The pervasive threat of jamming attacks, particularly from adaptive jammers capable of optimizing their strategies, poses a significant challenge to the security and reliability of wireless communications. This paper addresses this issue by investigating anti-jamming communications empowered by an active reconfigurable intelligent surface. The strategic interaction between the legitimate system and the adaptive jammer is modeled as a Stackelberg game, where the legitimate user, acting as the leader, proactively designs its strategy while anticipating the jammer’s optimal response. We prove the existence of the Stackelberg equilibrium and derive it using a backward induction method. Particularly, the jammer’s optimal strategy is embedded into the leader’s problem, resulting in a bi-level optimization that jointly considers legitimate transmit power, transmit/receive beamformers, and active reflection. We tackle this complex, non-convex problem by using a block coordinate descent framework, wherein subproblems are iteratively solved via convex relaxation and successive convex approximation techniques. Simulation results demonstrate the significant superiority of the proposed active RIS-assisted scheme in enhancing legitimate transmissions and degrading jamming effects compared to baseline schemes across various scenarios. These findings highlight the effectiveness of combining active RIS technology with a strategic game-theoretic framework for anti-jamming communications.

Satellite communications are envisioned as a key enabler for ubiquitous coverage in future 6G networks, yet the broadcast nature renders them vulnerable to eavesdropping, especially given the long-distance transmissions and associated high uncertainties. In this paper, we propose the physical layer security enhancement for multi-beam satellite communications with the assistance of an aerial reconfigurable intelligent surface (ARIS). Considering the high dynamics and uncertainties of channels, we characterize the channel distribution with moment-based ambiguity sets. Accordingly, a distributionally robust secrecy rate optimization is formulated through joint design of transmit and reflection beamforming. We then introduce a conditional value-at-risk-based reformulation to convert the probabilistic constraints into deterministic forms. An alternating optimization framework is subsequently employed to iteratively update the transmit and reflective beamforming vectors until convergence. Simulation results demonstrate that the proposed distributionally robust scheme significantly enhances secrecy performance, and maintains reliable performance across various channel error distributions.

Blockchain plays a crucial role in ensuring the security and integrity of decentralized systems, with the proof-of-work (PoW) mechanism being fundamental for achieving distributed consensus. As PoW blockchains see broader adoption, an increasingly diverse set of miners with varying computing capabilities participate in the network. In this paper, we consider the PoWblockchain mining, where the miners are associated with resource uncertainties. To characterize the uncertainty computing resources at different mining participants, we establish an ambiguous set representing uncertainty of resource distributions. Then, the networked mining is formulated as a non-cooperative game, where distributionally robust performance is calculated for each individual miner to tackle the resource uncertainties. We prove the existence of the equilibrium of the distributionally robust mining game. To derive the equilibrium, we propose the conditional value-at-risk (CVaR)-based reinterpretation of the best response of each miner. We then solve the individual strategy with alternating optimization, which facilitates the iteration among miners towards the game equilibrium. Furthermore, we consider the case that the ambiguity of resource distribution reduces to Gaussian distribution and the case that another uncertainties vanish, and then characterize the properties of the equilibrium therein along with a distributed algorithm to achieve the equilibrium. Simulation results show that the proposed approaches effectively converge to the equilibrium, and effectively tackle the uncertainties in blockchain mining to achieve a robust performance guarantee.

Malicious jamming presents a pervasive threat to the secure communications, where the challenge becomes increasingly severe due to the growing capability of the jammer allowing the adaptation to legitimate transmissions. This paper investigates the jamming mitigation by leveraging an active reconfigurable intelligent surface (ARIS), where the channel uncertainties are particularly addressed for robust anti-jamming design. Towards this issue, we adopt the Stackelberg game formulation to model the strategic interaction between the legitimate side and the adversary, acting as the leader and follower, respectively. We prove the existence of the game equilibrium and adopt the backward induction method for equilibrium analysis. We first derive the optimal jamming policy as the follower’s best response, which is then incorporated into the legitimate-side optimization for robust anti-jamming design. We address the uncertainty issue and reformulate the legitimate-side problem by exploiting the error bounds to combat the worst-case jamming attacks. The problem is decomposed within a block successive upper bound minimization (BSUM) framework to tackle the power allocation, transceiving beamforming, and active reflection, respectively, which are iterated towards the robust jamming mitigation scheme. Simulation results are provided to demonstrate the effectiveness of the proposed scheme in protecting the legitimate transmissions under uncertainties, and the superior performance in terms of jamming mitigation as compared with the baselines.

Semantic communication is an emerging technology to improve the communication efficiency in future networks. In this paper, we propose a multi-AP multi-user adaptive semantic and bit communication framework for image transmission, where each user can communicate with the access points via either semantic communication (SemCom) or bit communication mode. While the peak mean square error (PMSE) is a key parameter to characterize the difference between the original and corresponding recovered images, this metric has no closed form. We propose a data regression approach to approximate the PMSE. Then, the cost functions for the two types of communication modes are designed, where the delay and energy consumption for image transmission and the PMSE for the recovered image are considered simultaneously. Moreover, the computation delay and energy consumption for semantic feature extraction and recovery are also integrated into the SemCom cost function design. Then, an overall user cost minimization problem is formulated to jointly optimize the communication mode decision, user association, channel selection, power control, and computation resource allocation. To solve the formulated problem, we propose an improved particle swarm optimization based semi-cooperative matching (IPSO-SCM) algorithm, where a semi-cooperative matching (SCM) game is established to determine the communication mode decision, user association, and channel selection and the improved particle swarm optimization algorithm is designed to jointly optimize the power control and computation resource allocation in each step of the constructed SCM game. We further prove the effectiveness, convergence, stability, and extensibility of the proposed IPSO-SCM algorithm. Simulation results are provided to demonstrate the superiority of the proposed scheme.

Jamming attacks pose a significant threat to the security of air-ground communications, where the challenge becomes more severe when involving multiple unmanned aerial vehicles (UAVs) incurring complex interference. To address this issue, this paper proposes a graph attention-based reinforcement learning strategy for anti-jamming UAV communications. Specifically, we consider the multi-UAV transmission and deployment in the presence of jamming attacks. Then, we formulate a zero-sum game with the legitimate side and adversary to maximize and minimize the overall transmission rate, respectively. Given the complicated structure of the game, we decompose it into two layers, tackled in a hierarchical learning framework. Particularly, the inner layer addresses the legitimate beamforming, for which we establish the graph attention network (GAT) to track the complicated interference and jamming relationship based on the graph representation of the UAV network. The outer layer address the legitimate UAV deployment and adversarial jamming policy, which is reinterpreted in a multi-agent deep reinforcement learning framework to obtain the strategies of both sides. The inner GAT is then nested within the outer multi-agent learning framework in a hierarchical manner to approximate the equilibrium of the original game model. Simulation results demonstrate the convergence and the performance superiority of the proposed learning scheme in terms of anti-jamming transmission rate. Also, the results exhibit significant generalization capability to cover different network configurations and parameters with reliable communication performance.

