Pub Date : 2023-10-06DOI: 10.1109/JSTSP.2023.3322655
John A. Hodge;Kumar Vijay Mishra;Brian M. Sadler;Amir I. Zaghloul
Higher spectral and energy efficiencies are the envisioned defining characteristics of high data-rate sixth-generation (6G) wireless networks. One of the enabling technologies to meet these requirements is index modulation (IM), which transmits information through permutations of indices of spatial, frequency, or temporal media. In this paper, we propose novel electromagnetics-compliant designs of reconfigurable intelligent surface (RIS) apertures for realizing IM in 6G transceivers. We consider RIS modeling and implementation of spatial and subcarrier IMs, including beam steering, spatial multiplexing, and phase modulation capabilities. Numerical experiments for our proposed implementations show that the bit error rates obtained via RIS-aided IM outperform traditional implementations. We further establish the programmability of these transceivers to vary the reflection phase and generate frequency harmonics for IM through full-wave electromagnetic analyses of a specific reflect-array metasurface implementation.
更高的频谱效率和能效是第六代（6G）高数据速率无线网络的决定性特征。索引调制（IM）是满足这些要求的使能技术之一，它通过空间、频率或时间介质索引的排列来传输信息。在本文中，我们提出了符合电磁学原理的可重构智能表面（RIS）孔径新设计，以在 6G 收发器中实现 IM。我们考虑了空间和子载波 IM 的 RIS 建模和实现，包括波束转向、空间复用和相位调制功能。对我们提出的实现方法进行的数值实验表明，通过 RIS 辅助 IM 获得的误码率优于传统实现方法。通过对特定反射阵列元表面实施方案的全波电磁分析，我们进一步确定了这些收发器的可编程性，以改变反射相位并为 IM 生成频率谐波。
{"title":"Index-Modulated Metasurface Transceiver Design Using Reconfigurable Intelligent Surfaces for 6G Wireless Networks","authors":"John A. Hodge;Kumar Vijay Mishra;Brian M. Sadler;Amir I. Zaghloul","doi":"10.1109/JSTSP.2023.3322655","DOIUrl":"10.1109/JSTSP.2023.3322655","url":null,"abstract":"Higher spectral and energy efficiencies are the envisioned defining characteristics of high data-rate sixth-generation (6G) wireless networks. One of the enabling technologies to meet these requirements is index modulation (IM), which transmits information through permutations of indices of spatial, frequency, or temporal media. In this paper, we propose novel electromagnetics-compliant designs of reconfigurable intelligent surface (RIS) apertures for realizing IM in 6G transceivers. We consider RIS modeling and implementation of spatial and subcarrier IMs, including beam steering, spatial multiplexing, and phase modulation capabilities. Numerical experiments for our proposed implementations show that the bit error rates obtained via RIS-aided IM outperform traditional implementations. We further establish the programmability of these transceivers to vary the reflection phase and generate frequency harmonics for IM through full-wave electromagnetic analyses of a specific reflect-array metasurface implementation.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"17 6","pages":"1248-1263"},"PeriodicalIF":7.5,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136009024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.1109/JSTSP.2023.3312914
Li Wang;Xin Wu;Yi Zhang;Xinyun Zhang;Lianming Xu;Zhihua Wu;Aiguo Fei
The object inference for augmented reality (AR) requires a precise object localization within user's physical environment and the adaptability to dynamic communication conditions. Deep learning (DL) is advantageous in capturing highly-nonlinear features of diverse data sources drawn from complex objects. However, the existing DL techniques may have disfluency or instability issues when deployed on resource-constrained devices with poor communication conditions, resulting in bad user experiences. This article addresses these issues by proposing a deep adaptive inference network called DeepAdaIn-Net for the real-time device-edge collaborative object inference, aiming at reducing feature transmission volume while ensuring high feature-fitting accuracy during inference. Specifically, DeepAdaIn-Net encompasses a partition point selection (PPS) module, a high feature compression learning (HFCL) module, a bandwidth-aware feature configuration (BaFC) module, and a feature consistency compensation (FCC) module. The PPS module minimizes the total execution latency, including inference and transmission latency. The HFCL and BaFC modules can decouple the training and inference process by integrating a high-compression ratio feature encoder with the bandwidth-aware feature configuration, which ensures that the compressed data can adapt to the varying communication bandwidths. The FCC module fills the information gaps among the compressed features, guaranteeing high feature expression ability. We conduct extensive experiments to validate DeepAdaIn-Net using two object inference datasets: COCO2017 and emergency fire datasets, and the results demonstrate that our approach outperforms several conventional methods by deriving an optimal 123x feature compression for $640times 640$ images, which results in a mere 63.3 ms total latency and an accuracy loss of less than 3% when operating at a bandwidth of 16 Mbps.
