Pub Date : 2024-11-05DOI: 10.1109/JSTSP.2024.3459324
{"title":"IEEE Signal Processing Society Information","authors":"","doi":"10.1109/JSTSP.2024.3459324","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3459324","url":null,"abstract":"","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 4","pages":"C2-C2"},"PeriodicalIF":8.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10744618","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587589","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 : 2024-11-05DOI: 10.1109/JSTSP.2024.3459322
{"title":"IEEE Signal Processing Society Information","authors":"","doi":"10.1109/JSTSP.2024.3459322","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3459322","url":null,"abstract":"","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 4","pages":"C3-C3"},"PeriodicalIF":8.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10744789","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587617","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 : 2024-11-05DOI: 10.1109/JSTSP.2024.3465108
Ahmet M. Elbir;Kumar Vijay Mishra;Özlem Tuğfe Demir;Emil Björnson;Angel Lozano
{"title":"Introduction to the Special Issue Near-Field Signal Processing: Algorithms, Implementations and Applications","authors":"Ahmet M. Elbir;Kumar Vijay Mishra;Özlem Tuğfe Demir;Emil Björnson;Angel Lozano","doi":"10.1109/JSTSP.2024.3465108","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3465108","url":null,"abstract":"","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 4","pages":"541-545"},"PeriodicalIF":8.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10744777","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587615","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 : 2024-10-23DOI: 10.1109/JSTSP.2024.3435465
Ziyi Wang;Zhenyu Liu;Yuan Shen;Andrea Conti;Moe Z. Win
Reconfigurable intelligent surfaces (RISs) are proposed to control complex wireless environments in next-generation networks. In particular, wideband RISs can play a key role in high-accuracy location awareness, which calls for models that consider the frequency-selectivity of metasurfaces. This paper presents a general signal model for wideband systems with RISs and establishes a Fisher information analysis to determine the theoretical limits of wideband localization with RISs. In addition, synthetic RISs are proposed to mitigate the multiplicative fading effect caused by the scattering property of RISs. Special scenarios including complete coupling and complete decoupling are further investigated. Results show that with the proposed models, a wideband RIS with a polynomial phase response per element provides more position information than those with more degrees of freedom (DOFs) in piecewise-constant phase response per element. Furthermore, velocity-induced information allows a dynamic RIS to provide more position information than a static RIS. Additionally, a dynamic RIS can be synthesized through multiple measurements to outperform a large one.
{"title":"Holographic Localization With Synthetic Reconfigurable Intelligent Surfaces","authors":"Ziyi Wang;Zhenyu Liu;Yuan Shen;Andrea Conti;Moe Z. Win","doi":"10.1109/JSTSP.2024.3435465","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3435465","url":null,"abstract":"Reconfigurable intelligent surfaces (RISs) are proposed to control complex wireless environments in next-generation networks. In particular, wideband RISs can play a key role in high-accuracy location awareness, which calls for models that consider the frequency-selectivity of metasurfaces. This paper presents a general signal model for wideband systems with RISs and establishes a Fisher information analysis to determine the theoretical limits of wideband localization with RISs. In addition, synthetic RISs are proposed to mitigate the multiplicative fading effect caused by the scattering property of RISs. Special scenarios including complete coupling and complete decoupling are further investigated. Results show that with the proposed models, a wideband RIS with a polynomial phase response per element provides more position information than those with more degrees of freedom (DOFs) in piecewise-constant phase response per element. Furthermore, velocity-induced information allows a dynamic RIS to provide more position information than a static RIS. Additionally, a dynamic RIS can be synthesized through multiple measurements to outperform a large one.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 4","pages":"603-618"},"PeriodicalIF":8.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587536","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 : 2024-09-04DOI: 10.1109/JSTSP.2024.3424083
{"title":"IEEE Signal Processing Society Information","authors":"","doi":"10.1109/JSTSP.2024.3424083","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3424083","url":null,"abstract":"","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 3","pages":"C3-C3"},"PeriodicalIF":8.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10665749","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137526","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 : 2024-09-04DOI: 10.1109/JSTSP.2024.3424079
{"title":"IEEE Signal Processing Society Information","authors":"","doi":"10.1109/JSTSP.2024.3424079","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3424079","url":null,"abstract":"","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 3","pages":"C2-C2"},"PeriodicalIF":8.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10665923","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137558","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 : 2024-09-04DOI: 10.1109/JSTSP.2024.3445048
Wenbo Ding
{"title":"Editorial Introduction for the Special Issue on Intelligent Robotics: Sensing, Signal Processing and Interaction","authors":"Wenbo Ding","doi":"10.1109/JSTSP.2024.3445048","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3445048","url":null,"abstract":"","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 3","pages":"263-266"},"PeriodicalIF":8.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10665936","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137522","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 : 2024-08-15DOI: 10.1109/JSTSP.2024.3442469
Shekhar Kumar Yadav;S. R. M. Prasanna;Nithin V. George
Three-dimensional arrays can localize sources anywhere in the spatial domain without any ambiguity. Among these arrays, the spherical microphone array (SMA) has gained widespread usage in acoustic source localization and beamforming. However, SMAs are bulky, making them undesirable in applications with space and power constraints. To deal with this issue, arrays with microphones placed only in a sector of a sphere have been developed along with various techniques for localizing far-field sources in the spherical sector harmonics (S 2 H) domain. This work addresses near-field acoustic localization and beamforming using a spherical sector microphone array. We first introduce a representation of spherical waves from a near-field point source in the S 2 H domain using the orthonormal S 2 H basis functions. Then, using the representation, we develop an array model for when a spherical sector array is placed in a wavefield created by multiple near-field sources in the S 2 H domain. We highlight the advantages of the developed array model over the baseline near-field spatial domain array model. Using the developed array model, two algorithms are proposed for the joint estimation of the range, elevation and azimuth locations of near-field sources, namely NF-S 2 H-MUSIC and NF-S 2 H-MVDR. Further, a near-field beamforming algorithm capable of radial and angular filtering in the S 2 H domain is also presented. Finally, we present the Cramer-Rao Bound (CRB) for range, elevation and azimuth estimation in the S 2 H domain for near-field sources. The performances of the proposed algorithms are assessed using extensive near-field localization and beamforming simulations and an experiment.
