Alagan Anpalagan

Opportunistic Spectrum Access in Cognitive Radio Networks: Global Optimization Using Local Interaction Games

We investigate the problem of achieving global optimization for distributed channel selections in cognitive radio networks (CRNs), using game theoretic solutions. To cope with the lack of centralized control and local influences, we propose two special cases of local interaction game to study this problem. The first is local altruistic game, in which each user considers the payoffs of itself as well as its neighbors rather than considering itself only. The second is local congestion game, in which each user minimizes the number of competing neighbors. It is shown that with the proposed games, global optimization is achieved with local information. Specifically, the local altruistic game maximizes the network throughput and the local congestion game minimizes the network collision level. Also, the concurrent spatial adaptive play (C-SAP), which is an extension of the existing spatial adaptive play (SAP), is proposed to achieve the global optimum both autonomously as well as rapidly.

Opportunistic Spectrum Access in Unknown Dynamic Environment: A Game-Theoretic Stochastic Learning Solution

We investigate the problem of distributed channel selection using a game-theoretic stochastic learning solution in an opportunistic spectrum access (OSA) system where the channel availability statistics and the number of the secondary users are apriori unknown. We formulate the channel selection problem as a game which is proved to be an exact potential game. However, due to the lack of information about other users and the restriction that the spectrum is time-varying with unknown availability statistics, the task of achieving Nash equilibrium (NE) points of the game is challenging. Firstly, we propose a genie-aided algorithm to achieve the NE points under the assumption of perfect environment knowledge. Based on this, we investigate the achievable performance of the game in terms of system throughput and fairness. Then, we propose a stochastic learning automata (SLA) based channel selection algorithm, with which the secondary users learn from their individual action-reward history and adjust their behaviors towards a NE point. The proposed learning algorithm neither requires information exchange, nor needs prior information about the channel availability statistics and the number of secondary users. Simulation results show that the SLA based learning algorithm achieves high system throughput with good fairness.

Decision-Theoretic Distributed Channel Selection for Opportunistic Spectrum Access: Strategies, Challenges and Solutions

Opportunistic spectrum access (OSA) has been regarded as the most promising approach to solve the paradox between spectrum scarcity and waste. Intelligent decision making is key to OSA and differentiates it from previous wireless technologies. In this article, a survey of decision-theoretic solutions for channel selection and access strategies for OSA system is presented. We analyze the challenges facing OSA systems globally, which mainly include interactions among multiple users, dynamic spectrum opportunity, tradeoff between sequential sensing cost and expected reward, and tradeoff between exploitation and exploration in the absence of prior statistical information. We provide comprehensive review and comparison of each kind of existing decision-theoretic solution, i.e., game models, Markovian decision process, optimal stopping problem and multi-armed bandit problem. We analyze their strengths and limitations and outline further research for both technical contents and methodologies. In particular, these solutions are critically analyzed in terms of information, cost and convergence speed, which are key concerns for practical implementation. Moreover, it is noted that each kind of existing decision-theoretic solution mainly addresses one aspect of the challenges, which implies that two or more kinds of decision-theoretic solutions should be incorporated to address more challenges simultaneously.

Resource Allocation Techniques in Cooperative Cognitive Radio Networks

In the past decade, cognitive radio and cooperative communication techniques have been proposed in the literature for efficiently utilizing the radio resources. Cognitive radio is an emerging technology intended to enhance the utilization of the radio frequency spectrum. The cooperative communication system, with the same total power and bandwidth of legacy wireless communication systems, can increase the data rate of the future wireless communication system. A combination of cognitive radio with cooperative communication can further improve the future wireless network performance. Efficient resource allocation in cooperative cognitive radio network (CRN) is essential in order to meet the challenges of future wireless networks. In this article, a survey of resource allocation in cooperative CRN is presented. We discuss the taxonomy of objectives and protocols used in the literature for resource allocation in cooperative CRN. This paper also highlights the use of power control, cooperation types, network configurations and decision types used in cooperative CRN. Finally, directions for future research are outlined.

Routing in Opportunistic Networks

Routing in Opportunistic Networks focuses on the basics of opportunistic networks, modeling and communication in opportunistic networks, routing in opportunistic networks, and collaboration and cooperation in opportunistic networks. The editors will cover such topics as mobility characterization and discovery in opportunistic networks, scheduling and medium access control in opportunistic networks as well as testbed, tools, and measurements for opportunistic networks.

