Distributed Dynamic Resource Allocation for OFDMA-Based Cognitive Small Cell Networks Using a Regret-Matching Game Approach

Game theoretical approaches have been used to develop distributed resource allocation technologies for cognitive heterogeneous networks. In this chapter, we present a novel distributed resource allocation strategy for cognitive small cell networks based on orthogonal frequency-division multiple access. In particular, we consider a heterogeneous network consisting of macrocell networks overlaid with cognitive small cells that opportunistically access the available spectrum. We focus on a regret-matching game approach, aiming at maximizing the total throughput of the small cell network subject to cross-tier interference and quality of service (QoS) constraints. The regret-matching game approach exploits a regret procedure to learn the optimal resource allocation strategy from the regrets of the actions of cognitive users. Furthermore, the regret-matching game approach is extended to the joint resource allocation and user admission control problem. Numerical results are presented to demonstrate the effectiveness of the proposed regre-matching approaches.

Game Theory-Based Radio Resource Optimization in Heterogeneous Small Cell Networks (HSCNs)

To optimize radio resource allocation, the game theory is utilized as a powerful tool because its characteristic can be adaptive to the distribution characteristics of in heterogeneous small cell networks (HSCNs). This chapter summarizes the recent achievements for the game theory based radio resource allocation in HSCNs, where macro base stations (MBSs) and dense small cell base stations (SBSs) share the same frequency spectrum and interfere with each other. Two kinds of game models are introduced to optimize the radio resource allocation, namely the non-cooperative Stackelberg and the cooperative coalition. System models, optimization problem formulation, problem solution, and simulation results for these two kinds of game models are presented. Particularly, the Stackelberg models for HSCNs are presented with the Stackelberg equilibrium and the closed-form expressions. The coalition formations for traditional HCSNs, cloud small cell networks, and heterogeneous cloud small cell networks are introduced. Simulation results are shown to demonstrate the proposed game theory based radio resource optimization strategies converged and efficient.

Game Theory-Based Coverage Optimization for Small Cell Networks

Focusing on the coverage optimization of small cell networks (SCN), this chapter starts with a detailed analysis on various coverage problems, based on which the coverage optimization problem is formulated. Then centralized and distributed coverage optimization methods based on game theory are described. Firstly, considering the coverage optimization with a control center, a modified particle swarm optimization (MPSO) is presented for the self-optimization of SCN, which employs a heuristic power control scheme to search for the global optimum solution. Secondly, distributed optimization using game theory (DGT) without a control center is concerned. Considering both throughput and interference, a utility function is formulated. Then a power control scheme is proposed to find the Nash Equilibrium (NE). Simulation results show that MPSO and DGT significantly outperform conventional schemes. Moreover, compared with MPSO, DGT uses much less overhead. Finally, further research directions are discussed and conclusions are drawn.

Game Theoretic Infrastructure Sharing in Wireless Cellular Networks

The emerging traffic demand has fueled the rapid densification of cellular networks. The increased number of Base Stations (BSs) leads to augmented energy consumption and expenditures for the Mobile Network Operators (MNOs), especially during low traffic, when many of the BSs remain underutilized. Hence, the MNOs are encouraged to provide “green” and cost effective solutions for their networks. In this chapter, an innovative algorithm for infrastructure sharing in two-operator environments is proposed, based on BSs switching off during low traffic periods. Motivated by the conflicting interests of the operators, the problem is formulated in a game theoretic framework that enables the MNOs to act individually to estimate the switching off probabilities that reduce their financial cost. The authors analytically and experimentally estimate the potential energy and cost savings that can be accomplished. The obtained results show a significant reduction in both energy consumption and expenditures, thus giving the operators the necessary incentives for infrastructure sharing.

Applications of Game Theory for Physical Layer Security

This chapter provides a comprehensive review of the domain of game theory based physical layer security in wireless communications. By exploiting the wireless channel characteristic and secure cooperation of nodes, physical layer security is to enable the exchange of confidential messages over a wireless medium in the presence of unauthorized eavesdroppers, without relying on higher-layer encryption. However, the selfness of nodes seriously affects the secure cooperation; game theory can model the influence of the selfness on physical layer security. This chapter firstly describes the physical layer security issues in the wireless networks and the role of game theory in the research on physical layer security. And then the typical applications of game theory in physical layer security are subsequently covered, including zero-sum game, Stackelberg game, auction theory, coalition game. Finally, the chapter concludes with observations on potential research directions in this area.

