关键词： integrated communication and sensing   mobile edge computing   deep reinforcement learning   robust design   computation uncertainty  

摘要： In this paper, we consider an integrated sensing, communication, and computation (ISCC) system to alleviate the spectrum congestion and computation burden problem. Specifically, while serving communication users, a base station (BS) actively engages in sensing targets and collaborates seamlessly with the edge server to concurrently process the acquired sensing data for efficient target recognition. A significant challenge in edge computing systems arises from the inherent uncertainty in computations, mainly stemming from the unpredictable complexity of tasks. With this consideration, we address the computation uncertainty by formulating a robust communication and computing resource allocation problem in ISCC systems. The primary goal of the system is to minimize total energy consumption while adhering to perception and delay constraints. This is achieved through the optimization of transmit beamforming, offloading ratio, and computing resource allocation, effectively managing the trade-offs between local execution and edge computing. To overcome this challenge, we employ a Markov decision process (MDP) in conjunction with the proximal policy optimization (PPO) algorithm, establishing an adaptive learning strategy. The proposed algorithm stands out for its rapid training speed, ensuring compliance with latency requirements for perception and computation in applications. Simulation results highlight its robustness and effectiveness within ISCC systems compared to baseline approaches.

关键词： Digital twins   Heuristic algorithms   Approximation algorithms   Delays   Prediction algorithms   Cloud computing   Real-time systems   Digital twin   mobile edge computing   age of information   approximation and online algorithms   digital twin placement  

摘要： The emerging digital twin technique enhances the network management efficiency and provides comprehensive insights on network performance, through mapping physical objects to their digital twins. The user satisfaction on digital twin-enabled service relies on the freshness of digital twin data, which is measured by the Age of Information (AoI). Due to long service delays, the use of the remote cloud for delay-sensitive service provisioning faces serious challenges. Mobile Edge Computing (MEC), as an ideal paradigm for delay-sensitive services, is able to realize real-time data communication between physical objects and their digital twins at the network edge. However, the mobility of physical objects and dynamics of user query arrivals make seamless service provisioning in MEC become challenging. In this paper, we investigate dynamic digital twin placements for improving user service satisfaction in MEC environments, by introducing a novel metric to measure user service satisfaction based on the AoI concept and formulating two user service satisfaction enhancement problems: the static and dynamic utility maximization problems under static and dynamic digital twin placement schemes. To this end, we first formulate an Integer Linear Programming (ILP) solution to the static utility maximization problem when the problem size is small;otherwise, we propose a performance-guaranteed approximation algorithm. We then propose an online algorithm with a provable competitive ratio for the dynamic utility maximization problem, by considering dynamic user query services. Finally, we evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms outperform the comparison baseline algorithms, improving the algorithm performance by at least 10.7\%, compared to the baseline algorithms.

关键词： Virtual reality   video code quality   mobile edge computing   rendering offloading   resource allocation  

摘要： Fully immersive virtual reality (VR) applications require ultra-high data rates and time-varying viewport rendering to ensure smooth operation. Therefore, this letter proposes a mobile edge computing-assisted wireless VR streaming transmission scheme to pursue a better quality of experience (QoE) and alleviate the computing burden of VR users. Our goal is to minimize the weighted energy consumption and distortion while ensuring the required QoE. The formulated problem is NP-hard and involves the optimization of quantization parameters, rendering offloading decisions, transmit precoding, and computing resource allocation. Simulation results validate the efficiency of the proposed algorithm in terms of the QoE performance.

