Pub Date : 2024-06-24DOI: 10.1109/JSAC.2024.3413949
Seungnyun Kim;Jihoon Moon;Jiao Wu;Byonghyo Shim;Moe Z. Win
To meet the ever-increasing data rate demand expected in 6G networks, terahertz (THz) ultra-massive (UM) multiple-input multiple-output (MIMO) systems have gained much attention recently. One notable aspect of these systems is that the deployment of an extremely large-scale antenna array and high transmission frequency result in an expansion of the near-field region where the electromagnetic (EM) radiation is modeled as a spherical wave. In the near-field region, the channel becomes a function of a position of a user equipment (UE) rather than the direction, giving rise to a beam focusing operation that focuses the signal power onto the specific position. However, the traditional approaches relying on the sweeping of discretized beam codewords cannot support this ultra-sharp beam focusing operation in THz UM-MIMO systems. This paper proposes a novel beam focusing technique based on sensing and computer vision (CV) technologies. The essence of the proposed scheme is to estimate the UE’s position from the vision information using the CV technique and then generates the beam heading towards the estimated position. By replacing the discretized and time-consuming beam sweeping operation with a highly precise CV-based positioning, the positioning accuracy as well as the beam focusing gain can be improved significantly. Numerical results show that the proposed scheme achieves significant positioning accuracy and data rate gains over the conventional codebook-based beam focusing schemes.
{"title":"Vision-Aided Positioning and Beam Focusing for 6G Terahertz Communications","authors":"Seungnyun Kim;Jihoon Moon;Jiao Wu;Byonghyo Shim;Moe Z. Win","doi":"10.1109/JSAC.2024.3413949","DOIUrl":"10.1109/JSAC.2024.3413949","url":null,"abstract":"To meet the ever-increasing data rate demand expected in 6G networks, terahertz (THz) ultra-massive (UM) multiple-input multiple-output (MIMO) systems have gained much attention recently. One notable aspect of these systems is that the deployment of an extremely large-scale antenna array and high transmission frequency result in an expansion of the near-field region where the electromagnetic (EM) radiation is modeled as a spherical wave. In the near-field region, the channel becomes a function of a position of a user equipment (UE) rather than the direction, giving rise to a beam focusing operation that focuses the signal power onto the specific position. However, the traditional approaches relying on the sweeping of discretized beam codewords cannot support this ultra-sharp beam focusing operation in THz UM-MIMO systems. This paper proposes a novel beam focusing technique based on sensing and computer vision (CV) technologies. The essence of the proposed scheme is to estimate the UE’s position from the vision information using the CV technique and then generates the beam heading towards the estimated position. By replacing the discretized and time-consuming beam sweeping operation with a highly precise CV-based positioning, the positioning accuracy as well as the beam focusing gain can be improved significantly. Numerical results show that the proposed scheme achieves significant positioning accuracy and data rate gains over the conventional codebook-based beam focusing schemes.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141448675","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 : 2024-06-19DOI: 10.1109/JSAC.2024.3414600
Jingzhi Hu;Dusit Niyato;Jun Luo
Integrated sensing and communications (ISAC) is pivotal for 6G communications and is boosted by the rapid development of reconfigurable intelligent surfaces (RISs). Using the channel state information (CSI) across multiple frequency bands, RIS-aided multi-band ISAC systems can potentially track users’ positions with high precision. Though tracking with CSI is desirable as no communication overheads are incurred, it faces challenges due to the multi-modalities of CSI samples, irregular and asynchronous data traffic, and sparse labeled data for learning the tracking function. This paper proposes the X2Track framework, where we model the tracking function by a hierarchical architecture, jointly utilizing multi-modal CSI indicators across multiple bands, and optimize it in a cross-domain manner, tackling the sparsity of labeled data for the target deployment environment (namely, target domain) by adapting the knowledge learned from another environment (namely, source domain). Under X2Track, we design an efficient deep learning algorithm to minimize tracking errors, based on transformer neural networks and adversarial learning techniques. Simulation results verify that X2Track achieves decimeter-level axial tracking errors even under scarce UL data traffic and strong interference conditions and can adapt to diverse deployment environments with fewer than 5% training data, or equivalently, 5 minutes of UE tracks, being labeled.
