Direction Finding Estimators of Cyclostationary Signals in Array Processing for Microwave Power Transmission

A solar power satellite is paid attention to as a clean, inexhaustible large- scale base-load power supply. The following technology related to beam control is used: A pilot signal is sent from the power receiving site and after direction of arrival estimation the beam is directed back to the earth by same direction. A novel direction-finding algorithm based on linear prediction technique for exploiting cyclostationary statistical information (spatial and temporal) is explored. Many modulated communication signals exhibit a cyclostationarity (or periodic correlation) property, corresponding to the underlying periodicity arising from carrier frequencies or baud rates. The problem was solved by using both cyclic second-order statistics and cyclic higher-order statistics. By evaluating the corresponding cyclic statistics of the received data at certain cycle frequencies, we can extract the cyclic correlations of only signals with the same cycle frequency and null out the cyclic correlations of stationary additive noise and all other co-channel interferences with different cycle frequencies. Thus, the signal detection capability can be significantly improved. The proposed algorithms employ cyclic higher-order statistics of the array output and suppress additive Gaussian noise of unknown spectral content, even when the noise shares common cycle frequencies with the non-Gaussian signals of interest. The proposed method completely exploits temporal information (multiple lag ), and also can correctly estimate direction of arrival of desired signals by suppressing undesired signals. Our approach was generalized over direction of arrival estimation of cyclostationary coherent signals. In this paper, we propose a new approach for exploiting cyclostationarity that seems to be more advanced in comparison with the other existing direction finding algorithms

State-of-the-art assessment of 5G mmWave communications

Deliverable D2.1 del proyecto 5GWirelessMain objective of the European 5Gwireless project, which is part of the H2020 Marie Slodowska- Curie ITN (Innovative Training Networks) program resides in the training and involvement of young researchers in the elaboration of future mobile communication networks, focusing on innovative wireless technologies, heterogeneous network architectures, new topologies (including ultra-dense deployments), and appropriate tools. The present Document D2.1 is the first deliverable of Work- Package 2 (WP2) that is specifically devoted to the modeling of the millimeter-wave (mmWave) propagation channels, and development of appropriate mmWave beamforming and signal processing techniques. Deliver D2.1 gives a state-of-the-art on the mmWave channel measurement, characterization and modeling; existing antenna array technologies, channel estimation and precoding algorithms; proposed deployment and networking techniques; some performance studies; as well as a review on the evaluation and analysis toolsPostprint (published version

Noise Reduction with Microphone Arrays for Speaker Identification

The presence of acoustic noise in audio recordings is an ongoing issue that plagues many applications. This ambient background noise is difficult to reduce due to its unpredictable nature. Many single channel noise reduction techniques exist but are limited in that they may distort the desired speech signal due to overlapping spectral content of the speech and noise. It is therefore of interest to investigate the use of multichannel noise reduction algorithms to further attenuate noise while attempting to preserve the speech signal of interest. Specifically, this thesis looks to investigate the use of microphone arrays in conjunction with multichannel noise reduction algorithms to aid aiding in speaker identification. Recording a speaker in the presence of acoustic background noise ultimately limits the performance and confidence of speaker identification algorithms. In situations where it is impossible to control the noise environment where the speech sample is taken, noise reduction algorithms must be developed and applied to clean the speech signal in order to give speaker identification software a chance at a positive identification. Due to the limitations of single channel techniques, it is of interest to see if spatial information provided by microphone arrays can be exploited to aid in speaker identification. This thesis provides an exploration of several time domain multichannel noise reduction techniques including delay sum beamforming, multi-channel Wiener filtering, and Spatial-Temporal Prediction filtering. Each algorithm is prototyped and filter performance is evaluated using various simulations and experiments. A three-dimensional noise model is developed to simulate and compare the performance of the above methods and experimental results of three data collections are presented and analyzed. The algorithms are compared and recommendations are given for the use of each technique. Finally, ideas for future work are discussed to improve performance and implementation of these multichannel algorithms. Possible applications for this technology include audio surveillance, identity verification, video chatting, conference calling and sound source localization

Low-Complexity Reduced-Rank Beamforming Algorithms

A reduced-rank framework with set-membership filtering (SMF) techniques is presented for adaptive beamforming problems encountered in radar systems. We develop and analyze stochastic gradient (SG) and recursive least squares (RLS)-type adaptive algorithms, which achieve an enhanced convergence and tracking performance with low computational cost as compared to existing techniques. Simulations show that the proposed algorithms have a superior performance to prior methods, while the complexity is lower.Comment: 7 figure

Channel Modeling and Direction-of-Arrival Estimation in Mobile Multiple-Antenna Communication Systems

