2020 IEEE Wireless Communications and Networking Conference (WCNC)最新文献_第8页

Joint User-Centric Clustering and Frequency Allocation in Ultra-Dense C-RAN 超密集C-RAN中以用户为中心的联合聚类与频率分配

2020 IEEE Wireless Communications and Networking Conference (WCNC) Pub Date : 2020-05-01 DOI: 10.1109/WCNC45663.2020.9120743

Qiang Liu, Songlin Sun, Hui Gao

{"title":"Joint User-Centric Clustering and Frequency Allocation in Ultra-Dense C-RAN","authors":"Qiang Liu, Songlin Sun, Hui Gao","doi":"10.1109/WCNC45663.2020.9120743","DOIUrl":"https://doi.org/10.1109/WCNC45663.2020.9120743","url":null,"abstract":"This paper considers the downlink ultra-dense cloud radio access network (C-RAN), which employs multiple radio remote head (RRH) cooperation to guarantee the minimum achievable transmission rate for each user equipment (UE). However, due to the limited orthogonal frequency resources, it is difficult to achieve this goal. To maximize the coverage probability of the system, we focus on the joint user-centric clustering and frequency allocation problem. To reduce the computational complexity, this problem is split into two sub-problems: user-centric clustering and frequency allocation. Firstly, we propose a novel binary user-centric clustering strategy, which includes serving clusters and silent clusters. This strategy determines the acceptable combination of serving clusters and silent clusters to guarantee the minimum transmission rate for each UE and simplify the complexity of the subsequent frequency allocation. Then based on the generated clusters, a new graph generation method is proposed. The advantage of this graph is that we can allocate frequency resources by simply judging the relationship between the serving clusters in the graph without complicated calculations. Numerical simulation results show that the joint binary user-centric clustering and location-based frequency allocation scheme is superior to the benchmark solutions in terms of the coverage probability.","PeriodicalId":415064,"journal":{"name":"2020 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132287531","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: 3

Caching and Pricing based on Blockchain in a Cache-delivery Market 缓存交付市场中基于区块链的缓存和定价

2020 IEEE Wireless Communications and Networking Conference (WCNC) Pub Date : 2020-05-01 DOI: 10.1109/WCNC45663.2020.9120478

Yuanzhuo Lin, Hui Tian, Jiazhi Ren, Shaoshuai Fan

引用次数: 3

Location-Privacy-Aware Service Migration in Mobile Edge Computing 移动边缘计算中的位置隐私感知服务迁移

2020 IEEE Wireless Communications and Networking Conference (WCNC) Pub Date : 2020-05-01 DOI: 10.1109/WCNC45663.2020.9120551

Weixu Wang, Shuxin Ge, Xiaobo Zhou

{"title":"Location-Privacy-Aware Service Migration in Mobile Edge Computing","authors":"Weixu Wang, Shuxin Ge, Xiaobo Zhou","doi":"10.1109/WCNC45663.2020.9120551","DOIUrl":"https://doi.org/10.1109/WCNC45663.2020.9120551","url":null,"abstract":"To cope with user mobility and resource constraints of the edge servers, various service migration policies have been proposed in mobile edge computing (MEC) to achieve a trade-off between user-perceived delay and the service migration cost by moving the service to the user as close as possible. However, there is a risk of user location privacy leakage if a malicious eavesdropper tracks the service migration trajectory. In this paper, we investigate service migration in MEC by taking the risk of location privacy leakage into account. More specifically, we define the total cost of the system as the combination of the migration cost, user-perceived delay and the risk of location privacy leakage. We formulate the service migration problem as a Markov decision process, and propose an efficient algorithm to find the optimal solution that minimize the long-term total cost. Finally, the simulations based on real-world taxi traces in San Francisco show that the proposed method can make service migration decisions effectively protect the location privacy of users, as well as achieves a lower total cost than other baseline methods.","PeriodicalId":415064,"journal":{"name":"2020 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"300 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114000809","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: 12

User Slicing Scheme with Functional Split Selection in 5G Cloud-RAN 5G Cloud-RAN中具有功能拆分选择的用户切片方案

