Aims and scope

The aim of the Journal on Advances in Signal Processing is to highlight the theoretical and practical aspects of signal processing in new and emerging technologies. The journal is directed as much at the practicing engineer as at the academic researcher. Authors of articles with novel contributions to the theory and/or practice of signal processing are welcome to submit their articles for consideration. All manuscripts undergo a rigorous review process. Journal on Advances in Signal Processing employs a paperless, electronic review process to enable a fast and speedy turnaround in the review process.

The journal is an Open Access journal since 2007.

Special Issue on Signal processing for XL-MIMO and holographic MIMO
截稿日期: 2026-04-30

Multiple-input multiple-output (MIMO) technologies are ubiquitous in modern wireless communications and are expected to assume an even more prominent role in future wireless generations (6G and beyond). Massive MIMO allows to achieve flexible beamforming and large-scale spatial multiplexing thanks to the large number of antennas at the base station. 6G’s version of massive MIMO is envisioned to evolve in the direction of physically large and dense arrays to provide a significant boost in terms of spectral and energy efficiency. This vision, which is referred to as “extremely large MIMO” (XL-MIMO), deploys thousands of antennas with sub-wavelength spacing to enable higher spatial resolution for communication and sensing purposes. A further paradigm shift is represented by ``holographic MIMO,’’ where traditional antenna arrays are replaced by continuous surfaces (made of, e.g., metamaterials) that allow a full manipulation of the electromagnetic waves. In this setting, it is crucial to consider physically consistent channel and hardware models that build on electromagnetic and circuit theory. In addition to these aspects, the combination of physically large arrays and high operating frequencies, e.g., in the (sub-)THz band, leads to long-range near-field signal propagation, which can be leveraged to enhance both communication and sensing performances. XL- and holographic MIMO have recently sparked tremendous interest in signal processing techniques that are suitable to handle massive antenna and frequency dimensions, or that are directly applied at the electromagnetic level. This special session, that falls under EURASIP’s SPCN TAC, seeks to present the latest snapshot of the ongoing research on signal processing for XL-MIMO and holographic MIMO systems. This rapidly evolving field is of paramount theoretical and practical importance as it will define the physical layer of future wireless communications and sensing.

A short list of topics of interest:

• Performance analysis, channel estimation, and beamforming design for XL- and holographic MIMO.
• Signal processing under physically consistent channel and hardware models (e.g., based on electromagnetic and circuit theory).
• Signal processing for low-complexity and energy-efficient MIMO architectures.
• Signal processing for near-field communications, sensing, and localization.
• XL- and holographic MIMO for high frequency communications.

Special Issue on Advances in Signal Processing and Machine Learning for Localization and Sensing in Distributed, Reconfigurable and Multi-Sensor Systems
截稿日期: 2026-05-05

The landscape of signal processing for multi-sensor systems is rapidly evolving, driven by the increasing prevalence of advanced and flexible architectures such as reconfigurable intelligent surfaces (RIS), scatter arrays, distributed arrays, distributed massive-MIMO (D-MIMO), movable or fluid antennas, and cell-free setups leveraging emerging technologies like radio stripes. These emerging configurations are redefining the role of spatial diversity, enabling both accurate localization of active entities and environmental sensing of passive objects. Leveraging enhanced spatial resolution, near-field effects, and reconfigurable propagation environments, multi-sensor systems pave the way for next-generation localization and sensing solutions. These advancements are particularly crucial for applications such as autonomous navigation, industrial automation, smart environments, and next-generation wireless networks (B5G/6G). The ability to achieve accurate situational awareness anytime and anywhere requires novel signal processing and machine learning techniques that can extract meaningful information from complex, distributed sensing setups.

This Special Issue aims to provide a platform for cutting-edge research on both model-based and learning-driven approaches to localization and sensing. While classical signal processing techniques, such as beamforming and Bayesian inference, offer interpretability and robustness, learning-driven methods, including deep learning and graph-based approaches, enable adaptive processing in scenarios where analytical models may be unavailable. Hybrid approaches that combine model-based and learning-based methods present new exciting research directions.

We invite original research contributions on novel algorithms, theoretical advancements, and practical demonstrations addressing the challenges and opportunities in localization, tracking, sensing, and communication with reconfigurable and multi-sensor systems.

Topics of Interest

Topics of interest for this Special Issue include, but are not limited to:

•Beamforming and adaptive array processing for localization and sensing
•Bayesian inference and maximum likelihood methods
•Extremely large arrays and XL-MIMO for localization and sensing
•Compressive sensing and sparsity-based localization
•Reconfigurable intelligent surfaces (RIS) and scatter arrays
•Distributed MIMO, coherent cell-free architectures, and radio stripes
•Movable and fluid antennas for localization
•Cooperative and networked localization
•AI/ML for localization and sensing
•Neural networks, deep learning, and graph-based approaches
•Combined model-based and data-driven localization and sensing
•Multi-sensor fusion for localization and tracking
•Holographic localization and sensing
•Carrier-phase positioning

Article publication will be continuously, i.e. articles are published into the special issue one at a time as soon as they are ready.

