With the rapid development of reinforcement learning (RL)-enhanced anti-jamming wireless communication technologies and jamming technologies, intelligent communication confrontation has become an urgent problem to be solved. Most existing work assumed that detailed information of jammer was known in advance, which hardly holds in practice. Besides, some work was sensitive to the changing of jamming policy, leading to limited adaptability and scalability. This article extends the research to scenarios with unknown jammer and diverse jamming policies, including fixed, reactive, and deep RL (DRL)-based proactive jamming policies. The interaction between communication party and jammer is formulated as a partially observable adversarial team stochastic game (POATSG). To cope with unknown and diverse jamming policies, a prioritized fictitious rival play (PFRP)-based robust anti-jamming spectrum access scheme (RASAS) is proposed. First, a fictitious jammer is designed to force the communication party to promote robustness via adversarial training. Then, a synchronized update mechanism is adopted to mitigate the nonstationary issue. Finally, the fictitious agent pool is introduced to create diverse fictitious opponents and avoid overfitting. Simulation results show that the PFRP-based scheme is robust to the jamming policy, switching cycle of the jamming policy, and jamming channel number.

In traditional public blockchain networks, transaction fees are allocated only to full nodes (miners), neglecting relay nodes and diminishing participation incentives for lightweight nodesparticularly in energy-constrained wireless blockchain environments. This paper proposes a novel dual auction mechanism to allocate transaction fees for both relay and validation activities in the wireless blockchain network. The proposed one consists of two sub-auction stages: the relay sub-auction and the validation subauction. In the relay sub-auction, relay nodes select transactions to forward based on rewards. Additionally, nodes adjust the relay probability using a no-regret algorithm to enhance efficiency. In the validation sub-auction, full nodes use the Vickrey-Clarke-Groves (VCG) mechanism to select transactions and construct the block. Our mechanism demonstrably satisfies Incentive Compatible (IC), Individual Rational (IR), and Computationally Efficient (CE) while maintaining bounded social welfare optimization. Furthermore, we consider the impact of network complexity on blockchain performance. Extensive simulation results demonstrate that the proposed one reduces energy and bandwidth resource consumption without compromising the throughput and security of the wireless blockchain network.

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 paper addresses the challenge of anti-jamming in moving reactive jamming scenarios. The moving reactive jammer initiates high-power tracking jamming upon detecting any transmission activity, and when unable to detect a signal, resorts to indiscriminate jamming. This presents dual imperatives: maintaining hiding to avoid the jammer’s detection and simultaneously evading indiscriminate jamming. Spread spectrum techniques effectively reduce transmitting power to elude detection but fall short in countering indiscriminate jamming. Conversely, changing communication frequencies can help evade indiscriminate jamming but makes the transmission vulnerable to tracking jamming without spread spectrum techniques to remain hidden. Current methodologies struggle with the complexity of simultaneously optimizing these two requirements due to the expansive joint action spaces and the dynamics of moving reactive jammers. To address these challenges, we propose a parallelized deep reinforcement learning (DRL) strategy. The approach includes a parallelized network architecture designed to decompose the action space. A parallel exploration-exploitation selection mechanism replaces the$\varepsilon $-greedy mechanism, accelerating convergence. Simulations demonstrate a nearly 90% increase in normalized throughput.

This paper focuses on distributed resource management and task scheduling for multi-access MEC networks against the intelligent eavesdropping jammer (IEJ). Due to the lack of a central controller, the problem of joint task scheduling and network resource allocation is formulated as a distributed multi-user hybrid-integer non-convex model.The optimization objective is to maximize users’ satisfaction while meeting the Quality of Service (QoS) requirements of tasks and ensuring the high-reliable demands of data offloading. To overcome the challenge of partial observability for users, the channel observation matrix and Gramian Angular Field (GAF) are utilized to preprocess the limited channel state information and to mine the potential time-frequency characteristics of the external environment. Moreover, the hierarchical architecture and parallel networks are introduced for a parameterized redesign of the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) method to improve the decision accuracy. Finally, simulation results demonstrate the superiority of the proposed algorithm over existing methods in terms of delay, energy consumption, and security.

Blockchain has been widely implemented as a trusted platform. Previous works mainly focus on the computing capacity of devices while communication factors play a vital role in blockchain performance during dynamic wireless environments. High-speed movement causes frequent wireless connection interruptions and leads to severe performance degradation of blockchain. Besides, resource-constrained mobile devices are unwilling to selflessly contribute their energy and bandwidth for blockchain, hindering applications in dynamic mobile networks. This paper proposes a reverse auction mechanism to incentivize mobile devices to provide robust wireless connections. Devices submit their connection provision and expected rewards as bids. The mobile blockchain system uses smart contracts to autonomously execute the reverse auction to determine winners and allocate payments based on actual connections. We prove that the reverse auction mechanism is Individual Rationality (IR), Incentive Compatibility (IC), and Computational Efficiency (CE), and derive the approximation ratio 2$\sigma$of the mechanism. Extensive simulation results demonstrate that the proposed mechanism decreases up to half the energy and bandwidth consumption, but achieves a similar TPS and stale rate compared to the selfless scheme, where devices contribute all wireless connections for nothing in return. The proposed auction mechanism achieves more than 96% of the optimal social welfare.