{"title":"DeepAdaIn-Net: Deep Adaptive Device-Edge Collaborative Inference for Augmented Reality","authors":"Li Wang;Xin Wu;Yi Zhang;Xinyun Zhang;Lianming Xu;Zhihua Wu;Aiguo Fei","doi":"10.1109/JSTSP.2023.3312914","DOIUrl":"10.1109/JSTSP.2023.3312914","url":null,"abstract":"The object inference for augmented reality (AR) requires a precise object localization within user's physical environment and the adaptability to dynamic communication conditions. Deep learning (DL) is advantageous in capturing highly-nonlinear features of diverse data sources drawn from complex objects. However, the existing DL techniques may have disfluency or instability issues when deployed on resource-constrained devices with poor communication conditions, resulting in bad user experiences. This article addresses these issues by proposing a deep adaptive inference network called DeepAdaIn-Net for the real-time device-edge collaborative object inference, aiming at reducing feature transmission volume while ensuring high feature-fitting accuracy during inference. Specifically, DeepAdaIn-Net encompasses a partition point selection (PPS) module, a high feature compression learning (HFCL) module, a bandwidth-aware feature configuration (BaFC) module, and a feature consistency compensation (FCC) module. The PPS module minimizes the total execution latency, including inference and transmission latency. The HFCL and BaFC modules can decouple the training and inference process by integrating a high-compression ratio feature encoder with the bandwidth-aware feature configuration, which ensures that the compressed data can adapt to the varying communication bandwidths. The FCC module fills the information gaps among the compressed features, guaranteeing high feature expression ability. We conduct extensive experiments to validate DeepAdaIn-Net using two object inference datasets: COCO2017 and emergency fire datasets, and the results demonstrate that our approach outperforms several conventional methods by deriving an optimal 123x feature compression for \u0000<inline-formula><tex-math>$640times 640$</tex-math></inline-formula>\u0000 images, which results in a mere 63.3 ms total latency and an accuracy loss of less than 3% when operating at a bandwidth of 16 Mbps.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"17 5","pages":"1052-1063"},"PeriodicalIF":7.5,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135650942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1109/JSTSP.2023.3326801
Yongpeng Wu;Erik G. Larsson;Jing Li;Angel Lozano;Luce Morin;Mai Xu;Chengshan Xiao;Wei Yang
Future wireless networks are expected to support ubiquitous extended reality (XR) with human-to-human communications. XR is a term that refers to all real-and-virtual combined environments and human-machine interactions generated by computer technology and wearables, where the ‘X’ represents any current or future spatial computing technology. XR includes augmented reality (AR), mixed reality (MR), and virtual reality (VR) that all are immersive at different levels and entail distinct degrees of sensory inputs. The ultra-high resolution, detailed representation, panoramic scenery, and multi-stimuli of XR provide a unique immersive experience, allowing users to interact within an alternative world. Transmitting XR video, with its ultra-high bit rate and low latency, presents critical challenges to wireless networking.
