Enhanced nonlinear equalization for OFDM systems in IoT-based intelligent transportation using CGLS algorithms

Abstract

The rapid development of intelligent transportation systems and the Internet of Things (IoT) has increased the need for robust and efficient wireless communication in high-mobility environments, such as vehicle-to-everything networks. Orthogonal frequency division multiplexing is widely used due to its high data rates and resistance to multipath fading. However, in fast-moving scenarios, Doppler effects cause interference between subcarriers, reducing signal quality. This paper proposes a practical nonlinear equalization framework based on the conjugate gradient least squares (CGLS) method to tackle this problem. Two advanced designs—a CGLS-based block decision feedback equalizer and a regularized least squares CGLS sliding window equalizer—are introduced and tested. Simulation results show that the proposed methods significantly reduce bit error rates and achieve up to a 5 dB performance improvement over traditional approaches, while keeping computational costs low enough for real-time IoT applications. This work supports the development of safer, more reliable, and energy-efficient communication systems for smart and sustainable transportation. These contributions align with the United Nations Sustainable Development Goals (SDGs), specifically SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action) by supporting the development of resilient, energy-efficient, and scalable digital communication infrastructures for smart mobility and urban sustainability.