Low Signal-to-noise Ratio Regime Research Articles

Shear-Wave Particle-Velocity Estimation and Enhancement Using a Multi-Resolution Convolutional Neural Network.

Tissue mechanical properties are valuable markers for tissue characterization, aiding in the detection and staging of pathologies. Shear wave elastography (SWE) offers a quantitative assessment of tissue mechanical characteristics based on the SW propagation profile, which is derived from the SW particle motion. Improving the signal-to-noise ratio (SNR) of the SW particle motion would directly enhance the accuracy of the material property estimates such as elasticity or viscosity. In this paper, we present a 3-D multi-resolution convolutional neural network (MRCNN) to perform improved estimation of the SW particle velocity Vz. Additionally, we propose a novel approach to generate training data from real acquisitions, providing high SNR ground truth target data, one-to-one paired to inputs that are corrupted with real-world noise and disturbances. By testing the network on in vitro data acquired from a commercial breast elastography phantom, we show that the MRCNN outperforms Loupas' autocorrelation algorithm with an improved SNR of 4.47dB for the Vz signals, a two-fold decrease in the standard deviation of the downstream elasticity estimates, and a two-fold increase in the contrast-to-noise ratio of the elasticity maps. The generalizability of the network was further demonstrated with a set of ex vivo porcine liver data. The proposed MRCNN outperforms the standard autocorrelation method, in particular in low SNR regimes.

Efficient imaging using spiral acquisitions on a portable 50-mT MR head scanner.

Ultralow-field (ULF) magnetic resonance imaging (MRI) can suffer from inferior image quality because of low signal-to-noise ratio (SNR). As an efficient way to cover the k-space, the spiral acquisition technique has shown great potential in improving imaging SNR efficiency at ULF. The current study aimed to address the problems of noise and blurring cancelation in the ULF case with spiral trajectory, and we proposed a spiral-out sequence for brain imaging using a portable 50-mT MRI system. The proposed sequence consisted of three modules: noise calibration, field map acquisition, and imaging. In the calibration step, transfer coefficients were obtained between signals from primary and noise-pick-up coils to perform electromagnetic interference (EMI) cancelation. Embedded field map acquisition was performed to correct accumulated phase error due to main field inhomogeneity. Considering imaging SNR, a lower bandwidth for data sampling was adopted in the sequence design because the 50-mT scanner is in a low SNR regime. Image reconstruction proceeded with sampled data by leveraging system imperfections, such as gradient delays and concomitant fields. The proposed method can provide images with higher SNR efficiency compared with its Cartesian counterparts. An improvement in temporal SNR of approximately 23%-44% was measured via phantom and in vivo experiments. Distortion-free images with a noise suppression rate of nearly 80% were obtained by the proposed technique. A comparison was also made with a state-of-the-art EMI cancelation algorithm used in the ULF-MRI system. SNR efficiency-enhanced spiral acquisitions were investigated for ULF-MR scanners and future studies could focus on various image contrasts based on our proposed approach to widen ULF applications.

A General Framework for UAV-aided THz Communications Subject to Generalized Geometric Loss

In this article, we develop a general framework for the performance analysis of unmanned aerial vehicle (UAV) aided terahertz (THz) communications under generalized geometric loss, which considers the fluctuations of the UAV's position and orientation alongside the non-orthogonality of the THz beam with respect to the detector plane. In particular, we derive general and novel analytical expressions for the probability density function and the cumulative distribution function of the instantaneous signal-to-noise ratio (SNR) under generalized fading channels subject to generalized geometric loss. Consequently, novel, general, and accurate analytical expressions for the outage probability, average symbol error rate, and average channel capacity are derived. Furthermore, approximate asymptotic expressions in the high SNR regime are also derived. Moreover, A very accurate approximate expression for the channel capacity is also provided in the low SNR regime. As an example, the system performance under the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> fading channel model is analyzed in terms of the above-mentioned performance metrics.

