Optimal resource allocation solutions for heterogeneous cognitive radio networks

Awoyemi, Babatunde Seun; Maharaj, B. T.; Alfa, Attahiru Sule

doi:10.1016/j.dcan.2016.11.003

Cited by 31 publications

(13 citation statements)

References 21 publications

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“…The spectrum resource management problem was considered for co-located SUs with both streaming and intermittent data and seamless end-to-end service was ensured by effectively identifying the number of backup channels. In another contribution, the authors in [18] proposed the use of priority queues as a resource allocation (RA) model for different traffic classes. They used packet priority to explore the relevance and implications of various heterogeneous classifications.…”

Section: A Meeting Imt-2020 and Beyond Requirement Specificationsmentioning

confidence: 99%

QoS provisioning and energy saving scheme for distributed cognitive radio networks using deep learning

Hlophe

Maharaj

2020

J. Commun. Netw.

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One of the major challenges facing the realization of cognitive radios (CRs) in future mobile and wireless communications is the issue of high energy consumption. Since future network infrastructure will host real-time services requiring immediate satisfaction, the issue of high energy consumption will hinder the full realization of CRs. This means that to offer the required quality of service (QoS) in an energy-efficient manner, resource management strategies need to allow for effective trade-offs between QoS provisioning and energy saving. To address this issue, this paper focuses on single base station (BS) management, where resource consumption efficiency is obtained by solving a dynamic resource allocation (RA) problem using bipartite matching. A deep learning (DL) predictive control scheme is used to predict the traffic load for better energy saving using a stacked auto-encoder (SAE). Considered here was a base station (BS) processor with both processor sharing (PS) and first-come-first-served (FCFS) sharing disciplines under quite general assumptions about the arrival and service processes. The workload arrivals are defined by a Markovian arrival process while the service is general. The possible impatience of customers is taken into account in terms of the required delays. In this way, the BS processor is treated as a hybrid switching system that chooses a better packet scheduling scheme between mean slowdown (MS) FCFS and MS PS. The simulation results presented in this paper indicate that the proposed predictive control scheme achieves better energy saving as the traffic load increases, and that the processing of workload using MS PS achieves substantially superior energy saving compared to MS FCFS.

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Section: A Meeting Imt-2020 and Beyond Requirement Specificationsmentioning

confidence: 99%

QoS provisioning and energy saving scheme for distributed cognitive radio networks using deep learning

Hlophe

Maharaj

2020

J. Commun. Netw.

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“…Vehicles become as intelligent as smart mobile devices which can support various applications and services, such as autonomous driving, augmented reality (AR), virtual reality (VR), and online gaming [1,2]. Particularly, most of these applications are delay-sensitive and computation-intensive, which result in significant challenges of the computational capability and the battery capability of vehicles [3,4]. Moreover, offloading computation tasks and data traffic to a remote centralized cloud is considered as an effective approach for resolving the above challenges by employing abundant computation resources, which could relieve with cellular base stations to offer services to vehicles in the edge of the radio access network.…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient offloading decision-making for mobile edge computing in vehicular networks

Huang

Lai

et al. 2020

J Wireless Com Network

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Driven by the explosion transmission and computation requirement in 5G vehicular networks, mobile edge computing (MEC) attracts more attention than centralized cloud computing. The advantage of MEC is to provide a large amount of computation and storage resources to the edge of networks so as to offload computation-intensive and delay-sensitive applications from vehicle terminals. However, according to the mobility of vehicle terminals and the time varying traffic load, the optimal task offloading decisions is crucial. In this paper, we consider the uplink transmission from vehicles to road side units in the vehicular network. A dynamic task offloading decision for flexible subtasks is proposed to minimize the utility, which includes energy consumption and packet drop rate. Furthermore, a computation resource allocation scheme is introduced to allocate the computation resources of MEC server due to the differences in the computation intensity and the transmission queue of each vehicle. Consequently, a Lyapunov-based dynamic offloading decision algorithm is proposed, which combines the dynamic task offloading decision and computation resource allocation, to minimize the utility function while ensuring the stability of the queue. Finally, simulation results demonstrate that the proposed algorithm could achieve a significant improvement in the utility of vehicular networks compared with comparison algorithms.

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“…Several scientific researchers have shown that air pollutants formed by petroleum fires can degrade air quality, impair visibility and harm sensitive ecosystems. Serious pollutants, such as CO, CO 2 , NO x , SO 2 and airborne particulates, are generated when petroleum is burned [1][2][3][4][5]. Radio communication systems do not function reliably under these conditions due to the limitations in capacity and scalability [6].…”

Section: Introductionmentioning

confidence: 99%

Mitigating the Influence of Dense Oil Fires on Free-Space Optical Communication

Zaker

Taban

Mohammed

2020

EUREKA: Physics and Engineering

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This study estimates the performance of a free space optical system (FSO) affected by air pollutants from oil fires. Simulations are performed to evaluate the reliability of optical propagation according to the length of the FSO channels under two beam angle angles. The proposed FSO system parameters such as the Q-factor, BER and reception capacity are successfully used to reduce channel loss. Results demonstrate that the proposed FSO link performs satisfactorily when the divergence angle is 1 mrad and the distance is from 0.5 km to 0.9 km. Q-factor and receiving power decrease when the divergence angle of beam increases to 2 mrad, and a link is achieved when the distance is from 0.5 km to 0.7 km. The eye diagram is used to evaluate and confirm the quality of received data. An eye opening is observed at 0.5 km for both divergence angles. Then, the eye completely closes at 1 km for 2 mrad, thereby degrading the performance. Therefore, these results can be conducted for similar systems optimization options by applying our analysis

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Optimal resource allocation solutions for heterogeneous cognitive radio networks

Cited by 31 publications

References 21 publications

QoS provisioning and energy saving scheme for distributed cognitive radio networks using deep learning

QoS provisioning and energy saving scheme for distributed cognitive radio networks using deep learning

Energy-efficient offloading decision-making for mobile edge computing in vehicular networks

Mitigating the Influence of Dense Oil Fires on Free-Space Optical Communication

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