Collaborative Channel Access and Transmission for NR Sidelink and Wi-Fi Coexistence over Unlicensed Spectrum
- URL: http://arxiv.org/abs/2501.17878v2
- Date: Fri, 14 Feb 2025 07:09:24 GMT
- Title: Collaborative Channel Access and Transmission for NR Sidelink and Wi-Fi Coexistence over Unlicensed Spectrum
- Authors: Zhuangzhuang Yan, Xinyu Gu, Zhenyu Liu, Liyang Lu,
- Abstract summary: 5G-Advanced has introduced sidelink communication over the unlicensed spectrum (SL-U) to increase data rates.
The primary challenge of SL-U in the unlicensed spectrum is ensuring fair coexistence with other incumbent systems, such as Wi-Fi.
- Score: 7.098402998630272
- License:
- Abstract: With the rapid development of various internet of things (IoT) applications, including industrial IoT (IIoT) and visual IoT (VIoT), the demand for direct device-to-device communication to support high data rates continues to grow. To address this demand, 5G-Advanced has introduced sidelink communication over the unlicensed spectrum (SL-U) to increase data rates. However, the primary challenge of SL-U in the unlicensed spectrum is ensuring fair coexistence with other incumbent systems, such as Wi-Fi. In this paper, we address the challenge by designing channel access mechanisms and power control strategies to mitigate interference and ensure fair coexistence. First, we propose a novel collaborative channel access (CCHA) mechanism that integrates channel access with resource allocation through collaborative interactions between base stations (BS) and SL-U users. This mechanism ensures fair coexistence with incumbent systems while improving resource utilization. Second, to further enhance the performance of the coexistence system, we develop a cooperative subgoal-based hierarchical deep reinforcement learning (C-GHDRL) algorithm framework. The framework enables SL-U users to make globally optimal decisions by leveraging cooperative operations between the BS and SL-U users, effectively overcoming the limitations of traditional optimization methods in solving joint optimization problems with nonlinear constraints. Finally, we mathematically model the joint channel access and power control problem and balance the trade-off between fairness and transmission rate in the coexistence system by defining a suitable reward function in the C-GHDRL algorithm. Simulation results demonstrate that the proposed scheme significantly enhances the performance of the coexistence system while ensuring fair coexistence between SL-U and Wi-Fi users.
Related papers
- Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks [55.467288506826755]
Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks.
Most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance.
We propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme.
arXiv Detail & Related papers (2025-01-20T04:26:21Z) - Latency Optimization in LEO Satellite Communications with Hybrid Beam Pattern and Interference Control [20.19239663262141]
Low Earth orbit (LEO) satellite communication systems offer high-capacity, low-latency services crucial for next-generation applications.
The dense configuration of LEO constellations poses challenges in resource allocation optimization and interference management.
This paper proposes a novel framework for optimizing the beam scheduling and resource allocation in multi-beam LEO systems.
arXiv Detail & Related papers (2024-11-14T17:18:24Z) - FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression [55.992528247880685]
Decentralized training faces significant challenges regarding system design and efficiency.
We present FusionLLM, a decentralized training system designed and implemented for training large deep neural networks (DNNs)
We show that our system and method can achieve 1.45 - 9.39x speedup compared to baseline methods while ensuring convergence.
arXiv Detail & Related papers (2024-10-16T16:13:19Z) - Statistical QoS Provision in Business-Centric Networks [14.567380216501169]
Business-Centric Network (BCN) is a cross-layer framework that captures the relationship between application, transport parameters, and channels.
By jointly considering power and bandwidth allocation, transmission parameters, and AP network topology, we optimize weighted resource efficiency.
We introduce a novel multithreaded experience-sharing mechanism to accelerate training and enhance rewards.
arXiv Detail & Related papers (2024-08-28T08:03:04Z) - Multi-Agent Hybrid SAC for Joint SS-DSA in CRNs [17.162697767466085]
Opportunistic spectrum access has the potential to increase the efficiency of spectrum utilization in cognitive radio networks (CRNs)
In CRNs, both spectrum sensing and resource allocation (SSRA) are critical to maximizing system throughput.
We develop a novel multi-agent implementation of hybrid soft actor critic, MHSAC.
arXiv Detail & Related papers (2024-04-22T16:30:03Z) - A Federated Reinforcement Learning Framework for Link Activation in
Multi-link Wi-Fi Networks [3.093231349723552]
Multi-link operation (MLO) can result in higher interference and channel contention, leading to lower performance and reliability.
In this paper, we propose the use of a collaborative machine learning approach to train models across multiple distributed agents without exchanging data.
Results show that the FRL-based decentralized MLO-LA strategy achieves a better throughput fairness, and so a higher reliability.
arXiv Detail & Related papers (2023-04-28T09:39:10Z) - Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated
Split Learning [56.125720497163684]
We propose a hybrid federated split learning framework in wireless networks.
We design a parallel computing scheme for model splitting without label sharing, and theoretically analyze the influence of the delayed gradient caused by the scheme on the convergence speed.
arXiv Detail & Related papers (2022-09-02T10:29:56Z) - Federated Learning over Wireless IoT Networks with Optimized
Communication and Resources [98.18365881575805]
Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention.
It is of interest to investigate fast responding and accurate FL schemes over wireless systems.
We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
arXiv Detail & Related papers (2021-10-22T13:25:57Z) - Optimal Power Allocation for Rate Splitting Communications with Deep
Reinforcement Learning [61.91604046990993]
This letter introduces a novel framework to optimize the power allocation for users in a Rate Splitting Multiple Access network.
In the network, messages intended for users are split into different parts that are a single common part and respective private parts.
arXiv Detail & Related papers (2021-07-01T06:32:49Z) - Deep Multi-Task Learning for Cooperative NOMA: System Design and
Principles [52.79089414630366]
We develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL)
We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner.
arXiv Detail & Related papers (2020-07-27T12:38:37Z) - A Hybrid Model-based and Data-driven Approach to Spectrum Sharing in
mmWave Cellular Networks [37.00906872828011]
Inter-operator spectrum sharing in millimeter-wave bands has the potential of substantially increasing the spectrum utilization.
Traditional model-based spectrum sharing schemes make idealistic assumptions about inter-operator coordination mechanisms.
We propose hybrid model-based and data-driven multi-operator spectrum sharing mechanisms.
arXiv Detail & Related papers (2020-03-19T07:34:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.