The environmental shelters such as public links can enable covert communication by covering covert transmission. To further exploit shelters, this paper focuses on a two-hop system where multiple pairs of public users and one pair of covert users communicate through a multi-antenna relay. We aim to improve covertness performance while satisfying the covertness constraints of two hops and quality of service (QoS) requirements of public users. The covert throughput maximization problem is formulated via jointly optimizing transmit power and beamformer, which is challenging to solve. We introduce successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques to convert the problem into convex, where the joint optimization algorithm is developed. Considering the computational complexity, we further design a block diagonalization (BD) beamformer at the relay, which translates the beamformer optimization into a power allocation problem and derives the optimal solution in a closed form. We analytically show that the covert throughput first increases and then decreases as the number of public pairs increases in BD-based design, which has been verified numerically and can be generalized in other designs. Numerical results also evaluate the superiority of the joint optimization algorithm and the effectiveness of the BD-based efficient design. In particular, the BD-based design is very close to the joint optimization under small maximum transmit power of users or large maximum transmit power of relay.

This paper studies the anti-jamming channel selection problem in the unmanned aerial vehicle (UAV) communication scenario using machine learning. Recently, deep reinforcement learning (DRL) based anti-jamming approaches have drawn much attention, but most of them require lots of computing resources and power supply for training, which is impractical for the hardware-limited UAVs. What's more, the high complexity of DRL-based algorithms weakens their online learning ability, failing to rapidly adapt to the changing jamming environment. To be applicable to the hardware-limited UAVs, we propose a lightweight reinforcement learning algorithm based on the idea of spectrum state abstraction. We first assign similar spectrum states to clusters using the DRL and clustering algorithms. A state clustering network is deployed in the UAV to convert the large and redundant state space into a small number of state clusters. Based on the clustered states, the UAV uses a simple tabular Q-learning algorithm to online find the optimal anti-jamming policy. The simulation results show that, compared with the conventional DRL approach, the proposed algorithm can efficiently find the optimal anti-jamming policy and fast adapt to the change of jamming pattern in the complicated and dynamic jamming environment.

This paper investigates the problem of multidimensional resource management in multi-access mobile edge computing (MEC) networks against external dynamic jamming. The objective is to minimize the long-term computational cost of the MEC network while satisfying the task computation delay requirements of user equipment (UE) by jointly optimizing computing and communication resource allocation. To overcome challenges such as frequency conflict and dynamic jamming attacks, a distributed multi-agent hierarchical deep reinforcement learning (MAHDRL) MEC framework based on hybrid heterogeneous decision-making is proposed. Specifically, a hierarchical MEC anti-jamming data offloading optimization model is constructed, and the MEC resource management problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP). Based on this, a distributed MAHDRL algorithm based on the actor-critic (AC) model is designed to solve the multi-agent high-dimensional nonlinear hybrid integer programming NP-hard problem: the high-level network in the base station (BS) optimizes discrete channel access strategies, while the low-level network in UEs learns data offloading strategies. Additionally, the computational complexity is discussed and a theoretical proof of the algorithm convergence is presented. Simulation results demonstrate the superiority of the proposed algorithm, which reduces energy consumption and data processing delay across the network.

As a fundamental requirement for IoT communication systems the importance of highly reliable anti-jamming communication methods in safety use cases is growing. In this article, we propose a novel anti-intelligent jamming scheme called opponent awareness-based anti-jamming algorithm (OA3). The user-jammer-environment interaction is formulated as a two-player simultaneous action stochastic game where participators have the ability to update their strategies. The decision-making process of each agent is modeled as a Markov decision process (MDP). Begin with the intuition “learn how the jammer learns,” the opponent awareness-based iterative learning objective (OAL) of the user is presented by considering the learning awareness of the jammer to defeat the intelligent jamming. Finally, we introduce the framework, including offline policy learning and online policy exploiting to implement OAL and accelerate the learning. Simulations show that the OA3 outperforms the benchmark anti-jamming strategy in terms of packet success rate.

In this paper, we consider a novel covert communication scenario where the warden does not have prior knowledge of the transmitter’s power. In this scenario, the current widely adopted likelihood ratio test (LRT)-based detector is not optimal anymore. To address this, we formulate a detection framework based on the generalized likelihood ratio test (GLRT), which replaces the unknown parameter with maximum likelihood estimation (MLE) and is proven optimal in the scenario. Based on the GLRT-based framework, two different detection models are proposed, where one only utilizes observations of the current time and the other can exploit observations of past time slots. We analyze the estimation and fisher information of the transmit power, and even the detection and covert performance under two different detection models, respectively. Based on the analytical results, we further derive the maximum number of tolerable slots under which the transmitter can remain the same transmit power while the detection error probability of the warden is still larger than the regulated threshold. Moreover, the transmit power and the number of tolerable slots are jointly optimized to maximize the transmission throughput subjecting to covertness constraint. The numerical results demonstrate the correctness of the theoretical analysis. We show how the covert rate is influenced by the number of transmitting slots and the transmit power, which can guide the design of the covert transmission strategy.

