| 21. |
- Stoica, Petre, et al.
(författare)
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Comments on "Min-Norm interpretations and consistency of MUSIC, MODE, and ML"
- 1998
-
Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 46:8, s. 2262-2263
-
Tidskriftsartikel (refereegranskat)abstract
- The results and interpretations obtained in the above referred paper are shown to be well known or obvious. Additionally, corrections to some misleading statements in the aforementioned paper are presented.
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| 22. |
- Stoica, Petre, et al.
(författare)
-
Instrumental Variable Approach to Array Processing in Spatially Correlated Noise Fields
- 1994
-
Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 42:1, s. 121-133
-
Tidskriftsartikel (refereegranskat)abstract
- High-performance signal parameter estimation from sensor array data is a problem which has received much attention. A number of so-called eigenvector (EV) techniques such as MUSIC, ESPRIT, WSF, and MODE have been proposedin the literature. The EV techniques for array processing require knowledge of the spatial noise correlation matrix that constitutes a significant drawback. A novel instrumental variable (IV) approach to the sensor array problem is proposed. The IV technique relies on the same basic geometric properties as the EV methods to obtain parameter estimates. However, by exploiting the temporal correlation of the source signals, no knowledge of the spatial noisecovariance is required. The asymptotic properties of the IV estimator are examined and an optimal IV method is derived. Computer simulations are presented to study the properties of the IV estimators in samples of practical length. The proposed algorithm is also shown to perform better than MUSIC on a full-scale passive sonar experiment
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| 23. |
- Swindlehurst, A. Lee, et al.
(författare)
-
Multiple Invariance ESPRIT
- 1992
-
Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 40:4, s. 867-881
-
Tidskriftsartikel (refereegranskat)abstract
- A subspace-fitting formulation of the ESPRIT problem is presented that provides a framework for extending the algorithm to exploit arrays with multiple invariances. In particular, a multiple invariance (MI) ESPRIT algorithm is developed and the asymptotic distribution of the estimates is obtained. Simulations are conducted to verify the analysis and to compare the performance of MI ESPRIT with that of several other approaches. The excellent quality of the MI ESPRIT estimates is explained by recent results which state that, under certain conditions, subspace-fitting methods of this type are asymptotically efficient.
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| 24. |
- Tsinos, Christos G., et al.
(författare)
-
Symbol-Level Precoding with Low Resolution DACs for Large-Scale Array MU-MIMO Systems
- 2018
-
Ingår i: 2018 IEEE 19TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC). - : IEEE. - 9781538635124 ; , s. 671-675
-
Konferensbidrag (refereegranskat)abstract
- While (Multiple Input-Multiple Output) MIMO systems based on large-scale antenna arrays are seen as the solution to the continuously increasing demands in modern wireless systems, they require high hardware complexity and power consumption. To tackle this, solutions based on low resolution Analog-to-Digital Converters (ADCs) / Digital-to-Analog Converters (DACs) have been developed in the literature where they mainly propose quantized versions of typical channel dependent linear precoding solutions. Alternatively, nonlinear Symbol level Precoding techniques have been recently proposed for downlink Multi User (MU)-MIMO systems with low resolution DACs that achieve significantly improved performance in several cases. The existing SLP approaches support only DACs of 1-bit resolution which result in significant performance degradations, especially when constellations with order greater than 4 are employed. To that end, in this work a novel SLP approach is developed that supports systems with DACs of any resolution and it is applicable for any type of constellation. As it is verified by the presented numerical results, the proposed approach exhibits significantly improved performance when constellations with order greater than 4 are employed and require reduced computational complexity, compared to the existing solutions for the 1-bit DAC case.
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| 25. |
- Viberg, Mats, et al.
(författare)
-
Array Processing in Correlated Noise Fields Using Instrumental Variables and Subspace Fitting
- 1992
-
Ingår i: The Twenty-Sixth Asilomar Conference on Signals, Systems and Computers. - : IEEE. - 0818631600 ; , s. 1147-1151
-
Konferensbidrag (refereegranskat)abstract
- An improved technique for direction-of-arrival estimation of temporally correlated signals in the presence of spatially colored, but temporally uncorrelated, noise is presented. The method is particularly suited to applications in which the receiver bandwidth exceeds that of the emitter signals. A statistical performance analysis shows that the method nearly achieves the deterministic Cramer-Rao bound if the signals are sufficiently predictable. A Monte Carlo experiment suggests that the theoretical estimation error variance well predicts the empirical mean square error down to the threshold region.
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| 26. |
- Viberg, Mats, et al.
(författare)
-
Detection and Estimation in Sensor Arrays using Weighted Subspace Fitting
- 1991
-
Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 39:11, s. 2436-2449
-
Tidskriftsartikel (refereegranskat)abstract
- The problem of signal parameter estimation of narrowband emitter signals impinging on an array of sensors is addressed. A multidimensional estimation procedure that applies to arbitrary array structures and signal correlation is proposed. The method is based on the recently introduced weighted subspace fitting (WSF) criterion and includes schemes for both detecting the number of sources and estimating the signal parameters. A Gauss-Newton-type method is presented for solving the multidimensional WSF and maximum-likelihood optimization problems. The global and local properties of the search procedure are investigated through computer simulations. Most methods require knowledge of the number of coherent/noncoherent signals present. A scheme for consistently estimating this is proposed based on an asymptotic analysis of the WSF cost function. The performance of the detection scheme is also investigated through simulations.
