IEEE open journal of signal processing最新文献

{"title":"Deployment Strategy for Indoor Distributed MIMO System","authors":"Yujie Zhang;Juan Vidal Alegria;Jose Flordelis;Erik L. Bengtsson;Ove Edfors","doi":"10.1109/OJSP.2026.3654783","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3654783","url":null,"abstract":"The physical placement of antennas is a design factor for Distributed Multiple-Input Multiple-Output (D-MIMO) systems, but finding the optimal layout is a computationally intensive, non-convex problem. Prior research often addresses this by directly optimizing the coordinates of each distributed panels using complex numerical techniques, such as convex relaxation or iterative algorithms. While viable, these methods can be computationally demanding and offer limited insight into the structural properties of optimal deployments. In contrast, this paper introduces a structured, parametric optimization framework. We constrain the panel deployment to a lattice, reducing the high-dimensional problem to optimizing a few parameters that define the lattice's overall scale and shape. Through numerical simulations, our method is shown to perform nearly indistinguishable from that achieved by a highly complex benchmark, while it outperforms standard approaches like Majorization-Minimizing-Lloyd's algorithm (MM-Lloyd). Furthermore, we identify that a simple, non-optimized, evenly spaced grid can achieve 96% of the benchmark's performance, offering a highly efficient and practical heuristic.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"305-313"},"PeriodicalIF":2.7,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11355794","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Learning of Regularized Tyler's M-Estimator","authors":"Karim T. Abou–Moustafa","doi":"10.1109/OJSP.2026.3654886","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3654886","url":null,"abstract":"Tyler's <inline-formula><tex-math>$M$</tex-math></inline-formula>-estimator (TME) is an accurate and efficient robust estimator for the scatter matrix when the data are samples from an <italic>elliptical distribution</i> with heavy-tails and the number of samples <inline-formula><tex-math>$n$</tex-math></inline-formula> is larger than the number of variables <inline-formula><tex-math>$p$</tex-math></inline-formula>. Unfortunately, TME is not defined when <inline-formula><tex-math>$p &gt; n$</tex-math></inline-formula>, and various research works have proposed regularized versions of TME using the spirit of Ledoit & Wolf estimator whose performance depends on a carefully chosen <italic>shrinkage coefficient</i> <inline-formula><tex-math>$alpha in (0,1)$</tex-math></inline-formula>. In this paper, we consider the problem of estimating an optimal shrinkage coefficient <inline-formula><tex-math>$alpha$</tex-math></inline-formula> for Regularized TME (RTME). In particular, we propose to estimate an optimal shrinkage coefficient by setting <inline-formula><tex-math>$alpha$</tex-math></inline-formula> as the solution to a suitably chosen objective function; namely the leave-one-out cross-validated (LOOCV) log-likelihood loss. LOOCV, however, is computationally prohibitive even for moderate values of <inline-formula><tex-math>$n$</tex-math></inline-formula>. To this end, we propose a computationally efficient approximation for the LOOCV log-likelihood loss that eliminates the need for invoking the RTME procedure <inline-formula><tex-math>$n$</tex-math></inline-formula> times for each sample left out during the LOOCV procedure. This approximation yields an <inline-formula><tex-math>$O(n)$</tex-math></inline-formula> reduction in the running time complexity for the LOOCV procedure, which results in a significant speedup for computing the LOOCV estimate. We demonstrate the efficacy of the proposed approach on synthetic high-dimensional data sampled from heavy-tailed elliptical distributions, as well as on real high-dimensional datasets for object and face recognition. Our experiments demonstrate that the proposed method is efficient and consistently more accurate than other methods in the literature for shrinkage coefficient estimation.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"91-100"},"PeriodicalIF":2.7,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11355793","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User-to-User Clustering, Channel Estimation & Cross-Link Interference Mitigation for Dynamic TDD Systems","authors":"Martin Andersson;Anubhab Chowdhury;Erik G. Larsson","doi":"10.1109/OJSP.2026.3654888","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3654888","url":null,"abstract":"In dynamic time-division duplexing (TDD) systems, half-duplex access points (APs) scheduled either in uplink (UL) or downlink (DL), simultaneously serve users operating in UL and DL on the same time-frequency resources. This incurs cross-link interference from APs operating in DL to APs in UL, and similarly from users operating in UL to users in DL. In