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

Area of Science:

• Wireless communication systems
• Signal processing
• Telecommunications engineering

Background:

• Link adaptation (LA) optimizes modulation schemes (MS) and coding rates for radio link quality in LTE/LTE-A.
• Current LA methods using signal-to-noise ratio (SNR) are insufficient due to MS sensitivity to errors, leading to performance degradation.
• Optimizing LA requires integrating physical and MAC layer characteristics for improved channel efficiency and throughput.

Purpose of the Study:

• To propose and evaluate two novel link adaptation models: Cross-Layer Link Adaptation (CLLA) and Markov Decision Process over CLLA (MDP-CLLA).
• To address the accuracy issues in adaptive modulation selection and enhance overall system throughput and efficiency in LTE/LTE-A.
• To leverage a modified alpha-Shannon capacity formula within the MDP-CLLA model for superior link adaptation.

Main Methods:

• Developed a downward cross-layer approach for the CLLA model to improve adaptive modulation accuracy.
• Integrated a Markov Decision Process (MDP) with the CLLA model (MDP-CLLA) for enhanced modulation level selection.
• Utilized MATLAB and Simulink for simulations over Rayleigh fading channels in SISO mode, evaluating performance based on throughput and packet loss for various packet sizes.

Main Results:

• The CLLA model demonstrated throughput improvements of 87.5-89.6% (QPSK to 16-QAM) and 0-43.3% (16-QAM to 64-QAM) compared to existing systems.
• The MDP-CLLA model further enhanced throughput by 87.5-88.6% (QPSK to 16-QAM) and 0-43.2% (16-QAM to 64-QAM) over the CLLA model and existing systems.
• Phase productivity analysis and ANOVA confirmed the MDP-CLLA model's superior efficiency and optimal packet size selection.

Conclusions:

• The proposed CLLA and MDP-CLLA models offer significant improvements in LTE/LTE-A link adaptation.
• The MDP-CLLA model, incorporating a modified alpha-Shannon capacity, provides superior modulation selection and overall system performance.
• The study validates the effectiveness of cross-layer optimization and advanced decision-making processes for future wireless communication systems.