“If we know more, we can achieve more.” This adage also applies to communication networks, where more information about the network state translates into higher sum-rates. In this paper, we formalize this increase of sum-rate with increased knowledge of the network state. The knowledge of network state is measured in terms of the number of hops,  h , of information available to each transmitter and is labeled as  h -local view. To understand how much capacity is lost due to limited information, we propose to use the metric of normalized sum-capacity, which is the  h  -local view sum-capacity divided by global-view sum capacity. For the cases of one and two-local view, we characterize the normalized sum-capacity for many classes of deterministic and Gaussian interference networks. In many cases, a scheduling scheme called maximal independent graph scheduling is shown to achieve normalized sum-capacity. We also show that its generalization for 1-local view, labeled coded set scheduling, achieves normalized sum-capacity in some cases where its uncoded counterpart fails to do so.
['Vaneet Aggarwal', 'Amir Salman Avestimehr', 'Ashutosh Sabharwal']
On Achieving Local View Capacity Via Maximal Independent Graph Scheduling