A Novel Deep Learning Approach for Syndrome-Based Decoding of BCH Codes In this letter, we present a new decoder based on syndrome, where a deep neural network (DNN) is used …
Abstracts Category: Electronics and Communication
Learning-Based Maximum Likelihood Detection for Uplink Massive MIMO with One-Bit ADCs: Biased and Dithering Approaches The maximum likelihood (ML) detection based on learning is studied in this work for uplink …
Deep Generative Modeling of RF Communication Signals: A Study Using GANs for Synthesizing OFDM-QAM Waveforms In most cases, the recordings of radio frequency (RF) emissions from commercial communication hardware, carried …
Channel Coding as a Benchmark for Studying Task Distribution and Adaptation in Meta-Learning Meta-learning is an alternative family of methods which is well known and can be used in learning …
Enhancing MIMO Channel Estimation with Deep Learning: A Score-Based Generative Approach Channel estimation is considered one of the major tasks in the area of digital communications that has a significant …
Deep Learning-Based One-Bit Signal Detection: A Model-Aware Approach with LoRD-Net It is common in the fields of communication and sensing, to face the challenge of reconstructing a high-dimensional signal from …
Deep Learning-Driven Joint Channel Estimation and Signal Detection for Underwater Wireless Optical Communication A novel high-rate data service named underwater wireless optical communication (UWOC) method has been emerged recently. UWOC …
Introducing Sionna: A TensorFlow-Based Library for Prototyping and Evaluating Communication Systems Sionna is a TensorFlow based, open source library for link level simulations with GPU concurrency. It streamlines the iterative …
GAN-Based Joint Activity Detection and Channel Estimation for Grant-Free Random Access in IoT Networks Joint activity detection and channel estimation (JADCE) for grant-free random access relates to the fundamental problems …
Adaptive Receiver Design Using Predictive Meta-Learning for Dynamic A new trend is the use of deep neural networks (DNNs) in designing the receiver which can be perhaps used in an …
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