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Intelligent resource allocation decision using deep learning and optimization techniques for HetNets

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Abstract

Fifth generation (5G) cellular networks provide high speed services to the users in multimedia applications. Generally, a 5G network consists of large (Macro) and small (Femto/Micro) cells to provide variety of services to the User Equipments (UEs), having different requirements. Heterogeneous Networks (HetNets) contain small cells in the coverage region of macro cells. Resource allocation (RA) is a process of allocating available resources to the users according to their Quality of Service (QoS) demands. But resource allocation in 5G HetNets become challenging due to Interference among cells and Handoff. As Internet of Things (IoT) edge devices have dynamic traffic requirements, it will be difficult to allocate resources for them. In this paper, we propose an Intelligent RA Decision (IRAD) which is formulated and analyzed. In IRAD, Deep Learning based Encoder with Neural Network (DL-ENN) architecture is designed to classify the end IoT devices depending on their QoS requirements and traffic characteristics. From the output of classified results, optimum RA decisions are made to each device. In RA strategy of femtocells, the transmit power and allocated resources of each femtocell user are optimized such that minimum data rate requirements of femtocell users are satisfied and interference power from femtocells to the macro cells is reduced. The Improved Chicken Swarm Optimization (ICSO) algorithm is used for optimized RA decision to small cell users. By simulation results, we show that the proposed IRAD technique provides maximum resource utilization and throughput with reduced packet loss ratio.

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Abbreviations

5G:

Fifth Generation

ANN:

Artificial Neural Network

BRD:

Behavior of Resource Demand

CCI:

Co Channel Interference

CHO:

Chicken Swarm Optimization algorithm

CNN:

Convolution Neural network

CSI:

Channel State Indicator

CSO:

Chicken Swarm Optimization

D2D:

Device to Device

DL:

Deep Learning

DL_ENN:

Deep Learning_ Encoder Neural Network

DNN:

Deep Neural Network

EDRL:

Enhanced Deep Reinforcement Learning

FeNB:

Femtocell Base Station

HetNets:

Heterogeneous Networks

IoT:

Internet of Things

IRAD:

Intelligent Resource Allocation Decision

LTEA:

Long Term Evolution-Advanced

M2M:

Machine-to-Machine

MIMO:

Multiple Input Multiple Output

ML:

Machine Learning

NOMA:

Non-Orthogonal Multiple Access (NOMA).

PSO:

Particle Swarm Optimization

QoE:

Quality of Experience

QoS:

Quality of Service

QoS-ARS:

QoS-aware Resource Allocation and femtocell Selection

RA:

Resource Allocation

UE:

User Equipments

WiFi:

Wireless Fidelity

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Authors and Affiliations

  1. Department of CSE, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India

    Rosy Salomi Victoria D

  2. Department of ECE, St. Joseph’s Institute of Technology, Chennai, Tamil Nadu, India

    Princy Magdaline P

  3. Head of Computer Science Department, Women’s Christian College, Nagercoil, Tamil Nadu, India

    Glory Ratna Mary D

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D, R., P, P. & D, G. Intelligent resource allocation decision using deep learning and optimization techniques for HetNets. Wireless Netw 29, 3105–3119 (2023). https://doi.org/10.1007/s11276-023-03360-2

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