A novel user clustering and efficient resource allocation in non-orthogonal mutliple access for IoT networks

Area of Science:

• Wireless communication networks
• Resource allocation optimization
• Internet of Things (IoT)

Background:

• Optimal resource allocation is critical for 5G and beyond networks, particularly for connecting numerous IoT devices.
• Non-Orthogonal Multiple Access (NOMA) offers high system throughput but faces challenges in energy consumption and Quality of Service (QoS) for battery-limited IoT devices.
• Existing user clustering methods lack adaptability to diverse channel conditions, impacting overall throughput.

Purpose of the Study:

• To investigate user clustering and power allocation for enhanced resource management in multi-carrier NOMA systems.
• To improve system sum rate and energy efficiency while meeting QoS requirements for IoT devices.
• To develop a computationally efficient resource allocation scheme.

Main Methods:

• An iterative optimization process involving user clustering followed by power allocation.
• User clustering using a Partial Brute Force Search (P-BFS) method to reduce complexity.
• Optimal power allocation determined via the Lagrangian multiplier method with Karush-Kuhn-Tucker (KKT) conditions.
• A Deep Neural Network (DNN) integrated with P-BFS to further decrease resource allocation complexity.

Main Results:

• The proposed P-BFS user clustering significantly reduces complexity compared to exhaustive search methods.
• Optimal power allocation is achieved for users within each cluster and subchannel.
• The DNN-based P-BFS scheme further enhances the efficiency of resource allocation.
• Simulation results demonstrate a substantial improvement in the network's sum rate.

Conclusions:

• The developed P-BFS technique effectively addresses user clustering challenges in NOMA systems.
• The integration of DNN with P-BFS offers a promising approach for complex resource allocation problems.
• The proposed methods lead to significant performance gains in terms of sum rate for NOMA-enabled IoT networks.