IEEE Journal of Selected Topics in Signal Processing最新文献_第5页

Joint Communications and Sensing Hybrid Beamforming Design via Deep Unfolding 基于深度展开的联合通信与传感混合波束形成设计

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-09-20 DOI: 10.1109/JSTSP.2024.3463403

Nhan Thanh Nguyen;Ly V. Nguyen;Nir Shlezinger;Yonina C. Eldar;A. Lee Swindlehurst;Markku Juntti

{"title":"Joint Communications and Sensing Hybrid Beamforming Design via Deep Unfolding","authors":"Nhan Thanh Nguyen;Ly V. Nguyen;Nir Shlezinger;Yonina C. Eldar;A. Lee Swindlehurst;Markku Juntti","doi":"10.1109/JSTSP.2024.3463403","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3463403","url":null,"abstract":"Joint communications and sensing (JCAS) is envisioned as a key feature in future wireless communications networks. In massive MIMO-JCAS systems, hybrid beamforming (HBF) is typically employed to achieve satisfactory beamforming gains with reasonable hardware cost and power consumption. Due to the coupling of the analog and digital precoders in HBF and the dual objective in JCAS, JCAS-HBF design problems are very challenging and usually require highly complex algorithms. In this paper, we propose a fast HBF design for JCAS based on deep unfolding to optimize a tradeoff between the communications rate and sensing accuracy. We first derive closed-form expressions for the gradients of the communications and sensing objectives with respect to the precoders and demonstrate that the magnitudes of the gradients pertaining to the analog precoder are typically smaller than those associated with the digital precoder. Based on this observation, we propose a modified projected gradient ascent (PGA) method with significantly improved convergence. We then develop a deep unfolded PGA scheme that efficiently optimizes the communications-sensing performance tradeoff with fast convergence thanks to the well-trained hyperparameters. In doing so, we preserve the interpretability and flexibility of the optimizer while leveraging data to improve performance. Finally, our simulations demonstrate the potential of the proposed deep unfolded method, which achieves up to 33.5% higher communications sum rate and 2.5dB lower beampattern error compared with the conventional design based on successive convex approximation and Riemannian manifold optimization. Furthermore, it attains up to a 65% reduction in run time and computational complexity with respect to the PGA procedure without unfolding.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 5","pages":"901-916"},"PeriodicalIF":8.7,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684532","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938143","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}

引用次数: 0

Contrastive and Non-Contrastive Strategies for Federated Self-Supervised Representation Learning and Deep Clustering

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-09-18 DOI: 10.1109/JSTSP.2024.3461311

Runxuan Miao;Erdem Koyuncu

引用次数: 0

Sparse Training for Federated Learning With Regularized Error Correction

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-09-18 DOI: 10.1109/JSTSP.2024.3463505

Ran Greidi;Kobi Cohen

{"title":"Sparse Training for Federated Learning With Regularized Error Correction","authors":"Ran Greidi;Kobi Cohen","doi":"10.1109/JSTSP.2024.3463505","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3463505","url":null,"abstract":"Federated Learning (FL) is an emerging paradigm that allows for decentralized machine learning (ML), where multiple models are collaboratively trained in a privacy-preserving manner. However, training DNN models in FL systems face challenges such as elevated computational and communication costs in complex tasks. Sparse training schemes gain increasing attention in order to scale down the dimensionality of each client transmission. Specifically, sparsification with error correction methods is a promising technique, where only important updates are sent to the parameter server (PS) and the rest are accumulated locally. While error correction methods have shown to achieve a significant sparsification level without harming convergence, pushing sparsity further remains unresolved due to the staleness effect. In this paper, we propose a novel algorithm, dubbed Federated Learning with Accumulated Regularized Embeddings (FLARE), to overcome this challenge. FLARE presents a novel sparse training approach via accumulated pulling of the updated models with regularization on the embeddings in the FL process, providing a powerful solution to the staleness effect, and pushing sparsity to an exceptional level. Our theoretical analysis demonstrates that FLARE not only matches state-of-the-art performance in terms of convergence rate with time, but also achieves significant improvements in scalability with sparsity parameter. The empirical performance of FLARE is validated through extensive experiments on diverse and complex models, achieving a remarkable sparsity level (10 times and more beyond the current state-of-the-art) along with significantly improved accuracy. Additionally, an open-source software package has been developed for the benefit of researchers and developers in related fields.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 6","pages":"1085-1099"},"PeriodicalIF":8.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106526","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}