{"title":"Cross-Domain Learning Framework for Tracking Users in RIS-Aided Multi-Band ISAC Systems With Sparse Labeled Data","authors":"Jingzhi Hu;Dusit Niyato;Jun Luo","doi":"10.1109/JSAC.2024.3414600","DOIUrl":"10.1109/JSAC.2024.3414600","url":null,"abstract":"Integrated sensing and communications (ISAC) is pivotal for 6G communications and is boosted by the rapid development of reconfigurable intelligent surfaces (RISs). Using the channel state information (CSI) across multiple frequency bands, RIS-aided multi-band ISAC systems can potentially track users’ positions with high precision. Though tracking with CSI is desirable as no communication overheads are incurred, it faces challenges due to the multi-modalities of CSI samples, irregular and asynchronous data traffic, and sparse labeled data for learning the tracking function. This paper proposes the X2Track framework, where we model the tracking function by a hierarchical architecture, jointly utilizing multi-modal CSI indicators across multiple bands, and optimize it in a cross-domain manner, tackling the sparsity of labeled data for the target deployment environment (namely, target domain) by adapting the knowledge learned from another environment (namely, source domain). Under X2Track, we design an efficient deep learning algorithm to minimize tracking errors, based on transformer neural networks and adversarial learning techniques. Simulation results verify that X2Track achieves decimeter-level axial tracking errors even under scarce UL data traffic and strong interference conditions and can adapt to diverse deployment environments with fewer than 5% training data, or equivalently, 5 minutes of UE tracks, being labeled.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10565756","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-18DOI: 10.1109/JSAC.2024.3407451
{"title":"IEEE Journal on Selected Areas in Communications Publication Information","authors":"","doi":"10.1109/JSAC.2024.3407451","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3407451","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10561571","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-18DOI: 10.1109/JSAC.2024.3407471
{"title":"IEEE Open Access Publishing","authors":"","doi":"10.1109/JSAC.2024.3407471","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3407471","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10561891","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141422565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-18DOI: 10.1109/JSAC.2024.3379106
Angela Sara Cacciapuoti;Anne Broadbent;Eleni Diamanti;Jacquiline Romero;Stephanie Wehner
The Quantum Internet is envisioned as a global network, interconnecting heterogeneous quantum networks, able to transmit quantum information (qubits, qudits, or continuous variables) and to distribute entangled quantum states with no classical equivalent, by exploiting quantum links in synergy with classical links. The Quantum Internet is disruptive, since it is capable of supporting functionalities with no direct counterpart in classical networks, such as advanced quantum cryptographic services, blind quantum computing, and distributed quantum computing characterized by exponential increases in computing power and new forms of communication. These functionalities have the potential to fundamentally change the world in ways we cannot imagine yet.
{"title":"Guest Editorial The Quantum Internet: Principles, Protocols and Architectures","authors":"Angela Sara Cacciapuoti;Anne Broadbent;Eleni Diamanti;Jacquiline Romero;Stephanie Wehner","doi":"10.1109/JSAC.2024.3379106","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3379106","url":null,"abstract":"The Quantum Internet is envisioned as a global network, interconnecting heterogeneous quantum networks, able to transmit quantum information (qubits, qudits, or continuous variables) and to distribute entangled quantum states with no classical equivalent, by exploiting quantum links in synergy with classical links. The Quantum Internet is disruptive, since it is capable of supporting functionalities with no direct counterpart in classical networks, such as advanced quantum cryptographic services, blind quantum computing, and distributed quantum computing characterized by exponential increases in computing power and new forms of communication. These functionalities have the potential to fundamentally change the world in ways we cannot imagine yet.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10561892","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141422515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-18DOI: 10.1109/JSAC.2024.3407453
{"title":"IEEE Communications Society Information","authors":"","doi":"10.1109/JSAC.2024.3407453","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3407453","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10561898","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141422504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-18DOI: 10.1109/JSAC.2024.3407469
{"title":"TechRxiv: Share Your Preprint Research With the World!","authors":"","doi":"10.1109/JSAC.2024.3407469","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3407469","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10561572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141422502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-17DOI: 10.1109/JSAC.2024.3415082
Haijun Zhang;Bowen Chen;Xiangnan Liu;Chao Ren
The possibility of jointly optimizing location sensing and communication resources, facilitated by the existence of communication and sensing spectrum sharing, is what promotes the system performance to a higher level. However, the rapid mobility of user equipment (UE) can result in inaccurate location estimation, which can severely degrade system performance. Therefore, the precise UE location sensing and resource allocation issues are investigated in a spectrum sharing sixth generation network. An approach is proposed for joint subcarrier and power optimization based on UE location sensing, aiming to minimize system energy consumption. The joint allocation process is separated into two key phases of operation. In the radar location sensing phase, the multipath interference and Doppler effects are considered simultaneously, and the issues of UE’s location and channel state estimation are transformed into a convex optimization problem, which is then solved through gradient descent. In the communication phase, a subcarrier allocation method based on subcarrier weights is proposed. To further minimize system energy consumption, a joint subcarrier and power allocation method is introduced, resolved via the Lagrange multiplier method for the non-convex resource allocation problem. Simulation analysis results indicate that the location sensing algorithm exhibits a prominent improvement in accuracy compared to benchmark algorithms. Simultaneously, the proposed resource allocation scheme also demonstrates a substantial enhancement in performance relative to baseline schemes.
