Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118809
Xuechen Xie, Dongheng Zhang, Yadong Li, Jinbo Chen, Yang Hu, Qibin Sun, Yan Chen
The past decade has witnessed emerging applications of breath monitoring using off-the-shelf WiFi devices owing to their low-cost, non-intrusive, and privacy-friendly characteristics. While existing works have achieved promising results in certain scenarios, the performance degradation introduced by the interfering person who moves around the target user has not been fully investigated, which hinders practical applications of WiFi-based breath sensing. In this paper, we propose a robust respiration sensing system with WiFi which could achieve accurate respiration sensing under strong interference. To achieve this, we first design a 2-D Capon beamformer to maximize the signal-to-interference-plus-noise ratio (SINR). Then, the interfering user’s trajectory is estimated through spatial-temporal processing. Finally, we design a respiration extracting algorithm based on the constraint of the interferer’s trajectory and breath energy to find the optimal position to extract breath signals. Extensive experimental results show that the proposed framework can reduce the Mean Absolute Error (MAE) of breath rate estimation by up to 48% compared with the existing state-of-the-art methods, which demonstrates the superior robustness and effectiveness of our system.
{"title":"Robust Respiration Sensing with WiFi","authors":"Xuechen Xie, Dongheng Zhang, Yadong Li, Jinbo Chen, Yang Hu, Qibin Sun, Yan Chen","doi":"10.1109/WCNC55385.2023.10118809","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118809","url":null,"abstract":"The past decade has witnessed emerging applications of breath monitoring using off-the-shelf WiFi devices owing to their low-cost, non-intrusive, and privacy-friendly characteristics. While existing works have achieved promising results in certain scenarios, the performance degradation introduced by the interfering person who moves around the target user has not been fully investigated, which hinders practical applications of WiFi-based breath sensing. In this paper, we propose a robust respiration sensing system with WiFi which could achieve accurate respiration sensing under strong interference. To achieve this, we first design a 2-D Capon beamformer to maximize the signal-to-interference-plus-noise ratio (SINR). Then, the interfering user’s trajectory is estimated through spatial-temporal processing. Finally, we design a respiration extracting algorithm based on the constraint of the interferer’s trajectory and breath energy to find the optimal position to extract breath signals. Extensive experimental results show that the proposed framework can reduce the Mean Absolute Error (MAE) of breath rate estimation by up to 48% compared with the existing state-of-the-art methods, which demonstrates the superior robustness and effectiveness of our system.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116209894","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118783
Wenlin Yao, Jiayi Liu, Chen Wang, Qinghai Yang
In the 3GPP-based cellular vehicle-to-everything (C-V2X) architecture, the Roadside Units (RSU) plays an important role for the enhancement of Quality of Service (QoS) of the vehicular applications. The placement of RSUs has been studied in the literature. However, existing works assume known road traffic distribution with given task demands, which is a simplification of the complex real world situation. In this work, we investigate the optimum RSU placement for C-V2X with uncertain traffic density and task demands. We formulate this RSUs Placement in C-V2X Network (RPCN) problem to minimize the expected vehicle tasks offloading delay through uncertain programming where vehicles positions and tasks are treated as arbitrary stochastic variables. We propose a learning-based algorithm by integrating Stochastic Simulation (SS), Artificial Neural Network (ANN) and meta-heuristic algorithm to determine the placement from real traffic data. The proposed method is an offline design with high practicability. We conducted intensive real-trace driven simulations to demonstrate the effectiveness of our approach on placing RSUs with lower task offloading delay.