Interference Mitigation with Power Control and Allocation in the Heterogeneous Small Cell Networks

With the introduction of Small Cell into current cellular structure, the ever-growing demand for mobile traffic gets the opportunity to be fulfilled. But the overlapped dense deployment caused by the Small Cell Heterogeneous Network (HetNet) also arouses the interference problems. In order to solve those problems, this chapter focuses on Game Theory based uplink power control and downlink power allocation strategies for the interference mitigation of the heterogeneous Small Cell Network (SCN). For the uplink scenario, this chapter proposes the non-cooperative game model based power control algorithm, which can optimize the initial transmission power of both Macro Cell and Small Cell users through the Nash Equilibrium solution. For the downlink scenario with multiple service types in the SCN, the non-cooperative game model based scheme is proposed to optimize the transmission power allocation with constraints of different Quality of Service (QoS) requirements. The simulation results show the merits of the proposed strategies over current works.

Game-Theoretic Approaches in Heterogeneous Networks

Improving capacity and coverage is one of the main issues in next-generation wireless communication. Heterogeneous networks (HetNets), which is currently investigated in LTE-Advanced standard, is a promising solution to enhance capacity and eliminate coverage holes in a cost-efficient manner. A HetNet is composed of existing macrocells and various types of small cells. By deploying small cells into the existing network, operators enhance the users' quality of service which are suffering from severe signal degradation at cell edges or coverage holes. Nevertheless, there are numerous challenges in integrating small cells into the existing cellular network due to the characteristics: unplanned deployment, intercell interference, economic potential, etc. Recently, game theory has been shown to be a powerful tool for investigating the challenges in HetNets. Several game-theoretic approaches have been proposed to model the distributed deployment and self-organization feature of HetNets. In this chapter, the authors first give an overview of the challenges in HetNets. Subsequently, the authors illustrate how game theory can be applied to solve issues related to HetNets.

Distributed Learning of Equilibria with Incomplete, Dynamic, and Uncertain Information in Wireless Communication Networks

New decision-making paradigms addressing the requirements of flexibility, adaptability and intelligence are needed for future wireless networks. Moreover, mutual interactions should be captured when all the devices are autonomous and smart. Game theory is a powerful tool to study such interactions. However, since it is a branch of applied mathematic and mainly studied in economic, some featured challenges should be addressed when applied in wireless networks. This chapter bridges game theory and practical wireless applications, by focusing on the incomplete, dynamic and uncertain information constraints. Four kinds of distributed learning algorithms including stochastic learning automata, payoff-based log-linear learning, learning by trial and error, and no-regret learning are discussed. The learning procedures and basic theoretical results are presented, and their applications in wireless networks are reviewed. Contrastive analysis on environment dynamics, solution concepts, synchrony, convergence, and convergent results is discussed, and some future research directions are given.

Game Theoretic Analysis for Cooperative Video Transmission over Heterogeneous Devices

This chapter presents a mechanism for cooperative video transmission based on game theory for heterogeneous devices during broadcasting. Broadcasting is a multipoint delivery of transmission that sends data from a source to multiple destinations. The terminal is involved in cooperative transmission when the station broadcasts video data. To enhance performance, the heterogeneity and forwarding capabilities should be considered. This work studies power control and allocation in a collaborative transmission based on game theory, which provides an effective strategy when network resources are limited. First, a novel power-allocation model of the base station (BS) based on noncooperative game theory and bidding is presented in this study. Additionally, we also propose a utility function of Signal-to-Noise Ratios (SNRs) along with Signal-to-Interference Ratio (SIRs). Subsequently, based on such noncooperative game theory with a utility function, the model of the power distribution of terminals in cooperative transmission can be built. Experiments on the System-in-the-Loop (SITL) mode in OPNETs have proven the correctness of the designed model and superiority, verifying the effectiveness of the proposed power-control idea.

A Game Theoretic Framework for Green HetNets Using D2D Traffic Offload and Renewable Energy Powered Base Stations

This chapter investigates the interplay between cooperative device-to-device (D2D) communications and green communications in LTE heterogeneous networks (HetNets). Two game theoretic concepts are studied and analyzed in order to perform dynamic HetNet base station (BS) on/off switching. The first approach is a coalition-based method whereas the second is based on the Nash bargaining solution. Afterwards, a method for coupling the BS on/off switching approach with D2D collaborative communications is presented and shown to lead to increased energy efficiency. The savings are additionally increased when a portion of the small cell BSs in a HetNet are powered by renewable energy sources. Different utility functions, modeling the game theoretic framework governing the energy consumption balance between the cellular network and the mobile terminals (MTs), are proposed and compared, and their impact on MT quality of service (QoS) is analyzed.