The future integrated sensing and communication (ISAC) networks is expected to equip with sufficient computation resources. However, current research focuses on single-domain resource allocation in ISAC and computing force networks, leaving the joint optimization of sensing, communication, and computation resource allocation unexplored. In this paper, we propose a novel approach to this problem by deep incorporating computation resources, combined with a federated learning framework, while considering sensing precision and power consumption. Firstly, a multi-objective optimization is designed, involving Cramer-Rao Bound, sum rate of ISAC networks, and power consumption of computing force networks. Subsequently, the multi-objective optimization is transformed into a multi-task learning model. We aim to obtain joint optimization of sensing, communication, and computation resource allocation via deep learning techniques. Towards the multi-task learning model, the multiple-gradient descent algorithm is utilized to obtain the multi-objective optimization. Furthermore, a practical low-complexity the multiple-gradient descent algorithm is developed to reduce the computational cost. Finally, the effectiveness of the proposed deep learning algorithms is verified by simulations results.

One of the main challenges of popularizing the integration of sensing and communication (ISAC) network is mutual interference between the two functions. A viable solution is the time division scheme where communication and sensing are separated in time domain. This paper considers a multi-cluster ISAC network model, where the time-domain radio resources are allocated to sensing and communication. At the same time, the time resources can be reused in space-domain. Particularly, the terminals in different work phases can access radio resources of different or the same clusters simultaneously, depending on interference. In this way, the interference is isolated while the resources utilization is improved. Two different resource allocation approaches are proposed according to how interference is considered. The aim is to maximize the sensing detection probability under the constraint of network throughput. The performance improvement in terms of target detection probability brought by the proposed schemes is shown by numerical results compared with benchmark methods.

This paper investigates the application of artificial intelligence (AI) to wireless technology, specifically in the context of beam management (BM) in the advanced 5th-generation (5G) communication system. Our focus lies in aligning our study with the ongoing discussions within the Third Generation Partnership Project (3GPP) as of December 2022. Instead of evaluating the performance of specific AI models, we take user equipment (UE) receiver (Rx) beam prediction as an illustrative example of AI-based BM. We explore various aspects of AI model management, including model selection, monitoring, and activation/deactivation operations, from a 3GPP perspective. For model selection, we propose deploying distinct AI models for different propagation environments, categorized based on base station (BS) transmitter (Tx) beam measurement results. Reference Signal Received Power (RSRP) serves as a pivotal key performance index (KPI) for model performance monitoring. Our simulation results indicate that, instead of training one all-encompassing AI model with numerous layers for universal application, transitioning between domain-specific AI models with fewer layers yields superior performance. Model activation/deactivation procedures determine whether AI-based BM or traditional BM should be employed in a given scenario. We also introduce the use of AI for predicting the performance of both AI-based BM and traditional BM. By comparing the performance of these strategies, we can ascertain whether link performance degradation results from AI output errors or UE movement into challenging propagation environments. This approach enables the effective management of model switching between AI-based BM and traditional BM. The simulation shows that we can reduce the number of unnecessary switches by 10%.

The benefits of mobile edge computing (MEC) systems cannot be fully exploited when the communication link is blocked or the communication signal is weak. Intelligent reflective surface (IRS) technology is introduced to build an IRS-assisted MEC system and to solve this issue. In this paper, devices offload part of their computing tasks to MEC through the multi-antenna access point with the help of the IRS, thereby reducing the completion time of computing tasks. We consider the weighted sum-latency minimization for single-device and multi-device scenarios in the uplink, which are constrained by computing offload allocation, edge node computational capability, IRS practical phase shift, beamforming, and device transmitting power. Firstly, block coordinate descent technology is used to decouple latency minimization problem into two subproblems of computation and communication. Secondly, in single-device scenario, the original problem is simplified and solved by the continuous refinement scheme. In multi-device scenario, an algorithm that combines alternating optimization and the Jaya algorithm is proposed for the first time to solve the weighted sum-latency minimization problem. In addition, compared with the conventional MEC systems without IRS, the effectiveness and high-performance gain of the proposed algorithm are proved through simulations.

Vehicular ad hoc networks (VANETs) are characterized by dynamic behavior and unique characteristics that contribute to the complexity of network connectivity and reachability, thereby posing substantial challenges to achieving efficient data transmission. While one avenue for understanding these network properties involves the application of time-varying graphs, comprehending such methods may be hindered by absence of well-defined analogies to established properties of static graphs. Thereby, this study addresses this gap by exploring the properties of time-varying graphs as a means to depict network characteristics, aiming to enhance data dissemination efficiency within vehicular networks assisted by unmanned aerial vehicles (UAVs). Considering delay-sensitive data dissemination, we integrate dissemination delay into the time-varying graph, introducing the concept of a delay-constrained time-varying graph (DTVG). In this context, a data dissemination problem is formulated to optimize data transmission utility. To address this problem, we propose a novel data dissemination algorithm leveraging insights from $k$ -token dissemination problem. Theoretical analysis is employed to derive both upper and lower bounds of our proposed algorithm. Simulations are conducted for validating the feasibility of characterizing dynamic networks based on time-varying graphs. Finally, the effectiveness of our proposed approach is demonstrated through the evaluation of metrics such as dissemination delay, delivery ratio, and network throughput.

Artificial Intelligence (AI) has gained significant attention and extensive research across various fields in recent years. In the realm of wireless communication, researchers are exploring the use of AI to facilitate various physical layer (PHY) procedures. Within the standardization efforts of the Third Generation Partnership Project (3GPP), one prominent direction being explored is AI-based beam management (BM). The primary objective is to harness AI techniques for predicting optimal beams, thereby reducing measurement overhead and latency. This paper aims to discuss the progress made in AI-based beam management within the Release 18 standardization. Furthermore, through our research, we have identified the mobile speed of user equipment (UE) as a crucial factor that impacts the optimal time window size for collecting input data in AI models. We have observed an inverse correlation between UE speed and the time window size. Accordingly, to mitigate unnecessary measurement overhead and latency, we propose that the determination of the time window size for input data collection should be based on the UE speed. Additionally, we will present our simulation results and provide a comprehensive analysis of this relationship.