Network virtualization (NV) allows multiple heterogeneous virtual networks (VNs) to coexist and operate over the same physical network (PN) infrastructures. Some of the benefits of this advancement include flexibility in VN topologies, heterogeneity in VN technologies, and modularity of network operations. However, there are a few areas, such as resource allocation and economics, which challenge the implementation of NV. In this paper, we first introduce some NV parameters that influence the resource allocation and economics of an NV system. Next, we formulate an economic model for NV using the prey-predator food chain model. This model takes into account the dynamics in an NV system, such as the service, payoff, failure, and competition rates within each VN and PN. The solution point to this model represents the resource strategy of the service provider (SP) given the number of users trying to use its VN, as well as the resource strategy of the infrastructure provider (InP) given the strategy of the VN leasing its PN. In addition, we establish economic models that relate the capacities of the end users, the SP, and the InP. Finally, we provided simulations that show how the prey-predator food chain model fits well on an NV system.

Combined with a quickest detection algorithm for error propagation prevention in joint channel decoding and state estimation, a decoding procedure based on Pearl's belief propagation is proposed to exploit the redundancy of system states in time domain for cyber-physical systems. By applying the proposed scheme to a linear model of electric generator dynamic system, numerical simulations have demonstrated that a better performance of state estimation will be obtained by the joint channel decoding and state estimation, and the error propagation can be successfully eliminated by the error propagation detection algorithm during the state estimation process.

To reap the benefits of cache-enabled small cell networks, new backaul management mechanisms are needed to prevent the predicted files that are downloaded at the small base stations (SBSs) for caching purposes from jeopardizing the urgent requests that need to be served via the backhaul. Such mechanisms must account for the heterogeneity of the backhaul that will encompass both wireless backhaul links (at various frequency bands) and a wired backhaul component. In this paper, the heterogeneous backhaul management problem is formulated as a minority game in which each SBS has to define the number of predicted files to download, without affecting the required transmission rate of the current requests. For the formulated game, it is shown that a unique fair proper mixed Nash equilibrium (PMNE) exists. A self-organizing reinforcement learning algorithm is then proposed and shown to converge to a unique Boltzmann-Gibbs equilibrium, which approximates the desired PMNE. Simulation results show that the performance of the proposed approach can be close to that of the ideal optimal algorithm while it outperforms a centralized greedy approach in terms of the amount of data that is cached without jeopardizing the quality-of-service of current requests.

Demand-side management, together with the integration of distributed energy storage have an essential role in the process of improving the efficiency and reliability of the power grid. In this paper, we consider a smart power system in which users are equipped with energy storage devices. Users will request their energy demands from an energy provider who determines their energy payments based on the load profiles of users. By scheduling the energy consumption and storage of users regulated by a central controller, the energy provider tries to minimize the square euclidean distance between the instantaneous energy demand and the average demand of the power system. The users intend to reduce their energy payment by jointly scheduling their appliances and controlling the charging and discharging process for their energy storage devices. We apply game theory to formulate the energy consumption and storage game for the distributed design, in which the players are the users and their strategies are the energy consumption schedules for appliances and storage devices. Based on the game theory setup and proximal decomposition, we also propose two distributed demand side management algorithms executed by users in which each user tries to minimize its energy payment, while still preserving the privacy of users as well as minimizing the amount of required signaling with the central controller. In simulation results, we show that the proposed algorithms provide optimality for both energy provider and users.

Reliable detection of primary users is an important task in cognitive radio. It becomes challenging in the presence of malicious users' sharing false sensing data in a collaborative spectrum sensing. In this paper, we propose a Bayesian nonparametric clustering approach to estimate the primary user's channel behavior and identify malicious users' collaborative spectrum sensing. The proposed scheme clusters malicious attacks in a Bayesian nonparametric way and identifies malicious users. From the simulation results, we demonstrate the effectiveness of the proposed approach by using real wireless traces and comparing with the nonparametric mean-shift clustering approach.

In cognitive radio networks, the main objective of spectrum sensing is to exploit spectrum holes left by the primary users (PUs). Different PUs' traffic patterns might provide different opportunities for second user (SU) spectrum access. In this paper, we identify the PUs' traffic patterns and then maximize SU transmission accordingly. First a theoretical framework is developed to cluster PU traffic patterns based on a Bayesian nonparametric inference model, in which the number of traffic types is unknown. Second, in order to exploit the spectrum holes, we study a sensing-transmission structure to optimize the SU transmission strategy. Specifically, we exploit the short and long transmission opportunities based on the PU traffic pattern and channel idle time distribution. Finally, we propose a threshold-based sensing-transmission method that optimizes the SU utility, while protecting PU transmissions. Both sensing and transmission errors are considered for perfect sensing with/without acknowledgement-based transmission and imperfect sensing, respectively. From the simulation results, we show that the proposed technique outperforms the nonparametric mean shift clustering algorithm. Furthermore, we utilize these clustering results to optimize the SU's transmission strategy with perfect and imperfect sensing. We compare our proposed technique with the probabilistic sensing-transmission structure and show the performance gain in terms of throughput.