Neighbor discovery is the procedure to establish a first contact between two wireless devices. For duty-cycled low-power devices, energy consumption is closely related to neighbor discovery latency. Actually, in recent protocols, such as Bluetooth low energy (BLE) or ANT+, neighbor discovery latency is determined by the parameters used by the devices, such as advertising interval, scan window, scan interval, and so on. A fundamental problem of the BLE-like protocol is that the exact relation between parameters and discovery latency has not been fully analyzed. In this article, we propose a Euclidean-division-based low-complexity precise analytical approach that can derive the mathematical expressions of both worst-case latency and average latency for any parameter groups. It is confirmed by simulation results that our solution can make highly accurate predictions about the value of latencies. Simulation results also show that the proposed solution has an extremely low complexity. Moreover, we derive the lower bound of latency for given duty cycles, which provides useful guidelines for the choice of energy-efficient parameter groups for BLE.

Intelligent reflective surfaces (IRSs) as low energy consumption and easy to attach devices have been widely applied in the field of anti-jamming recently. In particular, the combination of IRS and unmanned aerial vehicle (UAV), as IRS-UAV, further expands the scope of IRS services. In this article, the joint IRS selection and beamforming optimization problem has been investigated in multiple IRS-UAV-assisted anti-jamming D2D networks. To solve the above optimization problem, a distributed matching-based selection and$Q$-learning-based beamforming optimization algorithm (DMQ) was proposed. In detail, the optimization problem is decomposed into two subproblems, namely, the IRS selection subproblem is formulated as a noncommutative many-to-many matching game model to describe peer effects and uncertainty selection quotas, and the passive beamforming optimization subproblem is solved by a reinforcement algorithm to satisfy the complex environment. Numerical simulations confirm the convergence and near-optimal performance of the proposed scheme with lower latency and greater robustness.

Blockchain is considered the critical backbone technology for secure and trusted Internet of Things (IoT) in the future 6G network. However, deploying a blockchain system in a complex wireless IoT network is challenging due to the limited resources, complex wireless environment, and the property of self-interested IoT devices. The existing incentive mechanism of blockchain is not compatible with the wireless IoT network. In this article, to incentivize IoT devices to join the construction of the wireless blockchain network, we propose a multidimensional contract to optimize the blockchain utility while addressing the issues of adverse selection and moral hazard. Specifically, the proposed contract considers the IoT device’s hash power and communication cost and especially explores the connectivity of devices from the perspective of complex network theory. We investigate the energy consumption and the block confirmation probability of the wireless blockchain network via simulations under varied network sizes and average link probability. Numerical results demonstrate that our proposed contract mechanism is feasible, achieves 35% more utility than existing approaches, and increases utility by four times compared with the original PoW-based incentive mechanism.

The primary objective of anti-jamming techniques is to ensure that the transmitted data arrives at the intended receiver without being disturbed or jammed with by any jamming signal or other hostile activities to ensuring the security of the communication system. Deep reinforcement learning (DRL) has been extensively utilized in solving the dynamic spectrum anti-jamming problem. However, most of existing DRL-based algorithms require lots of training time, which fails to adapt the fast-channging jamming environment. Our objective is to find a practical and fast-convergence anti-jamming learning solution. To achieve this, we redesign the DRL algorithm in the following two ways. First, we split the cycle of reinforcement learning into two parts: applying process and training process. Second, we use soft labels instead of rewards which bring more information. We further theoretically show that the information gain can help our proposed algorithm converge faster. Moreover, we also show that our labeled DRL algorithm is better than the idealized DRL-based scheme which can obtain the same information as the soft labels. Simulation results demonstrate that compared with existing DRL-based algorithms, our proposed algorithm reduces the number of iterations by up to 90%.

Recently, the development of unmanned aerial vehicle (UAV) mobile-edge computing (MEC) networks has brought unprecedented gains and opportunities. In this article, the joint computation offloading, UAV role, and location selection problem in hierarchical multicoalition UAV MEC network is investigated. To capture the hierarchical feature and discrete optimization, the discrete Stackelberg game with multiple leaders and followers is formulated. We prove that both the leader-level and member-level subgames are ordinal potential games (OPGs) with Nash equilibrium (NE). Thus, the Stackelberg equilibrium (SE) is guaranteed. To achieve the SE, the log-linear-based hierarchical learning algorithm (LHLA) is proposed and analyzed. The simulation results show that the LHLA can converge fast and achieve better performance compared with the existing schemes.

