Game Theoretic Issues in Cognitive Radio Systems (Invited Paper)

2007

The ongoing growth in wireless communication continues to increase demand on the frequency spectrum. The current rigid frequency band allocation policy leads to a significant under-utilization of this scarce resource. However, recent policy changes by the Federal Communications Commission (FCC) and research directions suggested by the Defense Advanced Research Projects Agency (DARPA) have been focusing on wireless devices that can adaptively and intelligently adjust their transmission characteristics, which are known as cognitive radios. This paper suggests a game theoretical approach that allows master-slave cognitive radio pairs to update their transmission powers and frequencies simultaneously. This is shown to lead to an exact potential game, for which it is known that a particular update scheme converges to a Nash Equilibrium (NE). Next, a Stackelberg game model is presented for frequency bands where a licensed user has priority over opportunistic cognitive radios. We suggest a modification to the exact potential game discussed earlier that would allow a Stackelberg leader to charge a virtual price for communicating over a licensed channel. We investigate virtual price update algorithms for the leader and prove the convergence of a specific algorithm. Simulations performed in Matlab verify our convergence results and demonstrate the performance gains over alternative algorithms.

2021

Cognitive radio enabled wireless sensor network is capable of reducing the spectrum scarcity problem of the wireless networks. Looking at the scarcity of available bandwidth, and the high growth in the number of communication devices in recent times, cognitive radio technology has proven to be a promising technology for the days to come. The application of Game Theory in cognitive radio networks has been visible in recent research works. However, only limited literature is available in which possibilities of applying the game-theory based approaches for the challenging task of channel assignment in cognitive radio wireless sensor are available in the literature. It is understood that the crux of the solution to the problem of scheming games for allocation of the channel is centered on the selection of the utility function in order to increase the efficiency of the channel allocation algorithm. Accordingly, the study regarding the influence of several utility functions on the perform...

The traditional approach of fixed spectrum allocation to licensed networks has resulted in spectrum underutilisation. Cognitive radio technology is envisioned as a promising solution that can be used to resolve the ineffectiveness of the fixed spectrum allocation policy by accessing the underutilised spectrum of existing technologies opportunistically. The implementation of cognitive radio networks (CRNs) faces distinct challenges due to the fact that two systems (i.e., cognitive radio (CR) and primary users (PUs)) with conflicting interests interact with each other. Specially, in self-organised systems such as ad-hoc CRNs (AHCRNs), the coordination of spectrum access introduces challenges to researchers due to rapid utilisation changes in the available spectrum, as well as the multi-hop nature of ad-hoc networks, which creates additional challenges in the analysis of resource allocation (e.g., power control, channel and rate allocation). Instead, game theory has been adopted as a powerful mathematical tool in analysing and modelling the interaction processes of AHCRNs. In this survey, we first review the most fundamental concepts and architectures of CRNs and AHCRNs. We then introduce the concepts of game theory, utility function, Nash equilibrium and pricing techniques. Finally, we survey the recent literature on the game theoretic analysis of AHCRNs, highlighting its applicability to the physical layer PHY, the MAC layer and the network layer.

2013 International Conference on Information Technology and Electrical Engineering (ICITEE), 2013

In a spectrum sharing system, lower-priority users are allowed to spatially reuse the spectrum allocated to higher priority users as long as they do not disrupt communications of the latter. Therefore, to improve spectrum utilization, an important requirement for the former users is to manage the interference and ensure that the latter users can maintain reliable communications. This paper presents a game theoretic framework to model the dynamic spectrum sharing in cognitive radio networks. First, a utility function that captures the selfish and cooperative behavior of the lower-priority users to manage the interference by selecting the best channel with minimal intra-and inter-system interference is defined. Next, based on the defined utility function, the proposed framework can be fo rmulated as a potential game; thus, the convergence to a Nash equilibrium point is ensured as long as the best response dynamic is adopted. At the equilibrium point, power allocation algorithm is proposed such that the interference to higher-priority users can be maintained below the maximum allowable level. The simulation results show the convergence of the proposed potential game and the performance improvement of higher-priority users in terms of SINR and outage probability.

2008

We consider the problem of efficient opportunistic spectrum access in cognitive radio networks where there are multiple secondary users trying to share access to multiple channels. In our formulation, each user has a potentially different valuation of each channel and wishes to pick a channel in such a way as to maximize its benefit without interfering with other users. There is a fundamental tradeoff in this problem-while information about other secondary users is useful in making a good channel access decision, the communication cost of gathering this information must be taken into account. We formulate the problem as a multi-round negotiation game in which the users try to gather "just-enoughinformation" to make their decisions. The channel valuations are modeled as independently uniformly distributed random variables between 0 and 1. We propose a thresholdbased channel sensing policy based on observations from a previous work. For a two-user two-channel setting, we calculate the optimal threshold, and obtain the corresponding performance for cases with no information exchange, partial information exchange, and full information exchange. We then show how the optimal choice of how much information is to be exchanged varies with the cost of negotiation.

