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 0 as well as a random phase. We identify the scaling laws of the information-theoretic capacity of the network when nodes can relay information for each other. 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. 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 20=2 for 2 < 3 and p n for 3. The best known earlier result of Xie and Kumar identified the scaling law for > 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 multiple-input multiple-output (MIMO) communication. The key ingredient is a hierarchical and digital architecture for nodal exchange of information for realizing the cooperation. Index Terms-Ad hoc wireless networks, capacity scaling, digital communications, distributed multiple-input multiple-output (MIMO), hierarchical cooperation.
Recently, Ozgur et al. REF demonstrated that the capacity of random wireless ad hoc network scales linearly with n by allowing nodes to cooperate intelligently using distributed MIMO communications.
Hierarchical Cooperation Achieves Optimal Capacity Scaling in Ad Hoc Networks
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Mathematics Computer Science