Lithium batteries have emerged as a more environmentally friendly and cost-efficient alternative. These have a smaller and lighter form factor compared to traditional lead acid batteries, saving space after installation. Lithium batteries have a significantly longer expected life-span (five or six years on average). The commonly used lead acid batteries are expected to be efficient for a much shorter period - around three years.
Further favorable aspects of lithium batteries are the improved charge and discharge capacities and related savings potential from the battery configuration. Backup batteries are fully charged at all times and discharge only when there is a power outage. By using a cycle-type lithium battery capable of daily charge and discharge, smart power control with a DC power controller can be performed, enabling flexible and efficient power supply to radio equipment. Voltage boosting is also an option with lithium technology; this can help operators increase voltage, save on energy transportation and serve newly installed 5G AAUs from longer distances more efficiently. In the event of theft, the battery is designed to automatically stop any output of power, rendering it useless to criminals. The batteries are also fitted with GPS modules, making them easily traceable.
HYBRID AND RENEWABLE ENERGY
Solar has become a competitive alternative to diesel in off-grid areas as the price of photovoltaic panels has fallen and base station battery solutions have become more advanced. Site-level hybrid energy solutions involve a mix of solar/diesel/wind/electricity/hydrogen-rich fuel and grid, providing a more efficient way to power sites. Custom-made hybrid solutions have a dedicated variable speed motor and a DC alternator which reduces loss caused by energy conversion. Advanced algorithms can select optimal energy sources and achieve significant energy efficiency improvements for the estimated 1 million cell sites globally.
More broadly, the shift to purchasing renewables will accelerate as prices continue to decline and future contracts enable long-term lock-ins. Operators can buy more of their energy from larger, centralized renewable energy sources and achieve long-term power purchase agreements or use their assets and produce their renewables in their cell sites.
RAN AND NETWORK EQUIPMENT INNOVATIONS
Turning off equipment even for a short period of time or putting it into a sleep mode when there is no traffic to serve, saves energy. With 2G, 3G and 4G, there are recurring transmissions of always-on signals called cell-specific reference signals to secure cell coverage and a connection with users. Significant energy-saving is possible from decreasing resources allocated to signaling and its ‘ping-pong’ effect between user equipment and the cell site.
The 5G NR standard allows more components to switch off or go to sleep when the base station is in idle mode and requires far fewer transmissions of always-on signaling transmissions. Overall, these factors allow deeper sleep periods for a longer time, which - everything being equal - confers a significant saving on network energy consumption per bit of data.
Massive MIMO requires an increased number of antennas compared with traditional MIMO technology. Laboratory tests suggest that the increased number of antennas improves energy efficiency, transmitting and receiving more data for a given amount of energy.
NETWORK-WIDE PLANNING AND OPTIMIZATION - SUNSETTING LEGACY NETWORKS
A key challenge is to square the improvement in energy efficiency per bit of data in 5G networks with the inevitability of rising traffic and the risk that overall power consumption could still increase. In this sense, strategies to reduce energy emissions have to be considered at an overall network planning level, incorporating all generations of mobility and their associated spectral elements.
Sunsetting legacy 2G and 3G networks is a major means of emissions reduction. The energy per bit of data with each new mobile generation is constantly improving, so sunsetting 2G and 3G networks can boost overall network energy efficiency. As legacy mobile technologies approach the end of their lifecycles, the importance of decommissioning and refarming certain spectrum bands to LTE or 5G is growing. 5G is particularly attractive as it is more efficient than legacy generations, given the NR standard.
Although the exact difference in energy efficiency between 5G and previous technologies varies, laboratory tests suggest 5G has a significant efficiency advantage. It can also save energy and space through using fewer active antenna units and other networking elements. The process of sunsetting is already in progress and will likely continue in a staggered manner over several years to balance the risk of stranded network assets if take-up of LTE and 5G tariffs lags expectations.
AI-DRIVEN NETWORK PLANNING
AI-driven network management and planning applications are not a particularly new concept, but many vendors and network operators have recently launched energy management solutions that leverage AI and advanced data analytics to optimize energy consumption. AI can help operators increase energy efficiency and deal with the 5G era’s increased data traffic in terms of network planning and optimization.
AI can also help in network planning by gaining insights from coverage areas, building heatmaps for network usage and recommending an optimal location for new cell sites. New algorithms could also help understand spatial and temporal patterns in the ever-changing nature of mobile data use and predict future usage profiles in different coverage areas. AI algorithms can support the interplay between indoor and outdoor small cells, Wi-Fi hotspots and macro sites to maximize energy efficiency.
AI-DRIVEN NETWORK OPTIMIZATION
Equipment vendors have started to offer AI-driven energy-saving solutions as an extension to existing network management platforms. Algorithms for power-saving in base stations can already be used to shut down power amplifiers, transceivers and other network elements. However, AI can improve efficiency and lengthen sleep periods.
Base stations are the ‘low-hanging fruit’ for such applications as they consume more than 70 percent of total energy. As each is unique, optimizing their operation one-by-one would be labor-intensive. AI was introduced to enable more precise energy-saving based on traffic and other site-related conditions, improving efficiency and reducing the manpower required. Large-scale deployments have shown an increase in power-saving activation of more than 80 percent.
