This paper investigates the optimal channel exploration in multiple spectrum band covert communication. Different from most existing covert communication works that only consider the static wardens, we also take the reactive wardens that release real-time suppression tracking jamming based on their receiving power into consideration. In channel exploration period, the covert transmitter Alice aims to find a channel with high channel gain while guaranteeing its transmission covertness. However, obtaining channel gains on different bands before transmission takes Alice time and Alice has to choose the right time to stop exploring for throughput maximization. Firstly, to address the threats of the inactive and reactive wardens, the strategies of channel inversion power control and feedback based anti-jamming are respectively adopted. Then, we formulate the channel exploration problem with optimal stopping theory after analyzing Alice’s transmission covertness performance. Furthermore, since the conventional approach to this problem requires intensively computation, the one stage look ahead (1-SLA) rule is adopted to reduce the computation complexity. In particular, we mathematically prove that this rule is optimal in maximizing Alice’s expected throughput. At last, the simulation results are provided to validate the analytical results and the superiority of the proposed scheme compared with the benchmarks.

This article investigates the problem of synchronous randomized neighbor discovery with directional antennas. Due to the long tail effect, it will take long time to discover the last few neighbors, which increases overall neighbor discovery time. This effect is due to small proportion of remaining undiscovered neighbors. Moreover, improper choices of reception probabilities make the discovery even worse. In this article, a cognitive framework is proposed to minimize the expectation of neighbor discovery time. We present a scheme in which reception probabilities are dynamically adjusted. We consider an ideal scenario and a practical scenario. In an ideal scenario where perfect information about the number of neighbors is available, reception probabilities are adjusted according to the number of neighbors. A method of dynamic programming is used to recursively calculate the optimal reception probabilities. In an actual scenario where perfect information about number of neighbors is unavailable, a neighbor estimation method based on maximum-likelihood estimation is executed before probability adjustment. Simulation results show that when perfect information about neighbor is available and total transmission probability is within a proper range (between 0.1 and 0.2), the average neighbor discovery time can be significantly reduced (by 38% to 43%, respectively) compared with an existing probability-fixed scheme. With imperfect information, the scheme also works well and realizes appreciable reduction in average neighbor discovery time compared with existing self-adaptive schemes.

Internet of Spectrum Devices (IoSD) has been proposed as a bridging network among various spectrum-monitoring devices and massive spectrum-utilizing devices to enable a highly efficient spectrum sharing and management paradigm for future wireless networks. Spectrum data analytics is one of the key enabling techniques in IoSD. Correlations between spectrum state evolutions of different frequency points measured by an IoSD have been exploited to realize joint time-frequency spectrum prediction for improving the prediction accuracy. However, this kind of interrelationship has not been utilized efficiently to enhance the positive influences or avoid the negative influences when inferring the spectrum state. To fill the above gap, characteristics of spectrum state evolutions in the frequency domain are first modeled as multidimensional feature vectors in this article. Then, extensive clustering analyzes based on bisecting the K-means clustering and the agglomerative hierarchical clustering are conducted on spectrum state evolutions with multidimensional feature vectors. Real-world experiments demonstrate that the proposed multidimensional features can represent the characteristics of spectrum state evolutions in a more comprehensive way. Furthermore, clustering with the proposed vectors is integrated to the joint time-frequency spectrum inference problem to form the clustering-based joint spectral-temporal-spectrum-prediction (C-JSTSP) scheme. Experiments verify that the proposed C-JSTSP scheme can improve the inference performance on both the inference accuracy and the runtime overhead.

This article focuses on the slot access problem for neighboring cooperation in unmanned aerial vehicle (UAV) swarms. To avoid the slot access process being hindered by unavailable topology information or information exchanges, a self-organized collision discovery mechanism is proposed. Each broadcaster can know whether its transmission is successful through the mechanism which provides the basic knowledge for finding the slot access strategy. Considering the distributed feature, the slot access problem is formulated as two game models. Both games are proved to have at least one Nash Equilibrum (NE) and the best NE is the optimum of the problem. Two distributed and synchronous algorithms are proposed to reach the NE. The first algorithm converges fast which satisfies the dynamic feature of UAV swarms and the second one converges to the optimum asymptotically. Moreover, to enhance the time efficiency of UAV swarms, the total number of required slots is investigated in some typical topologies and then conjectured to general ones. Simulation results verify that the proposed method is effective and the conjecture is true in almost all topologies.

