1,238 research outputs found
On the Interference Alignment Designs for Secure Multiuser MIMO Systems
In this paper, we propose two secure multiuser multiple-input multiple-output
transmission approaches based on interference alignment (IA) in the presence of
an eavesdropper. To deal with the information leakage to the eavesdropper as
well as the interference signals from undesired transmitters (Txs) at desired
receivers (Rxs), our approaches aim to design the transmit precoding and
receive subspace matrices to minimize both the total inter-main-link
interference and the wiretapped signals (WSs). The first proposed IA scheme
focuses on aligning the WSs into proper subspaces while the second one imposes
a new structure on the precoding matrices to force the WSs to zero. When the
channel state information is perfectly known at all Txs, in each proposed IA
scheme, the precoding matrices at Txs and the receive subspaces at Rxs or the
eavesdropper are alternatively selected to minimize the cost function of an
convex optimization problem for every iteration. We provide the feasible
conditions and the proofs of convergence for both IA approaches. The simulation
results indicate that our two IA approaches outperform the conventional IA
algorithm in terms of average secrecy sum rate.Comment: Updated version, updated author list, accepted to be appear in IEICE
Transaction
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
Interference Alignment via Message-Passing
We introduce an iterative solution to the problem of interference alignment
(IA) over MIMO channels based on a message-passing formulation. We propose a
parameterization of the messages that enables the computation of IA precoders
by a min-sum algorithm over continuous variable spaces -- under this
parameterization, suitable approximations of the messages can be computed in
closed-form. We show that the iterative leakage minimization algorithm of
Cadambe et al. is a special case of our message-passing algorithm, obtained for
a particular schedule. Finally, we show that the proposed algorithm compares
favorably to iterative leakage minimization in terms of convergence speed, and
discuss a distributed implementation.Comment: Submitted to the IEEE International Conference on Communications
(ICC) 201
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