This paper is the first to investigate both communication and control in traffic channel (TCH) and control channel (CCH) respectively when considering leader-follower formation keeping in UAV communication networks. In this paper, we analyze the relationship between the mutual interference and information exchange cost, and then formulate the joint channel and link selection problem as a two-way consensus game between CCH and TCH. To characterize the two-way choice of link selection, we creatively propose the generalized two-way consensus equilibrium (GTCE) to capture the stable state. Then, we prove that the formulated game has at least one pure-strategy GTCE which can maximize the UAV communication network utility. A distributed better reply based joint channel and link selection (BRJCLS) algorithm as well as two-dimensional minimum spanning tree (MST) based initialization (TMSTI) algorithm is proposed to achieve the GTCE. Simulation results are presented to show the convergence and effectiveness of the formulated two-way consensus game and proposed algorithms.

This paper investigates the anti-jamming problems for multi-user scenarios. On the one hand, users in the networks should coordinate their channel selection strategies to avoid spectrum conflicts among different users. On the other hand, because of the openness characteristic of wireless communications, malicious jammers can disrupt the legitimate communications of legitimate users by sending jamming signals, thus users also need to fully consider how to defend against malicious jamming attacks. To cope with the internal coordination and external confrontation problem, a context-aware dynamic spectrum coordinated anti-jamming approach is proposed. In detail, the multiuser anti-jamming scenario is modeled as an anti-jamming local altruistic game, and the existence of Nash Equilibrium (NE) is demonstrated. Besides, the proposed game model is proved to be an exact potential game. To obtain NEs, a multi-pattern stochastic learning algorithm (MSLA) is designed. Through local information exchange and distributed learning, users can achieve global optimization under the dynamic jamming environment.

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.

This paper investigates the anti-jamming problems in wireless communication networks. In these networks consisted of multiple devices (users), there exist two critical problems. On the one hand, users with various transmission requirements should coordinate their channel selection strategies distributedly to avoid spectrum conflicts and satisfy transmission demands. On the other hand, they also need to fully consider how to eliminate the effects of malicious attacks. To cope with the internal coordination and external confrontation challenges and accommodate the dynamic changing jamming attacks, a context-aware distributed coordinated anti-jamming channel access mechanism is proposed, which means for different cases of jamming attacks, different access strategies are adopted. In detail, to reflect the heterogeneous communication demands of users, the transmission satisfaction function is firstly introduced. Then, the multi-user anti-jamming scenario is modeled as a context-aware multi-pattern dynamic anti-jamming game, which can be decomposed into two sub-games. Here, for the case that the control channel is available, a local altruistic sub-game is introduced. While for the case that the control channel has been jammed, an anti-jamming congestion sub-game is designed. Besides, the existence of Nash Equilibriums is demonstrated. To obtain NEs, a context-aware distributed channel access (CDCA) algorithm is designed. Through game-theoretic analysis and distributed learning, global transmission satisfaction can be improved under the dynamic jamming environment. Furthermore, the fairness of the network can also be guaranteed.

Coalition structure is an efficient networking architecture for task implementation in unmanned aerial vehicle (UAV) networks. However, both the formation of coalition and the spectrum resource for intra-coalition communication affect the reconnaissance performance. In this paper, we investigate a cooperative reconnaissance and spectrum access (CRSA) scheme for task-driven heterogeneous coalition-based UAV networks by jointly optimizing task layer and resource layer. Specifically, coalition formation game (CFG) is formulated to jointly optimize task selection and bandwidth allocation. In addition to the traditional Pareto order and selfish order, coalition expected altruistic order maximizing coalitions' utility is proposed. The CFG under the proposed order is proved to be an exact potential game (EPG). Then the existence of stable coalition partition is guaranteed with the help of Nash equilibrium (NE). We propose a joint bandwidth allocation and coalition formation (JBACF) algorithm to achieve stable coalition partition wherein an efficient gradient projection (GP) based method is applied to solve bandwidth allocation. The effectiveness of the proposed scheme and algorithms are demonstrated through in-depth numerical simulations. The results show that our proposed CRSA scheme is superior to non-joint optimization scheme. Also, the proposed order is superior to traditional Pareto order and selfish order.

Device-to-device (D2D)-enabled mobile edge computing (MEC) is an emerging technology which has been widely investigated in terrestrial networks. Different from most existing relevant work, where the network is base station-assisted and offloadings are made on homogeneous channels, this paper focuses on an unmanned aerial vehicle (UAV) swarm, where both the decentralized character and the heterogeneous data computation demands are considered. To fully utilize the limited time, spectrum and computation resources, the joint computation offloading and variable-width channel access problem is investigated. The problem is solved by a game-theoretic based solution. Specifically, the problem is first formulated into a game model which is proved to be an exact constrained potential game (ECPG). The game has at least one pure strategy generalized Nash equilibrium (GNE) and the best GNE is the global optimum of the problem. After that, to enable the UAV swarm reach the GNE autonomously, a distributed collective best response (COBR) algorithm is then proposed. The algorithm can converge to a GNE of the game, which is the local or global optimum of the proposed problem. Simulation results show that the proposed method can save about 10% energy than offloading on homogeneous channels.