In this paper, we investigate the problem of distributed ferrying transmission in UAV-assisted device-to-device (D2D) communication networks. When drones are performing tasks with given trajectories, terrestrial communication devices can select them for loading data opportunistically, and then drones will offload the data to corresponding receivers in the appropriate later time. For the dynamic multi-device network, there are composite optimization problems including competition of drone selection, time allocation of data loading and offloading, as well as limited channel access. Due to the distributed feature, devices share resources through independent perception and decision making. Therefore, a dynamic hierarchical game is designed for the problem of joint UAV allocation and channel access. Specifically, a predictable dynamic matching market is constructed to address the problem of UAV selection and time allocation, while the problem of channel access is studied by the congestion game. Based on the game model, a distributed hierarchical algorithm is proposed and the property of convergence is discussed. Simulation results confirm that the effective selection of data ferrying approach can improve the transmission performance significantly, while unreasonable optimization approaches may lead to the decline of the transmission performance.

Message interaction among neighborhood is necessary for unmanned aerial vehicles (UAVs) and Time Division Multiple Access is a feasible implementation by which each UAV broadcasts in one dedicated slot. However, the dynamic character requires the access process fast and the constrained energy limits the feedbacks during the process uninformative. Therefore, this article focuses on the slot access problem in UAV swarms. To be energy saving, a collision discovery method which is independent of valid information is designed to play the role of feedback. Furthermore, considering the decentralized structure, the slot access problem is formulated as a game model. To reach the Nash Equilibrium of the game fast, a synchronous and uncoupled learning algorithm is proposed to, in which all UAVs update simultaneously based on the information they obtain. The algorithm releases the requirement for a scheduler or a common control channel and is therefore applicable in UAV swarms. Simulation results verify the effectiveness of the proposed method and some investigations about the requirement for the number of slots are made.

Unmanned aerial vehicles (UAVs) have attracted increasing attention in wireless communications due to the high mobility. This paper investigates a fixed-wing UAV-to-UAV (U2U) communications system, with the aim of minimizing the information transmission time via proactively designing the UAV paths. First, we propose a general optimization framework for U2U communications, which covers the communication throughput requirement, interference from terrestrial transmitters, UAV maximum/minimum speeds and accelerations, and minimum U2U distance. To tackle the formulated optimization, the communication throughput constraint that contains uncertain locations of terrestrial transmitters is transformed into a deterministic expression with the aid of S-procedure, and the nonlinear equality constraints on the UAV paths are replaced by linear equality constraints with additional positive semidefinite matrix constraints. Then, we develop a path planning algorithm based on the exact penalty method and successive convex approximation. Furthermore, we design a heuristic path planning algorithm that solves the completion time minimization problem by iteratively addressing a series of throughput maximization problems. The proposed heuristic algorithm strikes a good tradeoff between the computational complexity and the achievable performance. Finally, the simulation results are presented to verify the proposed path planning algorithms under various parameter configurations.

In this paper, we consider the spectrum sharing planning problem for a full-duplex unmanned aerial vehicle (UAV) relaying systems with underlaid device-to-device (D2D) communications, where a mobile UAV employed as a full-duplex relay assists the communication link between separated nodes without direct link. Our design aims to maximize the sum throughput under the transmit power budget, while guaranteeing the coexistence with terrestrial D2D pairs, satisfying the information causality and UAV's trajectory constraints. First, the transmit power planning with a given trajectory is investigated, where a successive convex algorithm is developed by leveraging the D.C. (difference of two convex) programming. Then, we propose a two-step trajectory design method for the given transmit power since the constraints of D2D pairs result in a non-convex feasible set. Furthermore, an efficient spectrum sharing method for an aerial UAV and terrestrial D2D communications is designed by alternately optimizing the transmit power and UAV's trajectory. Finally, simulation results under various parameter configurations are provided to show the effectiveness of the proposed algorithms.

Device-to-device communication and energy harvesting are both key technologies to improve spectrum and energy efficiency. In this paper, we investigate the resource allocation problem for the energy harvesting-powered D2D communication underlaying cellular networks, where D2D pairs firstly harvest energy and then transmit information signals. The goal is to maximize the sum throughput via joint time scheduling and power control while satisfying the SINR requirement of cellular user and taking into account the energy constraint. The formulated non-convex problem is transformed into a nonlinear fractional programming problem with a tactful reformulation. Coupled with D.C. (difference of two convex functions) programming, a near optimal solution of the non-convex problem can be obtained by iteratively solving a sequence of convex problems. Then, a first-order algorithm is employed to solve these convex problems. Numerical simulations are conducted to validate the effectiveness of the proposed algorithm and evaluate the system throughput performance.

