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Cooperative Communications, Distributed Coding and Machine Learning

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1247))

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Abstract

In this contribution, we will investigate how cooperative communications using relay nodes can achieve a higher channel capacity, compared to conventional transmissions. Then, a distributed coding scheme is designed for approaching the corresponding channel capacity. More specifically, a virtual Irregular Convolutional Code (IRCC) is designed based on an iterative learning algorithm and the resultant component encoders are distributed to multiple relay nodes. The near-capacity scheme is applied to an Unmanned Aerial Vehicle (UAV) network for improving the transmission rate at the cell-edge or isolated area. Machine learning algorithm is used to find the optimal location for the UAVs, which serve as the relay nodes. It is shown that a high performing next-generation wireless communications scheme can be created by incorporating cooperative communications, distributed coding and machine learning algorithms.

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Notes

  1. 1.

    This definition is in line with  [6, 15], but it is unconventional, because it relates the transmit power to the receiver noise measured at two distinct locations.

  2. 2.

    The rational of considering \(\gamma ^{sd}_\texttt {r}=-3.5\) dB is explained in Sect. 6.

  3. 3.

    The CSI knowledge is only needed at the receiver for decoding purposes, where each RN only has to know the CSI between the SN and itself, while the DN only has to know the CSI between the corresponding RNs/SN and itself.

  4. 4.

    The original DTTCM scheme of [38] employed 2/3-rate TTCM-8PSK at the SN and uncoded-4PSK at the RN. The DTTCM scheme considered here uses 1/2-rate TTCM-4PSK at the SN and uncoded-4PSK at the RN, in order to make its throughput as close as possible to the proposed DIRCC scheme for a fair comparison.

  5. 5.

    In terms of SNR per information bit, the gain of DIRCC over DTTCM is given by 2.0 dB \(+10\log _{10}(0.667)-10\log _{10}(0.50)=0.76\) dB.

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Authors and Affiliations

  1. School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK

    Soon Xin Ng

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Correspondence to Soon Xin Ng .

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  1. Fellow of IEEE, Dean of College of Computing & Informatics, University of Sharjah, Sharjah, United Arab Emirates

    Mohammad S. Obaidat

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Ng, S.X. (2020). Cooperative Communications, Distributed Coding and Machine Learning. In: Obaidat, M. (eds) E-Business and Telecommunications. ICETE 2019. Communications in Computer and Information Science, vol 1247. Springer, Cham. https://doi.org/10.1007/978-3-030-52686-3_2

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  • DOI: https://doi.org/10.1007/978-3-030-52686-3_2

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