Multimodality-aided Multicarrier Waveform Recognition in low SNR Regimes based on Denoised Cyclic Autocorrelation Transformation

Wireless signal recognition driven by artificial intelligence (AI) plays a pivotal role in 6G ultra-reliable wireless communications, facilitating spectrum surveillance to impede illegal radio interference. As a promising wireless technique, multicarrier waveform recognition (MWR) has been explored to enhance the reliability of wireless data transmission. However, existing works fail to achieve reliable recognition accuracy under strong noise. It remains a daunting task for MWR in signal-to-noise ratio (SNR) regimes. To deal with this issue, we propose a denoised cyclic autocorrelation-based multimodality fusion network (DCA-MFNet). Specifically, we first leverage the cyclic autocorrelation transformation to convert intercepted signals into cyclic autocorrelation (CA) features in cyclic frequency domains, which have the robust property of being insensitive to low SNR. Next, the singular value decomposition (SVD) method is employed to weaken the strong noise effects on useful CA peak values. Based on the denoised CA matrix (DCAM), the projection accumulation strategy is proposed to generate the time delay accumulation vector (TDV) and cyclic frequency accumulation vector (CFV), which can enlarge the discrimination among multicarrier signals. Finally, we fed the multimodality features of DCAM, TDV, and CFV into the developed DCA-MFNet to perform hierarchical learning, feature aggregation training, and multicarrier type prediction. Experimental results demonstrate that the proposed DCA-MFNet obtains better recognition performance than existing algorithms. Moreover, DCA-MFNet can effectively identify six multicarrier signals with a recognition accuracy of 100% at a low SNR of even -2 dB.

Deconvolution-Based Partial Volume Correction for Volumetric Blood Flow Measurement.

Ultrasound-based blood flow (BF) monitoring is vital in the diagnosis and treatment of a variety of cardiovascular and neurologic conditions. Finite spatial resolution of clinical color flow (CF) systems, however, has hampered measurement of vessel cross Section areas. We propose a resolution enhancement technique that allows reliable determination of BF in small vessels. We leverage sparsity in the spatial distribution of the frequency spectrum of routinely collected CF data to blindly determine the point spread function (PSF) of the imaging system in a robust manner. The CF data are then deconvolved with the PSF, and the volumetric flow is computed using the resulting velocity profiles. Data were collected from phantom blood vessels with diameters between 2 and 6 mm using a clinical ultrasound system at 2 MHz insonation frequency. The proposed method yielded a flow estimation bias of 0 mL/min, standard deviation of error (SDE) of 22 mL/min, and a root-mean-square error (RMSE) of 22 mL/min over a 150 mL/min range of mean flows. Recordings were also obtained in low signal-to-noise ratio (SNR) conditions using a skull mimicking element, resulting in an estimation bias of -13 mL/min, SDE of 23 mL/min, and an RMSE of 26 mL/min. The effect of insonation frequency was also investigated by obtaining recordings at 4.3 MHz, yielding an estimation bias of -16 mL/min, SDE of 16 mL/min, and an RMSE of 22 mL/min. The results indicate that our technique can lead to clinically acceptable flow measurements across a range of vessel diameters in high and low SNR regimes.

Synthetically trained convolutional neural networks for improved tensor estimation from free-breathing cardiac DTI.

Cardiac diffusion tensor imaging (cDTI) provides invaluable information about the state of myocardial microstructure. For further clinical dissemination, free-breathing acquisitions are desired, which however require image registration prior to tensor estimation. Due to the varying contrast and the intrinsically low signal-to-noise ratio (SNR), registration is very challenging and thus can introduce additional errors in the tensor estimation. In the work at hand it is hypothesized, that by incorporating spatial information and physiologically plausible priors into the fitting algorithm, the robustness of diffusion tensor estimation can be improved. To this end, we present a parameterized pipeline to generate synthetic data, that captures the statistics including spatial correlations of diffusion tensors and motion of the heart. The synthetic data is used to train a residual convolutional neural network (CNN) to estimate diffusion tensors from unregistered in-vivo cDTI data. Using in-silico data, the synthetically trained CNN is demonstrated to yield increased tensor estimation accuracy and precision when compared to conventional registration followed by least squares fitting. The network outputs fewer outliers especially at the myocardial borders. In-vivo feasibility using data from five healthy subjects demonstrates the utility of the synthetically trained network. The in-vivo results predicted by the synthetically trained CNN are found to be consistent with the registered least-squares estimates while showing fewer outliers and reduced noise. Even in low SNR regimes, the network results in robust tensor estimation, enabling scan time reduction by reduced-average acquisition in-vivo. Finally, to investigate the network's capability of discriminating between healthy and lesioned tissue, the in-vivo data was artificially augmented showing preserved classification of tissue states based on diffusion metrics.

