IEEE Journal of Selected Topics in Signal Processing最新文献_第2页

Distributed Massive MIMO With Low Resolution ADCs for Massive Random Access 基于低分辨率adc的大规模随机接入分布式大规模MIMO

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-01-10 DOI: 10.1109/JSTSP.2024.3516382

Yuhui Song;Zijun Gong;Yuanzhu Chen;Cheng Li

{"title":"Distributed Massive MIMO With Low Resolution ADCs for Massive Random Access","authors":"Yuhui Song;Zijun Gong;Yuanzhu Chen;Cheng Li","doi":"10.1109/JSTSP.2024.3516382","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3516382","url":null,"abstract":"Massive machine-type communications (mMTC), an essential fifth-generation (5G) usage scenario, aims to provide services for a large number of users that intermittently transmit small data packets in smart cities, manufacturing, and agriculture. Massive random access (MRA) emerges as a promising candidate for multiple access in mMTC characterized by the sporadic data traffic. Despite the use of massive multiple-input multiple-output (mMIMO) in MRA to achieve spatial division multiple access and mitigate small-scale fading, existing research endeavors overlook the near-far effect of large-scale fading by assuming perfect power control. In this paper, we present a cost-efficient, effective, and fully distributed solution for MRA to combat large-scale fading, wherein distributed access points (APs) cooperatively detect and serve active users. Each AP is equipped with low resolution analog-to-digital converters (ADCs) for energy-efficient system implementation. Specifically, we derive a rigorous closed-form expression for the uplink achievable rate, considering the impact of non-orthogonal pilots and low resolution ADCs. We also propose a scalable distributed algorithm for user activity detection under flat fading channels, and further adapt it to handle frequency-selective fading in popular orthogonal frequency division multiplexing (OFDM) systems. The proposed solution is fully distributed, since most processing tasks, such as activity detection, channel estimation, and data detection, are localized at each AP. Simulation results demonstrate the significant advantage of distributed systems over co-located systems in accommodating more users while achieving higher activity detection accuracy, and quantify performance loss resulting from the use of low resolution ADCs.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 7","pages":"1381-1395"},"PeriodicalIF":8.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993328","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

IEEE Signal Processing Society Information IEEE信号处理学会信息

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-01-07 DOI: 10.1109/JSTSP.2024.3511064

引用次数: 0

IEEE Signal Processing Society Information IEEE信号处理学会信息

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-01-07 DOI: 10.1109/JSTSP.2024.3511060

引用次数: 0

Editorial Introduction to the Special Issue on Learning-Based Signal Processing for Integrated Sensing and Communications 基于学习的集成传感与通信信号处理特刊编辑导言

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-01-07 DOI: 10.1109/JSTSP.2024.3522437

Kumar Vijay Mishra;M. R. Bhavani Shankar;Nuria González-Prelcic;Mikko Valkama;Wei Yu;Björn Ottersten

引用次数: 0

Latent Mixup Knowledge Distillation for Single Channel Speech Enhancement

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-01-07 DOI: 10.1109/JSTSP.2024.3524022

Behnam Gholami;Mostafa El-Khamy;Kee-Bong Song

引用次数: 0

Low-Latency Deep Analog Speech Transmission Using Joint Source Channel Coding

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-01-06 DOI: 10.1109/JSTSP.2024.3521277

Mohammad Bokaei;Jesper Jensen;Simon Doclo;Jan Østergaard

{"title":"Low-Latency Deep Analog Speech Transmission Using Joint Source Channel Coding","authors":"Mohammad Bokaei;Jesper Jensen;Simon Doclo;Jan Østergaard","doi":"10.1109/JSTSP.2024.3521277","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3521277","url":null,"abstract":"Low-latency configurable speech transmission presents significant challenges in modern communication systems. Traditional methods rely on separate source and channel coding, which often degrades performance under low-latency constraints. Moreover, non-configurable systems require separate training for each condition, limiting their adaptability in resource-constrained scenarios. This paper proposes a configurable low-latency deep Joint Source-Channel Coding (JSCC) system for speech transmission. The system can be configured for varying signal-to-noise ratios (SNR), wireless channel conditions, or bandwidths. A joint source-channel encoder based on deep neural networks (DNN) is used to compress and transmit analog-coded information, while a configurable decoder reconstructs speech from noisy compressed signals. The system latency is adaptable based on the input speech length, achieving a minimum latency of 2 ms, with a lightweight architecture of 25 k parameters, significantly fewer than state-of-the-art systems. The simulation results demonstrate that the proposed system outperforms conventional separate source-channel coding systems in terms of speech quality and intelligibility, particularly in low-latency and noisy channel conditions. It also shows robustness in fixed configured scenarios, though higher latency conditions and better channel environments favor traditional coding systems.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 8","pages":"1401-1413"},"PeriodicalIF":8.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184263","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

Federated Reinforcement Learning for Resource Allocation in V2X Networks V2X网络中资源分配的联邦强化学习

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-12-17 DOI: 10.1109/JSTSP.2024.3513692