Satellite communication is envisioned as a key enabler of future 6G networks, yet its wide coverage with high link attenuation poses significant challenges for physical layer security. In this paper, we investigate secure multi-beam, multi-group satellite communications assisted by aerial reconfigurable intelligent surfaces (ARISs). To maximize the sum of achievable multicast rates among the groups while constraining wiretap rates, we formulate a joint optimization problem involving transmission and reflection beamforming, ARIS-group association, and ARIS deployment. Due to the mixed-integral and non-convex nature of the formulated problem, we propose to decompose the problem and employ the block coordinate descent framework that iteratively solves the subproblems. Simulation results demonstrate that the proposed ARIS-assisted multi-beam satellite system provides a notable improvement in secure communication performance under various network scenarios, offering useful insights into the deployment and optimization of intelligent surfaces in future secure satellite networks.

This paper addresses the enhancement of physical layer security in multibeam satellite systems through the deployment of aerial reconfigurable intelligent surfaces (ARIS). We aim to maximize the sum achievable rate across multiple groups, subject to constraints on wiretap rates, by jointly optimizing the transmission beamforming and ARIS passive beamforming. We propose an alternating optimization framework, where the transmission beamforming and passive beamforming are optimized using semidefinite programming. Simulation results demonstrate that the proposed ARIS-assisted multibeam satellite systems can significantly enhance secure communication performance under various eavesdropping scenarios.

Federated Learning (FL) offers promising solutions for deploying AI in wireless networks, allowing resourceconstrained devices to collaboratively train machine learning models, and reducing deployment costs. However, FL faces challenges due to device heterogeneity and unreliable communication links, which extend training time. Unmanned Aerial Vehicles (UAVs), with their flexibility and deployment advantages, have emerged as valuable assets in addressing these limitations by enhancing line-of-sight communication and providing proximal computational resources. This paper proposes a UAV-assisted FL framework that jointly optimizes resource allocation, task loads, and UAV trajectories to minimize FL completion time. Through a block coordinate descent (BCD) approach, our framework addresses the formulated joint optimization problem. Simulation results demonstrate that our proposed framework effectively balances resource allocation and significantly reduces FL completion time compared to benchmark schemes.

Reconfigurable Intelligent Surface (RIS) has emerged as a key technology capable of economically and efficiently reconfiguring wireless communication environments, and has been widely utilized to assist secure transmission in cell-free (CF) system in recent years. However, due to the presence of multiplicative fading effects, traditional passive RIS typically performs poorly when deployed far from the access point (AP) and users. Additionally, eavesdroppers (Eves) are malicious users hidden within the system, making it difficult for the system to acquire their channel state information (CSI). To address above issues, this paper investigates secure transmission of CF system aided by multiple active RISs. By considering no Eves' CSI, we propose an artificial noise (AN) aided transmssion strategy to enhance the secure transmission of system. In this strategy, the system total power is divided into two parts at the APs, one for the signal transmission and one for the AN signaling. To allocate the first part of power, we propose an efficient alternating optimization (AO) algorithm to optimize the active and passive beamformers at APs and RISs by ensuring that the users' quality of service (QoS) for communication is satisfied. To allocate the second part of residual power, we propose equal and unequal power allocation strategies for transmitting artificial noise (AN) so as to degrade Eves' signal reception performance. The simulation results show that compared with the benchmark schemes, our proposed method with the aide of active RISs in CF system can more effectively suppresse the performance of Eves, which in turn significantly enhances the secrecy rate for users in the system.

The space-air-ground integrated network (SAGIN) reveals enormous potential towards ubiquitous access with pros-perous applications for future 6G wireless networks, yet the limited energy presents a significant challenge towards the efficient operation of SAGIN. In this paper, we propose to employ an un-manned aerial vehicle (UAV) to approach the ground nodes to help alleviate the computation burden, where the computed results are then forwarded to the satellite for remote use. We formulate the problem to minimize the weighted energy consumption in terms of data offloading, computation, and results forwarding, along with the UAV propulsion energy, while jointly investigating the transmissions, scheduling, computation, and trajectory strategy design. The problem is then decomposed and solved in a block coordinate descent framework. Simulation results demonstrate that the proposed joint optimization scheme effectively reduces the overall energy consumption compared to benchmark approaches.

This paper propose a graph neural network (GNN) framework to achieve physical layer security. We consider the secure communication between a multi-antenna base station and multiple users, in the presence of multiple eavesdroppers, where the GNN-based beamforming is conducted for secure transmissions. Particularly, we reinterpret the networks roles as graph elements and track the inter-user interference through the graph structure, and thus the secrecy rate maximization is obtained through neural network training. Numerical results indicates that the proposed GNN approach approximate the secrecy performance as compared with the conventional optimization techniques, while obtaining the solution in a more efficient manner, with the ability to adapt and scale in dynamic wireless networks.

This paper studies simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided spectrum sharing cell-free (CF) and cognitive radio (CR) combined IoT system, and focuses on enhancing the secondary user’s (SU’s) achievable rate (AR) while guaranteeing the primary user’s lowest AR requirement. Different from the existing studies, we consider a special condition of limited power supply at RIS in this work, in which only a part of the electromagnetic (EM) elements in RIS can function properly due to lack of energy. To this end, we formulate a worst-case SU’s AR optimization problem under both perfect channel state information (CSI) and imperfect CSI conditions. To tackle these two complicated non-convex problems, alternating optimization framework is proposed to jointly optimize the beamformer at primary and secondary transmitters, transmitting/reflecting phase shift as well as EM element selection at STAR-RIS with provable convergence. In particular, semi-definite relaxation (SDR)+successive convex approximation+penalty-convex concave procedure (PCCP) combined algorithm and SDR+PCCP+Dinkelbach combined algorithm are proposed to solve the highly coupled EM element selection and phase shift at RIS under perfect and imperfect CSI cases, respectively. Numerical results verify that given insufficient power supply and RIS, the proposed scheme significantly improves the AR of SUs and outperforms the benchmark schemes of traditional reflecting RIS-aided or no RIS aided case as well as random EM element selection strategies.

