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Distributed Downlink Precoding for Cell-Free Massive MIMO: A Quasi-Neural Network Approach

IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第2期73卷 1157-1168页

作者： Dai, Weijie Wang, Rui Liu, Junkai Jiang, Yi Fudan Univ Sch Informat Sci & Technol Dept Commun Sci & Engn Key Lab Informat Sci Electromagnet Waves MoE Shanghai 200438 Peoples R China

This paper proposes a novel downlink precoding method for a cell-free massive multiple-input multiple-output (CF-mMIMO) network, requiring no channel state information sharing between the access points via fronthaul links. By drawing analogies between a CF-mMIMO network and an artificial neural network, the proposed algorithm borrows the idea of backpropagation to train the precoders and the combiners through over-the-air ping-pong signaling between the access points and user equipments. It utilizes manifolds optimization to meet the per-AP power constraint and is named as distributed quasi-neural network precoding on manifold (DQNPM). The DQNPM algorithm can accommodate a large category of objective functions for fully distributed implementation. Numerical simulations show that our method outperforms the state-of-the-art approaches, and is robust against pilot contamination.

关键词： Precoding Downlink Manifolds Artificial neural networks Transmitting antennas signal to noise ratio signal processing algorithms Cell-free massive MIMO distributed precoding quasi-neural network Riemannian manifold

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Combining SEFDM Technique With IBFD Communication System in an ROF Link for Achieving Ultra-High Spectral Efficiency

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第3期43卷 1089-1097页

作者： Chen, Yu Chen, Yang East China Normal Univ Sch Commun & Elect Engn Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China

Oriented to the requirements of future high-frequency wireless communication systems, this paper proposes a method of combining spectrally efficient frequency-division multiplexing (SEFDM) technique with the in-band full-duplex (IBFD) communication system and applying it to microwave photonic transmission links to achieve ultra-high spectral efficiency. The microwave photonic link not only realizes the transmission of SEFDM signals received by the IBFD system but also realizes the analog self-interference cancellation (SIC) function. The digital SIC and SEFDM demodulation functions are seamlessly integrated following the analog SIC. Utilizing a cross-iterative algorithm, the adverse effects of the signal of interest (SOI) on the digital SIC of the received signal are mitigated, leading to a substantial improvement in both the SIC depth and demodulation performance of the SEFDM signal compared to the conventional least squares (LS) algorithm. An experiment has been conducted. The SOI and self-interference (SI) have symbol rates of 300 Msym/s and 600 Msym/s, respectively, and share a center frequency of 2 GHz and a bandwidth compression factor of 0.8. When the SI to SOI power ratio is 10.3 dB, the analog SIC depth is around 18.3 dB. The conventional LS algorithm achieves a digital SIC depth of 12.6 dB but the error vector magnitude (EVM) is 13.2%. In contrast, our proposed iterative method improves the SIC depth to 15.1 dB and reduces the EVM to 4.1%. The feasibility of the system is also verified by incorporating a 25.2-km fiber.

关键词： Interference cancellation Demodulation Microwave photonics Optical fibers Microwave theory and techniques Microwave communication Optical fiber dispersion Optical fiber communication Communication systems signal processing algorithms Digital signal processing in-band full-duplex microwave photonics self-interference cancellation (SIC) spectrally efficient frequency-division multiplexing (SEFDM)

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An Efficient Subswath-Subband Chirp Z Transform Algorithm for Multiple-Receiver SAS Considering the Differential Range Curvature

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Ning, Mingqiang Tang, Jinsong Wu, Haoran Zhong, Heping Ma, Mengbo Naval Univ Engn Wuhan 430033 Peoples R China Acad Mil Sci Beijing 100080 Peoples R China

