Showing posts from February, 2024

“En-caching” the RAN – the AI way

  RAN caching is an intuitive use-case for AI. Our report “AI and RAN – How fast will they run?”, places caching third in the list of top AI applications in the RAN. There is seriously nothing new about caching. In computing analogy, caching is as old as computing itself. The reason caching and RAN are being uttered in the same breath is primarily MEC. MEC is a practical concept. MEC attempts to leverage the distributed nature of the RAN infrastructure in response to the explosion in mobile data generation and consumption. Caching looks at practically every point in the RAN as a possible caching destination – it can be base stations, or RRHs, or BBUs, or femtocells, or macrocells and even user equipment. The caching dilemma is a multipronged – what to cache, where to cache and how much to cache. In an ideal world, one could have access to infinite storage and processing capacity interconnected with infinite throughput at zero latency. In the real world however, each of these a

AI burden in RAN - 5G Wins!

Does 5G wear the AI burden more lightly in the RAN than earlier ‘older’ generations? Most certainly! We aver in our upcoming report, “ AI ML and DL in the Mobile Core ”. To be sure, there was no shortage of motivation for the usage of AI in 4G, or for that matter ANY generation of RANs. Let us look at 4G networks alone. 4G networks offered a quantum leap in terms of throughput over its predecessors. That aspect alone would have incentivized telcos to view their RANs from the prism of AI-enabled insights. To be sure, they did. Then there was energy management. Practices and the possibility of using AI and ML technologies in energy management precede the advent of 5G RAN. The case for traffic optimization in the pre-5G era was advanced greatly by the hybrid mode of deployment - the 5GC coupled with old radios as the hybrid mode offered a spectacular rise in throughput, enough to keep network planners interested in AI and ML-based constructs. The principle driver for AI however is not thr

The Table, The RAN, The AI and The Serving

What is the singularly pivotal value addition that 5G networks bring to the table? Beyond doubt, it is their ability to become all things for everyone. Welcome aboard traffic optimization – better known as network slicing plus edge computing. And who serves the traffic optimization in all its flavor? Undoubtedly, AI. No wonder, we forecast the the addressable market for AI in RAN traffic optimization will grow by a whopping 31.5% during 2023-2028 in our upcoming report "AI and RAN – How fast will they run?" Let us look at network slicing first. To be sure, network slicing is offered to an extent on 4G networks as well. But is the full flavor of the feature that 5G promises to unleash, that is making matters ripe for intervention of AI and ML technologies. Simply put, network slicing puts forth a plethora of difficult decisions that network planners need to confront. These decisions involve the degree to which granularity in slicing should be achieved and how to optimally ma