Deep Learning based End-to-End Wireless Communication Systems without Pilots

The present world is data driven. From social sciences to frontiers of research in science and engineering, one common factor is the continuous data generation. It has started to affect our daily lives. Big data concepts are found to have significant impact in modern wireless communication systems. The analytical tools of big data have been identified as full scale autonomous mode of operation which necessitates a strong role to be played by learning based systems. The chapter has focused on the synergy of big data and deep learning for generating better efficiency in evolving communication frameworks. The chapter has also included discussion on machine learning and cognitive technologies w.r.t. big data and mobile communication. Cyber Physical Systems being indispensable elements of M2M communication, Wireless Sensor Networks and its role in CPS, cognitive radio networking and spectrum sensing have also been discussed. It is expected that spectrum sensing, big data and deep learning will play vital roles in enhancing the capabilities of wireless communication systems.

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Big Spectrum Data and Deep Learning Techniques for Cognitive Wireless Networks

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch055 ◽

2020 ◽

pp. 994-1015

Author(s):

Punam Dutta Choudhury ◽

Ankumoni Bora ◽

Kandarpa Kumar Sarma

Keyword(s):

Big Data ◽

Deep Learning ◽

Wireless Communication ◽

Spectrum Sensing ◽

Communication Systems ◽

Common Factor ◽

Wireless Communication Systems ◽

Data Generation ◽

Daily Lives ◽

Communication Frameworks

The present world is data driven. From social sciences to frontiers of research in science and engineering, one common factor is the continuous data generation. It has started to affect our daily lives. Big data concepts are found to have significant impact in modern wireless communication systems. The analytical tools of big data have been identified as full scale autonomous mode of operation which necessitates a strong role to be played by learning based systems. The chapter has focused on the synergy of big data and deep learning for generating better efficiency in evolving communication frameworks. The chapter has also included discussion on machine learning and cognitive technologies w.r.t. big data and mobile communication. Cyber Physical Systems being indispensable elements of M2M communication, Wireless Sensor Networks and its role in CPS, cognitive radio networking and spectrum sensing have also been discussed. It is expected that spectrum sensing, big data and deep learning will play vital roles in enhancing the capabilities of wireless communication systems.

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End-to-End QoS Provision and Control in Wireless Communication Systems by Means of Digital Watermarking Signal Processing

2006 3rd International Symposium on Wireless Communication Systems ◽

10.1109/iswcs.2006.4362382 ◽

2006 ◽

Cited By ~ 1

Author(s):

Francesco Benedetto ◽

Gaetano Giunta ◽

Alessandro Neri

Keyword(s):

Signal Processing ◽

Wireless Communication ◽

Digital Watermarking ◽

Communication Systems ◽

Wireless Communication Systems ◽

End To End ◽

And Control

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Online Deep Learning in Wireless Communication Systems

2018 52nd Asilomar Conference on Signals, Systems, and Computers ◽

10.1109/acssc.2018.8645312 ◽

2018 ◽

Cited By ~ 1

Author(s):

Mark Eisen ◽

Clark Zhang ◽

Luiz F. O. Chamon ◽

Daniel D. Lee ◽

Alejandro Ribeiro

Keyword(s):

Deep Learning ◽

Wireless Communication ◽

Communication Systems ◽

Wireless Communication Systems

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Implementation of Multiband Communication Using SDR for Remote Application

10.21203/rs.3.rs-60502/v1 ◽

2020 ◽

Author(s):

Madhuri Gummineni ◽

Trinatha Rao Polipalli

Keyword(s):

Wireless Communication ◽

Communication Systems ◽

Low Cost ◽

Wireless Communication Systems ◽

Simulation Based ◽

End To End ◽

To Come ◽

Multimode Operation ◽

Reconfigurable Radio ◽

Multi Mode

Abstract To develop Next Generation Wireless Communication a generic hardware design is required so that it can be driven by software to allow for future upgrades. Thus Reconfigurable Radio implements multi-band, multi-mode operation and interoperability with low-cost. For reducing response time between incompatible radios during emergencies interoperability is essential for a secure heterogeneous communication. Some of the Challenges identified for implementing reliable and reconfigurable wireless communication systems are: specific training required for using the equipment, end-to-end connectivity between devices, extending link capacity during the high peak utilization. Each device and architecture will differ based on the type of communication system. Interconnecting emerging fields enhances the performance , implementation helps to come across alternatives to overcome practical difficulties and challenges of connecting different fields to Cognitive Radio(CR). Earlier research gave prominence to theoretical and simulation-based work. This motivates us to verify interoperability in real time using SDR. This paper describes the implementation of a Multiband, multimode operation for establishing communication between different types of architecture i.e. VUSDR (HAM), Hack RF One, LoRa, RF module, GSM module and USRP N210 to prove reliability and end-to-end communication.

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