Design Optimization of Resource Allocation in OFDMA-Based Cognitive Radio-Enabled Internet of Vehicles (IoVs)
Joint optimal subcarrier and transmit power allocation with QoS guarantee for enhanced packet transmission over Cognitive Radio (CR)-Internet of Vehicles (IoVs) is a challenge. This open issue is considered in this paper. A novel SNBS-based wireless radio resource scheduling scheme in OFDMA CR-IoV n...
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doaj-5a93731a2fa24eeca36e8c6cb98ade4d2020-11-25T04:05:26ZengMDPI AGSensors1424-82202020-11-01206402640210.3390/s20216402Design Optimization of Resource Allocation in OFDMA-Based Cognitive Radio-Enabled Internet of Vehicles (IoVs)Joy Eze0Sijing Zhang1Enjie Liu2Elias Eze3Institute for Research in Applicable Computing (IRAC), School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UKInstitute for Research in Applicable Computing (IRAC), School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UKInstitute for Research in Applicable Computing (IRAC), School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UKInstitute for Research in Applicable Computing (IRAC), School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UKJoint optimal subcarrier and transmit power allocation with QoS guarantee for enhanced packet transmission over Cognitive Radio (CR)-Internet of Vehicles (IoVs) is a challenge. This open issue is considered in this paper. A novel SNBS-based wireless radio resource scheduling scheme in OFDMA CR-IoV network systems is proposed. This novel scheduler is termed the SNBS OFDMA-based overlay CR-Assisted Vehicular NETwork (SNO-CRAVNET) scheduling scheme. It is proposed for efficient joint transmit power and subcarrier allocation for dynamic spectral resource access in cellular OFDMA-based overlay CRAVNs in clusters. The objectives of the optimization model applied in this study include (1) maximization of the overall system throughput of the CR-IoV system, (2) avoiding harmful interference of transmissions of the shared channels’ licensed owners (or primary users (PUs)), (3) guaranteeing the proportional fairness and minimum data-rate requirement of each CR vehicular secondary user (CRV-SU), and (4) ensuring efficient transmit power allocation amongst CRV-SUs. Furthermore, a novel approach which uses Lambert-W function characteristics is introduced. Closed-form analytical solutions were obtained by applying time-sharing variable transformation. Finally, a low-complexity algorithm was developed. This algorithm overcame the iterative processes associated with searching for the optimal solution numerically through iterative programming methods. Theoretical analysis and simulation results demonstrated that, under similar conditions, the proposed solutions outperformed the reference scheduler schemes. In comparison to other scheduling schemes that are fairness-considerate, the SNO-CRAVNET scheme achieved a significantly higher overall average throughput gain. Similarly, the proposed time-sharing SNO-CRAVNET allocation based on the reformulated convex optimization problem is shown to be capable of achieving up to 99.987% for the average of the total theoretical capacity.https://www.mdpi.com/1424-8220/20/21/6402Cognitive Radiogame theoryInternet of VehiclesOFDMAvehicular networks |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Joy Eze Sijing Zhang Enjie Liu Elias Eze |
spellingShingle |
Joy Eze Sijing Zhang Enjie Liu Elias Eze Design Optimization of Resource Allocation in OFDMA-Based Cognitive Radio-Enabled Internet of Vehicles (IoVs) Sensors Cognitive Radio game theory Internet of Vehicles OFDMA vehicular networks |
author_facet |
Joy Eze Sijing Zhang Enjie Liu Elias Eze |
author_sort |
Joy Eze |
title |
Design Optimization of Resource Allocation in OFDMA-Based Cognitive Radio-Enabled Internet of Vehicles (IoVs) |
title_short |
Design Optimization of Resource Allocation in OFDMA-Based Cognitive Radio-Enabled Internet of Vehicles (IoVs) |
title_full |
Design Optimization of Resource Allocation in OFDMA-Based Cognitive Radio-Enabled Internet of Vehicles (IoVs) |
title_fullStr |
Design Optimization of Resource Allocation in OFDMA-Based Cognitive Radio-Enabled Internet of Vehicles (IoVs) |
title_full_unstemmed |
Design Optimization of Resource Allocation in OFDMA-Based Cognitive Radio-Enabled Internet of Vehicles (IoVs) |
title_sort |
design optimization of resource allocation in ofdma-based cognitive radio-enabled internet of vehicles (iovs) |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-11-01 |
description |
Joint optimal subcarrier and transmit power allocation with QoS guarantee for enhanced packet transmission over Cognitive Radio (CR)-Internet of Vehicles (IoVs) is a challenge. This open issue is considered in this paper. A novel SNBS-based wireless radio resource scheduling scheme in OFDMA CR-IoV network systems is proposed. This novel scheduler is termed the SNBS OFDMA-based overlay CR-Assisted Vehicular NETwork (SNO-CRAVNET) scheduling scheme. It is proposed for efficient joint transmit power and subcarrier allocation for dynamic spectral resource access in cellular OFDMA-based overlay CRAVNs in clusters. The objectives of the optimization model applied in this study include (1) maximization of the overall system throughput of the CR-IoV system, (2) avoiding harmful interference of transmissions of the shared channels’ licensed owners (or primary users (PUs)), (3) guaranteeing the proportional fairness and minimum data-rate requirement of each CR vehicular secondary user (CRV-SU), and (4) ensuring efficient transmit power allocation amongst CRV-SUs. Furthermore, a novel approach which uses Lambert-W function characteristics is introduced. Closed-form analytical solutions were obtained by applying time-sharing variable transformation. Finally, a low-complexity algorithm was developed. This algorithm overcame the iterative processes associated with searching for the optimal solution numerically through iterative programming methods. Theoretical analysis and simulation results demonstrated that, under similar conditions, the proposed solutions outperformed the reference scheduler schemes. In comparison to other scheduling schemes that are fairness-considerate, the SNO-CRAVNET scheme achieved a significantly higher overall average throughput gain. Similarly, the proposed time-sharing SNO-CRAVNET allocation based on the reformulated convex optimization problem is shown to be capable of achieving up to 99.987% for the average of the total theoretical capacity. |
topic |
Cognitive Radio game theory Internet of Vehicles OFDMA vehicular networks |
url |
https://www.mdpi.com/1424-8220/20/21/6402 |
work_keys_str_mv |
AT joyeze designoptimizationofresourceallocationinofdmabasedcognitiveradioenabledinternetofvehiclesiovs AT sijingzhang designoptimizationofresourceallocationinofdmabasedcognitiveradioenabledinternetofvehiclesiovs AT enjieliu designoptimizationofresourceallocationinofdmabasedcognitiveradioenabledinternetofvehiclesiovs AT eliaseze designoptimizationofresourceallocationinofdmabasedcognitiveradioenabledinternetofvehiclesiovs |
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