{"title":"An Adaptive Proximal Inexact Gradient Framework and Its Application to Per-Antenna Constrained Joint Beamforming and Compression Design","authors":"Xilai Fan;Bo Jiang;Ya-Feng Liu","doi":"10.1109/TSP.2025.3602976","DOIUrl":"10.1109/TSP.2025.3602976","url":null,"abstract":"In this paper, we propose an adaptive proximal inexact gradient (APIG) framework for solving a class of nonsmooth composite optimization problems involving function and gradient errors. Unlike existing inexact proximal gradient methods, the proposed framework introduces a new line search condition that jointly adapts to function and gradient errors, enabling adaptive stepsize selection while maintaining theoretical guarantees. Specifically, we prove that the proposed framework achieves an <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>-stationary point within <inline-formula><tex-math>$mathcal{O}(epsilon^{-2})$</tex-math></inline-formula> iterations for nonconvex objectives and an <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>-optimal solution within <inline-formula><tex-math>$mathcal{O}(epsilon^{-1})$</tex-math></inline-formula> iterations for convex cases, matching the best-known complexity in this context. We then custom-apply the APIG framework to an important signal processing problem: the joint beamforming and compression problem (JBCP) with per-antenna power constraints (PAPCs) in cooperative cellular networks. This customized application requires careful exploitation of the problem’s special structure such as the tightness of the semidefinite relaxation (SDR) and the differentiability of the dual. Numerical experiments demonstrate the superior performance of our custom-application over state-of-the-art benchmarks for the JBCP.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3433-3447"},"PeriodicalIF":5.8,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919542","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}
{"title":"Data Detection for Oversampled OTSM System with Low-bit ADCs","authors":"Xindi Yu, Wenqian Shen, Jiaxin Chen, Jianping An","doi":"10.1109/tsp.2025.3603612","DOIUrl":"https://doi.org/10.1109/tsp.2025.3603612","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"83 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919539","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}
{"title":"Remote Inference Over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantization","authors":"Eyal Fishel;May Malka;Shai Ginzach;Nir Shlezinger","doi":"10.1109/TSP.2025.3603943","DOIUrl":"10.1109/TSP.2025.3603943","url":null,"abstract":"A broad range of technologies rely on <italic>remote inference</i>, wherein data acquired is conveyed over a communication channel for inference in a remote server. Communication between the participating entities is often carried out over rate-limited channels, necessitating data compression for reducing latency. While deep learning facilitates joint design of the compression mapping along with encoding and inference rules, existing learned compression mechanisms are static, and struggle in adapting their resolution to changes in channel conditions and to dynamic links. To address this, we propose Adaptive Rate Task-Oriented Vector Quantization (ARTOVeQ), a learned compression mechanism that is tailored for remote inference over dynamic links. ARTOVeQ is based on designing nested codebooks along with a learning algorithm employing progressive learning. We show that ARTOVeQ extends to support low-latency inference that is gradually refined via successive refinement principles, and that it enables the simultaneous usage of multiple resolutions when conveying high-dimensional data. Numerical results demonstrate that the proposed scheme yields remote deep inference that operates with multiple rates, supports a broad range of bit budgets, and facilitates rapid inference that gradually improves with more bits exchanged, while approaching the performance of single-rate deep quantization methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3557-3571"},"PeriodicalIF":5.8,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919541","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}
Toan-Van Nguyen, Sajjad Nassirpour, Italo Atzeni, Antti Tölli, A. Lee Swindlehurst, Duy H. N. Nguyen
{"title":"MIMO Detection with Spatial Sigma-Delta ADCs: A Variational Bayesian Approach","authors":"Toan-Van Nguyen, Sajjad Nassirpour, Italo Atzeni, Antti Tölli, A. Lee Swindlehurst, Duy H. N. Nguyen","doi":"10.1109/tsp.2025.3602839","DOIUrl":"https://doi.org/10.1109/tsp.2025.3602839","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"10 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905720","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}
Tao Wang, Junli Liang, H. C. So, Zhaozhao Gao, Ming Gu
{"title":"Drone Swarm Waveform and Array Design for Range Profile-based Electromagnetic Disguise","authors":"Tao Wang, Junli Liang, H. C. So, Zhaozhao Gao, Ming Gu","doi":"10.1109/tsp.2025.3601671","DOIUrl":"https://doi.org/10.1109/tsp.2025.3601671","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"14 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898460","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}
