Compressive Sensing Framework for Signal Processing in Heterogeneous Cellular Networks
The authors consider a heterogeneous cellular network where each base station sends a unique training signal based on its physical layer cell identity. The received signal at the Mobile Terminal (MT) is a superposition of training signals from different Base Stations (BS). Neither the identities of BS nor their channel response is known a-priori at the MT. For this scenario, they consider the problem of finding the identities of constituent BS from the superimposed components in the received signal. Though the number of BS with unique identities can be quite large in a cellular network, in any given scenario, the actual number of BS interfering at a MT is relatively few.