{"title":"Joint Radar Sensing, Location, and Communication Resources Optimization in 6G Network","authors":"Haijun Zhang;Bowen Chen;Xiangnan Liu;Chao Ren","doi":"10.1109/JSAC.2024.3415082","DOIUrl":"10.1109/JSAC.2024.3415082","url":null,"abstract":"The possibility of jointly optimizing location sensing and communication resources, facilitated by the existence of communication and sensing spectrum sharing, is what promotes the system performance to a higher level. However, the rapid mobility of user equipment (UE) can result in inaccurate location estimation, which can severely degrade system performance. Therefore, the precise UE location sensing and resource allocation issues are investigated in a spectrum sharing sixth generation network. An approach is proposed for joint subcarrier and power optimization based on UE location sensing, aiming to minimize system energy consumption. The joint allocation process is separated into two key phases of operation. In the radar location sensing phase, the multipath interference and Doppler effects are considered simultaneously, and the issues of UE’s location and channel state estimation are transformed into a convex optimization problem, which is then solved through gradient descent. In the communication phase, a subcarrier allocation method based on subcarrier weights is proposed. To further minimize system energy consumption, a joint subcarrier and power allocation method is introduced, resolved via the Lagrange multiplier method for the non-convex resource allocation problem. Simulation analysis results indicate that the location sensing algorithm exhibits a prominent improvement in accuracy compared to benchmark algorithms. Simultaneously, the proposed resource allocation scheme also demonstrates a substantial enhancement in performance relative to baseline schemes.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933119","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 : 2024-06-17DOI: 10.1109/JSAC.2024.3414610
Guangyu Wu;Fuhui Zhou;Kai Kit Wong;Xiang-Yang Li
The clustering unmanned aerial vehicles (UAVs) positioning is significant for preventing unauthorized clustering UAVs from causing physical and informational damages. However, current positioning systems suffer from limited sensing view and positioning range, which result in poor positioning performance. In order to tackle those issues, a novel vehicle-mounted radar-vision clustering UAVs positioning system is developed, which achieves precise, wide-area, and dynamic-view sensing and positioning of the clustering UAVs. Moreover, a matching-based spatiotemporal fusion framework is established to mitigate cross-modal and cross-view spatiotemporal misalignment by adaptively exploiting the cross-modal and cross-view feature correlations. Furthermore, we propose an attention-based spatiotemporal fusion method that achieves a trinity projective attention with the unique structure and task-oriented format for effective feature matching and precise clustering UAVs positioning. Our method also exploited the modality-oriented cross-modal feature and the UAV-motion-oriented cross-view UAV spatiotemporal motion feature.We demonstrate the advantages of our proposed framework and positioning method in our developed clustering UAVs positioning system in practice. Experimental results confirm that our proposed method outperforms the benchmark methods in terms of the positioning precision, especially under the occlusion scenarios. Moreover, ablation studies confirm the effectiveness of each unit of our method.
