Improved Data-Aided Channel Estimation in LTE PUCCH Using a Tensor Modeling Approach

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.