To submit your manuscript for consideration as part of this topical collection, please follow the steps detailed on Submission Guideline below. When submitting a manuscript you will be asked (in details page) if you are submitting to a collection. Please ensure that you select correct collection title “Advances in Signal Processing and Machine Learning for Localization and Sensing in Distributed, Reconfigurable and Multi-Sensor Systems” from the drop-down list. Authors should also express their interest in the Collection in their cover letter.

This collection supports and amplifies research related to SDG9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation.

Special Issue on Advanced Signal Processing for Distributed and Autonomous Sensing Systems
截稿日期: 2026-05-15

EURASIP Journal on Advances in Signal Processing is calling for submissions to our Collection on Advanced Signal Processing for Distributed and Autonomous Sensing Systems. This Special Issuer is linked to the 32nd European Signal Processing Conerence (EUSIPCO 2024).

The past decade has seen a rise in the adoption of distributed autonomous sensing systems (DASS), in the field of drone swarms, automotives, satellite networks, industry automation, autonomous rovers and truck platooning to name a few. These networked cyber-physical systems are typically tasked with complex missions, which necessitate accurate PNT (Position, Navigation, and Timing), cooperative sensing, coordination and control, decision making, sensor fusion, distributed inference and learning, and timely decision making on the Edge.

In many cases, these DASS are also deployed in inaccessible or intermittently accessible environments e.g., drone swarms in BVLOS scenarios, with limited access to cloud services and other critical infrastructure, which necessitates on-board or in-network inference, control, and decision. Signal processing and Machine learning play a vital role in providing efficient, optimal and robust solutions for these challenges. This special issue is a platform to address these challenges by presenting research on novel data models, signal processing and machine learning algorithms, resource constrained real-time Edge AI solutions, and fundamental insights into advanced optimization and statistical tools.

In this special issue, we aim to push the boundaries of sensing, learning, decision making and autonomous mobility of DASS. Key topics of interest include, but not limited to

• Distributed (and decentralised) optimisation for multi-agent systems
• Collaborative position, navigation, and timing (PNT) of multi-agent systems
• Machine learning models for large-scale networked sensing systems
• Probabilistic and Physics-inspired sensor fusion for autonomous systems
• Sparsity exploiting methods for distributed sensing systems
• Security and Privacy of distributed signal processing in networked systems
• Distributed estimation, detection and decision making in networked systems
• Applications: Industrial automation, Automotive, Situational awareness, Smart cities, Drone swarms, Satellite networks, and more

Article publication will be continuously, i.e. articles are published into the special issue one at a time as soon as they are ready. Authors are encouraged to contact with the Guest Editors for submission scope related queries.

This Collection welcomes submission of Research Articles. Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system. During the submission process, under the section additional information, you will be asked whether you are submitting to a Collection, please select "Advanced Signal Processing for Distributed and Autonomous Sensing Systems" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s editorial standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.

Special Issue on Advanced Multi-channel Signal Processing for Sensing Systems
截稿日期: 2026-06-30

EURASIP Journal on Advances in Signal Processing is calling for submissions to our Collection on Advanced Multi-channel Signal Processing for Sensing Systems. Early sensing systems use a single or a few channels to infer information about the surrounding environment. These systems observe the environment from only one perspective and as such the informative content of collected data is limited by such a perspective. Moreover, the number of degrees of freedom provided by these systems does not allow for the development of advanced processing solutions. Nowadays, recent advancements in technology have led to the spread of the so-called multi-channel systems that overcome the limitations of early systems. Multi-channel systems exploit diversity in different domains (frequency, time, space, polarization, and others) and this diversity can increase the amount of information contained in data making more effective tasks such as parameter estimation, target detection, filtering, interference mitigation, etc. Modern sensing systems can benefit from diversity by means of multi-dimensional signal processing techniques that can capitalize on the information provided by multiple channels. This special issue looks at multiple channel sensing systems and the interest is on advanced signal processing solutions for detection, communication, classification, estimation, beamforming, waveform design, and filtering.

We invite investigators to contribute original research papers on the following recommended but not exclusive list of topics:

- Tensor-based approaches to adaptive processing
- Joint design of multi-channel sensing and communication systems
- Beamforming for MIMO and/or phase array systems
- Multidimensional interference estimation and mitigation
- Target detection
- Polarimetric data classification
- Multidimensional radar imaging
- Compressive-sensing-based learning techniques
- Signal classification

Article publication will be continuously, i.e. articles are published into the special issue one at a time as soon as they are ready.