Edge Intelligence (EI) combines edge computing and artificial intelligence to deliver low-latency and resource-efficient services. Integrated Sensing and Communication (ISAC) further empowers EI by enhancing edge perception and accelerating intelligent model training. However, integrating ISAC into EI complicates the coordination of dynamically varying sensing, communication, and computation resources, especially under device mobility and unpredictable network conditions, leading to degraded service performance. To address these coordination challenges and sustain high-quality service under mobility and dynamics, we aim to design an adaptive service provision framework that tightly couples real-time perception with intelligent decision-making at the edge. Specifically, we propose an adaptive service provision architecture for ISAC-assisted EI, where Digital Twins (DTs) hosted on edge servers represent edge devices and their contexts to enable accurate perception and intelligent decision-making, thereby enhancing the efficiency of ISAC-enabled services. By dynamically migrating DTs across edge servers based on device mobility and resource availability, the system supports continuous decision-making and seamless service delivery. We further integrate convex optimization for efficient multi-resource coordination and a Time-Varying Contextual Bandit (TVCB) algorithm to enable adaptive, context-aware DT migration in dynamic environments. Extensive simulations demonstrate that our approach significantly improves service quality, reliability, and adaptability in ISAC-assisted EI systems, reducing migration oscillations and overhead while achieving lower latency and higher utility compared with representative baselines.

Vehicular edge computing (VEC) is critical for the safe and efficient driving of intelligent vehicles, by which they can offload computation-intensive tasks (such as driving environment perception) to edge servers to overcome the limitations of onboard computational resources and cooperate with others. One of the major challenges faced by VEC is that the offloaded intelligent driving tasks generally generate large amounts of data, which can easily stretch and congest the vehicle communication channels. To address the above challenges, we first propose a novel semantic VEC (SVEC) architecture, which can extract the semantic information of tasks and offload them to edge servers, thereby achieving reliable and efficient offloaded task communication and computation adaptively. Considering the scarce channel resources of vehicles and the intelligent tasks with different priorities and modalities, we define a novel user utility model for SVEC and transform the problem of maximizing user utility into a joint optimization problem of semantic feature extraction, task offloading and resource allocation. Furthermore, to cope with the complexity of the solution space of the optimization problem, we propose a diffusion-based multi-agent reinforcement learning algorithm, which improves the ability of agents to explore the solution space through the diffusion process, thereby achieving optimal decisions for semantic feature extraction, task offloading and resource allocation. Simulation results show that the proposed scheme improves the overall performance of SVEC while reducing offload latency and average system cost.

Deep neural networks are powerful and popular learning models; however, recent studies have shown that deep neural network-based policies are susceptible to deception by adversarial attacks. A minimalistic attack is a specialized form of adversarial attack that aims to accomplish successful attacks at the lowest possible cost. Recently, transfer optimization algorithms have been applied to deceive previously trained policies by acquiring knowledge from previously solved tasks. Experiments indicate that the transfer optimization algorithms perform well compared to traditional optimization algorithms. However, current transfer algorithms for addressing minimalistic attacks not only select a single source task for knowledge transfer but also tend to overly rely on identified appropriate source tasks. To address this issue, this paper introduces a similar locality based transfer evolutionary optimization algorithm. It can adaptively select multiple source tasks and extract valuable knowledge from these source tasks. Moreover, by leveraging the concept of similar locality, the algorithm alleviates its excessive dependence on familiar tasks, thereby providing fresh knowledge for the optimization of the target task. On this basis, the algorithm can mine more valuable knowledge from the large source task space to achieve a successful attack in a shorter period. The algorithm is tested on three Atari games-BeamRider, Qbert, and Seaquest-demonstrating its ability and potential to outperform other transfer optimization algorithms currently available in solving this problem.

The performance of modern communication and radar systems can suffer severe performance degradation from oscillator phase noise. Existing receivers commonly assume that the carrier phase is a constant over a window of a few symbol intervals and average the received signals over these intervals to obtain an estimate of the carrier phase for data demodulation. For mmWave/THz wireless and optical communications with fast time-varying phase noise, this quasi-static carrier phase assumption will no longer be applicable to ensure optimum estimation and compensation for the unknown carrier phase. We present here an Mth-power receiver for$M$-ary phase-shift keying ($M\text{PSK}$) modulation and a Wiener process carrier phase model that is applicable in many situations, especially in optical communications. The receiver can eliminate the$M$-ary phase modulation by raising the received signal samples (one sample per symbol interval) with noise to the power of$M$. The resulting unmodulated phase samples then enable the receiver to perform joint maximum likelihood estimation and maximum a posteriori probability estimation of the unknown initial carrier phase and Wiener carrier phase noise process. The estimation performance improves with a relatively small data block length for any given signal-to-noise ratio and Wiener phase noise variance, and this leads to better error probability performance in data detection. Simulation results are obtained for the estimation mean square error of the noisy carrier phase and the error probability of the detected$M\text{PSK}$symbols.

Mobile edge computing (MEC) brings computational resources to the edge of network that triggers the paradigm shift of centralized machine learning towards federated learning. Federated learning enables edge nodes to collaboratively train a shared prediction model without sharing data. In MEC, heterogeneous edge nodes may join or leave the training phase during the federated learning process, resulting in slow convergence of dynamic communities and federated learning. In this paper, we propose a fine-grained training strategy for federated learning to accelerate its convergence rate in MEC with dynamic community. Based on multi-agent reinforcement learning, the proposed scheme enables each edge node to adaptively adjust its training strategy (aggregation timing and frequency) according to the network dynamics, while compromising with each other to improve the convergence of federated learning. To further adapt to the dynamic community in MEC, we propose a meta-learning-based scheme where new nodes can learn from other nodes and quickly perform scene migration to further accelerate the convergence of federated learning. Numerical results show that the proposed framework outperforms the benchmarks in terms of convergence speed, learning accuracy, and resource consumption.