The interpolation-free chirp z-transform (CZT) algorithm is efficient but requires approximating the accurate 2-D spectrum's phase. However, this approximation is invalid for data from multiple-receiver synthetic aperture sonar (MRSAS) systems with wide beamwidths, large fractional bandwidths, and wide subswaths. To address the issue above, this article derives a new range history and 2-D spectrum, and proposes a new method to limit the approximation error of the 2-D spectrum's phase. This article first represents the range history as a combination of basic functions and derives a new 2-D spectrum without any approximation. After the receiver dependence of the 2-D spectrum's phase is partially reduced, the echo data are divided into multiple subapertures in the receiver domain to ensure that the 2-D spectrum's phase difference of different receivers within each subaperture can be ignored. A frequency-domain filter is designed based on the 2-D spectrum of the reference receiver to compensate for all receivers' data in the subaperture. Therefore, only one imaging process is needed in a subaperture, rather than receiver-by-receiver imaging. The 2-D spectrum's phase is expanded up to the third range frequency and its error is limited by the subswath-subband processing rather than considering the higher order terms in Taylor expansion. The quadratic and cubic terms in each subband are linearized through subband-wise linear approximation (SLA) to facilitate the CZT algorithm. Finally, the high-resolution image of the whole swath is obtained through the subimage fusion. The superiority of the proposed range history and subswath-subband processing has been verified under wide beamwidths, large fractional bandwidths, and wide subswaths through simulation and measured data.

关键词： Receivers History Imaging Synthetic aperture sonar Accuracy Transmitters signal processing algorithms Approximation algorithms Chirp Azimuth 2-D spectrum Chirp z-transform (CZT) algorithm multiple-receiver synthetic aperture sonar (MRSAS) subband-wise linear approximation (SLA) subswath-subband processing

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Distributed Solvers for Network Linear Equations With Scalarized Compression

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2644-2651页

作者： Wang, Lei Ren, Zihao Yuan, Deming Shi, Guodong Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Univ Sydney Australia Ctr Robot Sydney NSW 2006 Australia

Distributed computing is fundamental to multiagent systems, with solving distributed linear equations as a typical example. In this article, we study distributed solvers for network linear equations over a network with node-to-node communication messages compressed as scalar values. Our key idea lies in a dimension compression scheme that includes a dimension-compressing vector and a data unfolding step. The compression vector applies to individual node states as an inner product to generate a real-valued message for node communication. In the unfolding step, such scalar message is then plotted along the subspace generated by the compression vector for the local computations. We first present a compressed consensus flow that relies only on such scalarized communication, and show that linear convergence can be achieved with well excited signals for the compression vector. We then employ such a compressed consensus flow as a fundamental consensus subroutine to develop distributed continuous-time and discrete-time solvers for network linear equations, and prove their linear convergence properties under scalar node communications. With scalar communications, a direct benefit would be the reduced node-to-node communication channel burden for distributed computing. Numerical examples are presented to illustrate the effectiveness of the established theoretical results.

关键词： Vectors Convergence signal processing algorithms Heuristic algorithms Electronic mail Consensus algorithm Communication channels Distributed algorithms Computational efficiency Australia Compression consensus scalar communication network linear equation

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Modern FDA-LFM Radar System and Efficient Range-Angle-Doppler processing Algorithm

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 3577-3588页

作者： Kim, Geon U. Kim, Jeong Phill Chung Ang Univ Dept Elect & Elect Engn Seoul South Korea

A radar system is proposed that combines frequency diverse array (FDA) and linear frequency modulation (LFM) signals to provide efficient and high-range resolution characteristics. The combined signal could be generated by using a multichannel direct digital synthesizer. Previous studies of the FDA-LFM system based on the delay line are reviewed. Some errors in the signal expressions are corrected and the associated characteristics are revised. For the proposed FDA-LFM radar system, the signal formulations and the signal processing algorithm for range, angle, and Doppler are detailed. The range-time information can initially be extracted by applying the short-time Fourier transform to the time-domain data. In addition, the target angle is extracted from the obtained range-time data through the coordinate transformation. The Doppler information is then extracted by applying the fast Fourier transform to the successive range-angle data. Numerical simulations are performed and the obtained results fully validate the proposed FDA-LFM radar configuration and the systematic and efficient signal processing algorithm. Further Monte Carlo simulations are performed to analyze the estimation accuracy of the proposed radar system.