Non-terrestrial network (NTN)-based integrated communication and computation empowers various emerging applications with global coverage. Yet this vision is severely challenged by the energy issue given the limited energy supply of NTN nodes and the energy-consuming nature of communication and computation. In this paper, we investigate the energy-efficient integrated communication and computation for the ground node data through a NTN, incorporating an unmanned aerial vehicle (UAV) and a satellite. We jointly consider ground data offloading to the UAV, edge processing on the UAV, and the forwarding of results from UAV to satellite, where we particularly address the uncertainties of the UAV-satellite links due to the large distance and high dynamics therein. Accordingly, we propose to minimize the weighted energy consumption due to data offloading, UAV computation, UAV transmission, and UAV propulsion, in the presence of angular uncertainties under Gaussian distribution within the UAV-satellite channels. The formulated problem with probabilistic constraints due to uncertainties is converted into a deterministic form by exploiting the Bernstein-type inequality, which is then solved using a block coordinate descent framework with algorithm design. Simulation results are provided to demonstrate the performance superiority of our proposal in terms of energy sustainability, along with the robustness against uncertain non-terrestrial environments.

Over-the-air computation (AirComp) is a promising technique that addresses big data collection and fast wireless data aggregation. However, in a network where wireless communication and AirComp coexist, mutual interference becomes a critical challenge. In this paper, we propose to employ an unmanned aerial vehicle (UAV) to enable integrated communication and AirComp, where we capitalize on UAV mobility with alleviated interference for performance enhancement. Particularly, we aim to maximize the sum of user transmission rate with the guaranteed AirComp accuracy requirement, where we jointly optimize the transmission strategy, signal normalizing factor, scheduling strategy, and UAV trajectory. We decouple the formulated problem into two layers where the outer layer is for UAV trajectory and scheduling, and the inner layer is for transmission and computation. Then, we solve the inner layer problem through alternating optimization, and the outer layer is solved through soft actor–critic-based deep reinforcement learning. Simulation results show the convergence of the proposed learning process and also demonstrate the performance superiority of our proposal as compared with the baselines in various situations.

While unmanned aerial vehicles (UAVs) with flexible mobility are envisioned to enhance physical layer security in wireless communications, the efficient security design that adapts to such high network dynamics is rather challenging. The conventional approaches extended from optimization perspectives are usually quite involved, especially when jointly considering factors in different scales such as deployment and transmission in UAV-related scenarios. In this paper, we address the UAV-enabled multi-user secure communications by proposing a deep graph reinforcement learning framework. Specifically, we reinterpret the security beamforming as a graph neural network (GNN) learning task, where mutual interference among users is managed through the message-passing mechanism. Then, the UAV deployment is obtained through soft actor-critic reinforcement learning, where the GNN-based security beamforming is exploited to guide the deployment strategy update. Simulation results demonstrate that the proposed approach achieves near-optimal security performance and significantly enhances the efficiency of strategy determination. Moreover, the deep graph reinforcement learning framework offers a scalable solution, adaptable to various network scenarios and configurations, establishing a robust basis for information security in UAV-enabled communications.

Over-the-air computation (AirComp) has emerged as a promising technology that enables simultaneous transmission and computation through wireless channels. In this paper, we investigate the networked AirComp in multiple clusters allowing diversified data computation, which is yet challenged by the transceiver coordination and interference management therein. Particularly, we aim to maximize the multi-cluster weighted-sum AirComp rate, where the transmission scalar as well as receive beamforming are jointly investigated while addressing the interference issue. From an optimization perspective, we decompose the formulated problem and adopt the alternating optimization technique with an iterative process to approximate the solution. Then, we reinterpret the iterations through the principle of algorithm unfolding, where the channel condition and mutual interference in the AirComp network constitute an underlying graph. Accordingly, the proposed unfolding architecture learns the weights parameterized by graph neural networks, which is trained through stochastic gradient descent approach. Simulation results show that our proposals outperform the conventional schemes, and the proposed unfolded graph learning substantially alleviates the interference and achieves superior computation performance, with strong and efficient adaptation to the dynamic and scalable networks.

Studying the characteristics of the satellite-toground (S2G) channel model is essential for the development and assessment of satellite communication systems. In this study, we introduce a unique approach by combining a low-earth-orbit (LEO) random geometric satellite channel model with line-of-sight (LoS) and single-bounced (SB) non-line-of-sight (NLoS) components for the S2G multiple-input multiple-output (MIMO) channel. By utilizing the coaxial cylinders reference model in a lightly shadow environment occluded by terrain features, we compute the space correlation function (SCF) and time correlation function (TCF). To simplify the simulation process, we present a deterministic simulation model using the finite number of scatterers and analyze the various factors that influence channel characteristics. The findings from the simulation indicate that the orientation of both the satellite and terrestrial receiver antennas, as well as their respective movement directions, distances, and the density of scatterers’ azimuth angles, all play a significant role in shaping the statistical properties of the channel model.

To facilitate UAV-based wireless communications, a comprehensive understanding of the wireless channel characteristics is critical. However, most existing studies focuse on the high-altitude channel, while limited attention is paied to the ultra-low altitude channels given the evident difficulties in the measurement of the latter scenario. In this paper, we have tackled the challenges of lightweight channel sounder system design enabling aerial mounting and flying to facilitate the ultra-low altitude UAV channel measurement. We specially focus on the ultra-low altitude channel at 1.4 GHz, which is recently authoried in China to particularly facilitate UAVs applications. With extensive measurements in a riverside area, we analyze the propagation properties and enlighten the large-scale fading and small-scale fading characteristics, where the large-scale parameters are revealed and the Log-logistic distribution is evaluated as the best fit to characterize the small-scale fading. These findings provide important guidance for the ultra-low altitude UAV communication particularly in the riverside scenarios.

Physical layer authentication (PLA) is a promising solution to address the security issue raised due to malicious jamming or spoofing. However, accurate and diversified channel state information is required to implement the PLA schemes. In this regard, reconfigurable intelligent surface (RIS) has the potential to quickly reshape the communication environment at a cheap cost, and thus has great potential to enhance the PLA. In this paper, we propose a RIS-assisted channel impulse response (CIR)-based dynamic PLA scheme. Specifically, the receiver exploits the geographic location information of the transmitters embedded in CIR to identify the message. In order to reduce the impact of the components representing environmental changes in CIR on the authentication, the method of regularly updating CIR database is adopted. In addition, with RIS enriched CIR information, we can achieve a high authentication rate by constructing a classification neural network. Experiments are conducted based on the communication system with DeepMIMO datasets, and the simulation results demonstrate that the proposed authentication scheme is effective for the identification of both first-attack and non-first-attack spoofers.