引用次数: 0

Dual Driven Leaning for Joint Activity Detection and Channel Estimation in Multibeam LEO Satellite Communications 多波束LEO卫星通信联合活动检测与信道估计的双驱动学习

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-09-13 DOI: 10.1109/JSTSP.2024.3461308

Shuntian Zheng;Sheng Wu;Haoge Jia;Jingjing Zhao;Yuanming Shi;Chunxiao Jiang

{"title":"Dual Driven Leaning for Joint Activity Detection and Channel Estimation in Multibeam LEO Satellite Communications","authors":"Shuntian Zheng;Sheng Wu;Haoge Jia;Jingjing Zhao;Yuanming Shi;Chunxiao Jiang","doi":"10.1109/JSTSP.2024.3461308","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3461308","url":null,"abstract":"This paper investigates the uplink massive connectivity by grant-free random access in intelligent reflecting surface (IRS) assisted low earth orbit satellite communications. By leveraging sporadic activity of the ground devices (GDs), the joint device activity detection and channel estimation (JADCE) problem can be addressed by compressive sensing (CS) algorithms, which either fail to satisfy estimation accuracy or suffer from high computation complexities. Consequently, we propose a general data and model dual driven architecture to efficiently solve the JADCE problem through an unfolded iterative network. Specifically, we improve the original multiple-measurement-vectors (MMV) orthogonal approximate message passing (OAMP) algorithm with an unrolled model driven neural network to exploit the sparse beamspace channel. Moreover, we incorporate the data driven in each iteration, termed model and data dual driven OAMP network (DOAMPNet), which adaptively learns channel sparsity and improves channel estimation performance with model guarantees. Extensive simulations are provided to demonstrate the superiority of the proposed model and data dual driven networks compared with existing methods in terms of estimation accuracy. Remarkably, the proposed DOAMPNet reduces pilot overhead by about 40%, and achieves a normalized mean-square error improvement of about 4 dB when signal-to-noise ratio is 10 dB.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 7","pages":"1194-1209"},"PeriodicalIF":8.7,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993250","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}

引用次数: 0

Deep Learning-Based Auto-Encoder for Time-Offset Sub-Faster-Than-Nyquist Downlink NOMA With Timing Errors and Imperfect CSI 基于深度学习的具有时序误差和不完全CSI的时间偏移亚快于nyquist下行NOMA自编码器

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-09-12 DOI: 10.1109/JSTSP.2024.3457014

Ahmed Aboutaleb;Mohammad Torabi;Benjamin Belzer;Krishnamoorthy Sivakumar

{"title":"Deep Learning-Based Auto-Encoder for Time-Offset Sub-Faster-Than-Nyquist Downlink NOMA With Timing Errors and Imperfect CSI","authors":"Ahmed Aboutaleb;Mohammad Torabi;Benjamin Belzer;Krishnamoorthy Sivakumar","doi":"10.1109/JSTSP.2024.3457014","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3457014","url":null,"abstract":"This paper presents architecture designs and performance evaluations for the encoding and decoding of transmitted and received sequences for downlink time-offset sub-faster-than-Nyquist non-orthogonal multiple access signaling (TO-sFTN-NOMA). A conventional singular value decomposition (SVD)-based scheme for TO-sFTN-NOMA is employed as a benchmark. While this SVD scheme provides reliable communication, our findings reveal that it is not optimal in terms of bit error rate (BER) performance. Moreover, the SVD scheme is sensitive to timing offset errors, and its complexity increases quadratically with the sequence length. To overcome these limitations and improve the TO-sFTN-NOMA's performance, we propose a convolutional neural network (CNN) auto-encoder (AE) technique for encoding and decoding with linear time complexity. We explain the design of the encoder and decoder architectures and the training criteria. By considering several variants of the proposed CNN AE, we show that the proposed CNN AE can achieve an excellent trade-off between performance and complexity. The proposed CNN AE surpasses the SVD method by approximately 10 dB in a TO-sFTN-NOMA system with no timing offset errors and no channel state information (CSI) estimation errors. In the presence of CSI error with variance of 1<inline-formula><tex-math>$%$</tex-math></inline-formula> and uniform timing error at <inline-formula><tex-math>$pm$</tex-math></inline-formula>4% of the symbol interval, the proposed CNN AE provides up to 16 dB SNR gain over the SVD method. We also propose a novel modified training objective function consisting of a weighted summation of the cross-entropy (CE) loss and a Q-function metric related to the BER. Simulations show that the modified objective loss function achieves SNR gains of up to 1 dB over the CE loss function alone.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 7","pages":"1178-1193"},"PeriodicalIF":8.7,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993251","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}

