Name
Abstract | ||
|---|---|---|
Sparse Superposition Codes are a class of capacity achieving codes for which decoding can be interpreted as a compressive sensing problem. The approximate message passing algorithm, proven to be effective in compressive sensing, has been proposed in different incarnations as a valid decoding approach. However, most literature focuses on infinite code length and asymptotic performance, while the strong reliance on matrix-and vector-wise operations suggests that a hardware-oriented approach might be more efficient. This work analyzes the performance of two decoding algorithms with finite code lengths and fixed point precision: 5-bit codeword symbol quantization is shown to cause performance degradation ≤ 0.15 dB. In-algorithm quantization values are proposed, together with code construction and algorithm approximations that cause negligible performance degradation. After selecting a set of codes as a case study, a decoding complexity estimation is performed, demonstrating that a fully parallel architecture is unfeasible. Suggestions and improvements towards partially-parallel solutions are given. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SiPS.2015.7344999 | 2015 IEEE Workshop on Signal Processing Systems (SiPS) |
Keywords | DocType | Citations |
Sparse Superposition Codes,Compressed Sensing,Approximate Message Passing | Conference | 4 |
PageRank | References | Authors |
0.42 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlo Condo | 1 | 132 | 21.40 |
| Warren J. Gross | 2 | 1106 | 113.38 |