{"title":"Learning-based RSU Placement for C-V2X with Uncertain Traffic Density and Task Demand","authors":"Wenlin Yao, Jiayi Liu, Chen Wang, Qinghai Yang","doi":"10.1109/WCNC55385.2023.10118783","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118783","url":null,"abstract":"In the 3GPP-based cellular vehicle-to-everything (C-V2X) architecture, the Roadside Units (RSU) plays an important role for the enhancement of Quality of Service (QoS) of the vehicular applications. The placement of RSUs has been studied in the literature. However, existing works assume known road traffic distribution with given task demands, which is a simplification of the complex real world situation. In this work, we investigate the optimum RSU placement for C-V2X with uncertain traffic density and task demands. We formulate this RSUs Placement in C-V2X Network (RPCN) problem to minimize the expected vehicle tasks offloading delay through uncertain programming where vehicles positions and tasks are treated as arbitrary stochastic variables. We propose a learning-based algorithm by integrating Stochastic Simulation (SS), Artificial Neural Network (ANN) and meta-heuristic algorithm to determine the placement from real traffic data. The proposed method is an offline design with high practicability. We conducted intensive real-trace driven simulations to demonstrate the effectiveness of our approach on placing RSUs with lower task offloading delay.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124389271","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118789
Lili Song, Zhenzhen Gao, Jian Huang, Boliang Han
The physical layer (PHY) security technology based on radio frequency (RF) fingerprint can effectively solve the secure access problem of wireless devices. The hardware impairments of the devices can be used to generate the unique RF fingerprint to identify different wireless devices. Fingerprint extraction as a key step in the process of identification faces the challenges of ensuring the identification accuracy with reduced sample dimension and low testing and training time. To address the above problems, we propose a lightweight RF fingerprint extraction scheme to extract the physical layer attributes and effectively reduce the data dimension and time consumption. Based on the proposed RF fingerprint, the Bayesian classifier is used to identify the wireless devices. Furthermore, a joint judgment strategy is proposed to improve the identification accuracy by using multiple segments of one signal frame. The experimental result shows that, compared to the existing RF fingerprint identification schemes, the proposed RF fingerprint identification scheme obtains the best identification accuracy with lower time and data consumption.
{"title":"A Lightweight Radio Frequency Fingerprint Extraction Scheme for Device Identification","authors":"Lili Song, Zhenzhen Gao, Jian Huang, Boliang Han","doi":"10.1109/WCNC55385.2023.10118789","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118789","url":null,"abstract":"The physical layer (PHY) security technology based on radio frequency (RF) fingerprint can effectively solve the secure access problem of wireless devices. The hardware impairments of the devices can be used to generate the unique RF fingerprint to identify different wireless devices. Fingerprint extraction as a key step in the process of identification faces the challenges of ensuring the identification accuracy with reduced sample dimension and low testing and training time. To address the above problems, we propose a lightweight RF fingerprint extraction scheme to extract the physical layer attributes and effectively reduce the data dimension and time consumption. Based on the proposed RF fingerprint, the Bayesian classifier is used to identify the wireless devices. Furthermore, a joint judgment strategy is proposed to improve the identification accuracy by using multiple segments of one signal frame. The experimental result shows that, compared to the existing RF fingerprint identification schemes, the proposed RF fingerprint identification scheme obtains the best identification accuracy with lower time and data consumption.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"466 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124439311","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118690
Qu Luo, Zeina Mheich, Gaojie Chen, Pei Xiao, Zilong Liu
Non-orthogonal multiple access (NOMA) is a promising candidate radio access technology for future wireless communication systems, which can achieve improved connectivity and spectral efficiency. Without sacrificing error rate performance, link adaptation combining with adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) can provide better spectral efficiency and reliable data transmission by allowing both power and rate to adapt to channel fading and enabling re-transmissions. However, current AMC or HARQ schemes may not be preferable for NOMA systems due to the imperfect channel estimation and error propagation during successive interference cancellation (SIC). To address this problem, a reinforcement learning based link adaptation scheme for downlink NOMA systems is introduced in this paper. Specifically, we first analyze the throughput and spectrum efficiency of NOMA system with AMC combined with HARQ. Then, taking into account the imperfections of channel estimation and error propagation in SIC, we propose SINR and SNR based corrections to correct the modulation and coding scheme selection. Finally, reinforcement learning (RL) is developed to optimize the SNR and SINR correction process. Comparing with a conventional fixed look-up table based scheme, the proposed solutions achieve superior performance in terms of spectral efficiency and packet error performance.
