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

Saeed Habibi Qomi¹, Fakhroddin Nazari², Farid Samsami Khodadad¹

¹Faculty of Engineering Modern Technologies, Amol University of Special Modern Technologies, Amol, 4616849767, Iran.

Scientific Reports

|June 27, 2026

Summary

This study optimizes Internet of Things (IoT) networks for smart cities using multi-carrier non-orthogonal multiple access (MC-NOMA) and 3D beamforming. The proposed method significantly enhances energy efficiency and network robustness for urban IoT deployments.

Keywords:

Device-to-device (D2D)Device-to-infrastructure (D2I)Energy efficiency Interference management Internet of Things (IoT)Memetic particle swarm optimization Non-orthogonal multiple access (NOMA)Quality of service (QoS)Resource allocation Three-dimensional (3D) beamforming

Related Experiment Videos

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.