| 1. |
- Carvalho, E. de, et al.
(author)
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EU FP7 INFSO-ICT-317669 METIS, D3.1 Positioning of multi-node/multi-antenna technologies
- 2013
-
Reports (other academic/artistic)abstract
- This document describes the research activity in multi-node/multi-antenna technologies within METIS and positions it with respect to the state-of-the-art in the academic literature and in the standardization bodies. Based on the state-of-the-art and as well as on the METIS objectives,we set the research objectives and we group the different activities (or technology components) into research clusters with similar research objectives. The technologycomponents and the research objectives have been set to achieve an ambidextrous purpose. On one side we aim at providing the METIS system with those technological components that are a natural but non-trivial evolution of 4G. On the other side, we aim at seeking for disruptivetechnologies that could radically change 5G with respect to 4G. Moreover, we mapped the different technology components to METIS’ other activities and to the overall goals of theproject.
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| 2. |
- Fantini, R, et al.
(author)
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EU FP7 INFSO-ICT-317669 METIS, D3.2 First performance results for multi-node/multi-antenna transmission technologies
- 2014
-
Reports (other academic/artistic)abstract
- This deliverable describes the current results of the multi-node/multi-antenna technologies investigated within METIS and analyses the interactions within and outside Work Package 3. Furthermore, it identifies the most promising technologies based on the current state of obtained results. This document provides a brief overview of the results in its first part. The second part, namely the Appendix, further details the results, describes the simulation alignment efforts conducted in the Work Package and the interaction of the Test Cases. The results described here show that the investigations conducted in Work Package 3 are maturing resulting in valuable innovative solutions for future 5G systems.
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| 3. |
- Chan, W. M., et al.
(author)
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Kolmogorov Model for Large Millimeter-Wave Antenna Arrays : Learning-based Beam-Alignment
- 2019
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In: Conference Record - Asilomar Conference on Signals, Systems and Computers. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 411-415
-
Conference paper (peer-reviewed)abstract
- A new approach is presented to the problem of beam alignment for large-dimensional millimeter-wave antenna systems, with a single radio-frequency chain, based on the application of the Kolmogorov Model (KM) framework to enable learning-based beam alignment. Unlike the conventional exhaustive search-based approach, the proposed KM does not require the entire beam space search, i.e., the number of beam soundings can be extremely smaller than the conventional approach, which is achieved by exploiting the predictive power of KM. We show, across several metrics, that by just sounding 25% of the beams, the proposed method approaches the performance of the exhaustive search method. Simulation results that validate the training and test performance of KM and illustrate the new method with significantly reduced overhead are presented.
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| 4. |
- Ghauch, H., et al.
(author)
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Learning Kolmogorov Models for Binary Random Variables
- 2020
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In: Conference Record - Asilomar Conference on Signals, Systems and Computers. - : IEEE Computer Society. ; , s. 1204-1209
-
Conference paper (peer-reviewed)abstract
- We consider a set of binary random variables and address the open problems of inferring provable logical relations among these random variables, and prediction. We propose to solve these two problems by learning a Kolmogorov model (KM) for these random variables. Our proposed framework allows us to derive provable logical relations, i.e., mathematical relations among the outcomes of the random variables in the training set, and thus, extract valuable relations from that set. The proposed method to discover the logical relations is established using implication in mathematical logic, thereby offering a provable analytical basis for asserting these relations, unlike similar factorization methods. We also propose an efficient algorithm for learning the KM model and show its first-order optimality, despite the combinatorial nature of the learning problem. We illustrate our general framework by applying to recommendation systems and gene expression data. In recommendation systems, the proposed logical relations identify groups of items for which a user liking an item logically implies that he/she likes all items in that group. Our work is a significant step toward interpretable machine learning.
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