Distributed Intelligence in Wireless Networks

The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, and security privacy concerns caused by billions of connected wireless devices typically zillions bytes data they produce at the network edge. A blend edge computing Artificial Intelligence (AI) techniques could optimally shift resourceful computation servers closer edge, which provides support for advanced AI applications (e.g., video/audio surveillance personal recommendation system) enabling intelligent decision making on point generation as when it is needed, distributed Machine Learning (ML) with its potential avoid transmission dataset possible compromise that may exist in centralized learning. Besides, deployment redesign end-to-end communication attracting attention improve performance. Therefore, interaction communications generates a new concept, named native networks. In this paper, we conduct comprehensive overview recent advances intelligence networks under umbrella networks, focus design learning architectures heterogeneous AI-enabled computing, communication-efficient technologies learning, AI-empowered communications. We highlight advantages hybrid compared state-of-the-art techniques. summarize challenges existing research contributions identify future opportunities.