The Changing Face of Telecommunications

  In end-February 2024, Marvell captured large bits of popular imagination when it open sourced OCTEON 10 ML/AI Accelerator Software to what it termed as optimization of 5G RAN Networks. Lying as it does at the intersection of AI and RAN, it is worth taking a deep dive into this development as AI in RAN remains an area of continued focus for Insight Research. It is worth noting that Marvell’s entry into the 5G domain was a result of the widely reported misfiring of Intel’s 10 nm promise to Nokia and Nokia’s subsequent unsuccessful tryst with Xilinx FPGA. That was 2019 and that was custom Silicon. This is of course, 2024; and we are taking about open-source accelerators. How did we get here? With a history of close to three decades, Marvell’s tryst with telecommunications is not new. Apart from in-house development, Marvell has over the years acquired companies engaged in ethernet switches, embedded networking software, 3G SoCs, IMS software, network processors and data center switc

  Of all the announcements at MWC24, the one that validates our focus on AI in RAN is the formation of the AI-RAN Alliance . Coinciding with the release of our report “AI and RAN – How fast can they run?” , the Alliance checks all the boxes to furthering AI from the periphery of the RAN to its very core (pardon the pun, not to be confused with the mobile core). The Alliance has trifurcated the engagement of AI with the RAN at the following levels in its demonstration at MWC24 :   AI on RAN deals with making the RAN work for furthering AI end-applications. It is well understood that the availability of high-quality and high-throughput bandwidth will help improve the reach and spread of AI. This approach has a low barrier for entry as the emphasis is primarily on harnessing existing RAN infrastructure. Here, AI is the tenant, and RAN is the provider.   AI for RAN turns the earlier tenant-provider equation upside down and looks at AI as a tool for improvement in the RAN functioning. T

  Excerpted from our report AI and RAN – How fast will they run? The above figure charts the progression of the revenue shares of the key end-applications for AI in the RAN. Insight Research identifies the following key end-applications for AI in the RAN ·       Traffic optimization ·       Caching ·       Energy management ·       Coding The impression surrounding the all-pervasiveness of AI is principally shaped by the somewhat recent unleashing of the power of generative AI. Generative AI and LLM are arguably the more flamboyant exponents of AI and definitely the more recent ones. AI as a construct, however, has its roots in the previous century. AI has made itself at home in the RAN almost as early as the advent of cellular mobility itself. There have been several compelling reasons for the technology to operate under the radar for a major portion of its existence: There was little appreciation of AI as a distinctive technology. This was because AI offered only piecemeal increment

  How important is coding in the RAN? The answer, “very important” is not going to win you any awards, as it is a no-brainer! Coding is not just about data representation; it's about optimizing data flow, enhancing error correction, and ensuring robustness in diverse network conditions. Let us look at a very specific intent – error correction. To prevent data from being corrupted by channel interference and noise, channel coding employs redundancy. At the heart of 5G RAN's coding mechanisms is the use of Polar Codes and LDPC (Low-Density Parity-Check) codes. Polar Codes work well in 5G control channels. This is because, they are well suited for limited or finite data lengths.   LDPC codes works well with longer lengths. Not surprising, given the “low density” properties of the codes. LDPC essentially ensures that even if some packets are corrupted during transmission, the original data can be reconstructed without retransmission. LDPC codes, are therefore employed for d