In blockchain systems characterized by computation competition, allocating computation resources is of paramount significance for the economic benefits of nodes. Besides, uncer-tainties of computation resources also affect the node's profits. In this paper, we address the computation resource allocation issue within a proof-of-work (PoW) blockchain system without exact information on the available resources, which impedes the direct investigation of the maximum mining profit. Correspondingly, we establish the chance-constrained threshold for maximum achievable profit through the blockchain in an uncertain environment and maximize this threshold under a given outage probability. Particularly, the uncertain computation resource is modeled only with its first and second statistics, which lack the exact distribution information. In this respect, we propose the distributionally robust approach to tackle the chance-constrained resource allocation strategy, which guarantees the intended profit threshold regardless of the actual distribution. We show that the considered problem admits a conditional value-at-risk (CVaR) approximation reformulation, which can be handled by alternately optimizing the resource allocation strategy and the profit threshold. Simulation results demonstrate that the proposed design is robust against the uncertainty distribution, and effectively guarantees the profits of miners.

Satellite communication is considered as one of the key technologies to achieve global seamless coverage and has attracted wide attention. It is crucial to establish an accurate clutter loss model for satellite-ground communication. Clutter loss refers to the extra path loss caused by the obstruction of the terrain and objects on the ground, especially when a satellite has low elevation angle. The clutter loss model proposed by ITU-R P.2108-0 only considers the influence of the elevation angle, and the traditional prediction model for the clutter loss is limited in accuracy and stability. In this work, we used a satellite ground station to carry out the channel measurement at 8.25 GHz on the clutter loss of the X-band satellite-to-ground (S2G) links in the suburban campus environment, and extracted the clutter loss data set involved in the process of satellite inbound and outbound from the received signal strength. We utilize the multi-layer perceptron (MLP), long short-term memory (LSTM), and bidirectional-long short-term memory (Bi-LSTM) neural networks to build channel models to predict the clutter loss based on the measurement data. The model prediction results show that the Bi-LSTM-based model has higher prediction accuracy than the MLP-based and LSTM-based models.

Over-the-air computation (AirComp) emerges as a promising method to integrate computation and communication in 5G and beyond network architecture. Nevertheless, the performance of AirComp, measured by mean-square error (MSE), can be severely bottlenecked by the availability of channel information. In this paper, we investigate the AirComp design in presence of channel uncertainties. Particularly, we consider the case that only the first and second moments of the channel, which can be easily obtained through actual measurement, are available, without the exact statistical information. Then, we establish the chance-constrained AirComp with a thresholded MSE under a given outage probability. Correspondingly, we address the distributionally robust AirComp design to guarantee the intended threshold regardless of the channel distribution. By leveraging conditional value-at-risk (CVaR), we reformulate the probabilistic-form constraint into its deterministic counterpart to facilitate the analysis. Then, the reformulated problem is decomposed to optimize the transmit and receive scaling factors alternatively. Simulation results demonstrate that our proposal rigorously ensures robustness amid uncertainties and effectively reduces computation distortion when compared to the baseline methods.

Efficient data processing and computation are essential for the Industrial Internet of Things (IIoT) to empower various applications, which can be significantly bottlenecked by the limited energy capacity and computation capability of the IIoT nodes. In this article, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processing, while considering the practical issue of UAV jittering. Specifically, we propose a joint design on trajectory and offloading strategies to minimize energy consumption due to local and edge computation, as well as data transmission. We particularly address UAV jittering that induces Gaussian-distributed uncertainties associated with flying waypoints, resulting in probabilistic-form flying speed and data offloading constraints. We exploit the Bernstein-type inequality to reformulate the constraints in deterministic forms and decompose the energy minimization to solve for trajectory and offloading separately within an alternating optimization framework. The subproblems are then tackled with the successive convex approximation technique. Simulation results show that our proposal strictly guarantees robustness under uncertainties and effectively reduces energy consumption as compared with the baselines.

Massive random access of devices brings great challenge to the management of radio access networks. Most of the time, the access requests in the network is sporadic. Exploiting the bursting nature, sparse active user detection (SAUD) is an efficient enabler towards efficient active user detection. However, the sparsity might be deteriorated in case of high concurrent request periods. To dynamically coordinate the access requests, a reinforcement-learning (RL)-assisted scheme of closed-loop access control utilizing the access class barring (ACB) technique is proposed, where the control policy is determined through continuous interaction between the RL agent and the environment. The proposed RL agent can be deployed at the next generation node base (gNB), supporting rapid switching between heterogeneous vertical applications, such as mMTC and uRLLC services. Moreover, a data-driven scheme of deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces, where a replay buffer is applied for automatic large-scale data collection. An Actor-Critic framework is formulated to incorporate the strategy-learning modules into the intelligent control agent. Simulation results show that the proposed schemes can achieve superior performance in both access efficiency and user detection accuracy over the benchmark scheme for different heterogeneous services with massive access requests.

Reconfigurable intelligent surfaces (RISs) are recognized with great potential to strengthen wireless security, yet the performance gain largely depends on the deployment location of RISs in the network topology. In this article, we consider the anti-eavesdropping communication established through an RIS at a fixed location, as well as an aerial platform mounting another RIS and a friendly jammer to further improve the secrecy. The aerial RIS helps enhance the legitimate signal and the aerial cooperative jamming is strengthened through the fixed RIS. The security gain with aerial reflection and jamming is further improved with the optimized deployment of the aerial platform. We particularly consider the imperfect channel state information issue and address the worst case secrecy for robust performance. The formulated robust secrecy rate maximization problem is decomposed into two layers, where the inner layer solves for reflection and jamming with robust optimization, and the outer layer tackles the aerial deployment through deep reinforcement learning. Simulation results show the deployment under different network topologies and demonstrate the performance superiority of our proposal in terms of the worst case security provisioning as compared with the baselines.

The Internet of Things (IoT) can be conveniently deployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. In this paper, we propose to employ unmanned aerial vehicles (UAVs) to assist the clustered IoT data collection with blockchain-based security provisioning. In particular, the UAVs generate candidate blocks based on the collected data, which are then audited through a lightweight proof-of-stake consensus mechanism within the UAV-based blockchain network. To motivate efficient blockchain while reducing the operational cost, a stake pool is constructed at the active UAV while encouraging stake investment from other UAVs with profit sharing. The problem is formulated to maximize the overall profit through the blockchain system in unit time by jointly investigating the IoT transmission, incentives through investment and profit-sharing, and UAV deployment strategies. Then, the problem is solved in a distributed manner while being decoupled into two layers. The inner layer incorporates IoT transmission and incentive design, which are tackled with large-system approximation and one-leader-multi-follower Stackelberg game analysis, respectively. The outer layer for UAV deployment is undertaken with a multi-agent deep deterministic policy gradient approach. Results show the convergence of the proposed learning process and the UAV deployment, and also demonstrated the performance superiority of our proposal as compared with the baselines.

