SigSag: Iterative detection through soft message-passing

Abstract

The multiple-access framework of ZigZag decoding[1] is a useful technique for combating interference via multiple repeated transmissions, and is known to be compatible with distributed random access protocols. However, in the presence of noise this type of decoding can magnify errors, particularly when packet sizes are large. We present a simple soft-decoding version, called SigSag, that improves performance. We show that for two users, collisions result in a cycle-free factor graph that can be optimally decoded via belief propagation. For collisions between more than two users, we show that if a simple bit-permutation is used then the graph is locally tree-like with high probability, and hence belief propagation is near optimal. Through simulations we show that our scheme performs better than coordinated collision free time division multiple access (TDMA) and the ZigZag decoder.978-1-4244-9921-2/11/$26.00 ©2011 IEEE

SigSag: Iterative Detection Through Soft Message-Passing

“…We conjecture that this is due to effect of the joint cycles, i.e., this is due to the joint factor graph structure that makes the optimized 155 Tanner code perform worse. (10,20,2), and (20,40,1) with the ZigZag and collisionfree system followed by the sum-product decoder over the packets coded by the 155-Tanner code.…”

“…Further, observe that there can be no cycle of length two since a bit cannot collide with itself. Here we use the same notation introduced in II-A1, where D (1) and D (2) are the time difference between user transmissions in first and second collisions. Arrange the variable nodes according to their order of appearance in the packets.…”

“…As mentioned before, we want to continue connecting the bits from y j and get back to x i . The path is {x i , y j , x k , y k−D (1) , x k−D (1) +D (2) , . .…”

“…Each user relies on carrier sensing to detect if other users are transmitting. If this fails (the case This work appeared in part at the IEEE INFOCOM conference, Shanghai, China, April 2011 [1]. This work was supported in part by one or more of the following: the DARPA IT-MANET grant W911NF07-0028, the NSF grant CCF-0964479.…”

Random access on graphs: A survey and new results

“…The principal practical applications are i) satellite networks [5], [12]; ii) terrestrial wireless networks [6], [10] including cellular, ad-hoc, and sensor networks; iii) machine-to-machine communications [14]; iv) shared memories. In all cases, the main problem is the contention among the sources and the need to share the channel resource [1], [2], [15], [16].…”