Light Weight Deep Learning Algorithm for Voice Call Quality of Services (Qos) in Cellular Communication

Area of Science:

• Telecommunications Engineering
• Computer Science

Background:

• Cellular networks facilitate communication via analog (NMT-450) or digital (DAMPS, GSM) protocols.
• Network performance and coverage are enhanced by diverse base station standards and inter-operator connectivity.
• Seamless communication is enabled between mobile and landline networks.

Purpose of the Study:

• To propose a deep learning algorithm for improving voice call quality in cellular networks.
• To ensure reliable message transmission between transmitters and receivers through continuous monitoring of voice data packets.

Main Methods:

• A deep learning algorithm was developed to monitor voice data packets.
• The algorithm ensures proper message exchange between the phone and base station.
• Simulations were performed in Matlab to evaluate performance metrics, focusing on Quality of Service (QoS).

Main Results:

• The proposed deep learning method demonstrated superior performance in maintaining voice call quality.
• The simulation results indicated an average improvement of 97.35% in the Quality of Service (QoS) rate compared to existing methods.

Conclusions:

• The deep learning algorithm effectively enhances voice call quality in cellular communication.
• The proposed method offers a significant improvement in Quality of Service (QoS) over traditional approaches.