Internet of Things (IoT) can be conveniently de-ployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. In this paper, we propose to employ unmanned aerial vehicles (UAVs) to assist the IoT data collection with blockchain-based security provisioning, towards efficient and safeguarded IoT operations. In particular, a blockchain with proof-of-stake (PoS) consensus mechanism is constructed among the UAVs with the collected IoT data. Correspondingly, we optimize the IoT communication and the UAV deployment for the maximum blockchain throughput considering the PoS procedure. The problem is solved with a deep deterministic policy gradient-based approach, where the power allocation is obtained with closed-form solutions and the UAV deployment is learned with actor-critic networks. Simulation results are provided to show the deployment and performance, corroborating the effectiveness of our proposal.

The unbalanced traffic distribution is a severe problem in cellular networks, which leads to congestion and reduces spectrum efficiency. To tackle this problem, we propose an unmanned aerial vehicle (UAV)-assisted wireless network architecture in which UAV acts as relay to divert the traffic from the overloaded cell to its neighbor underloaded cell. Considering that UAV communications are easily eavesdropped, we use the secrecy capacity to evaluate the performance of the network. To fully exploit the advantages of the proposed architecture, we formulate a joint UAV position optimization, user association, and time allocation problem to maximize the sum-log-rate of all users in two adjacent cells. To tackle the complicated joint optimization problem, we first design a genetic-based algorithm to optimize the UAV position, and then use the branch-and-bound method to devise a low-complexity algorithm to get the optimal user association and time allocation schemes. The simulation results indicate that the proposed UAV-assisted wireless network architecture is superior to the terrestrial network, and the proposed algorithms can further improve the network performance in comparison with the other schemes.

Buffer-aided relay technique can improve the diversity order and offer secrecy provision. To further improve secrecy performance, this paper proposes a secure link selection for relay networks where a new link selection policy is first designed under the constraint on the buffers and channel states using a Markov chain. The stationary state and the corresponding state transition matrix can be derived, and they are used to analyze the secrecy performance. Through the derivation of secrecy outage probability, we can get its closed-form expressions. Numerical results demonstrate that the proposed secure transmission scheme has a better performance than the conventional buffer-aided secure transmission schemes in terms of secrecy outage probability.

The Viterbi algorithm is widely adopted in digital communication systems because of its capability of realizing maximum-likelihood signal sequence detection. However, implementation of the Viterbi algorithm requires instantaneous channel state information (CSI) to be available at the receiver. This is difficult to satisfy in some emerging communication systems such as molecular communications, underwater optical communications, etc, where the underlying channel models are highly complex or completely unknown. ViterbiNet, developed in the prior literature, is a promising framework to cope with this challenge, where deep learning (DL) techniques are combined with the Viterbi Algorithm to enable near-optimal signal detection without CSI. This paper offers a non-trivial variation of ViterbiNet based on generative adversarial networks (GAN). Specifically, a novel architecture using GAN is designed to directly learn the channel transition probability (CTP) from receiver observations, which is the only part of the Viterbi algorithm that is channel-dependent. With the learned CTP, the classical Viterbi algorithm can be implemented without modifications. To make the proposed architecture applicable to time-varying channels, we further develop two methods to fine-tune the learned CTP online. In the first method, pilots within each frame are exploited to update the CTP learning network; In the second method, a decision-directed approach is devised to generate training data in real-time, which is utilized to re-train the learning network. By combining these two approaches, the receiver is able to track the dynamic channel conditions without being trained from scratch. Numerical simulations demonstrate the superiority of the proposed design compared to existing methods.

In this paper, we investigate the application of the non-orthogonal multiple access (NOMA) technique into the unmanned aerial vehicle (UAV) aided relay networks. Specifically, we first incorporate the NOMA protocol with the decode-and-forward (DF) relay protocol to enhance the performance of the cell edge users in a macrocell network. Theoretical analysis indicates that the NOMA-DF-relay protocol outperforms the conventional orthogonal multiple access (OMA) based DF-relay protocol in terms of data rate. To fully exploit the advantages of the proposed protocol, we formulate a joint UAV height optimization, channel allocation, and power allocation problem with the objective to maximize the total data rate of the cell edge users under the coverage of the UAV. For solving the formulated problem effectively, we first analyze its property and employ the golden section method to propose a general framework to obtain the optimal height of the UAV. Then, we design a low-complexity iterative algorithm to solve the joint channel-and-power allocation problem based on the matching theory and the Lagrangian dual decomposition technique. Finally, simulation results demonstrate that the NOMA-DF-relay protocol is superior to the OMA-DF-relay protocol even when the system parameters are not optimized, and the proposed algorithms can further significantly improve the network performance in comparison with the other schemes.

Electric Vehicle (EV) public charging is important to meet the exploding charging demand and to address the range anxiety issue. In this paper, we focus on the EV public charging market with heterogeneous charging stations (CSs) under the time-based billing model. We jointly consider the charging time optimization for EVs, the EV-CS pairing, and the pricing mechanism for CSs. A hierarchical game, which mathematically corresponds to an equilibrium problem with equilibrium constraints (EPEC), is then developed to formulate the three coupled problems. In the proposed hierarchical game, each CS sets the charging price to maximize its own revenue first, then the EVs choose their desired CSs and determine the charging time. We analyze the optimal charging time strategies for EVs, and a many-to-one matching algorithm is applied to solve the EV-CS pairing problem. Besides, a block coordinate descent (BCD) based algorithm is applied for each CS to solve the pricing problem. Simulation results show that our proposed schemes can achieve the performance improvement of the charging system.

Attracted by the advantages of multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA), this article studies the resource allocation problem of a NOMA-MEC system in an ultra-dense network (UDN), where each user may opt for offloading tasks to the MEC server when it is computationally intensive. Our optimization goal is to minimize the system computation cost, concerning the energy consumption and task delay of users. In order to tackle the non-convexity issue of the objective function, we decouple this problem into two sub-problems: user clustering as well as jointly power and computation resource allocation. Firstly, we propose a user clustering matching (UCM) algorithm exploiting the differences in channel gains of users. Then, relying on the mean-field game (MFG) framework, we solve the resource allocation problem for intensive user deployment, using the novel deep deterministic policy gradient (DDPG) method, which is termed by a mean-field-deep deterministic policy gradient (MF-DDPG) algorithm. Finally, a jointly iterative optimization algorithm (JIOA) of UCM and MF-DDPG is proposed to minimize the computation cost of users. The simulation results demonstrate that the proposed algorithm exhibits rapid convergence, and is capable of efficiently reducing both the energy consumption and task delay of users.

