@techreport{TD:100200, att_abstract={{�If we know more, we can achieve more.� This adage also applies to 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 of information available to each node and is labeled each node�s 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-hop 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 one-hop local view, labeled coded set scheduling, achieves capacity whenever its uncoded counterpart fails to do so.}}, att_authors={va037f}, att_categories={C_CCF.3}, att_copyright={{IEEE}}, att_copyright_notice={{test}}, att_donotupload={true}, att_private={false}, att_projects={}, att_tags={}, att_techdoc={true}, att_techdoc_key={TD:100200}, att_url={}, author={Vaneet Aggarwal and Salman Avestimehr and Ashutosh Sabharwal}, institution={{IEEE Trans. Inf. Theory}}, month={April}, title={{On Achieving Local View Capacity Via Maximal Independent Graph Scheduling}}, year=2011, }