{"title":"Guest Editorial Signal Processing for XR Communications and Systems","authors":"Yongpeng Wu;Erik G. Larsson;Jing Li;Angel Lozano;Luce Morin;Mai Xu;Chengshan Xiao;Wei Yang","doi":"10.1109/JSTSP.2023.3326801","DOIUrl":"https://doi.org/10.1109/JSTSP.2023.3326801","url":null,"abstract":"Future wireless networks are expected to support ubiquitous extended reality (XR) with human-to-human communications. XR is a term that refers to all real-and-virtual combined environments and human-machine interactions generated by computer technology and wearables, where the ‘X’ represents any current or future spatial computing technology. XR includes augmented reality (AR), mixed reality (MR), and virtual reality (VR) that all are immersive at different levels and entail distinct degrees of sensory inputs. The ultra-high resolution, detailed representation, panoramic scenery, and multi-stimuli of XR provide a unique immersive experience, allowing users to interact within an alternative world. Transmitting XR video, with its ultra-high bit rate and low latency, presents critical challenges to wireless networking.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"17 5","pages":"913-918"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10319123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138138065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1109/JSTSP.2023.3323654
Chenxing Li;Yiping Duan;Qiyuan Du;Shiqi Sun;Xin Deng;Xiaoming Tao
With the development of computer science and deep learning networks, AI generation technology is becoming increasingly mature. Video has become one of the most important information carriers in our daily life because of their large amount of data and information. However, because of their large amount of information and complex semantics, video generation models, especially High Definition (HD) video, have been a difficult problem in the field of deep learning. Video semantic representation and semantic reconstruction are difficult tasks. Because video content is changeable and information is highly correlated, we propose a HD video generation model from a spatio-temporal scene graph: the spatio-temporal scene graph to video (StSg2vid) model. First, we enter the spatio-temporal scene graph sequence as the semantic representation model of the information in each frame of the video. The scene graph used to describe the semantic information of each frame contains the motion progress of the object in the video at that moment, which is equivalent to a clock. A spatio-temporal scene graph transmits the relationship information between objects through the graph convolutional neural network and predicts the scene layout of the moment. Lastly, the image generation model predicts the frame image of the current moment. The frame at each moment depends on the scene layout at the current moment and the frame and scene layout at the previous moment. We introduced the flow net, wrapping prediction model and the spatially-adaptive normalization (SPADE) network to generate images of each frame forecast. We used the Action genome dataset. Compared with the current state-of-the-art algorithms, the videos generated by our model achieve better results in both quantitative indicators and user evaluations. In addition, we also generalized the StSg2vid model into virtual reality (VR) videos of indoor scenes, preliminarily explored the generation method of VR videos, and achieved good results.
{"title":"VR+HD: Video Semantic Reconstruction From Spatio-Temporal Scene Graphs","authors":"Chenxing Li;Yiping Duan;Qiyuan Du;Shiqi Sun;Xin Deng;Xiaoming Tao","doi":"10.1109/JSTSP.2023.3323654","DOIUrl":"10.1109/JSTSP.2023.3323654","url":null,"abstract":"With the development of computer science and deep learning networks, AI generation technology is becoming increasingly mature. Video has become one of the most important information carriers in our daily life because of their large amount of data and information. However, because of their large amount of information and complex semantics, video generation models, especially High Definition (HD) video, have been a difficult problem in the field of deep learning. Video semantic representation and semantic reconstruction are difficult tasks. Because video content is changeable and information is highly correlated, we propose a HD video generation model from a spatio-temporal scene graph: the spatio-temporal scene graph to video (StSg2vid) model. First, we enter the spatio-temporal scene graph sequence as the semantic representation model of the information in each frame of the video. The scene graph used to describe the semantic information of each frame contains the motion progress of the object in the video at that moment, which is equivalent to a clock. A spatio-temporal scene graph transmits the relationship information between objects through the graph convolutional neural network and predicts the scene layout of the moment. Lastly, the image generation model predicts the frame image of the current moment. The frame at each moment depends on the scene layout at the current moment and the frame and scene layout at the previous moment. We introduced the flow net, wrapping prediction model and the spatially-adaptive normalization (SPADE) network to generate images of each frame forecast. We used the Action genome dataset. Compared with the current state-of-the-art algorithms, the videos generated by our model achieve better results in both quantitative indicators and user evaluations. In addition, we also generalized the StSg2vid model into virtual reality (VR) videos of indoor scenes, preliminarily explored the generation method of VR videos, and achieved good results.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"17 5","pages":"935-948"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136258951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1109/JSTSP.2023.3296231
