Deep learning-based optimal spatial subsampling in ultrasound nondestructive testing. - In: 31st European Signal Processing Conference (EUSIPCO 2024), (2023), S. 1863-1867
Traditional ultrasound synthetic aperture imaging relies on closely spaced measurement positions, where the pitch size is smaller than half the ultrasound wavelength. While this approach achieves high-quality images, it necessitates the storage of large data sets and an extended measurement time. To address these issues, there is a burgeoning interest in exploring effective subsampling techniques. Recently, Deep Probabilistic Subsampling (DPS) has emerged as a feasible approach for designing selection matrices for multi-channel systems. In this paper, we address spatial subsampling in single-channel ultrasound imaging for Nondestructive Testing (NDT) applications. To accomplish a model-based data-driven spatial subsampling approach within the DPS framework that allows for the optimal selection of sensing positions on a discretized grid, it is crucial to build an adequate signal model and design an adapted network architecture with a reasonable cost function. The reconstructed image quality is then evaluated through simulations, showing that the presented subsampling pattern approaches the performance of fully sampling and substantially outperforms uniformly spatial subsampling in terms of signal recovery quality.
https://doi.org/10.23919/EUSIPCO58844.2023.10289868
Leakage-based coordinated beamforming for reconfigurable intelligent surfaces-aided dynamic TDD systems. - In: 31st European Signal Processing Conference (EUSIPCO 2024), (2023), S. 1688-1692
The integration of dynamic time-division-duplexing (DTDD) and reconfigurable intelligent surfaces (RISs) has been proposed as a solution to meet the small cells traffic fluctuations and to tune the wireless propagation channels in real-time. The most critical problem of an RIS-aided DTDD system is cross-link interference. Therefore, in this paper we jointly optimize the base stations transmit precoders and the RIS reflection vector to maximize the sum signal-to leakage-plus-noise ratio (SLNR), with the objective of improving communication efficiency while reducing the impact of cross-link interference. Our numerical results demonstrate a significant performance improvement using the proposed method as compared to some baseline schemes.
https://doi.org/10.23919/EUSIPCO58844.2023.10289836
Near-field beamforming for MU-MIMO millimeter wave communication system. - In: 31st European Signal Processing Conference (EUSIPCO 2024), (2023), S. 1683-1687
Employing a large number of antennas in conjunction with the exploitation of higher frequencies is a promising solution for improving the rate of future wireless systems. The use of large antenna arrays with high transmission frequencies results in the devices operating in the near-field region of the large-scale antenna arrays. This paper studies near-field beamforming for a multi-user multiple-input multiple-output (MU-MIMO) millimeter wave (mmWave) communication system. We exploit the distance discrimination potentials of the near-field beamforming to facilitate an efficient de-ployment of high-rate multi-user downlink MIMO mmWave systems. To this end, we study the performance of the near-field beamforming using several precoding schemes. Our numerical results demonstrate a significant performance improvement due to the capability of the near-field beamforming to support reliable communications even for devices that are located at the same angular direction which corresponds to the “worst case” situation.
https://doi.org/10.23919/EUSIPCO58844.2023.10289803
Nested PARAFAC tensor-based channel estimation method for double RIS-aided MIMO communication systems. - In: 31st European Signal Processing Conference (EUSIPCO 2024), (2023), S. 1674-1678
In this paper, we consider a double-RIS (D-RIS) aided MIMO system, where one RIS is deployed closer to the transmitter and another is placed closer to the receiver. We show that the received signals in flat-fading D-RIS aided MIMO systems can be represented as a 4-way tensor satisfying a nested PARAFAC decomposition model. Exploiting such a structure, a closed-form channel estimation method is proposed, where two out of three channels are estimated in parallel using the low-complexity Khatri-Rao factorization technique. Furthermore, we propose an alternating least squares (ALS)-based channel estimation method with an efficient initialization. The simulation results show that both proposed methods have a comparable performance as long as the identifiability conditions of the Khatri-Rao factorization are satisfied. The proposed ALS-based method can achieve a satisfactory performance with less training overhead. Moreover, the proposed ALS-based method performance can further be improved by using the Khatri-Rao factorization as an initialization.
https://doi.org/10.23919/EUSIPCO58844.2023.10290107
Robust beamforming for reconfigurable intelligent surfaces-aided dynamic TDD systems. - In: 31st European Signal Processing Conference (EUSIPCO 2024), (2023), S. 1624-1628
We propose a robust beamforming design for a reconfigurable intelligent surface (RIS) aided dynamic time-division-duplexing system. The main focus is to design the optimal transmit beamforming vectors and the passive RIS reflection vector to minimize the total transmit power of the downlink cells in the presence of channel imperfections. We consider a conventional worst-case formulation that has deterministic upper bounds on the norms of the channel imperfection. We adopt a semidefinite relaxation (SDR) technique and an S-procedure to reformulate the problem into a semidefinite programming (SDP) form with linear matrix inequality (LMI) constraints. Then, we adopt the alternating optimization approach to update the active transmit beamforming vectors and the passive RIS reflection vector sequentially. Numerical results are presented showing the effectiveness of the proposed method as compared to the non-robust design.