{"title":"Theory and Practice of Light-Weight Sequential SBL Algorithm: An Alternative to OMP","authors":"Rohan R. Pote;Bhaskar D. Rao","doi":"10.1109/TSP.2025.3600492","DOIUrl":"10.1109/TSP.2025.3600492","url":null,"abstract":"We propose a low complexity forward selection algorithm for the sparse signal recovery (SSR) problem based on the sparse Bayesian learning (SBL) formulation. The proposed algorithm, called as light-weight sequential SBL (LWS-SBL), offers an alternative to the widely used iterative and greedy algorithm known as orthogonal matching pursuit (OMP). In contrast to OMP, which models the unknown sparse vector as a deterministic variable, the same is modeled as a stochastic variable within LWS-SBL. Specifically, the proposed algorithm is derived from the stochastic maximum likelihood estimation framework, and it iteratively selects columns that maximally increase the likelihood. We derive efficient recursive procedure to update the internal parameters of the algorithm, and maintain a similar asymptotic computational complexity as OMP. Additional two perspectives, one based on array processing beamforming interpretations and the other based on a local high-resolution analysis, are provided to understand the underlying differences in the mechanisms of the two algorithms. They reveal avenues where LWS-SBL improves over OMP. These are verified in the numerical section in terms of improved support recovery performance. Similar to the counterparts in OMP, for SSR problems involving parametric dictionaries, the flexibility of the proposed approach is demonstrated by extending LWS-SBL to recover multi-dimensional parameters, and in a <italic>gridless</i> manner.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3528-3542"},"PeriodicalIF":5.8,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898689","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}
{"title":"Interleaved Hidden Markov Processes Inference for Deinterleaving Radar Pulse Sequences","authors":"Jiadi Bao;Mengtao Zhu;Yunjie Li;Shafei Wang","doi":"10.1109/TSP.2025.3597790","DOIUrl":"10.1109/TSP.2025.3597790","url":null,"abstract":"The Hidden Markov Process (HMP) has been widely used to model radar pulse sequences. For the radar signal deinterleaving task in an electronic reconnaissance system, the intercepted radar pulse sequences are assumed to be interleaved hidden Markov processes (IHMP). In this context, this paper proposes a generative model to represent the IHMP and reformulates the deinterleaving problem as a posterior inference task. To compute the posterior probability, we first design an exact inference algorithm. However, due to the combinatorial nature of the hidden state representation, exact inference becomes computationally intractable. To address this limitation, we further develop a sampling-based method and two variational-based methods, yielding tractable solutions for the posterior computation. Finally, a theoretical lower bound on the error probability is derived based on the likelihood ratio test, with the proposed methods shown to get reasonably close to the bound. Simulations on diverse radar pulse signal datasets verify that variational inference with a structured approximation delivers a superior balance between deinterleaving accuracy and computational efficiency, making it a promising alternative to exact inference methods and search-based methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3448-3462"},"PeriodicalIF":5.8,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898673","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}
{"title":"Distributed Extended Kalman Consensus Filtering for Multi-Sensor Networked Nonlinear Systems Under Stochastic Communication Protocol","authors":"Han Zhou;Shuli Sun","doi":"10.1109/TSP.2025.3599848","DOIUrl":"https://doi.org/10.1109/TSP.2025.3599848","url":null,"abstract":"The distributed extended Kalman consensus filtering problem under stochastic communication protocol (SCP) is investigated for multi-sensor networked nonlinear systems. In the sensor network, each sensor node and its neighbor nodes occupy a limited number of communication channels when exchanging measurement data. Utilizing the SCP-equipped communication network ensures that the neighbor nodes of each sensor node randomly access these channels and send measurement data based on the number of channels at each step. A set of random variables is introduced to represent the neighbor nodes whose measurements are selected for transmission at each step. When each sensor node is aware of the measurement data received from its neighbor nodes at each step, a distributed extended Kalman consensus filter dependent on random variables is designed. To improve the estimation consensus among nodes, a consensus term is added to the performance metric, and a weighting factor is introduced to assign weight between estimation accuracy and consensus. The optimal filtering gain is derived by minimizing this performance metric. A sufficient condition for the exponential mean-square boundedness of the filtering error is given. Finally, the proposed algorithm’s effectiveness is validated through a simulation example.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3629-3640"},"PeriodicalIF":5.8,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315295","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}
