocation and power control. Since the four-dimensional matching involved in the joint optimization problem is NP-hard, a pricing-based two-stage matching algorithm is proposed to reduce dimensionality and provide a tractable solution. In the first stage, the spectrum resources reused by relay-to-receiver links are determined by a two-dimensional matching. Then, a three-dimensional matching is conducted to match users, relays and the spectrum resources reused by transmitter-to-relay links. In the process of preference establishment of the second stage, the optimal transmit power is solved to guarantee that the D2D link has the maximized energy efficiency. Simulation results show that the proposed algorithm not only has a good performance on energy efficiency, but also enhances the average number of served users compared to the case without any relay

Energy-Efficient Matching for Resource Allocation in D2D Enabled Cellular Networks

Energy-efficiency (EE) is critical for device-to-device (D2D) enabled cellular networks due to limited battery capacity and severe co-channel interference. In this paper, we address the EE optimization problem by adopting a stable matching approach. The NP-hard joint resource allocation problem is formulated as a one-to-one matching problem under two-sided preferences, which vary dynamically with channel states and interference levels. A game-theoretic approach is employed to analyze the interactions and correlations among user equipments (UEs), and an iterative power allocation algorithm is developed to establish mutual preferences based on nonlinear fractional programming. We then employ the Gale-Shapley (GS) algorithm to match D2D pairs with cellular UEs (CUs), which is proved to be stable and weak Pareto optimal. We provide a theoretical analysis and description for implementation details and algorithmic complexity. We also extend the algorithm to address scalability issues in large-scale networks by developing tie-breaking and preference deletion based matching rules. Simulation results validate the theoretical analysis and demonstrate that significant performance gains of average EE and matching satisfactions can be achieved by the proposed algorithm