Product Information
Description
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.