Many device companies are now using machine learning and AI to optimize functions such as antenna tuning and power amplifier biasing to improve efficiency of the transmit and receive chains in the radio. This is a future trend to improving efficiency at the device level.
In the 5G era, the energy optimization offered by the first and second generations of algorithms is not sufficient to deliver the needed energy-saving to keep up with growing data traffic. The third generation of AI-driven energy-saving solutions can take account of the different efficiency levels of frequency bands and factors in that the power efficiency of different networks can vary. The new AI can help base stations direct services to the optimal network, resulting in greater network energy efficiency.
Major vendors are currently offering solutions that can make energy savings of 5 to 15 percent on the RAN. New software can forecast data traffic based on historical patterns, weather, events nearby and other factors, before identifying the necessary thresholds, activation and sleep periods. Based on the information, the algorithm can shut down power amplifiers, transceivers and other network elements to save energy. Alongside this power-saving potential, AI-driven shutdown solutions can constantly monitor customer experience, network availability and data traffic to ensure there is no impact on network performance.
AI can also reduce energy consumption outside the RAN - in central offices, shops and data centers - by continuously calibrating the optimal settings of heating and cooling systems, pumps and fans. Engineers can use AI-driven building management systems to prioritize work, reduce unproductive travel time, identify equipment issues, avoid costly unscheduled callouts and help ensure network reliability. Going forward, AI-driven energy-saving platforms are expected to focus more on data harvested from user devices. Anonymized coverage and data traffic insights from devices can help optimize the network further and adjust more capacity layers.
Overall, AI-driven network shutdown solutions can be broken down into three areas:
- Module (transceiver, baseband processing, etc.): The AAU components can be shut down in real-time during idle periods
- Equipment (AAU, RRU): Equipment can be completely shut down during periods of low traffic, usually at night
- Network: Large-scale, AI-driven solutions can schedule data traffic between different 5G bands (for example, from C-Band to sub-3 GHz bands) or between 5G and 4G, in a similar way to the smart data mode seen in new smartphones.
CONTENT CACHING NEARER TO THE END-USER
A surge in the popularity of video streaming over the last five years has made placing content caching facilities closer to end users strategically important - to maintain quality and for competitive reasons. Most video content passes through content delivery networks (CDNs), which transfer media across hundreds of servers worldwide. CDNs can reduce power demand as a video stream only has to travel through the network once to reach thousands of customers.
The CDN market historically mostly comprised independent groups such as Level 3 and Akamai, but major internet and consumer tech companies (Google, Facebook, Apple, Amazon, Netflix) have established their own servers to ensure control over their own content.
Reducing the distance between cache points and users results in improved latency, which preserves the customer experience for high- and super-high-definition video. As fixed-wireless access over 5G gains traction as a last-mile alternative for home broadband in some markets, the requirement for caching nearer to end-user premises would become even more pressing. CDN analytics platforms and network management systems can together capture, locate and analyze trends and events across the RF, RAN, backhaul and core, providing operators with unprecedented insight to optimize their network, save energy and monitor the customer experience.
THE WAY FORWARD
Alongside technical improvements to reduce energy leakage as power passes through the network phases, a range of measures are available to improve efficiency holistically across the network. These include the following:
- User equipment and devices - energy consumption and extended battery life of end-user terminals, mostly handsets
- Site-level innovations - new lithium-ion battery solutions, rectifiers, liquid cooling, air-con systems and simplification of site set-up
- RAN and network equipment innovations - AI-driven software focused on maximizing sleep states to avoid unnecessary energy consumption in the RAN
- Network planning and optimization - including the sunsetting of legacy 2G and 3G networks and long-term purchasing contracts for renewable energy.
The big picture for operators of ultimately reducing emissions to net zero depends on wrapping energy efficient technologies into a broader ‘green’ strategy that encompasses all facets of operations. To put teeth behind public commitments, many large operators have implemented key performance indicators and reporting targets in line with the independent Science Based Targets initiative.
Emissions reduction goals have been set in a phased approach to first reach carbon-neutral status before the more difficult and ambitious objective of net zero. Our analysis indicates that progress has generally been solid so far, enabled by advances in the renewable energy markets.
Despite this progress, reporting targets are not yet in place in most operators. There are also several persistent barriers, including emissions data availability and tracking mechanisms, lack of partnerships with energy sector producers and, in some cases, outdated organizational structures that augur for more cross-team working and less hierarchy.
The data aspect is of particular importance; we hope this research will help raise awareness of the issue. The construction of comprehensive data ‘pipelines’ with associated analytics would help uncover costly anomalies. Deploying smart sensors at various points of the network would help measure equipment-level energy consumption, battery status, active hours of generators, fuel levels, outside and indoor temperatures and air conditioning. Operators would need to build their comprehensive and real-time data repository, but we believe this would be money well spent. With reliable measurements and data pipelines established, big data applications can monitor and adjust network power - a key ability for the software-defined networks set to be the default option in the 5G era.
For the full report, 5G Energy Efficiencies: Green is the New Black, visit:
