Pub Date : 2022-05-16DOI: 10.1109/ICCWorkshops53468.2022.9882153
Jiacheng Hou, Haoye Lu, A. Nayak
As people spend more time watching movies and sharing videos online, it is crucial to provide users with a satisfactory quality of experience (QoE). With the help of the in-network caching feature in named data networking (NDN), our paper aims to improve user experience through caching. We propose a graph neural network-gain maximization (GNN-GM) cache placement algorithm. First, we use a GNN model to predict users’ ratings of unviewed videos. Second, we consider the total predicted rating of a video as the gain of caching the video. Third, we propose a cache placement algorithm to maximize the caching gains and proactively cache videos. We also design a caching replacement strategy based on the gain of caching the video. We utilize a real-world dataset to evaluate our caching strategy. Compared to state-of-the-art caching approaches, experimental results show that our caching policy improves cache hit rate by 25%, reduces latency by 5%, and reduces server load by 7%.
{"title":"GNN-GM: A Proactive Caching Scheme for Named Data Networking","authors":"Jiacheng Hou, Haoye Lu, A. Nayak","doi":"10.1109/ICCWorkshops53468.2022.9882153","DOIUrl":"https://doi.org/10.1109/ICCWorkshops53468.2022.9882153","url":null,"abstract":"As people spend more time watching movies and sharing videos online, it is crucial to provide users with a satisfactory quality of experience (QoE). With the help of the in-network caching feature in named data networking (NDN), our paper aims to improve user experience through caching. We propose a graph neural network-gain maximization (GNN-GM) cache placement algorithm. First, we use a GNN model to predict users’ ratings of unviewed videos. Second, we consider the total predicted rating of a video as the gain of caching the video. Third, we propose a cache placement algorithm to maximize the caching gains and proactively cache videos. We also design a caching replacement strategy based on the gain of caching the video. We utilize a real-world dataset to evaluate our caching strategy. Compared to state-of-the-art caching approaches, experimental results show that our caching policy improves cache hit rate by 25%, reduces latency by 5%, and reduces server load by 7%.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129451855","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}
In this paper, we study a UAV-enabled wireless sensor network (WSN), where a UAV is dispatched to charge multiple sensors deployed on the ground floor via the wireless power transfer (WPT) technique. A probabilistic line-of-sight (PLoS) channel model and a practical nonlinear energy harvesting (EH) model are considered in the characterization of the harvested energy by sensors. Focusing on a WPT task where a minimum energy budget is required by each sensor, we formulate a UAV trajectory optimization problem minimizing the corresponding completion time. To simplify the analysis, we reformulate the completion time minimization problem via inserting a successive-hover-and-fly (SHF) structure into UAV trajectory without loss of optimality. Afterwards, we proved the convexity in the LoS probability and nonlinear harvested power with respect to a higher-order power of a horizontal distance. Based on the proved convexity, we construct a convex approximation for the harvested energy at each sensor and propose an iterative solution for iteratively reducing the completion time until a convergence to a suboptimal point. At last, the simulation results are presented to confirm the convergence of the proposed algorithm and reveal the benefits in adopting the more practical PLoS model.
{"title":"UAV Trajectory Design on Completion Time Minimization of WPT Task in UAV-Enabled Multi-User Network","authors":"Xiaopeng Yuan, Guodong Sun, Yulin Hu, Lihua Wu, Hao Wang, A. Schmeink","doi":"10.1109/iccworkshops53468.2022.9814521","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814521","url":null,"abstract":"In this paper, we study a UAV-enabled wireless sensor network (WSN), where a UAV is dispatched to charge multiple sensors deployed on the ground floor via the wireless power transfer (WPT) technique. A probabilistic line-of-sight (PLoS) channel model and a practical nonlinear energy harvesting (EH) model are considered in the characterization of the harvested energy by sensors. Focusing on a WPT task where a minimum energy budget is required by each sensor, we formulate a UAV trajectory optimization problem minimizing the corresponding completion time. To simplify the analysis, we reformulate the completion time minimization problem via inserting a successive-hover-and-fly (SHF) structure into UAV trajectory without loss of optimality. Afterwards, we proved the convexity in the LoS probability and nonlinear harvested power with respect to a higher-order power of a horizontal distance. Based on the proved convexity, we construct a convex approximation for the harvested energy at each sensor and propose an iterative solution for iteratively reducing the completion time until a convergence to a suboptimal point. At last, the simulation results are presented to confirm the convergence of the proposed algorithm and reveal the benefits in adopting the more practical PLoS model.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129837657","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 : 2022-05-16DOI: 10.1109/ICCWorkshops53468.2022.9882145
Yaxin Song, Shaoyi Xu, Yuanjie Wang
Intelligent reflecting surface (IRS) has recently been envisioned as a cost-effective solution to enhance the received signal power of the desired user and to suppress interference of the unintended user. In this paper, we investigate the IRS-aided multi-cell millimeter wave (mmWave) communication system for suppressing inter-cell interference (ICI) to assist the downlink transmission of cell-edge users. We aim for maximizing the minimum weighted signal-to-interference-plus-noise ratio (SINR) through jointly optimizing the active beamforming vectors of mmWave base stations (MBSs), the phase shifts of the IRS, and the location of the IRS in the case of imperfect CSI. To tackle this non-convex problem, we propose a majorization-minimization (MM)-based beamforming algorithm, in which three sets of variables can be updated alternately. The proposed algorithm is also extended to multi-IRS-aided multi-cell mmWave scenarios. The simulation results show the advantages in terms of the SINR of cell-edge users after introducing the IRS to mitigate the ICI.
{"title":"IRS-aided Multi-cell mmWave Communication Systems for Suppressing Interference","authors":"Yaxin Song, Shaoyi Xu, Yuanjie Wang","doi":"10.1109/ICCWorkshops53468.2022.9882145","DOIUrl":"https://doi.org/10.1109/ICCWorkshops53468.2022.9882145","url":null,"abstract":"Intelligent reflecting surface (IRS) has recently been envisioned as a cost-effective solution to enhance the received signal power of the desired user and to suppress interference of the unintended user. In this paper, we investigate the IRS-aided multi-cell millimeter wave (mmWave) communication system for suppressing inter-cell interference (ICI) to assist the downlink transmission of cell-edge users. We aim for maximizing the minimum weighted signal-to-interference-plus-noise ratio (SINR) through jointly optimizing the active beamforming vectors of mmWave base stations (MBSs), the phase shifts of the IRS, and the location of the IRS in the case of imperfect CSI. To tackle this non-convex problem, we propose a majorization-minimization (MM)-based beamforming algorithm, in which three sets of variables can be updated alternately. The proposed algorithm is also extended to multi-IRS-aided multi-cell mmWave scenarios. The simulation results show the advantages in terms of the SINR of cell-edge users after introducing the IRS to mitigate the ICI.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132542625","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814609
Jiaqi Zou, Yuanhao Cui, Yuyang Liu, Songlin Sun
This paper investigates an energy-efficient beam-forming design in integrated sensing and communication (ISAC) systems, with the transmitted waveform jointly designed in the scenario of multi-user communication and moving target estimation at the same time. To improve the energy efficiency (EE) of transmission waveform while guaranteeing target es-timation performance, we maximize the EE of the emitted dual-functional waveform, under a Cramer-Rao bound (CRB) constraint. However, the considered optimization problem is highly non-convex as a result of its fractional form. In our previous work, we solve this problem by fractional programming based on Dinkelbach's method. Nevertheless, the previous method suffers from a slow speed of convergence. To deal with this problem, we explore a sequential convex approximation method to calculate the results effectively. Numerical results demonstrate its improvement compared with the benchmark on iteration speed and EE performance.
{"title":"Optimal Energy-Efficient Beamforming for Integrated Sensing and Communications Systems","authors":"Jiaqi Zou, Yuanhao Cui, Yuyang Liu, Songlin Sun","doi":"10.1109/iccworkshops53468.2022.9814609","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814609","url":null,"abstract":"This paper investigates an energy-efficient beam-forming design in integrated sensing and communication (ISAC) systems, with the transmitted waveform jointly designed in the scenario of multi-user communication and moving target estimation at the same time. To improve the energy efficiency (EE) of transmission waveform while guaranteeing target es-timation performance, we maximize the EE of the emitted dual-functional waveform, under a Cramer-Rao bound (CRB) constraint. However, the considered optimization problem is highly non-convex as a result of its fractional form. In our previous work, we solve this problem by fractional programming based on Dinkelbach's method. Nevertheless, the previous method suffers from a slow speed of convergence. To deal with this problem, we explore a sequential convex approximation method to calculate the results effectively. Numerical results demonstrate its improvement compared with the benchmark on iteration speed and EE performance.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132579422","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814592
Zhicheng Xiao, Guodong Sun, Yulin Hu, Chao Shen, A. Schmeink
In this paper, we study the capacity of massive multiple-input multiple-output (MIMO) systems in the finite blocklength (FBL) regime. Due to the impact of FBL and joint coding among MIMO multiplexing, the multi-stream transmission enabled by the MIMO technique does not perform in the same manner as the one in the infinite blocklength (IBL) regime where transmissions are arbitrarily reliable at Shannon's capacity. Having more streams in a MIMO transmission exploits a longer (equivalent) blocklength, which is more preferred in the FBL regime. On the other hand, with a given total power budget/limit, having more streams with poor channel gains sharing the budget actually reduce the contributions from the strong streams on the FBL performance. This tradeoff is addressed in this work. In particular, we characterize the FBL capacity of massive MIMO under selected multi-stream transmission (SMST) scheme and investigate the optimal multi-stream configuration maximizing the effective channel capacity with a given target decoding error probability. For the scenarios with no channel state information (CSI), the FBL capacity with equal power allocation policy among the selected streams is studied. In addition, when CSI is available an instantaneous power allocation policy is provided.
{"title":"Channel Capacity in the Finite Blocklength Regime for Massive MIMO with Selected Multi-Streams (Invited Paper)","authors":"Zhicheng Xiao, Guodong Sun, Yulin Hu, Chao Shen, A. Schmeink","doi":"10.1109/iccworkshops53468.2022.9814592","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814592","url":null,"abstract":"In this paper, we study the capacity of massive multiple-input multiple-output (MIMO) systems in the finite blocklength (FBL) regime. Due to the impact of FBL and joint coding among MIMO multiplexing, the multi-stream transmission enabled by the MIMO technique does not perform in the same manner as the one in the infinite blocklength (IBL) regime where transmissions are arbitrarily reliable at Shannon's capacity. Having more streams in a MIMO transmission exploits a longer (equivalent) blocklength, which is more preferred in the FBL regime. On the other hand, with a given total power budget/limit, having more streams with poor channel gains sharing the budget actually reduce the contributions from the strong streams on the FBL performance. This tradeoff is addressed in this work. In particular, we characterize the FBL capacity of massive MIMO under selected multi-stream transmission (SMST) scheme and investigate the optimal multi-stream configuration maximizing the effective channel capacity with a given target decoding error probability. For the scenarios with no channel state information (CSI), the FBL capacity with equal power allocation policy among the selected streams is studied. In addition, when CSI is available an instantaneous power allocation policy is provided.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130253971","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 : 2022-05-16DOI: 10.1109/ICCWorkshops53468.2022.9915019
Mingyao Cui, Zi-Yang Wu, Yuhao Chen, Shenheng Xu, Fan Yang, L. Dai
Ultra- massive multiple-input- multiple-output (UM-MIMO) is promising to meet the high rate requirements for future 6G. However, due to the large number of antennas and high path loss, the hardware power consumption and computing power consumption of UM-MIMO will be unaffordable. To address this problem, we implement a low-power communication system based on reconfigurable intelligent surface (RIS) and artificial intelligence (AI) for 6G. For hardware design, we employ a 256-element RIS at the base station to replace the traditional phased array. Moreover, a 2304-element RIS is developed as a relay to assist communication with much reduced transmit power. For software implementation, we develop an AI-based transmission design to reduce computing power consumption. By jointly designing the hardware and software, this prototype can realize real-time 4K video transmission with much reduced power consumption.
