Abstract
Programming and behavioral autonomy constitutes the main features of any cognitive system. In this regard, Cognitive Radio (CR) uses the concept of understanding-by-building in order to achieve two important objectives. These objectives are namely, establishing a long-lasting reliable communication, and allowing the most efficient use of the spectrum resources. Achieving said objectives is possible by supporting secondary users to access to the licensed spectrum in an opportunistic fashion, after vacant spectrum holes are detected once a primary user exits.
In this paper, we will look to enhance the cellular networks by integrating a cognitive radio to have the so-called cognitive radio cellular network (CRCN). We aim to overcome the current issues and difficulties facing the integration of CR in cellular networks. Overcoming these obstacles comes to improving the handover process in primary network and managing the secondary network using, respectively, the reinforcement learning (RL) and the k-nearest neighbors algorithm (k-NN). We also will come with a new architecture describing our vision for every case scenario in the two types of network.
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Notes
- 1.
Studies in the Federal Communications Commission in the United States (FCC) and the Federal Office of Communications in the United Kingdom (OFCOM).
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Hamdouchi, A., El Biari, A., Benmammar, B., Tabii, Y. (2018). Towards the Use of Cognitive Radio to Solve Cellular Network Challenges. In: Auer, M., Tsiatsos, T. (eds) Interactive Mobile Communication Technologies and Learning. IMCL 2017. Advances in Intelligent Systems and Computing, vol 725. Springer, Cham. https://doi.org/10.1007/978-3-319-75175-7_1
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