How AI Can Reduce Energy Consumption and Drive Sustainability in FM Radio Broadcasting
Originally Aired - Tuesday, April 18 | 10:20 AM - 10:40 AM PT
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With energy costs soaring across the globe, reducing energy consumption is increasingly a top priority among radio broadcasters, to reduce operating costs as well as to meet the world’s growing need to implement greener solutions. Over the past few decades, we have seen huge advancements in transmitter design and performance. However, much of this progress was achieved thanks to LDMOS (laterally-diffused metal-oxide semiconductor) technology which has now reached its maximum potential for improving transmitter efficiency.
In the broadcasting chain, the transmitter represents the most impactful equipment as it continuously delivers a fixed output power to the antenna. In the FM chain, transmitters go from a few watts to dozens of kilowatts depending on the coverage area, landscape and radio listeners’ profile. The efficiency of a 10kW FM transmitter on the market today is about 74%, which means that the direct electrical consumption is approximately 13kWh, 24/7. Total electricity consumption to feed one 10kW FM transmitter during one year is then estimated at 120MWh.
To support broadcasters in meeting their sustainability goals and economic challenges, WorldCast developed its worldwide patented software SmartFM. With SmartFM, total consumption for the exact same system drops by 10% to 40% - a maximum reduction of 50MWh per year for a 10kW FM transmitter. In terms of CO2 emissions, the annual savings with SmartFM reach 18 tons.
This paper will discuss the AI technology behind SmartFM, the industry’s first and only patented AI technology that enables FM broadcasters to reduce their energy costs up to 40% without any compromise in audio quality or coverage.
The paper will outline the three-year, five-phase development journey that WorldCast undertook to create SmartFM:
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Phase 0: A historical approach of FM Broadcasting and FM Reception. This phase enabled them to identify a new way to broadcast while reducing energy consumption
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Phase 1: A statistical approach for content breakdown and classification (this phase corresponds to the retrieval and analysis of large numbers of data – their “big data”). This enabled them to create a library of audio content classified by their signal characteristics as well as by their impact on the acoustic experience
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Phase 2: A probabilistic approach in the implementation of content detection. This enabled them to develop the algorithm that identifies the content based on its characteristics and qualifies it with a mathematical calculation.
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Phase 3: A deterministic approach in the automation of RF management. It consisted of deploying the tools to translate the calculation into commands for the RF stages while maintaining product robustness and security.
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Phase 4: From theory to practice. This final phase enabled them to define an objective method to qualify the impact of the technology during on-field broadcasting.
The optimized power consumption of SmartFM cascades into multiple benefits for broadcasters - including reduced cooling and maintenance, electricity savings, and longer transmitter lifespan. And, instead of paying high energy bills, broadcasters can now invest that saved sum into more valuable business assets.
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