{"title":"Reinforcement Learning Aided Link Adaptation for Downlink NOMA Systems With Channel Imperfections","authors":"Qu Luo, Zeina Mheich, Gaojie Chen, Pei Xiao, Zilong Liu","doi":"10.1109/WCNC55385.2023.10118690","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118690","url":null,"abstract":"Non-orthogonal multiple access (NOMA) is a promising candidate radio access technology for future wireless communication systems, which can achieve improved connectivity and spectral efficiency. Without sacrificing error rate performance, link adaptation combining with adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) can provide better spectral efficiency and reliable data transmission by allowing both power and rate to adapt to channel fading and enabling re-transmissions. However, current AMC or HARQ schemes may not be preferable for NOMA systems due to the imperfect channel estimation and error propagation during successive interference cancellation (SIC). To address this problem, a reinforcement learning based link adaptation scheme for downlink NOMA systems is introduced in this paper. Specifically, we first analyze the throughput and spectrum efficiency of NOMA system with AMC combined with HARQ. Then, taking into account the imperfections of channel estimation and error propagation in SIC, we propose SINR and SNR based corrections to correct the modulation and coding scheme selection. Finally, reinforcement learning (RL) is developed to optimize the SNR and SINR correction process. Comparing with a conventional fixed look-up table based scheme, the proposed solutions achieve superior performance in terms of spectral efficiency and packet error performance.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127931388","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118999
Zachary Osterwisch, Alexander Mauntel, Nathanael Nisbett, Dibbya Barua, Ahmad Alsharoa
This paper proposes a novel portable prototype and self-contained Air Quality (AQ) monitoring device that utilizes Light Detection and Ranging (LiDAR) technology to take its measurements. The novel device aims to improve mining safety by collecting and analyzing the AQ inside mines and displaying the real-time conditions to personnel. The intent is to create a 3D map of the environment and display potentially hazardous Atmospheric Particulate Matter (APM). To achieve this goal, we prototype a portable, compact, and easy-to-operate system that utilizes LiDAR to detect APM. Then, we propose how the collected data can be used to calculate real-time AQ conditions. Finally, we illustrate selected results to show the importance and feasibility of our novel prototype.
{"title":"Particulate Matter Detection in Mines Using 3D Light Detection and Ranging Technology","authors":"Zachary Osterwisch, Alexander Mauntel, Nathanael Nisbett, Dibbya Barua, Ahmad Alsharoa","doi":"10.1109/WCNC55385.2023.10118999","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118999","url":null,"abstract":"This paper proposes a novel portable prototype and self-contained Air Quality (AQ) monitoring device that utilizes Light Detection and Ranging (LiDAR) technology to take its measurements. The novel device aims to improve mining safety by collecting and analyzing the AQ inside mines and displaying the real-time conditions to personnel. The intent is to create a 3D map of the environment and display potentially hazardous Atmospheric Particulate Matter (APM). To achieve this goal, we prototype a portable, compact, and easy-to-operate system that utilizes LiDAR to detect APM. Then, we propose how the collected data can be used to calculate real-time AQ conditions. Finally, we illustrate selected results to show the importance and feasibility of our novel prototype.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128785410","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118924
Minqing Yu, Callum T. Geldard, W. Popoola
This paper presents an experimental comparison of pulse amplitude modulation (PAM), carrierless amplitude and phase (CAP), and quadrature amplitude modulation-orthogonal frequency division multiplexing (QAM-OFDM) in a turbid underwater optical wireless communications (UOWC) channel. It is shown that the transmission rates of PAM and CAP are maintained at around 180 Mbps even as turbidity increases. However, the highest transmission rate of 472 Mbps is achieved using 16-QAM-OFDM in tap water. Finally, bit power loading (BPL) is applied to further improve the performance of QAM-OFDM, yielding an increased maximum transmission rate of 557 Mbps in tap water.
