Energy-Efficient ICI Mitigation with Dynamic and Location-Based Power Allocation in Mobility-Based 5G HetCN

Borah, Janmoni; Bora, Joyatri

doi:10.1007/s11277-020-07930-x

Cited by 8 publications

(14 citation statements)

References 20 publications

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“…Noncooperative power control game had been established for the power allocation (PA) in 5G technology. Borah and Bora 23 had proposed power allocation scheme (dynamic power allocation based on user location–DPAUL) in HetNet based on the joint contribution of both dynamic and location. The adaptation of the proposed mechanism coordinate both macro and small cells to moderate the inter‐cell interference (ICI) effects.…”

Section: Literature Surveymentioning

confidence: 99%

Energy and spectral efficient resource allocation in 5G HetNet using optimized deep bi‐BRLSTM model

Tejasvi

Manjaiah

2022

Trans Emerging Tel Tech

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The heterogeneous deployment of ultra‐dense femto cell dispenses an enhanced coverage and capacity for the users in the 5G wireless communication system. Due to the substantial growth in wireless data traffic and the inflation of energy consumption, the design of energy and spectrum efficient wireless network is seems to be an extreme concern. Therefore, there is a need to develop an energy and spectrum efficient resource allocation model. The joint contribution of the maximization of energy efficiency (EE) and spectral efficiency (SE) with its constraints is formulated as a multiobjective optimization problem. The resource allocation (RA) issue in the network is handled by the hybrid deep bidirectional battle royale long short term memory (Deep bi‐BRLSTM) model. The loss function in the network model is taken as a consideration to optimize the SE and EE. Fuzzy based energy efficient adaptive atom search optimizer (FEEAAS) is instigated to optimize the trade‐off of EE and SE consecutively. It is embedded with high convergence rate with maximum efficiency which is suitable for power optimization with maximum resource allocation. The performance of the proposed model is implemented in Matlab Simulink platform. The simulation result reveals that the presented model attains an optimal trade‐off between the EE and SE and also improves the overall throughput and delay when compared with existing solutions.

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Section: Literature Surveymentioning

confidence: 99%

Energy and spectral efficient resource allocation in 5G HetNet using optimized deep bi‐BRLSTM model

Tejasvi

Manjaiah

2022

Trans Emerging Tel Tech

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“…The radio propagation in the setup network is considered with both large‐scale distance and small‐scale channel fading models. In this setup, the part of the authors' previously proposed CUM model is considered for mobile SCs mobility 21,22 . The mSCs in the setup network is considered to be mounted on vehicles as shown in Figure 2.…”

Section: System Modelmentioning

confidence: 99%

“…The downlink SINR of a j th MC user u could be expressed as 21,22

γ_{u, j} goodbreak= \frac{P_{mc} g_{j, u}}{(\sum_{\binom{j' = 1}{j' \neq j}}^{N_{italicMB} - 1} {normalP}_{italicmc} {normalg}_{j', u} + \sum_{m = 1}^{N_{mSB}} {normalP}_{italicsc} {normalg}_{m, u} + \sum_{\begin{matrix} center \\ f = 1 \end{matrix}}^{N_{fSB}} {normalP}_{italicsc} {normalg}_{f, u} + {normalN}_{normalo})}

where path loss models in the network are

g_{j, u} = {|h_{ju}|}^{2} normalC d_{uj}^{- α}

g_{j^{'}, u} = {|h_{j^{'} u}|}^{2} normalC d_{u j^{'}}^{- α}

g_{m, u} = {|h_{mu}|}^{2} normalC d_{um}^{- α}

, and

g_{f, u} = {|h_{fu}|}^{2} normalC d_{uf}^{- α}

. The terms

h_{ju}

h_{j^{'} u}

…”

