Intelligent Radio Signal Processing: A Survey
arXiv:2008.08264
Abstract
Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive number of connections, and various radio characteristics. Owing to recent advancements in big data and computing technologies, artificial intelligence (AI) has become a useful tool for radio signal processing and has enabled the realization of intelligent radio signal processing. This survey covers four intelligent signal processing topics for the wireless physical layer, including modulation classification, signal detection, beamforming, and channel estimation. In particular, each theme is presented in a dedicated section, starting with the most fundamental principles, followed by a review of up-to-date studies and a summary. To provide the necessary background, we first present a brief overview of AI techniques such as machine learning, deep learning, and federated learning. Finally, we highlight a number of research challenges and future directions in the area of intelligent radio signal processing. We expect this survey to be a good source of information for anyone interested in intelligent radio signal processing, and the perspectives we provide therein will stimulate many more novel ideas and contributions in the future.
arXiv commentAccepted for publication
References in corpus (22)
- Over the Air Deep Learning Based Radio Signal Classification
- Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
- Metaheuristic Design of Feedforward Neural Networks: A Review of Two Decades of Research
- Learning to Detect
- Model-Driven Deep Learning for MIMO Detection
- Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems
- Recurrent Neural Networks For Accurate RSSI Indoor Localization
- Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions
- End-to-end Deep Learning of Optical Fiber Communications
- A Deep Learning Framework for Optimization of MISO Downlink Beamforming
- Learning the MMSE Channel Estimator
- Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination
- Machine Learning for Intelligent Optical Networks: A Comprehensive Survey
- Detection Algorithms for Communication Systems Using Deep Learning
- PrecoderNet: Hybrid Beamforming for Millimeter Wave Systems with Deep Reinforcement Learning
- Deep Learning Based Sphere Decoding
- Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach
- Neural Network-Optimized Channel Estimator and Training Signal Design for MIMO Systems with Few-Bit ADCs
- Reconfigurable Intelligent Surface for Green Edge Inference
- Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications
- Cascade-Net: a New Deep Learning Architecture for OFDM Detection
- A Modular Neural Network Based Deep Learning Approach for MIMO Signal Detection