Reading Damaged Scripts: Partial Packet Recovery Based on Compressive Sensing for Efficient Random Linear Coded Transmission

Random linear coding (RLC) can improve the performance of multicast transmissions in terms of throughput and energy efficiency. However, RLC and linear codes in general cannot necessarily attain the optimal performance in arbitrary networks. In this regard, partial packet recovery can be considered as a nonlinear strategy to complement such approaches for more general networks. In this paper, we propose a partial packet recovery scheme that benefits from the sparsity of bit errors in partially corrupted RLC packets. As opposed to many previous schemes, it performs without introducing preliminary checksums or preambles, demanding physical layer soft information, or requesting post-redundancy from the transmitter. It relies only on algebraic coding and data processing techniques, the existing knowledge at the receiver, and the conventional acknowledgment messages in RLC. By reconstructing and utilizing the partially corrupted packets that are usually discarded, it can reduce the average number of transmitted RLC packets required for successful decoding by typically 50%, which improves throughput and energy efficiency at the transmitter. We formulate our partial packet recovery in the form of a sparse recovery problem, present its different solutions using compressive sensing theory, discuss their complexity, and present and evaluate a Markov chain model for its performance.