2020 IEEE Wireless Communications and Networking Conference (WCNC) Pub Date : 2020-05-01 DOI: 10.1109/WCNC45663.2020.9120828

Salma Matoussi, Ilhem Fajjari, N. Aitsaadi, R. Langar

{"title":"User Slicing Scheme with Functional Split Selection in 5G Cloud-RAN","authors":"Salma Matoussi, Ilhem Fajjari, N. Aitsaadi, R. Langar","doi":"10.1109/WCNC45663.2020.9120828","DOIUrl":"https://doi.org/10.1109/WCNC45663.2020.9120828","url":null,"abstract":"Next Generation 5G Radio Access Network (NGRAN) is envisioned to integrate the slicing approach to build a flexible network supporting diverse use-cases with customized architectures, features and services. RAN processing functional splits have been standardized to add new deployment design capabilities and enhance cost efficiency. A further challenge consists in how to meet the multitude use-case’s requirements while considering different design models in the physical infrastructure. Current related works are tackling the slice embedding problem from a cell-centric perspective. However, to achieve greater flexibility and better resource utilization, a user-centric approach should be more exploited. In this paper, we propose a SLICE-HPSO scheme that jointly harnesses radio, processing and link resources at the user level to build multiple user slices on top of the physical infrastructure. Our proposal is tailored to different user quality-of-service requirements and to the diverse functional splits resource requests. SLICE-HPSO is in compliance with the 3GPP and optimizes further the heterogeneous resource usage while meeting the scalability requirement.","PeriodicalId":415064,"journal":{"name":"2020 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"35 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114100128","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: 6

Characterization for High-Speed Railway Channel enabling Smart Rail Mobility at 22.6 GHz 实现22.6 GHz智能铁路机动性的高速铁路信道特性

2020 IEEE Wireless Communications and Networking Conference (WCNC) Pub Date : 2020-05-01 DOI: 10.1109/WCNC45663.2020.9120474

Lei Ma, K. Guan, Dong Yan, Danping He, B. Ai, Junhyeong Kim, Heesang Chung

引用次数: 4

Performance Analysis of Temporal Correlation in Finite-Area UAV Networks with LoS/NLoS 具有LoS/NLoS的有限区域无人机网络时间相关性性能分析

2020 IEEE Wireless Communications and Networking Conference (WCNC) Pub Date : 2020-05-01 DOI: 10.1109/WCNC45663.2020.9120822

Ruixin Jin, Liyun Yang, Hongtao Zhang

{"title":"Performance Analysis of Temporal Correlation in Finite-Area UAV Networks with LoS/NLoS","authors":"Ruixin Jin, Liyun Yang, Hongtao Zhang","doi":"10.1109/WCNC45663.2020.9120822","DOIUrl":"https://doi.org/10.1109/WCNC45663.2020.9120822","url":null,"abstract":"In the existing works, the finite-area distribution of Unmanned Aerial Vehicle (UAV) and the effects of LoS and NLoS in air-to-ground channels have not been modeled when analyzing the temporal correlation in UAV networks, which makes the current analyses unsuitable for the practical deployment in hotspots. This paper analyzes the temporal correlation by deriving the expression of the interference correlation and joint coverage probability in mobile UAV networks, where all UAVs move independently in a finite area and the channel fading is calculated based on air-to-ground channel model with LoS and NLoS. Specifically, the temporal correlation is measured by incorporating the fluctuations caused by probability variation of LoS and the reliable network topology caused by finite mobility into the wireless channels. Furthermore, the non-uniform distribution caused by random mobility of UAVs is considered over different time slots. The results show that the interference correlation decreases as the moving distance of UAVs increases, and the decreasing interference correlation offsets part of the decrease in joint coverage probability caused by increasing moving distance of UAVs.","PeriodicalId":415064,"journal":{"name":"2020 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"174 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113973039","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: 8

A Linear Bayesian Learning Receiver Scheme for Massive MIMO Systems 大规模MIMO系统的一种线性贝叶斯学习接收方案

2020 IEEE Wireless Communications and Networking Conference (WCNC) Pub Date : 2020-05-01 DOI: 10.1109/WCNC45663.2020.9120718

