Efficient models for enhancing the link adaptation performance of LTE/LTE-A networks

Ali Abdulqader Bin-Salem,Tat-Chee Wan,Mohammed Anbar,Hamad Naeem,Abdellah Redjaimia,Sabri M Hanshi

doi:10.1186/s13638-022-02091-w

Abstract

Link adaptation (LA) is the ability to adapt the modulation scheme (MS) and the coding rate of the error correction in accordance with the quality of the radio link. The MS plays an important role in enhancing the performance of LTE/LTE-A, which is typically dependent on the received signal to noise ratio (SNR). However, using the SNR to select the proper MSs is not enough given that adaptive MSs are sensitive to error. Meanwhile, non-optimal MS selection may seriously impair the system performance and hence degrades LA. In LTE/ LTE-A, the LA system must be designed and optimized in accordance with the characteristics of the physical (e.g., MSs) and MAC layers (e.g., Packet loss) to enhance the channel efficiency and throughput. Accordingly, this study proposes using two LA models to overcome the problem. The first model, named the cross-layer link adaptation (CLLA) model, is based on the downward cross-layer approach. This model is designed to overcome the accuracy issue of adaptive modulation in existing systems and improve the channel efficiency and throughput. The second model, named the Markov decision process over the CLLA (MDP-CLLA) model, is designed to improve on the selection of modulation levels. Besides that, our previous contribution, namely the modified alpha-Shannon capacity formula, is adopted as part of the MDP-CLLA model to enhance the link adaptation of LTE/LTE-A. The effectiveness of the proposed models is evaluated in terms of throughput and packet loss for different packet sizes using the MATLAB and Simulink environments for the single input single output (SISO) mode for transmissions over Rayleigh fading channels. In addition, phase productivity, which is defined as the multiplication of the total throughput for a specific modulation with the difference between adjacent modulation SNR threshold values, is used to determine the best model for specific packet sizes in addition to determine the optimal packet size for specific packet sizes among models. Results generally showed that the throughput improved from 87.5 to 89.6% for (QPSK rightarrow 16-QAM) and from 0 to 43.3% for (16-QAM rightarrow 64-QAM) modulation transitions, respectively, using the CLLA model when compared with the existing system. Moreover, the throughput using the MDP-CLLA model was improved by 87.5–88.6% and by 0–43.2% for the (QPSK rightarrow 16-QAM)and (16-QAM rightarrow 64-QAM) modulation transitions, respectively, when compared with the CLLA model and the existing system. Results were also validated for each model via the summation of the phase productivity for every modulation at specific packet sizes, followed by the application one-way analysis of variance (ANOVA) statistical analysis with a post hoc test, to prove that the MDP-CLLA model improves with best high efficiency than the CLLA model and the existing system.

Highlights

In 2004, an initial study of long-term evolution (LTE) was introduced and viewed as a path for migration to 4G networks [1]
The throughput of the Markov decision process (MDP)-cross-layer link adaptation (CLLA) model is improved by 87.5–88.6% and by 0–43.2% for (QPSK → 16-QAM) and (16-QAM → 64-QAM) switching modulation, respectively, compared with the CLLA model and the existing system
For 64-QAM, the 8000; 8000; and 1000 bits are the optimal packet sizes in the existing system, the CLLA, and the MDP-CLLA models, respectively. This exceptional case is due to the signal to noise ratio (SNR) distance and the throughput that have a significant impact on the phase productivity results of both existing system and the CLLA model in the case of 8000 bits compared with the other packet sizes

Summary

Introduction

In 2004, an initial study of long-term evolution (LTE) was introduced and viewed as a path for migration to 4G networks [1]. LTE and LTE-A are the primary communication technology and have a capability for the deployment in a place where it becomes complicated to get with other technologies such as digital subscriber line (DSL) or cable This complicated process is because of the deployment’s cost; maintenance of such technologies; and the distinguished features of LTE, such as the high capacity of transmission bandwidth that reaches to 20 and 100 MHz in LTE and LTE-A, respectively [3]. Such bandwidth will lead to raise the data rate up to 100 Mbps and 1000 Mbps for downlink (DL) LTE and DL LTE-A, respectively [4], and achieve good quality of service (QoS). Modulations can be integrated with optimal packet size and other upperlayer factors to enhance link adaptation to improve the performance of throughput and packet loss in the network

Objectives

Results

Conclusion