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| 27. |
- Viberg, Mats, et al.
(författare)
-
Sensor Array Processing Based on Subspace Fitting
- 1991
-
Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 39:5, s. 1110-1121
-
Tidskriftsartikel (refereegranskat)abstract
- Algorithms for estimating unknown signal parameters from the measured output of a sensor array are considered in connection with the subspace fitting problem. The methods considered are the deterministic maximum likelihood method (ML), ESPRIT, and a recently proposed multidimensional signal subspace method. These methods are formulated in a subspace-fitting-based framework, which provides insight into their algebraic and asymptotic relations. It is shown that by introducing a specific weighting matrix, the multidimensional signal subspace method can achieve the same asymptotic properties as the ML method. The asymptotic distribution of the estimation error is derived for a general subspace weighting, and the weighting that provides minimum variance estimates is identified. The resulting optimal technique is termed the weighted subspace fitting (WSF) method. Numerical examples indicate that the asymptotic variance of the WSF estimates coincides with the Cramer-Rao bound. The performance improvement compared to the other techniques is found to be most prominent for highly correlated signals.
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| 28. |
- Viberg, Mats, et al.
(författare)
-
Source Localization in the Presence of Model Uncertainties
- 1992
-
Ingår i: Proceedings of the 2nd Workshop on Adaptive Algorithms in Communications.
-
Konferensbidrag (refereegranskat)abstract
- In many signal processing applications high accuracy signal parameter estimation from sensor array data is a significant problem. Much of the recent work in array processing has focused on methods for high-resolution location estimation. Model based estimation techniques requre accurate knowledge of the so-called array manifold. In practise, the array response is often determined by measuring the array response when only one emitter is radiating and the signal parameters of which are allowed to vary in a known way. This paper addresses some of the practical issues that arise in generating the so-called array manifold from a finite collection of caibration vectors. For high-resolution signal parameter estimation techniques to be successful, the interpolated array manifold has to satisfy certain smoothness conditions. A paradigm for generating an array model from noise corrupted calibration vectors is developed. The key idea is to use a local parametric model of the sensor responses. The potential improvement using the suggested scheme rather than an ideal array model is demonstrated on real data collected from a full-scale hydro-acoustic array.
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| 29. |
- You, Lei, et al.
(författare)
-
Learning-Assisted Optimization for Energy-Efficient Scheduling in Deadline-Aware NOMA Systems
- 2019
-
Ingår i: IEEE Transactions on Green Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2473-2400. ; 3:3, s. 615-627
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, we study a class of minimum-energy scheduling problems in non-orthogonal multiple access (NOMA) systems. NOMA is adopted to enable efficient channel utilization and interference mitigation, such that base stations can consume minimal energy to empty their queued data in presence of transmission deadlines, and each user can obtain all the requested data timely. Due to the high computational complexity in resource scheduling and the stringent execution-time constraints in practical systems, providing a time-efficient and high-quality solution to 5G real-time systems is challenging. The conventional iterative optimization approaches may exhibit their limitations in supporting online optimization. We herein explore a viable alternative and develop a learning-assisted optimization framework to improve the computational efficiency while retaining competitive energy-saving performance. The idea is to use deep-learning-based predictions to accelerate the optimization process in conventional optimization methods for tackling the NOMA resource scheduling problems. In numerical studies, the proposed optimization framework demonstrates high computational efficiency. Its computational time is insensitive to the input size. The framework is able to provide optimal solutions as long as the learning-based predictions satisfy a derived optimality condition. For the general cases with imperfect predictions, the algorithmic solution is error-tolerable and performance scaleable, leading the energy-saving performance close to the global optimum.
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| 30. |
- You, Lei, et al.
(författare)
-
Power and Load Optimization in Interference-Coupled Non-Orthogonal Multiple Access Networks
- 2018
-
Ingår i: 2018 IEEE Global Communications Conference (GLOBECOM). - : IEEE. - 9781538647271
-
Konferensbidrag (refereegranskat)abstract
- Towards energy savings in large-scale non-orthogonal multiple access (NOMA) networks, we investigate power and load optimization for multi-cell and multi-carrier NOMA systems in this paper. To capture the coupling relation of mutual interference among cells, firstly, we extend a load-coupling model from orthogonal multiple access (OMA) to NOMA networks. Next, with this analytical tool, we formulate the considered optimization problem in NOMA-based load-coupled systems, where optimizing load, power, and determining decoding order are the key aspects in the optimization. Theoretically, we prove that the minimum network energy consumption can be achieved by using all the time-frequency resources in each cell to deliver users' demand. To achieve the optimal load and enable efficient power optimization, we develop a power-adjustment algorithm. Numerical results demonstrate promising energy-saving gains of NOMA over OMA in large-scale cellular networks, in particular for the high-demand and resource-limited scenarios.
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