Kaidi Xu;Shenglong Zhou;Geoffrey Ye Li

{"title":"Federated Reinforcement Learning for Resource Allocation in V2X Networks","authors":"Kaidi Xu;Shenglong Zhou;Geoffrey Ye Li","doi":"10.1109/JSTSP.2024.3513692","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3513692","url":null,"abstract":"Resource allocation significantly impacts the performance of vehicle-to-everything (V2X) networks in next generation multiple access (NGMA). Most existing algorithms for resource allocation are based on optimization or machine learning (e.g., reinforcement learning). In this paper, we explore resource allocation in a NGMA V2X network under the framework of federated reinforcement learning (FRL). On one hand, the usage of RL overcomes many challenges from the model-based optimization schemes. On the other hand, federated learning (FL) enables agents to deal with a number of practical issues, such as privacy, communication overhead, distributed learning, and exploration efficiency. The framework of FRL is then implemented by the inexact alternative direction method of multipliers (ADMM), where subproblems are solved approximately using policy gradients and accelerated by an adaptive step size calculated from their second moments. The developed algorithm, PASM, is proven to be convergent under mild conditions and has a nice numerical performance compared with some baseline methods for solving the resource allocation problems in a NGMA V2X network.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 7","pages":"1210-1221"},"PeriodicalIF":8.7,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993249","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

Signal Processing and Learning for Next Generation Multiple Access in 6G 下一代6G多址的信号处理与学习

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-12-09 DOI: 10.1109/JSTSP.2024.3511403

Wei Chen;Yuanwei Liu;Hamid Jafarkhani;Yonina C. Eldar;Peiying Zhu;Khaled B. Letaief

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SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-11-26 DOI: 10.1109/JSTSP.2024.3506286

Haohe Liu;Xuenan Xu;Yi Yuan;Mengyue Wu;Wenwu Wang;Mark D. Plumbley

{"title":"SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound","authors":"Haohe Liu;Xuenan Xu;Yi Yuan;Mengyue Wu;Wenwu Wang;Mark D. Plumbley","doi":"10.1109/JSTSP.2024.3506286","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3506286","url":null,"abstract":"Large languagemodels (LLMs) have significantly advanced audio processing through audio codecs that convert audio into discrete tokens, enabling the application of language modelling techniques to audio data. However, traditional codecs often operate at high bitrates or within narrow domains such as speech and lack the semantic clues required for efficient language modelling. Addressing these challenges, we introduce SemantiCodec, a novel codec designed to compress audio into fewer than a hundred tokens per second across diverse audio types, including speech, general sound, and music, without compromising quality. SemantiCodec features a dual-encoder architecture: a semantic encoder using a self-supervised pre-trained Audio Masked Autoencoder (AudioMAE), discretized using k-means clustering on extensive audio data, and an acoustic encoder to capture the remaining details. The semantic and acoustic encoder outputs are used to reconstruct audio via a diffusion-model-based decoder. SemantiCodec is presented in three variants with token rates of 25, 50, and 100 per second, supporting a range of ultra-low bit rates between 0.31 kbps and 1.40 kbps. Experimental results demonstrate that SemantiCodec significantly outperforms the state-of-the-art Descript codec on reconstruction quality. Our results also suggest that SemantiCodec contains significantly richer semantic information than all evaluated state-of-the-art audio codecs, even at significantly lower bitrates.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 8","pages":"1448-1461"},"PeriodicalIF":8.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184457","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

Coding Speech Through Vocal Tract Kinematics

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-11-20 DOI: 10.1109/JSTSP.2024.3497655

Cheol Jun Cho;Peter Wu;Tejas S. Prabhune;Dhruv Agarwal;Gopala K. Anumanchipalli

{"title":"Coding Speech Through Vocal Tract Kinematics","authors":"Cheol Jun Cho;Peter Wu;Tejas S. Prabhune;Dhruv Agarwal;Gopala K. Anumanchipalli","doi":"10.1109/JSTSP.2024.3497655","DOIUrl":"https://doi.org/10.1109/JSTSP.2024.3497655","url":null,"abstract":"Vocal tract articulation is a natural, grounded control space of speech production. The spatiotemporal coordination of articulators combined with the vocal source shapes intelligible speech sounds to enable effective spoken communication. Based on this physiological grounding of speech, we propose a new framework of neural encoding-decoding of speech – Speech Articulatory Coding (SPARC). SPARC comprises an articulatory analysis model that infers articulatory features from speech audio, and an articulatory synthesis model that synthesizes speech audio from articulatory features. The articulatory features are kinematic traces of vocal tract articulators and source features, which are intuitively interpretable and controllable, being the actual physical interface of speech production. An additional speaker identity encoder is jointly trained with the articulatory synthesizer to inform the voice texture of individual speakers. By training on large-scale speech data, we achieve a fully intelligible, high-quality articulatory synthesizer that generalizes to unseen speakers. Furthermore, the speaker embedding is effectively disentangled from articulations, which enables accent-perserving zero-shot voice conversion. To the best of our knowledge, this is the first demonstration of universal, high-performance articulatory inference and synthesis, suggesting the proposed framework as a powerful coding system of speech.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"18 8","pages":"1427-1440"},"PeriodicalIF":8.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184455","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