Sparse reconstruction-based detection of spatial dimension holes in cognitive radio networks

In this paper, we investigate a spectrum-sensing algorithm for detecting spatial dimension holes in Multiple-Input Multiple-Output (MIMO) transmissions for OFDM systems using Compressive Sensing (CS) tools. This extends the energy detector to allow for detecting transmission opportunities even if the band is already energy filled. We show that the task described above is not performed efficiently by regular MIMO decoders (such as MMSE decoder) due to possible sparsity in the transmit signal. Since CS reconstruction tools take into account the sparsity order of the signal, they are more efficient in detecting the activity of the users. Building on successful activity detection by the CS detector, we show that the use of a CS-aided MMSE decoder yields better performance rather than using either CS-based or MMSE decoders separately.