—Compressed Sensing (CS) is an emerging field in mathematics that is used to measure few measurements of sparse vectors for lossless reconstruction. In this paper we use results from channel coding to create the recovery algorithm RSCS for CS in the Multiple Measurement Vector case (MMV) that can be used with a deterministic measurement matrix by using error correction schemes. In particular, we show that a modified Reed Solomon encoding-decoding structure can be used to measure sparsely representable vector systems down to the theoretical minimum number of measurements with guaranteed reconstruction, even in the low dimensional case.