{"title":"Demo: Low-power Communications Based on RIS and AI for 6G","authors":"Mingyao Cui, Zi-Yang Wu, Yuhao Chen, Shenheng Xu, Fan Yang, L. Dai","doi":"10.1109/ICCWorkshops53468.2022.9915019","DOIUrl":"https://doi.org/10.1109/ICCWorkshops53468.2022.9915019","url":null,"abstract":"Ultra- massive multiple-input- multiple-output (UM-MIMO) is promising to meet the high rate requirements for future 6G. However, due to the large number of antennas and high path loss, the hardware power consumption and computing power consumption of UM-MIMO will be unaffordable. To address this problem, we implement a low-power communication system based on reconfigurable intelligent surface (RIS) and artificial intelligence (AI) for 6G. For hardware design, we employ a 256-element RIS at the base station to replace the traditional phased array. Moreover, a 2304-element RIS is developed as a relay to assist communication with much reduced transmit power. For software implementation, we develop an AI-based transmission design to reduce computing power consumption. By jointly designing the hardware and software, this prototype can realize real-time 4K video transmission with much reduced power consumption.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114242738","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814461
H. Lee, Sang Hyun Lee, Tony Q. S. Quek
This paper studies a deep learning approach for multi-objective network optimizations. Heterogeneous performance measures are maximized simultaneously to identify complete Pareto-optimal tradeoffs. To this end, a multi-objective optimization (MOO) problem is first reformulated as a collection of constrained single objective optimization (SOO) problems, each associated with a Pareto-optimal point. A novel MOO learning mechanism is developed to address multiple instances of such SOO problems concurrently. A constrained optimization technique is parameterized with neural networks to find an individual solution of the Pareto boundary points. The developed scheme proves efficient in characterizing the optimal tradeoffs of conflicting performance metrics in interfering networks.
{"title":"Learning Multi-Objective Network Optimizations","authors":"H. Lee, Sang Hyun Lee, Tony Q. S. Quek","doi":"10.1109/iccworkshops53468.2022.9814461","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814461","url":null,"abstract":"This paper studies a deep learning approach for multi-objective network optimizations. Heterogeneous performance measures are maximized simultaneously to identify complete Pareto-optimal tradeoffs. To this end, a multi-objective optimization (MOO) problem is first reformulated as a collection of constrained single objective optimization (SOO) problems, each associated with a Pareto-optimal point. A novel MOO learning mechanism is developed to address multiple instances of such SOO problems concurrently. A constrained optimization technique is parameterized with neural networks to find an individual solution of the Pareto boundary points. The developed scheme proves efficient in characterizing the optimal tradeoffs of conflicting performance metrics in interfering networks.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116482416","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814554
Yue Qi, M. Vaezi, W. Shin
Secure communication over two related multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks is investigated. Both networks consist of two legitimate (internal) users and an eavesdropper (external user). In one network, in addition to the external eavesdropper, one of the internal users is also seen as an eavesdropper to the other internal user, resulting in layered confidentiality. The secrecy capacity expressions of these networks are composed of nonconvex functions of transmit covariance matrices, which makes it intractable to find optimal transmit covariance matrices. To overcome this challenge, we form a weighted sum-rate maximization problem for each network and apply the block successive maximization method. This method updates the primal variable blocks successively by maximizing local lower-bounds of the original problem, followed by an update for the dual variable in a closed form. The solutions for these problems design covariance matrices corresponding to the transmitted messages such that they are transmitted securely and reliably. Numerical results illustrate the efficacy of the proposed signaling design for both MIMO-NOMA networks.
