University of Oulu

C. K. Singh, P. K. Upadhyay and J. J. Lehtomäki, "Performance Analysis and Deep Learning Assessment of Full-Duplex Overlay Cognitive Radio NOMA Networks Under Non-Ideal System Imperfections," in IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 3, pp. 664-682, June 2023, doi: 10.1109/TCCN.2023.3246532.

Performance analysis and deep learning assessment of full-duplex overlay cognitive radio NOMA networks under non-ideal system imperfections

Saved in:
Author: Singh, Chandan Kumar1; Upadhyay, Prabhat Kumar1,2; Lehtomäki, Janne J.2
Organizations: 1Department of Electrical Engineering, Indian Institute of Technology Indore, Indore, India
2Centre for Wireless Communications, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.9 MB)
Persistent link:
Language: English
Published: IEEE Communications Society, 2023
Publish Date: 2023-09-11


In this paper, we investigate the effectiveness of an overlay cognitive radio (OCR) coupled with non-orthogonal multiple access (NOMA) system using a full-duplex (FD) cooperative spectrum access with a maximal ratio combining (MRC) scheme under the various non-ideal system imperfections. In view of practical realization, we ponder the impact of loop self-interference, transceiver hardware impairments, imperfect successive interference cancellation, and channel estimation errors on the system performance. We investigate the performance of the proposed system by obtaining closed-form expressions for outage probability and ergodic rate for primary as well as secondary users using Nakagami- m fading channels. As a result, we reveal some notable ceiling effects and present efficacious power allocation strategy for cooperative spectrum access. We further evaluate the system throughput and ergodic sum-rate (ESR) to assess the system’s overall performance. Our findings manifest that the FD-based OCR-NOMA can comply with the non-ideal system imperfections and outperform the competing half-duplex (HD) and orthogonal multiple access (OMA) counterparts. Due to the massive complexity of the suggested system model, direct derivation of the closed-form formula for the ESR becomes cumbersome. To address this problem, we develop a deep neural network (DNN) framework for ESR prediction in real-time situations.

see all

Series: IEEE transactions on cognitive communications and networking
ISSN: 2372-2045
ISSN-E: 2332-7731
ISSN-L: 2372-2045
Volume: 9
Issue: 3
Pages: 664 - 682
DOI: 10.1109/TCCN.2023.3246532
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work is carried out under the Nokia Foundation Visiting Professor Grant and Visvesvaraya PhD Scheme of Ministry of Electronics & Information Technology (MeitY), Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia). The work of Janne J. Lehtomäki is supported in part by the Academy of Finland 6Genesis Flagship under Grant 318927.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
Copyright information: © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.