IEEE Transactions on Signal Processing, 2010

The concept of cognitive radio (CR) has recently received great attention from the research community as a promising paradigm to achieve efficient use of the frequency resource by allowing the coexistence of licensed (primary) and unlicensed (secondary) users in the same bandwidth. In this paper we propose and analyze a totally decentralized approach, based on game theory, to design cognitive MIMO transceivers, who compete with each other to maximize their information rate. The formulation incorporates constraints on the transmit power as well as null and/or soft shaping constraints on the transmit covariance matrix, so that the interference generated by secondary users be confined within the temperature-interference limit required by the primary users. We provide a unified set of conditions that guarantee the uniqueness and global asymptotic stability of the Nash equilibrium of all the proposed games through totally distributed and asynchronous algorithms. Interestingly, the proposed algorithms overcome the main drawback of classical waterfilling based algorithms-the violation of the temperature-interference limit-and they have the desired features required for CR applications, such as low-complexity, distributed implementation, robustness against missing or outdated updates of the users, and fast convergence behavior.

PeerJ Computer Science

The wireless networks face challenges in efficient utilization of bandwidth due to paucity of resources and lack of central management, which may result in undesired congestion. The cognitive radio (CR) paradigm can bring efficiency, better utilization of bandwidth, and appropriate management of limited resources. While the CR paradigm is an attractive choice, the CRs selfishly compete to acquire and utilize available bandwidth that may ultimately result in inappropriate power levels, causing degradation in network’s Quality of Service (QoS). A cooperative game theoretic approach can ease the problem of spectrum sharing and power utilization in a hostile and selfish environment. We focus on the challenge of congestion control that results in inadequate and uncontrolled access of channels and utilization of resources. The Nash equilibrium (NE) of a cooperative congestion game is examined by considering the cost basis, which is embedded in the utility function. The proposed algorithm ...

Wireless Communications and Mobile Computing, 2012

The invention of cognitive radio (CR) concept aims to overcome the spectral scarcity issues of emerging radio systems by exploiting under-utilization of licensed spectrum. Determining how to allocate unused frequency bands among CR is one of the most important problems in CR networks. Because different CRs may have different quality-of-service requirements, they may have different objectives. In voice communication, high-speed transmission is the most important factor; hence, voice radios always try to maximize their transmission rate. However, in data communication, the most important factor is the bit error rate. The data radios always try to maximize their signal-to-interference-plus-noise ratio (SINR). In this paper, two non-cooperative games named interference minimization game and capacity maximization game, which reflect the target of data radios and voice radios, respectively, are proposed. From the simulations, after these games are applied, the average SINRs of all players at each channel are improved. The average SINR of players in each channel after applying the capacity maximization game is smaller than that after applying the interference minimization game. However, in comparison with that after applying the interference minimization game, the average capacity of players after applying capacity maximization approach is larger.

Lecture Notes in Computer Science, 2011

In this paper we present an approach based on game-theoretical mechanism design for dynamic spectrum allocation in cognitive radio networks. Secondary users (SU) detect when channels can be used without disrupting any primary user and try to use them opportunistically. When an SU detects a free channel, it estimates its capacity and sends the valuation of it to a central manager. The manager calculates a conflict-free allocation by implementing a truthful mechanism. The SUs have to pay for the allocation an amount which depends on the set of valuations, and they behave as benefit maximizers. We present and test two mechanisms implementing this idea which are proved to be truthful, and that are tractable and approximately efficient. We show the flexibility of these mechanisms by illustrating how they can be modified to achieve other objectives such as fairness and also how they can operate without really charging the SUs.

IEEE Transactions on Wireless Communications, 2009

In this paper we develop a framework for resource allocation in a secondary spectrum access scenario, where a group of Cognitive Radios (CR) access the resources of a primary system. We assume the primary system is a cellular OFDMbased network operating in uplink. We develop an optimum resource allocation strategy, using cooperative Game Theory, which guarantees the primary's required QoS and allocates an achievable rate at a given bit error rate for the secondary, when possible. The proposed Cognitive Radio Game (CRG) is a network-assisted resource management method, where users (both primary and secondary) inform the primary system's BS of their channel state information and power limitation and the base station calculates the optimum sub-channel and power allocation for all users. Using Game theoretic axiom of fairness, i.e., Nash Bargaining Solutions (NBS), we develop an alternative efficient and fair resource allocation and compare its performance with the proposed CRG method. We use Sequential Quadratic Programming (SQP) to solve the proposed nonlinearly constrained CRG optimization problem.