Deep learning for wireless physical layer: Opportunities and challenges

Tianqi Wang; Chao-Kai Wen; Hanqing Wang; Feifei Gao; Tao Jiang; Shi Jin

doi:10.1109/cc.2017.8233654

Implementation of conventional communication system in deep learning

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.1.10835 ◽

2017 ◽

Vol 7 (1.1) ◽

pp. 696

Author(s):

Satyanarayana P ◽

Charishma Devi. V ◽

Sowjanya P ◽

Satish Babu ◽

N Syam Kumar ◽

...

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Communication Network ◽

Deep Learning ◽

Communication System ◽

Communication Systems ◽

Physical Layer ◽

Learning Capacity ◽

Constrained Learning ◽

Complex Channel

Machine learning (ML) has been broadly connected to the upper layers of communication systems for different purposes, for example, arrangement of cognitive radio and communication network. Nevertheless, its application to the physical layer is hindered by complex channel conditions and constrained learning capacity of regular ML algorithms. Deep learning (DL) has been as of late connected for some fields, for example, computer vision and normal dialect preparing, given its expressive limit and advantageous enhancement ability. This paper describes about a novel use of DL for the physical layer. By deciphering a communication system as an auto encoder, we build up an essential better approach to consider communication system outline as a conclusion to-end reproduction undertaking that tries to together enhance transmitter and receiver in a solitary procedure. This DL based technique demonstrates promising execution change than traditional communication system.

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Deep Learning in Physical Layer Communications

IEEE Wireless Communications ◽

10.1109/mwc.2019.1800601 ◽

2019 ◽

Vol 26 (2) ◽

pp. 93-99 ◽

Cited By ~ 93

Author(s):

Zhijin Qin ◽

Hao Ye ◽

Geoffrey Ye Li ◽

Biing-Hwang Fred Juang

Keyword(s):

Deep Learning ◽

Physical Layer

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Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics

IEEE Access ◽

10.1109/access.2019.2903130 ◽

2019 ◽

Vol 7 ◽

pp. 30792-30801 ◽

Cited By ~ 9

Author(s):

Hongmei Wang ◽

Zhenzhen Wu ◽

Shuai Ma ◽

Songtao Lu ◽

Han Zhang ◽

...

Keyword(s):

Deep Learning ◽

Wireless Communications ◽

Physical Layer ◽

Signal Demodulation

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Redefining Wireless Communication for 6G:Signal Processing Meets Deep Learning with Deep Unfolding

10.36227/techrxiv.16434855.v1 ◽

2021 ◽

Author(s):

Anu Jagannath ◽

Jithin Jagannath

Keyword(s):

Signal Processing ◽

Deep Learning ◽

Wireless Communication ◽

Communication Systems ◽

Domain Knowledge ◽

Physical Layer ◽

Learning Approaches ◽

Communication Architecture ◽

Recent Emergence ◽

Rate Improvement

The year 2019 witnessed the rollout of the 5G standard, which promises to offer significant data rate improvement over 4G. While 5G is still in its infancy, there has been an increased shift in the research community for communication technologies beyond 5G. The recent emergence of machine learning approaches for enhancing wireless communications and empowering them with much-desired intelligence holds immense potential for redefining wireless communication for 6G. The evolving communication systems will be bottlenecked in terms of latency, throughput, and reliability by the underlying signal processing at the physical layer. In this position paper, we motivate the need to redesign iterative signal processing algorithms by leveraging deep unfolding techniques to fulfill the physical layer requirements for 6G networks. To this end, we begin by presenting the service requirements and the key challenges posed by the envisioned 6G communication architecture. We outline the deficiencies of the traditional algorithmic principles and data-hungry deep learning (DL) approaches in the context of 6G networks. Specifically, deep unfolded signal processing is presented by sketching the interplay between domain knowledge and DL. The deep unfolded approaches reviewed in this article are positioned explicitly in the context of the requirements imposed by the next generation of cellular networks. Finally, this article motivates open research challenges to truly realize hardware-efficient edge intelligence for future 6G networks.<br>

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