Machine Learning-Based Spectrum Forecasting for Ultra-Dense IoT Environments
The reach of Internet of Things (IoT) devices in cross domain applications has recently resulted in a significant rise in the burden on wireless communication systems. The modern wireless communication systems are under stress due to fast deployment of the Internet of Things’ (IoT) in homes, businesses, and smart cities. It becomes more challenging to balance the bandwidth in these ultra-dense environments. Here many heterogeneous devices are involved and need a finite amount of radio spectrum. It becomes difficult for conventional spectrum assignment and sensing techniques to manage due to unpredictable traffic volumes, varying interference, and quickly changing channel conditions. Hence, there is the necessity for future systems to find a reactive mechanism and implement predictive strategies that can predict the changes in the spectrum in coming times. Machine learning has become a promising technology facilitator in this context. The predictive abilities of ML make it possible to have a smarter transmission scheduling, enhanced interference avoidance, and more effective use of limited spectrum resources. This chapter showcases a review and assessment of ML-based spectrum forecasting methods designed for dense IoT environments and setups. It reviews a wide range of learning techniques, like deep learning architectures, supervised and unsupervised methods, and reinforcement learning models, and discusses on how it can be deployed for significant tasks like channel occupancy prediction, interference mitigation, and enabling dynamic access decisions. This chapter also includes the review and assessment of emerging system architectures that can reduce latency, save device energy, and ensure privacy in distributed IoT networks, like edge-assisted and federated learning. The issues like how ML-enabled forecasting improves spectrum efficiency, reduces collision rates, and helps in maintaining quality of service (QoS) in large-scale IoT deployments through comparative analysis and case-driven discussion is also discussed in this chapter. The chapter finally summarizes new research directions that impacts the development of spectrum-intelligent IoT and 6G communication systems in future.