In this letter, a new precoded filter bank system based on the pruned discrete Fourier transform spread filter-bank multicarrier (Pruned DFTs FBMC) modulation is proposed. The new scheme utilizes only the double rate transmission idea combined with a data allocation strategy, without the use of Offset Quadrature Amplitude Modulation (OQAM). In addition, we introduce an iterative equalizer in order to obtain better performance, work with larger filter overlap factors and consequently a better spectral localization. The whole system analysis is presented in a new matrix structure in order to make the precoding stage clearer.

The use of monitoring sensors is increasingly present in the context of precision agriculture. Usually, these sensor nodes (SNs) alternate their states between periods of activation and hibernation to reduce battery usage. When employing unmanned aerial vehicles (UAVs) to collect data from SNs distributed over a large agricultural area, we must synchronize the UAV route with the activation period of each SN. In this article, we address the problem of optimizing the UAV path through all the SNs to reduce its flight time, while also maximizing the SNs’ lifetime. Using the concept of timeslots for time base management combined with the idea of flight prohibition list, we propose an efficient algorithm for discovering and reconfiguring the activation time of the SNs. Experimental results were obtained through the development of our own simulator—UAV Simulator. These results demonstrate a considerable reduction in the distance traveled by the UAV and also in its flight time. In addition, the model provides a reduction in transmission time by SNs after reconfiguration, thus ensuring a longer lifetime for the SNs in the monitoring environment, as well as improving the freshness and continuity of the gathered data, which support the decision-making process.

This work proposes a new precoded filter bank (FB) system via a two-dimensional (2D) fast Fourier transform (2D-FFT). Its structure is similar to Orthogonal Time Frequency Space (OTFS) systems, where the OFDM transmitter is changed to a filter bank multi-carrier (FBMC) one, thus obtaining a lower out-of-band emission. The complex orthogonality of the FBMC transmission is guaranteed by using pre-coding based on a discrete Fourier transform, which is also used to implement the two-dimensional fast Fourier transform. Furthermore, due to the filtering process, the proposed system obtains a good error performance using a simple equalizer in the time-frequency domain. In this sense, we also introduce a hybrid receiver in the proposed system to exploit this feature. First, the time-frequency domain equalization is performed, followed by an interference cancellation on the delay-Doppler domain. Using the delay-Doppler domain detection, the simulation results show that the proposed system obtains an error performance similar to the OTFS system. Already using the simple equalizer in the time-frequency domain, the proposed scheme presents great advantage compared to the OTFS with the same equalizer in the studied scenarios.

Orthogonal Time Frequency Space (OTFS) is a promising modulation scheme that works in the delay-Doppler (DD) domain, offering resistance to frequency selective fading and time-varying channels. Thus, OTFS channel estimation assumes great significance for successful transmission. Typically, it requires allocating pilots in the DD domain, which often results in an increase in the peak-to-average power ratio (PAPR) and high complexity to detect the received signal. In response to these issues, we present a solution that estimates the channel in the time-frequency (TF) domain. In addition, although several works in the literature present solutions for OTFS channel estimation, few consider the presence of high-power amplifiers (HPAs) and explain the impact of nonlinear effects on channel estimation and system performance. Starting from channel estimation based on preambles and pilots in the TF domain, we present a solution capable of obtaining reliable channel estimation using a long short-term memory (LSTM) network in highly selective channel conditions, effectively compensating for nonlinearities in signal reception. Our results validate the effectiveness of the proposed solution, highlighting its potential to improve the performance and robustness of OTFS communication systems in real scenarios with nonlinear HPA effects.

Vehicular communications are an important focus of studies for 5G applications and beyond. However, in a scenario with doubly-selective and highly variable channel characteristics, tracking the wireless channel to ensure communication reliability is one of the main goals to provide communication efficiency. Moreover, multicarrier modulation schemes usually employed in these scenarios are susceptible to nonlinear distortions caused by high power amplifiers (HPA) at the transmitter, impairing the channel estimation and detection capability of the receivers. In view of these requirements and challenges, in the present work we propose a low complexity estimator based on the long short-term memory (LSTM) network, followed by a neural network (NN) in order to improve the data-pilot aided (DPA) estimation. In addition, we propose a new technique to exploit the characteristics of the vehicular channel, by sampling the subcarriers used at the input of the LSTM. Thus, besides tracking the variations of the wireless channel, the LSTM network is also used to interpolate the channel estimates for all subcarriers. The simulation results show the superiority of the proposed scheme in comparison with other state-of-the-art schemes, especially in high signal-to-noise ratio (SNR) regimes. Furthermore, the proposed scheme significantly reduces the computational complexity due to the subcarrier sampling procedure.

The discrete Fourier transform (DFT) can be used as a precoding technique in a filter bank system to partially recover complex orthogonality. In this work, a proposal is made to make the waveform design more flexible through the introduction of a parameter called rate factor. With this parameter, we generalize the DFT precoded filter bank system and modify the data transmission format when compared to the conventional system. Such modification allows for better resource allocation and lower PAPR. Furthermore, theoretical and simulation results show better error performance in high mobility scenarios.