IEEE Transactions on Signal Processing最新文献_第6页

Revised Maximum-Likelihood Detector With Quantization Design for One-Bit Massive MIMO Systems 一比特大规模MIMO系统的改进最大似然检测器量化设计

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-13 DOI: 10.1109/TSP.2026.3653950

Shumei Wei;Jin Xu;Xiaofeng Tao;Shixun Gong;Rui Meng

{"title":"Revised Maximum-Likelihood Detector With Quantization Design for One-Bit Massive MIMO Systems","authors":"Shumei Wei;Jin Xu;Xiaofeng Tao;Shixun Gong;Rui Meng","doi":"10.1109/TSP.2026.3653950","DOIUrl":"10.1109/TSP.2026.3653950","url":null,"abstract":"One-bit analog-to-digital converters (ADCs) offer a practical solution for reducing both cost and power consumption in massive multiple-input multiple-output (MIMO) systems. Nevertheless, the severe distortion induced by extremely coarse quantization significantly deteriorates the data detection performance. For 1-bit ADC, the conventional symmetric quantization may not necessarily be the optimal choice. This paper examines the impact of quantization thresholds on the performance of maximum likelihood (ML) data detection in one-bit massive MIMO systems, and proposes a revised ML (rML) detector with an iterative adaptive quantization design. Initially, the original ML detection problem, constrained by discrete constellations, is reformulated into the rML problem. By optimizing the quantization thresholds, the solution to the rML problem can be guided to converge to the true transmitted signal. Leveraging this characteristic, we propose the rML detector with iterative adaptive quantization design to progressively refine the quantization thresholds during data detection. We further present two implementation strategies for the rML detector: one iteration one quantization (1I1Q) and one iteration two quantization (1I2Q). Additionally, an rML-based joint channel estimation and data detection (JED) method is introduced, where the decoded data from 1I1Q is utilized to enhance the pilot data vectors, thereby refining the estimated channel and ultimately enhancing data detection performance. Finally, numerical results demonstrate that the proposed rML detector with iterative adaptive quantization design is both efficient and robust, significantly outperforming the state-of-the-art methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"409-422"},"PeriodicalIF":5.8,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

One-Bit MIMO Detection: From Global Maximum-Likelihood Detector to Amplitude Retrieval Approach 位MIMO检测：从全局最大似然检测器到幅度恢复方法

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-13 DOI: 10.1109/TSP.2026.3653849

Mingjie Shao;Wei-Kun Chen;Cheng-Yang Yu;Ya-Feng Liu;Wing-Kin Ma

{"title":"One-Bit MIMO Detection: From Global Maximum-Likelihood Detector to Amplitude Retrieval Approach","authors":"Mingjie Shao;Wei-Kun Chen;Cheng-Yang Yu;Ya-Feng Liu;Wing-Kin Ma","doi":"10.1109/TSP.2026.3653849","DOIUrl":"10.1109/TSP.2026.3653849","url":null,"abstract":"As communication systems advance towards the future 6G era, the incorporation of large-scale antenna arrays in base stations (BSs) presents challenges such as increased hardware costs and energy consumption. To address these issues, the use of one-bit analog-to-digital converters (ADCs)/digital-to-analog converters (DACs) has gained significant attentions. This paper focuses on one-bit multiple-input multiple-output (MIMO) detection in an uplink multiuser transmission scenario where the BS employs one-bit ADCs. One-bit quantization retains only the sign information and loses the amplitude information, which poses a unique challenge in the corresponding detection problem. The maximum-likelihood (ML) formulation of one-bit MIMO detection has a challenging likelihood function that hinders the application of many high-performance detectors developed for classic MIMO detection (under high-resolution ADCs). While many approximate methods for the ML detection problem have been studied, it lacks an efficient <italic>global</i> algorithm. This paper fills this gap by proposing an efficient branch-and-bound algorithm, which is guaranteed to find the global solution of the one-bit ML MIMO detection problem. Additionally, a new amplitude retrieval (AR) detection approach is developed, incorporating explicit amplitude variables into the problem formulation. The AR approach yields simpler objective functions that enable the development of efficient algorithms offering both global and approximate solutions. The paper also contributes to the computational complexity analysis of both ML and AR detection problems. Extensive simulations are conducted to demonstrate the effectiveness and efficiency of the proposed formulations and algorithms.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"765-780"},"PeriodicalIF":5.8,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