{"title":"Experimental Comparison of Modulation Techniques for LED-based Underwater Optical Wireless Communications","authors":"Minqing Yu, Callum T. Geldard, W. Popoola","doi":"10.1109/WCNC55385.2023.10118924","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118924","url":null,"abstract":"This paper presents an experimental comparison of pulse amplitude modulation (PAM), carrierless amplitude and phase (CAP), and quadrature amplitude modulation-orthogonal frequency division multiplexing (QAM-OFDM) in a turbid underwater optical wireless communications (UOWC) channel. It is shown that the transmission rates of PAM and CAP are maintained at around 180 Mbps even as turbidity increases. However, the highest transmission rate of 472 Mbps is achieved using 16-QAM-OFDM in tap water. Finally, bit power loading (BPL) is applied to further improve the performance of QAM-OFDM, yielding an increased maximum transmission rate of 557 Mbps in tap water.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127399044","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118986
Xinliang Zhang, M. Vaezi
A deep autoencoder (DAE)-based communication over the two-user Z-interference channel (ZIC) is introduced in this paper. The proposed DAE-ZIC is designed to minimize the bit error rate (BER) in the presence of interference by jointly optimizing the encoders and decoders. Effectively, this is an end-to-end communication that designs new constellations for the ZIC. Normalization layers are embedded in the proposed DAE design to realize an average power constraint so that there are no regular shape restrictions on the constellation symbols. We compare the performance of the DAE-ZIC with two baseline methods, which are ZIC with regular and rotated constellations. Simulation results show a significant gain in BER reduction. On average, in weak, moderate, and strong regimes, 31%–75% BER improvement is achieved compared to the best existing methods.
{"title":"Deep Autoencoder-based Z-Interference Channels","authors":"Xinliang Zhang, M. Vaezi","doi":"10.1109/WCNC55385.2023.10118986","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118986","url":null,"abstract":"A deep autoencoder (DAE)-based communication over the two-user Z-interference channel (ZIC) is introduced in this paper. The proposed DAE-ZIC is designed to minimize the bit error rate (BER) in the presence of interference by jointly optimizing the encoders and decoders. Effectively, this is an end-to-end communication that designs new constellations for the ZIC. Normalization layers are embedded in the proposed DAE design to realize an average power constraint so that there are no regular shape restrictions on the constellation symbols. We compare the performance of the DAE-ZIC with two baseline methods, which are ZIC with regular and rotated constellations. Simulation results show a significant gain in BER reduction. On average, in weak, moderate, and strong regimes, 31%–75% BER improvement is achieved compared to the best existing methods.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128984424","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118894
J. Wang, Xinxin Ma, Le Zheng, Kai Yang, Zhao Chen, Qiaoqiao Xia
This paper studies the secrecy wireless information and power transfer problem in ultra-dense cloud radio access network (UD-CRAN) with wireless fronthaul, which is a promising framework for future Internet of Things (IoT). The transmission schemes of wireless fronthaul and access links are jointly designed, while addressing the characteristics of ultra-dense network such as base station diversity and high probability of line-of-sight transmission. Specifically, we employ the idea of block diagonalization to deal with the fronthaul interference, which support multi-stream fronthaul transmission for each remote radio head (RRH). We then jointly optimize the power allocation in the fronthaul and the resource allocation in the access link which includes beamforming for information and energy transmission, on/off of RRHs, and user-RRH association. In order to solve the formulated mixed integer non-convex optimization problem, we leverage the sparsity of beamforming vectors brought by the ultra-dense RRHs. We then solve the reformulated problem by employing the successive convex approximation approach. Finally, numerical results are presented to demonstrate the effectiveness of the proposed scheme.