https://doi.org/10.23919/EUSIPCO58844.2023.10289990
Misspecification under the narrowband assumption: a Cramér-Rao bound perspective. - In: 31st European Signal Processing Conference (EUSIPCO 2024), (2023), S. 1524-1528
To efficiently extract estimates about the propagation behavior of electromagnetic waves in a radio environment it is common to invoke the narrowband-assumption. It essentially states that the relative bandwidth of the measurement system is so low that the frequency response of a single propagation path only depends on it Time-of-Flight and the response of the measurement device can be calibrated independently of the measured channel. Recent advances into higher relative bandwidths and antenna arrays with larger spatial aperture render this assumption less likely to be satisfied, which leads to a model mismatch during estimation. In this case estimates are inherently biased and have a special statistical behavior. This behavior can be captured by the so-called Misspecified Cramér-Rao Bound, which formulates a lower bound for the variance of estimates that are biased due to model mismatch. We analyze this bound in contrast to the traditional Cramér-Rao Bound and show the shortcomings in the setting of joint ToF-DoA estimation in the mmWave spectrum. The conducted numerical studies also show that planar array geometries inherently suffer from violation of the narrowband assumption irrespective of the individual elements' frequency response, whereas circular structures show it to a lesser degree.
https://doi.org/10.23919/EUSIPCO58844.2023.10289949
Low complexity PMI selection for BICM-MIMO rate maximization in 5G new radio systems. - In: 31st European Signal Processing Conference (EUSIPCO 2024), (2023), S. 1445-1449
This paper presents novel methods for selecting the best precoding matrix index (PMI) from the Type- I codebook adopted in 5G New Radio (5G NR (gNB) , in terms of the achievable rate for MIMO-BICM systems. To overcome the complexity of dealing with a multi-variable problem with discrete domains, we introduce heuristic algorithms that exploit the Kronecker and DFT structure of the codebook. Our proposed methods utilize a combination of direct estimation and a low-dimensional search to derive the optimal PMI indices, and the singular value (SV) pre-coder serves as an optimal reference. The approach significantly reduces the number of codebook precoder candidates, resulting in a much lower complexity compared to the exhaustive search methods. Simulation results demonstrate the effectiveness of our proposed algorithms in achieving a performance comparable to the performance obtained by an exhaustive search.
https://doi.org/10.23919/EUSIPCO58844.2023.10290121
Optimization of transmission parameters in fast pulse-echo ultrasound imaging using sparse recovery. - In: 31st European Signal Processing Conference (EUSIPCO 2024), (2023), S. 441-445
In pulse-echo ultrasound imaging, the goal is to achieve a certain image quality while minimizing the duration of the signal acquisition. In the past, fast ultrasound imaging methods applying sparse signal recovery have been implemented by accepting a single pulse-echo measurement. However, they have experienced a certain amount of reconstruction error. In sparse signal recovery, reducing the correlation between the samples of the measurements observed by the different receivers is beneficial for lowering the reconstruction error. Exploiting the Born approximation and Green's function for the wave equation, the analytical inverse scattering problem can be defined in matrix-vector form. Adopting this setting, it has been suggested in the past to reduce the correlation between the samples of the measurement using Cylindrical Waves (CWs) with randomly selected delays and weights. In a similar setting, we created an optimization problem accepting transmission delays and weights as variables to minimize the correlation between the samples of the measurement in each receiver. We demonstrate via simulations that CWs employing the optimized transmission parameters outperformed the cases with Plane Wave Imaging (PWI) and CWs with random transmission parameters in terms of reconstruction accuracy.
https://doi.org/10.23919/EUSIPCO58844.2023.10290105
Machine learning based channel prediction for NR type II CSI reporting. - In: IEEE ICC 2023, (2023), S. 4967-4972
The application of artificial intelligence and machine learning (AI/ML) into the wireless physical layer is under discussion at 3GPP. Channel state information (CSI) prediction is among the sub use cases being studied. In this work, we propose an AI/ML CSI predictor that aims to compensate the scheduling delays at the base station. The AI/ML CSI predictor operates at the user equipment side and generates the channel reporting based on its prediction. Our AI/ML CSI predictor is designed for the intended prediction time, e.g., 5 ms, by collecting a few past measurements at the input. Our architecture is flexible regarding the number of physical resource blocks and can be used by all user equipments within the cell. Our results show that the proposed AI/ML CSI predictor has the 90 % normalized squared error performance around −13 dB and less than 1.4 % of the predicted eigenvectors have a squared generalized cosine similarity below 0.9, which is much better than zero order hold.
https://doi.org/10.1109/ICC45041.2023.10279531
Learning based compressive beam detection using real-valued beamspace covariance processing for mmWave communications. - In: 2023 IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), (2023), S. 641-645
In this paper, we present a learning based beam detection scheme using compressive measurements in mmWave band communications. By considering that the measured beam-space covariance (BSC) is the compressive projection of the antenna-element-space covariance (AESC) of the spatial channel, while the latter is directly associated to the optimal communication beam, the upper triangular part of the BSC matrix is selected as the input feature of a feed-forward neural network (NN) which directly detects the best communication beam. We also show that, by designing the training beams with structured random phases to be conjugate symmetric, the real part of the BSC becomes the compressive projection of the forward-backward (FB) averaged version of the AESC. This property leads to a small real-valued NN with less nodes. Simulations show that the proposed scheme outperforms the traditional two-step approach, with only a few measurements.
https://doi.org/10.1109/SPAWC53906.2023.10304433