{"title":"IEEE Signal Processing Society Information","authors":"","doi":"10.1109/JSTSP.2023.3296231","DOIUrl":"10.1109/JSTSP.2023.3296231","url":null,"abstract":"","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"17 5","pages":"C2-C2"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10319125","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135783221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1109/JSTSP.2023.3296235
{"title":"IEEE Signal Processing Society Information","authors":"","doi":"10.1109/JSTSP.2023.3296235","DOIUrl":"10.1109/JSTSP.2023.3296235","url":null,"abstract":"","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"17 5","pages":"C3-C3"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10319122","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135783222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-31DOI: 10.1109/JSTSP.2023.3310654
Chaowei Wang;Yehao Li;Feifei Gao;Danhao Deng;Jisong Xu;Yuhan Liu;Weidong Wang
Semantic communication is a novel paradigm that conveys intention or goal from the source to the destination. It can greatly improve communication efficiency, especially for the applications that require extremely low latency and high reliability, such as augmented reality (AR), virtual reality (VR) or extended reality (XR). An adaptive semantic-bit communication structure based on resource efficiency enhancement for XR is proposed, in which part of the XR users employ semantic communication, while others employ the conventional way. We utilize adaptive communication and power allocation to maximize the system-level achievable performance indicated by equivalent semantic rate. The formulated problem is addressed by a two-step optimization. First, we propose a signal-to-interference-plus-noise ratio (SINR)-based paradigm selection scheme as the semantic communication outperforms the conventional way in low and moderate SINR regimes. Then we propose a genetic algorithm-based power allocation to solve the non-convex optimization. Simulation results demonstrate that the proposed scheme achieves a higher equivalent semantic rate against the baseline schemes.
{"title":"Adaptive Semantic-Bit Communication for Extended Reality Interactions","authors":"Chaowei Wang;Yehao Li;Feifei Gao;Danhao Deng;Jisong Xu;Yuhan Liu;Weidong Wang","doi":"10.1109/JSTSP.2023.3310654","DOIUrl":"10.1109/JSTSP.2023.3310654","url":null,"abstract":"Semantic communication is a novel paradigm that conveys intention or goal from the source to the destination. It can greatly improve communication efficiency, especially for the applications that require extremely low latency and high reliability, such as augmented reality (AR), virtual reality (VR) or extended reality (XR). An adaptive semantic-bit communication structure based on resource efficiency enhancement for XR is proposed, in which part of the XR users employ semantic communication, while others employ the conventional way. We utilize adaptive communication and power allocation to maximize the system-level achievable performance indicated by equivalent semantic rate. The formulated problem is addressed by a two-step optimization. First, we propose a signal-to-interference-plus-noise ratio (SINR)-based paradigm selection scheme as the semantic communication outperforms the conventional way in low and moderate SINR regimes. Then we propose a genetic algorithm-based power allocation to solve the non-convex optimization. Simulation results demonstrate that the proposed scheme achieves a higher equivalent semantic rate against the baseline schemes.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"17 5","pages":"1080-1092"},"PeriodicalIF":7.5,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76618608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-28DOI: 10.1109/JSTSP.2023.3309463
Jukka Talvitie;Mikko Säily;Mikko Valkama
Accurate knowledge of the three-dimensional (3D) orientations and 3D locations of the user devices, such as wearable glasses, is of paramount importance in different extended reality (XR) use cases and applications. In this article, we address the corresponding six degrees-of-freedom (6DoF) tracking challenge of 5G-empowered XR devices. We describe a new uplink (UL) carrier phase measurements based estimation approach, allowing for low-latency 3D orientation and 3D location tracking directly at the 5G network base-stations or gNodeBs (gNBs). extended Kalman filter (EKF) based practical signal processing algorithms are described while also the applicable Cramér-Rao lower-bounds (CRLBs) are derived and presented. Also, the related aspect of over-the-air estimation of the XR headset antenna constellation or antenna geometry is addressed. Additionally, the important practical challenges related to user equipment (UE) clock drifting as well as integer ambiguities in carrier phase based methods are both considered. Finally, an extensive set of numerical results is provided in an example indoor factory like environment, covering both 3.5 GHz and 28 GHz network deployments. The obtained results demonstrate the feasibility of continuous 6DoF tracking through the proposed approach, with root mean squared error (RMSE) accuracies below one degree for the 3D orientation and below one centimeter for the 3D location, respectively. The results also demonstrate that UE clock drifting and carrier phase integer ambiguities can both be efficiently estimated and tracked, as part of the overall proposed concept and methods.
