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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | da Silva, I. de Almeida, A. Cavalcanti, F. Baldemair, R. Falahati, S. |
| Copyright Year | 2010 |
| Abstract | NA In 3rd. Generation Partnership Project (3GPP) Long Term Evolution (LTE) systems, when no resources has been assigned in the uplink to a given user, the control information associated with Layers 1 and 2 in the protocol stack is conveyed back to the LTE base station (also known as eNodeB) through the so-called Physical Uplink Control Channel (PUCCH). In this work we consider the Format 2 of LTE PUCCH which conveys information about the channel status. At the eNodeB, conventional receivers generally resort to reference signals (RS), or pilot symbols, to perform channel estimation prior to symbol detection. In this paper, we propose a tensor modeling approach for a Data-Aided (DA) channel estimation in PUCCH. First, we formulate the practical channel estimation problem in PUCCH using the Parallel Factor (PARAFAC) tensor model. Based in this model, we resort to the Alternating Least Squares (ALS) algorithm as a DA-based channel estimator. Contrary to conventional RS-based channel estimation operating only on reference signals, the proposed algorithm also simultaneously exploits the energy of the data symbols of all the users, which is contained in PUCCH slots in order to iteratively estimate the user channel coefficients. As will be shown in our simulation results, improved channel estimation accuracy is obtained. |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 157352 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424464029 |
| ISSN | 19381883 |
| e-ISBN | 9781424464043 |
| DOI | 10.1109/ICC.2010.5502479 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-05-23 |
| Publisher Place | South Africa |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Channel estimation Tensile stress Base stations Radio access networks Diversity reception Maximum likelihood estimation Long Term Evolution Control systems Protocols Least squares approximation |
| Content Type | Text |
| Resource Type | Article |
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