A Novel Privacy Enhancement Scheme with Dynamic Quantization for Federated Learning 一种新的联邦学习动态量化隐私增强方案

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-13 DOI: 10.1109/TSP.2026.3653846

Yifan Wang;Xianghui Cao;Shi Jin;Mo-Yuen Chow

{"title":"A Novel Privacy Enhancement Scheme with Dynamic Quantization for Federated Learning","authors":"Yifan Wang;Xianghui Cao;Shi Jin;Mo-Yuen Chow","doi":"10.1109/TSP.2026.3653846","DOIUrl":"10.1109/TSP.2026.3653846","url":null,"abstract":"Federated learning (FL) has been widely regarded as a promising paradigm for privacy preservation of raw data in machine learning. Although data privacy in FL is locally protected to some extent, it is still a desideratum to enhance privacy and alleviate communication overhead caused by repetitively transmitting model parameters. Typically, these challenges are addressed separately, or jointly via a unified scheme that consists of noise-injected privacy mechanism and communication compression, which may lead to model corruption due to the introduced composite noise. In this work, we propose a novel model-splitting privacy-preserving FL (MSP-FL) scheme to achieve private FL with precise accuracy guarantee. Based upon MSP-FL, we further propose a model-splitting privacy-preserving FL with dynamic quantization (MSPDQ-FL) to mitigate the communication overhead, which incorporates a shrinking quantization interval to reduce the quantization error. We provide privacy and convergence analysis for both MSP-FL and MSPDQ-FL under non-i.i.d. dataset, partial clients participation and finite quantization level. Numerical results are presented to validate the superiority of the proposed schemes.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"453-470"},"PeriodicalIF":5.8,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Tensor-Based Target Sensing for Resource-Irregular ISAC Systems 基于张量的非规则ISAC系统目标感知

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-12 DOI: 10.1109/TSP.2026.3653322

Lin Chen;Li Ge;Xue Jiang;Zhiyuan Jiang;Hongbin Li

{"title":"Tensor-Based Target Sensing for Resource-Irregular ISAC Systems","authors":"Lin Chen;Li Ge;Xue Jiang;Zhiyuan Jiang;Hongbin Li","doi":"10.1109/TSP.2026.3653322","DOIUrl":"https://doi.org/10.1109/TSP.2026.3653322","url":null,"abstract":"Most existing target sensing approaches in integrated sensing and communication (ISAC) systems assume a regular time-frequency resource allocation. However, in practical ISAC systems, resources are often allocated irregularly because of the randomness of user scheduling. This paper addresses such resource-irregular scenarios by integrating the CANDECOMP/PARAFAC decomposition (CPD) framework with tensor completion. The proposed structured tensor completion and decomposition (STCD) method enhances target sensing by not only processing echo signals from irregularly allocated resource regions but also interpolating those from unallocated ones. Moreover, tensor completion reconstructs the Vandermonde structure of steering matrices. By enforcing a tensor rank-1 constraint, the STCD method leverages the Vandermonde structure to establish more relaxed uniqueness conditions for CPD compared with existing approaches. Additionally, we present the Cramér-Rao bound results for STCD in angle-range-velocity estimation, extending prior analyses from resource-regular to resource-irregular scenarios. Simulation results validate the effectiveness of the proposed STCD method for resource-irregular target sensing, demonstrating improved performance over traditional methods and its unstructured counterpart.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"605-621"},"PeriodicalIF":5.8,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Distributed Poisson Multi-Bernoulli Filtering via Generalized Covariance Intersection 基于广义协方差交集的分布泊松多伯努利滤波

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-12 DOI: 10.1109/TSP.2026.3651805

Ángel F. García-Fernández;Giorgio Battistelli

引用次数: 0

Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction 通过元学习上下文相关的适形预测校准无线AI

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-05 DOI: 10.1109/TSP.2026.3650912

