Optimal performance of resource allocation in LTE-A for heterogeneous cellular network

Kukade, Shweta; Sutaone, M. S.; Patil, Rajendrakumar A.

doi:10.1007/s11276-021-02635-w

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…The distribu- tion of the arrival rate of users is uniform with the values as in Table 2. Outcomes of the proposed scheme is compared with HORA and CRBA algorithm [20]; whale optimization algorithm (WOA)-based resource allocation scheme [25]; RSMA technique with Han-Kobayashi scheme for RA in multicell mm-wave environment [29]; amended barnacles mating optimizer-based RA algorithm(ABMO) [24] and energy-aware mode selection for D2D resource allocation using Hungarian algorithm [19]. Unit cell that are uniformly distributed MTCDs and H2H UEs are considered.…”

Section: Resultsmentioning

confidence: 99%

“…The chunk-based and heuristic-based RA algorithm (CBRA & HORA) developed in [20] solves the tradeoff that exists between the performance of QoS parameter and computational complexity of the algorithm. The HORA algorithm allocates resource blocks to users, taking into account their QoS requirements, while CRBA assigns RB chunks to user groups based on channel conditions.…”

Section: Related Workmentioning

confidence: 99%

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Joint delay and energy aware dragonfly optimization‐based uplink resource allocation scheme for LTE‐A networks in a cross‐layer environment

Moses,

Sambantham,

Faheem

et al. 2024

The Journal of Engineering

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The exponential growth in data traffic from smart devices has led to a need for highly capable wireless networks with faster data transmission rates and improved spectral efficiency. Allocating resources efficiently in a 5G communication system with a huge number of machine type communication (MTC) devices is essential to ensure optimal performance and meet the diverse requirements of different applications. The LTE‐A network offers high‐speed mobile data services and caters to MTC devices and has relatively low data service requirements compared to human‐to‐human (H2H) communications. LTE‐A networks require advanced scheduling schemes to manage the limited spectrum and ensure efficient transmissions. This necessitates effective resource allocation schemes to minimize interference between cells in future networks. To address this issue, a joint delay and energy aware Levy flight Brownian movement‐based dragonfly optimization (DELFBDO)‐based uplink resource allocation scheme for LTE‐A Networks is proposed in this work to optimize energy efficiency, maximize the throughput and reduce the latency. The DELFDO algorithm efficiently organizes packets in both time and frequency domains for H2H and MTC devices, resulting in improved quality of service while minimizing energy consumption. The Simulation results demonstrate that the proposed method increases the energy efficiency by producing the appropriate channel and power assignment for UEs and MTC devices.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Joint delay and energy aware dragonfly optimization‐based uplink resource allocation scheme for LTE‐A networks in a cross‐layer environment

Moses,

Sambantham,

Faheem

et al. 2024

The Journal of Engineering

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“…The channel gains between the k th UE and BS are denoted by

{H_k}^{UL}\in {\mathbb{C}}^{{T\mathrm{x}}_m\times {R\mathrm{x}}_b}

. The received UL signal

{Y}_A

{BS}_A

can be modeled as 39 :

{Y}_A=\sum \limits_{l=1}^L\sum \limits_{k=1}^K{H}_{lk}^A{x}_{lk}^A+{z}_A,

{Y}_A=\sum \limits_{k=1}^{K_A}{H}_{Ak}^A{x}_{Ak}+{\sum}_{\begin{array}{c}l\ne A\\ {}l=1\end{array}}^L\sum \limits_{q=1}^{K_l}{H}_{lq}^A{x}_{lq}+{z}_A,

{Y_{A} = H}_{Ak}^{A} x_{Ak} goodbreak+ \sum_{k = 1}^{k_{A}} H_{Ak}^{A} x_{Ak} goodbreak+ \sum_{\begin{matrix} l goodbreak= 1 \\ l \neq A \end{matrix}}^{L} \sum q<...

…”

Section: System Modelmentioning

confidence: 99%

“…This study proposes BNSRME algorithm, which is used as an uplink multiuser scheduler. It performs the resource allocation among the active users in cellular networks 39 Algorithm 1. shows the mathematical working of the modified RME scheduler.…”

Section: Uplink Scheduling Schemementioning

confidence: 99%

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Uplink transmission with multiuser scheduling in non‐stand‐alone cellular network using virtual MIMO system

Kukade

Sutaone²,

Patil³

2022

Trans Emerging Tel Tech

Self Cite

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5G architecture allows the use of new radio alongside long‐term evolution‐A (LTE‐Advanced) and enables cellular mobile networks to handle extraordinarily high traffic volume data. The major challenge in current uplink data transmission systems is making resource blocks (RBs) available in a continuous form and assigning them to active users in an optimum manner to improve the system's spectral efficiency. The best N‐subset (BNS) minimization technique is suggested to identify the best of the best continuous chunks of RBs from the existing system bandwidth, followed by a modified recursive maximum expansion (RME) algorithm to assign RBs to users in the most optimal manner. The algorithm together is referred to as a multiuser uplink scheduler BNSRME algorithm. A constraint utility maximization problem is formulated to allocate RBs to UEs in the most optimized manner. Utility matrix is then converted into a weighted sum‐rate maximization problem allowing weights to be updated adaptively based on marginal utility values. In addition, a threshold limit is considered based on the signal‐to‐noise ratio in the BNSRME‐TH algorithm, which satisfies multiusers in terms of assigned resources and improves system performance, spectral efficiency, and throughput. This MU scheduling strategy is used in the 5G uplink non‐stand‐alone cellular network. The findings exhibit that the proposed multiuser algorithm increases system spectral efficiency by 31.65% in comparison with the existing opportunistic algorithms. It has been observed that the performance of the system is further increased by using the MU‐MIMO framework.

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Cell Zooming With Energy‐Efficient Offloading in Cellular Network: An Optimization‐Assisted Model

Prabha,

Senthil,

Suganthi

2024

Int J Communication

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With the speedy development of cellular users, conventional mobile network communications could not satisfy the requirements of rising traffic. The design of the user association approach should have the ability to balance the energy saving and traffic hotspot providence from the traffic cell sleeping system. Nevertheless, with the gradually rising denser deployment of small cells (SCs), the HetNets face novel challenges. This work aims to introduce a cell zooming (CZ) scheme that admits the EE traffic offloading. Thereby, the given model includes two phases, (i) energy efficient offloading: In this phase, appropriate traffic flow is predicted for the hybrid deep learning model that includes deep Q network (DQN) and reinforcement learning (RL). This prediction is related to the offload traffic from macrocell to small cell based on constraints like QoS and Energy, respectively. (ii) Cell zooming: After the prediction, adaptive CZ is preceded by an improved CZ factor with optimal threshold selection. For optimization, a new scaled bald eagle search optimization (SBESO) is used. The betterment of SBESO is established on throughput, SINR, and so forth.

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Optimal performance of resource allocation in LTE-A for heterogeneous cellular network

Cited by 5 publications

References 36 publications

Joint delay and energy aware dragonfly optimization‐based uplink resource allocation scheme for LTE‐A networks in a cross‐layer environment

Joint delay and energy aware dragonfly optimization‐based uplink resource allocation scheme for LTE‐A networks in a cross‐layer environment

Uplink transmission with multiuser scheduling in non‐stand‐alone cellular network using virtual MIMO system

Cell Zooming With Energy‐Efficient Offloading in Cellular Network: An Optimization‐Assisted Model

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