In this article, we address the security issue in a tiered small-cell network aiming at security optimization for small-cell users (SUEs) to defend against eavesdropping. Meanwhile, the transmissions from small-cell base stations (SBSs) are subject to the aggregate interference constraints of macro-cell users (MUEs). In particular, we consider two-fold information uncertainties in small cells, i.e., the uncertainties regarding the eavesdroppers and interference channels to the MUEs. As such, the SBSs compete for robust secrecy rate with robust protection for the MUEs. We adopt the generalized robust Nash equilibrium problem (GRNEP) formulation, for which we confirm the existence of equilibrium and analyze the condition for the uniqueness with variational inequality-assisted analysis. Furthermore, to solve for the equilibrium, we introduce the pricing mechanism and decompose the original GRNEP as a nonlinear complementarity problem with a priced NEP, where the former provides solution of price coefficients and the latter for resource allocation strategies based on given prices. Finally, extensive simulation results are provided to demonstrate the impacts of the interference constraint and uncertainties upon the security performance of an individual SUE and the overall network, which also corroborate the effectiveness of our proposal in security provisioning for the SUEs and interference protection for the MUEs.

Mobile edge caching (MEC), content delivery network (CDN) and data center (DC) serve Internet content providers (ICPs) with different advantages and disadvantages. In this paper, we propose an equilibrium problem with equilibrium constraints (EPEC) to investigate the delivery strategies for files and pricing mechanisms for MEC, CDN, and DC. At the upper level, MEC and CDN predict the files' rational delivery strategies and set the delivery price for each byte to provide the content delivery service. DC serves as origin servers to provide free content delivery service. At the lower level, the files observe the price strategy and determine their delivery strategies. In the proposed EPEC problem, there exist Nash equilibriums, which are coupled with each other, at both the upper level and lower level. We adopt a block coordinate descent (BCD) method to find the equilibrium solutions at both the upper level and lower level. Simulation results show that our proposed approach yields high utilities at the equilibrium.

As industrial production gradually becomes more intelligent, 5G technology has great application prospects in industrial environments. However, the channel in the industrial environment is different from the channel in the other scenarios, and hence need to be characterized specifically. In this paper, a measurement campaign and modeling of industrial scenarios are presented. And we study the main three frequency bands of 3.5GHz, 4.9GHz and 5.8GHz in Sub-6G. Then we model the path loss to fill the gaps in measurement modeling currently in large indoor industrial scenarios. At the same time, we also modeled and analyzed the shadow fading. The results show that the shadow fading in the industrial environment conforms to the lognormal distribution.

In this paper, we study the offloading problem in a mobile edge computing (MEC) network consisting of two-tier UAV. The high-altitude platform unmanned aerial vehicle (HAP-UAV) is equipped with a MEC server to complete the computing tasks of the low altitude platform unmanned aerial vehicle (LAP-UAV). We propose a multi-leader multi-follower Stackelberg game to formulate the two-tier UAV MEC offloading problem. As the leaders of the game, the HAP-UAVs optimize their pricing by considering the behavior of their competitors to maximize their revenue. Each LAP-UAV selects the best computing tasks offload strategy to minimize latency. From this perspective, the stochastic equilibrium problem of equilibrium program with equilibrium constraints (EPEC) model is proposed to develop the optimal supply strategies for HAP-UAVs to maximize their profits and minimize LAP-UAVs' cost. Simulation results show that the offloading delay of LAP-UAVs can be reduced by the proposed scheme.

The broadcast nature of wireless communications makes transmission susceptible to eavesdropping. Recently, physical-layer security has been extensively studied to secure the wireless transmissions, but the security is severely compromised when the eavesdropper has the superiority in the channel quality. Toward this issue, we in this paper propose a joint ARQ and network coding method exploiting the characteristics of both the physical layer and the link layer. Specifically, we relate the message to be transmitted in the current slot with the successfully decoded messages in previous slots, where the ARQ is employed at the legitimate side to guarantee the successful transmission and decoding. Once one or more received messages fail to be decoded at the eavesdropper, it further prevents the eavesdropper from decoding the transmissions in later slots and thus the message transmitted currently and later is secured at the legitimate side. Moreover, for the more disadvantageous case that the eavesdropper is geographically closer to the transmitter, we further propose a destination-aided network coding scheme, which secures private information no matter where the eavesdropper is. The closed-form expressions of intercept probability and loss probability of the private information are analytically presented. The simulation results are provided to confirm our theoretical findings.

Due to the spectrum-sharing feature of cognitive radio networks (CRNs) and the broadcasting nature of wireless channels, providing quality-of-service (QoS) provisioning for primary users (PUs) and protecting information security for secondary users (SUs) are two crucial and fundamental issues for CRNs. Consequently, in this paper, we establish a unified QoS and security provisioning framework for wiretap CRNs. Specifically, different from the widely used deterministic QoS provisioning method and information-theoretical security protection approach, our established framework, which is built on the theory of statistical queueing analysis, can quantitatively characterize the PU's QoS and the SU's security requirements. By adopting the theories of effective capacity and effective bandwidth, we further convert the QoS and security requirements to the equivalent PU's effective capacity and SU's effective bandwidth constraints. Following our developed framework, we formulate the nonconvex optimization problem, which aims at maximizing the average throughput of SU subject to PU's QoS requirement, SU's security constraint, as well as SU's average and peak transmit power limitations. Then, we adopt the techniques of convex hull and probabilistic transmission to convert the original nonconvex problem to the equivalent convex problem and obtain the optimal power allocation scheme through the Lagrangian method. Moreover, we also develop a fixed power allocation scheme which is suboptimal but has low complexity. The simulation results are also provided, which demonstrate the impact of the PU's QoS and the SU's security requirements on SU's throughput as well as the advantage of our proposed optimal power allocation scheme over the fixed power allocation scheme and the conventional security-based water-filling policy.

Small cell networks are envisioned as one of the critical enabling technologies for the next-generation wireless systems. However, due to the limited capability of small cell base stations as compared with the macro-cell base stations, the secure wireless communication faces significant challenges. Towards this issue, we target at enhancing the wireless security for small cell networks by employing the physical layer security techniques. Specifically, we maximize the secrecy rate for each individual small cell in a distributed manner, while protecting the transmissions in the macro-cell by imposing the aggregate interference constraint over the small cell transmissions. The distributed secrecy competition is formulated as a generalized Nash equilibrium problem, for which we adopt its equivalence in the form of the quasi-variational inequality to analyze the existence and uniqueness of the Nash equilibrium. Furthermore, we tackle the interference constraint as the penalty over the secrecy rate of the small cells and introduce the Nash equilibrium problem formulation. Then, the distributed algorithm is proposed based on the best-response strategy to solve for the Nash equilibrium of the secrecy competition game. Finally, simulation results are provided to corroborate our theoretical findings.