Seonghoon Yoo;Sangwoo Park;Petar Popovski;Joonhyuk Kang;Osvaldo Simeone

{"title":"Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction","authors":"Seonghoon Yoo;Sangwoo Park;Petar Popovski;Joonhyuk Kang;Osvaldo Simeone","doi":"10.1109/TSP.2026.3650912","DOIUrl":"10.1109/TSP.2026.3650912","url":null,"abstract":"Modern software-defined networks, such as Open Radio Access Network (O-RAN) systems, rely on artificial intelligence (AI)-powered applications running on controllers interfaced with the radio access network. To ensure that these AI applications operate reliably at runtime, they must be properly calibrated before deployment. A promising and theoretically grounded approach to calibration is conformal prediction (CP), which enhances any AI model by transforming it into a provably reliable set predictor that provides error bars for estimates and decisions. CP requires calibration data that matches the distribution of the environment encountered during runtime. However, in practical scenarios, network controllers often have access only to data collected under different contexts – such as varying traffic patterns and network conditions – leading to a mismatch between the calibration and runtime distributions. This paper introduces a novel methodology to address this calibration-test distribution shift. The approach leverages meta-learning to develop a zero-shot estimator of distribution shifts, relying solely on contextual information. The proposed method, called meta-learned context-dependent weighted conformal prediction (ML-WCP), enables effective calibration of AI applications without requiring data from the current context. Additionally, it can incorporate data from multiple contexts to further enhance calibration reliability.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"423-438"},"PeriodicalIF":5.8,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Fractional Programming for Stochastic Precoding Over Generalized Fading Channels 广义衰落信道上随机预编码的分式规划

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-03-12 DOI: 10.1109/TSP.2026.3673062

Wenyu Wang;Kaiming Shen

{"title":"Fractional Programming for Stochastic Precoding Over Generalized Fading Channels","authors":"Wenyu Wang;Kaiming Shen","doi":"10.1109/TSP.2026.3673062","DOIUrl":"10.1109/TSP.2026.3673062","url":null,"abstract":"This paper seeks an efficient algorithm for stochastic precoding to maximize the long-term average weighted sum rates throughout a multiple-input multiple-output (MIMO) network. Unlike many existing works that assume a particular probability distribution model for fading channels (which is typically Gaussian), our approach merely relies on the first and second moments of fading channels. For the stochastic precoding problem, a naive idea is to directly apply the fractional programming (FP) method to the data rate inside the expectation; it does not work well because the auxiliary variables introduced by FP are then difficult to decide. To address the above issue, we propose using a lower bound to approximate the expectation of data rate. This lower bound stems from a nontrivial use of the matrix FP, and outperforms the existing lower bounds in that it accounts for generalized fading channels whose first and second moments are known. The resulting approximate problem can be efficiently solved in closed form in an iterative fashion. Furthermore, for large-scale MIMO, we improve the efficiency of the proposed algorithm by eliminating the large matrix inverse. Simulations show that the proposed stochastic precoding method outperforms the benchmark methods in both Gaussian and non-Gaussian fading channel cases.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"1414-1427"},"PeriodicalIF":5.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Over-the-Air Computation on Network Edge for Collaborative Estimation 基于网络边缘的空中计算协同估计

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-03-30 DOI: 10.1109/TSP.2026.3678844

Vincent Huynh;Weiwei Wang;Zhi Ding

{"title":"Over-the-Air Computation on Network Edge for Collaborative Estimation","authors":"Vincent Huynh;Weiwei Wang;Zhi Ding","doi":"10.1109/TSP.2026.3678844","DOIUrl":"10.1109/TSP.2026.3678844","url":null,"abstract":"Parameter estimation is an important task in distributed sensing systems, where data from multiple devices contributes to real-time decision-making. Over-the-air computation (AirComp) provides a new approach for spectrally efficient edge computing in collaborative parameter estimation. In this work, we propose a novel AirComp estimation framework based on the natural aggregation of pre-processed signals from participating agents in wireless multiple access channels. We focus on maximum likelihood estimation of source signal magnitude and time delay, proposing two approaches to complete this task. Our first approach involves a noisy approximation of the joint log-likelihood function (LLF) of agent observations. Our second approach employs noise-whitening to address the effects of pulse shaping. We present four channel compensation schemes to address destructive inter-agent channel interference and derive the corresponding Cramér-Rao lower bound (CRLB) in each scheme. We apply a practical partial phase pre-compensation that achieves nearly the same performance as complete phase compensation, thereby simplifying the conditions of the proposed AirComp estimation in real-world deployment.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"1600-1615"},"PeriodicalIF":5.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147577738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Beamforming Design for Minimizing the Signal Power Estimation Error 最小化信号功率估计误差的波束成形设计