A key feature of cognitive radio network is the intelligence of secondary users who can collaborate to improve the system performance. The collaboration in terms of channel recommendation is studied in this paper. The recommendation mechanism results in dynamics of the channel preferences of secondary users, thus causing a behavior propagation in a social network. For cognitive radio networks having a grid topology, the ergodicity of the dynamics is studied using the model of interacting particles in nonequilibrium statistical mechanics. For networks having a grid topology or being randomly deployed, mean field descriptions using ordinary differential equation are used to explicitly describe the dynamics of behavior propagation. The analytic results are demonstrated by numerical simulations.

A futures market is proposed for spectrum market in order to manage the financial risk in spectrum trade and discovering future price. The similarities between the spectrum market and electricity market are pointed out. The model of spot market price in the spectrum market is discussed using a set of real activity measurements of cellular base stations. The characteristics of price are analyzed using Black-Scholes model. Based on the models, the option pricing strategies, which determine the price of futures options, and hedging policies, which determine the investment in the futures markets in order to reduce the financial risk, are discussed analytically. Both strategies are tested using the measurement data.

In cognitive radio networks where secondary users (SUs) access spectral white spaces left by primary users (PUs), there exists a trade-off between sensing and transmission. However, the white spaces depend on the PUs' traffic patterns, and this fact can be utilized to optimize SU transmissions after traffic identification. This paper studies the effective approaches to adapt SU transmissions to the inherit dynamics associated to each PU traffic application. We define a threshold for sensing-transmission trade-off that is optimal under the technical constraints, and exploit short time and long time opportunities left by PUs. In the simulation results, we demonstrate the effectiveness of the proposed scheme by comparing with the threshold-based sensing transmission structure for throughput gains and show the sensing-transmission threshold under various traffic applications (patterns). Moreover, we illustrate the effects of different sensing times on throughput for different traffic applications using some real traces.

In cognitive radio (CR) wireless mesh networks (WMNs), wireless mesh routers work as secondary users (SUs) that can opportunistically and intelligently access the idle licensed spectrum. When developing efficient routing techniques in CR networks, the aggregate interference from the SUs to the primary users (PUs) should be considered. Though the interference from a single SU that is outside the PUs' footprints is small, the aggregate interference from a great number of SUs transmitting at the same time may be significant, and this will greatly influence the PUs' performance. In this paper, a distributed routing algorithm is proposed using the network formation game to minimize the aggregate interference from the SUs to the PUs. The proposed distributed algorithm can avoid the problems in the centralized approach, such as the high cost for building the centralized coordinate nodes, high information-gathering delay, and system breakdown caused by the possible failures in the centralized nodes. From the simulation results, we can observe that the proposed algorithm can find the routes with lower interference to the PUs compared to the Dijkstra's algorithm. Compared to an upper bound, the distributed solution shows near optimum.

Cognitive radio (CR) networks have attracted many attentions recently, while the security issues are not fully studied yet. In this paper, we propose a new and powerful network layer attack, routing-toward-primary-user (RPU) attack in CR networks. In this attack, malicious nodes intentionally route a large amount of packets toward the primary users (PUs), aiming to cause interference to the PUs and to increase delay in the data transmission among the secondary users. In the RPU attack, it is difficult to detect the malicious nodes since the malicious nodes may claim that those nodes, to which they forward the packets, behave dishonestly and cause problems in the data transmission. To defend against this attack without introducing high complexity, we develop a defense strategy using belief propagation. First, an initial route is found from the source to the destination. Each node keeps a table recording the feedbacks from the other nodes on the route, exchanges feedback information and computes beliefs. Finally, the source node can detect the malicious nodes based on the final belief values. Simulation results show that the proposed defense strategy against the RPU attack is effective and efficient in terms of significant reduction in the delay and interference caused by the RPU attack.

In contrast to traditional communication networks, the data traffic of cognitive radio network is interrupted by primary users. To evaluate the impact of primary user activities, the transient data traffic dynamics of cognitive radio networks, after being interrupted by primary users, are studied. The Onsager's regression approach, a powerful principle in non-equilibrium statistical mechanics, is applied. The autocorrelation function in the equilibrium state is used to study the time evolution of system deviation from the equilibrium state, according to the principle of Onsager's regression. Analytic approximations have been derived for the autocorrelation function for both generic and fluid approximation cases. Numerical simulations are carried out for a Jackson network model. The validity of the Onsager's regression is verified. The derived approximation of the correlation function is demonstrated to match the true value at the beginning of the relaxation. A scheduling algorithm for fast recovery of equilibrium is also proposed and demonstrated by numerical results.