{"title":"A Vehicle-Mounted Radar-Vision System for Precisely Positioning Clustering UAVs","authors":"Guangyu Wu;Fuhui Zhou;Kai Kit Wong;Xiang-Yang Li","doi":"10.1109/JSAC.2024.3414610","DOIUrl":"10.1109/JSAC.2024.3414610","url":null,"abstract":"The clustering unmanned aerial vehicles (UAVs) positioning is significant for preventing unauthorized clustering UAVs from causing physical and informational damages. However, current positioning systems suffer from limited sensing view and positioning range, which result in poor positioning performance. In order to tackle those issues, a novel vehicle-mounted radar-vision clustering UAVs positioning system is developed, which achieves precise, wide-area, and dynamic-view sensing and positioning of the clustering UAVs. Moreover, a matching-based spatiotemporal fusion framework is established to mitigate cross-modal and cross-view spatiotemporal misalignment by adaptively exploiting the cross-modal and cross-view feature correlations. Furthermore, we propose an attention-based spatiotemporal fusion method that achieves a trinity projective attention with the unique structure and task-oriented format for effective feature matching and precise clustering UAVs positioning. Our method also exploited the modality-oriented cross-modal feature and the UAV-motion-oriented cross-view UAV spatiotemporal motion feature.We demonstrate the advantages of our proposed framework and positioning method in our developed clustering UAVs positioning system in practice. Experimental results confirm that our proposed method outperforms the benchmark methods in terms of the positioning precision, especially under the occlusion scenarios. Moreover, ablation studies confirm the effectiveness of each unit of our method.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933120","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 : 2024-06-17DOI: 10.1109/JSAC.2024.3413986
Xiulong Liu;Bojun Zhang;Sheng Chen;Xin Xie;Xinyu Tong;Tao Gu;Keqiu Li
Wireless signal analytics in IoT systems can enable various promising wireless sensing applications such as localization, anomaly detection, and human activity recognition. As a matter of fact, there are significant correlations in terms of dimension, spatial and temporal aspects among wireless signals from multiple sensors. However, none of the wireless sensing research currently in use directly incorporates or exploits the signal correlations. Therefore, there is still substantial scope for improvement in regards to accuracy and robustness. We are introducing a novel framework called Signal Correlation Learning (SCL). This framework utilizes a directed graph to explicitly represent the signal correlation across various wireless sensors. We use signal embedding to depict the correlation features of a multi-dimensional sensor that arise from a multi-sensor system. Then, we perform Kullback-Leibler (KL) divergence on embedding vectors of any pair of sensors in the system to construct a subgraph at a given time point, which can measure the spatial signal correlation of sensors. Subsequently, several subgraphs spanning a specific time frame are fused into a coherent universal graph based on the small-world theory. This universal graph represents the three types of signal correlation simultaneously. A signal correlation aggregation structure is utilized to extract the features from the universal graph. These features can be used to address target sensing problems. We implement SCL in real RFID, Bluetooth, WIFI, and Zigbee systems, and evaluate its performance in three common wireless sensing problems including localization, anomaly detection, and human activity recognition. Extensive experiments demonstrate that our SCL framework significantly outperforms state-of-the-art wireless sensing algorithms by increasing $80%sim 190%$ in terms of accuracy, and by increasing $160%sim 220%$ in terms of robustness.
{"title":"A Wireless Signal Correlation Learning Framework for Accurate and Robust Multi-Modal Sensing","authors":"Xiulong Liu;Bojun Zhang;Sheng Chen;Xin Xie;Xinyu Tong;Tao Gu;Keqiu Li","doi":"10.1109/JSAC.2024.3413986","DOIUrl":"10.1109/JSAC.2024.3413986","url":null,"abstract":"Wireless signal analytics in IoT systems can enable various promising wireless sensing applications such as localization, anomaly detection, and human activity recognition. As a matter of fact, there are significant correlations in terms of dimension, spatial and temporal aspects among wireless signals from multiple sensors. However, none of the wireless sensing research currently in use directly incorporates or exploits the signal correlations. Therefore, there is still substantial scope for improvement in regards to accuracy and robustness. We are introducing a novel framework called Signal Correlation Learning (SCL). This framework utilizes a directed graph to explicitly represent the signal correlation across various wireless sensors. We use signal embedding to depict the correlation features of a multi-dimensional sensor that arise from a multi-sensor system. Then, we perform Kullback-Leibler (KL) divergence on embedding vectors of any pair of sensors in the system to construct a subgraph at a given time point, which can measure the spatial signal correlation of sensors. Subsequently, several subgraphs spanning a specific time frame are fused into a coherent universal graph based on the small-world theory. This universal graph represents the three types of signal correlation simultaneously. A signal correlation aggregation structure is utilized to extract the features from the universal graph. These features can be used to address target sensing problems. We implement SCL in real RFID, Bluetooth, WIFI, and Zigbee systems, and evaluate its performance in three common wireless sensing problems including localization, anomaly detection, and human activity recognition. Extensive experiments demonstrate that our SCL framework significantly outperforms state-of-the-art wireless sensing algorithms by increasing \u0000<inline-formula> <tex-math>$80%sim 190%$ </tex-math></inline-formula>\u0000 in terms of accuracy, and by increasing \u0000<inline-formula> <tex-math>$160%sim 220%$ </tex-math></inline-formula>\u0000 in terms of robustness.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933121","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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