2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)最新文献_第7页

Hierarchical Resource Allocation: Balancing Throughput and Energy Efficiency in Wireless Systems 分层资源分配:平衡无线系统的吞吐量和能源效率

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-02-05 DOI: 10.1109/WCNCW49093.2021.9420039

Bho Matthiesen, Eduard Axel Jorswieck, P. Popovski

{"title":"Hierarchical Resource Allocation: Balancing Throughput and Energy Efficiency in Wireless Systems","authors":"Bho Matthiesen, Eduard Axel Jorswieck, P. Popovski","doi":"10.1109/WCNCW49093.2021.9420039","DOIUrl":"https://doi.org/10.1109/WCNCW49093.2021.9420039","url":null,"abstract":"A main challenge of 5G and beyond wireless systems is to efficiently utilize the available spectrum and simultaneously reduce the energy consumption. From the radio resource allocation perspective, the solution to this problem is to maximize the energy efficiency instead of the throughput. This results in the optimal benefit-cost ratio between data rate and energy consumption. It also often leads to a considerable reduction in throughput and, hence, an underutilization of the available spectrum. Contemporary approaches to balance these metrics based on multi-objective programming theory often lack operational meaning and finding the correct operating point requires careful experimentation and calibration. Instead, we propose the novel concept of hierarchical resource allocation where conflicting objectives are ordered by their importance. This results in a resource allocation algorithm that strives to minimize the transmit power while keeping the data rate close the maximum achievable throughput. In a typical multi-cell scenario, this strategy is shown to reduces the transmit power consumption by 65% at the cost of a 5% decrease in throughput. Moreover, this strategy also saves energy in scenarios where global energy efficiency maximization fails to achieve any gain over throughput maximization.","PeriodicalId":262379,"journal":{"name":"2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116704270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Deep Reinforcement Learning for Energy-Efficient Beamforming Design in Cell-Free Networks 基于深度强化学习的无蜂窝网络节能波束形成设计

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-02-05 DOI: 10.1109/WCNCW49093.2021.9420002

Weilai Li, Wanli Ni, Hui Tian, Meihui Hua

{"title":"Deep Reinforcement Learning for Energy-Efficient Beamforming Design in Cell-Free Networks","authors":"Weilai Li, Wanli Ni, Hui Tian, Meihui Hua","doi":"10.1109/WCNCW49093.2021.9420002","DOIUrl":"https://doi.org/10.1109/WCNCW49093.2021.9420002","url":null,"abstract":"Cell-free network is considered as a promising architecture for satisfying more demands of future wireless networks, where distributed access points coordinate with an edge cloud processor to jointly provide service to a smaller number of user equipments in a compact area. In this paper, the problem of uplink beamforming design is investigated for maximizing the long-term energy efficiency (EE) with the aid of deep reinforcement learning (DRL) in the cell-free network. Firstly, based on the minimum mean square error channel estimation and exploiting successive interference cancellation for signal detection, the expression of signal to interference plus noise ratio (SINR) is derived. Secondly, according to the formulation of SINR, we define the long-term EE, which is a function of beam-forming matrix. Thirdly, to address the dynamic beamforming design with continuous state and action space, a DRL-enabled beamforming design is proposed based on deep deterministic policy gradient (DDPG) algorithm by taking the advantage of its double-network architecture. Finally, the results of simulation indicate that the DDPG-based beamforming design is capable of converging to the optimal EE performance. Furthermore, the influence of hyper-parameters on the EE performance of the DDPG-based beamforming design is investigated, and it is demonstrated that an appropriate discount factor and hidden layers size can facilitate the EE performance.","PeriodicalId":262379,"journal":{"name":"2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123933893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Performance of Cell-Free MmWave Massive MIMO Systems with Fronthaul Compression and DAC Quantization 采用前传压缩和DAC量化的无小区毫米波海量MIMO系统性能研究

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-01-25 DOI: 10.1109/WCNCW49093.2021.9420030

In-soo Kim, Junil Choi

引用次数: 6

AI-Empowered VNF Migration as a Cost-Loss-Effective Solution for Network Resilience 人工智能支持的VNF迁移作为网络弹性的成本-损失效益解决方案

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-01-22 DOI: 10.1109/WCNCW49093.2021.9420029

Amina Lejla Ibrahimpasic, B. Han, H. Schotten

引用次数: 5

Channel Estimation for Practical IRS-Assisted OFDM Systems 实用irs辅助OFDM系统的信道估计

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2020-12-25 DOI: 10.1109/WCNCW49093.2021.9419982

Wanning Yang, Hongyu Li, Ming Li, Yang Liu, Li-Yu Daisy Liu

{"title":"Channel Estimation for Practical IRS-Assisted OFDM Systems","authors":"Wanning Yang, Hongyu Li, Ming Li, Yang Liu, Li-Yu Daisy Liu","doi":"10.1109/WCNCW49093.2021.9419982","DOIUrl":"https://doi.org/10.1109/WCNCW49093.2021.9419982","url":null,"abstract":"Intelligent reflecting surface (IRS), composed of a large number of hardware-efficient passive elements, is deemed as a potential technique for future wireless communications since it can adaptively enhance the propagation environment. In order to effectively utilize IRS to achieve promising beamforming gains, the problem of channel state information (CSI) acquisition needs to be carefully considered. However, most recent works assume to employ an ideal IRS, i.e., each reflecting element has constant amplitude, variable phase shifts, as well as the same response for the signals with different frequencies, which will cause severe estimation error due to the mismatch between the ideal IRS and the practical one. In this paper, we study channel estimation in practical IRS-aided orthogonal frequency division multiplexing (OFDM) systems with discrete phase shifts. Different from the prior works which assume that IRS has an ideal reflection model, we perform channel estimation by considering amplitude-phase shift-frequency relationship for the response of practical IRS. Aiming at minimizing normalized-mean-square-error (NMSE) of the estimated channel, a novel IRS time-varying reflection pattern is designed by leveraging the alternating optimization (AO) algorithm for the case of using low-resolution phase shifters. Moreover, for the high-resolution IRS cases, we provide another practical reflection pattern scheme to further reduce the complexity. Simulation results demonstrate the necessity of considering practical IRS model for channel estimation and the effectiveness of our proposed channel estimation methods.","PeriodicalId":262379,"journal":{"name":"2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126879159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

Reconfigurable Intelligent Surfaces based Cognitive Radio Networks 基于认知无线电网络的可重构智能表面

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2020-08-25 DOI: 10.1109/WCNCW49093.2021.9419976

Abubakar U. Makarfi, R. Kharel, Khaled Maaiuf Rabie, Omprakash Kaiwartya, Xingwang Li, D. Do

引用次数: 17

IRS-Assisted Millimeter Wave Communications: Joint Power Allocation and Beamforming Design irs辅助毫米波通信:联合功率分配和波束成形设计

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2020-01-21 DOI: 10.1109/WCNCW49093.2021.9419998

Yue Xiu, Yang Zhao, Yang Liu, Jun Zhao, Osman Yağan, Ning Wei

引用次数: 32

WCNCW 2021 Workshops Organizing Committee WCNCW 2021工作坊组委会

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 1900-01-01 DOI: 10.1109/wcncw49093.2021.9419983

引用次数: 0

Index 指数

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 1900-01-01 DOI: 10.1109/wcncw49093.2021.9419973

引用次数: 0