{"title":"Orientation and Location Tracking of XR Devices: 5G Carrier Phase-Based Methods","authors":"Jukka Talvitie;Mikko Säily;Mikko Valkama","doi":"10.1109/JSTSP.2023.3309463","DOIUrl":"10.1109/JSTSP.2023.3309463","url":null,"abstract":"Accurate knowledge of the three-dimensional (3D) orientations and 3D locations of the user devices, such as wearable glasses, is of paramount importance in different extended reality (XR) use cases and applications. In this article, we address the corresponding six degrees-of-freedom (6DoF) tracking challenge of 5G-empowered XR devices. We describe a new uplink (UL) carrier phase measurements based estimation approach, allowing for low-latency 3D orientation and 3D location tracking directly at the 5G network base-stations or gNodeBs (gNBs). extended Kalman filter (EKF) based practical signal processing algorithms are described while also the applicable Cramér-Rao lower-bounds (CRLBs) are derived and presented. Also, the related aspect of over-the-air estimation of the XR headset antenna constellation or antenna geometry is addressed. Additionally, the important practical challenges related to user equipment (UE) clock drifting as well as integer ambiguities in carrier phase based methods are both considered. Finally, an extensive set of numerical results is provided in an example indoor factory like environment, covering both 3.5 GHz and 28 GHz network deployments. The obtained results demonstrate the feasibility of continuous 6DoF tracking through the proposed approach, with root mean squared error (RMSE) accuracies below one degree for the 3D orientation and below one centimeter for the 3D location, respectively. The results also demonstrate that UE clock drifting and carrier phase integer ambiguities can both be efficiently estimated and tracked, as part of the overall proposed concept and methods.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"17 5","pages":"919-934"},"PeriodicalIF":7.5,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10232971","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79758062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-14DOI: 10.1109/JSTSP.2023.3304846
Teng Ma;Yue Xiao;Xia Lei;Ming Xiao
Future wireless networks will witness ubiquitous human-machine interactions, where extended reality (XR) is expected to be a key scenario in next-generation mobile systems. In this article, we examine the integrated sensing and communication (ISAC) framework in XR, where a reconfigurable intelligent surface (RIS) may assist user (UE) positioning and communication. Specifically, a practical positioning algorithm based on multiple signal classification (MUSIC) with the aid of specially designed RIS configurations is conceived. Furthermore, we formulate the joint optimization of the UE beamformer and RIS phase shifter to maximize the channel capacity under Cramér-Rao lower bound (CRLB) constraints, which is solved by alternating optimization with gradient projection and manifold optimization. Finally, we use simulation results to demonstrate the feasibility of the conceived positioning algorithm and corroborate the effectiveness of the proposed optimization approach.
