Today, RAN protocols are agreed at the industry level in a waterfall process that includes research, design, standardization, implementation and testing. This takes years and consumes a formidable amount of resources. Standardization is important to establish consensus between hardware manufacturers and guarantee cross-vendor inter-operability, but these negotiations not always technically driven. Can this approach be accelerated, optimized and, above all, automated? In this talk, protocols are postulated as the language that machines speak and an ML-based frameworkis described to teach independent radio nodes how to develop their own language in a performance-maximization manner.
Related solution: AI and ML for 6G networks