关键词： Autonomous aerial vehicles   Sensors   Radar   Optimization   Precoding   Radar antennas   Energy consumption   Joint communication   sensing and computing   mobile edge computing   radar sensing   successive convex approximation   unmanned aerial vehicle  

摘要： In this paper, we propose a joint communication, sensing and computing (JCSC) framework for unmanned aerial vehicle (UAV) system that utilizes mobile edge computing (MEC) technology to offload radar sensing data to the ground for processing. To achieve optimal performance in sensing data processing, it is necessary to design the communication and radar precoding matrix of the UAV systems and optimize the location of the UAV system. The joint optimization model is established using the linear weighting method, which transforms the joint optimization problem into a single objective optimization problem. To solve this problem, the master problem is decomposed into two subproblems: one for precoding design in data offloading and the other for precoding design in radar sensing. Fractional programming and successive convex approximation (SCA) are used to transform each of these subproblems respectively into a convex problem, and two algorithms named "precoding design and location optimization for data offloading (PLDO)" and "precoding design and location optimization for radar sensing (PLRS)" are proposed to solve the optimization problem. The simulation results show that the proposed algorithm can greatly reduce the energy consumption of UAV compared to local processing, and it has a faster convergence speed.

关键词： Decoding   Task analysis   Energy consumption   Eavesdropping   Wireless networks   Signal to noise ratio   NOMA   Mobile edge computing   non-orthogonal multiple access   semi-grant-free transmission   scheduling  

摘要： This letter investigates a non-orthogonal multiple access (NOMA) based mobile edge computing system with semi-grant-free (SGF) transmission, where a grant-based user (GBU) wants to securely offload its data to a computational access point (CAP) for processing with a time deadline in the presence of an eavesdropper, and a grant-free user (GFU) is scheduled from a group of GFUs to simultaneously offload its data to the CAP for processing based on SGF-NOMA and at the same time interfere with the eavesdropper to improve the GBU secrecy performance. Two GFU scheduling strategies, where one schedules the GFU with the maximum communication channel gain, and the other schedules the GFU with the maximum jamming channel gain, are proposed. The closed-form exact expressions of the system success probability are derived, from which the impacts of various system parameters are obtained. Simulation results confirm the theoretical results and show that the performance comparison between the two strategies depends on the system parameters such as the offloaded data size.

关键词： Jamming   Task analysis   Computational modeling   Servers   Optimization   Data models   Resource management   Mobile edge computing   anti-jamming communications   hybrid hidden strategy   hierarchical reinforcement learning  

摘要： This letter investigates the scenario of resisting intelligent reactive jammer in mobile edge computing (MEC) networks. To solve the multi-dimensional offloading allocation problem while avoiding malicious jamming attacks, a hierarchical reinforcement learning (HRL) based hybrid hidden strategy is proposed to allocate multi-dimensional resources and reduce the probability of being perceived in the offloading process. Specifically, the HRL framework is divided into two layers for asynchronous update: the upper layer with the discrete policy network taking charge of outputting the channel access strategies to resist the dynamic jamming attack in the frequency domain, and the lower layer with the continuous policy network mainly offering hidden offloading strategies to avoid the detection of intelligent reactive jammers. By integrating the double deep Q-Network and twin delayed deep deterministic network, and exploiting their advantages in solving the hybrid strategy problems, the mobile device (MD) is able to execute effective hidden offloading strategies to reduce the computing cost as well as to significantly avoid intelligent reactive jamming in dynamic and unknown environments.

关键词： offloading decision   Lagrange duality optimization   mobile edge computing   real-time IoT   resource allocation  

摘要： Internet of Things (IoT) devices can integrate with applications requiring intensive contextual data processing, intelligent vehicle control, healthcare remote sensing, VR, data mining, traffic management, and interactive applications. However, there are computationally intensive tasks that need to be completed quickly within the time constraints of IoT devices. To address this challenge, researchers have proposed computation offloading, where computing tasks are sent to edge servers instead of being executed locally on user devices. This approach involves using edge servers located near users in cellular network base stations, and also known as Mobile Edge Computing (MEC). The goal is to offload tasks to edge servers, optimizing both latency and energy consumption. The main objective of this paper mentioned in the summary is to design an algorithm for time- and energy-optimized task offloading decision-making in MEC environments. Therefore, we developed a Lagrange Duality Resource Optimization Algorithm (LDROA) to optimize for both decision offloading and resource allocation for tasks, whether to locally execute or offload to an edge server. The LDROA technique produces superior simulation outcomes in terms of task offloading, with improved performance in computation latency and cost usage compared to conventional methods like Random Offloading, Load Balancing, and the Greedy Latency Offloading scheme.