In cognitive radio networks, a secondary user needs to estimate the primary users' traffic patterns so as to optimize its transmission strategy. In this paper, we propose a nonparametric Bayesian method for identifying traffic applications, since the traffic applications have their own distinctive patterns. In the proposed algorithm, the collapsed Gibbs sampler is applied to cluster the traffic applications using the infinite Gaussian mixture model over the feature space of the packet length, the packet inter-arrival time, and the variance of packet lengths. We analyze the effectiveness of our proposed technique by extensive simulation using the measured data obtained from the WiMax networks.

A key feature of cognitive radio network is the intelligence of secondary users which can collaborate to improve the performance. The collaboration in terms of channel recommendation is studied. Via recommendations, the channel preferences of secondary users become dynamic. The corresponding behavior dynamics of secondary users are studied. Particularly, the ergodicity of the dynamical progress is studied using the model of interacting particles in nonequilibrium statistical mechanics, which is important for predicting the long term dynamics of the cognitive radio network. A mean field description using ordinary differential equation for the dynamics is also used to explicitly describe the behavior dynamics. A Jackson network with lattice topology is simulated to demonstrate the conclusions.

For reducing the channel switching frequency due to uncertain and frequent primary users' interruptions, we design a nonparametric channel allocation algorithm which can effectively manage the spectrum resources in distributed cognitive radio networks and serve for delay-sensitive data traffics. In the proposed algorithm, an infinite Gaussian mixture model is used to get the posterior decision from the feature spaces of the channel idle duration and the primary user's frequency channel, while the collapsed Gibbs sampling is applied for fast convergence. Experimental and simulation results demonstrate that the proposed channel allocation algorithm outperforms the random channel allocation method and the idle probability based channel allocation algorithm in terms of throughput and packet drops.

This paper introduces a novel approach that enables a number of cognitive radio devices that are observing the availability pattern of a number of primary users (PUs), to cooperate and use Bayesian nonparametric techniques to estimate the distributions of the PUs' activity pattern. To address this problem, a coalitional game is formulated between the cognitive devices and an algorithm for cooperative coalition formation is proposed. It is shown that the proposed coalition formation algorithm allows the cognitive nodes that are experiencing a similar behavior from some PUs to self-organize into disjoint, independent coalitions. Inside each coalition, the cooperative cognitive nodes use Bayesian nonparametric techniques so as to improve the accuracy of the estimated PUs' activity distributions. Simulation results show that the proposed algorithm significantly improves the estimates of the PUs' activity patterns.

Cognitive radio (CR) is a promising technology for future wireless spectrum allocation to improve the usage of the licensed bands. However, CR wireless networks are susceptible to various attacks and cannot offer efficient security. Primary user emulation (PUE) is one of the most serious attacks for CR networks, which can significantly increase the spectrum access failure probability. In this paper, we propose a defense strategy against the PUE attack in CR networks using belief propagation, which avoids the deployment of additional sensor networks and expensive hardware in the networks used in the existing literatures. In our proposed approach, each secondary user calculates the local function and the compatibility function, computes the messages, exchanges messages with the neighboring users, and calculates the beliefs until convergence. Then, the PUE attacker will be detected, and all the secondary users in the network will be notified in a broadcast way about the characteristics of the attacker's signal. Therefore, all SUs can avoid the PUE attacker's primary emulation signal in the future. Simulation results show that our proposed approach converges quickly, and is effective to detect the PUE attacker.

In this paper, we introduce a distributed dynamic routing algorithm in multi-hop cognitive radio (CR) networks, in which secondary users (SUs) want to minimize their interference to the primary users (PUs) while keeping the delay along the route low. We employ a cognitive pilot channel (CPC) for SUs to be able to access the information about PUs, including PUs' locations and channel conditions. Medial axis with a relaxation factor is used as a reference path for the routing, along which we develop a hierarchical structure for multiple sources to reach their destinations. We introduce a temporal and spatial dynamic non-cooperative game to model the interactions among the SUs as well as their influences on the PUs, and obtain by backward induction a set of mixed (behavioral) Nash equilibrium strategies. We also employ a multi-stage fictitious play learning algorithm for distributed routing, which minimizes the overall interference from the SUs to the PUs, as well as the average packet delay along the route from the SU nodes to their destinations. Simulation results show that our proposed algorithm can avoid congestion in the CR network and minimize delay while keeping the interference level low.