Closed-Form Sum-Rate Analysis of Interference Alignment with Limited Feedback Based on Scalar Quantization and Random Vector Quantization

Interference alignment (IA) is a promising interference management technique to achieve the theoretical optimal degree of freedom (DoF) performance in multi-user cooperation scenarios. However, the effective achievable sum-rate performance of IA is largely affected by the feedback overhead and accuracy of channel state information (CSI) and decoding information (DI). Therefore, it is critical to establish the exact relationship between feedback overhead and the achievable sum-rate of IA to obtain the optimal effective performance. Most existing IA performance analysis approaches focus on the vector quantization (VQ)-based feedback strategy, but the implementation complexity of VQ will be excessive when more quantization bits are required to achieve the expected quantization accuracy for larger-sized matrices or higher signal-to-noise ratio (SNR) regimes. Moreover, the obtained achievable sum-rate formulas are too complicated for quick performance evaluation. In this paper, a new sum-rate performance analysis method for IA under different quantization and feedback strategies is proposed to achieve a trade-off between accuracy and complexity, and the closed-form achievable sum-rate expressions are derived. First, in the IA case with random vector quantization (RVQ)-based CSI feedback, the quantization error of RVQ is transformed into the equivalent VQ error of the Gaussian channel error, based on which the achievable sum-rate formula is obtained. Second, in the IA case with scalar quantization (SQ)-based CSI feedback, the relationship between the effective sum-rate and SQ bits is established. Third, in the IA case with SQ-based CSI feedback and RVQ-based DI feedback, the achievable sum-rate formula is derived by combining these two kinds of quantization errors. Finally, the simulation results confirm that the theoretical results are accurate enough, which can help to determine the optimal CSI feedback overhead in practical channel conditions. Moreover, the theoretical and simulation results demonstrate that RVQ may be more applicable to IA scenarios with fewer receiving antennas and low SNR regimes.

Throughput Analysis of α-κ-µ Extreme/Gamma Composite Fading Channel

In this paper, we consider α-κ-µ-Extreme/gamma composite fading channel model, which is used to characterize non-linear severe fading and shadowing impairments in enclosed wireless propagation scenarios. For this model, we first obtain the probability density function (PDF) expression for the received signal-to-noise ratio(SNR). Using the derived PDF, throughput analysis is provided by deriving expressions for the average channel capacity(ACC) and effective capacity(EC) in closed-form. The derived expressions are evaluated for several values of fading and shadowing indexes to study their effect on the ACC and EC. Furthermore, asymptotic analysis of ACC and EC are carried out to show better approximation under the high and low SNR regimes.

Far-Field DOA Estimation of Uncorrelated RADAR Signals through Coprime Arrays in Low SNR Regime by Implementing Cuckoo Search Algorithm

For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, aiming to improve DOF. The optimization features of the cuckoo search (CS) algorithm are utilized for DOA estimation of far-field sources in a low signal-to-noise ratio (SNR) environment. The analytical approach of the proposed CSAs, CS and global and local minima in terms of cumulative distribution function (CDF), fitness function and SNR for DOA accuracy are presented. The parameters like root mean square error (RMSE) for frequency distribution, RMSE variability analysis, estimation accuracy, RMSE for CDF, robustness against snapshots and noise and RMSE for Monte Carlo simulation runs are explored for proposed model performance estimation. In conclusion, the proposed DOA estimation in radar technology through CS and CSA achievements are contrasted with existing tools such as particle swarm optimization (PSO).

“Self-Wiener” Filtering: Data-Driven Deconvolution of Deterministic Signals

We consider the problem of robust deconvolution, and particularly the recovery of an unknown deterministic signal convolved with a known filter and corrupted by additive noise. We present a novel, non-iterative data-driven approach. Specifically, our algorithm works in the frequency-domain, where it tries to mimic the optimal unrealizable non-linear Wiener-like filter as if the unknown deterministic signal were known. This leads to a threshold-type regularized estimator, where the threshold at each frequency is determined in a data-driven manner. We perform a theoretical analysis of our proposed estimator, and derive approximate formulas for its Mean Squared Error (MSE) at both low and high Signal-to-Noise Ratio (SNR) regimes. We show that in the low SNR regime our method provides enhanced noise suppression, and in the high SNR regime it approaches the optimal unrealizable solution. Further, as we demonstrate in simulations, our solution is highly suitable for (approximately) bandlimited or frequency-domain sparse signals, and provides a significant gain of several dBs relative to other methods in the resulting MSE.

