2006/11/15 by Ayfer Ozgur, Ozgur, Ayfer, Olivier Leveque +3
Computer Science · Mathematics · #FOS: Computer and information sciences #Information Theory (cs.IT) #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.cs/0611070
56 pages, 16 figures, submitted to IEEE Transactions on Information Theory
arxiv created 2007/06/12 · arxiv updated 2009/12/01
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 n2/3 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 n2-alpha/2 for 2<alpha<3 and sqrtn 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.