Unmanned aerial vehicles (UAVs) have attracted a lot of research attention because of their high mobility and low cost in serving as temporary aerial base stations (BSs) and providing high data rates for next-generation communication networks. To protect user privacy while avoiding detection by a warden, we investigate a jammer-aided UAV covert communication system, which aims to maximize the user’s covert rate with optimized transmit and jamming power. The UAV is equipped with multi-antennas to serve multi-users simultaneously and enhance the Quality of Service. By considering the general composite fading and shadowing channel models, we derive the exact probability density (PDF) and cumulative distribution functions (CDF) of the signal-to-interference-plus-noise ratio (SINR). The obtained PDF and CDF are used to derive the closed-form expressions for detection error probability and covert rate. Furthermore, the covert rate maximization problem is formulated as a Nash bargaining game, and the Nash bargaining solution (NBS) is introduced to investigate the negotiation among users. To solve the NBS, we propose two algorithms, i.e., particle swarm optimization-based and joint two-stage power allocation algorithms, to achieve covertness and high data rates under the warden’s optimal detection threshold. All formulated problems are proven to be convex, and the complexity is analyzed. The numerical results are presented to verify the theoretical performance analysis and show the effectiveness and success of achieving the covert communication of our algorithms.

The semantic communication mechanism enables wireless devices in vehicular networks to communicate more effectively with the semantic meaning. However, in high-dynamic vehicular networks, the transmission of semantic information faces challenges in terms of reliability and stability. To address these challenges, a long-term robust resource allocation scheme is proposed under the Device-to-Device (D2D) vehicular (D2D-V) networks, where multiple performance indicators (user satisfaction, queue stability, and communication delay) are considered. Due to the sophisticated probabilistic form with consideration of channel fluctuations, the Bernstein approximation is introduced to acquire the deterministic constraint more efficiently. The robust resource allocation problem is proposed and separated into two independent subproblems by the Lyapunov optimization method, which includes semantic access control in the application layer and power control in the physical layer. After that, the successive convex approximation method and Karush-Kuhn-Tucher conditions are adopted to solve the subproblems, thereby proposing a robust resource allocation algorithm. The simulations reveal the trade-off relationship between user satisfaction, queue stability, and communication delay, which is on the premise of meeting the user SINR requirement. Moreover, the simulations also prove the necessity of considering channel uncertainty in high-speed mobile vehicular communication scenarios.

In this paper, a novel scheme is proposed to optimize the effectiveness of the device-to-device (D2D)-enabled vehicular communications (D2D-V) which are underlaid in cellular networks, where the uplink channel allocated to one cellular user (CU) is reused by the multiple D2D-V links. To develop a well-functioning D2D-V system, the sum rate of all D2D-V links is necessary to be maximized, and also the reliability of the cochannel CU to infrastructure has to be guaranteed by the CU interference constraint. Unlike the traditional statical D2D systems, the D2D-V system suffers with high mobility and channel uncertainty. Therefore, the CU interference constraint is formulated as a probabilistic function by the Bernstein approximation; the constraint attempts to address the mobile channel fluctuations. Besides, the tractable approximate constraint is reformulated as two separable structure; this reformulated constraint attempts to determine the near-optimal solutions more efficiently. Since the objective function with a logarithmic form is nonconvex, successive convex approximation is applied to transform the nonconvex problem into the convex problem. The dual decomposition is used to determine the near-optimal solutions. After the problem is reformulated, a distributed robust power control algorithm is proposed to perform the chance-constrained optimization. Numerical simulations are used to evaluate the performance of the proposed scheme. The simulation results show the impacts of system performance when applying the proposed scheme in vehicular environments with high mobility. The proposed optimization schemes are further verified by comparing with the existing methods.

This paper studies how to apply game theory to realize a well-function device-to-device enabled vehicular (D2D-V) communication system, where the uplink channel allocated to the cellular user (CU) is reused by multiple D2D-V users. Considering a non-cooperative setting where the CU and D2D-V users are selfish and profit-driven, a novel Stackelberg game framework is proposed to model the single-leader-multiple-follower hierarchical competition, where the CU and D2D-V users act as the leader and the followers, respectively. For the current D2D-V networks, the interference in the dense vehicle scene often leads to extremely poor communication quality. Moreover, a vehicle's mobility leads to an uncertain channel state and further affects the stability of communication. To achieve effective communication, robust Stackelberg game-based resource allocation is developed, and a price-penalty mechanism is further proposed. Unlike previous Stackelberg games, the robust game is highlighted by handling the channel uncertainty which is embedded in the interference probability constraints. Besides, a game equilibrium (GE) is considered to be the solution and its existence and uniqueness are investigated. Also, a distributed robust power control and nonuniform price bargaining algorithm is proposed to approach the GE. Numerical simulations are performed to evaluate the algorithm performances, and the results indicate that the proposed algorithm is effective in high mobility vehicular networks under uncertain channel environments.

This paper exploits the mobility of an unmanned aerial vehicle (UAV) to improve the physical layer (PLS) security against potential eavesdropping scenarios, where the sensitive data transmitted from a ground base station (GBS) to multiple information receivers (IRs) is easily eavesdropped by multiple non-colluding eavesdroppers (Eves). Specifically, the UAV is employed as a friendly jammer to transmit artificial noise to degrade the wiretap channels. By leveraging the UAV's mobility and the possibility of establishing line-of-sight links, the UAV can move close to the Eves and opportunistically jam them. Especially, two practical factors, namely, the imperfect location information of Eves and the existence of no-fly zones are considered. Since only the partial position information of the Eves can be known, as the worst case, the minimum average secrecy rate of IRs is robustly optimized by jointly designing the trajectory and jamming power of the UAV, and the transmission power of the GBS to ensure the security of all IRs. However, the formulated problem with coupled variables is non-convex and intractable. As such, an efficient alternative iterative suboptimal algorithm is proposed to solve this problem by applying the block coordinate descent, successive convex approximation, slack variables, and $\mathcal {S}$ -Procedure. Numerical results reveal that the proposed robust algorithm can substantially improve the max-min average secrecy rate, and the effectiveness of the proposed algorithm is further verified by comparing it with three benchmark schemes.

Smart and green cities impose stringent requirements on spectral efficiency (SE) and energy efficiency (EE) of vehicular networks. For the current vehicular ad-hoc networks (VANETs), vehicle's mobility leads to rapid topology changes and high channel uncertainty. However, clustering schemes for establishing stable clusters and robust power control combating with channel fluctuation are investigated independently. In this paper, joint clustering and robust power control schemes are proposed to optimize the SE and EE of the involved VANETs. Via the same fixed-length slot, the synchronized interference constraints of cluster heads (CHs) are formed and offer conditions for robust power control. Due to the random channel fluctuations, all CHs' synchronized interference constraints are formulated as probability constraints. Besides, a pricing-based utility which avoids the separate optimization between SE and EE is introduced and the price's impact on the tradeoff between them is involved. Since the probability constraints are intractable and the unified utility is nonconvex, the Bernstein approximation and successive convex approximation (SCA) are used to transform the problem into a tractable convex one. Through dual decomposition, two robust power control algorithms are proposed to determine the optimal solutions for the fixed price C and the optimal price C*, respectively. Numerical simulations are used to evaluate the algorithmic performances in high-dynamic system, and the results show that the proposed algorithms are effective. The validity of the clustering method and the proposed robust power control scheme is further verified by comparison.