45,450 research outputs found
A low-complexity equalizer for video broadcasting in cyber-physical social systems through handheld mobile devices
In Digital Video Broadcasting-Handheld (DVB-H) devices for cyber-physical social systems, the Discrete Fractional Fourier Transform-Orthogonal Chirp Division Multiplexing (DFrFT-OCDM) has been suggested to enhance the performance over Orthogonal Frequency Division Multiplexing (OFDM) systems under time and frequency-selective fading channels. In this case, the need for equalizers like the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) arises, though it is excessively complex due to the need for a matrix inversion, especially for DVB-H extensive symbol lengths. In this work, a low complexity equalizer, Least-Squares Minimal Residual (LSMR) algorithm, is used to solve the matrix inversion iteratively. The paper proposes the LSMR algorithm for linear and nonlinear equalizers with the simulation results, which indicate that the proposed equalizer has significant performance and reduced complexity over the classical MMSE equalizer and other low complexity equalizers, in time and frequency-selective fading channels. © 2013 IEEE
Holographic particle localization under multiple scattering
We introduce a novel framework that incorporates multiple scattering for
large-scale 3D particle-localization using single-shot in-line holography.
Traditional holographic techniques rely on single-scattering models which
become inaccurate under high particle-density. We demonstrate that by
exploiting multiple-scattering, localization is significantly improved. Both
forward and back-scattering are computed by our method under a tractable
recursive framework, in which each recursion estimates the next higher-order
field within the volume. The inverse scattering is presented as a nonlinear
optimization that promotes sparsity, and can be implemented efficiently. We
experimentally reconstruct 100 million object voxels from a single 1-megapixel
hologram. Our work promises utilization of multiple scattering for versatile
large-scale applications
Quantum resource estimates for computing elliptic curve discrete logarithms
We give precise quantum resource estimates for Shor's algorithm to compute
discrete logarithms on elliptic curves over prime fields. The estimates are
derived from a simulation of a Toffoli gate network for controlled elliptic
curve point addition, implemented within the framework of the quantum computing
software tool suite LIQ. We determine circuit implementations for
reversible modular arithmetic, including modular addition, multiplication and
inversion, as well as reversible elliptic curve point addition. We conclude
that elliptic curve discrete logarithms on an elliptic curve defined over an
-bit prime field can be computed on a quantum computer with at most qubits using a quantum circuit of at most Toffoli gates. We are able to classically simulate the
Toffoli networks corresponding to the controlled elliptic curve point addition
as the core piece of Shor's algorithm for the NIST standard curves P-192,
P-224, P-256, P-384 and P-521. Our approach allows gate-level comparisons to
recent resource estimates for Shor's factoring algorithm. The results also
support estimates given earlier by Proos and Zalka and indicate that, for
current parameters at comparable classical security levels, the number of
qubits required to tackle elliptic curves is less than for attacking RSA,
suggesting that indeed ECC is an easier target than RSA.Comment: 24 pages, 2 tables, 11 figures. v2: typos fixed and reference added.
ASIACRYPT 201
Quantum Computing in the NISQ era and beyond
Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the
near future. Quantum computers with 50-100 qubits may be able to perform tasks
which surpass the capabilities of today's classical digital computers, but
noise in quantum gates will limit the size of quantum circuits that can be
executed reliably. NISQ devices will be useful tools for exploring many-body
quantum physics, and may have other useful applications, but the 100-qubit
quantum computer will not change the world right away --- we should regard it
as a significant step toward the more powerful quantum technologies of the
future. Quantum technologists should continue to strive for more accurate
quantum gates and, eventually, fully fault-tolerant quantum computing.Comment: 20 pages. Based on a Keynote Address at Quantum Computing for
Business, 5 December 2017. (v3) Formatted for publication in Quantum, minor
revision
Training Passive Photonic Reservoirs with Integrated Optical Readout
As Moore's law comes to an end, neuromorphic approaches to computing are on
the rise. One of these, passive photonic reservoir computing, is a strong
candidate for computing at high bitrates (> 10 Gbps) and with low energy
consumption. Currently though, both benefits are limited by the necessity to
perform training and readout operations in the electrical domain. Thus, efforts
are currently underway in the photonic community to design an integrated
optical readout, which allows to perform all operations in the optical domain.
In addition to the technological challenge of designing such a readout, new
algorithms have to be designed in order to train it. Foremost, suitable
algorithms need to be able to deal with the fact that the actual on-chip
reservoir states are not directly observable. In this work, we investigate
several options for such a training algorithm and propose a solution in which
the complex states of the reservoir can be observed by appropriately setting
the readout weights, while iterating over a predefined input sequence. We
perform numerical simulations in order to compare our method with an ideal
baseline requiring full observability as well as with an established black-box
optimization approach (CMA-ES).Comment: Accepted for publication in IEEE Transactions on Neural Networks and
Learning Systems (TNNLS-2017-P-8539.R1), copyright 2018 IEEE. This research
was funded by the EU Horizon 2020 PHRESCO Grant (Grant No. 688579) and the
BELSPO IAP P7-35 program Photonics@be. 11 pages, 9 figure
Robust massive MIMO Equilization for mmWave systems with low resolution ADCs
Leveraging the available millimeter wave spectrum will be important for 5G.
In this work, we investigate the performance of digital beamforming with low
resolution ADCs based on link level simulations including channel estimation,
MIMO equalization and channel decoding. We consider the recently agreed 3GPP NR
type 1 OFDM reference signals. The comparison shows sequential DCD outperforms
MMSE-based MIMO equalization both in terms of detection performance and
complexity. We also show that the DCD based algorithm is more robust to channel
estimation errors. In contrast to the common believe we also show that the
complexity of MMSE equalization for a massive MIMO system is not dominated by
the matrix inversion but by the computation of the Gram matrix.Comment: submitted to WCNC 2018 Workshop
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