Here the intelligent jammer issue is studied. With the rapid development of cognitive radio technology, current cognitive terminals can adaptively or intelligently switch channel by spectrum sensing and decision‐making. Most of the traditional jamming methods, such as swept jamming and comb jamming, generally work in a relatively fixed pattern, which are not able to effectively jam the terminals empowered with cognition and spectrum decision‐making capability. In view of this problem, the authors propose an intelligent jamming decision‐making system based on reinforcement learning. First, in order to jam a pair of transmitter and receiver with adaptive frequency hopping capability, a jammer with spectrum sensing, offline training and learning scheme is proposed. Second, a reinforcement learning‐based algorithm for jamming decision‐making is proposed and simulated. A special feature of the proposed scheme is that considering the reward is difficult to obtain in the actual communication system, a virtual jamming decision‐making method is used to enable the jammer to learn and jam efficiently without the user's prior information. Finally, the proposed jamming model and algorithm are implemented and verified on Universal software radio peripheral testbed.

In this article, we study the unmanned aerial vehicle (UAV)-enabled opportunistic data collection in wireless sensor networks (WSNs). The UAV performing remote missions is expected to collect data from the WSN during the return flights. Due to the specified task and safety restrictions, flight trajectory and time of the UAV are strictly constrained, resulting in the limited coverage ability in the data collection process. Moreover, the unknown distribution of active sensors makes it difficult for ground sensors and the UAV to complete the offline optimization of flight mode and transmission. To tackle these problems, we develop an air-ground collaborative online planning method. On the one hand, ground sensors actively form terrestrial transmission clusters to improve the data upload efficiency. After analyzing the Line-of-Sight (LoS) reliability and transmission correlation, we construct a coalition formation game model for the clustering of ground sensors. We discuss the equilibrium property of the game model, which can be achieved by the proposed distributed coalition formation algorithm. On the other hand, to avoid conflicts during the data collection, a data upload protocol is designed. We further discuss various flight speed planning schemes based on different detection capabilities of the UAV. The simulation results show that the performance of ground coalition-based air-ground collaborative online optimization is much better than that of the unilateral data collection by the UAV. Moreover, UAV flight online planning can further improve data uploading efficiency.

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 order to promote the spectral utilization, this article investigates the partially overlapping channel (POC) accessing problem in the wireless network. To reflect the heterogeneous characteristics of users, the optimization goal is set as maximizing the quality of service (QoE), instead of maximizing the throughput or minimizing the interference. The problem is formulated as a QoE maximization game and is then proved to be an ordinal potential game by utilizing the approximate relationship between interference and QoE. The proposed game is proved to have at least one pure Nash equilibrium (NE) and the best pure strategy NE point is an approximate global optimum of maximizing network QoE. A distributed algorithm is designed to reach the NE and it is proved that when the learning parameter is large enough, the algorithm asymptotically maximizes the network QoE. Simulation results verify the effectiveness of utilizing POCs and the proposed method.

This paper mainly investigates the unmanned aerial vehicle (UAV) communication networks under the threat of a static malicious jammer. While taking the flying process of the user (UAV transmitter-receiver pair) into consideration, we propose a joint power and trajectory optimization method. Moreover, a Stackelberg framework is formulated to solve the proposed optimization problem. In addition, a joint power and trajectory optimization algorithm (JPTOA) based on best-response (BR) is designed to obtain the user's strategy as well as Stackelberg equilibrium (SE) in each time stage. Furthermore, as an extension of single stage optimization, the I-step exploration and one-step decision (IEOD) scheme is designed to enhance the user's cumulative utility. Finally, simulation results are presented to show the performance of the proposed JPTOA scheme.

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.

The problem of distributed and dynamic sensing user selection in cognitive systems is studied in this paper, where channel sensing consumes resources and users behavior is distributed. Since users can obtain the channel state from the fusion center, if there are other users sensing the channel, users may enjoy the results sensed by others rather than sense the channel themselves. Such selfish behavior decreases both network utility and individual rewards. Inspired by the social expectation that no one should always enjoy the fruits of others' labor and that one should provide compensation after obtaining a benefit, we propose a distributed sensing compensation algorithm in this paper. The main concept of this algorithm is that after a channel is accessed successfully, users must sense the channel as a compensation for enjoying others' sensing results. The system state probabilities are obtained using Markov chain analysis. We show that there are always an optimal or near optimal number of users sensing the channel and hence, the near-optimal performance is achieved on average using the proposed algorithm. Additionally, the algorithm achieves good fairness performance with respect to the sensing cost. It is further shown that the proposed algorithm is not only suitable for static scenarios but also adaptable for dynamic scenarios with a changing active user set.

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.

