Abstract-We compare filter bank multicarrier (FBMC) and orthogonal frequency-division multiplexing (OFDM) in the uplink of a multiple access network. Our study reveals that the high sensitivity of OFDM to carrier frequency offset (CFO) among different users and the need for interference cancellation methods to reduce this sensitivity leads to very complex and yet not very high performance systems. In FBMC-based networks, on the other hand, near-perfect performance is achieved without any need for interference cancellation, thanks to the excellent frequency localized filters used in the realization of FBMC systems.Index Terms-Carrier frequency offset (CFO), computational complexity, filter bank multicarrier (FBMC), offset quadrature amplitude modulation (OQAM), orthogonal frequency division multiple access (OFDMA).

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Accurate estimation and correction of channel distortions and carrier frequency offset (CFO) are of a great importance in any multicarrier communication system. Hence, in this paper, we propose data‐aided CFO and channel estimation techniques for both multiuser uplink and downlink of the generalized frequency division multiple access (GFDMA). Our proposed solutions jointly estimate the CFO and channel responses based on the maximum‐likelihood criterion. To simplify the implementation of the proposed estimation algorithms, we suggest a preamble composed of two similar Zadoff‐Chu training sequences in a generalized frequency division multiplexing block. It is worth mentioning that our proposed technique can estimate both integer and fractional CFO values without any limitation on the acquisition range of CFO. In the uplink phase, each user aligns its carrier frequency with the base station using the estimated CFO in the downlink. However, the CFO estimates may get outdated for the uplink transmission. Thus, residual CFOs may still remain in the received signal at the base station. While being trivial in the downlink, CFO correction is a challenging task in the uplink. Thus, we also propose a joint CFO correction and channel equalization technique for the uplink of GFDMA systems. Finally, we evaluate our proposed estimation and correction algorithms in terms of estimation mean square error and bit error rate performance through simulations.

GFDM (generalized frequency division multiplexing) is a non-orthogonal waveform that is being discussed as a candidate for the fifth generation of wireless communication systems (5G). GFDM is a multicarrier technique with circular pulse shaping that is designed in a way to address emerging applications in 5G networks such as Internet of Things (IoT) and machine-to-machine communications (M2M). The same as other multicarrier systems, GFDM suffers from a high peak to average power ratio (PAPR). To attack PAPR problem, in this paper, we propose a polynomial based companding method with iterative expansion that is called polynomial-based companding technique (PCT). Based on our simulation results, a great amount of PAPR reduction can be achieved through utilization of our proposed technique. Through simulations, we have also investigated the bit error rate (BER) performance of the system while adopting our PCT method. Our simulations reveal that there is a tradeoff between PAPR reduction and BER performance.

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Estimation of carrier frequency offset (CFO) is a challenging task in practical systems specifically in the uplink of multiuser systems where multiple CFOs are present in the received signal. Massive MIMO as a multiuser technique has recently attracted a great deal of attention among researchers. However, to the best of our knowledge, there is no study looking into the joint estimation of CFOs and wireless channel in orthogonal frequency division multiplexing (OFDM) based massive MIMO systems. Therefore, in this paper, we propose joint estimation of multiple CFOs and the users' channel responses based on the maximum likelihood (ML) criteria in such systems. We propose to use the zadoff-chu (ZC) training sequences to reduce the implementation complexity. Additionally, utilization of ZC sequences for training simplifies the multidimensional grid search problem of estimating multiple CFOs and converts it into a set of line search problems, i.e., one line search problem per user. Also this sequence has a low peak to average ratio (PAPR). Finally, we show the efficacy of our proposed algorithm through numerical simulations.

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