Yong Xiao

A Hierarchical Game Theoretic Framework for Cognitive Radio Networks

We consider OFDMA-based cognitive radio (CR) networks where multiple secondary users (SUs) compete for the available sub-bands in the spectrum of multiple primary users (PUs). We focus on maximizing the payoff of both SUs and PUs by jointly optimizing transmit powers of SUs, sub-band allocations of SUs, and the prices charged by PUs. To further improve the performance of SUs, we allow SUs who share the same sub-band to cooperate with each other to send and receive signals. To help us understand the interaction among SUs and PUs, we study the proposed network model from a game theoretic perspective. More specifically, we first formulate a coalition formation game to study the sub-band allocation problem of SUs and then integrate the coalition formation game into a Stackelberg game-based hierarchical framework. We propose a simple distributed algorithm for SUs to search for the optimal sub-bands. We prove that the transmit power and sub-band allocation of SUs and the price charged by PUs are interrelated by the pricing function of PUs. This makes the joint optimization possible. More importantly, we prove that if the pricing coefficients of PUs have a fixed linear relationship, the sub-band allocation of SUs will be stable and the Stackelberg equilibrium of the hierarchical game framework will be unique and optimal. We propose a simple distributed algorithm to achieve the Stackelberg equilibrium of the hierarchical game. Our proposed algorithm does not require SUs to know the interference temperature limit of each PU, and has low communication overheads between SUs and PUs.

Game Theoretic Analysis for Spectrum Sharing with Multi-Hop Relaying

This paper studies spatial spectrum sharing (SSS) based multi-user cognitive radio (CR) networks that allow secondary users (SU) to access the licensed spectrum as long as the interference powers of primary users (PU) to be lower than a certain threshold. Although recent results have shown that multi-hop relaying has a great potential on improving the performance of CR networks, finding effective methods to control and manage SUs to achieve the optimal performance is still a challenging problem. In this paper, we model CR networks as a non-cooperative game in which each SU obtains benefits through both spectrum sharing by paying prices to PUs and multi-hop relaying by paying price to nearby SUs. Optimal power allocation methods for SUs are investigated under different assumptions and pricing functions. The conditions under which the optimal Nash Equilibrium (NE) is obtained when all SUs use multi-hop relaying are discussed. Our results are extended into large multi-user CR networks with K source-to-destination pairs. Two distributed algorithms are proposed. The first one is a sub-gradient based power allocation algorithm in which SUs can iteratively adjust their transmit powers to approach the payoff of a NE. The other one is a Q-learning based relay selection algorithm which enables each SU to iteratively search for a NE-achieving relaying scheme.

A Simple Distributed Power Control Algorithm for Cognitive Radio Networks

This paper studies the power control problem for spectrum sharing based cognitive radio (CR) networks with multiple secondary source-to-destination (SD) pairs. A simple distributed algorithm is proposed for the secondary users (SUs) to iteratively adjust their transmit powers to improve the performance of the network. The proposed algorithm does not require each SU (or PU) to negotiate with other SUs (or PUs) during the communication. It is proved that the proposed algorithm can obtain a time average performance as good as that achieved when the Nash equilibrium (NE) is chosen in hindsight. More specifically, the average performance of CR networks will converge to an ε-Nash equilibrium at a rate of Tε = O (exp (1/ε)). A sub-optimal algorithm is also introduced to further improve the convergence rate to Tε/log Tε = O (1/ε). Numerical results are presented to show the performance of the proposed algorithms under different settings.

Spatial spectrum sharing-based carrier aggregation for heterogeneous networks

This paper considers spatial spectrum sharing-based carrier aggregation (SSS-CA) from a game theoretic perspective. In SSS-CA, a network operator can not only transmit on its own licensed spectrum but it can also access and aggregate the licensed spectrum of other operators on payment of a certain price. The difference between operators and the aggregators in each licensed spectrum makes this network heterogeneous. We first model the pairing problem between potential operators as a pairing game and then derive the condition for which both operators are incentivized to form an SSS-CA pair. We then introduce the power control game to derive the optimal transmit power of each spectrum aggregator. Finally, we consider the pricing optimization problem by forming a pricing adjustment game. We observe that these three problems are linked by the price function of the operators and hence can be jointly optimized by using a hierarchical game theoretic framework. We derive the Stackelberg equilibrium for the pricing and power joint optimization problem and present the numerical results to compare the performance improvement brought by our proposed joint optimization method.