Alva Kosasih, Wibowo Hardjawana, B. Vucetic, Chao-Kai Wen

{"title":"A Linear Bayesian Learning Receiver Scheme for Massive MIMO Systems","authors":"Alva Kosasih, Wibowo Hardjawana, B. Vucetic, Chao-Kai Wen","doi":"10.1109/WCNC45663.2020.9120718","DOIUrl":"https://doi.org/10.1109/WCNC45663.2020.9120718","url":null,"abstract":"Much stringent reliability and processing latency requirements in ultra-reliable-low-latency-communication (URLLC) traffic make the design of linear massive multiple-input-multiple-output (M-MIMO) receivers becomes very challenging. Recently, Bayesian concept has been used to increase the detection reliability in minimum-mean-square-error (MMSE) linear receivers. However, the latency processing time is a major concern due to the exponential complexity of matrix inversion operations in MMSE schemes. This paper proposes an iterative M-MIMO receiver that is developed by using a Bayesian concept and a parallel interference cancellation (PIC) scheme, referred to as a linear Bayesian learning (LBL) receiver. PIC has a linear complexity as it uses a combination of maximum ratio combining (MRC) and decision statistic combining (DSC) schemes to avoid matrix inversion operations. Simulation results show that the bit-error-rate (BER) and latency processing performances of the proposed receiver outperform the ones of MMSE and best Bayesian-based receivers by minimum 2 dB and 19 times for various M-MIMO system configurations.","PeriodicalId":415064,"journal":{"name":"2020 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123798867","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: 3

UAV-based Air-to-Ground Channel Modeling for Diverse Environments 基于无人机的多环境空对地信道建模

2020 IEEE Wireless Communications and Networking Conference (WCNC) Pub Date : 2020-05-01 DOI: 10.1109/WCNC45663.2020.9120659

Muhammad Usaid Akram, Usama Saeed, Syed Ali Hassan, Haejoon Jung

引用次数: 3

WCNC 2020 Committees

2020 IEEE Wireless Communications and Networking Conference (WCNC) Pub Date : 2020-05-01 DOI: 10.1109/wcnc45663.2020.9120449

引用次数: 0

Distributed V2V Computation Offloading Based on Dynamic Pricing Using Deep Reinforcement Learning 基于深度强化学习动态定价的分布式V2V计算卸载

2020 IEEE Wireless Communications and Networking Conference (WCNC) Pub Date : 2020-05-01 DOI: 10.1109/WCNC45663.2020.9120816

Jinming Shi, Jun Du, Jian Wang, Jian Yuan

{"title":"Distributed V2V Computation Offloading Based on Dynamic Pricing Using Deep Reinforcement Learning","authors":"Jinming Shi, Jun Du, Jian Wang, Jian Yuan","doi":"10.1109/WCNC45663.2020.9120816","DOIUrl":"https://doi.org/10.1109/WCNC45663.2020.9120816","url":null,"abstract":"Vehicular computation offloading is a promising paradigm that improves the computing capability of vehicles to support autonomous driving and various on-board infotainment services. Comparing with accessing the remote cloud, distributed vehicle-to-vehicle (V2V) computation offloading is more efficient and suitable for delay-sensitive tasks by taking advantage of vehicular idle computing resources. Due to the high dynamic vehicular environment and the variation of available vehicular computing resources, it is a great challenge to design an effective task offloading mechanism to efficiently utilize vehicular computing resources. In this paper, we investigate the computation task allocation among vehicles, and propose a distributed V2V computation offloading framework, in which wireless channel states and variation of idle computing resources are both considered. Specially, we formulate the task allocation problem as a sequential decision making problem, which can be solved by using deep reinforcement learning. Considering that vehicles with idle computing resources may not share their computing resources voluntarily, we thus propose a dynamic pricing scheme that motivates vehicles to contribute their computing resources according to the price they receive. The performance of designed task allocation mechanism is validated by simulation results which reveal the effectiveness of our mechanism compared to the other algorithms.","PeriodicalId":415064,"journal":{"name":"2020 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128527122","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: 13