With rapid urbanization, noise levels have been increasing around us, either in the form of locomotive noise, construction noise, traffic noise, industrial noise, community noise, or domestic noise, which can seriously impact our health. The World Health Organization (WHO) has identified hearing loss as a major challenge, particularly affecting the younger population. Prolonged exposure to loud sounds, especially in recreational settings, contributes to hearing loss exacerbated by environmental noise. Noise levels in locomotive cabs vary depending on the type of operation. Diesel locomotive cabs experience worse noise environments compared to electric locomotives, primarily due to the diesel engine's noise, which fluctuates with load and speed. In electric locomotives, the main noise source is the electric traction motor drive system. Continuous exposure to these noises affects locomotive pilots' concentration and decision-making. Over extended periods, this exposure can lead to fatigue and noise-induced hearing loss. There is a need to develop a robust active noise control system that enhances locomotive pilots' concentration and decision-making while reducing fatigue and noise-induced hearing loss.
As per the International Telecommunication Union (ITU), it is expected that global mobile data traffic will reach 607 Exabytes per month by 2025, further increasing to 5016 Exabytes by 2030. This tremendous growth in data traffic shows that internet connectivity has become crucial for the home, healthcare, transportation, infrastructure, and military applications. As the number of connected devices increases rapidly, additional frequency spectrum resources are required to manage them. The primary spectrum candidate for the future 6G networks is the mid-spectrum (7-20 GHz), as it provides the trade-off between extreme coverage and capacity. Similarly, it is expected that 6G will use the extreme massive MIMO, which consists of antenna elements ranging from 256 to 1024 and beyond. However, the advantage of the extreme massive MIMO comes at the cost of high computational complexity, as it uses the antenna elements in order of hundreds to thousands. The primary technical challenge in extreme massive MIMO lies in reducing this complexity, which involves designing a low-complexity equalizer, developing lowcomplexity signal processing for digital beamforming, and reduction in fronthaul load. As the number of connected devices, such as smart appliances, smart grids, and connected vehicles, increases tremendously, the noise model is no longer Gaussian. It is shifting towards a non-Gaussian/impulsive nature, which presents a major challenge at the physical layer for 6G. There is a need to develop robust algorithms for channel estimation, equalization, and detection to tackle non-Gaussian/impulsive noise.