Joint Synchronization and Compressive Channel Estimation for Frequency-Selective Hybrid mmWave MIMO Systems

Synchronization is a fundamental procedure in cellular systems whereby user equipment (UE) acquires the time and frequency information required to decode the data transmitted by a base station (BS). Due to the small antenna aperture at millimeter wave (mmWave), large antenna arrays are used to compensate for the low signal-to-noise ratio (SNR) at the receiver. For this reason, synchronization is often performed jointly with beam training as in 5G New Radio (NR) to boost the receive SNR. However, if the mmWave multiple-input multiple-output (MIMO) channel is to be estimated, then synchronization must be performed in the low SNR regime owing to the lack of directional beamforming during compressive channel estimation. Current mmWave channel estimation algorithms implicitly take synchronization for granted, which is a not practical assumption in the low SNR regime. To solve this problem, this work proposes the first synchronization framework for hybrid mmWave MIMO that is robust to carrier frequency offset (CFO) and phase noise (PHN) synchronization errors. We propose two novel multi-stage algorithms to jointly estimate the various unknown parameters, based on the expectation-maximization (EM) algorithm. By using the QuaDRiGa 5G NR channel simulator, we show that compressive channel estimation without prior synchronization is possible, and the proposed approaches outperform current solutions for joint beam training and synchronization currently considered in 5G NR.

Design and Testbed Implementation of Multiuser CFOs Estimation for MIMO SC-FDMA System

In this paper, we propose and implement blind multiuser carrier frequency offsets (CFOs) estimation for multi-input multi-output (MIMO) single carrier frequency division multiple access (SC-FDMA) uplink system using orthogonal propagator method (OPM) over a noisy frequency-selective channel. The performance of typical OPM is limited at low signal-to-noise ratio (SNR) due to its noise-free signal model. The additive white Gaussian noise mainly impacts the diagonal elements of the data covariance matrix (DCM) of the received signal. First, the proposed fractional CFOs estimator minimizes the noise effect by reconstructing the denoised diagonal elements of the DCM for the received signal through cubic Hermite interpolation. Therefore, we use modified OPM to estimate multiple CFOs for MIMO SC-FDMA systems. Second, we have used an iterative method based on the first-order Taylor series approximation of the CFO vector to avoid peak ambiguity and reduce the number of iterations required in the grid search. The mean square error of the proposed CFO estimator considerably improves compared with the existing methods at a low SNR regime. The bit error rate performance of the proposed method for MIMO SC-FDMA is also compared with the existing methods. Finally, the proposed estimator is validated by implementing it on radio frequency hardware testbed over an indoor propagation environment.

Mixed RF/FSO Communications With Outdated-CSI-Based Relay Selection Under Double Generalized Gamma Turbulence, Generalized Pointing Errors, and Nakagami-m Fading

This paper investigates the performance of dual-hop amplify-and-forward (AF) mixed radio frequency (RF)/free-space optical (FSO) transmissions with partial relay selection (PRS) based on outdated channel state information (CSI) estimates. Turbulence-induced fading, path loss, and pointing errors, are all considered in the FSO channel modeling. Both the fading and the path loss are described through general models which encompass the commonly used models. Novel expressions for the cumulative distribution function, probability density function, moment generating function, and moments of the end-to-end signal-to-noise ratio (SNR) are obtained in closed-form. Thereafter, novel closed-form expressions for key performance metrics, namely, outage probability, average bit error probability and spectral efficiency, are derived. The analysis is unified, applying to both types of detection techniques, intensity modulation/direct direction and heterodyne detection. Asymptotic analysis is further conducted, which open the door to additional important results. Monte Carlo simulation results confirm the effectiveness of the proposed analysis, and attest that in AF RF/FSO systems with PRS based on outdated CSI estimates, where implementing higher number of relays is highly difficult and costly, the low SNR regime may be prohibitive because it hinders the diversity advantages promised by the use of more relays in PRS systems.

