Approaching the Capacity of Wireless Networks through Distributed Interference Alignment

By Krishna Srikanth Gomadam, Viveck R. Cadambe, Syed Ali Jafar. IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference, 2008

788 citations recorded by the source corpus.

Recent results establish the optimality of interference alignment to approach the Shannon capacity of interference networks at high SNR. However, the extent to which interference can be aligned over a finite number of signalling dimensions remains unknown. Another important concern for interference alignment schemes is the requirement of global channel knowledge. In this work we provide examples of iterative algorithms that utilize the reciprocity of wireless networks to achieve interference alignment with only local channel knowledge at each node. These algorithms also provide numerical insights into the feasibility of interference alignment that are not yet available in theory.

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Citations in the corpus

Degrees of Freedom of the K User M×N MIMO Interference Channel (2008, methodology): “If interference alignment is achieved on the SIMO channel it can also be achieved on the MISO channel, due to a reciprocity of alignment [20].”
On Feasibility of Interference Alignment in MIMO Interference Networks (2009, methodology): “The zero-forcing ﬁlters at the receiver are denoted by U [ k ] .”
Optimal Resource Allocation for MIMO Ad Hoc Cognitive Radio Networks (2008, background): “Recent advances in the degrees-of-freedom characterization of MIMO interference channels (ICs) motivated development of optimization algorithms based on interference alignment [9], [20].”
Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013, background): “, |Kt j=1hjkCjkDjivji|(2) is small without enforcing that every term in the sum is small) is more difficult under local CSI, if not impossible in noniterative multi-cell systems [18, 91].”
Interference alignment via alternating minimization (2009, methodology): “In this case, the maximum achievable spatial degrees of freedom are unknown [4], except for the 3-user case [1], where frequency or time selectivity do not increase the spatial degrees of freedom.”
An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel (2011, methodology): “method [ 22 ]. Obviously, the WMMSE algorithm significantly outperformstheMMSEalgorithmintermsof theachievedsumrate.”