{"title":"Novel Signaling Design for MIMO-NOMA Against External and Internal Eavesdroppers","authors":"Yue Qi, M. Vaezi, W. Shin","doi":"10.1109/iccworkshops53468.2022.9814554","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814554","url":null,"abstract":"Secure communication over two related multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks is investigated. Both networks consist of two legitimate (internal) users and an eavesdropper (external user). In one network, in addition to the external eavesdropper, one of the internal users is also seen as an eavesdropper to the other internal user, resulting in layered confidentiality. The secrecy capacity expressions of these networks are composed of nonconvex functions of transmit covariance matrices, which makes it intractable to find optimal transmit covariance matrices. To overcome this challenge, we form a weighted sum-rate maximization problem for each network and apply the block successive maximization method. This method updates the primal variable blocks successively by maximizing local lower-bounds of the original problem, followed by an update for the dual variable in a closed form. The solutions for these problems design covariance matrices corresponding to the transmitted messages such that they are transmitted securely and reliably. Numerical results illustrate the efficacy of the proposed signaling design for both MIMO-NOMA networks.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114824405","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814502
Zhitong Ni, J. A. Zhang, Xiaojing Huang, Kai Yang
This paper proposes a scheme that solves two challenging problems in parameter estimation using communication signals: (1) asynchronous transmitter and receiver; and (2) sensing receiver with a small number of antennas. These problems exist in parameter estimation for perceptive mobile networks and WiFi. The geometrically-separated transmitter and receiver in communications are typically asynchronous at clock level. For a small base-station or WiFi, the number of antenna elements in an array is usually limited, which limits the resolution of estimating the angle-of-arrivals (AOAs) of multipath signals. In this paper, we employ cross-antenna cross-correlation (CACC) operation to resolve the asynchronous issue and use the CACC outputs to generate a multi-domain signal block that combines three-domain receive samples to efficiently increase the resolution of AOAs. The proposed scheme enables the direct use of uplink communication signals for radio sensing, without requiring any modifications on infrastructure or advanced hardware, such as a full-duplex transceiver. It also enables the estimation of more number of paths than the number of antennas, hence sensing in a small base-station or WiFi becomes possible.
{"title":"Asynchronous Uplink Sensors Fused in Perceptive Mobile Networks","authors":"Zhitong Ni, J. A. Zhang, Xiaojing Huang, Kai Yang","doi":"10.1109/iccworkshops53468.2022.9814502","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814502","url":null,"abstract":"This paper proposes a scheme that solves two challenging problems in parameter estimation using communication signals: (1) asynchronous transmitter and receiver; and (2) sensing receiver with a small number of antennas. These problems exist in parameter estimation for perceptive mobile networks and WiFi. The geometrically-separated transmitter and receiver in communications are typically asynchronous at clock level. For a small base-station or WiFi, the number of antenna elements in an array is usually limited, which limits the resolution of estimating the angle-of-arrivals (AOAs) of multipath signals. In this paper, we employ cross-antenna cross-correlation (CACC) operation to resolve the asynchronous issue and use the CACC outputs to generate a multi-domain signal block that combines three-domain receive samples to efficiently increase the resolution of AOAs. The proposed scheme enables the direct use of uplink communication signals for radio sensing, without requiring any modifications on infrastructure or advanced hardware, such as a full-duplex transceiver. It also enables the estimation of more number of paths than the number of antennas, hence sensing in a small base-station or WiFi becomes possible.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130527363","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9882151
{"title":"Welcome Message from the Technical Program Committee Chair","authors":"","doi":"10.1109/iccworkshops53468.2022.9882151","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9882151","url":null,"abstract":"","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133146297","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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