The integration of Roadside Units (RSUs) is vital for the development of autonomous driving technologies. Challenges arise from sinking computing capabilities into RSUs and vehicles in the paradigm of Vehicle Edge Computing (VEC), particularly due to heterogeneous computation and communication capacities of network nodes and multiple sources of computing tasks (node-mounted and offloading tasks). These challenges complicate network stability from the perspective of a long-term optimization evolving over time, considering unpredictable task distribution and environmental states. To tackle these challenges, we approach the problem of partial task offloading to minimize task delay while meeting the demand of system stability over time as a dynamic long-term optimization. Utilizing Lyapunov stochastic optimization tools, we successfully decouple the long-term delay minimization and stability constraint, transforming it into a per-slot scheduling problem. Since the per-slot scheduling problem with complicated Lyapunov drift functions can not be solved by numerical optimization at each time step, our solution leverages a proposed deep reinforcement learning algorithm, leading to extensive simulations that demonstrate the superior effectiveness and efficiency of our proposal compared to existing schemes.

With the rapid advancement of intelligent transportation systems, Road Side Units (RSUs) in the Internet of Vehicles (IoV) play a crucial role in sensing environments information for vehicles to make driving decisions and transportation management. By offloading computation tasks from RSUs to other nodes such as cloud servers, other RSUs, and vehicles, the network computation resources can be fully utilized, however, at the expense of increasing task delay. In multi-hop task offloading, tasks can be forwarded to vehicles outside the coverage area of the source RSU where tasks are generated. However, due to the movement of vehicles, stable transmission among nodes is not guaranteed, posing a challenge in determining the next node for task forwarding. In this paper, we ensure the connectivity among nodes by establishing a mobility model and design a mechanism for selecting forwarding vehicles. The goal is to find those vehicles that can communicate stably with the source RSU and determine the optimal communication path. We formulate task offloading as a 0-1 mathematical model with the objective of minimizing the task delay. Subsequently, we propose a solution based on asynchronous deep reinforcement learning A3C. Through extensive simulations, we validate the effectiveness of our approach.

Unmanned aerial vehicles (UAVs) are playing an increasingly pivotal role in modern communication networks, offering flexibility and enhanced coverage for a variety of applications. However, UAV networks pose significant challenges due to their dynamic and distributed nature, particularly when dealing with tasks such as power allocation, channel assignment, caching, and task offloading. Traditional optimization techniques often struggle to handle the complexity and unpredictability of these environments, leading to suboptimal performance. This survey provides a comprehensive examination of how deep reinforcement learning (DRL) can be applied to solve these mathematical optimization problems in UAV communications and networking. Rather than simply introducing DRL methods, the focus is on demonstrating how these methods can be utilized to solve complex mathematical models of the underlying problems. We begin by reviewing the fundamental concepts of DRL, including value-based, policy-based, and actor-critic approaches. Then, we illustrate how DRL algorithms are applied to specific UAV network tasks by discussing from problem formulations to DRL implementation. By framing UAV communication challenges as optimization problems, this survey emphasizes the practical value of DRL in dynamic and uncertain environments. We also explore the strengths of DRL in handling large-scale network scenarios and the ability to continuously adapt to changes in the environment. In addition, future research directions are outlined, highlighting the potential for DRL to further enhance UAV communications and expand its applicability to more complex, multi-agent settings.

Supporting Mobile Edge Computing (MEC) in satellite-terrestrial networks (STN) provides essential offloading services for devices for the Internet of Things (IoT) devices in remote areas. However, when terrestrial demands for computing resources are high, the MEC servers on visible LEO satellites may suffer from insufficient capacity, while those on more distant LEO satellites remain underutilized. To address this issue, this paper investigates cooperative task offloading across multiple LEO satellites within an MEC-based satellite-terrestrial network. We propose a Quality-of-Service (QoS)-aware offloading decision and resource allocation scheme supported by a Software-Defined Network (SDN) for a satellite-terrestrial network architecture. This architecture integrates the LEO Walker constellation with satellite ground stations (SGS), with the aim of providing edge computing services to IoT devices in remote areas. To meet the task’s QoS requirements, the tasks can be offloaded to either SGS or LEO satellites within the constellation. To address the challenges of a vast state space and complex action space within the system, we introduce the QOS-Aware MUlti-Hop Task Offloading in Satellite-Terrestrial EDge NEtworks (OUTSIDE) algorithm, which combines the global search capabilities of genetic algorithms with the local refinement strengths of the Lagrangian multiplier method to minimize the total task computation latency while satisfying QoS demands. Finally, comparative analysis and simulation experiments were conducted. These demonstrate that the OUTSIDE algorithm outperforms other approaches in terms of efficiency and effectiveness.

Owing to its outstanding parallel computing capabilities, quantum computing (QC) has been a subject of continuous attention. With the gradual maturation of QC platforms, it has increasingly played a significant role in various fields such as transportation, pharmaceuticals, and industrial manufacturing, achieving unprecedented milestones. In modern society, wireless communication stands as an indispensable infrastructure, with its essence lying in optimization. Although artificial intelligence (AI) algorithms such as reinforcement learning (RL) and mathematical optimization have greatly enhanced the performance of wireless communication, the rapid attainment of optimal solutions for wireless communication problems remains an unresolved challenge. QC, however, presents a new alternative. This paper aims to elucidate the fundamentals of QC and explore its applications in wireless communications and networking. First, we provide a tutorial on QC, covering its basics, characteristics, and popular QC algorithms. Next, we introduce the applications of QC in communications and networking, followed by its applications in miscellaneous areas such as security and privacy, localization and tracking, and video streaming. Finally, we discuss remaining open issues before concluding.

Cloud-edge collaboration and edge intelligence have greatly driven the growth of the Industrial Internet of Things (IIoT). However, the jittery network delay and limited computational resources of edge servers make it difficult to meet the stringent latency requirements in IIoT, and so far there is no good solution to solve this problem. To this end, we introduce scalable neurocomputing, which provides neural networks with different utilities and computation resource requirements, to be deployed on edge servers of cloud-edge IIoT systems. We then optimize such systems by formulating data scheduling and system computational resource allocation as an infinite horizon optimization problem, considering that the data collection from end devices is an infinite long-term process. To solve this hard problem, we design a rolling prediction-optimization framework that transforms the infinite horizon problem into a truncated finite horizon optimization that maximizes the average system utility while satisfying the stringent delay constraints. We have conducted extensive simulations and built a prototype system, which verify the feasibility and performance of our proposed scheme.