Distributed optimization for cognitive radio networks using Stackelberg game

This paper considers frequency-division spatial spectrum sharing (FD-SSS) based cognitive radio (CR) networks. In this network, the spectrum of primary users (PU) can be divided into a number of sub-bands each of which can only be accessed by one secondary user (SU). This setting combines both advantages of spatial spectrum sharing and temporal spectrum sharing methods. However, there are three major problems for this system: 1) how to optimally allocate the available subbands to all SUs, 2) how to distributively optimize the transmit powers for SUs to maintain the received interference power of PUs, 3) how to control the accessibility of SUs in the licensed spectrum. In this paper, we introduce a Stackelberg game based hierarchical framework to optimize the performance of FD-SSS based CR networks. More importantly, we show that, by using a simple pricing function for PUs, the above three problems can be solved simultaneously. We prove that the proposed game has a unique Stackelberg equilibrium (SE) and then introduce a simple distributed algorithm to let SUs and PUs converge to the SE. Comparing to the existing results, our setting does not require SUs to communicate with each other or to know the interference temperature of PUs.

Dynamic spectrum scheduling for carrier aggregation: A game theoretic approach

In this paper, we investigate the performance of the dynamic allocation of resources between separate cellular networks. We propose a Dynamic Internetworking Carrier Aggregation (DI-CA) framework which involves every network operator releasing some of its exclusive, but excess, spectrum to another network operator for a limited time. We derive the basic condition for which DI-CA can improve the performance for all the operators and then propose a distributed scheduling framework that uses coalition formation with uncertainty, in which each independent operator can decide whether or not DI-CA can improve its performance without having information regarding channel conditions or load experienced by other operators. We propose a distributed Bayesian coalition formation algorithm to approach a neighborhood of the Bayesian Nash equilibrium.

Secondary Users Entering the Pool: A Joint Optimization Framework for Spectrum Pooling

Spectrum pooling has been shown to have a great potential to improve the spectrum utilization, especially when primary users (PUs) and secondary users (SUs) are allowed to utilize a common spectrum pool. This paper studies the joint optimization problem for a spectrum pooling system with both PUs and SUs. We develop a novel hierarchical game theoretic model which consists of an overlapped coalition formation game model to analyze the pricing cooperation/competition strategy among PUs and a non-cooperative game model to investigate the resource competition among SUs. These two game models are interrelated in a hierarchical game structure, in which we also study the interaction between SUs and PUs. Our model does not require SUs to have information about spectrum access scheduling of PUs. Furthermore, we propose a simple distributed joint optimization algorithm that can optimize the coalition formation of PUs as well as the sub-band allocation and transmit powers of SUs. To study different fairness criteria and their effects on the payoff divisions among PUs, we derive the optimal payoff division schemes of two popular fairness criteria, namely Nash bargaining solution and Shapley value fairness.

Dynamic pricing coalitional game for cognitive radio networks

We consider a hierarchical game theoretic model for cognitive radio (CR) networks in which primary users (PU) set the price to charge secondary users (SU) for accessing the licensed spectrum and SUs optimize their transmit powers according to the price imposed by PUs. Pricing strategies can be tailored to steer SUs to a Stackelberg equilibrium. We establish a coalition formation game framework to study the possible cooperation among PUs. In our framework, the PUs who can detect the same SUs form a coalition to select the pricing function as long as each member of the coalition is allocated a fair share of the payoff. We show that allowing all PUs to cooperatively decide the price for every SU is generally not the optimal solution. We then propose a distributed algorithm that allows PUs to dynamically approach a unique and stable partition of the grand coalition, as well as a Stackelberg equilibrium point of the hierarchical game.

Dynamic Energy Trading for Wireless Powered Communication Networks

Wireless powered communication systems have recently attracted significant interest because of their potential to provide a ubiquitous and sustainable energy supply for communication networks. However, the energy that can be harvested from external energy sources is generally uncontrollable and intermittent. By allowing multiple devices to exchange their harvested energy, dynamic energy trading (DET) is introduced to improve the energy supply reliability and performance of wireless powered communication networks. This article provides an overview of the possible architecture and functional components that enable DET in communication networks. Various design issues on how to implement DET in practice are discussed. An optimal policy is proposed for delay-tolerant wireless powered communication networks in which each wireless powered device can schedule its data transmission and energy trading operations according to current and future energy availability. Finally, some potential topics and challenges for future research are highlighted.

Fairness and efficiency tradeoffs for user cooperation in distributed wireless networks

We propose a general framework to analyze incentives for user cooperation, and characterize the tradeoff between fairness and efficiency for cooperative networks. More specifically, we define the incentive region as a set of action profiles that provides cooperation benefits to all users and focus on the optimization of efficiency and fairness within this region. We introduce a linear resource allocation (LRA) scheme and show that most existing fairness measures can be converted to LRA with different linear coefficient vectors. We then propose the concept of strong price of fairness (SPoF) to study the network efficiency of the strong equilibrium. We show that both the SPoF and fairness measures are connected to the linear coefficient vector of LRA, which makes it possible to study the fairness and efficiency relationship. We then use the random access (RA) system as an example to show how to use the proposed framework to study a specific wireless network.