In this paper, we propose a dynamic secure routing game framework to effectively combat jamming attacks in distributed cognitive radio networks. We first propose a stochastic multi-stage zero-sum game framework based on the directional exploration of ad hoc on-demand distance vector (AODV) algorithms. The zero-sum game captures the conflicting goals between malicious attackers and honest nodes and considers packet error probability and delay as performance metrics. The game-theoretic routing protocol guarantees a performance level given by the value of the game. Distributed Boltzmann-Gibbs learning is used for an on-line routing algorithm, in which the users do not have the knowledge of the attackers and the utility function. Instead, the users learn the payoffs based on their past observations. We use simulations to illustrate the proposed routing mechanism and compare the algorithm with fictitious-play learning. Unlike typical distributed routing algorithms such as AODV routing, the proposed secure routing algorithm supports a novel recovery of routing path failure against unknown attackers.

In this paper, we introduce a distributed dynamic routing algorithm for secondary users (SUs) to minimize their interference with the primary users (PUs) in multi-hop cognitive radio (CR) networks. We use the medial axis with a relaxation factor as a reference path which is contingent on the states of the PUs. Along the axis, we construct a hierarchical structure for multiple sources to reach cognitive pilot channel (CPC) base stations. We use a temporal and spatial dynamic non-cooperative game to model the interactions among SUs as well as their influences from PUs in the multi-hop structure of the network. A multi-stage fictitious play learning is used for distributed routing in multi-hop CR networks. We obtain a set of mixed (behavioral) Nash equilibrium strategies of the dynamic game in closed form by backward induction. The proposed algorithm minimizes the overall interference and the average packet delay along the routing path from SU nodes to CPC base stations in an optimal and distributed manner.

In order to maintain a conflict-free environment among licensed primary users (PUs) and unlicensed secondary users (SUs) in cognitive radio networks, providing frequency and geographical information through control channels, such as the cognitive pilot channel (CPC), has been recently proposed. While existing literature focused on the type of information that these control channels need to carry, this paper investigates the problem of gathering this information cooperatively, among a network of secondary base stations (SBSs). In this regard, given a cognitive network where every SBS can only have accurate knowledge on a small number of different primary users (PUs) or channels, each SBS can cooperate with neighboring SBSs in order to improve its view of the spectrum, i.e., learn about new PUs that can subsequently be used by its served SUs. We model the problem as a hedonic coalition formation game among the SBSs and we propose an algorithm for forming the coalitions. Using the proposed algorithm, each SBS can take an individual decision to join or leave a coalition while maximizing its overall potential utility, which accounts for the tradeoff between the benefit from learning new channels through coalition members and the cost from receiving inaccurate information. Simulation results show that the proposed algorithm yields a performance advantage, in terms of the average payoff per SBS reaching up to 165% relative to the non-cooperative case for a large network with 27 SBSs.

Cognitive radio is a new communication paradigm that is able to observe, learn, optimize, and intelligently adapt to achieve optimal frequency band usage. Cognitive radio (CR)-based wireless mesh networks (WMNS) provide infrastructures for secondary users' transmission, and it is necessary to develop techniques with low interference to primary users in such networks. In this paper, we propose an interference-minimization routing and scheduling algorithm for CR-WMNs. The flow control, route selection, time sharing, and opportunistic spectrum access are jointly formulated to minimize the aggregate interference to primary users. The optimization problem can be solved in a centralized way, and a distributed algorithm is also developed by adaptively modifying the individual link cost by the interference to the primary user and flow conditions. Simulation results show that the proposed scheme finds better routes in terms of interference to the primary users compared to the shortest path scheme, and the distributed solution shows near optimum compared with the centralized solution.

We suggested a joint algorithm for opportunistic spectrum and time sharing in cognitive radio (CR) wireless mesh networks (WMNs) in this paper. The problem was formulated using linear programming and solved by the simplex-II method. The flow optimization problem was first developed for grid topology of CR WMNs. Results showed optimized flow of the CR WMNs with ideal interference free link scheduling. Second, a practical interference free link scheduling algorithm was suggested. Simulation results showed that the normalized cost of the optimized algorithm was close to 1 and offered a cross-layer optimized solution between scheduling and network flow. The throughput of the CR WMNs using the suggested algorithm was optimally enhanced.

We derive the exact maximal-ratio combining (MRC) performance of dual-hop cooperative diversity systems with L-antenna destination reception in Nakagami-m fading channels, where a single-antenna relay operates in fixed-gain (semi-blind) amplify-and-forward (AF) mode. We also show that if m0, m1, and m2are the respective Nakagami parameters for the source-to-destination, source-to-relay, and relay-to-destination links, the fixed-gain AF cooperation with MRC achieves the diversity order of m0L + min {m1, m2L}.