The rapid proliferation of new applications poses great challenges to the computation capability of mobile devices. To tackle this problem, computation offloading was proposed as a promising paradigm. In this paper, we consider a cellular device-to-device (D2D) mobile edge computing (MEC) system consisting of one base station (BS) deployed with an MEC server and users. We assume that a portion of users have task computation requirements and may perform local computing, MEC offloading, or D2D offloading. Further assuming that tasks can be partitioned into small-sized data and can be executed simultaneously via various computation modes, we jointly study computation offloading and resource allocation problem. To achieve efficient information interaction and task management, we first propose a joint task management architecture. Defining task execution cost as the weighted sum of task execution latency and energy consumption, we then formulate the joint optimization problem as a task execution cost minimization problem. As the formulated problem is a mixed integer nonlinear problem, which cannot be solved conveniently, we propose a heuristic algorithm that successively solves computation offloading subproblem and resource allocation subproblem by Kuhn–Munkres algorithm and Lagrangian dual method, respectively. Numerical results demonstrate the effectiveness of the proposed algorithm.

This paper proposes a novel knowledge-driven approach for resource allocation in wireless networks using the graph neural network (GNN) architecture. To meet the millisecond-level timeliness and scalability required for the dynamic network environment, our proposed approach, named UWGNN, incorporates the deep unrolling of the weighted minimum mean square error (WMMSE) algorithm, referred to as domain knowledge, into GNN, thereby reducing computational delay and sample complexity while adapting to various data distributions. Specifically, by unrolling WMMSE algorithm into a series of interconnected submodules, UWGNN aligns closely with the optimization steps of the algorithm. Our analysis reveals the effectiveness of the deep unrolling method within UWGNN, which decomposes complicated end-to-end mappings, leading to a reduction in model complexity and parameter count. Experimental results demonstrate that UWGNN maintains optimal performance with computation latency 3 to 4 orders of magnitude lower than the WMMSE algorithm and exhibits strong performance and generalization across diverse data distributions and communication topologies without the need for retraining. Our findings contribute to the development of efficient and scalable wireless resource management solutions for distributed and dynamic networks with strict latency requirements.

Mobile edge computing (MEC) has recently emerged as an effective paradigm to enhance the computing capability of capability-limited mobile devices (MDs). In this article, we consider an MEC system consisting of a number of MEC servers and one MD which generates a series of tasks characterized by their dependency relationships. We study computation scheduling and offloading problem of the tasks. To improve the task processing performance, we first propose a parallel transmission and execution (PTE) scheme, based on which we design a computation scheduling and offloading algorithm. Considering the fairness among tasks in terms of task transmission and execution time, we formulate the computation scheduling and offloading problem as a constrained worst-case latency optimization problem which minimizes the maximum completion time of all the tasks. As the original optimization problem cannot be solved conveniently, we first categorize the tasks into high priority tasks (HPTs), medium priority tasks (MPTs) and low priority tasks (LPTs) based on their task execution status and causal relationship. Then, a dynamic priority-based computation scheduling and offloading algorithm is proposed, which designs computation scheduling and offloading strategy for dynamically-changed HPTs and MPTs, respectively. In particular, for HPTs, a multiple knapsack-based heuristic algorithm is proposed, and a task weight and data size-based computation scheduling and offloading algorithm is further proposed for MPTs. Numerical results demonstrate the effectiveness of the proposed scheme.

Mobile computation offloading (MCO) is a technique that can help reduce energy consumption of mobile devices (MDs) by offloading their tasks to more powerful devices for execution. In MCO, the offloading decision for a task depends on availability of both communication and computation resource. In small cell cellular networks, cloudlet servers are usually co-located with the small base stations (SBSs). As a result, offloading decisions of the MDs are coupled with SBS associations, while strong overlapping coverage between the SBSs can result in complicated interference conditions in wireless transmissions that affect the offloading performance. In this paper, offloading decisions and SBS associations are jointly optimized with transmission power and channel assignments in a small cell cellular network. The objective is to minimize the total energy consumption of all MDs, subject to task's latency constraints. The problem is first formulated as a mixed binary nonlinear programming problem, then transformed and solved using the general bender decomposition (GBD). A heuristic solution is proposed that recursively allows more MDs to make offloading decisions based on the current transmission conditions. Compared to using GBD, this solution results in much lower worst-case complexity, while achieving good energy performance for a wide range of system parameters.

Benefited from the characteristics of high mobility, low cost and convenient deployment, unmanned aerial vehicles (UAVs) can be deployed in wireless communication systems as mobile base stations (BSs) to improve the communication performance of users. In addition, by deploying communication and sensing equipment and supporting efficient resource sharing of communication and sensing technologies, UAVs are expected to act as high-performance aerial platforms which integrate communication and sensing technologies. In this paper, a multiantenna UAV-enabled joint communication and sensing scenario is examined by jointly considering the flight energy of the UAV, multi-antenna transmissions, and the user service requirements. Two optimization problems are respectively formulated for various UAV states. In particular, the problem of communication precoding and UAV flight trajectory optimization is formulated as a minimum user rate maximization problem and the joint optimization problem of UAV sensing position, communication and sensing precoding is formulated as a minimum target detection probability maximization problem. Since the minimumrate maximization problem is a non-convex optimization problem, which is difficult to solve directly, we decompose the original optimization problem into a communication precoding design subproblem and a UAV trajectory design subproblem, and solve the two subproblems successively by applying an alternate iteration method. Specifically, a zero forcing (ZF) algorithm is put forward for solving the communication precoding design subproblem. A successive convex approximation (SCA) algorithm is applied to determine the optimal trajectory of the UAV. Based on the optimal trajectory of the UAV, the sensing position optimization problem is modeled as a weighted distance minimization problem, and then a heuristic algorithm is proposed to obtain the optimal positions. Finally, a ZF algorithm-based joint communication and sensing precoding is presented. The effectiveness of the proposed algorithm is verified by simulations.

In this work, we investigate content delivery and target sensing problem in unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) networks where UAVs are allowed storing user-requested contents, delivering the content to users and performing target sensing as well. To jointly address the performance of content transmission and target sensing, we define utility function and formulate the UAV deployment, communication and sensing precoder design problem as a constrained utility maximization problem. As the formulated problem is a mixed-integer nonlinear programming problem, which cannot be solved conveniently, we transform it into two subproblems, namely, user grouping and UAV deployment subproblem, and communication and sensing precoder design subproblem, and solve the two subproblems by using an alternate iteration-based algorithm. Specifically, we first design a mean-shift-based user grouping strategy which divides users into different groups and then propose a UAV deployment strategy based on successive convex approximation (SCA)-based iterative algorithm and the first order Taylor expansion method. To solve communication and sensing precoder design subproblem, we propose a two-layer penalty-based SCA algorithm. Simulation results demonstrate the effectiveness of the proposed algorithms.