{"title":"Secrecy Wireless Information and Power Transfer in Ultra-Dense Cloud-RAN with Wireless Fronthaul","authors":"J. Wang, Xinxin Ma, Le Zheng, Kai Yang, Zhao Chen, Qiaoqiao Xia","doi":"10.1109/WCNC55385.2023.10118894","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118894","url":null,"abstract":"This paper studies the secrecy wireless information and power transfer problem in ultra-dense cloud radio access network (UD-CRAN) with wireless fronthaul, which is a promising framework for future Internet of Things (IoT). The transmission schemes of wireless fronthaul and access links are jointly designed, while addressing the characteristics of ultra-dense network such as base station diversity and high probability of line-of-sight transmission. Specifically, we employ the idea of block diagonalization to deal with the fronthaul interference, which support multi-stream fronthaul transmission for each remote radio head (RRH). We then jointly optimize the power allocation in the fronthaul and the resource allocation in the access link which includes beamforming for information and energy transmission, on/off of RRHs, and user-RRH association. In order to solve the formulated mixed integer non-convex optimization problem, we leverage the sparsity of beamforming vectors brought by the ultra-dense RRHs. We then solve the reformulated problem by employing the successive convex approximation approach. Finally, numerical results are presented to demonstrate the effectiveness of the proposed scheme.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132274346","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118862
Zhaojie Li, T. Ohtsuki, Guan Gui
Federated Learning is now widely used to train neural networks under distributed datasets. One of the main challenges in Federated Learning is to address network training under local data heterogeneity. Existing work proposes that taking similarity into account as an influence factor in federated learning can improve the speed of model aggregation. We propose a novel approach that introduces Centered Kernel Alignment (CKA) into loss function to compute the similarity of feature maps in the output layer. Compared to existing methods, our method enables fast model aggregation and improves global model accuracy in non-IID scenario by using Resnet50.
{"title":"Communication Efficient Heterogeneous Federated Learning based on Model Similarity","authors":"Zhaojie Li, T. Ohtsuki, Guan Gui","doi":"10.1109/WCNC55385.2023.10118862","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118862","url":null,"abstract":"Federated Learning is now widely used to train neural networks under distributed datasets. One of the main challenges in Federated Learning is to address network training under local data heterogeneity. Existing work proposes that taking similarity into account as an influence factor in federated learning can improve the speed of model aggregation. We propose a novel approach that introduces Centered Kernel Alignment (CKA) into loss function to compute the similarity of feature maps in the output layer. Compared to existing methods, our method enables fast model aggregation and improves global model accuracy in non-IID scenario by using Resnet50.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"928 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127015751","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118751
Aleksandar Ichkov, P. Mähönen, L. Simić
We study the problem of joint user association and beam pair link (BPL) allocation in millimeter-wave (mm-wave) cellular networks. We propose two interference-aware strategies – a centralized and a distributed one – and evaluate their performance based on site-specific directional channel data and realistic antenna models. Our results show that using idealized sectored antenna models severely underestimates the spatial interference, considering the non-negligible sidelobes of realistic antenna arrays which strongly limit the achievable spatial separation of the allocated BPLs in mm-wave networks using beam codebooks. We also show that intra-cell interference is the dominant interference component for all allocated users, in contrast to assumptions in the prior literature. By exploiting non line-of-sight BPLs, our interference-aware strategies achieve significant performance gains over interference-agnostic 5G-NR default user association to the strongest base station and BPL, as well as outperforming a centralized, load-balancing literature benchmark. Our proposed strategies rely solely on downlink 5G-NR reference signals for channel state information updates, making them attractive for practical codebook-based mm-wave cellular networks.
{"title":"Interference-Aware User Association and Beam Pair Link Allocation in mm-Wave Cellular Networks","authors":"Aleksandar Ichkov, P. Mähönen, L. Simić","doi":"10.1109/WCNC55385.2023.10118751","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118751","url":null,"abstract":"We study the problem of joint user association and beam pair link (BPL) allocation in millimeter-wave (mm-wave) cellular networks. We propose two interference-aware strategies – a centralized and a distributed one – and evaluate their performance based on site-specific directional channel data and realistic antenna models. Our results show that using idealized sectored antenna models severely underestimates the spatial interference, considering the non-negligible sidelobes of realistic antenna arrays which strongly limit the achievable spatial separation of the allocated BPLs in mm-wave networks using beam codebooks. We also show that intra-cell interference is the dominant interference component for all allocated users, in contrast to assumptions in the prior literature. By exploiting non line-of-sight BPLs, our interference-aware strategies achieve significant performance gains over interference-agnostic 5G-NR default user association to the strongest base station and BPL, as well as outperforming a centralized, load-balancing literature benchmark. Our proposed strategies rely solely on downlink 5G-NR reference signals for channel state information updates, making them attractive for practical codebook-based mm-wave cellular networks.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130376326","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}
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