Antennas that are able to adaptively direct the transmitted (and received) energy are of great interest in future wireless communication systems. The directivity implies reduced transmit power and interference, and also a potential for increased capacity. This thesis treats some modeling and estimation problems in mobile communication systems that employ multiple antennas, primarily at the base stations. With multiple antennas at the receive side, the spatial dimension is added, and processing is performed in both the temporal and spatial domains. The potential benefits are increased range, fading diversity and spatially selective transmission. Specifically, the problems dealt in this thesis are mainly related to the uplink transmission from mobile to the base station. Two main topics are studied, channel modeling and estimation of channel parameters. This thesis first describes the modeling of the reflected power distribution due to the scatterers close to the mobile stations, in terms of the received signal azimuth at the base station with multiple-antenna. As a more realistic channel modeling, a multipath fading deterministic channel model is proposed to generate properly correlated faded waveforms with appropriate power distribution through azimuth spread of received signal. The purpose of the proposed channel model is to model fading received signal waveforms with Laplacian distribution of power through received signal azimuth spread. This thesis is divided into two parts; in the first part multipath fading by local scattering are used to derive a channel model including the spatial dimension for non frequency-selective fading. This means that the mobile is not modeled as a point source but as a cluster of a large number of independent scatterers with small time delay spread to take into account angular spreading of the signal. Properly correlated fading waveforms are obtained by taking into account the angular spread of the scattered signals from a particular distribution of scatterers. By appropriate scaling of the array response vector (ARV) based on non-equal locations for various received signal components as a function of distance from the transmitter, the reflected power from a given scatterer is no longer constant but varies as a function of the distance from the transmitter. Our proposed channel model is able to produce fading signal waveform which agrees with the results of reflected angular power dispersions measured in the field, e.g. Laplacian distribution of power in azimuth. It is also shown that the channel response can be modeled as a complex Gaussian vector. Although the channel will be frequency selective in the case of multipath propagation with considerable time spread, this can be modeled as having more than one cluster of scatterers. By employing Walsh-Hadamard codewo VdLrs)l wideband multipath fading model is achieved. It is shown that the statistical properties of proposed model such as signal waveform's correlation, autocorrelation and crosscorrelation between generated paths, are in good agreement with the theory in space and time domain. The model can be applied to evaluate smart antenna systems and beamforming algorithms in the uplink by generating uncorrelated multipaths Rayleigh fading waveforms with certain spatio-temporal correlation and spatial coordinates relative to base stations to simulate received signals from mobiles and interferers. Bit-error-rate (BER) performance analysis of uniform linear array antenna (ULA) based on correlation - matrix is also presented as an application of our proposed model for multipleantenna evaluations. Our simulated results show 5% improvement than other published related works. One problem when modeling frequency selective fading is that each cluster has to be assigned spatial parameters. Since the joint spatial and temporal characteristics are unknown, non-parametric channel estimation approaches are required in this case in order to estimate the channel parameter, which is the subject of the second part. The second part of the thesis deals with channel parameter estimation of distributed scattering sources. Because of local scattering around the transmitter the signal waveforms appears spatially distributed at the receiver. The characterization of the spatial channel, in particular mean direction of arrival and spatial spread, is of prime interest for system optimization and performance prediction. Low-complexity spectral-based estimators are used for the estimation of direction and spatial spread of the distributed source by employing the proposed channel model for simulation. Estimated parameters from recent measurements ([PMFOO]) are compared with estimated parameters from model generated waveforms as well as theoretical distribution of received signal's angular spread. Good agreement between them is observed which shows the correctness of our proposed channel model for simulating spatio-temporally correlated received signal at an antenna array. The estimated parameter error improved by 5% over the other published related works

Spatio-Temporal Analysis of Urban Acoustic Environments with Binaural Psycho-Acoustical Considerations for IoT-based Applications

Sound pleasantness or annoyance perceived in urban soundscapes is a major concern in environmental acoustics. Binaural psychoacoustic parameters are helpful to describe generic acoustic environments, as it is stated within the ISO 12913 framework. In this paper, the application of a Wireless Acoustic Sensor Network (WASN) to evaluate the spatial distribution and the evolution of urban acoustic environments is described. Two experiments are presented using an indoor and an outdoor deployment of a WASN with several nodes using an Internet of Things (IoT) environment to collect audio data and calculate meaningful parameters such as the sound pressure level, binaural loudness and binaural sharpness. A chunk of audio is recorded in each node periodically with a microphone array and the binaural rendering is conducted by exploiting the estimated directional characteristics of the incoming sound by means of DOA estimation. Each node computes the parameters in a different location and sends the values to a cloud-based broker structure that allows spatial statistical analysis through Kriging techniques. A cross-validation analysis is also performed to confirm the usefulness of the proposed system.Ingeniería, Industria y Construcció