Wireless Network Simplification: The Performance of Routing
Abstract
Consider a wireless Gaussian network where a source wishes to communicate with a destination with the help of N fullduplex relay nodes. Most practical systems today route information from the source to the destination using the best path that connects them. In this paper, we show that routing can in the worst case result in an unbounded gap from the network capacity  or reversely, physical layer cooperation can offer unbounded gains over routing. More specifically, we show that for $N$relay Gaussian networks with an arbitrary topology, routing can in the worst case guarantee an approximate fraction $\frac{1}{\left\lfloor N/2 \right\rfloor + 1}$ of the capacity of the full network, independently of the SNR regime. We prove that this guarantee is fundamental, i.e., it is the highest worstcase guarantee that we can provide for routing in relay networks. Next, we consider how these guarantees are refined for Gaussian layered relay networks with $L$ layers and $N_L$ relays per layer. We prove that for arbitrary $L$ and $N_L$, there always exists a route in the network that approximately achieves at least $\frac{2}{(L1)N_L + 4}$ $\left(\mbox{resp.}\frac{2}{LN_L+2}\right)$ of the network capacity for odd $L$ (resp. even $L$), and there exist networks where the best routes exactly achieve these fractions. These results are formulated within the network simplification framework, that asks what fraction of the capacity we can achieve by using a subnetwork (in our case, a single path). A fundamental step in our proof is a simplification result for MIMO antenna selection that may also be of independent interest. To the best of our knowledge, this is the first result that characterizes, for general wireless network topologies, what is the performance of routing with respect to physical layer cooperation techniques that approximately achieve the network capacity.
 Publication:

arXiv eprints
 Pub Date:
 November 2017
 arXiv:
 arXiv:1711.01007
 Bibcode:
 2017arXiv171101007E
 Keywords:

 Computer Science  Information Theory