In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refe...
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In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.
Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substa...
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The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light c...
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This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously colle...
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Mobile edge computing (MEC) has produced incredible outcomes in the context of computationally intensive mobile applications by offloading computation to a neighboring server to limit the energy usage of user equipmen...
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This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects f...
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In this paper, a multi-modal data based semisupervised learning (SSL) framework that jointly use channel state information (CSI) data and RGB images for vehicle positioning is designed. In particular, an outdoor posit...
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The next generation of wireless communications seeks to deeply integrate artificial intelligence (AI) with user-centric communication networks, with the goal of developing AI-native networks that more accurately addre...
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The next generation of wireless communications seeks to deeply integrate artificial intelligence (AI) with user-centric communication networks, with the goal of developing AI-native networks that more accurately address user requirements. The rapid development of large language models (LLMs) offers significant potential in realizing these goals. However, existing efforts that leverage LLMs for wireless communication often overlook the considerable gap between human natural language and the intricacies of real-world communication systems, thus failing to fully exploit the capabilities of LLMs. To address this gap, we propose a novel LLM-driven paradigm for wireless communication that innovatively incorporates the nature language to structured query language (NL2SQL) tool. Specifically, in this paradigm, user personal requirements is the primary focus. Upon receiving a user request, LLMs first analyze the user intent in terms of relevant communication metrics and system parameters. Subsequently, a structured query language (SQL) statement is generated to retrieve the specific parameter values from a high-performance real-time database. We further utilize LLMs to formulate and solve an optimization problem based on the user request and the retrieved parameters. The solution to this optimization problem then drives adjustments in the communication system to fulfill the user’s requirements. To validate the feasibility of the proposed paradigm, we present a prototype system. In this prototype, we consider user-request centric semantic communication (URC-SC) system in which a dynamic semantic representation network at the physical layer adapts its encoding depth to meet user requirements, such as improved data transmission quality or reduced data transmission latency. Additionally, two LLMs are employed to analyze user requests and generate SQL statements, respectively. Simulation results demonstrate the effectiveness of the prototype in personalizing and addressing user r
In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air compu...
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In order to reduce the interference,a novel,cluster-based medium access control(MAC)protocol with load aware for VANETs is proposed in this ***,all vehicles on roads are grouped into stable clusters in the light of th...
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In order to reduce the interference,a novel,cluster-based medium access control(MAC)protocol with load aware for VANETs is proposed in this ***,all vehicles on roads are grouped into stable clusters in the light of their direction,number of neighbors,link reliability,and traffic *** utilizing the advantages of centralized control in software defined VANETs(SDVN),cluster stability can be maintained in ***,a contention-free MAC mechanism composed of inter-cluster multi-channel allocation and intra-cluster dynamic TDMA frame allocation is proposed to prevent co-channel interference and hidden terminal *** results show that the proposed protocol outperforms some existing protocols in cluster stability,delivery ratio,throughput and delay performance.
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