In this paper, we establish a unified Quality-of-Service (QoS) and security provisioning framework for wiretap cognitive radio networks (CRN) by employing the theories of statistical queueing analysis, effective bandwidth, and effective capacity, which can quantitatively characterize the QoS and security requirements. Based on our developed framework, we formulate the nonconvex optimization problem that aims at maximizing the average throughput of secondary user (SU) subject to PU's QoS requirement, CRN's security constraint, as well as SU's average and peak transmit power limitations. By using the techniques of convex hull and probabilistic transmission, we convert the original nonconvex problem to the equivalent convex problem and then obtain the optimal power allocation via Lagrangian method. Simulation results demonstrate the impact of PU's QoS and CRN's security requirements on SU's throughput as well as the advantage of our proposed scheme over the fixed power allocation and the conventional security-based water-filling policy.

Information security is a critical yet challenging issue for wireless communications. In this paper, we consider the distributed resource competition in a network that consists of both security-oriented users (SeUs) and regular users (ReUs) which, respectively, intend for secrecy rate and transmission rate maximization. To enhance wireless security, the SeUs are given higher priorities such that they are allowed to take action first in the competition, which gives rise to the multi-leader-follower hierarchical game formulation where the SeUs are the leaders in the upper layer and ReUs are the followers in the lower layer. However, the solution to the lower sub-game among the ReUs, in the form of a Nash equilibrium parameterized by the upper strategy, lacks closed-form expression, which hinders us from solving the hierarchical game effectively. To tackle this issue, we first consider the case with one leader and reformulate the game as a mathematical program with equilibrium constraints (MPEC). Then, the MPEC is transformed as a single-level optimization and solved through successive concave approximation. For the general case that comprises multiple leaders, the equilibrium program with equilibrium constraints (EPEC) is introduced for the game reformulation. Due to the inherent difficulties of EPEC, the relaxed concept of local Nash equilibrium (LNE) is introduced as the solution. Furthermore, the existence and uniqueness of the LNE are investigated with the variational inequality-based analysis. Finally, simulation results are provided to corroborate our theoretical findings.

With large coverage area, high data rate, low latency and high spectral efficiency, LTE-V has been considered as a promising communication technology in the vehicular networks. However, as all vehicles share the same wireless resource, in LTE-V, how to design power allocation strategy for each vehicle while motivating other vehicles to forward the data remains challenging. In this paper, we model the data transmission in the uplink scenario of the vehicular network as an equilibrium program with equilibrium constraints (EPEC), where the upper-layer vehicles as receivers (VaRs) provide priced relaying service to vehicle as a transmitter (VaT) in the bottom layer. Observing the prices set by serving VaRs in allocated channels, we adopt multi-level water- filling algorithm at the VaT for power allocation. With joint consideration on the optimal reaction of the VaT and the pricing strategy of other VaRs, each VaR optimizes its setting price by adopting the sub-gradient algorithm such that the equilibrium of the formulated EPEC is finally achieved. Simulation results corroborate our theoretical analysis and demonstrate the performance superiority of our proposal as compared with classic pricing strategies of VaRs.

Information security is a critical yet challenging issue for wireless communications. In this paper, we investigate this problem from a networked perspective. Specifically, we consider a wireless network where there coexists users with and without security concerns, which respectively maximize the secrecy rate and transmission rate. To enhance wireless security, we give the security-oriented user higher priorities and thus formulate the hierarchical power competition game with the security-oriented user at the upper layer as the leader and the regular users at the lower layer as followers. However, the solution to the power competition at the lower layer, which is a Nash equilibrium parameterized by the upper power strategy, lacks closed-form expressions and thus impedes us from solving the hierarchical game by directly applying the backward induction method. As such, we instead employ the mathematical program with equilibrium constraints (MPEC) formulation for our considered problem. Leveraging the concavity of the lower-layer problem, we then transform the MPEC problem into a single-level optimization and solve for the optimal by applying the difference-of-two-concave-functions (D.C.) programming. Numerical results are provided to validate our theoretical analysis, which also demonstrates the advantages of our model to enhance security as compared with the case of single-level competition.

Physical layer security has emerged as a promising technique to safeguard the information security in wireless networks. In this paper, we investigate the physical layer security issue for a wireless network where there coexist multiple users with security concerns. Specifically, we tackle the problem from a distributed perspective and formulate the secure transmissions at different users as a non-cooperative game. Consider the practical situation that the legitimate transmitter may not always have the perfect information regarding the channel state information of the eavesdropper, we adopt the robust secrecy rate to combat the potential worst cases. Accordingly, the robust Nash equilibrium is employed as the solution to the resource competition game among the users. Further, we analyze properties of the equilibrium and derive the optimal transmission strategy for each individual user to maximize its own robust secrecy rate, following which the distributed algorithm is proposed for the network-wide competition to reach the equilibrium. Finally, simulation results are provided to corroborate our theoretical findings.

Ultra-dense networks are envisioned as essential to embrace the skyrocketed traffic for the next-generation wireless networks. In this paper, we consider the uplink transmissions in ultra-dense networks, where the increased interference along with the increased density significantly challenges the efficient utilization of network resources as well as the provisioning of users' quality of services (QoS). Targeting these issues, we consider the QoS in terms of target signal-to-interference-plus-noise ratio (SINR) and power consumption simultaneously for each user within a multi-objective optimization model. We then investigate the interactions among users by leveraging the non-cooperative game-theoretical framework. By characterizing the properties of Nash equilibrium, we develop the target-SINR oriented resource allocation (TORA) algorithm, which features distributed implementation. Moreover, we obtain the condition to guarantee the convergence of our proposed TORA algorithm and demonstrate that it adapts to different interfering scenarios. Furthermore, considering the heterogeneous service requirements in real practice, we also design the target-SINR constrained resource allocation (TCRA) algorithm, such that TORA and TCRA are able to cope with voice and data services, respectively. Also provided are the simulation results, which demonstrate that, compared with the counterparts, our proposals more effectively guarantee the target-SINR for users with efficient power utilization.

In this paper, we consider the physical layer security issue for the multi-channel full-duplex (FD) communications in the presence of eavesdroppers. There co-exist multiple FD pairs, where the two users in each pair perform bi-directional transmissions. The secure communication is then challenged by the users' self-interference, external interference from other pairs, and threats from the eavesdroppers. We investigate the problem from a distributed perspective and formulate the problem as a non-cooperative game, where each user optimizes their power allocation over the channels to maximize their own secrecy rate. Confirming the existence of the Nash equilibrium, we introduce an equivalent variational inequality (VI) formulation to derive the sufficient condition for the equilibrium to be unique. We then develop the iterative security-aware water-filling (ISWF) algorithm that can be implemented at each individual user in a distributed manner and prove that the condition for the unique equilibrium also claims the convergence of ISWF algorithm. Furthermore, we extend our formulation to the heterogeneous cases that there co-exist FD and half-duplex users with different security requirements in the networks, and demonstrate that they can all be covered as special cases under our formulated VI framework. Finally, we present simulation results to validate our theoretical findings.

