169 research outputs found
A Novel Synchronization Method in Terahertz Large-Scale Antenna Array System
We focus on the problems of the accurate time delay estimation, the design of training pilots, and hybrid matrix optimization within the large-scale antenna array Terahertz (THz) broadband communication system. In contrast to the existing researches based on narrow-band arrays, we hereby shed light on the time delay estimation of broadband arrays. In THz broadband communication systems, the data symbol duration is relatively short when comparing with the dimension of the antenna array. In large-scale antenna systems, signals received in each antenna are no longer different phase-shifted copies of the same symbol, but completely different symbols in which occasion traditional narrow-band structure is no longer suitable. Based on the above conclusion, firstly, we put forward a system model based on large-scale antenna arrays and Time delay line (TDL) structure. Secondly, we deduce the Cramer-Rao lower bound (CRLB) of the time delay estimation, and present a time delay estimation algorithm that could reach the CRLB. Thirdly, by minimizing the CRLB, we address the design of the training pilot and optimized TDL structure under the condition of constant envelope training pilot and modulus TDL structure. Finally, we disclose the numerical simulation results. According to the simulation results, the aforementioned method is workable in reaching the CRLB, the TDL structure can significantly surpass that of the traditional model, and the optimal pilot design method outperforms the pseudo-random pilot structure
Wideband Power Spectrum Sensing: a Fast Practical Solution for Nyquist Folding Receiver
The limited availability of spectrum resources has been growing into a
critical problem in wireless communications, remote sensing, and electronic
surveillance, etc. To address the high-speed sampling bottleneck of wideband
spectrum sensing, a fast and practical solution of power spectrum estimation
for Nyquist folding receiver (NYFR) is proposed in this paper. The NYFR
architectures is can theoretically achieve the full-band signal sensing with a
hundred percent of probability of intercept. But the existing algorithm is
difficult to realize in real-time due to its high complexity and complicated
calculations. By exploring the sub-sampling principle inherent in NYFR, a
computationally efficient method is introduced with compressive covariance
sensing. That can be efficient implemented via only the non-uniform fast
Fourier transform, fast Fourier transform, and some simple multiplication
operations. Meanwhile, the state-of-the-art power spectrum reconstruction model
for NYFR of time-domain and frequency-domain is constructed in this paper as a
comparison. Furthermore, the computational complexity of the proposed method
scales linearly with the Nyquist-rate sampled number of samples and the
sparsity of spectrum occupancy. Simulation results and discussion demonstrate
that the low complexity in sampling and computation is a more practical
solution to meet the real-time wideband spectrum sensing applications
Neural Networks for improved signal source enumeration and localization with unsteered antenna arrays
Direction of Arrival estimation using unsteered antenna arrays, unlike mechanically scanned or phased arrays, requires complex algorithms which perform poorly with small aperture arrays or without a large number of observations, or snapshots. In general, these algorithms compute a sample covriance matrix to obtain the direction of arrival and some require a prior estimate of the number of signal sources. Herein, artificial neural network architectures are proposed which demonstrate improved estimation of the number of signal sources, the true signal covariance matrix, and the direction of arrival. The proposed number of source estimation network demonstrates robust performance in the case of coherent signals where conventional methods fail. For covariance matrix estimation, four different network architectures are assessed and the best performing architecture achieves a 20 times improvement in performance over the sample covariance matrix. Additionally, this network can achieve comparable performance to the sample covariance matrix with 1/8-th the amount of snapshots. For direction of arrival estimation, preliminary results are provided comparing six architectures which all demonstrate high levels of accuracy and demonstrate the benefits of progressively training artificial neural networks by training on a sequence of sub- problems and extending to the network to encapsulate the entire process
Distributed UAV Swarm Augmented Wideband Spectrum Sensing Using Nyquist Folding Receiver
Distributed unmanned aerial vehicle (UAV) swarms are formed by multiple UAVs
with increased portability, higher levels of sensing capabilities, and more
powerful autonomy. These features make them attractive for many recent
applica-tions, potentially increasing the shortage of spectrum resources. In
this paper, wideband spectrum sensing augmented technology is discussed for
distributed UAV swarms to improve the utilization of spectrum. However, the
sub-Nyquist sampling applied in existing schemes has high hardware complexity,
power consumption, and low recovery efficiency for non-strictly sparse
conditions. Thus, the Nyquist folding receiver (NYFR) is considered for the
distributed UAV swarms, which can theoretically achieve full-band spectrum
detection and reception using a single analog-to-digital converter (ADC) at low
speed for all circuit components. There is a focus on the sensing model of two
multichannel scenarios for the distributed UAV swarms, one with a complete
functional receiver for the UAV swarm with RIS, and another with a
decentralized UAV swarm equipped with a complete functional receiver for each
UAV element. The key issue is to consider whether the application of RIS
technology will bring advantages to spectrum sensing and the data fusion
problem of decentralized UAV swarms based on the NYFR architecture. Therefore,
the property for multiple pulse reconstruction is analyzed through the
Gershgorin circle theorem, especially for very short pulses. Further, the block
sparse recovery property is analyzed for wide bandwidth signals. The proposed
technology can improve the processing capability for multiple signals and wide
bandwidth signals while reducing interference from folded noise and subsampled
harmonics. Experiment results show augmented spectrum sensing efficiency under
non-strictly sparse conditions
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