The key characteristic of future radio access networks is massive interconnectivity with highly diverse service requirements. The latter are pushed to extremes, as bandwidth and reliability need to be further augmented to accommodate the exchange of colossal amounts of data while latency needs to be further lowered even at high mobility.
Increased bandwidth will be achieved with the advent of sub-THz and THz bands in 6G but is particularly difficult to harness (detecting weak signals is extremely difficult, increased complexity and parallelism in RF hardware, reduced beam width, poor signal penetration, etc.).
Moreover, 6G RAN (Radio Access Networks) will be characterized by beyond-terrestrial communications covering various challenging areas such as space, underwater and aerial zones (Air-duct/Water-duct communications). Such communications require support for reliable ultra-long-distance transmissions. Furthermore, 6G RAN requires putting more stress on energy efficiency than 5G RAN as future use cases are more energy-intensive. Intelligence is further pushed to the edge with increased demand for distributed computing and caching techniques to address the unmatched requirements in computation and transmission load.
Finally, in a general manner, Artificial Intelligence will continue to play a vital role in future access networks. In fact, optimization of multi-objective performance-based problems in 6G is typically NP hard and is difficult to solve in real time. Hence, learning algorithms will aid optimization for efficient resource allocation such as data offloading, caching, interference management, dynamic tuning of the physical layer parameters, and so on.