关键词： signal resolution signal processing algorithms Radar signal processing Frequency diversity Chirp modulation Systematics Frequency modulation Doppler radar Time-frequency analysis Synthesizers Beat frequency coordinate transform direct digital synthesizer (DDS) Doppler processing frequency diverse array (FDA) linear frequency modulation (LFM) range-angle processing short-time Fourier transform (STFT)

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USSD: Unsupervised Sleep Spindle Detector

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IEEE ACCESS 2025年 13卷 18644-18659页

作者： Ramirez, Edgardo Estevez, Pablo A. Adams, Martin D. Perez, Claudio A. Garrido Gonzalez, Marcelo Peirano, Patricio Univ Chile Dept Elect Engn Santiago 8330015 Chile Millennium Inst Intelligent Healthcare Engn Santiago 1025000 Chile Ctr Intervent Med Precis & Adv Cellular Therapy IMPACT Santiago 1025000 Chile Univ Chile Inst Nutr & Food Technol Sleep Lab Santiago 8330015 Chile Univ Diego Portales UDP Fac Educ Santiago 8370109 Chile

Sleep spindles (SSs) appear in electroencephalogram (EEG) recordings during sleep stage N2, and they are usually detected through visual inspection by an expert. Labeling SSs in large datasets is time-consuming and depends on the expert criteria. In this work, we propose an unsupervised SS detector based on dictionary learning called the Unsupervised Sleep Spindle Detector (USSD). The proposed detector learns prototype SSs of different lengths (called atoms). An unsupervised adaptive threshold method based on the distribution of the automatically detected SS lengths is developed, which allows the adaptation of the USSD algorithm to different datasets in an unsupervised way. For each detection, the USSD estimates the probability of being an SS. The USSD performances on the labeled MASS-SS2 and INTA-UCH datasets yield F1-scores of $0.72 \pm 0.02$ and $0.72 \pm 0.04$ , respectively. The USSD outperforms the A7 and LUNA detectors, which are traditional unsupervised models. Next, we fine-tune the resulting USSD model with 20% of the labeled MASS-SS2 and INTA-UCH datasets, achieving F1 scores of $0.78 \pm 0.06$ and $0.75 \pm 0.05$ , respectively. In addition, the SSs detected by USSD on the unlabeled CAP dataset are used to pre-train a supervised deep learning method, which after fine-tuning with 20% of the MODA dataset, reaches an F1-score of $0.81 \pm 0.02$ .

关键词： Sleep Electroencephalography signal processing algorithms Brain modeling Detectors Dictionaries Matching pursuit algorithms Machine learning Recording Filtering algorithms EEG sleep spindle unsupervised learning dictionary learning

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Low Complexity and Mobility-Aware Robust Radio, Storage, Computing, and Cost Management for Cellular Vehicular Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 3327-3344页

作者： Abedi, Mohammad Reza Mokari, Nader Javan, Mohammad Reza Saeedi, Hamid Jorswieck, Eduard A. Zorba, Nizar Tarbiat Modares Univ ECE Dept Tehran 14115111 Iran Shahrood Univ Technol Fac Elect Engn Shahrood *** Iran Univ Doha Sci & Technol Coll Engn & Technol Doha 24449 Qatar TU Braunschweig Inst Commun Technol D-38106 Braunschweig Germany Qatar Univ Coll Engn Doha 2713 Qatar

In this paper, a novel two-time scale task offloading/delivery framework is presented which dynamically manages radio, storage, computing, and cost aspects through multiple Mobile Edge Computing (MEC) servers distributed across Road Side Units (RSUs), Base Stations (BSs), vehicles, and pedestrians. To enhance flexibility, we introduce a smart dynamic task partitioning and cooperative offloading/delivery mechanism, enabling users to offload task partitions to various MEC servers. Uncertainties stemming from users' high mobility and traffic variations may lead to unpredictable task offloading demands and Imperfect Channel State Information (I-CSI), potentially causing unsuccessful task offloading/delivery. In response, we propose a robust and cooperative two-time-scale task offloading/delivery approach, adept at handling demand uncertainty and I-CSI. The problem formulation involves a two-time scale Markov decision process, aimed at minimizing task offloading/delivery costs while maximizing task completion rates. Large time-scale slots incorporate intelligent RSU activation/deactivation, while short time-scale slots focus on task offloading/delivery. To efficiently implement this framework, we propose Federated Reinforcement Learning (FRL) algorithm based on Deep Deterministic Policy Gradient (DDPG);namely Low overhead Multi-Agent DDPG (LoMADDPG) where at each slot, all agents tune their Actor and Critic neural network weights with the help of Global Software-defined Networking (SDN) Controller (GSC) which acts as a global weight tuner. Simulation results illustrate that our proposed task offloading/delivery scheme can result in significant improvement in task completion rate and cost reduction.