{"title":"Integrated Sensing and Communication for Wireless Extended Reality (XR) With Reconfigurable Intelligent Surface","authors":"Teng Ma;Yue Xiao;Xia Lei;Ming Xiao","doi":"10.1109/JSTSP.2023.3304846","DOIUrl":"10.1109/JSTSP.2023.3304846","url":null,"abstract":"Future wireless networks will witness ubiquitous human-machine interactions, where extended reality (XR) is expected to be a key scenario in next-generation mobile systems. In this article, we examine the integrated sensing and communication (ISAC) framework in XR, where a reconfigurable intelligent surface (RIS) may assist user (UE) positioning and communication. Specifically, a practical positioning algorithm based on multiple signal classification (MUSIC) with the aid of specially designed RIS configurations is conceived. Furthermore, we formulate the joint optimization of the UE beamformer and RIS phase shifter to maximize the channel capacity under Cramér-Rao lower bound (CRLB) constraints, which is solved by alternating optimization with gradient projection and manifold optimization. Finally, we use simulation results to demonstrate the feasibility of the conceived positioning algorithm and corroborate the effectiveness of the proposed optimization approach.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"17 5","pages":"980-994"},"PeriodicalIF":7.5,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81444716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-10DOI: 10.1109/JSTSP.2023.3304117
Yuquan Xiao;Qinghe Du;Wenchi Cheng;Nan Lu
Privacy and security assurance over wireless transmissions is one of critical issues for future extended reality (XR) communication systems expected to be supported by the sixth generation of mobile communications networks (6G). In light of the strong anti-eavesdropping capability, physical layer security (PLS) techniques have been recognized as a competitive candidate to provide secure transmissions for XR services. However, existing key performance evaluation metrics, such as the security capacity and the outage security capacity, cannot well capture diverse features of quality-of-security (QoSec) requirements raised by XR services. To overcome the problem, in this article we propose a unified statistical security model that can characterize fine-grained security requirements for various XR applications. Specifically, the eavesdropping process at the eavesdropper is modeled by a queuing system. The arrival process represents the legitimate user's streaming data correctly-captured by the eavesdropper. The departure process represents data that are outdated and moved out of the queue, fitting the essential time-sensitive nature of XR applications. Yet storing the overheard data in the queue is not equivalent to data recovery, the eavesdropper has to accumulate a sufficient of amount correctly-captured data in the queue to successfully decipher some data each time. Under this framework, leveraging the effective bandwidth theory in statistically queuing analyses, we develop the concept of statistical security capacity, which is used to evaluate the legitimate user's throughput with the constrained information level leaked to the eavesdropper. The statistical security model is featured with a parameter called QoSec exponent, quantitatively indicating the fine-grained level of security requirement. Following this model, we formulate the nonconvex statistical-security-capacity maximization problems with the internal and external eavesdroppers, respectively, associated with the cases with and without eavesdropper's CSI known at the legitimate transmitter. Solving the two problems, we derive the corresponding optimal resource schemes over the time-varying fading channels. Simulation results demonstrate our proposal as an effective model for security requirements, and our scheme can significantly improve security-constrained throughput in XR communications compared to the baseline schemes.
{"title":"Secure Communication Guarantees for Diverse Extended-Reality Applications: A Unified Statistical Security Model","authors":"Yuquan Xiao;Qinghe Du;Wenchi Cheng;Nan Lu","doi":"10.1109/JSTSP.2023.3304117","DOIUrl":"10.1109/JSTSP.2023.3304117","url":null,"abstract":"Privacy and security assurance over wireless transmissions is one of critical issues for future extended reality (XR) communication systems expected to be supported by the sixth generation of mobile communications networks (6G). In light of the strong anti-eavesdropping capability, physical layer security (PLS) techniques have been recognized as a competitive candidate to provide secure transmissions for XR services. However, existing key performance evaluation metrics, such as the security capacity and the outage security capacity, cannot well capture diverse features of quality-of-security (QoSec) requirements raised by XR services. To overcome the problem, in this article we propose a unified statistical security model that can characterize fine-grained security requirements for various XR applications. Specifically, the eavesdropping process at the eavesdropper is modeled by a queuing system. The arrival process represents the legitimate user's streaming data correctly-captured by the eavesdropper. The departure process represents data that are outdated and moved out of the queue, fitting the essential time-sensitive nature of XR applications. Yet storing the overheard data in the queue is not equivalent to data recovery, the eavesdropper has to accumulate a sufficient of amount correctly-captured data in the queue to successfully decipher some data each time. Under this framework, leveraging the effective bandwidth theory in statistically queuing analyses, we develop the concept of statistical security capacity, which is used to evaluate the legitimate user's throughput with the constrained information level leaked to the eavesdropper. The statistical security model is featured with a parameter called QoSec exponent, quantitatively indicating the fine-grained level of security requirement. Following this model, we formulate the nonconvex statistical-security-capacity maximization problems with the internal and external eavesdroppers, respectively, associated with the cases with and without eavesdropper's CSI known at the legitimate transmitter. Solving the two problems, we derive the corresponding optimal resource schemes over the time-varying fading channels. Simulation results demonstrate our proposal as an effective model for security requirements, and our scheme can significantly improve security-constrained throughput in XR communications compared to the baseline schemes.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"17 5","pages":"1007-1021"},"PeriodicalIF":7.5,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75239149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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