It is our pleasure to write the Editorial for the Special Issue on Evolution and Development of 5G Wireless Communication Systems. Upon conclusion of fourth generation (4G) cellular network standardization tasks a few years ago, the direction of research has started to shift systematically towards fifth generation (5G) communication systems. The difference between 4G and 5G is not limited to the increased throughput and performance. 5G systems are supposed to be flexible to accommodate heterogeneous traffic and devices, and various applications with different quality-of-service (QoS) requirements. Particularly, the goal is to take full benefit of advances in technology including cloud computing, Internet of Things (IoT), ultra-dense networks, massive MIMO, device-to-device communication, pervasive and social computing. In order to meet stringent goals, 5G communication systems build upon the evolution of the existing technologies and the development of the new technologies mentioned above. The Special Issue contains 11 papers, each paper covers the subject from different prospective, and thus, offer readers a holistic view of different research challenges currently under investigation by research communities. The papers can be grouped under following topics: C. Hua et al. present a paper entitled “Wireless backhaul resource allocation and user-centric clustering in ultra-dense wireless networks”. It considers optimization of resource allocation in wireless backhaul links and user-centric clustering in the access links. The objective is to maximise the weighted sum rate of all users under the backhaul resource constraints. An iterative algorithm is proposed to solve the transformed problem based on its special property. Simulation results show that the proposed algorithm outperforms other existing schemes under different network settings. Z. Wang et al. present a paper entitled “Interference pricing in 5G ultra-dense small cell networks: a Stackelberg game approach” which models the scenario as a Stackelberg game, where the macrocell base stations (MBS) act as the leader and all small cell base stations (SCBSs) as followers. Simulation results show the correctness of the analysis and the significant benefits when the power control and channel allocation are jointly considered in the proposed schemes. Z. Kaleem et al. present “Public safety users’ priority-based energy and time-efficient device discovery scheme with contention resolution for ProSe in third generation partnership project long-term evolution-advanced systems”, which proposes a time and energy-efficient contention-resolving device discovery resource allocation (TEECR-DDRA) scheme that has the capability to enhance the success ratio for discovery of D2D users by reducing collisions among users. Moreover, the proposed TEECR-DDRA scheme has the ability to prioritise PS users to meet their QoS and latency requirements. System-level simulations show that the proposed TEECR-DDRA scheme performs remarkably well under D2D network. M. T. Gul et al. present a paper entitled “Merge-and-forward: a cooperative multimedia transmissions protocol using RaptorQ codes”, proposing a cooperative multimedia transmission protocol based on a novel merge-and-forward relaying and the best relay selection (RS) schemes. Moreover, to combat the packet loss for enhanced and reliable video delivery, they adopt application layer forward error correction scheme which is based on the most improved and advanced version of fountain codes (i.e., RaptorQ codes). They evaluate the performance of the proposed scheme in terms of decoding failure probability, decoding overhead, peak signal-to-noise ratio, and mean opinion score. K. Yang et al.'s paper “Edge aware cross-tier base station cooperation in heterogeneous wireless networks with non-uniformly-distributed nodes” investigates the cross-tier base station (BS) cooperation in non-uniform heterogeneous networks where the distribution of pico BSs (PBSs) is modelled as Neyman–Scott cluster process. The authors propose an edge aware cross-tier cooperation scheme to improve the performance of edge hotspot users that have weaker signal-to-interference-plus noise ratio (SINR). Stochastic geometry is utilised to derive the SINR and energy efficiency performance of the proposed scheme, which is compared with other classical schemes such as full cooperation (FC) and traditional non-cooperation scheme. Y. Cai et al. present “Secure transmission in the random cognitive radio networks with secrecy guard zone and artificial noise” which proposes a simple and decentralised secure transmission scheme by jointly incorporating the secrecy guard zone and artificial noise in cognitive radio networks. Numerical results show how the system parameters affect the achievable maximum secrecy throughput, the optimal transmission power and the optimal power allocation between the information-bearing signal and the artificial noise. Y. Sun et al. present “Local altruistic coalition formation game for spectrum sharing and interference management in hyper-dense cloud-RANs” and investigate the spectrum sharing and interference management in hyper-dense cloud radio access networks (C-RANs). The authors formulate this problem as a local altruistic coalition formation game (LACF) with externalities. The authors propose a distributed coalitional formation algorithm based on modified recursive core to obtain the final stable coalition partition. Furthermore, the system stability, convergence and complexity of the proposed algorithm are analysed. W. Chang et al. paper “Effects of non-uniform quantisation on the interference mitigation using multi-cell multiple-input and multiple-output coordinated beamforming” proposes a low complexity cumulative distribution function (CDF)-based non-uniform quantisation method with a limited number of feedback bits for applying more quantisation levels to represent feedback CSI, which occurs with higher probability. The simulation results proved the higher transmission rate, particularly in cases with fewer feedback bits. D. Liu et al.'s paper “Self-organising multiuser matching in cellular networks: a score-based mutually beneficial approach” studies the self-organising user assignment problem for the multi-user cooperation network. Furthermore, the multi-user assignment problem is formulated as a one-to-one matching game, in which idle users and active users rank one another individually based on their own preference. Simulation results show that the proposed distributed algorithm yields well matching performance between source users and relay users, which is close to the optimal centralised results. D. C. Araújo et al.'s paper “Massive MIMO: survey and future research topics” presents an overview of the basic concepts of massive multiple-input multiple-output, with a focus on the challenges and opportunities, based on contemporary research. R. Sun et al. present the paper “Transceiver design for cooperative nonorthogonal multiple access systems with wireless energy transfer”. The paper considers an energy harvesting-based cooperative non-orthogonal multiple access (NOMA) system. Transmitter beamforming, power splitter and receiver filter are jointly designed to maximise rate with the predefined QoS constraint of weaker node and the power constraint of node which simultaneously sends independent signals to a stronger node and weaker node. Since the problem is non-convex, they propose an iterative approach to solve it. Moreover, a zero-forcing based low-complexity solution is also presented. Simulation results demonstrate that, both two proposed schemes have better performance than the direction transmission. All of the papers in Special Issue show that 