n source and destination pairs randomly located in an area want to
communicate with each other. Signals transmitted from one user to another at
distance r apart are subject to a power loss of r^{-alpha}, as well as a random
phase. We identify the scaling laws of the information theoretic capacity of
the network. In the case of dense networks, where the area is fixed and the
density of nodes increasing, we show that the total capacity of the network
scales linearly with n. This improves on the best known achievability result of
n^{2/3} of Aeron and Saligrama, 2006. In the case of extended networks, where
the density of nodes is fixed and the area increasing linearly with n, we show
that this capacity scales as n^{2-alpha/2} for 2<alpha<3 and sqrt{n} for
alpha>3. The best known earlier result (Xie and Kumar 2006) identified the
scaling law for alpha > 4. Thus, much better scaling than multihop can be
achieved in dense networks, as well as in extended networks with low
attenuation. The performance gain is achieved by intelligent node cooperation
and distributed MIMO communication. The key ingredient is a hierarchical and
digital architecture for nodal exchange of information for realizing the
cooperation.Comment: 56 pages, 16 figures, submitted to IEEE Transactions on Information
Theor
