Re-establishing emergency communication and ensuring rapid response are critical for rescue operations in natural disaster scenarios, such as earthquakes, floods, and wildfires. Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks have emerged as a promising solution to re-establish communication links and provide flexible computational support in these complex environments. However, the existing UAV-assisted MEC research has not fully investigated the joint optimization of task offloading and resource allocation (JTORA) problem, considering both terrain obstacles and high-interference zones. In this paper, we investigate the JTORA problem to minimize the energy consumption for communication and computation in a rate-splitting multiple access (RSMA)-based UAV-assisted mobile edge computing (MEC) network. RSMA is utilized to enhance interference management and improve spectral efficiency. We propose a proximal policy optimization (PPO)-based method to optimize the task offloading ratio, message splitting ratio, and RSMA precoding matrix for the proposed JTORA problem. Simulation results show that the proposed approach effectively enhances system efficiency and sustainability.

Natural disasters often destroy critical infrastructure, such as terrestrial communication networks and transportation routes, thereby severely disrupting post-disaster rescue operations. To rapidly re-establish communication links and provide flexible computational support in disaster rescue scenarios, the integration of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) has emerged as a promising solution. Nevertheless, the highly complex and resource-constrained characteristics of disaster environments pose significant challenges for UAV-assisted computation task offloading. In this paper, we investigate the joint task offloading and resource allocation (JTORA) problem to minimize the energy consumption associated with communication and computation during task offloading. Specifically, we develop a twin-delayed deep deterministic policy gradient (TD3)-based JTORA (JTORA-TD3) algorithm, which enables the UAV to optimize decisions of task offloading and resource allocation intelligently. To further enhance the training efficiency of the JTORA-TD3 algorithm in a complex disaster rescue environment, we integrate a large AI model (LAM) into the TD3 framework. Based on the textual interaction, we propose an LAM-enabled TD3-based JTORA (JTORA-LAM4TD3) algorithm. Simulation results demonstrate that the proposed JTORA-LAM4TD3 algorithm significantly outperforms baselines. These findings confirm the effectiveness of integrating LAMs with deep reinforcement learning (DRL) for solving the decision optimization problem.

Given that the locations of base stations (BSs) remain fixed after installation, direct data forwarding to remote user equipment (UE) becomes challenging. Unmanned aerial vehicles (UAVs) offer a hopeful solution as mobile relays for next generation wireless communications to realize data forwarding with the flexible and cost-effective deployment. However, the limited onboard energy of UAVs and slow progress in energy storage technology pose significant challenges to achieving energy-efficient communication. Therefore, in this article, we investigate a wireless communication network utilizing a UAV as a high-altitude relay for data forwarding, and formulate a UAV relay deployment optimization problem (URDOP) to minimize the energy consumption of data forwarding and UAV hovering by optimizing UAV deployment, including the locations and number of UAV hover points. Given that the URDOP is a mixed-integer programming problem, conventional gradient-based approaches face limitations. To address this, we propose a self-adaptive differential evolution with a variable population size (SaDEVPS) algorithm to solve the URDOP. The performance of proposed SaDEVPS is verified through simulations, and the results show that it can successfully decrease the energy consumption of system when compared to other benchmark algorithms.

In the multi-server multi-access edge computing (MEC) system, the computing capacity could be insufficient and the computing resource could be over-utilized when an excessive number of computing tasks are offloaded for execution. To alleviate the excessive burden in the multi-server MEC system and tap the underutilized resources of wired edge devices, which are called edge computers in this paper, we investigate the computation offloading and shunting scheme in the wireless wireline internetwork. We formulate an optimization problem to minimize the average total time delay and use the deep reinforcement learning method to obtain the optimal shunting policy. By sensing the state of the wireless wireline internetwork, the proposed scheme can adaptively adjust the shunting ratio and utilize the resources of MEC servers and edge computers intelligently and jointly. Finally, we evaluate the computational complexity and validate the convergence property of this scheme. The extensive simulation results demonstrate that this scheme can improve the resource productivity and reduce the average total time delay.

To satisfy the explosively mounting requirements of computing resources, the device-to-device (D2D) communications are integrated into the mobile edge computing (MEC) system to share the computing resources of mobile devices. However, most existing literature neglects the selfish of mobile devices. They are reluctant or even refuse to help others. Motivated by this, we propose an auction scheme for computing resource allocation (ASCRA) in D2D- assisted MEC system. The ASCRA algorithm consists of three steps which are identification confirmation, candidate selection, and matching & pricing. There are two kinds of sellers in ASCRA algorithm, where both mobile devices and edge server can share and sell their computing resources. The edge server is also the auctioneer of the system, which controls and manages the whole system. Moreover, both the resource condition and the delay condition are considered in ASCRA algorithm. Numerical results show that the system efficiency, individual rationality, budget-balanced and truthful properties are satisfied in our ASCRA algorithm.

In this study, the performance of the mixed radio‐frequency (RF)/free‐space optical (FSO) system with co‐channel interference (CCI) is investigated. The RF links are modelled as Rayleigh fading, whereas FSO links follow M‐distribution with pointing errors. Assume the amplify‐and‐forward relay is corrupted by multiple CCIs, and both fixed and variable gain relay schemes are considered. Novel expressions for the end‐to‐end outage probability (OP), symbol error rate (SER) and outage capacity in different cases are presented and analysed. Numerical results demonstrate the existence of OP and SER floors in the presence of CCIs, which implies system performance is dominant by RF links, and interference is the main factor affecting the system performance. Additionally, the fixed gain scheme provides a performance enhancement compared with the variable gain scheme in the authors’ system.

In this study, routing metric design for multi‐gateway (MG) wireless mesh networks (WMNs) where partially overlapped channels (POCs) are used for data transmissions is investigated. POCs, multiple gateways and routing are effective solutions for capacity improvement in WMNs. Previous research designs routing metrics for WMNs where only orthogonal channels (OCs) are used. As interference estimation of POCs is quite different from that of OCs, these routing metrics are not suitable for POCs WMNs. In this paper, a light‐weight uniform description of interference‐based routing metric is proposed, and to the best of our knowledge, this is the first routing metric specifically designed for MG POCs WMNs. Equivalent bandwidth is defined to capture the unique characteristics of POCs WMNs accurately, and gateways are also paid close attention to. This metric combines the optimal gateway selection together with the optimum path selection, and simulation results show that it can help select low‐interference and light‐load paths to light‐load gateway for data flows.