this paper, we develop a scalable method for UL-user-to-DL-user interference (UUI) mitigation in dynamic TDD networks. To this end, we note that the UUI observed at each DL user will be predominantly caused by the UL users in its close vicinity. Hence, we propose to form local clusters of UL users for each DL user, including only the UL users that are expected to cause significant UUI to the DL user. Then, we present a graph-coloring-based pilot reuse algorithm that ensures orthogonal pilots among the UL users within the local clusters of each DL user, while maximizing the pilot reuse (i.e., minimizing the pilot length) for scalability. Further, we introduce beamformed DL pilots to estimate effective DL channels (the physical DL channel multiplied by the DL precoding matrix), which together with estimated user-to-user channels enable the multi-antenna DL users to design combining vectors maximizing their signal-to-interference-and-noise ratios, by taking both desired signal amplification and UUI suppression into consideration. We derive an achievable DL spectral efficiency with our proposed pilot schemes and combining vectors. Numerical results show the effectiveness of our proposed UUI mitigation scheme, both in terms of pilot overhead reduction and UUI suppression with our designed combining vectors.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"238-246"},"PeriodicalIF":2.7,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11355849","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GLRT-Based CFAR Detection in the Latent Space for Extended Targets in Gaussian and Non-Gaussian Disturbance","authors":"Angelo Coluccia;Emanuele Mele;Alessio Fascista","doi":"10.1109/OJSP.2026.3653668","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3653668","url":null,"abstract":"The design of detectors with constant false alarm rate (CFAR) property is a cornerstone challenge in radar signal processing. To this aim, a forward-thinking strategy is to construct decision statistics as functions of suitable maximal invariants associated with invariant tests that guarantee, through a suitable transformation group, the CFAR property by construction. However, the distribution of such maximal invariants is often difficult to handle, especially for complicated non-Gaussian models, making the derivation of the generalized likelihood ratio test (GLRT) challenging. In this work, we introduce a novel learning-based CFAR detection framework, in which a trained probabilistic encoder maps maximal invariant statistics (or functions thereof) to a convenient low-dimensional latent space where a latent GLRT-based detector (L-GLRT) is easy to derive. A cross-entropy loss with Kullback-Leibler (KL) divergence regularization is adopted to encourage the latent distributions under both <inline-formula><tex-math>$H_{0}$</tex-math></inline-formula> (target free) and <inline-formula><tex-math>$H_{1}$</tex-math></inline-formula> (target present) hypotheses to be as close as possible, in an information-theoretic sense, to Gaussian densities. Mismatched data incorporated under either hypotheses are introduced to promote robustness or selectivity. The approach unifies Gaussian and non-Gaussian settings, spanning from point-like to extended targets under the complex multivariate elliptically contoured matrix (CMECM) family, and is benchmarked against state-of-the-art classical and data-driven detectors. Moreover, since the latent space is low dimensional, insightful visualization of the behavior of the designed L-GLRT detectors can be obtained. Numerical results show that the proposed method achieves superior robustness/selectivity trade-offs while preserving CFAR guarantees by design and containing <inline-formula><tex-math>$P_{d}$</tex-math></inline-formula> losses under matched conditions.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"285-295"},"PeriodicalIF":2.7,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11347597","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Objective Evaluation of Prosody and Intelligibility in Speech Synthesis via Conditional Prediction of Discrete Tokens","authors":"Ismail Rasim Ulgen;Zongyang Du;Junchen Lu;Philipp Koehn;Berrak Sisman","doi":"10.1109/OJSP.2026.3653666","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3653666","url":null,"abstract":"Objective evaluation of synthesized speech is critical for advancing speech generation systems, yet existing metrics for intelligibility and prosody remain limited in scope and weakly correlated with human perception. Word Error Rate (WER) provides only a coarse text-based measure of intelligibility, while F0-RMSE and related pitch-based metrics offer a narrow, reference-dependent view of prosody. To address these limitations, we propose <italic>TTScore</i>, a targeted and reference-free evaluation framework based on conditional prediction of discrete speech tokens. TTScore employs two sequence-to-sequence predictors conditioned on input text: <italic>TTScore-int</i>, which measures intelligibility through content tokens, and <italic>TTScore-pro</i>, which evaluates prosody from the perspective of pitch, through prosody tokens. For each synthesized utterance, the predictors compute the likelihood of the corresponding token sequences, yielding interpretable scores that capture alignment with intended linguistic content and prosodic structure. Experiments on the SOMOS, VoiceMOS, and TTSArena benchmarks demonstrate that TTScore-int and TTScore-pro provide reliable, aspect-specific evaluation and achieve stronger correlations with human judgments of overall quality than existing intelligibility and prosody-focused metrics.