Performance assessment of orthogonal space‐time block codes in Nakagami‐m/inverse Gaussian fading MIMO channels

This paper evaluates the performance of multiple-input multiple-output systems with orthogonal space-time block code transmit diversity technique experiencing over G composite fading channels. Particularly, closed form expressions for ergodic capacity, average symbol error rate and outage probability are presented in high SNR regime. The ergodic capacity of multiple-input multiple-output systems with orthogonal space-time block code is also measured in low signal-to noise ratio regime. Other essential statistical properties such as variance and probability density function of capacity are also explored for comprehensive analysis. Compared to the exact expressions, the derived analytical formulations are found to be involved with considerably less mathematical exercise and provide additional insights into the implications of model parameters on system performance. The accuracy of G distribution in approximating Nakagami- m / lognormal over several realistic scenario are examined and simultaneously the efficiency of distribution is compared with that of Nakagami- m / Gamma distribution. The obtained analytical expressions are corroborated using Monte-Carlo simulations where it is observed that the approximated results remain notably tight in asymptotically high signal-to noise ratio regime.

Time-frequency DOA estimation of chirp signals based on multi-subarray

Spatial time-frequency distribution (STFD) utilizes the spatial time-frequency characteristics of chirp signals and effectively improves the direction of arrival (DOA) estimation performance. However, the existing methods based on the STFD matrix assumed that each source has good time-frequency point selection performance. In practice, the time-frequency point selection accuracy of chirp signals suffers from the signal-to-noise ratio (SNR). Especially in the case of low SNR, the time-frequency point selection error increased, leading to the reduction of the SNR and degradation of the DOA estimation performance in the time-frequency domain. To solve the above problems, we propose a time-frequency DOA estimation method based on multiple subarrays. The array is firstly divided into several overlapping subarrays, and the data received by the array is transformed from the element space into beamspace by using beamforming, which improves the source separation and the SNR. Then, chirp signals possess the ideal energy collection features in the time-frequency domain so that the time-frequency analysis tools are used for separated sources in beamspace to further improve the source separation and the SNR in the time-frequency domain. This results in better time-frequency point selection accuracy of chirp signals at a low SNR regime. Finally, the averaged STFD matrix can be obtained through averaging over multiple single-source time-frequency points in the time-frequency domain, and the DOAs can be obtained by combining with the subspace-based method. The theoretical analysis and simulation results indicate that compared with the existing STFD-based methods, the proposed method in this paper provides good performance on estimation and resolution in cases with low input SNRs due to beamspace processing. Furthermore, in cases where the DOAs between the coherent sources are closely spaced and the snapshot number is low, our proposed method significantly improves the performance of the DOA estimation.

Deep Learning-Based Spectrum Sensing in Space-Air-Ground Integrated Networks

To complement terrestrial connections, the space-air-ground integrated network (SAGIN) has been proposed to provide wide-area connections with improved quality of experience (QoE). Spectrum management is an important issue in SAGIN due to the explosive proliferation of wireless devices and services. While the progress on enabling dynamic spectrum access shows promise in advancing increased spectrum sharing, the issue of reliable spectrum sensing under low signal-to-noise ratio (SNR) remains one of the key challenges faced by the spectrum management. As artificial intelligence can provide wireless networks intelligence through learning and data mining, deep learning-based spectrum sensing is proposed in order to improve the spectrum sensing performance, where a deep neural network-based detection framework is built to extract features in a data-driven way based on the covariance matrix of the received signal. To eliminate the impact of noise uncertainty, a blind threshold setting scheme is proposed without using the system prior information. Numerical analyses on simulated and real-world signals show that the detection performance of the proposed scheme is improved under a low SNR regime.

On the Throughput of Wireless Communication With Combined CSI and H-ARQ Feedback

In this paper, we characterize the combined channel state information (CSI) and re-transmission feedback on wireless communication. Feedback, often assumed free, has traditionally been used to either learn the channel state, or to request the re-transmission of a failed reception. We propose here a generalized framework for feedback that captures the channel time-variation. We consider two-way communication model where all the resources needed for training, feedback (CSI and automatic repeat request), and data. In order to maximize the throughput and optimize resource allocations, we provide the expressions of outage probabilities for different rate/power adaptation and re-transmission protocols. The simulation results show that, besides signal-to-noise ratio (SNR) and Doppler, the optimal pilot overhead depends on whether we are using rate/power adaptation and the number of re-transmissions. We also prove that in slow-fading channels with high SNR regimes, varying the rate provides the best throughput. When dealing with fast time-varying channels and low SNR regimes, using constant power and rate gives the best throughput performance.