{"title":"NeaSource Localization and Beamforming in the Spherical Sector Harmonics Domain","authors":"Shekhar Kumar Yadav;S. R. M. Prasanna;Nithin V. George","doi":"10.1109/JSTSP.2024.3442469","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3442469","url":null,"abstract":"Three-dimensional arrays can localize sources anywhere in the spatial domain without any ambiguity. Among these arrays, the spherical microphone array (SMA) has gained widespread usage in acoustic source localization and beamforming. However, SMAs are bulky, making them undesirable in applications with space and power constraints. To deal with this issue, arrays with microphones placed only in a sector of a sphere have been developed along with various techniques for localizing far-field sources in the spherical sector harmonics (S\u0000<sup>2</sup>\u0000H) domain. This work addresses near-field acoustic localization and beamforming using a spherical sector microphone array. We first introduce a representation of spherical waves from a near-field point source in the S\u0000<sup>2</sup>\u0000H domain using the orthonormal S\u0000<sup>2</sup>\u0000H basis functions. Then, using the representation, we develop an array model for when a spherical sector array is placed in a wavefield created by multiple near-field sources in the S\u0000<sup>2</sup>\u0000H domain. We highlight the advantages of the developed array model over the baseline near-field spatial domain array model. Using the developed array model, two algorithms are proposed for the joint estimation of the range, elevation and azimuth locations of near-field sources, namely NF-S\u0000<sup>2</sup>\u0000H-MUSIC and NF-S\u0000<sup>2</sup>\u0000H-MVDR. Further, a near-field beamforming algorithm capable of radial and angular filtering in the S\u0000<sup>2</sup>\u0000H domain is also presented. Finally, we present the Cramer-Rao Bound (CRB) for range, elevation and azimuth estimation in the S\u0000<sup>2</sup>\u0000H domain for near-field sources. The performances of the proposed algorithms are assessed using extensive near-field localization and beamforming simulations and an experiment.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 4","pages":"546-560"},"PeriodicalIF":8.7,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587556","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 : 2024-07-22DOI: 10.1109/JSTSP.2024.3431203
Kevin Riou;Kaiwen Dong;Kevin Subrin;Patrick Le Callet
Traditional passive point cloud acquisition systems, such as lidars or stereo cameras, can be impractical in real-life and industrial use cases. Firstly, some extreme environments may preclude the use of these sensors. Secondly, they capture information from the entire scene instead of focusing on areas relevant to the end task, such as object recognition and localization. In contrast, we propose to train a Reinforcement Learning (RL) agent with dual objectives: i) control a robot equipped with a tactile (or laser) sensor to iteratively collect a few relevant points from the scene, and ii) recognize and localize objects from the sparse point cloud which has been collected. The iterative point sampling strategy, referred to as an active sampling strategy, is jointly trained with the classifier and the pose estimator to ensure efficient exploration that focuses on areas relevant to the recognition task. To achive these two objectives, we introduce three RL reward terms: classification, exploration, and pose estimation rewards. These rewards serve the purpose of offering guidance and supervision in their respective domain, allowing us to delve into their individual impacts and contributions. We compare the proposed framework to both active sampling strategies and passive hard-coded sampling strategies coupled with state-of-the-art point cloud classifiers. Furthermore, we evaluate our framework in realistic scenarios, considering realistic and similar objects, as well as accounting for uncertainty in the object's position in the workspace.