Throughput Analysis of Opportunistic Access Strategies in Hybrid Underlay—Overlay Cognitive Radio Networks

In cognitive radio networks, it is important to effectively use the under-utilized spectrum resources without affecting the primary users. In an underlay system, secondary users are allowed to share the channel simultaneously with primary users (with the restriction on interference level) but not in an overlay system. In this article, we consider a system where a secondary user can switch between overlay and underlay modes of operation in order to improve its throughput with limited sensing capability (i.e. sensing only one channel at a time). The results based on Markov chain analysis are satisfactorily verified using Monte-Carlo simulation. It is found that proper selection of transmission mode can provide greater improvement in throughput for a secondary user. The mode selection depends on the transition characteristics of primary users and the throughput ratio between the two modes of operation.

Opportunistic Spectrum Access with Spatial Reuse: Graphical Game and Uncoupled Learning Solutions

This article investigates the problem of distributed channel selection for opportunistic spectrum access systems, where multiple cognitive radio (CR) users are spatially located and mutual interference only emerges between neighboring users. In addition, there is no information exchange among CR users. We first propose a MAC-layer interference minimization game, in which the utility of a player is defined as a function of the number of neighbors competing for the same channel. We prove that the game is a potential game with the optimal Nash equilibrium (NE) point minimizing the aggregate MAC-layer interference. Although this result is promising, it is challenging to achieve a NE point without information exchange, not to mention the optimal one. The reason is that traditional algorithms belong to coupled algorithms which need information of other users during the convergence towards NE solutions. We propose two uncoupled learning algorithms, with which the CR users intelligently learn the desirable actions from their individual action-utility history. Specifically, the first algorithm asymptotically minimizes the aggregate MAC-layer interference and needs a common control channel to assist learning scheduling, and the second one does not need a control channel and averagely achieves suboptimal solutions.

Opportunistic Spectrum Access Using Partially Overlapping Channels: Graphical Game and Uncoupled Learning

This article investigates the problem of distributed channel selection in opportunistic spectrum access (OSA) networks with partially overlapping channels (POC) using a game-theoretic learning algorithm. Compared with traditional non-overlapping channels (NOC), POC can increase the full-range spectrum utilization, mitigate interference and improve the network throughput. However, most existing POC approaches are centralized, which are not suitable for distributed OSA networks. We formulate the POC selection problem as an interference mitigation game. We prove that the game has at least one pure strategy NE point and the best pure strategy NE point minimizes the aggregate interference in the network. We characterize the achievable performance of the game by presenting an upper bound for aggregate interference of all NE points. In addition, we propose a simultaneous uncoupled learning algorithm with heterogeneous exploration rates to achieve the pure strategy NE points of the game. Simulation results show that the heterogeneous exploration rates lead to faster convergence speed and the throughput improvement gain of the proposed POC approach over traditional NOC approach is significant. Also, the proposed uncoupled learning algorithm achieves satisfactory performance when compared with existing coupled and uncoupled algorithms.

Emerging cognitive radio technology: Principles, challenges and opportunities

Due to the increasing demand for new wireless services and applications as well as the increasing number of wireless users, the available spectrum is becoming increasingly scarce. As a result, the federal communications commission (FCC) has been investigating new ways to manage the radio frequency resources. Cognitive radio (CR) technology is an innovative radio design philosophy which aims to increase spectrum utilization by exploiting unused and under-utilized spectrum in dynamically changing environments. The basic idea is to let unlicensed users use licensed frequencies, provided they can guarantee minimum interference perceived by the primary licensed users. However, allowing opportunistic use of the wireless spectrum creates new problems such as peaceful coexistence with other wireless technologies as well as understanding the influence of interference that each of these networks can create. In this article, we discuss the key CR operations and principles, and then discuss some of the main challenges and research opportunities that exist in CR-based emerging wireless networks.

A Survey of Distributed Relay Selection Schemes in Cooperative Wireless Ad hoc Networks

Cooperative communication aims to achieve spatial diversity gain via the cooperation of user terminals in transmission without requiring multiple transceiver antennas on the same node. It employs one or more terminals as relays in the neighbourhood of the transmitter and the receiver, which collaborate in the transmission and serve as a virtual MIMO antenna array. Allowing cooperation in wireless communication engenders new problems related to resource allocation and relay selection. Optimal relay selection is vital for reaping the performance benefit of cooperative communication. It is a challenging task to share channel information in timely and distributed manner and at the same time make optimal selection of relay in a time varying radio environment. This paper presents a comprehensive survey of distributed relay selection schemes for cooperative communication that have been proposed in the literature. We discuss various classifications of relay selection schemes and describe their characteristics and functionality. We then present a qualitative comparison of their performance against a set of representative metrics. Finally, we discuss some of their shortcomings and suggest some research directions.