## 1 Introduction
In [24], second-order statistics of the channel are exploited to derive the signal-to-interference-plus-noise ratio in each sub-carrier, so to realize adaptive coding and bit-power loading. An OFDM acoustic modem is presented in [25], with adaptive modulation being performed based on the channel delay and Doppler spread, measured by using chirp pilots. An information-dependent sub-carrier mapping is proposed for underwater video transmission in [26], so that important data are conveyed on the most reliable OFDM sub-channels, while less useful data are transmitted through the lower quality sub-carriers. The authors in [27] proposed a continuous phase modulation-based OFDM, outperforming standard implementations, and, thus, demonstrating itself to be more suited to UWACs. In [28], a novel fractional fast Fourier transform (FFT) OFDM system is presented, with amplitude shift keying (ASK) employed for sub-carriers' modulation. Specifically, the use of ASK achieves a better bandwidth efficiency with respect to other conventionally considered schemes and, furthermore, the use of fractional FFT in place of the conventional one allows problems related to carrier frequency offset to be more efficiently mitigated. All the solutions for OFDM optimization rely on channel state information (CSI), made available from estimation or prediction. Overall, channel estimation typically considers a bi-directional transmission of pilot signals between the communication nodes, in order to measure the corresponding CIR [29] and feed back the channel coefficients. For example, in [30] a novel adaptive denoising scheme is proposed to achieve a reliable channel estimation in OFDM-based UWACs affected by strong noise. The authors in [31] propose an optimized pilot-assisted channel estimation for OFDM, even though performance was measured under the unrealistic assumption of ideal synchronization and Doppler compensation. Unfortunately, due to the low speed of sound, estimation procedures are time consuming and they must be performed frequently in order to keep the CSI updated. So, the communication rate may be significantly reduced. Furthermore, the available channel coefficients may be outdated, since, during the estimation process, the channel may have already changed [32]. In order to overcome such problems, channel prediction can be exploited in place of estimation. Predictors are typically based on channel statistical models [33] and are developed in different approaches, such as recursive least squares (RLS) [34] and deep neural networks [35]. Channel prediction does not impact on data rate since it allows the reduction of overhead information to be transmitted. On the other hand, if the predicted channel deviates from the real one, inaccurate system optimization occurs and problems of reliability may occur. The work in [36] considers, instead, the joint use of channel estimation and prediction. However, it is proposed for a single-carrier binary phase shift keying (BPSK) scheme where transmission parameter adaptation is not addressed. The worthiness of estimation and/or prediction is discussed in [37] by comparing minimum mean square error channel estimation and auto-regressive (AR) channel prediction. Furthermore, the authors propose a mechanism for bandwidth adaptation, in order to provide channel-tailored performance and avoid interchannel interference (ICI). Finally, a possible approach for channel estimation may rely on deep-learning, as presented in [38]. The proposed solution is designed for Multiple-Input Multiple-Output (MIMO) OFDM systems, even though not specifically related to the underwater acoustic case.