How can radio frequency machine learning solve the biggest challenges in high-frequency radio communications?
Frequency selection is a complex and critical process in high-frequency (HF) radio communications. Choosing the optimal frequency requires significant expertise. Operators must understand which frequencies will propagate effectively based on time of day, geographic location, and current ionospheric conditions. Making the wrong choice can result in complete communication failure.
Despite its strategic value, old legacy HF technology’s dependence on human expertise has been one of its most significant limitations. The core innovation in cognitive radio and machine learning is automating this critical frequency selection process.
Radio frequency machine learning has emerged as a groundbreaking solution to these challenges. Read on to discover how KNL’s machine learning transforms communications in the most demanding environments where conventional technologies fail.
When discussing machine learning for radio frequency, we’re essentially talking about cognitive radio technology. While this topic could also be approached through AI, KNL developed cognitive radio long before the current AI breakthrough, which is why we primarily refer to it as cognitive, machine learning-based radio technology.
In short: Radio frequency machine learning
- Radio frequency machine learning converts traditional HF radio from a specialised tool into a high-performance communication solution accessible for modern defence and security operations.
- The technology addresses traditional limitations of HF communications, including slow connection times, vulnerability to jamming, and complex operational requirements.
- KNL’s cognitive radios deliver capabilities previously thought impossible in HF communications.
- As electromagnetic spectrum contestation increases globally, these adaptive capabilities will become vital for maintaining reliable communications.
KNL’s cognitive radio
How radio frequency machine learning transforms critical radio communication
Unlike conventional radios that operate on fixed frequencies or require manual adjustment based on statistical models, radios equipped with frequency machine learning technology or as we call it cognitive spectrum sensing, create situational awareness of their entire network. They continuously monitor all HF traffic, listen for messaging across the network, and build a comprehensive understanding of signal propagation and interference conditions in real-time.
“Traditional HF radio systems struggle with three critical limitations: slow connection times, vulnerability to jamming, and dependence on expert operators. Advanced machine learning provides a solution that addresses all these challenges.”
This awareness constantly evolves as electromagnetic conditions change. The learning process should be swift and rely on current data instead of outdated predictions, allowing immediate adaptation to changing conditions. Machine learning technology enables radios to comprehend their operational environment by monitoring the entire radio spectrum, detecting signal propagation patterns, and identifying sources of interference.
While conventional systems might scan a handful of frequencies sequentially, KNL has taken this concept much further – our radios constantly listen to over 4,000 HF channels simultaneously. Through this continuous learning process, the radio makes intelligent decisions about optimal frequency selection, transmission power, and networking routes without human intervention.
Let’s examine machine learning’s two most significant advantages: ease of use and superior frequency discovery.
Making complex technology more accessible
Traditional HF radios require deep expertise in ionospheric propagation, frequency selection, and procedures — a barrier that has limited broader adoption.
KNL’s machine learning radios eliminate this need. If you know how to use a computer or mobile device, learning to use the radio is easy.
With KNL’s cognitive radios:
- Personnel can be fully operational after just 1-2 days of training (versus weeks or months).
- No specialised radio operator knowledge is required to achieve optimal performance.
- Standard web-based interfaces for email and messaging require no complex training.
- The cognitive system handles all complex frequency selection and adaptation automatically.
This democratisation of HF radio technology creates tangible operational benefits, such as minimised vulnerability, broader deployment potential, and lower training costs.
KNL’s radio is easy to operate
Finding frequencies that others miss
A second significant advantage of machine learning is the dramatically expanded range of usable frequencies compared to manually operated radios.
Field tests conducted at a distance of approximately 950 kilometres demonstrate a groundbreaking capability of our radio frequency machine learning technology: the ability to identify and utilise operational frequencies that traditional statistical models overlook.
Our cognitive radios consistently discover usable frequencies in higher bands (up to 19 MHz), which conventional propagation models typically consider unreliable. These higher frequencies often perform up to 10 MHz better than what prediction software calculated as optimal. This finding was validated in testing published in Viestimies Journal 1/2025, a Finnish Signal Officers Association publication.
The technical data is compelling: while lower frequencies (2-5 MHz) followed expected daily variations, our cognitive radios successfully utilised frequencies up to 19 MHz.
Measurements demonstrated that modulation error rates at lower frequencies (below 5 MHz) were highest at 10-15 dB but improved linearly with frequency, reaching over 40 dB at higher frequencies. This improvement occurs because natural and artificial noise is concentrated in the lower part of the HF spectrum.
Read the full Viestimies article (written in Finnish)
This expanded frequency range delivers two critical benefits:
- First, our radios can choose from a much wider selection of available channels
- Second, these higher frequencies experience significantly less interference, resulting in clearer communication and enabling faster transmission parameters.
“Machine learning radio systems are exclusive in their ability to utilise these higher-quality frequencies—traditional radios simply cannot adapt quickly enough to identify and exploit these dynamic opportunities.”
Delivering critical operational benefits
Tactical advantages of machine learning in radio communications
KNL’s advanced radio technology also delivers critical operational benefits by overcoming traditional HF radio limitations. Our machine learning algorithms aka cognitive engine reduces connection time to just 0.5 seconds, whereas conventional radios take minutes to establish links.
By continuously analysing the entire spectrum, these radios maintain real-time awareness of optimal frequencies, eliminating the need for manual scanning. In addition, this feature makes it extremely difficult to jam.
Unlike vulnerable satellite systems that can be jammed or disabled, KNL’s cognitive radios provide inherently resilient communications that automatically adapt to maintain connectivity in contested environments.
Radios with fully automatic cognitive systems
KNL offers unique solutions that utilise advanced radio frequency machine learning technology.
CNHF1 Radio is designed for vessels or fixed installation use like base stations. It monitors over 2,500 calling channels simultaneously and supports wideband HF communications up to 153 kbps.
CNHF Manpack brings the same technology to a portable form factor, monitoring over 4,000 channels simultaneously with wideband HF communications up to 300 kbps in extreme conditions from -40°C to +55°C.
“Our communication system functions autonomously without relying on external third-party infrastructure or satellite networks while ensuring maximum security through user-controlled encryption key management.”
Explore all features: CNHF1 Radio | CNHF Manpack
KNL’s radio solutions transforming communications capabilities
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