Qihui Wu

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.

Cognitive Internet of Things: A New Paradigm Beyond Connection

Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the observations. In this paper, we argue that only connected is not enough, beyond that, general objects should have the capability to learn, think, and understand both physical and social worlds by themselves. This practical need impels us to develop a new paradigm, named cognitive Internet of Things (CIoT), to empower the current IoT with a “brain” for high-level intelligence. Specifically, we first present a comprehensive definition for CIoT, primarily inspired by the effectiveness of human cognition. Then, we propose an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning. Furthermore, we provide a systematic tutorial on key enabling techniques involved in the cognitive tasks. In addition, we also discuss the design of proper performance metrics on evaluating the enabling techniques. Last but not the least, we present the research challenges and open issues ahead. Building on the present work and potentially fruitful future studies, CIoT has the capability to bridge the physical world (with objects, resources, etc.) and the social world (with human demand, social behavior, etc.), and enhance smart resource allocation, automatic network operation, and intelligent service provisioning.

Kernel-Based Learning for Statistical Signal Processing in Cognitive Radio Networks: Theoretical Foundations, Example Applications, and Future Directions

Kernel-based learning (KBL) methods have recently become prevalent in many engineering applications, notably in signal processing and communications. The increased interest is mainly driven by the practical need of being able to develop efficient nonlinear algorithms, which can obtain significant performance improvements over their linear counterparts at the price of generally higher computational complexity. In this article, an overview of applying various KBL methods to statistical signal processing-related open issues in cognitive radio networks (CRNs) is presented. It is demonstrated that KBL methods provide a powerful set of tools for CRNs and enable rigorous formulation and effective solutions to both long-standing and emerging design problems.

Spatial-Temporal Opportunity Detection for Spectrum-Heterogeneous Cognitive Radio Networks: Two-Dimensional Sensing

This paper investigates the issue of spatial-temporal opportunity detection for spectrum-heterogeneous cognitive radio networks, where at a given time secondary users (SUs) at different locations may experience different spectrum access opportunities. Most prior studies address either spatial or temporal sensing in isolation and explicitly or implicitly assume that all SUs share the same spectrum opportunity. However, this assumption is not realistic and the traditional non-cooperative sensing (NCS) and cooperative sensing (CS) schemes are not very effective in a more realistic setting considering the heterogeneous spectrum availability among SUs. We define new performance metrics to guide the spatial-temporal opportunity detection and propose a two-dimensional sensing (TDS) framework to improve the opportunity detection performance, which exploits correlations in time and space simultaneously by effectively fusing sensing results in a spatial-temporal sensing window. Furthermore, in terms of maximum interference constrained transmission power (MICTP), we classify the spatial opportunities for SUs into three groups: black, grey, and white, and propose a TDS-based distributed power control scheme to further improve the spectrum utilization by exploiting both grey and white spectrum opportunities. The effectiveness of the proposed scheme is demonstrated through in-depth numerical simulations under a variety of scenarios.

Cellular-Base-Station-Assisted Device-to-Device Communications in TV White Space

This paper presents a systematic approach to exploiting TV white space (TVWS) for device-to-device (D2D) communications with the aid of the existing cellular infrastructure. The goal is to build a location-specific TVWS database, which provides a lookup table service for any D2D link to determine its maximum permitted emission power (MPEP) in an unlicensed digital TV (DTV) band. To achieve this goal, the idea of mobile crowd sensing is first introduced to collect active spectrum measurements from massive personal mobile devices. Considering the incompleteness of crowd measurements, we formulate the problem of unknown measurements recovery as a matrix completion problem and apply a powerful fixed point continuation algorithm to reconstruct the unknown elements from the known elements. By joint exploitation of the big spectrum data in its vicinity, each cellular base station further implements a nonlinear support vector machine algorithm to perform irregular coverage boundary detection of a licensed DTV transmitter. With the knowledge of the detected coverage boundary, an opportunistic spatial reuse algorithm is developed for each D2D link to determine its MPEP. Simulation results show that the proposed approach can successfully enable D2D communications in TVWS while satisfying the interference constraint from the licensed DTV services. In addition, to our best knowledge, this is the first try to explore and exploit TVWS inside the DTV protection region resulted from the shadowing effect. Potential application scenarios include communications between internet of vehicles in the underground parking and D2D communications in hotspots such as subway, game stadiums, and airports.

Robust Spectrum Sensing With Crowd Sensors

This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where the sensing data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute with abnormal data, which makes the existing cooperative sensing schemes ineffective. To tackle these challenges, we first propose a generalized modeling approach for sensing data with an arbitrary abnormal component. Under this model, we then analyze the impact of general abnormal data on the performance of the cooperative sensing, by deriving closed-form expressions of the probabilities of global false alarm and global detection. To improve sensing data quality and enhance cooperative sensing performance, we further formulate an optimization problem as stable principal component pursuit, and develop a data cleansing-based robust spectrum sensing algorithm to solve it, where the under-utilization of licensed spectrum bands and the sparsity of nonzero abnormal data are jointly exploited to robustly cleanse out the potential nonzero abnormal data component from the original corrupted sensing data. Extensive simulation results demonstrate that the proposed robust sensing scheme performs well under various abnormal data parameter configurations.

Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). Over the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Then, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we analyze the spear-and-shield relation between Byzantine attack and defense from an interactive game-theoretical perspective. Moreover, we highlight the unsolved research challenges and depict the future research directions.

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.