| 1. |
- Bacharach, Lucien, et al.
(författare)
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A TIGHTER BAYESIAN CRAMER-RAO BOUND
- 2019
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Ingår i: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP). - : IEEE. - 9781479981311 ; , s. 5277-5281
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Konferensbidrag (refereegranskat)abstract
- It has been shown lately that any "standard" Bayesian lower bound (BLB) on the mean squared error (MSE) of the Weiss-Weinstein family (WWF) admits a "tighter" form which upper bounds the "standard" form. Applied to the Bayesian Cramer-Rao bound (BCRB), this result suggests to redefine the concept of efficient estimator relatively to the tighter form of the BCRB, an update supported by a noteworthy example. This paper lays the foundation to revisit some Bayesian estimation problems where the BCRB is not tight in the asymptotic region.
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| 2. |
- Batstone, K., et al.
(författare)
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Robust Self-calibration of Constant Offset Time-difference-of-arrival
- 2019
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Ingår i: 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. - 9781479981311 ; 2019-May, s. 4410-4414
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Konferensbidrag (refereegranskat)abstract
- In this paper we study the problem of estimating receiver and sender positions from time-difference-of-arrival measurements, assuming an unknown constant time-difference-of-arrival offset. This problem is relevant for example for repetitive sound events. In this paper it is shown that there are three minimal cases to the problem. One of these (the five receiver, five sender problem) is of particular importance. A fast solver (with run-time under 4 μs) is given. We show how this solver can be used in robust estimation algorithms, based on RANSAC, for obtaining an initial estimate followed by local optimization using a robust error norm. The system is verified on both real and synthetic data.
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| 3. |
- Becirovic, Ema, et al.
(författare)
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Detection of Pilot-Hopping Sequences for Grant-Free Random Access in Massive MIMO Systems
- 2019
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Ingår i: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP). - : IEEE. - 9781479981311 ; , s. 8380-8384
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Konferensbidrag (refereegranskat)abstract
- In this paper, we study an active user detection problem for massive machine type communications (mMTC). The users transmit pilot-hopping sequences and detection of active users is performed based on the received energy. We utilize the channel hardening and favorable propagation properties of massive multiple- input multipleoutput (MIMO) to simplify the user detection. We propose and compare a number of different user detection methods and find that using non- negative least squares (NNLS) is well suited for the task at hand as it achieves good results as well as having the benefit of not having to specify further parameters.
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| 4. |
- Elvander, Filip, et al.
(författare)
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NON-COHERENT SENSOR FUSION VIA ENTROPY REGULARIZED OPTIMAL MASS TRANSPORT
- 2019
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Ingår i: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP). - : IEEE. - 9781479981311 ; , s. 4415-4419
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Konferensbidrag (refereegranskat)abstract
- This work presents a method for information fusion in source localization applications. The method utilizes the concept of optimal mass transport in order to construct estimates of the spatial spectrum using a convex barycenter formulation. We introduce an entropy regularization term to the convex objective, which allows for low-complexity iterations of the solution algorithm and thus makes the proposed method applicable also to higher-dimensional problems. We illustrate the proposed method's inherent robustness to misalignment and miscalibration of the sensor arrays using numerical examples of localization in two dimensions.
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| 5. |
- Ghauch, Hadi, 1986-, et al.
(författare)
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Compressive Sensing with Applications to Millimeter-wave Architectures
- 2019
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Ingår i: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP). - : IEEE. - 9781479981311 ; , s. 7834-7838
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Konferensbidrag (refereegranskat)abstract
- To make the system available at low-cost, millimeter-ave (mmWave) multiple-input multiple-output (MIMO) architectures employ analog arrays, which are driven by a limited number of radio frequency (RF) chains. One primary challenge of using large hybrid analog-digital arrays is that the digital baseband cannot directly access the signal to/from each antenna. To address this limitation, recent research has focused on retransmissions, iterative precoding, and subspace decomposition methods. Unlike these approaches that exploited the channel's low-rank, in this work we exploit the sparsity of the received signal at both the transmit/receive antennas. While the signal itself is de facto dense, it is well-known that most signals are sparse under an appropriate choice of basis. By delving into the structured compressive sensing (CS) framework and adapting them to variants of the mmWave hybrid architectures, we provide methodologies to recover the analog signal at each antenna from the (low-dimensional) digital signal. Moreover, we characterizes the minimal numbers of measurement and RF chains to provide this recovery, with high probability. We discuss their applications to common variants of the hybrid architecture. By leveraging the inherent sparsity of the received signal, our analysis reveals that a hybrid MIMO system can be " turned into" a fully digital one: the number of needed RF chains increases logarithmically with the number of antennas.
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| 6. |
- Ghazanfari, Amin, et al.
