Satellite-terrestrial integrated network (STIN) has been recognized as a promising paradigm to provide ubiquitous and reliable coverage for billions of devices over the world. Integrated sensing and communication (ISAC) can achieve higher spectrum resource utilization efficiency, reduce the hardware size and lighten the payload of satellites for STIN. Multi-access mobile edge computing (MEC) leverages distributed edge servers to alleviate the computational burden for sensing data processing on the satellites. In this paper, we propose multi-access MEC empowered ISAC for STIN. Specifically, a group of low earth orbit (LEO) satellites perform radar sensing operations with optimized scheduling. While a portion of the acquired sensing data undergoes onboard processing at the satellites, the remaining part is processed remotely on multiple terrestrial edge servers. We formulate an optimization problem which concurrently optimizes the following strategy variables: the sensing scheduling, the beamforming for offloading transmission, the beamforming for radar sensing, the duration for sensing and data offloading, the offloaded workload and the computing capacity allocation of each edge server. Notwithstanding the non-convex nature of the formulated optimization problem, we develop a hierarchical decomposition algorithm for achieving the solution efficiently. Extensive numerical simulations confirm the superior performance of our proposed multi-access MEC-enabled ISAC framework in STIN scenarios while simultaneously verifying the efficiency of our optimization algorithm.

Integrated Sensing and Communication (ISAC) is becoming increasingly important in next-generation wireless networks. This paper focuses on an ISAC system supported by a reconfigurable intelligent surface (RIS), where a full-duplex base station (BS) simultaneously performs uplink multi-user communication, downlink multi-user communication, and radar sensing tasks with the assistance of the RIS. To maximize the sum rate of all downlink and uplink users, an optimization problem is formulated, subject to multiple constraints, including target detection signal-to-interference-plus-noise ratio, self-interference, BS transmission power, user transmission power, and unit-modulus constraints of RIS reflection coefficients. To address the complex non-convex optimization problems, efficient solving algorithms are proposed, and their performance is validated through simulations. The results demonstrate that the RIS-assisted full-duplex ISAC (RAFD-ISAC) system significantly enhances both communication and sensing performance. The proposed joint beamforming and reflection design offers a novel solution for the deep integration of sensing and communication in next-generation networks.

Integrated sensing and communication (ISAC), as a rapidly advancing technique, introduces a fresh approach for achieving secure communication and intelligent sensing for future wireless networks. An ISAC framework empowered by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is explored in this paper, where a base station equipped with multiple antennas establishes wireless links to users each with a single antenna during the detection of a point target. The point target, regarded as an eavesdropper, trying to intercept users’ information. Cramér-Rao bound (CRB) serves as evaluation criterion to assess sensing accuracy of point eavesdropper, whereas the secrecy rate is employed to quantify the security level of the communication link. To optimize sensing-communication tradeoff, a joint optimization problem is constructed. To approach the formulated problem, a hybrid Block Coordinate Descent (BCD)-based algorithm is developed, which alternately updates the transmission beamforming and STAR-RIS phase shifts, using successive convex approximation (SCA) technique, penalty dual decomposition (PDD) framework and projected gradient method (PGM).

Reconfigurable intelligent surface (RIS)-enabled integrated sensing and communication (ISAC) systems enhance spectrum efficiency and sensing accuracy. Building on this, we propose a novel self-sustainable multi-functional RIS (S-MFRIS) concept that supports multiple functionalities: reflection, refraction, amplification, energy harvesting, and target sensing. By harvesting energy from incident signals, the S-MFRIS can reflect, refract, and amplify signals without needing an external power supply, effectively overcoming double-fading attenuation. Furthermore, by deploying low-cost sensor elements, the S-MFRIS can capture echo signals from multiple targets, mitigating the signal attenuation commonly associated with multi-hop links. Then, we establish an S-MFRIS-enabled ISAC system and formulate an optimization problem to maximize the signal-to-interference-plus-noise ratio (SINR) of the sensing targets, subject to constraints on communication rate, power budget, and reflection coefficients. To solve this non-convex problem, we decompose it into three sub-problems, which are efficiently addressed using an iterative algorithm. Simulation and numerical results demonstrate the following key findings: (1) The proposed algorithm achieves better convergence and performance than the semidefinite relaxation-based and random-based algorithms. (2) The performance of the MFRIS-aided system varies under different operating protocols, with the self-sustainable MFRIS outperforming other schemes, particularly when the power budget is sufficient. (3) The proposed S-MFRIS achieves$30\%-46\%$sensing SINR gains at most for the same total power budget or element configuration. (4) The number of sensing elements improves sensing performance up to a certain point, after which further increases in the number of elements yield diminishing returns.

