In this letter, we investigate an unmanned aerial vehicle (UAV) communication system, where an intelligent reflecting surface (IRS) is deployed to assist in the transmission from a ground node (GN) to the UAV in the presence of a jammer. We aim to maximize the average rate of the UAV commnunication by jointly optimizing the GN’s transmit power, the IRS’s passive beamforming and the UAV’s trajectory. However, the formulated problem is difficult to solve due to the non-convex objective function and the coupled optimization variables. Thus, to tackle it, we propose an alternating optimization (AO) based algorithm by exploiting the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques. Simulation results show that the proposed algorithm can significantly improve the average rate compared with the benchmark algorithms. Moreover, it also shows that when the jamming power is large and the number of IRS elements is relatively small, deploying the IRS near the jammer outperforms deploying it near the GN, and vice versa.

To build the new generation of ubiquitous communication and service integration networks with “network omnipresence and computing ubiquitous,” it is urgent to improve the in-orbit computing ability of low earth orbit (LEO) constellation networks and develop intelligent technology for satellite–ground collaborative edge computing. Communication tasks between ground nodes and satellites are increasing, but the satellite-to-ground spectrum resources are limited. The reasonable application of channels determines the performance of the network, which in turn affects the users’ experience. An outstanding issue is how to allocate channels rationally and control the power of data transmission to reduce co-channel interference and minimize system overhead effectively. This article studies multiuser computation offloading for low earth orbit (LEO) constellation networks under dynamic environment, wherein the system overhead is minimized by joint offloading strategy and power optimization. First, we propose a generic network architecture for computation offloading of LEO constellation networks under the dynamic environment. Then, from a game-theoretic perspective, we model the overhead minimization problem as a potential game and prove that the Nash equilibrium (NE) minimizes the system overhead. After that, to reach the NE, we design the Synchronous log-linear learning-based power control algorithm and joint offloading strategy and power optimization algorithm based on SLA (JOPAS), and prove the convergence of the algorithms. Finally, the effectiveness of the proposed algorithm is verified through extensive simulations and comparisons with benchmark algorithms, and the proposed algorithm achieves near-optimal performance.

In this paper, we investigate the resource management for quality of experience (QoE)-centric low earth orbit (LEO) satellite-terrestrial integrated network (STIN) based on non-orthogonal multiple access (NOMA). In satellite communication, existing efforts are devoted to maximizing quality of service (QoS) metrics, such as throughput. The optimization mechanism centered around QoS may to some extent impact the fairness of the system, leading to a decrease in resource utilization. To achieve more rational resource allocation, we focus on maximizing the user's QoE. We apply NOMA to LEO STIN to improve spectral efficiency and utilize the mean opinion score (MOS) to assess user satisfaction. Then, we maximize the sum MOSs of all users by jointly optimizing channel access, user pairing and power allocation. However, the proposed problem is a complex non-convex problem that is challenging to solve directly. To tackle this issue, we propose a coalition formation algorithm based on better response for user pairing and channel access. The power allocation is performed by deriving and presenting a linear search algorithm. Simulation results show that the proposed QoE optimization framework can effectively improve the QoE performance of the system compared with the QoS-centric optimization scheme and the OMA transmission method. Furthermore, in contrast to existing work, the QoE optimization proposed in this paper enhances the fairness of the system and increases resource utilization without incurring additional system costs.

Device-to-Device (D2D) multicast communication is an important component of wireless network design, bringing greater flexibility and efficiency to 5G and future network architectures. Utilizing semantic communication for D2D multicast is considered a promising approach. However, two challenges need to be addressed: user clustering and resource management based on semantic communication. For the first issue of user clustering, we introduce a semantic triple-based structure to extract semantic features of images and define user similarity for image delivery tasks based on semantic features and other indicators. Subsequently, improvements are made to the K-medoids algorithm to achieve efficient user clustering. For the other issue of resource management, a scaling compression factor is first designed to adjust the fidelity of image delivery for the semantic image communication process of semantic encoding and decoding. Then, we propose a mini-batch model training method by randomly selecting batches to train sub-semantic models, balancing model performance and training complexity. Secondly, we establish an optimization problem to maximize user quality of experience (QoE) through optimizing semantic compression ratios and D2D multicast channel selection. We analyze it from a game theory perspective, modelling the maximization of average QoE for all cell users as a potential game. Finally, we design a joint semantic compression ratio selection and channel allocation strategy based on the spatial adaptive (JSCSA) algorithm to achieve Nash Equilibrium (NE) and demonstrate the convergence of the algorithm. Simulation results confirm the superiority of the proposed algorithm.

This paper studies the anti-jamming problem of unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) communication networks in the presence of a jammer under the accurate probabilistic line-of-sight (LoS) model. Our goal is to maximize the information collection throughput of the system under the assumption that only the jammer’s approximate location is known. To this end, we formulate a throughput maximization problem by optimizing the UAV trajectory, the IoT devices’ transmit power, and communication scheduling under the accurate real-time probabilistic LoS channel. However, the proposed optimization problem is non-convex and coupled, and hence intractable to be solved. In order to tackle the problem, a robust iterative algorithm is proposed by leveraging the block coordinate descent (BCD) method, the successive convex approximation (SCA) technology, the difference of convex (D.C) programming approach, and the $\mathcal{S}$-procedure. Extensive simulation results show that our proposed algorithm significantly improves the system throughput while achieving a practical anti-jamming effect compared with the benchmark algorithms.

Device-to-device (D2D) communication is considered a key technology of 5G. Integrating semantic communication (SemCom) into D2D communication has the potential to optimize information content based on user intent and context, thereby enhancing efficiency in utilizing limited wireless resources and improving user experience. However, SemCom introduces additional computational costs. Thus, addressing the practical considerations of whether and how to integrate SemCom and D2D communication remains a pressing issue. This paper proposes a transmission framework in which the traditional mode (BitCom) and the SemCom mode coexist, and then an efficient distributed dynamic resource management problem is proposed to minimize the system’s cost. Firstly, we construct cost models for the two communication modes based on latency and energy consumption. Next, we propose an optimization problem for joint semantic extraction ratio, transmission mode selection, and channel allocation. To efficiently address the problem, we decompose the original problem into two parts. With the given D2D mode selection and channel allocation, we optimize the semantic extraction ratio by leveraging convex optimization methods. Subsequently, with the given semantic extraction ratio, we formulate the mode selection and channel allocation problem as a stochastic game and demonstrate the existence of a Nash equilibrium (NE). Finally, we propose a stochastic learning algorithm with multi-agent to converge to the NE solution. Simulation results verify that under conditions of severe mutual interference, the system cost can even be reduced by 39% compared to the single BitCom mode.