Security is an issue of paramount importance, yet is it a significant challenge for wireless communications, which becomes more intricate when facing a full duplex (FD) active eavesdropper capable of performing eavesdropping and jamming simultaneously. In this paper, we investigate the physical layer security issue in the presence of an FD active eavesdropper, who launches jamming attacks to further improve the eavesdropping. The jamming, however, also results in self-interference at the eavesdropper itself. This security problem is formulated within a hierarchical game framework where the eavesdropper acts as the leader and the legitimate user is the follower. In particular, we first investigate the follower's secrecy rate maximization problem and derive the optimal legitimate transmission strategy. Then, the leader's wiretap rate maximization is expressed as a mathematical program with equilibrium constraints (MPEC). Leveraging the concavity of the follower's problem, we transform the MPEC problem into a single-level optimization and obtain the jamming power allocation strategy by applying the primal-dual interior-point method. Moreover, we analyze the situations where only partial channel state information is available at the legitimate user and the corresponding impacts on the game. Finally, we present extensive simulation results to validate our theoretical analysis.

In this paper, we consider the multi-channel power optimization to enhance security for a full-duplex (FD) transmission pair. The FD-enabled concurrent transmissions between the users, on one hand, induce self-interference at their own receivers, and on the other hand, act as friendly jamming to degrade the eavesdropping for the other. To elaborate on such a tradeoff for secrecy maximization, we investigate the power allocation problem from both centralized and distributed perspectives. For the centralized approach, we intend to maximize users' sum secrecy rate. The non-concave problem is tackled by the difference-of- two-concave-functions programming, where the local optimal is obtained by iteratively solving a series of concave problems. For the distributed approach, we formulate the secrecy rate competition between the users as a game. The Nash equilibrium of the game is then achieved by the users' best-response iterations. In particular, by leveraging the theory of variational inequality, we derive the conditions for the equilibrium to be unique. Finally, we present simulation results that verify our theoretical analysis. Also, it is demonstrated that, for the security performance of FD and half- duplex transmissions, one may outperform the other depending on the self-interference cancellation.

Security issue is of paramount importance yet significant challenge for wireless communications, and this problem can be even more intricate when facing with a full-duplex adversary. In this paper, we investigate the physical layer security of a legitimate transmission link in the presence of a full-duplex active eavesdropper, who is capable to perform eavesdropping and jamming simultaneously. The legitimate user aims at a target secrecy rate while the eavesdropper intends to maximize its wiretap rate. To this end, the eavesdropper imposes a jamming signal at the legitimate receiver to stimulate higher-power legitimate transmissions and thus facilitates its eavesdropping. This, however, generates residual self-interference at the eavesdropper itself and is subject to a linear price for the jamming power. The problem is then formulated within a game-theoretic framework, where the closed-form strategies of both the legitimate user and active eavesdropper are obtained. Moreover, we analyze the performance in terms of secrecy outage probability for the legitimate link in such a hostile situation. Also provided are the simulation results which validate our theoretical analysis.

Reactive jamming, which performs jamming attacks on condition of detecting the legitimate transmissions, is widely considered as one of the most serious security challenges in wireless communications. In this paper, we tackle the reactive jamming issue from a novel yet realistic perspective -- the jammer may not always be able to accurately detect the legitimate transmissions, which in turn, can be exploited by the legitimate user to enhance security. In accordance with the detection- then-jamming characteristic of reactive jamming, we formulate the transmitting-jamming problem within a Stackelberg game framework, where the legitimate user takes action first, followed by the reactive jammer. To optimize its own utility, the legitimate user needs to determine the transmission strategy by elaborately achieving the tradeoff between the signal-to- interference-plus-noise ratio (SINR) and the probability to be accurately detected and thus jammed by its adversary. The investigation on Stackelberg equilibrium provides the solution to the game model. Furthermore, we consider the more practical situation that the legitimate user has only incomplete knowledge regarding its adversary and analyze the corresponding impact on the game and equilibrium. Simulation results demonstrate significant performance superiority in terms of secure legitimate transmissions compared with the classical approach.

In heterogeneous networks, users are usually confronted with multiple covering base stations (BSs) that differ in the respects of transmit power, bandwidth resources, and so forth, which makes the user association problem more challenging. In this paper, we consider this problem by emphasizing the long-term effect of the user association policy against the dynamic wireless environment for each individual user. In particular, we exploit the stochastic game model to characterize users' non-cooperative behaviors that they compete for the limited resources at BSs for better services, where the reward function for users is defined as their infinite-horizon discounted sum rate. Such a formulation has the advantage to track the users' performance in the long run with respect to the channel state variations. The Nash equilibrium of the game is obtained from users' best-reply playing, which is formulated as a Markov decision process with the value iteration algorithm providing the solution. Furthermore, we specially analyze the two-BS scenario and derive the threshold-based results for the association policy. The simulation results demonstrate that, compared with the counterparts, our proposal achieves higher system sum rate with relatively lower frequency of handovers, and improves the fairness in terms of transmission rate among users.

In this paper, we develop a macro-femto cooperation strategy for uplink transmissions of multi-channel two-tier networks, which aims at alleviating the co-channel interference and optimizing the energy efficiency of macro-users (MUEs) and femto-users (FUEs) simultaneously. Specifically, the features of our work include three folds. First, our proposed strategy allows the MUE to select a femto-access point (FAP) to perform hybrid access, which efficiently eliminates the cross-tier interference. Second, by adopting the coalitional game in partition form, the users with strong mutual interference form a coalition to share the channel in a time-division multiplexing manner such that the intra-coalition interference can be avoided. The corresponding time-division policy is obtained by employing the Nash bargaining solution. Third, the inter-coalition resource competition problem is solved within a non-cooperative energy efficiency game framework and the transmit power for each user is derived through Nash equilibrium. Theoretical analysis shows that our proposed strategy can efficiently improve the energy efficiency of FUEs. Also provided are simulation results which demonstrate the performance superiority of our developed strategy over the non-cooperative scheme in terms of user's energy efficiency and data transmission rate.

Power control strategy that guarantees users' quality-of-service (QoS) in a power-efficient manner is a critical yet challenging issue in distributed wireless networks. In this paper, we investigate the problem by considering the energy consumption and QoS provisioning simultaneously, where the QoS requirement is specified by the target signal-to- interference-plus-noise ratio (SINR). The problem is represented as multi-objective optimization at each individual user. Then, we cast the formulation within a non-cooperative game framework where the weighted sum of the original objectives is the payoff function. Following our analyses on the properties of Nash equilibrium, we propose the target-SINR oriented power control (TOPC) strategy, which has the advantage of distributed implementation. Further, we reveal the condition for TOPC to converge and illustrate its performance in the extreme cases. Simulation results confirm our analytical results and demonstrate that, compared with the counterparts, our proposal more effectively guarantees users' QoS with efficient power utilization.