Intelligent reflecting surface (IRS)-assisted wireless communication has demonstrated notable performance enhancements despite challenges such as double-fading attenuation and dependency on external power sources. This paper introduces a self-sustainable IRS (S-IRS) architecture that integrates energy harvesting with information transmission, unlocking its potential in simultaneous wireless information and power transfer (SWIPT) systems. A time-splitting-based S-IRS-aided SWIPT protocol is proposed to analyze the system’s energy transfer and communication efficiency. To address the trade-off between these interdependent metrics, a sum-rate optimization problem is formulated under the energy constraints of the S-IRS and wireless devices. A hierarchical optimization framework, incorporating advanced learning and optimization techniques, is developed to solve this problem. The simulation results validate the effectiveness of the proposed approach, highlighting its advantages and the impact of critical design parameters on system performance.

Large Language Models (LLMs), as advanced AI technologies for processing and generating natural language text, bring substantial benefits to the Industrial Internet of Things (IIoT) by enhancing efficiency, decision-making, and automation. Nevertheless, their deployment faces significant obstacles due to high computational and energy demands, which often exceed the capabilities of many industrial devices. To overcome these challenges, edge-cloud collaboration has become increasingly essential, assisting in offloading LLMs tasks to reduce the computational load. However, traditional reinforcement learning (RL)-based strategies for LLMs task offloading encounter difficulties with generalization ability and defining explicit, appropriate reward functions. Therefore, in this paper, we propose a novel framework for offloading LLMs inference tasks in IIoT, utilizing a Decentralized Identifier (DID)-based identity management system for trusted task offloading. Furthermore, we introduce an intelligence-based RL (IRL) approach, which sidesteps the need for defining specific reward functions. Instead, it uses “intelligence” as a metric to evaluate cognitive improvements and adapt to varying environmental preferences, significantly improving generalizability. In our experiments, we employ the GPT-J-6B model and utilize the Human Eval dataset to assess its ability to tackle programming challenges, demonstrating the superior performance of our proposed solution compared to existing methods.

“Read-write-own” based Web3 has been proposed as a promising user-centric Internet to open the new generation of the World Wide Web, where Web3 users can independently manage data and derive value from creating content without relying on intermediaries. Connected and autonomous vehicles (CAVs) in Web3 can trade models in a self-controlled and decentralized credible way, which is a fundamentally and principally innovation based on novel architecture. Effectively implementing such paradigms involves proper model trading strategies. However, reinforcement learning (RL)-based strategies face challenges of poor generalization ability, low feasibility, and the exploration-exploitation dilemma. It is also difficult to define an explicit and appropriate reward function. Therefore, in this paper, we propose an intelligence-based reinforcement learning (IRL) approach for CAVs in Web3. We present a framework to enable model transactions between CAVs. Also, we provide a decentralized identifier (DID)-based identity management system for resource description and data verification to access Web3, followed by the mechanism and supporting smart contracts. Furthermore, we formulate the model trading issue as an active inference to form higher-level cognition about the environment without rewards. Then we use IRL to solve it. And we use “intelligence”, a high-level indicator, to quantify the efficiency of such cognition. It can evaluate the difference between the predicted state and the real state in policy exploration. The proposed scheme shows good generalization and can auto-balance exploration and exploitation, simultaneously achieving outperforming performance on the model trading issue with no rewards. In simulations, the performance of the proposed scheme is compared with existing methods.