During my 25 years working in the wireless industry, there has never been a shortage of technology change but my experience over the last couple of years has demonstrated a marked increase in the complexity of deploying and operating wireless networks.
The exponential growth in data traffic with resulting capacity challenges is forcing Operators to use all tools at their disposal to manage the traffic increase. Network architectures, by necessity, are becoming much more layered and complicated and this will likely continue for the foreseeable future.
The wireless network environment is undergoing a revolution with planned massive deployments of small cells that are both driven and enabled by LTE. Operators that have managed a few thousand cell sites must now create processes to manage tens of thousands of sites, all of which will constantly be changing. As two examples, Verizon Wireless has recently reported capacity issues and stated small cells as a specific element of their solution, and AT&T Inc. (NYSE: T) has stated that they plan on deploying 40,000 small cells in the future.
Furthermore, the expanding array of wireless devices and applications accessing the networks is also challenging operations management. Operator core networks are changing to support the influx of different services by implementing new platforms that are far beyond traditional wireless equipment. I have already seen Network Operations organizations working full-out just to keep up with current demands, and it is clear that the coming exponential growth will not be able to be handled by simply throwing more bodies at the issue. The net result is that without strategic technology enhancements, networks will very soon start outpacing the operators' capabilities to manually manage and optimize deployed assets, and correspondingly, the end-user experience.
I believe there are two technology solutions now coming to the forefront and, when combined, these solutions will become powerful tools necessary for operators to meet the complexity challenge. The first technology is the Self-Organizing Network (SON) and the second is big data analytics. Overall, the combination of these two developments permit operators to introduce automation allowing efficient deployment and management of significantly larger number of network elements.
Wireless operators are now beginning to deploy the first versions of SON technology. SON holds the promise of creating a set of functionalities that allows the automation of many operations processes, reducing the manual effort required and increasing the quality of those processes. One SON application being deployed today, called Automatic Neighbor Relation (ANR), enables a cell site to self-configure its acceptable neighbor list based on information it receives with minimal to no human intervention. Therefore, a cell site installation process that previously could have taken hours to perform manually can now happen, autonomously, in minutes.
The second technology that will need to be added to the mix is the appropriate and timely use of data analytics. In the classic sense of "garbage in-garbage out," the benefits of SON automation will only be maximized to the degree that the information used to trigger and implement a response is highly accurate. Achieving improved input data will often come about by correlating information from a wider set of sources that, individually, provide different perspectives on a particular situation.
The interest and expectations for SON and big data are already very high. However, as we have seen throughout history, the implementation of capabilities that radically alter the operational environment tend to be more evolutionary than revolutionary. Therefore, the expectation is that the introduction of SON will happen pragmatically and incrementally as experience and trust levels grow. Two primary dimensions will likely drive the actual evolution of SON capability. The first dimension defines three types of operator activity impacted by SON: (1) self-configuration, which often occurs during the initial implementation of a new element; (2) self-optimization, where the element modifies its operation based on new information, learning or long-term network changes; and (3) self-healing, where the network elements react to particular disruptions, outages or environmental situations and minimize impact to end users.
The second dimension centers on the type and consistency of network elements involved in a SON process. In operations today, SON approaches usually address a single network platform, such as the eNodeB, within a single vendor's domain.
In time, SON solutions will likely involve more standardized interfaces and accommodate multi-vendor solutions. The migration will also move beyond operational platforms to involve business support systems, resulting in a holistic integrated ecosystem. In some configurations the variety of data sources will allow new use cases from different inputs. Often, existing use cases will gain incremental improvements from more refined and appropriate decisions based on deeper knowledge sets. Overall, operators benefit by optimizing their entire networks rather than individual network segments.
I think that operators are still very nascent in implementing the combination of SON and big data, however, experience gained in the near future will allow for more full scale introductions in the future. My sense is that to maximize SON optimization efforts, the data analytics services in these situations must be able to process billions or more transactions from a wide variety of data sources and provide actionable outputs in near real-time.
Achieving this is possible but not simple, requiring a very concerted and cross-functional effort that may be disruptive for many operators. However, those operators who are successful in implementing SON and Big Data will be able to manage their growth most efficiently and also, potentially, provide a point of differentiation in an increasingly competitive world.
— Rob Chimsky, VP of Insights, Guavus Inc.