Machine learning (ML) has been widely applied to the upper layers of wireless communication systems for various purposes, such as deployment of cognitive radio and communication network. However, its application to the physical layer is hampered by sophisticated channel environments and limited learning...
Deep Learning Methods for Physical-Layer Wireless Communications - Recent Advances and Future Trendsdoi:10.1016/j.phycom.2021.101546Mohammad HammoudehElsevier BVPhysical Communication
Intelligent communication is gradually considered as the mainstream direction in future wireless communications. As a major branch of machine learning, deep learning (DL) has been applied in physical layer communications and has demonstrated an impressive performance improvement in recent years. However, ...
●【An introduction to deep learning for the physical layer】 ●【Deep learning based communication over the air】 ●【Backpropagating through the air: Deep learning at physical layer without channel models】 ●【Joint transceiver optimization for wireless communication PHY using neural network】 ●【M...
for thef i rst time that polymorphic receivers are feasible and ef f ective.CCS CONCEPTS• Hardware → Wireless devices.KEYWORDSDeep Learning, FPGA, Wireless, System on Chip, Embedded, 5GACM Reference Format:Francesco Restuccia and Tommaso Melodia. 2020. PolymoRF: Polymor-phic Wireless ...
Ben is the Director of Engineering at DeepSig Inc., which is commercializing the foundational research behind deep learning applied to wireless communications and signal processing. He also runs GNU Radio, the world's most widely used open-source signal processing toolkit, and is very active in th...
Recent explorations of Deep Learning in the physical layer (PHY) of wireless communication have shown the capabilities of Deep Neuron Networks in tasks like channel coding, modulation, and parametric estimation. However, it is unclear if Deep Neuron Networks could also learn the advanced waveforms of...
“Machine learning paradigms for next-generation wireless networks,” IEEE Wireless Communications Magazine, vol. 24, no. 2, pp. 98–105, 2017. [17] Y. Wang, J. Guo, H. Li, L. Li, Z. Wang, and H. Wang, “CNNbased modulation classifcation in the complicated communication channel,”...
MIST: A Novel Training Strategy for Low-latency Scalable Neural Net Decoders MIST_CNN_Decoder Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors modulation_classif Learning Physical-Layer Communication with Quantized Feedback quantizedfeedback Reinforcement Learni...
Recent explorations of Deep Learning in the physical layer (PHY) of wireless communication have shown the capabilities of Deep Neuron Networks in tasks like channel coding, modulation, and parametric estimation. However, it is unclear if Deep Neuron Networks could also learn the advanced waveforms of...