@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,
}