Fault tolerant machine learning for nanoscale cognitive radio
Research output: Contribution to journal › Article › Scientific › peer-review
|Number of pages||12|
|Publication status||Published - Feb 2011|
|Publication type||A1 Journal article-refereed|
We introduce a machine learning-based classifier that identifies free radio channels for cognitive radio. The architecture is designed for nanoscale implementation, under nanoscale implementation constraints; we do not describe all physical details but believe future physical implementation to be feasible. The system uses analog computation and consists of cyclostationary feature extraction and a radial basis function network for classification. We describe a model for nanoscale faults in the system, and simulate experimental performance and fault tolerance in recognizing WLAN signals, under different levels of noise and computational errors. The system performs well under expected non-ideal manufacturing and operating conditions.