Cognitive radio attracts researchers' attention recently in radio resource management due to its ability to exploit environment awareness in configuring radio system parameters. Cognitive engine (CE) is the structure known for deciding system parameters' adaptation using optimization and machine learning techniques. However, these techniques have strengths and weaknesses depending on the experienced network scenario that make one more appropriate than others. In this paper, we propose a novel design for the cognitive system called supervised cognitive system (SCS), which aims to perform radio parameters adaptation with the most appropriate CE learning technique for the encountered network scenario. To realize SCS, it is required to evaluate the performance of different CEs in different network scenarios and according to certain performance objectives. In addition, the ability to select the most appropriate CE learning technique for adaptation in the current network scenario is also a priority in our design. Therefore, SCS investigates the relationship between learning and performance improvement and it employs online learning to classify scenarios and select the most appropriate CE learning technique. The testbed implementation and evaluation results in terms of goodput, packet error rate, and spectral efficiency show that the proposed SCS achieves more than 50% in performance gain compared to the best standalone CE.
|Original language||English (US)|
|Title of host publication||2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||8|
|State||Published - Mar 19 2018|