Adaptive MCS selection and resource planning for energy-efficient communication in LTE-M based IoT sensing platform

Area of Science:

• Wireless Communication
• Internet of Things (IoT)
• Telecommunications Engineering

Background:

• Wireless sensing technologies are integral to the growing Internet of Things (IoT).
• Long Term Evolution Advanced (LTE-A) with Machine Type Communication (LTE-M) supports IoT sensing platforms.
• Existing LTE-M may be energy-inefficient for low-data-rate IoT devices due to its high-data-rate origins.

Purpose of the Study:

• To address energy consumption challenges in LTE-M based IoT sensing platforms.
• To propose a novel adaptive modulation and coding selection (AMCS) algorithm for enhanced energy efficiency.
• To develop a quantity-oriented resource planning (QORP) technique for optimized spectrum allocation.

Main Methods:

• Developed an adaptive modulation and coding selection (AMCS) algorithm.
• The AMCS algorithm identifies optimal Modulation and Coding Scheme (MCS) and primary resource blocks (#PRBs).
• Implemented a quantity-oriented resource planning (QORP) technique using optimal MCS for spectrum allocation.

Main Results:

• The proposed AMCS algorithm significantly reduces energy consumption in IoT sensing nodes.
• Energy consumption reduction of up to 23.09% was observed.
• A reduction in primary resource blocks (#PRBs) by up to 25.98% was achieved.

Conclusions:

• The novel AMCS algorithm effectively lowers energy usage for IoT sensing devices on LTE-M.
• The QORP technique enhances spectrum allocation adaptability to sensing node requirements.
• The proposed methods offer a viable solution for energy-efficient IoT deployments in cellular networks.