Integrated sensing and communication (ISAC) systems may face a heavy computation burden since the sensory data needs to be further processed. This paper studies a novel system that integrates sensing, communication, and computation, aiming to provide services for different objectives efficiently. This system consists of a multi-antenna multi-functional base station (BS), an edge server, a target, and multiple single-antenna communication users. The BS needs to allocate the available resources to efficiently provide sensing, communication, and computation services. Due to the heavy service burden and limited power budget, the BS can partially offload the tasks to the nearby edge server instead of computing them locally. We consider the estimation of the target response matrix, a general problem in radar sensing, and utilize Cramér-Rao bound (CRB) as the corresponding performance metric. To tackle the non-convex optimization problem, we propose both semidefinite relaxation (SDR)-based alternating optimization and SDR-based successive convex approximation (SCA) algorithms to minimize the CRB of radar sensing while meeting the requirement of communication users and the need for task computing. Furthermore, we demonstrate that the optimal rank-one solutions of both the alternating and SCA algorithms can be directly obtained via the solver or further constructed even when dealing with multiple functionalities. Simulation results show that the proposed algorithms can provide higher target estimation performance than state-of-the-art benchmarks while satisfying the communication and computation constraints.

Autonomous surface vessels (ASVs) play a crucial role in military, civilian and scientific research fields, and path planning technology is the foundation for ensuring the navigation of ASVs. However, reinforcement learning algorithms face challenges in path planning, such as low sample utilization efficiency and difficulties in collecting effective data. To address these issues, this paper proposes an efficient reinforcement learning path planning algorithm based on knowledge transfer. First, a state similarity model is introduced to measure the differences between source and target samples. By identifying and transferring similar samples, the learning speed for new tasks is significantly accelerated. Second, considering the complex rules of the International Regulations for Preventing Collisions at Sea (COLREGs), high-quality collision avoidance samples from the source task are selected as knowledge transfer objects, thereby reducing the failure rate of agents in the collision avoidance process. Finally, the impact of the proportion of source task samples in the total sample pool on algorithm performance is analyzed. A progressive exploration method based on sample proportion is established to mitigate the issue of negative transfer. Simulation results demonstrate that the collision avoidance success rate of the algorithm has been significantly improved, with an average success rate of 91% across five typical collision avoidance scenarios—an increase of over 30% compared to the traditional DDPG algorithm. The convergence speed of the reward value improved by 50% after 1000 training episodes. Real-world ship tests verified that it can achieve real-time collision avoidance in complex environments, with a path safety compliance rate of 100%. The algorithm effectively enhances the reliability and safety of autonomous navigation for ASVs.

The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and gigantic data aggregation. However, the scarce radio resources and stringent latency requirement make it challenging to meet these demands. To tackle these challenges, over-the-air computation (AirComp) emerges as a potential technology. Specifically, AirComp seamlessly integrates the communication and computation procedures through the superposition property of multiple-access channels, which yields a revolutionary multiple-access paradigm shift from “compute-after-communicate” to “compute-when-communicate”. By this means, AirComp enables spectral-efficient and low-latency wireless data aggregation by allowing multiple devices to occupy the same channel for transmission. In this paper, we aim to present the recent advancement of AirComp in terms of foundations, technologies, and applications. The mathematical form and communication design are introduced as the foundations of AirComp, and the critical issues of AirComp over different network architectures are then discussed along with the review of existing literature. The technologies employed for the analysis and optimization on AirComp are reviewed from the information theory and signal processing perspectives. Moreover, we present the existing studies that tackle the practical implementation issues in AirComp systems, and elaborate the applications of AirComp in Internet of Things and edge intelligent networks. Finally, potential research directions are highlighted to motivate the future development of AirComp.

-1To efficiently exploit the massive amounts of raw data that are increasingly being generated in mobile edge networks, federated learning (FL) has emerged as a promising distributed learning technique by collaboratively training a shared learning model on edge devices. The number of resource blocks when using traditional orthogonal transmission strategies for FL linearly scales with the number of participating devices, which conflicts with the scarcity of communication resources. To tackle this issue, over-the-air computation (AirComp) has emerged recently which leverages the inherent superposition property of wireless channels to performone-shotmodel aggregation. However, the aggregation accuracy in AirComp suffers from the unfavorable wireless propagation environment. In this paper, we consider the use of intelligent reflecting surfaces (IRSs) to mitigate this problem and improve FL performance with AirComp. Specifically, a novel performance-oriented long-term design scheme that integrated design multiple communication rounds to minimize the optimality gap of the loss function is proposed. We first analyze the convergence behavior of the FL procedure with the absence of channel fading and noise. Based on the obtained optimality gap which characterizes the impact of channel fading and noise in different communication rounds on the ultimate performance of FL, we propose both online and offline schemes to tackle the resulting design problem. Simulation results demonstrate that such a long-term design strategy can achieve higher test accuracy than the conventional isolated design approach in FL. Both the theoretical analysis and numerical results exhibit a “later-is-better” principle, which demonstrates the later rounds in the FL procedure are more sensitive to aggregation error, and hence more resources are required over time.

Recently, reconfigurable intelligent surface (RIS) is emerging as an enabling technology to further enhance the performance of wireless communication systems. RIS is actually a metasurface which is consist of a large number of reflection elements. Each of the elements is capable of re-scatter the incident wave by an independent reflection coefficient. Signals can be redirected to the receiver by delicately assigning reflection coefficients to the elements. Thus a better receiving condition can be realized, and system performance can be enhanced. Existing studies already proved the superiority of general RIS-aided systems, e.g., single-user and multi-user, unicast and multicast, orthogonal and non-orthogonal multiple access. However, most of them considered ideal reflection elements. They assumed that full reflection can be realized at any values of phase-shift, which however is unrealistic due to energy dissipation in reflection elements. To circumvent this problem, in this paper we consider a practical phase shift model where reflection amplitude and phase shift are coupled. Specifically, under this practical model we try to maximize the achievable rate of an RIS-aided MIMO system. An alternating optimization based algorithm is proposed to solve the corresponding optimization problem. Reflection coefficients at the RIS and transmit covariance matrix at the transmitter are optimized alternately with the others being fixed. Simulation results are presented to manifest the validity of the proposed scheme.

High data throughput is desired in the wireless communication system design. Rate adaptation is an efficient way to update the data rate in the dynamic wireless environment. Conventional rate adaptation algorithms rely on the feedback of acknowledgment/negative acknowledgment (ACK/NACK) messages or signal to noise ratio (SNR). Existing rate adaptation algorithms can not achieve satisfactory transmission rates in time-varying environments. In this paper, we model the rate selection problem as a multi-armed bandit (MAB) problem and propose an online learning rate adaptation algorithm that learns the channel status from both RSSI and ACK/NACK signals. Compared with existing rate adaptation algorithms, the proposed algorithm can adapt to the time-varying channel better and achieve near-optimal transmission rate performance.