Interference Alignment and Degrees of Freedom of the $K$-User Interference Channel

By Viveck R. Cadambe, Syed Ali Jafar. IEEE Transactions on Information Theory, 2008

2,988 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e8ff8a0aa1b615ffdf4ad74946e6b9676aeff3db

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Group Sparse Beamforming for Green Cloud-RAN (2013, background): “Moreover, in the low interference region, treat ing interference as noise can be optimal [22].”
Completely Stale Transmitter Channel State Information is Still Very Useful (2010, methodology): “In interference channels, CSIT can be used to align the interference from multiple receivers to reduce the aggregate interference footprint [1], [2].”
Interference Alignment and Spatial Degrees of Freedom for the K User Interference Channel (2007, background): “The proof for general K ≥ 3 is provided in the full paper [19].”
What is a Spectrum Hole and What Does it Take to Recognize One? (2009, background): “[9], [ 10 ]) that design transmissions so that the interference at receivers is aligned roughly orthogonal to their desired signals.”
Interference Alignment: A New Look at Signal Dimensions in a Communication Network (2011, methodology): “In this section, we review the early applications of interference alignment in [13, 14, 22, 67, 90].”
Green Small-Cell Networks (2011, neutral): “Other promising but complex methods comprise interference alignment [9] or multicell processing [6].”
Stochastic geometry and random graphs for the analysis and design of wireless networks (2009, methodology): “Two important new techniques from information theory for characterizing interference i n wireless networks include the deterministic capacity [102 ] and the degrees-of-freedom region [103].”
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey (2018, methodology): “In Multi-Input Multi-Output (MIMO) networks, IA uses linear precoding technique to align transmission signals in such a way that the interference signal lies in a reduced dimensional subspace at each receiver [38], [39].”
Compute-and-Forward: Harnessing Interference Through Structured Codes (2009, methodology): “Work on interference alignment by Maddah-Ali, Motahari, and Khandani [40] and Cadambe and Jafar [ 41 ] has shown that large gains are possible for interference channels at high signal-to-noise ratio (SNR).”
A Survey on Network Codes for Distributed Storage (2010, background): “The new idea is interference alignment [28], [ 29 ].”
Interference alignment via alternating minimization (2009, methodology): “In particular, [1] showed IA achieves the maximum degrees of freedom of the interference channel with time or frequency selectivity, though the degree of freedom frontier for constant channels is still unknown except with 3 users where it coincides with the time or frequency selective case.”
Compressive sensing based channel feedback protocols for spatially-correlated massive antenna arrays (2012, neutral): “tions (such as zero-forcing beamforming [11] and interference alignments [12]), the knowledge on full MIMO channel matrix H is needed at the transmitter.”
Interference alignment and cancellation (2009, background): “Recent work has argued that pre-processing signals at the senders in a manner that aligns interference at the receivers increases the total capacity of wireless networks [3, 6 , 9, 21].”