Deep learning-aided 6G wireless networks: A comprehensive survey of revolutionary PHY architectures
Deep learning (DL) has proven its unprecedented success in diverse fields such as
computer vision, natural language processing, and speech recognition by its strong …
computer vision, natural language processing, and speech recognition by its strong …
[HTML][HTML] Application of reinforcement learning and deep learning in multiple-input and multiple-output (MIMO) systems
The current wireless communication infrastructure has to face exponential development in
mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems …
mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems …
Semantic communication systems for speech transmission
Semantic communications could improve the transmission efficiency significantly by
exploring the semantic information. In this paper, we make an effort to recover the …
exploring the semantic information. In this paper, we make an effort to recover the …
Learning optimal resource allocations in wireless systems
This paper considers the design of optimal resource allocation policies in wireless
communication systems, which are generically modeled as a functional optimization …
communication systems, which are generically modeled as a functional optimization …
Model-free training of end-to-end communication systems
The idea of end-to-end learning of communication systems through neural network (NN)-
based autoencoders has the shortcoming that it requires a differentiable channel model. We …
based autoencoders has the shortcoming that it requires a differentiable channel model. We …
DeepNOMA: A unified framework for NOMA using deep multi-task learning
Non-orthogonal multiple access (NOMA) will provide massive connectivity for future Internet
of Things. However, the intrinsic non-orthogonality in NOMA makes it non-trivial to approach …
of Things. However, the intrinsic non-orthogonality in NOMA makes it non-trivial to approach …
End-to-end learning of communications systems without a channel model
The idea of end-to-end learning of communications systems through neural network (NN)-
based autoencoders has the shortcoming that it requires a differentiable channel model. We …
based autoencoders has the shortcoming that it requires a differentiable channel model. We …
Deep learning for wireless communications
Existing communication systems exhibit inherent limitations in translating theory to practice
when handling the complexity of optimization for emerging wireless applications with high …
when handling the complexity of optimization for emerging wireless applications with high …
Joint learning of geometric and probabilistic constellation shaping
The choice of constellations largely affects the performance of communication systems.
When designing constellations, both the locations and probability of occurrence of the points …
When designing constellations, both the locations and probability of occurrence of the points …
Implementation methodologies of deep learning-based signal detection for conventional MIMO transmitters
MS Baek, S Kwak, JY Jung, HM Kim… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
In this paper, simple methodologies of deep learning application to conventional multiple-
input multiple-output (MIMO) communication systems are presented. The deep learning …
input multiple-output (MIMO) communication systems are presented. The deep learning …