2,234 research outputs found
CSI Feedback Reduction for MIMO Interference Alignment
Interference alignment (IA) is a linear precoding strategy that can achieve
optimal capacity scaling at high SNR in interference networks. Most of the
existing IA designs require full channel state information (CSI) at the
transmitters, which induces a huge CSI signaling cost. Hence it is desirable to
improve the feedback efficiency for IA and in this paper, we propose a novel IA
scheme with a significantly reduced CSI feedback. To quantify the CSI feedback
cost, we introduce a novel metric, namely the feedback dimension. This metric
serves as a first-order measurement of CSI feedback overhead. Due to the
partial CSI feedback constraint, conventional IA schemes can not be applied and
hence, we develop a novel IA precoder / decorrelator design and establish new
IA feasibility conditions. Via dynamic feedback profile design, the proposed IA
scheme can also achieve a flexible tradeoff between the degree of freedom (DoF)
requirements for data streams, the antenna resources and the CSI feedback cost.
We show by analysis and simulations that the proposed scheme achieves
substantial reductions of CSI feedback overhead under the same DoF requirement
in MIMO interference networks.Comment: 30 pages, 7 figures, accepted for publication by IEEE transactions on
signal processing in June, 201
Generalized Interference Alignment --- Part I: Theoretical Framework
Interference alignment (IA) has attracted enormous research interest as it
achieves optimal capacity scaling with respect to signal to noise ratio on
interference networks. IA has also recently emerged as an effective tool in
engineering interference for secrecy protection on wireless wiretap networks.
However, despite the numerous works dedicated to IA, two of its fundamental
issues, i.e., feasibility conditions and transceiver design, are not completely
addressed in the literature. In this two part paper, a generalised interference
alignment (GIA) technique is proposed to enhance the IA's capability in secrecy
protection. A theoretical framework is established to analyze the two
fundamental issues of GIA in Part I and then the performance of GIA in
large-scale stochastic networks is characterized to illustrate how GIA benefits
secrecy protection in Part II. The theoretical framework for GIA adopts
methodologies from algebraic geometry, determines the necessary and sufficient
feasibility conditions of GIA, and generates a set of algorithms that can solve
the GIA problem. This framework sets up a foundation for the development and
implementation of GIA.Comment: Minor Revision at IEEE Transactions on Signal Processin
Dynamic Interference Mitigation for Generalized Partially Connected Quasi-static MIMO Interference Channel
Recent works on MIMO interference channels have shown that interference
alignment can significantly increase the achievable degrees of freedom (DoF) of
the network. However, most of these works have assumed a fully connected
interference graph. In this paper, we investigate how the partial connectivity
can be exploited to enhance system performance in MIMO interference networks.
We propose a novel interference mitigation scheme which introduces constraints
for the signal subspaces of the precoders and decorrelators to mitigate "many"
interference nulling constraints at a cost of "little" freedoms in precoder and
decorrelator design so as to extend the feasibility region of the interference
alignment scheme. Our analysis shows that the proposed algorithm can
significantly increase system DoF in symmetric partially connected MIMO
interference networks. We also compare the performance of the proposed scheme
with various baselines and show via simulations that the proposed algorithms
could achieve significant gain in the system performance of randomly connected
interference networks.Comment: 30 pages, 10 figures, accepted by IEEE Transaction on Signal
Processin
Interference Alignment for Cognitive Radio Communications and Networks: A Survey
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe
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