910 research outputs found
Angular CMA: A modified Constant Modulus Algorithm providing steering angle updates
Conventional blind beamforming algorithms have no direct notion of the physical Direction of Arrival angle of an impinging signal. These blind adaptive algorithms operate by adjusting the complex steering vector in the case of changing signal conditions and directions. This paper presents Angular CMA, a blind beamforming method that calculates steering angle updates (instead of weight vector updates) to keep track of the desired signal. Angular CMA and its respective steering angle updates are particularly useful in the context of mixed-signal hierarchical arrays as means to find and distribute steering parameters. Simulations of Angular CMA show promising convergence behaviour, while having a lower complexity than alternative methods (e.g., MUSIC)
Design exploration and performance strategies towards power-efficient FPGA-based achitectures for sound source localization
Many applications rely on MEMS microphone arrays for locating sound sources prior to their execution. Those applications not only are executed under real-time constraints but also are often embedded on low-power devices. These environments become challenging when increasing the number of microphones or requiring dynamic responses. Field-Programmable Gate Arrays (FPGAs) are usually chosen due to their flexibility and computational power. This work intends to guide the design of reconfigurable acoustic beamforming architectures, which are not only able to accurately determine the sound Direction-Of-Arrival (DoA) but also capable to satisfy the most demanding applications in terms of power efficiency. Design considerations of the required operations performing the sound location are discussed and analysed in order to facilitate the elaboration of reconfigurable acoustic beamforming architectures. Performance strategies are proposed and evaluated based on the characteristics of the presented architecture. This power-efficient architecture is compared to a different architecture prioritizing performance in order to reveal the unavoidable design trade-offs
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels
In this paper, we establish a general framework on the reduced dimensional
channel state information (CSI) estimation and pre-beamformer design for
frequency-selective massive multiple-input multiple-output MIMO systems
employing single-carrier (SC) modulation in time division duplex (TDD) mode by
exploiting the joint angle-delay domain channel sparsity in millimeter (mm)
wave frequencies. First, based on a generic subspace projection taking the
joint angle-delay power profile and user-grouping into account, the reduced
rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived
for spatially correlated wideband MIMO channels. Second, the statistical
pre-beamformer design is considered for frequency-selective SC massive MIMO
channels. We examine the dimension reduction problem and subspace (beamspace)
construction on which the RR-MMSE estimation can be realized as accurately as
possible. Finally, a spatio-temporal domain correlator type reduced rank
channel estimator, as an approximation of the RR-MMSE estimate, is obtained by
carrying out least square (LS) estimation in a proper reduced dimensional
beamspace. It is observed that the proposed techniques show remarkable
robustness to the pilot interference (or contamination) with a significant
reduction in pilot overhead
Low-Complexity Hybrid Beamforming for Massive MIMO Systems in Frequency-Selective Channels
Hybrid beamforming for frequency-selective channels is a challenging problem
as the phase shifters provide the same phase shift to all of the subcarriers.
The existing approaches solely rely on the channel's frequency response and the
hybrid beamformers maximize the average spectral efficiency over the whole
frequency band. Compared to state-of-the-art, we show that substantial sum-rate
gains can be achieved, both for rich and sparse scattering channels, by jointly
exploiting the frequency and time domain characteristics of the massive
multiple-input multiple-output (MIMO) channels. In our proposed approach, the
radio frequency (RF) beamformer coherently combines the received symbols in the
time domain and, thus, it concentrates signal's power on a specific time
sample. As a result, the RF beamformer flattens the frequency response of the
"effective" transmission channel and reduces its root mean square delay spread.
Then, a baseband combiner mitigates the residual interference in the frequency
domain. We present the closed-form expressions of the proposed beamformer and
its performance by leveraging the favorable propagation condition of massive
MIMO channels and we prove that our proposed scheme can achieve the performance
of fully-digital zero-forcing when number of employed phase shifter networks is
twice the resolvable multipath components in the time domain.Comment: Accepted to IEEE Acces
Symmetric complex-valued RBF receiver for multiple-antenna aided wireless systems
A nonlinear beamforming assisted detector is proposed for multiple-antenna-aided wireless systems employing complex-valued quadrature phase shift-keying modulation. By exploiting the inherent symmetry of the optimal Bayesian detection solution, a novel complex-valued symmetric radial basis function (SRBF)-network-based detector is developed, which is capable of approaching the optimal Bayesian performance using channel-impaired training data. In the uplink case, adaptive nonlinear beamforming can be efficiently implemented by estimating the systemâs channel matrix based on the least squares channel estimate. Adaptive implementation of nonlinear beamforming in the downlink case by contrast is much more challenging, and we adopt a cluster-variationenhanced clustering algorithm to directly identify the SRBF center vectors required for realizing the optimal Bayesian detector. A simulation example is included to demonstrate the achievable performance improvement by the proposed adaptive nonlinear beamforming solution over the theoretical linear minimum bit error rate beamforming benchmark
Cost-effective aperture arrays for SKA Phase 1: single or dual-band?
An important design decision for the first phase of the Square Kilometre
Array is whether the low frequency component (SKA1-low) should be implemented
as a single or dual-band aperture array; that is, using one or two antenna
element designs to observe the 70-450 MHz frequency band. This memo uses an
elementary parametric analysis to make a quantitative, first-order cost
comparison of representative implementations of a single and dual-band system,
chosen for comparable performance characteristics. A direct comparison of the
SKA1-low station costs reveals that those costs are similar, although the
uncertainties are high. The cost impact on the broader telescope system varies:
the deployment and site preparation costs are higher for the dual-band array,
but the digital signal processing costs are higher for the single-band array.
This parametric analysis also shows that a first stage of analogue tile
beamforming, as opposed to only station-level, all-digital beamforming, has the
potential to significantly reduce the cost of the SKA1-low stations. However,
tile beamforming can limit flexibility and performance, principally in terms of
reducing accessible field of view. We examine the cost impacts in the context
of scientific performance, for which the spacing and intra-station layout of
the antenna elements are important derived parameters. We discuss the
implications of the many possible intra-station signal transport and processing
architectures and consider areas where future work could improve the accuracy
of SKA1-low costing.Comment: 64 pages, 23 figures, submitted to the SKA Memo serie
DNN-Based Multi-Frame MVDR Filtering for Single-Microphone Speech Enhancement
Multi-frame approaches for single-microphone speech enhancement, e.g., the
multi-frame minimum-variance-distortionless-response (MVDR) filter, are able to
exploit speech correlations across neighboring time frames. In contrast to
single-frame approaches such as the Wiener gain, it has been shown that
multi-frame approaches achieve a substantial noise reduction with hardly any
speech distortion, provided that an accurate estimate of the correlation
matrices and especially the speech interframe correlation vector is available.
Typical estimation procedures of the correlation matrices and the speech
interframe correlation (IFC) vector require an estimate of the speech presence
probability (SPP) in each time-frequency bin. In this paper, we propose to use
a bi-directional long short-term memory deep neural network (DNN) to estimate a
speech mask and a noise mask for each time-frequency bin, using which two
different SPP estimates are derived. Aiming at achieving a robust performance,
the DNN is trained for various noise types and signal-to-noise ratios.
Experimental results show that the multi-frame MVDR in combination with the
proposed data-driven SPP estimator yields an increased speech quality compared
to a state-of-the-art model-based estimator
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