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Found 34 papers, 3 papers with code
Collaborative Computing in Non-Terrestrial Networks: A Multi-Time-Scale Deep Reinforcement Learning Approach
To address the above challenges, in this paper, a multi-time-scale deep reinforcement learning (DRL) scheme is developed for achieving the radio resource optimization in NTNs, in which the LEO satellite and user equipment (UE) collaborate with each other to perform individual decision-making tasks with different control cycles.
Collaborative Deep Reinforcement Learning for Resource Optimization in Non-Terrestrial Networks
Non-terrestrial networks (NTNs) with low-earth orbit (LEO) satellites have been regarded as promising remedies to support global ubiquitous wireless services.
Channel-Feedback-Free Transmission for Downlink FD-RAN: A Radio Map based Complex-valued Precoding Network Approach
As the demand for high-quality services proliferates, an innovative network architecture, the fully-decoupled RAN (FD-RAN), has emerged for more flexible spectrum resource utilization and lower network costs.
Digital Twin-Based User-Centric Edge Continual Learning in Integrated Sensing and Communication
A DT of the ISAC device is constructed to predict the impact of potential decisions on the long-term computation cost of the server, based on which the decisions are made with closed-form formulas.
Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocation
We focused on the PC sub-problem and proposed the MTCC-PC algorithm to learn an optimal PC policy given an RRA policy.
Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control
It is proved that the optimal policy for the augmented state MDP is optimal for the original PC problem with observation delay.
Artificial Intelligence for Web 3.0: A Comprehensive Survey
Then, we thoroughly analyze the current state of AI technology applications in the four layers of Web 3. 0 and offer some insights into its potential future development direction.
Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation
Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation.
A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC
To promote the development of this new breed of paradigm, in this article, we propose a system architecture of mobile AIGC-driven HDT and highlight the corresponding design requirements and challenges.
Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learning
In this paper, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL).
Guiding AI-Generated Digital Content with Wireless Perception
Experiments results verify the effectiveness of the WP-AIGC framework, highlighting its potential as a novel approach for guiding AI models in the accurate generation of digital content.
On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds
A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc.
Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks
To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for optimal ASP selection.
Performance Analysis and Enhancement of Beamforming Training in 802.11ad
Since the derived BF training efficiency is an implicit function, to reveal the relationship between system parameters and BF training performance, we also derive an approximate expression of BF training efficiency.
Beef up mmWave Dense Cellular Networks with D2D-Assisted Cooperative Edge Caching
In this paper, we propose a device-to-device (D2D) assisted cooperative edge caching (DCEC) policy for millimeter (mmWave) dense networks, which cooperatively utilizes the cache resource of users and SBSs in proximity.
Holistic Network Virtualization and Pervasive Network Intelligence for 6G
The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively.
Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications
Trainable parameters are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern and the large-scale fading coefficient.
Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning
Specifically, sampling rate adaption, inference task offloading and edge computing resource allocation are jointly considered to minimize the average service delay while guaranteeing the long-term accuracy requirements of different inference services.
Cost-Effective Two-Stage Network Slicing for Edge-Cloud Orchestrated Vehicular Networks
Since the problem is NP-hard due to coupled network planning and network operation stages, we develop a Two timescAle netWork Slicing (TAWS) algorithm by collaboratively integrating reinforcement learning (RL) and optimization methods, which can jointly make network planning and operation decisions.
Spectral Efficiency Analysis of Uplink-Downlink Decoupled Access in C-V2X Networks
The uplink (UL)/downlink (DL) decoupled access has been emerging as a novel access architecture to improve the performance gains in cellular networks.
Spectral Efficiency Analysis of Uplink-Downlink Decoupled Access in C-V2X Networks
Semantic Communications for Wireless Sensing: RIS-aided Encoding and Self-supervised Decoding
To select the task-related signal spectrums for achieving efficient encoding, a semantic hash sampling method is introduced.
Semantic-Aware Sensing Information Transmission for Metaverse: A Contest Theoretic Approach
Inspired by emerging semantic communication, in this paper, we propose a semantic transmission framework for transmitting sensing information from the physical world to Metaverse.
Digital Twin-Empowered Network Planning for Multi-Tier Computing
Using a multi-tier computing paradigm with servers deployed at the core network, gateways, and base stations to support stateful applications, we aim to optimize long-term resource reservation by jointly minimizing the usage of computing, storage, and communication resources and the cost from reconfiguring resource reservation.
Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services
With the help of UOAL, we propose an attention-aware network resource allocation algorithm that has two steps, i. e., attention prediction and QoE maximization.
Personalized QoE Enhancement for Adaptive Video Streaming: A Digital Twin-Assisted Scheme
In this paper, we present a digital twin (DT)-assisted adaptive video streaming scheme to enhance personalized quality-of-experience (PQoE).
Heterogeneous Ultra-Dense Networks with Traffic Hotspots: A Unified Handover Analysis
By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived.
Physical Layer Security Assisted Computation Offloading in Intelligently Connected Vehicle Networks
In this paper, we propose a secure computation offloading scheme (SCOS) in intelligently connected vehicle (ICV) networks, aiming to minimize overall latency of computing via offloading part of computational tasks to nearby servers in small cell base stations (SBSs), while securing the information delivered during offloading and feedback phases via physical layer security.
Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement Learning
In this chapter, the regulation of Unmanned Aerial Vehicle (UAV) communication network is investigated in the presence of dynamic changes in the UAV lineup and user distribution.
AI-Native Network Slicing for 6G Networks
Then, network slicing solutions are studied to support emerging AI services by constructing AI instances and performing efficient resource management, i. e., slicing for AI.
Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in Space-Air-Ground Integrated Network
Our objective is to design a task scheduling policy that minimizes offloading and computing delay of all tasks given the UAV energy capacity constraint.
Fatigue-aware Bandits for Dependent Click Models
Based on user's feedback, the platform learns the relevance of the underlying content as well as the discounting effect due to content fatigue.
Energy and Information Management of Electric Vehicular Network: A Survey
In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation.
Fast mmwave Beam Alignment via Correlated Bandit Learning
Beam alignment (BA) is to ensure the transmitter and receiver beams are accurately aligned to establish a reliable communication link in millimeter-wave (mmwave) systems.
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges
Next, a comprehensive survey of the state-of-art research on DRL for AIoT is presented, where the existing works are classified and summarized under the umbrella of the proposed general DRL model.
