Compressed Sensing Bayes Risk Minimization for Under-Determined Systems Via Sphere Detection
The application of Compresses Sensing is a promising physical layer technology for the joint activity and data detection of signals. Detecting the activity pattern correctly has severe impact on the system performance and is therefore of major concern. In contrast to previous work, in this paper the authors optimize joint activity and data detection in under-determined systems by minimizing the Bayes-Risk for erroneous activity detection. They formulate a new Compressed Sensing Bayes-Risk detector which directly allows to influence error rates at the activity detection dynamically by a parameter that can be controlled at higher layers. They derive the detector for a general linear system and show that their detector outperforms classical Compressed Sensing approaches by investigating an overloaded CDMA system.