In this paper, we present a secrecy outage performance analysis of wireless powered communication networks with multiple eavesdroppers, where an energy-limited information source with multiple antennas harvests the radio frequency (RF) energy from a dedicated power beacon (PB) before transmission. To exploit the benefits of multiple antennas at source, two popular multi-antenna transmission schemes, i.e., maximal ratio transmission and transmit antenna selection, are investigated for two intercepting ways at Eves, i.e., non-colluding and colluding scenarios, respectively. Specifically, adopting the time-switching protocol at PB, we derive exact and asymptotic closed-form expressions of the secrecy outage probability for both two transmission schemes taking into account the outdated channel state information (CSI). From our analysis, several important concluding remarks are obtained as follows: a) Full secrecy diversity order can be achieved by both two transmission schemes with no feedback delay, however, it reduces to zero in the presence of feedback delay; b) MRT scheme always outperforms TAS scheme with no feedback delay. However, TAS scheme achieves a similar performance as MRT scheme or even better in moderate and even serious feedback delay conditions.

In this paper, we investigate the average throughput of a multi-antenna wireless powered communication network where an energy-constrained user harvests energy from a hybrid access-point (AP) equipped with multiple antennas in the downlink, and then transmits information to the AP in the uplink using the harvested energy. Specifically, we consider a more practical scenario, i.e., nonlinear energy harvester, as compared with the traditional linear model. In order to evaluate the key parameters, such as the transmit power, antenna numbers, time-splitting, channel fading severity, on the performance of the considered system, we derive closed-form expressions of the average throughput for both delay tolerant and delay intolerant transmission modes in Nakagami-m fading channel. In addition, to further exploit the insights on the application of the considered system, the asymptotic analysis for the achievable throughput are also provided in two special cases, i.e., high transmit power regime and high saturation threshold regime. Finally, our results demonstrate that the considered system exhibits the throughput saturation phenomenon, and the parameters of channel fading severity produce a different impact on the average throughput in the two transmission modes.

This paper investigates the problem of dynamic spectrum access for canonical wireless networks, in which the channel states are time-varying. In the most existing work, the commonly used optimization objective is to maximize the expectation of a certain metric (e.g., throughput or achievable rate). However, it is realized that expectation alone is not enough since some applications are sensitive to fluctuations. Effective capacity is a promising metric for time-varying service process since it characterizes the packet delay violating probability (regarded as an important statistical quality-of-service index), by taking into account not only the expectation but also other high-order statistic. Therefore, we formulate the interactions among the users in the time-varying environment as a non-cooperative game, in which the utility function is defined as the achieved effective capacity. We prove that it is an ordinal potential game which has at least one pure strategy Nash equilibrium. Based on an approximated utility function, we propose a multi-agent learning algorithm which is proved to achieve stable solutions with dynamic and incomplete information constraints. The convergence of the proposed learning algorithm is verified by simulation results. Also, it is shown that the proposed multi-agent learning algorithm achieves satisfactory performance.

In this paper, we consider a multiple-input-multiple-output optical wireless communication (MIMO-OWC) system in the presence of log-normal fading. In this scenario, a general criterion for the design of full-diversity space code (FDSC) with the maximum likelihood detector is developed. This criterion reveals that in a high signal-to-noise ratio regime, MIMO-OWC offers both large-scale diversity gain, governing the exponential decay of the error curve, and small-scale diversity gain, producing traditional power-law decay. Particularly for a two by two MIMO-OWC system with unipolar pulse amplitude modulation, a closed-form solution to the design problem of a linear FDSC optimizing both diversity gains is attained by taking advantage of the available properties on the successive terms of Farey sequences in number theory as well as by developing new properties on the disjoint intervals formed by the Farey sequence terms to attack the continuous and discrete variables mixed max-min design problem. In fact, this specific design not only proves that a repetition code is the optimal linear FDSC optimizing both the diversity gains but also uncovers a significant difference between MIMO radio frequency communications and MIMO-OWC that space dimension alone is sufficient for a full large-scale diversity achievement. Computer simulations demonstrate that FDSC substantially outperforms uncoded spatial multiplexing with the same total optical power and spectral efficiency, and the latter provides only the small-scale diversity gain.

