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In this paper, a practical precoding method for the downlink of filter bank multicarrier-based (FBMC-based) massive multiple-input multiple-output (MIMO) is developed. The proposed method includes a two-stage precoder consisting of a fractionally spaced prefilter (FSP) per subcarrier for flattening/equalizing the channel across the subcarrier band, followed by a conventional precoder whose goal is to concentrate the signals of different users at their spatial locations. This way, each user receives only the intended information. In this paper, we take note that channel reciprocity may not hold perfectly in practical scenarios due to the mismatch of radio chains in uplink and downlink. Additionally, channel state information (CSI) at the base station may not be perfectly known. This, together with imperfect channel reciprocity can lead to detrimental effects on the downlink precoder performance. We theoretically analyze the performance of the proposed precoder in the presence of imperfect CSI and channel reciprocity calibration errors. This leads to an effective method for compensating these effects. Finally, we numerically evaluate the performance of the proposed precoder. Our results show that the proposed precoder leads to an excellent performance when benchmarked against OFDM. 

In this paper, we consider channel estimation problem in the uplink of filter bank multicarrier (FBMC) systems. We propose a pilot structure and a joint multiuser channel estimation method for FBMC. Opposed to the available solutions in the literature, our proposed technique does not rely on the flat-channel condition over each subcarrier band or any requirement for placing guard symbols between different users’ pilots. Our proposed pilot structure reduces the training overhead by interleaving the users’ pilots in time and frequency. Thus, we can accommodate a larger number of training signals within the same bandwidth and improve the spectral efficiency. Furthermore, this pilot structure inherently leads to a reduced peak-to-average power ratio (PAPR) compared with the solutions that use all the subcarriers for training. We analytically derive the Cramér-Rao lower bound (CRLB) and mean square error (MSE) expressions for our proposed method. We show that these expressions are the same. This confirms the optimality of our proposed method, which is numerically evaluated through simulations. Relying on its improved spectral efficiency, our proposed method can serve a large number of users and relax pilot contamination problem in FBMC-based massive MIMO systems. This is corroborated through simulations in terms of sum-rate performance for both single cell and multicell scenarios.