CoFi: Coding-Assisted File Distribution over a Wireless LAN

Abstract

The wireless channel is volatile in nature, due to various signal attenuation factors including path-loss, shadowing, and multipath fading. Existing media access control (MAC) protocols, such as the widely adopted 802.11 wireless fidelity (Wi-Fi) family, advocate masking harsh channel conditions with persistent retransmission and backoff, in order to provide a packet-level best-effort service. However, the asymmetry of the network environment of client nodes in space is not fully considered in the method, which leads to the decline of the transmission efficiency of the good ones. In this paper, we propose CoFi, a coding-assisted file distribution protocol for 802.11 based wireless local area networks (LANs). CoFi groups data into batches and transmits a random linear combination of packets within each batch, thereby reducing redundant packet and acknowledgement (ACK) retransmissions when the channel is lossy. In addition, CoFi adopts a MAC layer caching scheme that allows clients to store the overheard coded packets and use such cached packets to assist nearby peers. With this measure, it further improves the effective throughput and shortens the buffering delay when running applications such as bulk data transmission and video streaming. Our trace based simulation demonstrates that CoFi can maintain a similar level of packet delay to 802.11, but increases the throughput performance by a significant margin in a lossy wireless LAN. Furthermore, we perform a reverse-engineering on CoFi and 802.11 using a simple analytical framework, proving that they asymptotically approach different fairness measures, thus resulting in a disparate performance.

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

“…networks [3], cellular and radio access networks [4], [5], vehicular and wireless sensor networks [6], [7], and general wireless networks [8]- [12], as well as unreliable complex information technology infrastructures, such as data caching infrastructures [13]- [15]. One main hurdle that prevents the widespread adoption of RLNC in communication networks and information technology systems is the computationally highly demanding matrix multiplication and matrix inversion required for RLNC decoding [16]- [19].…”

“…Illustration of Fulcrum outer and inner encoding [26] with n = 4 data packets and r = 2 expansion packets. The illustrated outer encoding is a systematic encoding that treats the original data packets as outer coded packets [15], [33]- [35] while the r = 2 expansion packets are linear combinations of the original data packets with outer coding coefficients c ,j from GF (2 h ). The n + r = 6 outer coded packets form the packet set O, and these packets in the set O are linearly combined with inner coding coefficients λ ,j from GF (2) to give the inner coded packets.…”

“…The Fulcrum RLNC approach enables the flexible recoding and decoding in either a small Galois field (mainly at the expense of accumulating more coded packets to compensate for linear dependencies) or a large Galois field (incurring the high complexities), or a combination thereof [26]. An alternative approach to reduce computation complexity is systematic network coding [15], [33]- [35], which transmits the original packets in uncoded form and inserts a small number of coded packets into a generation to compensate for network transport losses. We employ systematic network coding in the outer coding of the sparse Fulcrum coding examined in this study.…”

Progressive Multicore RLNC Decoding With Online DAG Scheduling

“…Random linear network coding (RLNC) can significantly enhance the communication over unreliable complex networks, such as body area networks [1], caching networks [2]- [4], cellular networks [5], the Internet of Things (IoT) [6]- [8], radio access networks [9], vehicular networks [10], wireless sensor networks [11], and general wireless networks [12]- [16]. One main challenge of RLNC based communication is that the decoding in receiver nodes involves computationally highly demanding matrix multiplication and matrix inversion [17], [18].…”

“…Illustration of steps of backward substitution phase: New pivots found in step 3 of the forward elimination in C (Fig. 3(d)) are backward substituted into C and D. The helper matrix records the operations on the first applicable block of C [e.g., block with elements (9,1) to(12,4), i.e., the third block from the left in the top-most block row for the first row, i.e., Step 1;Step 2 uses block (9,5 to 12,8)] for replication on the remaining blocks of C and D.…”