Yuan Liu;John M. Martyn;Jasmine Sinanan-Singh;Kevin C. Smith;Steven M. Girvin;Isaac L. Chuang
{"title":"Toward Mixed Analog-Digital Quantum Signal Processing: Quantum AD/DA Conversion and the Fourier Transform","authors":"Yuan Liu;John M. Martyn;Jasmine Sinanan-Singh;Kevin C. Smith;Steven M. Girvin;Isaac L. Chuang","doi":"10.1109/TSP.2025.3599462","DOIUrl":"https://doi.org/10.1109/TSP.2025.3599462","url":null,"abstract":"Signal processing stands as a pillar of classical computation and modern information technology, applicable to both analog and digital signals. Recently, advancements in quantum information science have suggested that quantum signal processing (QSP) can enable more powerful signal processing capabilities. However, the developments in QSP have primarily leveraged <italic>digital</i> quantum resources, such as discrete-variable (DV) systems like qubits, rather than <italic>analog</i> quantum resources, such as continuous-variable (CV) systems like quantum oscillators. Consequently, there remains a gap in understanding how signal processing can be performed on hybrid CV-DV quantum computers. Here we address this gap by developing a new paradigm of mixed analog-digital QSP. We demonstrate the utility of this paradigm by showcasing how it naturally enables analog-digital conversion of quantum signals—specifically, the transfer of states between DV and CV quantum systems. We then show that such quantum analog-digital conversion enables new implementations of quantum algorithms on CV-DV hardware. This is exemplified by realizing the quantum Fourier transform of a state encoded on qubits via the free-evolution of a quantum oscillator, albeit with a runtime exponential in the number of qubits due to information theoretic arguments. Collectively, this work marks a significant step forward in hybrid CV-DV quantum computation, providing a foundation for scalable analog-digital signal processing on quantum processors.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3641-3655"},"PeriodicalIF":5.8,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11129874","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352002","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}
{"title":"Movable Antenna Enhanced Beampattern Synthesis for Spectrum Constrained MIMO Radar","authors":"Dongxu An;Jinfeng Hu;Ren Wang;Xin Tai;Yongfeng Zuo;Kai Zhong;Fulvio Gini;Maria Sabrina Greco","doi":"10.1109/TSP.2025.3594474","DOIUrl":"https://doi.org/10.1109/TSP.2025.3594474","url":null,"abstract":"Designing Constant Mode (CM) waveforms to synthesize spectrum-compatible beampatterns is crucial for Multiple-Input Multiple-Output (MIMO) radar systems. Unlike traditional Fixed Position Antenna (FPA) arrays, we propose a Movable Antenna (MA) enhanced MIMO radar that improves sensing performance by introducing new degrees of freedom (DoFs). This flexibility allows for the adjustment of each antenna’s position based on sensing requirements and optimizing the allocation of sensing power. Specifically, we co-design the transmit waveform and Antenna Position Vector (APV) to minimize the Mean Squared Error (MSE) of beampattern synthesis while adhering to constraints on MA array position, spectrum compatibility, and CM. This leads to a non-convex Quadratic-Constrained Quartic-Programming (QCQP) problem with two highly coupled variables. To solve this problem, we propose an Exact-Penalized-Product-Manifold (EPPM) method. First, we construct a Product Positivity Complex-Circle Manifold (PPC<inline-formula><tex-math>${}^{2}$</tex-math></inline-formula>M) space based on the constraint features of MA array position and CM, projecting both the APV and transmit waveform onto this space. Then, we employ a smoothing technique to transform the spectrum compatibility constraint into an exact penalty function, converting the problem into an unconstrained one on the PPC<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>M space. Finally, we derive a Parallel Conjugate Gradient (PCG) method to optimize the APV and transmit waveform in parallel. Simulation results show that compared with the existing FPA-based method, the proposed method reduces the beampattern sidelobe by 2.3 dB and the beampatterns matching MSE by about 4 dB.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3478-3495"},"PeriodicalIF":5.8,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256008","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}