{"title":"Reinforcement Learning Based Tactile Sensing for Active Point Cloud Acquisition, Recognition and Localization","authors":"Kevin Riou;Kaiwen Dong;Kevin Subrin;Patrick Le Callet","doi":"10.1109/JSTSP.2024.3431203","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3431203","url":null,"abstract":"Traditional passive point cloud acquisition systems, such as lidars or stereo cameras, can be impractical in real-life and industrial use cases. Firstly, some extreme environments may preclude the use of these sensors. Secondly, they capture information from the entire scene instead of focusing on areas relevant to the end task, such as object recognition and localization. In contrast, we propose to train a Reinforcement Learning (RL) agent with dual objectives: i) control a robot equipped with a tactile (or laser) sensor to iteratively collect a few relevant points from the scene, and ii) recognize and localize objects from the sparse point cloud which has been collected. The iterative point sampling strategy, referred to as an active sampling strategy, is jointly trained with the classifier and the pose estimator to ensure efficient exploration that focuses on areas relevant to the recognition task. To achive these two objectives, we introduce three RL reward terms: classification, exploration, and pose estimation rewards. These rewards serve the purpose of offering guidance and supervision in their respective domain, allowing us to delve into their individual impacts and contributions. We compare the proposed framework to both active sampling strategies and passive hard-coded sampling strategies coupled with state-of-the-art point cloud classifiers. Furthermore, we evaluate our framework in realistic scenarios, considering realistic and similar objects, as well as accounting for uncertainty in the object's position in the workspace.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 3","pages":"299-311"},"PeriodicalIF":8.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137576","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 : 2024-07-22DOI: 10.1109/JSTSP.2024.3431919
Hongwei Hou;Xuan He;Tianhao Fang;Xinping Yi;Wenjin Wang;Shi Jin
This paper investigates the uplink channel estimation of the millimeter-wave (mmWave) extremely large-scale multiple-input-multiple-output (XL-MIMO) communication system in the beam-delay domain, taking into account the near-field and beam-squint effects due to the transmission bandwidth and array aperture growth. Specifically, we model spatial-frequency domain channels in the beam-delay domain to explore inter-antenna and inter-subcarrier correlations. Within this model, the frequency-dependent hybrid-field beam domain steering vectors are introduced to describe the near-field and beam-squint effects. The independent and non-identically distributed Bernoulli-Gaussian models with unknown prior hyperparameters are employed to capture the sparsity in the beam-delay domain, posing a challenge for channel estimation. Under the constrained Bethe free energy minimization framework, we design different structures and constraints on trial beliefs to develop hybrid message passing (HMP) algorithms, thus achieving efficient joint estimation of beam-delay domain channel and prior hyperparameters. To further improve the model accuracy, the multidimensional grid point perturbation (MDGPP)-based representation is presented, which assigns individual perturbation parameters to each multidimensional discrete grid. By treating the MDGPP parameters as unknown hyperparameters, we propose the two-stage HMP algorithm for MDGPP-based channel estimation, where the output of the initial stage is pruned for the refinement stage to reduce the computational complexity. Numerical simulations demonstrate the significant superiority of the proposed algorithm over benchmarks with both near-field and beam-squint effects.
{"title":"Beam-Delay Domain Channel Estimation for mmWave XL-MIMO Systems","authors":"Hongwei Hou;Xuan He;Tianhao Fang;Xinping Yi;Wenjin Wang;Shi Jin","doi":"10.1109/JSTSP.2024.3431919","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3431919","url":null,"abstract":"This paper investigates the uplink channel estimation of the millimeter-wave (mmWave) extremely large-scale multiple-input-multiple-output (XL-MIMO) communication system in the beam-delay domain, taking into account the near-field and beam-squint effects due to the transmission bandwidth and array aperture growth. Specifically, we model spatial-frequency domain channels in the beam-delay domain to explore inter-antenna and inter-subcarrier correlations. Within this model, the frequency-dependent hybrid-field beam domain steering vectors are introduced to describe the near-field and beam-squint effects. The independent and non-identically distributed Bernoulli-Gaussian models with unknown prior hyperparameters are employed to capture the sparsity in the beam-delay domain, posing a challenge for channel estimation. Under the constrained Bethe free energy minimization framework, we design different structures and constraints on trial beliefs to develop hybrid message passing (HMP) algorithms, thus achieving efficient joint estimation of beam-delay domain channel and prior hyperparameters. To further improve the model accuracy, the multidimensional grid point perturbation (MDGPP)-based representation is presented, which assigns individual perturbation parameters to each multidimensional discrete grid. By treating the MDGPP parameters as unknown hyperparameters, we propose the two-stage HMP algorithm for MDGPP-based channel estimation, where the output of the initial stage is pruned for the refinement stage to reduce the computational complexity. Numerical simulations demonstrate the significant superiority of the proposed algorithm over benchmarks with both near-field and beam-squint effects.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 4","pages":"646-661"},"PeriodicalIF":8.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587630","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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