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Machine learning assisted adaptive index modulation for mmWave communications

Machine learning assisted adaptive index modulation for mmWave communications
Machine learning assisted adaptive index modulation for mmWave communications
In this paper, we propose an orthogonal frequency-division multiplexing system supported by the compressed sensing assisted index modulation, termed as (OFDM-CSIM), applied to millimeter-wave (mmWave) communications. In the OFDM-CSIM mmWave system, information is conveyed not only by the classic constellation symbols but also by the on/off status of subcarriers, where the size of constellation symbols and the number of active subcarriers can be beneficially configured for maximizing the system's throughput.
We conceive a machine learning (ML) assisted adaptive OFDM-CSIM mmWave system, which simultaneously benefits from the OFDM with index modulation (IM), compressed sensing (CS) and the hybrid beamforming techniques.
Specifically, a ML-assisted link adaptation scheme is designed based on the $k$-nearest neighbors (k-NN) algorithm with the objective to maximize the system's throughput. Our studies show that the proposed ML-assisted link adaptation is capable of providing higher throughput than the conventional threshold-based link adaptation when different antenna structures are considered.
Furthermore, the achievable data rates of four types of antenna arrays, including uniform linear array (ULA), uniform rectangular planar array (URPA), uniform circle planar array (UCPA) and uniform cylindrical array (UCYA), are investigated and compared over mmWave channels. The simulation results show that the UCYA achieves the highest data rate among these antenna arrays.
2644-125X
Liu, Haochen
280c47ba-6330-47b5-b5cd-2ca84878ce0a
Lu, Siyao
3ff52ef0-ecd2-4327-9096-562f96360970
Yang, Lieliang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Liu, Haochen
280c47ba-6330-47b5-b5cd-2ca84878ce0a
Lu, Siyao
3ff52ef0-ecd2-4327-9096-562f96360970
Yang, Lieliang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f

Liu, Haochen, Lu, Siyao, Yang, Lieliang and El-Hajjar, Mohammed (2020) Machine learning assisted adaptive index modulation for mmWave communications. IEEE Open Journal of the Communications Society. (In Press)

Record type: Article

Abstract

In this paper, we propose an orthogonal frequency-division multiplexing system supported by the compressed sensing assisted index modulation, termed as (OFDM-CSIM), applied to millimeter-wave (mmWave) communications. In the OFDM-CSIM mmWave system, information is conveyed not only by the classic constellation symbols but also by the on/off status of subcarriers, where the size of constellation symbols and the number of active subcarriers can be beneficially configured for maximizing the system's throughput.
We conceive a machine learning (ML) assisted adaptive OFDM-CSIM mmWave system, which simultaneously benefits from the OFDM with index modulation (IM), compressed sensing (CS) and the hybrid beamforming techniques.
Specifically, a ML-assisted link adaptation scheme is designed based on the $k$-nearest neighbors (k-NN) algorithm with the objective to maximize the system's throughput. Our studies show that the proposed ML-assisted link adaptation is capable of providing higher throughput than the conventional threshold-based link adaptation when different antenna structures are considered.
Furthermore, the achievable data rates of four types of antenna arrays, including uniform linear array (ULA), uniform rectangular planar array (URPA), uniform circle planar array (UCPA) and uniform cylindrical array (UCYA), are investigated and compared over mmWave channels. The simulation results show that the UCYA achieves the highest data rate among these antenna arrays.

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Accepted/In Press date: 19 August 2020

Identifiers

Local EPrints ID: 443637
URI: http://eprints.soton.ac.uk/id/eprint/443637
ISSN: 2644-125X
PURE UUID: 414caaa7-6f0f-4f70-83d9-6e972c7c0b13
ORCID for Haochen Liu: ORCID iD orcid.org/0000-0001-9794-5278
ORCID for Lieliang Yang: ORCID iD orcid.org/0000-0002-2032-9327
ORCID for Mohammed El-Hajjar: ORCID iD orcid.org/0000-0002-7987-1401

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Date deposited: 07 Sep 2020 16:30
Last modified: 07 Oct 2020 02:23

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