Integrated Access and Backhauling with Energy Harvesting and Dynamic Sleeping in HetNets

Alqasir, Abdullah; Aldubaikhy, Khalid; Kamal, Ahmed E.

doi:10.1109/icc42927.2021.9500432

Cited by 8 publications

(2 citation statements)

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“…1) The optimal solution of P1 is used to provide a benchmark of the algorithm performance in total energy consumption for the whole system and the computation requirements needed to reach the optimal solution. 2) Algorithm HBDS in [30], which is based in BSC and without ECUs. BSC helps in deciding which SBS can be deactivated and which is not, since SBS that is at the middle of the network (hence, has higher BSC) is more probable on being deactivated without affecting the performance of the network than an SBS that is at the edge of the network.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Second, continuous variables are solved by an nonlinear problem (NLP) approximation of the main optimization problem. 4) Finally, a validation and a comparison between the heuristic approach, heuristic backhauling and dynamic sleeping (HBDS) [30], and the optimal solution are presented using computer simulation, which shows significant computation efficiency improvement.…”

Section: Introductionmentioning

confidence: 99%

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An Energy-Saving Scheme With Edge Computing and Energy Harvesting in mmWaves Backhauling HetNets

Alqasir

2023

IEEE Access

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The exponential growth of data traffic from mobile devices requires the implementation of heterogeneous networks (HetNets), which densely deploy multiple radio access technologies to match such demands. The deployment of many small base stations (SBSs) leads to backhauling problems where wired backhauling is neither available nor efficient. millimeter waves (mmWaves) can potentially mitigate the backhauling problem by providing high throughput and low capital expenditure (CAPEX). However, due to the high attenuation rate in mmWaves, the increase in the distance between SBS and macro base station (MBS) can severely degrade the system's overall performance. On the other hand, densely deployed SBSs with wireless backhauling can cause high energy consumption in the system. Therefore, in this work, a novel network model is presented in which a combination of SBS, active antenna units (AAUs), and edge computing units (ECUs) are deployed to minimize the overall energy consumption of the network while maintaining the sufficient quality of service (QoS). A mathematical model based on optimization modeling is introduced to solve user equipments (UEs) association, dynamic sleeping, backhauling and fronthauling, and power transmission. Due to the complexity of the formulated problem, a heuristic algorithm is introduced. Namely, integrated access and backhauling (IAB) with Edge Computing and Dynamic Sleeping Algorithm (IEDS) algorithm is introduced to decompose the formulated problem into two parts and solve them iteratively. Finally, computer simulation results that demonstrate the model's performance are presented for comparison between optimal solution, IEDS, and HBDS, which shows that IEDS outperformed HBDS in performance with negligible computation difference.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%