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"247-256"},"PeriodicalIF":2.7,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11347554","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Speech DF Arena: A Leaderboard for Speech DeepFake Detection Models","authors":"Sandipana Dowerah;Atharva Kulkarni;Ajinkya Kulkarni;Hoan My Tran;Joonas Kalda;Artem Fedorchenko;Benoit Fauve;Damien Lolive;Tanel Alumäe;Mathew Magimai.-Doss","doi":"10.1109/OJSP.2026.3652496","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3652496","url":null,"abstract":"Parallel to the development of advanced deepfake audio generation, audio deepfake detection has also seen significant progress. However, a standardized and comprehensive benchmark is still missing. To address this, we introduce Speech DeepFake (DF) Arena, the first comprehensive benchmark for audio deepfake detection. Speech DF Arena provides a toolkit to uniformly evaluate detection systems, currently across 14 diverse datasets and attack scenarios, standardized evaluation metrics and protocols for reproducibility and transparency. It also includes a leaderboard to compare and rank the systems to help researchers and developers enhance their reliability and robustness. We include 14 evaluation sets, 14 state-of-the-art open-source and 4 proprietary detection systems, totalling 18 systems in the leaderboard. Our study presents many systems exhibiting high EER in out-of-domain scenarios, highlighting the need for extensive cross-domain evaluation. The leaderboard is hosted on HuggingFace¹ and a toolkit for reproducing results across the listed datasets is available on GitHub².","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"73-81"},"PeriodicalIF":2.7,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11345101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Complexity Machine Learning Models for Active Noise Control in Nonlinear Systems","authors":"Miguel Ferrer;María de Diego;Alberto Gonzalez","doi":"10.1109/OJSP.2025.3650619","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3650619","url":null,"abstract":"Active Noise Control (ANC) systems are typically based on adaptive filters. However, electroacoustic transducers and their associated electronic components often exhibit nonlinear behaviors that linear controllers cannot accurately model, resulting in suboptimal performance. While neural networks and other advanced models have been proposed to address these limitations, their high computational demands and inherent latency frequently restrict real-time deployment. This work investigates the use of lightweight, computationally efficient machine learning (ML) models that operate on a sample-by-sample basis using simple digital operators. The proposed models are applied to ANC under nonlinear conditions, including distortions in the primary path, the secondary path, and the reference signal. The approach enhances noise attenuation while preserving low computational complexity, thereby enabling real-time implementation on embedded systems. Simulation results confirm the effectiveness of the method across a variety of nonlinear scenarios, demonstrating superior noise reduction and control accuracy compared to conventional linear ANC schemes, and achieving this at a significantly lower cost than alternative nonlinear approaches.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"165-172"},"PeriodicalIF":2.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11328793","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Adaptive Multiparameter Penalty Selection Method for Multiconstraint and Multiblock ADMM","authors":"Luke Lozenski;Michael T. McCann;Brendt Wohlberg","doi":"10.1109/OJSP.2026.3664275","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3664275","url":null,"abstract":"This work presents a new method for online selection of multiple penalty parameters for the alternating direction method of multipliers (ADMM) algorithm applied to optimization problems with multiple constraints or functions with block matrix components. ADMM is widely used for solving constrained optimization problems in a variety of fields, including signal and image processing. Implementations of ADMM often utilize a single hyperparameter, referred to as the penalty parameter, which needs to be tuned to control the rate of convergence. However, in problems with multiple constraints, ADMM may demonstrate slow convergence regardless of penalty parameter selection due to scale differences between constraints. Accounting for scale differences between constraints to improve convergence in these cases requires introducing a penalty parameter for each constraint. The proposed method is able to adaptively account for differences in scale between constraints, providing robustness with respect to problem transformations and initial selection of penalty parameters. It is also simple to understand and implement. Our numerical experiments demonstrate that the proposed method performs favorably compared to a variety of existing penalty parameter selection methods.