In this paper, we investigate the secrecy performance of dual-hop amplify-and-forward (AF) multi-antenna relaying systems over Rayleigh fading channels by taking into account the direct link between the source and destination. To improve the secrecy performance, two linear processing schemes at relay and maximal ratio combining (MRC) at destination are proposed, namely, Zero-forcing/MRC (ZF/MRC) and Maximal ratio transmission/MRC (MRT/MRC). For these schemes, we present new tight analytical expressions of the secrecy outage probability. In addition, we examine the performance in high signal-to-noise ratio (SNR) regimes, and present simple secrecy outage approximations for all schemes. The results reveal that: 1) The MRT/MRC scheme achieves a full diversity order of M+1, while the ZF/MRC scheme achieves a diversity order ofM, where M is the number of antennas at relay. 2) The ZF/MRC scheme outperforms the MRT/MRC scheme in the low SNR regime, while becomes inferior to the MRT/MRC scheme in the high SNR regime.

The optimal detection of generalized spatial modulation (GSM) technique is the maximum likelihood (ML) algorithm. However, the computational complexity of ML detection is high and increases dramatically with the increase of the number of transmit antennas and active antennas. To tackle this problem, we propose low-complexity suboptimum detectors by exploiting a special property of GSM-MIMO systems, i.e., the spatial constraint of active antennas. Specifically, the proposed detections are designed based on a greedy algorithm termed Multipath Matching Pursuit (MMP). Using the spatial constraint of active antennas, the proposed detections achieve better performance than that of the original MMP algorithm with lower computational complexity. Moreover, the numerical results are also provided to demonstrate the superiority of the proposed detections.

In this paper, we investigate the hybrid access control in two-tier small cell networks from the perspective of incentive mechanism design, considering macro base station's private information. We formulate this problem as a Stackelberg game. To be specific, the macro base station (MBS) and small cell base stations (SBSs) are modeled as a leader with specific payoff preference and followers, respectively. A subsidy mechanism is adopted by MBS. Only when the SBS can provide acceptable service level for macro user (MUE), then MBS handovers MUE to SBS and provides subsidy to SBS; otherwise, MBS serves MUE by itself. We consider the impacts of MBS's private information on the final equilibrium of the proposed game. Theoretical analysis and simulation results show it is better for MBS to broadcast the private information to get more payoff from the perspective of incentive mechanism design.

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.

Spectrum auction is one of the most effective solutions to allocate the spectrum resource following the market rules and has attracted much attention from both academia and industry. However, most of the existing studies assume that the spectrum buyers' demands are homogeneous and the interference relationship is fixed without any change with the variation of spectrum. Furthermore, the economical efficiency of auction outcome has not drawn enough attention. That motivates us to design an auction scheme to jointly consider the multi-demand of buyers, heterogeneous spectrum, and economical efficiency. In this paper, we propose a novel overlapping coalition formation-based double auction, called VERACITY, to address this problem. The auctioneer groups the conflict free buyers into the same coalition and allows a buyer to join multiple coalitions based on the heterogeneous demand. Dynamic overlapping coalition formation implemented by the auctioneer is to find the approximately optimal coalition structure corresponding to the economical efficiency outcome, i.e., maximizing the social welfare. Furthermore, we prove that VERACITY is individually rational, budget balanced, truthful, and economically efficient. Simulation results are presented to show the convergence and effectiveness of the proposed VERACITY.

