Abstract
In this paper, we consider large-scale MIMO systems and we address the channel estimation problem. We propose an iterative receiver consisting of the cascade of a semi-blind least-squares channel estimation algorithm with a simplicity-based detection algorithm for finite-alphabet signals (FAS and FAS-SAC). A minimum number of pilot sequences is used to get an initial channel estimation. The detection algorithm outputs are then used to refine it gradually. Two feeding methods are studied. The first one uses raw detection outputs. The second one is based on hard decisions and enables better performance. Theoretical MSEs are calculated in both cases. Simulations assess the efficiency of the proposed iterative procedure compared to the state-of-the-art methods and show that it performs very close to the ideal scenario where all the communication frame sequences are known.
Highlights
The predicted exponential growth in the number of mobile connected machines and the traffic of data they represent motivate 5G designers to look for new technologies and approaches to address the mounting demand
We show that the proposed approach can be applied in underdetermined systems contrary to the stateof-the art methods. (ii) an analytical expression of the Cramer-Rao bound (CRB) when the channel estimator is fed by raw detection outputs, (iii) a second feeding strategy based on hard decisions on data from detection outputs for further channel state information (CSI) estimate accuracy (iv) and the theoretical computation of corresponding asymptotic
1) Comparison with EM algorithm: In Fig. 4, we compare the mean square error (MSE) of the proposed iterative channel estimation algorithm based on soft decision FAS-output under one iteration given in (23) to the Maximum Likelihood (ML) estimators and the EM algorithm with two iterations described in Section III for an overdetermined system with N = 8 and n = 64
Summary
It was theoretically demonstrated that usual schemes cannot achieve the sumrate capacity of multiuser wireless systems and the maximum number of supported users is limited by the total amount of orthogonal resources [1]. To overcome this problem and in order to support massive connectivity of users and devices, enhanced technologies are needed. CSI is usually estimated thanks to known training sequences inserted in the data frame This approach is well investigated in several works. With uniform power allocation between pilots and data, the optimal number can be much larger than the number of transmit antennas, which reduces the spectrum efficiency and limits the benefit of large-scale MIMO systems.
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