Cyclic Interference Alignment and Cancellation in 3-User X-Networks with Minimal Backhaul

We consider the problem of Cyclic Interference Alignment (IA) on the 3-user X-network and show that it is infeasible to exactly achieve the upper bound of $\frac{K^2}{2K-1}=\frac{9}{5}$ degrees of freedom for the lower bound of n=5 signalling dimensions and K=3 user-pairs. This infeasibility goes beyond the problem of common eigenvectors in invariant subspaces within spatial IA. In order to gain non-asymptotic feasibility with minimal intervention, we first investigate an alignment strategy that enables IA by feedforwarding a subset of messages with minimal rate. In a second step, we replace the proposed feedforward strategy by an analogous Cyclic Interference Alignment and Cancellation scheme with a backhaul network on the receiver side and also by a dual Cyclic Interference Neutralization scheme with a backhaul network on the transmitter side.Comment: 8 pages, short version submitted to ISIT 201

Software Defined Radio Implementation of Carrier and Timing Synchronization for Distributed Arrays

The communication range of wireless networks can be greatly improved by using distributed beamforming from a set of independent radio nodes. One of the key challenges in establishing a beamformed communication link from separate radios is achieving carrier frequency and sample timing synchronization. This paper describes an implementation that addresses both carrier frequency and sample timing synchronization simultaneously using RF signaling between designated master and slave nodes. By using a pilot signal transmitted by the master node, each slave estimates and tracks the frequency and timing offset and digitally compensates for them. A real-time implementation of the proposed system was developed in GNU Radio and tested with Ettus USRP N210 software defined radios. The measurements show that the distributed array can reach a residual frequency error of 5 Hz and a residual timing offset of 1/16 the sample duration for 70 percent of the time. This performance enables distributed beamforming for range extension applications.Comment: Submitted to 2019 IEEE Aerospace Conferenc

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

Generalized multi-photon quantum interference

Non-classical interference of photons lies at the heart of optical quantum information processing. This effect is exploited in universal quantum gates as well as in purpose-built quantum computers that solve the BosonSampling problem. Although non-classical interference is often associated with perfectly indistinguishable photons this only represents the degenerate case, hard to achieve under realistic experimental conditions. Here we exploit tunable distinguishability to reveal the full spectrum of multi-photon non-classical interference. This we investigate in theory and experiment by controlling the delay times of three photons injected into an integrated interferometric network. We derive the entire coincidence landscape and identify transition matrix immanants as ideally suited functions to describe the generalized case of input photons with arbitrary distinguishability. We introduce a compact description by utilizing a natural basis which decouples the input state from the interferometric network, thereby providing a useful tool for even larger photon numbers