We investigate the secrecy performance of dual-hop amplify-and-forward multi-antenna relaying systems over Rayleigh fading channels, considering the direct link between the source and the destination. In order to exploit the available direct link and the multiple antennas for secrecy improvement, different linear processing schemes at the relay and different diversity combining techniques at the destination are proposed, namely: 1) zero-forcing/maximal ratio combining (ZF/MRC); 2) ZF/selection combining (ZF/SC); 3) maximal ratio transmission/MRC (MRT/MRC); and 4) MRT/SC. For all these schemes, we present new closed-form approximations for the secrecy outage probability. Moreover, we investigate a benchmark scheme, i.e., cooperative jamming/ZF (CJ/ZF), where the secrecy outage probability is obtained in exact closed-form. In addition, we present asymptotic secrecy outage expressions for all the proposed schemes in the high signal-to-noise ratio (SNR) regime, in order to characterize key design parameters, such as secrecy diversity order and secrecy array gain. The outcomes of this paper can be summarized as follows: 1) MRT/MRC and MRT/SC achieve a full diversity order of M + 1, ZF/MRC and ZF/SC achieve a diversity order of M, while CJ/ZF only achieves unit diversity order, where M is the number of antennas at the relay; 2) ZF/MRC (ZF/SC) outperforms the corresponding MRT/MRC(MRT/SC) in the low SNR regime, while becomes inferior to the corresponding MRT/MRC (MRT/SC) in the high SNR; and 3) all the proposed schemes tend to outperform the CJ/ZF with moderate number of antennas, and linear processing schemes with MRC attain better performance than those with SC.

In this paper, we consider a multiple-input-multiple-output optical wireless communication (MIMO-OWC) system suffering from log-normal fading. In this scenario, a general criterion for the design of full large-scale diversity space code (FLDSC) with the maximum likelihood (ML) detector is developed. Based on our criterion, FLDSC is attained if and only if all the entries of the space coding matrix are positive. Particularly for 2×2 MIMO-OWC with unipolar pulse amplitude modulation (PAM), a closed-form linear FLDSC satisfying this criterion is attained by smartly taking advantage of some available properties as well as by developing some new interesting properties on Farey sequences in number theory to rigorously attack the continuous and discrete variables mixed max-min problem. In fact, this specific design not only proves that a repetition code (RC) is the best linear FLDSC, but also uncovers a significant difference between MIMO radio frequency (RF) communications and MIMO-OWC that space-only transmission is sufficient for a full diversity achievement. Computer simulations demonstrate that FLDSC substantially outperforms spatial multiplexing with the same total optical power and spectral efficiency and the latter obtains only the small-scale diversity gain.

In this paper, we propose general-order transmit antenna selection to enhance the secrecy performance of multiple-input-multiple-output multieavesdropper channels with outdated channel state information (CSI) at the transmitter. To evaluate the effect of the outdated CSI on the secure transmission of the system, we investigate the secrecy performance for two practical scenarios, i.e., Scenarios I and II, where the eavesdropper's CSI is not available at the transmitter and is available at the transmitter, respectively. For Scenario I, we derive exact and asymptotic closed-form expressions for the secrecy outage probability in Nakagami-m fading channels. In addition, we also derive the probability of nonzero secrecy capacity and the ε-outage secrecy capacity, respectively. Simple asymptotic expressions for the secrecy outage probability reveal that the secrecy diversity order is reduced when the CSI is outdated at the transmitter, and it is independent of the number of antennas at each eavesdropper NE, the fading parameter of the eavesdropper's channel mE, and the number of eavesdroppers M. For Scenario II, we make a comprehensive analysis of the average secrecy capacity obtained by the system. Specifically, new closed-form expressions for the exact and asymptotic average secrecy capacity are derived, which are valid for general systems with an arbitrary number of antennas, number of eavesdroppers, and fading severity parameters. Resorting to these results, we also determine a high signal-to-noise ratio power offset to explicitly quantify the impact of the main channel and the eavesdropper's channel on the average secrecy capacity.

Radio resource management (RRM) is crucial for improving resource utilization in heterogeneous wireless networks. Existing work attempts to exploit the network diversity to gain throughput improvement for users, which, however, neglects the impact of user demand on RRM. Armed with the idea that the ultimate goal of communications is to serve users with personalized demand, we introduce another dimension of potential performance gain, user demand diversity gain. This gain derives from the elaborate matching between user demand and radio resource, which can not be directly attained in existing throughput-centric optimization due to users' blindness in maximizing throughput. Aiming at obtaining this gain, we propose the user demand-centric optimization, where users seek to maximize quality of experience (QoE), instead of throughput. This shift enables us to propose a novel game formulation, QoE game. We derive the condition on the existence of the QoE equilibrium, validate the user demand diversity gain and propose a distributed QoE equilibrium learning algorithm. Finally, a cloud assisted learning framework is proposed to accommodate the learning algorithm with significantly reduced cost. Simulation results validate the existence of user demand diversity gain and the effectiveness of the proposed learning algorithm in improving the system efficiency and QoE fairness.