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"410-427"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11395612","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GAN-Based Bayer Pattern Image Synthesis for Raw Domain Computer Vision Tasks","authors":"Wei Zhou;Hongyu Wang;Haoyan Li;Yuxing Mao;Dezhi Ji;Xin Lou","doi":"10.1109/OJSP.2026.3701313","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3701313","url":null,"abstract":"In this paper, we introduce a novel approach for converting established color image datasets into their RAW counterparts using generative adversarial networks (GANs), aiming to tackle the challenge of obtaining RAW training data for RAW domain computer vision tasks, such as computer vision-oriented ISP pipeline design, RAW domain denoise, etc. We provide theoretical analysis to demonstrate how leveraging transformed data during GAN training can bolster the model’s capacity to learn the underlying distribution of the original data. The RAW Bayer pattern images can therefore be generated by configuring the transformations in the ISP pipeline into the GAN’s training process. Evaluation results show that RAW images generated by our method outperform the existing methods in the downstream computer vision tasks, and the training process of the proposed method is more stable. Our experiments show that the characteristics of the synthesized images are similar to those of the real images. By training denoising and object detection frameworks using the synthesized RAW Bayer images, we showcase that they can be used in end-to-end vision system (from RAW images to vision tasks with ISP in the loop) with negligible performance degradation.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"717-729"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11556389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148478706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EM-ADSNet: Joint Detection and Separation of Aliasing Electromagnetic Signals With an Unknown Number of Sources","authors":"Jiajie Wu;Benzhou Jin;Chang Liu;Yuyang Duan;Xiaofei Zhang","doi":"10.1109/OJSP.2026.3733342","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3733342","url":null,"abstract":"The rapid growth of electronic systems, including communication and radar platforms, has made electromagnetic environments increasingly complex. Signals with different modulation formats can overlap in space, time and frequency, creating severe aliasing in electromagnetic signal monitoring. Conventional separation methods typically require prior knowledge of the number of sources. This requirement limits their use in open electromagnetic environments, where the number of active signals is often unknown and difficult to estimate. To address this limitation, we propose EM-ADSNet (Electromagnetic Adaptive Detection-Separation Network), a single-channel deep-learning framework for joint signal-number detection and blind separation. Specifically, EM-ADSNet first employs a high-dimensional feature processing network to enhance the representation capacity for complex mixed electromagnetic signals. A Multi-Kernel Attention (MKA) encoder is then proposed to capture multi-scale signal characteristics through multi-kernel convolutions while enhancing the discriminative representation of mixed components via a lightweight attention mechanism. To handle mixtures with an unknown number of constituent signals, a dedicated detection module and an adaptive mask generation module are jointly designed. The detection module estimates the signal number by formulating it as a multi-class classification problem, and the estimated count further guides the mask generation module to activate the corresponding number of mask outputs for per-signal separation. Experiments on simulated datasets and the real-world MIT RF Challenge benchmark show that EM-ADSNet effectively separates complex mixed signals under a blind separation setting. The model remains robust under varying noise conditions and consistently outperforms state-of-the-art baseline methods.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"957-971"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11690779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}