A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

摘要: The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide fully data-driven evaluating and optimizing the end-to-end behavior big volumes of real-time within data rate Tb/s. In addition, 6G adopts an average 1000+ massive number connections per person in one decade (2030 virtually instantaneously). is novel service paradigm offers new application for future architecture. It enables ultra-reliable low latency (URLLC) enhancing information transmission up around 1 Tb/s while achieving 0.1 millisecond latency. main limitation this computational power available distributing with greatly designed artificial neural networks. work carried out paper aims highlight improvements multi-level architecture by enabling intelligence (AI) URLLC providing designing This done through learning, predicting, decision-making manage stream individuals trained data. secondary aim research improve user level device intelligence, cell edge cloud URLLC. improvement mainly depends on using training process unsupervised learning developing resource management. improving deep (DL) would facilitate creation AI system, networks intelligent devices, technologies based effective capability. These investigational problems are essential addressing requirements smart Moreover, provides further ideas several gaps between DL up-to-date unknown.