引用次数: 0

Contention-Based mMTC/URLLC Coexistence Through Coded Random Access and Massive MIMO 编码随机接入和大规模MIMO下基于争用的mMTC/URLLC共存

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-09-10 DOI: 10.1109/JSTSP.2024.3457381

Lorenzo Valentini;Elena Bernardi;Fabio Saggese;Marco Chiani;Enrico Paolini;Petar Popovski

{"title":"Contention-Based mMTC/URLLC Coexistence Through Coded Random Access and Massive MIMO","authors":"Lorenzo Valentini;Elena Bernardi;Fabio Saggese;Marco Chiani;Enrico Paolini;Petar Popovski","doi":"10.1109/JSTSP.2024.3457381","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3457381","url":null,"abstract":"Radio access network slicing is considered a key feature in next-generation multiple access. In this paper, we investigate the coexistence between massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC) services. To meet their heterogeneous requirements, we propose a novel grant-free scheme that leverages coded random access, massive multiple-input multiple-output (MIMO) processing, and both the preamble and the power domain to enable non-orthogonal access on shared frequency and time resources. To illustrate the concept, mMTC users transmit packet replicas having different preambles in various time slots, capitalizing on the temporal domain. Meanwhile, the URLLC users apply a more aggressive strategy that leverages pilot mixture and power diversity to meet the stringent latency and reliability requirements. Contention resolution is achieved through a signal processing algorithm based on successive interference cancellation (SIC). We show that the co-design of signal processing and access protocol is crucial to meet both service requirements, and we derive fundamental limits where possible. In instances where direct derivation proves impractical, we conduct symbol-level simulations of the whole system to gain comprehensive insights. The simulations reveal that the proposed scheme can satisfy mMTC/URLLC coverage density, reliability, and latency requirements, while outperforming orthogonal allocation schemes.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 7","pages":"1265-1280"},"PeriodicalIF":8.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670292","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993245","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}

引用次数: 0

INDIGO+: A Unified INN-Guided Probabilistic Diffusion Algorithm for Blind and Non-Blind Image Restoration

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-09-09 DOI: 10.1109/JSTSP.2024.3454957

Di You;Pier Luigi Dragotti

{"title":"INDIGO+: A Unified INN-Guided Probabilistic Diffusion Algorithm for Blind and Non-Blind Image Restoration","authors":"Di You;Pier Luigi Dragotti","doi":"10.1109/JSTSP.2024.3454957","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3454957","url":null,"abstract":"Generative diffusion models are becoming one of the most popular prior in image restoration (IR) tasks due to their remarkable ability to generate realistic natural images. Despite achieving satisfactory results, IR methods based on diffusion models present several limitations. First of all, most non-blind approaches require an analytical expression of the degradation model to guide the sampling process. Secondly, most existing blind approaches rely on families of pre-defined degradation models for training their deep networks. The above issues limit the flexibility of these approaches and so their ability to handle real-world degradation tasks. In this paper, we propose a novel INN-guided probabilistic diffusion algorithm for non-blind and blind image restoration, namely INDIGO and BlindINDIGO, which combines the merits of the perfect reconstruction property of invertible neural networks (INN) with the strong generative capabilities of pre-trained diffusion models. Specifically, we train the forward process of the INN to simulate an arbitrary degradation process and use the inverse to obtain an intermediate image that we use to guide the reverse diffusion sampling process through a gradient step. We also introduce an initialization strategy, to further improve the performance and inference speed of our algorithm. Experiments demonstrate that our algorithm obtains competitive results compared with recently leading methods both quantitatively and visually on synthetic and real-world low-quality images.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 6","pages":"1108-1122"},"PeriodicalIF":8.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106603","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}

引用次数: 0

A Feature-Domain Channel Acquisition Scheme for MIMO-OFDM MIMO-OFDM的一种特征域信道采集方案

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-09-09 DOI: 10.1109/JSTSP.2024.3454948

