Joint Activity and Data Detection for Machine to Machine Communication Via Bayes Risk Optimization
Performing joint detection of activity and data is a promising approach to reduce management overhead in Machine-to-Machine communication. However, erroneous activity detection has severe impacts on the system performance. Estimating an active node or user erroneously to be inactive results in a loss of data. To optimally balance activity and data detection, the authors derive a novel joint activity and data detector that bases on the minimization of the Bayes Risk. The Bayes Risk detector allows to control error rates with respect to the activity detection dynamically by a parameter that can be controlled by higher layers.