The challenges that operators face when trying to densify their outdoor networks are well documented. It is tough to gain access to the right sites without a time-consuming process of individually negotiating for each one, but even the best efforts to achieve a standardized template – let alone a common scale of pricing – have run into many problems (see separate item).
The latest opposition from US cities to proposals by the FCC shows the significance of the problem – and the USA has progressed a lot further than most countries in seeking to address the issue, as its operators gear up for major densification programs, which will only intensify with 5G.
Even if the MNO succeeds in negotiating the rights to large numbers of appropriate sites, at a reasonable cost, it still faces challenges in operating the network affordably. When deployed sparsely, small cells have low capex and opex cost compared to macrocells, because of their small footprint and power consumption and relatively simple workings.
However, two trends are presenting opex barriers. Some operators are deploying ‘macro equivalent’ small cells, which come with most of the functionality of a large base station, crammed into a small box, but with accompanying complexity. And others are starting to densify in earnest, which means rolling out large numbers of cells per city, with a collective operating cost that may be more than that of a macro covering the same area.
The only answer, according to Mazin Gilbert, VP of advanced technology and systems at AT&T, is artificial intelligence (AI). Gilbert heads up the operator’s open source efforts in AI (via its Acumos project) and edge compute (Akrainos – both these are hosted by the Linux Foundation). He is also chair of the governing board of the Foundation’s latest umbrella initiative, the LF Deep Learning Foundation, which is designed to coordinate a number of separate projects in this field to accelerate progress and avoid duplication of effort.
“Today, we have 35,000 microcells. We are going to 100,000 microcells building out 5G and more,” he said in an interview with LightReading. “Where do you put those? What building? What pole? It takes a year to put one of those out today. That cannot scale. The question is how do you make this mainstream, reduce the time cycle, and take into account traffic changes?”
The answer is AI, he argues. AI and machine learning (ML) can “redo completely the network planning process”, enabling operators to understand “on the spot” where a small cell would be best placed and how it will interact with others nearby, and with the macrocell. “We can’t send an army of people every time we want to build one, it’s not possible to scale 5G without it,” he added.
The beginning of densification at AT&T was one reason why the telco took its AI efforts into the open source world at a far earlier stage than it did with other developments such as ECOMP (now the basis of the Open Network Automation Protocol open project) or xRAN (soon to be part of the Open RAN Alliance). It is becoming urgent to scale up the deployment of small cells, white box switches and routers, and edge compute nodes, to support the new-look 5G infrastructure, which in turn will enable 5G to support many new use cases requiring dense capacity and low latency.
In a study Rethink published earlier this year – ‘AI, SON and the Self-driving Cellular Network – network planning and optimization were found to be the main reasons why operators expected to invest in AI/ML in the next 3-4 years.
The survey of almost 100 telco executives found that, between 2018 and 2022, between 60% and 74% of MNOs plan to deploy AI/ML to support SON in various areas of automated network planning, management and optimization.
The results show that 74% of MNOs plan to do this for RAN optimization by 2022 and about 70% for planning and to support RAN-based customer experience management. And 68% plan to do this for network maintenance, especially by harnessing predictive maintenance tools, while 60% are expecting to combined AI and SON in the management and orchestration of virtualized networks.
AT&T is already trialling the use of AI-driven drones to monitor infrastructure, including about 8mn poles. Gilbert continued in the interview: “I can send a drone with video capabilities and machine learning that can tell me what is wrong and diagnose the problem. And in the future, that drone will have a robot that can fix the problem that doesn’t jeopardize someone’s safety.”
Acumos, the first project within the Deep Learning Foundation, has links to other projects, such as ONAP, but it was deliberately not included in another of the Linux Foundation’s umbrellas, LF Networking, because it has broader applicability.
“We are building solutions and capabilities for different verticals,” Gilbert said – with generic foundations that will suit many industries, and specific tools, applications and marketplaces on top of those, to support each vertical’s specific needs. “AT&T and a bunch of other operators, we are interested in telecom sector, so how does the marketplace for AI fit into the LFN and into ONAP and into 5G? That is where we are coming in. But other companies who joined are interested in different aspects of AI, more to do with green energy, more to do with engineering and infrastructure and healthcare, so that the specialization happens at the solution level not at the foundation level.”
Acumos addresses AI model discovery, development and sharing, especially in the content and media sectors. It aims to establish a common platform for the exchange of machine learning solutions, while making AI more accessible to all companies.
AT&T announced in November that it was working with Indian integrator Tech Mahindra to build Acumos, with the aim of making it cheaper and simpler for operators to deploy and share AI applications, via a marketplace system. That could accelerate the uptake of AI-driven telco processes, from network planning and optimization, to consumer services; and it will also reduce the power of the major AI platform providers.
In January, Amdocs signed up, saying it would contribute knowledge of AI data, mapping and data tools from a customer experience, network and media standpoint.
Acumos is an extensible framework for AI and machine learning (ML) solutions, built on open source technologies and running on AT&T’s Indigo data sharing and collaboration platform. It can federate across various AI tools available today (for instance connecting two microservices derived from Google and Amazon). It allows those AI microservices to be edited, integrated, packaged, trained and deployed and they can then be accessed from the marketplace, and chained to create more complex services.
To this end, Acumos will package established toolkits, such as TensorFlow and SciKit Learn. The latter is a set of off-the-shelf algorithms for recognizing patterns, such as Random Forests and Logistic Regression, while TensorFlow is more of a low-level library providing the bricks for building machine learning algorithms and so provides greater flexibility and scope while requiring more effort.
Whichever is used, the framework provides an API, enabling developers to connect the algorithms together as if they came from the same development team. This frees data scientists to concentrate on tuning their data sets for the problem, while the model trainers can focus on the application without worrying about the underlying AI platform.
The framework also supports relevant non-AI tools such as microservices and containers to package and export production-ready AI applications as Docker files, which help developers and system administrators port applications, including all the dependencies, so that they can run across target machines automatically without further programming effort. Docker achieves this by creating safe Linux environments for applications called Docker containers.
Tech Mahindra will work with enterprises to help them apply the AI services and tap into intelligent telecoms connectivity to enable new use cases. The firm’s SVP and strategic business unit head, Raman Abrol, said: “Our ultimate goal with the Acumos Project is to accelerate and industrialize the deployment of AI at enterprises and get developers and businesses to collaborate effectively in order to improve how we all live, work and play.”