Spectrum intelligence using IoT data as a service

A real-time distributed IoT-based monitoring solution can deliver competitive RF insights. #sponsored

Ruth Brown, Principal Analyst – Mobile Networks, Heavy Reading

January 10, 2023

5 Min Read
Spectrum intelligence using IoT data as a service

Spectrum is the lifeblood of mobile networks, but it comes with a price. To satisfy network capacity demands, operators spend billions of dollars to acquire and optimize spectrum. And while MNOs have long invested in RF monitoring capabilities, a new approach to real-time monitoring using data captured from a distributed Internet of Things (IoT) sensor network can potentially give operators better tools to maximize the performance of their own networks and keep a close eye on competitor RAN strategies.

The rapidly increasing number of public and private wireless deployments will add additional pressure and contention in the prime spectrum ranges. Smarter real-time RF monitoring solutions will be imperative, especially where closely transmitting frequencies are deployed (e.g., adjacent, overlapping or shared spectrum bands). To remain competitive and capitalize on their spectrum assets, operators must consider what RF measurements to collect, technology options for RF monitoring and how to employ these to gain better visibility into competitor approaches and performance.

RF measurement collection

Gathering the correct RF information can help optimize a network and guide more complex planning and investment decisions, from spectrum acquisition to spectrum refarming and technology strategy. In other cases, spectrum monitoring is often the first step to prevent signaling attacks and interference.

There is a vast array of KPIs available for network analysis, and selecting datasets that enable cost optimization and identify opportunities for competitive advantage will be critical. For example, channel loading and channel quality insights would allow operators to compare their own network against a competitor's network for a given location to identify potential radio optimizations. These types of insights would also indicate a need to add new capacity or react with other customer care strategies. Additional new capabilities, such as setting up event-triggered data (e.g., an update initiated when quality moves outside a set threshold), may also enable trends to be discovered and acted on more quickly.

Competitor analysis

Knowing what your competitors are doing is essential. The right observations can assist operators with technology and location deployment decisions and help them react to subscriber network utilization changes. With a finite budget, competitor insights enable MNOs to prioritize areas more critical for investment. Service providers have always been able to track and access their own network data, but it has been challenging to gather and analyze competitive behavior in a timely enough fashion to drive action.

"Best network" benchmarking programs and "best network" accolades provide some differentiation and KPI visibility between rival mobile operators. However, with the race to establish new technologies such as 5G NR, geographical spectrum occupancy and deployment insights, such as 5G NSA vs. SA, are becoming vital to comprehend. An awareness of the entire market can help operators identify and prioritize areas for investment and support critical marketing campaigns.

Technology options for RF monitoring

Choosing the best RF monitoring solution is a prime concern for mobile operators seeking to ensure a great customer experience, reduce churn and make their networks as competitive as possible (e.g., by reducing spectrum acquisition and deployment costs). Several monitoring methodologies can generate network intelligence:

  • RF drive testing: For real-time spectrum monitoring, wide-area RF drive testing is the primary option today, and for many operators, it is the gold standard. While this is a very established and recognized method for gaining detailed visibility, it can be time-consuming and often involves driving vast service areas. Typically, this style of test is performed reactively in response to unexplained behavior. The large datasets are specific in time and location and require trained expertise to analyze and operate the tools.

  • Crowdsourcing data: Crowdsourcing is a useful technique for operators; yet, these methods often produce non-normalized data due to the use of different handsets and measurement apps, leading to some inconsistency and inaccuracy. Where there is a gap in specific RF data or it is not available (e.g., for signal strength or signal quality measurements), median values or extrapolation can be used. However, ambiguity in the data and extended lag between collection and analysis make it difficult to derive conclusions and practical actions from crowdsourced data.

  • Networked RF spectrum monitoring: A new alternative approach is cloud-based real-time spectrum monitoring using an RF data as a service (DaaS) approach with data captured from a distributed RF sensor network. These platforms provide continuous data feeds across a wide range of frequencies and offer cloud-scale advantages for fast data processing and analysis across multiple data points from broad geographic areas. By accessing a breadth of data across continuous time periods, it is possible to make quicker, accurate and more informed decisions about optimization, performance and strategy.

Yet, there are still some challenges to address. For operators to maximize the full benefit of a DaaS RF solution, they must demonstrate a geographical density of sensors. Thus, operators may choose to initially use a DaaS RF solution in combination with other traditional RF methods, or they may consider deploying their own private network of distributed IoT sensors.

In today's market, it is vital to understand competitive RF insights. A real-time distributed IoT-based monitoring solution that offers clear visibility and intelligence can deliver invaluable competitive intelligence and help operators deliver better network performance to their customers.

For more information, check out the archived webinar sponsored by thinkRF, Using Real-time Wireless Network Insights and Intelligence to Drive Competitive Advantage.

— Ruth Brown, Principal Analyst – Mobile Networks, Heavy Reading

This blog is sponsored by thinkRF.

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About the Author(s)

Ruth Brown

Principal Analyst – Mobile Networks, Heavy Reading

Ruth covers mobile network research for Heavy Reading. Key coverage areas include system architecture, core infrastructure and services, and supporting cloud technologies. Prior to joining Heavy Reading, Ruth worked in mobile and fixed network research and design for BT for over 20 years. Her research interests have included convergence, mobile QoS, network slicing, private networks, cloud native mobile core technology and automation. She has filed more than 40 patents on both real world applications and enhancements to mobile core networks. Ruth is an advocate for women in engineering.

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