关键词： Mobile edge computing   Trust management   Edge caching   Blockchain   Deep reinforcement learning  

摘要： Mobile edge computing (MEC) has introduced content edge caching to offload network backhaul and improve user quality of experience by transferring frequently requested content to edge nodes near end-users. However, the implementation of edge caching is fraught with significant security and confidentiality issues due to the lack of trust between the different entities involved in the caching process. Most existing solutions for managing trust in edge caching only consider the trust between caching edge servers and ignore the trust between edge servers and content providers. This paper aims to fill this gap by proposing a trust management-based edge caching system that exploits the potential of blockchain technology to secure the caching process by enabling trust between content providers and edge servers. The proposed trust management system establishes trust in both directions of the interaction, providing higher confidence between the caching actors. Our trust system is based on a direct trust score calculated using the subjective three value logic scheme (3VSL). This is complemented by a reputation-based indirect trust score managed by the blockchain. A deep reinforcement learning (DRL) algorithm is proposed to efficiently make secure and intelligent caching decisions. The simulation results show that the proposed blockchain-based trust management system is effective and guarantees a good level of security and confidentiality for edge caching. They also demonstrate that our DRL caching algorithm provides secure and intelligent caching decisions in MEC, outperforming the baseline edge caching schemes.

关键词： Task offloading   Space-air-ground integrated network   Mobile edge computing   Game theory   Nash equilibrium  

摘要： The limited coverage of terrestrial networks makes it difficult to provide low -latency and high -reliable computing services for users and Internet of Thing (IoT) devices in remote areas such as mountainous regions and oceans. Space -Air -Ground Integrated Network (SAGIN) combined with Mobile Edge Computing (MEC) can provide seamless three-dimensional services for users by deploying edge servers on satellites, Unmanned Aerial Vehicles (UAVs) and ground infrastructures. However, due to the limited heterogeneous computing resources in satellites-UAV clusters network and the energy resources of IoT devices, it brings a significant challenge in determining how to offload the computing tasks generated by ground user devices to satellite edge nodes, UAV edge nodes or locally for processing. In this paper, we first propose a satellites-UAV clusters -ground three -layer edge computing network architecture consisting of a global controller, inter -domain controllers and MEC servers. We then model the task offloading problem as a Binary Integer Linear Programming (BILP) aiming at minimizing the offloading cost composed of delay and energy consumption and prove it is NP -hard. Next, the original offloading problem is transformed to a noncooperative strategic game and the existence of Nash equilibrium is proven using potential games. Finally, we propose a Nash Equilibrium Iteration Offloading algorithm based on Game theory (NEIO-G) to find the optimal offloading strategy. Compared with other baseline algorithms, simulation results demonstrate that the NEIO-G can significantly reduce the system offloading overhead in terms of delay and energy consumption.

关键词： Mobile edge computing   fractional programming   active reconfigurable intelligent surface  

摘要： In this letter, we consider an active reconfigurable intelligent surface (ARIS)-assisted uplink mobile edge computing (MEC) system. We propose an ARIS assisted non-orthogonal multiple access (NOMA) scheme, denoted by ARIS-NOMA, in which multiple users process their local computing and simultaneously perform task offloading in NOMA manner to a base station (BS) with the help of a common ARIS. A total computation bits (TCB) maximization problem is formulated to improve the computing capability of proposed ARIS-NOMA scheme by jointly optimizing the BS's receive beamforming (RBF), ARIS's reflection coefficients, local computing frequency (LCF), and transmit power of each user. Specifically, we first use a fractional programming (FP) technique to reformulate the original objective function, and then utilize a block coordinate descent (BCD) method to separate the reformulated problem into several sub-problems and iteratively optimize one set of variables with others being fixed. In particular, we obtain closed-form solutions for BS RBF, ARIS reflection coefficients, LCF, and the transmit power of each user by jointly leveraging the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions. Numerical results validate the advantages of the proposed ARIS-NOMA scheme compared to the conventional benchmarks regarding to the TCB.