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Why Energy Sustainability Can’t Be an Afterthought
As mobile networks scale to meet the demands of 5G and look ahead to 6G, Mobile Network Operators (MNOs) are facing a critical shift. Traditional network optimization has long prioritized throughput and service quality, often overlooking energy efficiency. But with rising energy costs, increasing regulatory pressure, and growing environmental responsibility, that load-centric approach is no longer sustainable.
To stay competitive and compliant, MNOs need smarter, more efficient design and operational strategies that align with Environmental, Social, and Governance (ESG) principles. That’s where the Energy Sustainability Class (ESC) comes in.
What Is the Energy Sustainability Class?
The ESC is a composite metric that allows MNOs to track and improve energy efficiency and spectral performance simultaneously. It combines:
- Spectral Efficiency (SE) – measured in bps/Hz
- Energy Efficiency (EE) – measured in bps/W
Each base station (BS) is evaluated and assigned a grade—A, B, C, or D—based on normalized SE and EE values. When mapped on a scatter plot, each BS appears as a color-coded dot, giving operators a quick visual on where things stand.
The meaning of each ESC class is defined as follows:
- A - The sustainable zone, balancing the spectral and energy efficiency.
- B - High energy efficiency, but low spectral efficiency, denoting low QoS.
- C - High QoS but low energy efficiency, denoting a too high energy consumption.
- D - The unsustainable zone, with low spectral efficiency and low energy efficiency.
ESC in Action: Three Key Use Cases
The power of the ESC lies in how it supports different stages of the network lifecycle—from live monitoring to long-term planning. It enables operators to continuously assess energy and spectral performance, identify underperforming sites, and benchmark improvements over time.
Cyient’s VISMON platform strengthens this process by automating the collection of Performance Measurements (PM), Energy Measurements (EM), and base station topology data. This makes it possible to compute ESC scores in real time and generate actionable insights without manual intervention. With this foundation in place, ESC becomes a practical, high-impact tool across three key use cases:
- Real-Time Monitoring
Continuous data collection ensures visibility into evolving trends and performance gaps. This is especially valuable when evaluating the impact of new network configurations and identifying the worst-performing base stations (BSs). As shown in Fig. 2, this use case highlights how ESC supports real-time monitoring of energy and performance metrics, enabling timely and informed operational decisions.
Fig. 2 - Real-Time Monitoring use case - Custom Optimization
ESC delivers site-specific recommendations based on each network’s traffic, topology, and energy characteristics. Fig. 3 illustrates how ESC enables operators to classify sites at the local level and apply tailored optimization strategies that go beyond one-size-fits-all adjustments.
Fig. 3 – Custom optimization use case - Data-Driven Benchmarking
MNOs can assess their network performance against anonymized peer data to identify efficiency gaps and define measurable improvement targets. As shown in Fig. 4, Cyient supports this use case through a global, anonymized MNO database, providing meaningful performance comparisons and helping operators position themselves within a broader industry context.
Fig. 4 – Data driven benchmarking
Together, ESC and VISMON give operators a powerful combination: a clear sustainability benchmark, and the data intelligence needed to act on it—quickly, locally, and at scale.
ESC Optimization: Moving from Metrics to Root Causes
Building on research conducted at Cyient, a scientific article was recently published in IEEE Accesss, presenting the ESC alongside a machine learning-based approach to identify key indicators driving its classification. This analysis supports the identification of root causes behind energy performance outcomes. For instance:
- The frequent use of higher-order Modulation and Coding Schemes (MCS) was found to reduce the probability of a base station being classified as "D" by 50%, highlighting the role of better channel conditions.
- Similarly, RAN sharing with other MNOs was shown to double the likelihood of a base station being classified into the more efficient classes "A" or "B" compared to standalone deployments.
By surfacing these root causes, the ESC framework helps operators move beyond surface-level performance and directly address the underlying contributors to energy inefficiency. This has been demonstrated in the field through Cyient’s collaborations with Tier-1 operators. In one such deployment, a Tier-1 operator adopted Multi-Operator RAN (MORAN) sharing, and the ESC framework was used to evaluate its real-world impact.
By analyzing ESC scores before and after implementation, the operator saw a significant improvement: the share of base stations in the least efficient ESC class "D" dropped from 50% to 20%. This outcome reinforces how ESC enables operators to quantify the benefits of optimization efforts and make more informed decisions toward energy-efficient operations.
Looking Ahead
As networks evolve, the ability to unify performance intelligence with sustainability insights will define the next era of operational excellence. ESC is more than a metric, it’s a mindset shift that aligns technical innovation with environmental responsibility. The future belongs to operators who act on both.
About the Author
Luís Mata is a recognized leader in the field of mobile network intelligence, currently serving as Lead Researcher at the R&D at CYIENT. His current work focuses on applying AI to enhance the operational efficiency of mobile networks, with a particular emphasis on sustainability and next-generation technologies. He is also pursuing a Ph.D. at Instituto Superior Técnico, Universidade de Lisboa (IST/UL), and teaches as an Adjunct Professor in Computer and Internet Networks at Instituto Superior de Engenharia de Lisboa (ISEL).
Luís received his B.S. degree in Electrical and Computer Engineering from IST/UL in 1996, an Executive M.B.A. from AESE/IESE Business School in 2007, and a Specialist degree in Telecommunications from the Polytechnic Institute of Lisbon (IPL) in 2022. With over 25 years of experience across mobile and fixed networks, he has held roles in engineering, marketing, product management, and business strategy. His interests include data science, AI/ML, 5G, 6G, and Industry 4.0.