Section: System Modelmentioning

confidence: 99%

“…The terms

h_{ju}

h_{j^{'} u}

h_{mu}

, and

h_{fu}

denote Rayleigh channel gains to the u th user from its serving j th MC, interfering MCs, m th mSCs, and f th fSCs, respectively. The serving distance of u th user form j th MC is

d_{uj}

, and interfering distances to the user u from other setups MCs, deployed mSCs, and fSCs are

d_{u j^{'}}

d_{um}

, and

d_{uf}

, respectively.The SINR obtained by the m th mSC user k and the f th fSC user k , respectively, could be written in Equations () and () as 21,22

γ_{k, m} goodbreak= \frac{P_{sc} g_{m, k}}{(\sum_{j = 1}^{N_{MB}} {normalP}_{italicmc} {normalg}_{j, k} + \sum_{\binom{m' = 1}{m' \neq m}}^{N_{italicmSB} - 1} {normalP}_{italicsc} {normalg}_{m', k} + \sum_{\begin{matrix} center \\ f = 1 \end{matrix}}^{N_{fSB}} {normalP}_{italicsc} {normalg}_{f, k} + {normalN}_{normalo})}

γ_{k^{'}, f} goodbreak= \frac{P_{sc} g_{f, k^{'}}}{(\sum_{j = 1})}

…”

Section: System Modelmentioning

confidence: 99%

“…The work investigates the performance of HetCN with mmWave bands at fSCs and mSCs and pre‐existing sub 6 GHz bands inside the MC exposure area. The part of the authors' previously proposed Cell‐User Mobility (CUM) model has been adopted to analyze cell mobility 21,22 . The paper also provides a clearer comparison of the proposed DMCSM mechanism with the authors' prior proposed Dynamic Fixed Region Cooperation (DFRC), 23 Dynamic Power Allocation Mechanism (DPAM), 21 and Dynamic Power Allocation based on User Location (DPAUL) 22 ICI mitigation techniques.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic cell sleeping mechanism: An energy‐efficient approach for Mobile 5G HetCN

Borah¹,

Sheikh²,

Bora

2022

Int J Communication

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The paper proposes a novel cell sleeping mechanism for enhancing network energy-efficiency and to combat dynamic downlink interferences linked with mobile Small Cells (mSCs) in a 5G Heterogeneous Cellular Network (HetCN). The proposed Dynamic Mobile Cell Sleeping Mechanism (DMCSM) allows deployed vehicle-mounted mSCs to dynamically go into sleep based on the calculated distances of users from its serving mSCs BS. With this mechanism, vehicular mSCs during mobility dynamically calculate their distances with the Macrocell (MC) users. The mSCs go into sleep or get deactivated for a while based on the calculated distance and the cell radius defined for mSCs. The mSCs will get active and starts to provide services to the users that are found within their coverage radius. The setup 5G HetCN is investigated with a MC superimpose with fixed SCs (fSCs) and mobile SCs (mSCs). The proficiency of DMCSM is investigated with the exploitation of existing sub 6 GHz groups at MCs and the millimeter wave (mmWave) spectrums at deployed fSCs and mSCs. The network downlink performance metrics: user throughput, sumrate, energy-efficiency, and outage probability, have been explored. The work also depicts a comparison of the proposed DMCSM mechanism with the author's previously proposed ICI mitigation techniques.

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Encryption Based Strategy to Overcome the Problem of Pilot Contamination Within Multi-cellular Massive MIMO Systems

Boulouird

Belhabib

Riadi

2021

Wireless Pers Commun

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Energy-Efficient ICI Mitigation with Dynamic and Location-Based Power Allocation in Mobility-Based 5G HetCN

Cited by 8 publications

References 20 publications

Energy and spectral efficient resource allocation in 5G HetNet using optimized deep bi‐BRLSTM model

Energy and spectral efficient resource allocation in 5G HetNet using optimized deep bi‐BRLSTM model

Dynamic cell sleeping mechanism: An energy‐efficient approach for Mobile 5G HetCN

Encryption Based Strategy to Overcome the Problem of Pilot Contamination Within Multi-cellular Massive MIMO Systems

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