信号处理：2025–2026 年度综述

An annual survey of the eess.SP arXiv category, covering research published from September 2025 – August 2026. Built on a close-reading set of 132 papers and organised into 37 chapters, it maps the year by the field's research sub-directions.

Book framework (share of the year's output):

• Reconfigurable Intelligent Surfaces and Massive MIMO (46.2%) — Focuses on optimizing RIS-assisted wireless systems, massive MIMO precoding, and channel estimation techniques to enhance spectral and energy efficiency.
• Deep Learning for Signal Processing and Sensing (30.2%) — Applies deep learning models to diverse signal processing tasks including EEG, radar, and acoustic sensing for robust detection and classification.
• Near-Field Channel Modeling and Antenna Arrays (5.7%) — Investigates near-field propagation characteristics, antenna array design, and channel estimation challenges in millimeter-wave and sub-6 GHz bands.
• Satellite Communications and OTFS Modulation (5.1%) — Addresses resource allocation, channel estimation, and modulation schemes for LEO satellite constellations and high-mobility wireless environments.
• Adaptive Filtering and Parameter Estimation (3.4%) — Develops adaptive filtering, Kalman filtering, and parameter estimation methods for noisy signal environments and system identification.
• IoT Sensor Networks and Multimodal Monitoring (2.5%) — Utilizes IoT sensor networks, graph transformers, and multimodal data fusion for continuous health, environmental, and structural monitoring.
• Semantic Communications and Foundation Models (2.2%) — Explores semantic communication paradigms, foundation models, and generative AI for efficient information transmission in 6G networks.
• Channel State Information Prediction and Feedback (2.0%) — Focuses on reducing CSI feedback overhead and improving channel prediction accuracy using deep learning and statistical modeling techniques.
• Sparse Recovery and Inverse Problems (1.3%) — Solves sparse signal recovery, inverse problems, and sampling challenges using generative priors, diffusion models, and optimization algorithms.
• UAV and Vehicular Network Connectivity (1.0%) — Optimizes connectivity, coverage, and trajectory planning for UAV-assisted and vehicular networks in heterogeneous wireless environments.
• Audio-Visual Quality Assessment and Coding (0.2%) — Evaluates and improves audio and video quality through advanced coding standards, quantization techniques, and perceptual assessment metrics.

Each sub-direction is covered across three chapters — how it framed its questions, the strength of its key findings, and its open debates and frontiers — so the reader gets a traceable picture of what this part of the field actually produced over the year.