Multi-Dimensional Small-Scale Cooperative Spectrum Sensing Approach for Cognitive Radio Receivers

Cognitive radio (CR) is one of the most important emerging technologies that have been introduced to meet the heavy data traffic of future wireless networks. Effective implementation of CR networks strongly requires devolution of efficient spectrum sensing (SS) techniques. Recently many SS techniques based on the utilization of multiple antenna elements (MAE) and matched filtering (MF) at the CR receiver are emerged due to their high detection performance. In this paper, a multi-dimensional small-scale cooperative SS approach based on the use of MAE, MF, and DOA estimation is proposed. The multi-dimensional detection is performed by identifying both the primary user (PU) signal and its DOA, which corresponds to the temporal domain and spatial domain, respectively. The use of MAE provides many versions of the received PU signal, which considered as the backbone of many signal processing operations such as DOA estimation of PU signals. DOA estimation is based on calculating time difference of arrival (TDOA) between the output correlation signals coming from fast convolution blocks. The received PU signal versions are also used as inputs for separate MF detectors to obtain various decisions taking the advantage of the spatial diversity at the CR receiver antennas. Moreover, the estimated DOA of the PU signal is used to make a combination of the signal versions emanating from each antenna branch, using the delay and sum combiner to produce a strong signal with high signal to noise ratio (SNR). This strong signal is also used as input to a separate MF to produce an accurate decision. Finally, the principle of cooperation is achieved by combining these various decisions to obtain a reliable decision. Several simulation scenarios are carried out to verify the superiority of the proposed SS approach compared to the traditional MAE based SS technique under extremely low SNR regimes and high interference. Moreover, the simulations and theoretical closed-form expressions are highly matched together.

Variational Bayesian Learning for Decentralized Blind Deconvolution of Seismic Signals Over Sensor Networks

This work discusses a variational Bayesian learning approach towards decentralized blind deconvolution of seismic signals within a sensor network. Blind seismic deconvolution is cast into a probabilistic framework based on Sparse Bayesian learning developed for blind image deconvolution. The posterior distribution of the signals of interest is then approximated using a variational Bayesian method. Depending on a particular form of selected variational factors, the scheme is shown to generalize the state-of-the-art distributed seismic blind deconvolution algorithm. The algorithm operates by repeatedly alternating between two stages: (i) estimation of seismic source wavelet and (ii) reflectivity estimation. The wavelet is estimated in a distributed fashion using Alternating Direction Method of Multipliers. Based on this estimate, each sensor then locally obtains a sparse reflectivity estimate. Numerical evaluation with synthetic seismic data shows that the proposed method outperforms existing deconvolution algorithms in a high signal-to-noise ratio (SNR) region. In low SNR regime a higher sensitivity of sparse Bayesian learning regarding model mismatches in the estimated source wavelet is observed. Also, processing of seismic data generated with an acoustic wave equation showed that the proposed method is able recover the original reflectivities more accurately, with more distinct support of the reflectivities, as compared to existing methods despite present model mismatches. The algorithm is also applied to the estimation of real seismic data, and shows improved performance as compared to state-of-the-art estimation method.

Two-Dimensional Channel Parameter Estimation for Millimeter-Wave Systems Using Butler Matrices

In this paper, a novel two-dimensional parameter estimation method is proposed for frequency-selective millimeter-wave (mmWave) channels by probing a limited number of Kronecker products of discrete Fourier transform (DFT) beams, which are efficiently implemented in the analog domain by a novel combination of Butler matrices. The proposed strategy firstly estimates the channel parameters by using a modified parameter estimation via interpolation based on a DFT grid (PREIDG) algorithm. In a second step, high-resolution channel parameter estimation is achieved even in the low signal-to-noise-ratio (SNR) using the space-alternating generalized expectation-maximization (SAGE) algorithm. The proposed modified PREIDG algorithm outperforms state-of-the-art methods, e.g., the auxiliary beam pair (ABP) method while the SAGE algorithm achieves the derived Cramér-Rao lower bound (CRLB). Numerical results demonstrate that excellent estimation performance can be achieved for angle of departure (AoD) azimuth and elevation with addition to delay and complex path gain of each path even in the low SNR regime.