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 one critical challenge is the uncertainty of the quality of 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 abnormal data, which makes the existing defense schemes ineffective. To tackle these unique challenges, we propose a robust spectrum sensing scheme by developing a data cleansing framework, where the underutilization 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. Simulation results demonstrate that the proposed robust sensing scheme outperforms the state-of-art schemes under various abnormal data parameter configurations.

Starting from Neyman-Pearson criterion, this paper derives an optimal spectrum sensing scheme which exploits spatial diversity among multiple cognitive sensors and temporal diversity among consecutive time slots jointly. In the proposed scheme, the impact of the imperfect reporting channel on the design of the spectrum sensing scheme is effectively integrated. Simulation results show that compared with singular (either spatial or temporal) diversity-based sensing schemes, the proposed scheme brings not only improvement of sensing performance, but also significant reduction of sensing overhead.

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.

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.

This paper presents a comprehensive performance analysis of multiuser multiple antenna amplify-and-forward relaying networks employing opportunistic scheduling with feedback delay and co-channel interference over Rayleigh fading channels. Specifically, we derive exact as well as approximate closed-form expressions for the outage probability and average symbol error rate (SER) of the system. In addition, simple asymptotic expressions at the high signal-to-noise ratio (SNR) regime are obtained, which facilitate the characterization of the achievable diversity order and coding gain of the system. Moreover, two novel ergodic capacity bounds valid for general systems with arbitrary number of antennas and users are proposed. Finally, the optimum power allocation scheme in terms of minimizing the average SER is studied, and simple analytical solutions are obtained. Simulation results are provided to corroborate the derived analytical expressions, and it is demonstrated that the ergodic capacity bounds remain sufficiently tight across the entire range of SNRs and the proposed power allocation scheme offers significant improvements on the SER performance. The findings of the paper suggest that the full diversity order can only be achieved when there is ideal feedback, i.e., no feedback delay, and the diversity order always reduces to one in the presence of feedback delay. Also, the impact of key parameters such as the number of antennas and users on the system performance is intimately dependent on the level of feedback delay.

In this paper, we propose transmit antenna selection with maximal ratio combining (TAS/MRC) in dual-hop decode-and-forward spectrum-sharing relaying networks with the primary user's interference and outdated channel state information (CSI). In this network, a single antenna that maximizes the received SNR is selected at the secondary transmitter, and the MRC is adopted at the secondary receiver. To efficiently evaluate the impact of key parameters on the system performance, we derive the exact analytical expression for the outage probability of the secondary network in a Rayleigh fading channel. Moreover, we present simple asymptotic expressions for the outage probability in a high SNR regime, which reveal practical insights on the achievable diversity order and coding gain. The findings suggest that whether the outdated CSI concerning the secondary transmission links has significant impact on the outage probability of the system depends on the interference power constraint at primary receivers. Specifically, under the proportional interference power constraint, the achievable diversity order is affected by imperfect CSI regarding the secondary transmission links, and the diversity-multiplexing tradeoff is independent of the primary network. However, under the fixed interference power constraint, the error floor is displayed, and the achievable diversity order reduces to zero regardless of the CSI concerning the secondary transmission links.

For cognitive wireless networks, one challenge is that the status and statistics of the channels' availability are difficult to predict. Numerous learning based online channel sensing and accessing strategies have been proposed to address such challenge. In this work, we propose a novel channel sensing and accessing strategy that carefully balances the channel statistics exploration and multichannel diversity exploitation. Unlike traditional MAB-based approaches, in our scheme, a secondary cognitive radio user will sequentially sense the status of multiple channels in a carefully designed order. We formulate the online sequential channel sensing and accessing problem as a sequencing multi-armed bandit problem, and propose a novel policy whose regret is in optimal logarithmic rate in time and polynomial in the number of channels. We conduct extensive simulations to compare the performance of our method with traditional MAB-based approach. Simulation results show that the proposed scheme improves the throughput by more than 30% and speeds up the learning process by more than 100%.

In this paper, we investigate the ergodic capacity and the average symbol error probability (SEP) of uplink cognitive cellular networks with opportunistic scheduling in Nakagami-m fading channels. Considering the same opportunistic scheduling scheme as [1], we derive closed-form expressions for the ergodic capacity and the average SEP of the system. Depending on closed-form expressions, we further investigate the impact of various key system parameters, i.e., channel fading severity, primary user's target outage probability and primary user's transmission rate, on cognitive user's performance. Theoretical results, verified by simulations, about the ergodic capacity and the average SEP are expressed in terms of the Meijer's G-function and the confluent hypergeometric function of the second kind, respectively. From the simulations, we get that the ergodic capacity and the average SEP are independent of the number of cognitive users and the transmit power of primary user.

