Artificial Intelligence in Radio Frequencies

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Artificial Intelligence in Radio Frequencies

Tuesday, 10 March, 2020
Fatemeh Shah Mohammadi

Artificial intelligence (AI) and machine learning (ML) as an application of AI, has today become an inevitable part of major industries such as healthcare, financial trending, and transportation. Future urgent need to intelligently utilize wireless resources to meet the need of ever-increasing diversity in services and user behavior, has actuated the wireless communication industry to deploy AI and ML techniques. Intelligent wireless communication aims at maximizing the efficiency of resource allocation by enabling the system to first recognize the available resources, then perceive and learn the wireless environment, and finally reconfigure its operating mode to adapt to the perceived wireless environment. The cognition capability and reconfigurability are the essential features of intelligent radio (also called cognitive radio-CR) in which machine learning techniques mark immense potential in system adaptation.

Application of Machine Learning in Cognitive Radio

The concept of CR was first proposed by Joseph Mitola [1] in order for mitigating the scarcity in limited radio spectrum by improving spectrum allocation efficiency by allowing unlicensed users (cognitive radio users) to identify and transmit over the frequency bands which are already assigned to the licensed users (primary users), but idle over specific time/space (spectrum holes). Spectrum hole identification is usually achieved by sensing the radio frequency (RF) environment through a process called spectrum sensing. It should be noted that the title of CR is not limited only to unlicensed wireless users and implied to any wireless user that adds the cognition capability along with reconfigurability to its system function.

The current applications of machine learning in CR can be listed as follows.

  • Spectrum sensing and management: One application of machine learning techniques in realizing intelligent wireless communication is to track the occupancy statistics of the primary user and estimate the detection performance of the CR users. This process will lead to detecting the subbands, which are sensed and accessed consistently as spectrum holes. Sensing the potential subbands instead of the entire frequency band will minimize the number of sensors and, accordingly, improves energy efficiency.
  • Power allocation: The strict limit for the aggregated interference caused by CR users on the primary network brings the role of efficient power allocation for CR users into the picture. The main goal here is to allocate power to the CR users such that not only meet the primary user interference constraint but maximize the quality of CR user’s provided service. Various learning algorithms have been proposed to accomplish this task among which reinforcement learning is the most popular one.
  • Radio access technology: Given the numerous wireless technologies over the same frequency band, automatic network recognition is an important task that necessitates the application of machine learning. The main task here is to classify technologies and interference entities operating over the same band of interest.
  • Signal classification: CR users often require a capability to recognize used waveforms either for communications or detection purposes. Multi-class signal classification based on automatic modulation recognition is the famous task in this regard and shines another application of machine learning in intelligent wireless communications.
  • Medium access control (MAC) protocol identification: Sensing and identifying the MAC protocol types of any existing transmissions will be used by CR users to adaptively change their transmission parameters to not only improve spectrum utilization but facilitate the communications among heterogeneous CR networks. The identification capability here necessitates the utilization of machine learning techniques.
  • Attack detection: Machine learning has been widely proposed to be used in jamming detection at the base stations.

We offer the major application scenarios for cognitive radio networks, wherein machine learning techniques have been widely deployed. It shows the significance of machine learning in realizing the future of intelligent wireless communication.

[1] J. Mitola and G. Q. Maguire, Jr., "Cognitive radio: making software radios more personal," IEEE Pers. Commun., vol. 6, no. 4, pp. 13–18, Aug. 1999,
DOI: 10.1109/98.788210.


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