A network information theory for wireless communication: scaling laws and optimal operation

By Liang-Liang Xie, P. Arun Raj Kumar. IEEE Transactions on Information Theory, 2004

777 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/b3f62ce285003952b5849175bbb89ec3d6d3dfbd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

Citations in the corpus

A deterministic approach to throughput scaling in wireless networks (2002, result): “Recent results in [13], [8] show upper bounds that are tight in the scaling law sense (for the scenario when is growing linearly with ).”
Optimal throughput-delay scaling in wireless networks - part I: the fluid model (2006, background): “, [20], [14], [12]), the information-theoretic notion of network capacity is studied.”
Stochastic geometry and random graphs for the analysis and design of wireless networks (2009, neutral): “Starting with the work of Xie and Kumar [77], information-theoretic scaling laws, independent of any strategy used for communication, have been established by many authors [78]– [82].”
The Effect of Fading, Channel Inversion, and Threshold Scheduling on Ad Hoc Networks (2007, result): “The transmission capacity (TC) is closely related to the popular transport capacity metric introduced by Gupta and Kumar [6].”
Wireless Network Information Flow: A Deterministic Approach (2009, background): “Xi e and Kumar generalized the decode-forward encoding scheme for a network of multiple re lays [14].”
Capacity scaling laws in MIMO relay networks (2006, methodology): “Results of the Gupta-Kumar [20], [22] and Gastpar-Vetterli [21] type have recently been reported for single antenna fading channels in [23], for the case of node mobility and large-scale fading in [24], for extended AWGN networks in [25], [26], and for multihop routing in [27].”
Transmission Capacity of Wireless Ad Hoc Networks With Successive Interference Cancellation (2007, background): “In fact, it is well known that the matched-filter receiver considered in [12], while dominant in commercial CDMA systems, is dramatically suboptimal in theory relative to multiuser receivers, particularly in the presence of widely variant receiver powers [15], [16].”
On the capacity of large Gaussian relay networks (2005, methodology): “8A more restrictive dead zone assumption, namely, a fixed zone around every node in the network (and hence not leading to dense networks), has recently been studied in [ 27 ].”
Computing and communicating functions over sensor networks (2005, background): “Briefly, it would be one of communicating possibly correlated sources over a multiterminal wireless network, of the kind described in [2], to a specified destination, at a desired fidelity with respect to a joint distortion criterion (which will depend on the particular function of interest).”
Capacity of ad hoc wireless networks with infrastructure support (2005, background): “…when one minimizes the transmission power of each node resulting in a quadratic reduction in the interference region of a transmission.1 Minimizing the interference region permits one to schedule as many noninterfering concurrent transmissions as possible resulting in maximized transport capacity.”
Closing the Gap in the Capacity of Wireless Networks Via Percolation Theory (2007, background): “Nevertheless, the information theoretic upper bound of [18] does not preclude a rate higher than to be achievable.”
Hierarchical Cooperation Achieves Optimal Capacity Scaling in Ad Hoc Networks (2006, background): “Since the publication of [1], there have been several works d ealing with information theoretic scaling laws of wireless adhoc networks [6], [7], [8], [9], [ 10].”