This paper investigates the problem of distributed channel selection for interference mitigation in cognitive radio networks (CRNs) with block-fading channels, using a game-theoretic solution. Specifically, the channel gains are blockfixed in a slot and change randomly in the next slot. Existing algorithms, which are originally designed for static channels, can not converge in the presence of time-varying channels. We formulate this problem as a non-cooperative game with random payoffs, in which the utility of each player (CR user) is defined as the expected weighted experienced interference. This game is proved to be a potential game with the network utility, the expected weighted aggregate interference, serving as the potential function. Then, we propose a stochastic learning automata based distributed channel selection algorithm, with which the CR users learn the desirable channel selections from their action-payoff history. It is analytically shown that the proposed learning algorithm converges to pure strategy Nash equilibrium (NE), which maximizes the network utility globally or locally, without information exchange. Moreover, simulation results show that it achieves higher normalized transmission rate.

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.

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.

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.

In this paper, decentralized cooperative spectrum sensing in cognitive radio networks is studied based on the recent advances in unsupervised learning. To balance a tradeoff between the sensing reliability and the cooperation overhead (e.g., energy, delay, and signaling, etc.), a distributed clustering algorithm, without any central coordinator, is introduced for inducing the sensors with the best detection performance to join together and take charge of cooperative spectrum sensing. Numerical results show that the proposed scheme can obtain detection performance comparable to that of optimal soft combination scheme with reduced cooperation overhead. Moreover, the proposed scheme does not require any priori knowledge of spectrum sensors' received signal-to-noise-ratios (SNRs) or locations.

In multichannel system, user could keep transmitting over an instantaneous “on peak” channel by opportunistically accessing and switching among channels. Previous studies rely on constant transmission duration, which would fail to leverage more opportunities in time and frequency domain. In this paper, we consider opportunistic channel accessing/releasing scheme in multichannel system with Rayleigh fading channels. Our main goal is to derive a throughput-optimal strategy for determining when and which channel to access and when to release it. We formulate this real-time decision-making process as a two-dimensional optimal stopping problem. We prove that the two-dimensional optimal stopping rule can be reduced to a simple threshold-based policy. Leveraging the absorbing Markov chain theory, we obtain the optimal threshold as well as the maximum achievable throughput with computational efficiency. Numerical and simulation results show that our proposed channel utilization scheme achieves up to 140 percent throughput gain over opportunistic transmission with a single channel and up to 60 percent throughput gain over opportunistic channel access with constant transmission duration.

In this work, we study the problem of how to strike a balance between the QoS provisioning and the energy efficiency when a cooperative communication scheme is applied to a clustered wireless sensor network. Specifically, we first characterize the tradeoff by a multi-variable optimization problem, with the goal of balancing the outage performance and the network lifetime. Then, we horizontally decompose the problem into the concatenation of two subproblems: i) the long-haul transmit power per sensor node, and ii) the set of assisting cluster nodes. For the former one, an optimal long-haul transmit power solution is proposed based on the Lambert W function. The latter one is modeled as a coalition formation game, where the characteristic function is designed based on the combination of the former subproblem's results. Furthermore, an optimal algorithm is proposed by using a dynamic coalition formation process based on the best-reply process with trial opportunity. Extensive simulation results are presented to demonstrate the effectiveness of our proposed scheme.

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.

The issue of distributed channel selection in opportunistic spectrum access is investigated in this paper. We consider a practical scenario where the channel availability statistics and the number of competing secondary users are unknown to the secondary users. Furthermore, there is no information exchange between secondary users. We formulate the problem of distributed channel selection as a static non-cooperative game. Since there is no prior information about the licensed channels and there is no information exchange between secondary users, existing approaches are unfeasible in our proposed game model. We then propose a learning automata based distributed channel selection algorithm, which does not explicitly learn the channel availability statistics and the number of competing secondary users but learns proper actions for secondary users, to solve the proposed channel selection game. The convergence towards Nash equilibrium with respect to the proposed algorithm also has been investigated.