5G systems can support the specialized use cases which are not supported by the current access systems. In addition, authors investigated the issues related to backhaul for 5G systems and latency reduction. The integration of new technologies with the evolved current systems bring tremendous improvement in 5G systems. Alagan Anpalagan received the B.A.Sc., M.A.Sc., and Ph.D. degrees in electrical engineering from the University of Toronto, Toronto, ON, Canada. In 2001, he joined the Department of Electrical and Computer Engineering, Ryerson University, Toronto, where he was promoted to Full Professor in 2010. He served the department as the Graduate Program Director (2004–2009) and the Interim Electrical Engineering Program Director (2009–2010). He directs a research group working on radio resource management and radio access and networking areas within the WINCORE Lab. During his sabbatical (2010–2011), he was a Visiting Professor with Asian Institute of Technology and a Visiting Researcher with Kyoto University, Kyoto, Japan. His industrial experience includes working at Bell Mobility, Nortel Networks, and IBM Canada. He has coauthored three edited books, namely, Design and Deployment of Small Cell Networks (Cambridge University Press, 2014), Routing in Opportunistic Networks (Springer, 2013), and Handbook on Green Information and Communication Systems (Academic Press, 2012). His current research interests include cognitive radio resource allocation and management, wireless cross-layer design and optimization, cooperative communication, machine-to-machine communication, small cell networks, and green communications technologies. Dr. Anpalagan has served as an Associate Editor of the IEEE Communications Surveys & Tutorials since 2012 and Springer Wireless Personal Communications since 2009. Adnan Shahid received the B.Eng. and the M.Eng. degrees in computer engineering with communication specialization from the University of Engineering and Technology, Taxila, Pakistan in 2006 and 2010, respectively, and the Ph.D degree in information and communication engineering from the Sejong University, South Korea in 2015. He is currently working as a Postdoctoral Researcher at iMinds/IBCN, Department of Information Technology, University of Ghent, Belgium. From Sep 2015 – Jun 2016, he was with the Department of Computer Engineering, Taif University, Saudi Arabia. From Mar 2015 – Aug 2015, he worked as a Postdoc Researcher at Yonsei University, South Korea. From Aug 2012 – Feb 2015, he worked as a PhD research assistant in Sejong University, South Korea. From Mar 2007 – Aug 2012, he served as a Lecturer in electrical engineering department of National University of Computer and Emerging Sciences (NUCES-FAST), Pakistan. He was also the recipient of the prestigious BK 21 plus Postdoc program at Yonsei University, South Korea. He is a member of IEEE and actively involved in various research activities. He is also serving as an Associate Editor at IEEE Access Journal and Annals of Telecommunication Journal. His research interests includes the next generation wireless communication and networks with prime focus on resource management, interference management, cross-layer optimization, self-organizing networks, small cell networks, device to device communications, machine to machine communications, 5G wireless communications, etc. Waleed Ejaz (S’12, M’14, SM‱16) is a Senior Research Associate at the Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada. Prior to this, he was a Post-doctoral fellow at Queen's University, Kingston, Canada. He received his Ph.D. degree in Information and Communication Engineering from Sejong University, Republic of Korea in 2014. He earned his M.Sc. and B.Sc. degrees in Computer Engineering from National University of Sciences & Technology, Islamabad, Pakistan and University of Engineering & Technology, Taxila, Pakistan, respectively. He worked in top engineering universities in Pakistan and Saudi Arabia as a Faculty Member.His current research interests include Internet of Things (IoT), energy harvesting, 5G cellular networks, and mobile cloud computing. He is currently serving as an Associate Editor of the Canadian Journal of Electrical and Computer Engineering and the IEEE ACCESS. In addition, he is handling the special issues in IET Communications, the IEEE ACCESS, and the Journal of Internet Technology. He also completed certificate courses on Teaching and Learning in Higher Education from the Chang School at Ryerson University. Muhammad Ali Imran received his M.Sc. (Distinction) and Ph.D. degrees from Imperial College London, UK, in 2002 and 2007, respectively. He is currently a Reader in the Centre for Communication Systems Research (CCSR) at the University of Surrey, UK. He has a global collaborative research network spanning both academia and key industrial players in the field of wireless communications. He has lead role in a number of multimillion international research projects including the new physical layer work area for 5G innovation centre at Surrey. He has supervised 17 successful PhD graduates and published over 150 peer-reviewed research papers including more than 20 IEEE Journals. His research interests include the derivation of information theoretic performance limits, energy efficient design of cellular system and learning/self-organizing techniques for optimization of cellular system operation. He is a senior member of IEEE and a Fellow of Higher Education Academy (FHEA), UK. Kandeepan Sithamparanathan has a PhD from the University of Technology, Sydney and is currently with the School of Electrical and Computer Engineering at RMIT University. He is also a NICTA Researcher at the NICTA Victoria Research Laboratory (VRL, Melbourne). In the past he had worked with the National ICT Australia (Canberra Research Laboratory) and CREATE-NET (Trento). Kandeepan served as one of the Vice Chairs for the IEEE Technical Committee on Cognitive Networks (TCCN) and has published a book together with Dr Andrea Giorgetti from the University of Bologna, Italy, titled ‘Cognitive Radio Techniques: Spectrum Sensing, Interference Mitigation and Localization’, published by Artech House (Boston). He currently Chairs the IEEE VIC Communication Society Chapter and is a Senior Member of the IEEE. He was awarded as one of the best IEEE Reviewers by the IEEE Communications Society. Kandeepan has published around ninety peer reviewed journal and conference papers. He has chaired several IEEE workshops and other conferences. His research interests are in 5G communications, cognitive radios and signal processing techniques. Yuhua Xu received his B.S. degree in Communications Engineering, and Ph.D. degree in Communications and Information Systems from College of Communications Engineering, PLA University of Science and Technology, in 2006 and 2014 respectively. He has been with College of Communications Engineering, PLA University of Science and Technology since 2012, and currently as an Assistant Professor. His research interests focus on opportunistic spectrum access, learning theory, game theory, and distributed optimization techniques for wireless communications. He has published several papers in international conferences and reputed journals in his research area. He served as Associate Editor for Wiley Transactions on Emerging Telecommunications Technologies and KSII Transactions on Internet and Information Systems. In 2011 and 2012, he was awarded Certificate of Appreciation as Exemplary Reviewer for the IEEE Communications Letters. He was selected to receive the IEEE Signal Processing Society's (SPS) 2015 Young Author Best Paper Award, and the Funds for Distinguished Young Scholars of Jiangsu Province in 2015.