(författare)
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A Fair and Scalable Power Control Scheme in Multi-Cell Massive MIMO
- 2019
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Ingår i: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781479981311 ; , s. 4499-4503
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Konferensbidrag (refereegranskat)abstract
- This paper studies the transmit power optimization in a multi-cell massive multiple-input multiple-output (MIMO) system. To overcome the scalability issue of network-wide max-min fairness (NW-MMF), we propose a novel power control (PC) scheme. This scheme maximizes the geometric mean (GM) of the per-cell max-min spectral efficiency (SE). To solve this new optimization problem, we prove that it can be rewritten in a convex form and then solved using standard tools. To provide a fair comparison with the available utility functions in the literature, we solve the network-wide proportional fairness (NW-PE) PC as well. The NW-PE focuses on maximizing the sum SE, thereby ignoring fairness, but gives some extra attention to the weakest users. The simulation results highlight the benefits of our model which is balancing between NW-PE and NW-MMF.
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| 7. |
- Khirirat, Sarit, et al.
(författare)
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CONVERGENCE BOUNDS FOR COMPRESSED GRADIENT METHODS WITH MEMORY BASED ERROR COMPENSATION
- 2019
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Ingår i: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP). - : IEEE. - 9781479981311 ; , s. 2857-2861
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Konferensbidrag (refereegranskat)abstract
- The veritable scale of modern data necessitates information compression in parallel/distributed big-data optimization. Compression schemes using memory-based error compensation have displayed superior performance in practice, however, to date there are no theoretical explanations for these observed advantages. This paper provides the first theoretical support for why such compression schemes yields higher accuracy solutions in optimization. Our results cover both gradient and incremental gradient algorithms for quadratic optimization. Unlike previous works, our theoretical results explicitly quantify the accuracy gains from error compensation, especially for ill-conditioned problems. Finally, the numerical results on linear least-squares problems validate the benefit of error compensation and demonstrate tightness of our convergence guarantees.
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| 8. |
- Kiranyaz, Serkan, et al.
(författare)
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1-D Convolutional Neural Networks for Signal Processing Applications
- 2019
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Ingår i: 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing. - : IEEE. - 9781479981311 - 9781479981328 ; , s. 8360-8364
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Konferensbidrag (refereegranskat)abstract
- 1D Convolutional Neural Networks (CNNs) have recently become the state-of-the-art technique for crucial signal processing applications such as patient-specific ECG classification, structural health monitoring, anomaly detection in power electronics circuitry and motor-fault detection. This is an expected outcome as there are numerous advantages of using an adaptive and compact 1D CNN instead of a conventional (2D) deep counterparts. First of all, compact 1D CNNs can be efficiently trained with a limited dataset of 1D signals while the 2D deep CNNs, besides requiring 1D to 2D data transformation, usually need datasets with massive size, e.g., in the "Big Data" scale in order to prevent the well-known "overfitting" problem. 1D CNNs can directly be applied to the raw signal (e.g., current, voltage, vibration, etc.) without requiring any pre- or post-processing such as feature extraction, selection, dimension reduction …
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| 9. |
- Klintberg, Jacob, 1994, et al.
(författare)
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AN IMPROVED METHOD FOR PARAMETRIC SPECTRAL ESTIMATION
- 2019
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Ingår i: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. - 9781479981311 ; 2019-May, s. 5551-5555
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Konferensbidrag (refereegranskat)abstract
- One important class of problems within spectral estimation is when the signal can be well represented by a parametric model. These kind of problems can be found in many applications such as radar, sonar and wireless communication, and has therefore been extensively investigated. The main problem is to estimate frequencies and their corresponding amplitudes and damping factors from noisy measurements. One approach to this problem is to form a matrix of measurements, and then find an approximation to the range space of the matrix, with requirements that the approximation is of low rank and have a Hankel structure. From the approximation, the signal parameters can be extracted. In this work, we investigate three different methods which follows this methodology. The main contribution will be an illustration of how the problem formulation and rank constraint management affects the accuracy of the estimate. Numerical simulations indicates that a method which formulates a single convex envelope of a least squares fit to the measurement matrix and to the rank constraint jointly is more accurate than the other two investigated methods.
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| 10. |
- Larsson, Martin, et al.
(författare)
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Optimal Trilateration Is an Eigenvalue Problem
- 2019
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Ingår i: 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. - 9781479981311 ; 2019-May, s. 5586-5590
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Konferensbidrag (refereegranskat)abstract
- The problem of estimating receiver or sender node positions from measured receiver-sender distances is a key issue in different applications such as microphone array calibration, radio antenna array calibration, mapping and positioning using UWB or using round-trip-time measurements between mobile phones and WiFi-units. In this paper we address the problem of optimally estimating a receiver position given a number of distance measurements to known sender positions, so called trilateration. We show that this problem can be rephrased as an eigenvalue problem. We also address different error models and the multilateration setting where an additional offset is also unknown, and show that these problems can be modeled using the same framework.
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