关键词： Uncertainty Servers Resource management Optimization Costs Delays signal processing algorithms Machine learning algorithms Vehicle-to-everything Vehicle dynamics Mobile edge computing two-time scale task offloading/delivery RSU activation/deactivation demand and channel uncertainties task partitioning task migration

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Optimizing Optical Engine Features for Subsea Cable Systems

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第4期43卷 1662-1670页

作者： Varughese, Siddharth Bennett, Geoff Lavery, Domanic Sun, Han Mertz, Pierre Infinera Corp San Jose MD 20701 USA Infinera Canada Inc Ottawa ON K2K 2X3 Canada

We introduce a taxonomy for subsea optical fiber cables as used for intercontinental telecommunications systems. With reference to this new classification, we describe how the design of optical engines has both influenced, and been influenced by, the development of subsea cables. We outline the pivotal role coherent transceivers have played in the development of high-capacity subsea cables, specifically with reference to advances in digital signal processing and coded modulation, and we highlight the opportunities and challenges of upgrading legacy systems with state-of-the-art coherent transceivers. In this work, we also look to future cable designs and ask how the availability of fundamentally new fiber types (specifically multicore and hollow core fibers) will have an influence on subsea cables and, once again, change transceiver requirements.

关键词： Transceivers Optical fiber cables Underwater cables Optical fiber dispersion Dispersion Optical fibers Optical fiber amplifiers Optical fiber networks optical receivers optical transmitters signal processing algorithms

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On the Convergence of Decentralized Stochastic Gradient Descent With Biased Gradients

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 549-558页

作者： Jiang, Yiming Kang, Helei Liu, Jinlan Xu, Dongpo Northeast Normal Univ Sch Math & Stat Key Lab Appl Stat MOE Changchun 130024 Peoples R China Changchun Normal Univ Dept Math Changchun 130032 Peoples R China

Stochastic optimization algorithms are widely used to solve large-scale machine learning problems. However, their theoretical analysis necessitates access to unbiased estimates of the true gradients. To address this issue, we perform a comprehensive convergence rate analysis of stochastic gradient descent (SGD) with biased gradients for decentralized optimization. In non-convex settings, we show that for decentralized SGD utilizing biased gradients, the gradient in expectation is bounded asymptotically at a rate of O(1/root nT+ n/T), and the bound is linearly correlated to the biased gradient gap. In particular, we can recover the convergence results in the unbiased stochastic gradient setting when the biased gradient gap is zero. Lastly, we provide empirical support for our theoretical findings through extensive numerical experiments.

关键词： Optimization Convergence signal processing algorithms Stochastic processes Machine learning Estimation Vectors Machine learning algorithms Costs Convex functions Decentralized optimization non-convex optimization biased stochastic gradient descent convergence

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Hypothesis Perturbation for Active Learning

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2025年第1期19卷 115-128页

作者： Cho, Seong Jin Kim, Gwangsu Yoo, Chang D. Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea Korea Inst Oriental Med KM Sci Res Div Daejeon 34054 South Korea Jeonbuk Natl Univ Res Inst Mat & Energy Sci Dept Stat Jeonju 54896 South Korea

This paper introduces a computationally efficient Query-by-Committee (QBC) algorithm specifically designed for deep active learning. The algorithm leverages the concept of hypothesis perturbation (HP) to construct the committee. The conventional QBC algorithms often incur high computational costs due to the independent training required for each committee member. In contrast, the HP constructs the committee by strategically sampling hypotheses around a given hypothesis, and efficiently identifies data points located near the decision boundary of the current hypothesis. To quantify uncertainty, the algorithm leverages a novel metric termed disagreement in hypothesis perturbation (DHP). DHP quantifies the disagreement in predictions between the given hypothesis and its perturbed hypotheses. This metric effectively identifies data points with high uncertainty, making them ideal candidates for active learning. The effectiveness of the proposed DHP-based active learning algorithm is empirically validated through extensive experimentation. The results demonstrate that the algorithm consistently achieves superior performance compared to other established algorithms across various datasets and deep network architectures considered in the study.

关键词： Perturbation methods Complexity theory Uncertainty Training signal processing algorithms Measurement uncertainty Prediction algorithms Machine learning active learning uncertainty hypothesis perturbation version space

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