While these PoCs were quite promising, they uncovered a less explicit gap, but a serious one. Today, mobile networks are built end-to-end using a monolithic architecture, offered by a handful of vendors. The task of exposing the RAN data needed by our AI model, extracting it, and feeding it to the models, is both cumbersome and non-systematic. However, the timing could not be better! A paradigm shift in how mobile networks are built has been underway since 2018, i.e., Open RAN (O-RAN). The industry had been moving towards making mobile networks more open, softwarized, cloudified, and virtualized – exactly what is needed to accomplish our goals and drive our mission forward.

Multiple live O-RAN trials have been conducted in our region to explore the supply chain landscape, use cases, and value proposition of the technology [GCCO]. stc and other regional mobile network operators took to the field in a collaborative effort aimed at ensuring effective cross-migration of industry knowledge and expertise within the region. While the outcomes were largely promising, the trial also reaffirmed the conviction that further evolution of the hardware and software is required to address use cases that may not necessarily relate to pure connectivity and content services.

Non-Conventional Wisdom

The conventional wisdom behind Open RAN is breaking vendor lock-in, access to best-of-breed software and hardware, with the goal of eventually improving total cost of ownership (TCO). An essential element in the Open RAN stack is the RAN intelligent controller (RIC). The dominant view of RIC is that it is a tool for “closed-loop” value creation. This involves extracting data from the Open RAN system and employing it to improve the network’s energy efficiency and reduce the amount of labor needed for network maintenance. This provides a pathway for Open RAN technology to also reduce an operator’s OPEX. This approach is referred to as closed-loop (Figure 2) because it extracts data and then communicates control actions back to the RAN.

On the flip side of the coin, open-loop value creation involves extracting data from various layers, levels, and touchpoints within the RAN stack. The data is run in previously-ratified AI models to generate analytics work streams. We can package the associated outputs into business intelligence (BI) and decision intelligence (DI) insights used in a number of market verticals. This presents the telco with opportunities to unlock new value, and address a wide range of smart and cognitive city applications at scale.

Practical Use Cases

As part of our customer and market discovery exercise, we came across a number of use cases across multiple smart city verticals, including mega-events, large-scale retail spaces, and public transportation (Figure 3). The latter is probably the most impactful as it can contribute significantly to the efforts to reduce greenhouse gas (GHG) emissions. Today, 95% of Riyadh’s residents use private cars [RUHV]. The introduction of metro services is expected to reduce that, but the impact may be less than hoped for, due to the last/first mile problem. By analyzing commute behaviors and door-to-door journey patterns, first/last mile journey challenges can be singled out and, therefore, better addressed, which may lead to increased uptake of metro services. To appreciate the potential impact, every 10% increase in metro uptake will equal a reduction of approximately 3 million tons of CO2 emissions in Riyadh [BNFT].

Another notable use case is LEAP, the Saudi Arabia’s global tech-event that has seen more than 100K+ visitors in 2023. However, the collective feedback on social media was that there was a significant amount of time spent moving into/out and within the exhibition floor. Any attempt from organizers to create a more seamless visitor experience would involve a lot of improvised effort based on anticipations unless visitor mobility data is measured. By only improving the last/first mile commute experience, the time spent by visitors can be boosted by more than 25%, based on a pilot project we carried out.

Challenges Ahead

In a very recent and insightful article, industry veterans point out a potential shortfall in Open RAN systems that could result in stifling innovation in the Open RAN domain [ODSL]. This is related to the limited coverage and breadth of KPMs (RAN data) available today in commercial Open RAN systems. In a recent study we commissioned from Kelley Business School on the addressable market opportunity that can be captured by open-loop is estimated near USD 10B [KLLY]. Given the massive opportunity that Open RAN offers under the closed-loop paradigm, this article is in essence calling for expedited improvement to RIC interfaces, namely: O1, E2, and the recently introduced Y1.

Other challenges include – from the point of view of mobile network operators:

• End-to-end (E2E) Integration: This challenge, due to multi-vendor ecosystem, include validations, version issues, interoperability, and feature parity, among others. 
• Operational Challenges: Due to multi-vendor setup, a big challenge exist in how to ensure efficient conduction of tasks such as root cause analysis (RCA) and network issues accountability, inter-operability, regular software & patches upgrade in production issues.
• Cyber Security Challenges: Considering the regional regulator guidelines, adoption of open-source software issues, non-standardized or partial-standardized interfaces need to be critically examined, with particular emphasis on applicable data privacy and protection regulations.
• RIC & SMO Challenges: RIC & SMO automation layer is challenging, especially due to 3^rd party xApps and rApps, interoperability with E2 nodes and multi-vendor components support.

Conclusions

Advocates of Open RAN have been so far primarily focused on the potential cost savings and supply chain diversification advantages it may bring to mobile operators. In this article, we rather shed light on the potential for Open RAN to unlock new value creation opportunities outside the network itself. We discuss in this article how Open RAN is well-positioned to address the growing needs of smart cities. Open RAN’s software-centric architecture lends itself to the exposure of various datasets and the application of AI workflows. With that kind of capability, a plethora of analytics and insights can be generated in the context of making our cities smarter and more cognitive.

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