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-03-12 DOI: 10.1109/TSP.2026.3673368

Esa Ollila;Xavier Mestre;Elias Raninen

{"title":"Beamforming Design for Minimizing the Signal Power Estimation Error","authors":"Esa Ollila;Xavier Mestre;Elias Raninen","doi":"10.1109/TSP.2026.3673368","DOIUrl":"10.1109/TSP.2026.3673368","url":null,"abstract":"We study the properties of beamformers in their ability to either maintain or estimate the true signal power of the signal of interest (SOI). Our focus is particularly on the Capon beamformer and the minimum mean squared error (MMSE) beamformer. The Capon beamformer, also known as the minimum power distortionless response (MPDR) or the minimum variance distortionless response (MVDR) beamformer, is a widely used method in array signal processing. A curious feature of both the Capon and the MMSE beamformers is their tendency to either overestimate or underestimate the signal power. That is, the power estimates of their output signals are not asymptotically unbiased (as the sample size approaches infinity). To address this issue, we propose to shrink the Capon beamformer by finding a scaling factor that minimizes the mean squared error (MSE) of the signal power estimate. The new beamformer, referred to as the Capon<inline-formula><tex-math>${}^{+}$</tex-math></inline-formula> beamformer, is evaluated against the Capon and MMSE beamformers in terms of signal power bias and MSE, and signal waveform MSE. The Capon<inline-formula><tex-math>${}^{+}$</tex-math></inline-formula> beamformer strikes a better balance between signal power and waveform estimation while also exhibiting minimal bias, which approaches zero as the sample size increases.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"1206-1217"},"PeriodicalIF":5.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11432948","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Jamming Detection Based on Source Enumeration in Massive Channel Systems 基于源枚举的海量信道系统干扰检测

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-03-11 DOI: 10.1109/TSP.2026.3672303

Zhicheng Zhang;Haijun Tan;Ning Xie;Ye Tian;Lei Huang;Hongbin Li

{"title":"Jamming Detection Based on Source Enumeration in Massive Channel Systems","authors":"Zhicheng Zhang;Haijun Tan;Ning Xie;Ye Tian;Lei Huang;Hongbin Li","doi":"10.1109/TSP.2026.3672303","DOIUrl":"10.1109/TSP.2026.3672303","url":null,"abstract":"This paper addresses the jamming detection problem in colored noise environments. Low-power attacks are particularly dangerous due to their stealthiness and persistent harmful effects. The fluctuations caused by colored noise backgrounds further exacerbate this issue, making it easier for these attacks to remain concealed within the noise. We apply a source enumerator, namely the second-order-difference (SOD) algorithm that is robust against both white and colored noise, for jamming detection, and the resulting method is referred to as the SOD jamming detection (SODJD) method. Our analysis of jamming power’s impact on SODJD performance demonstrates that while SODJD can address colored noise, it is limited in scenarios with low-power signals mixed with several higher-power signals. To overcome these limitations, we propose an invariant SOD jamming detection (ISODJD) method by redesigning the criterion rule and introducing an adjustable parameter. Further analysis reveals that larger parameter values lead to improved detection performance, and the increased false alarm rate can be mitigated in massive channel systems, making ISODJD suitable for cooperative wireless communication scenarios. We implement our schemes and conduct extensive performance comparisons. Simulations demonstrate the effectiveness and superiority of the proposed method. Compared to the SODJD method, a 60-antenna MIMO system achieves a 4 dB improvement in detection sensitivity for low-power jamming signals under colored noise, aligning with our theoretical analysis.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"1263-1276"},"PeriodicalIF":5.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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