Elements of information theory
Elements of information theory
On energy efficiency and optimum resource allocation of relay transmissions in the low-power regime
IEEE Transactions on Wireless Communications
Energy-efficient broadcasting with cooperative transmissions in wireless sensor networks
IEEE Transactions on Wireless Communications
Spectral efficiency in the wideband regime
IEEE Transactions on Information Theory
Bounds on capacity and minimum energy-per-bit for AWGN relay channels
IEEE Transactions on Information Theory
On the power efficiency of sensory and ad hoc wireless networks
IEEE Transactions on Information Theory
Cooperative multihop broadcast for wireless networks
IEEE Journal on Selected Areas in Communications
On the power efficiency of cooperative broadcast in dense wireless networks
IEEE Journal on Selected Areas in Communications
Minimum energy per bit for Gaussian broadcast channels with common message and cooperating receivers
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Minimum energy per bit for wideband wireless multicasting: performance of decode-and-forward
INFOCOM'10 Proceedings of the 29th conference on Information communications
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We consider scaling laws for maximal energy efficiency of communicating a message to all the nodes in a random wireless network, as the number of nodes in the network becomes large. Two cases of large wireless networks are studied -- dense random networks and constant density (extended) random networks. We first establish an information-theoretic lower bound on the minimum energy per bit for multicasting that holds for arbitrary wireless networks when the channel state information is not available at the transmitters. These lower bounds are then evaluated for two cases of random networks. Upper bounds are also obtained by constructing a simple flooding scheme that requires no information at the receivers about the channel states or the locations and identities of the nodes. The gap between the upper and lower bounds is only a constant factor for dense random networks and differs by a poly-logarithmic factor for extended random networks. Furthermore, the proposed upper and lower bounds hold almost surely in the node locations as the number of nodes approaches infinity.