Optimal resource allocation in urban Internet of Things using 3D beamforming.

Area of Science:

• Wireless Communications
• Network Optimization
• Internet of Things (IoT)

Background:

• Dense Internet of Things (IoT) deployments in smart cities face challenges in spectral efficiency, energy consumption, and interference.
• Existing networks struggle with urban propagation uncertainties and coverage gaps.

Purpose of the Study:

• To jointly optimize 3D beamforming, subcarrier assignment, and power allocation in MC-NOMA networks for both device-to-infrastructure (D2I) and device-to-device (D2D) communications.
• To enhance spectral efficiency, energy efficiency, and robustness in urban IoT networks.

Main Methods:

• Utilized a percentile-based channel model with spatial shadowing correlation and an elliptical footprint model for urban propagation.
• Employed a three-layer memetic particle swarm optimization (Hybrid PSO) algorithm to solve the mixed-integer nonlinear programming problem.
• Integrated a Successive Interference Cancellation (SIC-aware) power solver, Hungarian method for subcarrier assignment, and adaptive local search.

Main Results:

• Achieved fast convergence with network power consumption stabilizing at 88 mW at a 600 MHz carrier frequency.
• Demonstrated superior performance compared to OFDMA and uniform linear array schemes, especially under challenging conditions.
• Validated significant enhancements in energy efficiency and robustness.

Conclusions:

• The proposed joint optimization framework for MC-NOMA with 3D beamforming offers a scalable solution for next-generation urban IoT networks.
• The approach effectively addresses spectral efficiency, energy consumption, and interference management challenges.
• The method proves robust against channel estimation errors, external interference, and high user densities.