Shuai Gao;Fan Xu;Qingjiang Shi

引用次数: 0

Multi-RIS-Empowered Multiple Access: A Distributed Sum-Rate Maximization Approach multi - ris授权多址：一种分布式和速率最大化方法

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-09-09 DOI: 10.1109/JSTSP.2024.3455102

Konstantinos D. Katsanos;Paolo Di Lorenzo;George C. Alexandropoulos

{"title":"Multi-RIS-Empowered Multiple Access: A Distributed Sum-Rate Maximization Approach","authors":"Konstantinos D. Katsanos;Paolo Di Lorenzo;George C. Alexandropoulos","doi":"10.1109/JSTSP.2024.3455102","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3455102","url":null,"abstract":"The plethora of wirelessly connected devices, whose deployment density is expected to largely increase in the upcoming sixth Generation (6G) of wireless networks, will naturally necessitate substantial advances in multiple access schemes. Reconfigurable Intelligent Surfaces (RISs) constitute a candidate 6G technology capable to offer dynamic over-the-air signal propagation programmability, which can be optimized for efficient non-orthogonal access of a multitude of devices. In this paper, we study the downlink of a wideband communication system comprising multiple multi-antenna Base Stations (BSs), each wishing to serve an associated single-antenna user via the assistance of a Beyond Diagonal (BD) and frequency-selective RIS. Under the assumption that each BS performs Orthogonal Frequency Division Multiplexing (OFDM) transmissions and exclusively controls a distinct RIS, we focus on the sum-rate maximization problem and present a distributed joint design of the linear precoders at the BSs as well as the tunable capacitances and the switch selection matrices at the multiple BD RISs. The formulated non-convex design optimization problem is solved via successive concave approximation necessitating minimal cooperation among the BSs. Our extensive simulation results showcase the performance superiority of the proposed cooperative scheme over non-cooperation benchmarks, indicating the performance gains with BD RISs via the presented optimized frequency selective operation for various scenarios.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 7","pages":"1324-1338"},"PeriodicalIF":8.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993326","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}

引用次数: 0

Multi-Sources Fusion Learning for Multi-Points NLOS Localization in OFDM System OFDM系统中多点NLOS定位的多源融合学习

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-09-06 DOI: 10.1109/JSTSP.2024.3453548

Bohao Wang;Zitao Shuai;Chongwen Huang;Qianqian Yang;Zhaohui Yang;Richeng Jin;Ahmed Al Hammadi;Zhaoyang Zhang;Chau Yuen;Mérouane Debbah

{"title":"Multi-Sources Fusion Learning for Multi-Points NLOS Localization in OFDM System","authors":"Bohao Wang;Zitao Shuai;Chongwen Huang;Qianqian Yang;Zhaohui Yang;Richeng Jin;Ahmed Al Hammadi;Zhaoyang Zhang;Chau Yuen;Mérouane Debbah","doi":"10.1109/JSTSP.2024.3453548","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3453548","url":null,"abstract":"Accurate localization of mobile terminals is a pivotal aspect of integrated sensing and communication systems. Traditional fingerprint-based localization methods, which infer coordinates from channel information within pre-set rectangular areas, often face challenges due to the heterogeneous distribution of fingerprints inherent in non-line-of-sight (NLOS) scenarios, particularly within orthogonal frequency division multiplexing systems. To overcome this limitation, we develop a novel multi-sources information fusion learning framework referred to as the Autosync Multi-Domains NLOS Localization (AMDNLoc). Specifically, AMDNLoc employs a two-stage matched filter fused with a target tracking algorithm and iterative centroid-based clustering to automatically and irregularly segment NLOS regions, ensuring uniform distribution within channel state information across frequency, power, and time-delay domains. Additionally, the framework utilizes a segment-specific linear classifier array, coupled with deep residual network-based feature extraction and fusion, to establish the correlation function between fingerprint features and coordinates within these regions. Simulation results reveal that AMDNLoc achieves an impressive NLOS localization accuracy of 1.46 meters on typical wireless artificial intelligence research datasets and demonstrates significant improvements in interpretability, adaptability, and scalability.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 7","pages":"1339-1350"},"PeriodicalIF":8.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993329","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}

引用次数: 0