  We study the sparse regression codes over flat-fading channels with multiple
receive antennas. We consider a practical scenario where the channel state
information is not available at the transmitter and the receiver. In this
setting, we study the maximum likelihood (ML) detector for SPARC, which has a
prohibitively high search complexity. We propose a novel practical decoder,
named maximum likelihood matching pursuit (MLMP), which incorporates a greedy
search mechanism along with the ML metric. We also introduce a parallel search
mechanism for MLMP. Comparing with the existing block-orthogonal matching
pursuit based decoders, we show that MLMP has significant gains in the block
error rate (BLER) performance. We also show that the proposed approach has
significant gains over polar codes employing pilot-aided channel estimation.