In this study, the authors study the self‐organising user assignment problem for the multi‐user cooperation network. In the cellular network with user cooperation, idle users near the base station are seen as potential relay nodes, which can assist cell edge users to transmit data. Practically, the selfish nature of users is considered in the authors’ model. They design a score‐based system, where the strategies of relay assignment are affected by the historical behaviours of users. That means not only the transmission performance, but also the accumulative contributions of users are considered. The proposed score‐based system sufficiently encourages idle users to assist active users. Furthermore, the multi‐user assignment problem is formulated as a one‐to‐one matching game, in which idle users and active users rank one another individually based on their own preference. To address the issue, they propose a self‐organising mutually beneficial matching algorithm, which is proven to converge to a stable matching. Simulation results show that the proposed distributed algorithm yields well matching performance between source users and relay users, which is close to the optimal centralised results.

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.

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.

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 proposes a novel base station (BS) coordination approach for intercell interference mitigation in the orthogonal frequency-division multiple access based cellular networks. Specifically, we first propose a new performance metric for evaluating end user's quality of experience (QoE), which jointly considers spectrum efficiency, user fairness, and service satisfaction. Interference graph is applied here to capture and analyze the interactions between BSs. Then, a QoE-oriented resource allocation problem is formulated among BSs as a local cooperation game, where BSs are encouraged to cooperate with their peer nodes in the adjacent cells in user scheduling and power allocation. The existence of the joint-strategy Nash equilibrium (NE) has been proved, in which no BS player would unilaterally change its own strategy in user scheduling or power allocation. Furthermore, the NE in the formulated game is proved to lead to the global optimality of the network utility. Accordingly, we design an iterative searching algorithm to obtain the global optimum (i.e., the best NE) with an arbitrarily high probability in a decentralized manner, in which only local information exchange is needed. Theoretical analysis and simulation results both validate the convergence and optimality of the proposed algorithm with fairness improvement.

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 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.

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.

Opportunistic spectrum access is an important issue in cognitive radio systems. In this paper, we propose a distributed opportunistic spectrum access scheme based on sequential channel-sensing in decentralized CRN (cognitive radio network). Different from the traditional spectrum sensing scheme, the scheme proposed allows a secondary users to sense many channels one by one sequentially in a slot. Simulation results show that the proposed scheme can attain better performance of throughput. At the same time, we analyze three sequential channel-sensing orders of the proposed scheme. The simulation results show that the learning based channel-sensing order is better in the performance of throughput.