Iterative adaptive signal predistortion for satellite communications

Abstract

The expected increase of data rates in future satellite communications (DVB-S2X) will require higher-order modulations and sharper roll-off, which makes the transmission more sensitive to non-linear interference due to on-board amplifiers. One solution is the predistortion of the signal that is carried out at the transmitter. Iterative predistortion based on the contraction mapping theorem has shown to be a good solution to mitigate the effects of nonlinearities but suffers from convergence depending on the chosen norm. In this paper, we propose to adapt the method with a feedback from the channel to ensure the convergence with the best linearization performance for a given number of iterations. We compare both schemes according to different figures of merit (adjacent channel interference factor, total degradation and normalized mean square error).

Predistortion-Based Linearization for 5G and Beyond Millimeter-Wave Transceiver Systems: A Comprehensive Survey

“…PD-based linearization, while considering satellite communications, has been integrated with a more practical and sophisticated adaptation process to meet the target performance of future high throughput satellites [237]- [241]. Both APD and DPD solutions may be considered for realizing a nearoptimal linearization across numerous frequency applications [241].…”

Comparison of signal predistortion schemes based on the contraction mapping for satellite communications with channel identification

“…This section focuses on the data-optimized signal predistortion technique proposed in [4]. In that case, the nonlinear channel consists of the transmitter filter, the satellite transponder and the receiver.…”

“…The condition for the operator to be a contraction mapping is the same as the one defined in Section III. Now, let us use the data predistortion scheme to define a data-optimized signal predistortion scheme as in [4]. The data-based model, denoted by F, and the signal-based model, denoted by R can be linked as illustrated in Fig.…”

“…In [4], we considered satellite communications and we proposed to apply the contraction mapping theorem to develop a data-optimized signal predistortion, with an optimization error defined from the data sequence. Assuming perfect channel identification, the resulting scheme, referred to as dataoptimized signal predistortion in this paper, outperforms one of the best signal predistortion technique in satellite communications [3] 1 .…”

“…We first describe the linearization method of [5]- [8] that leads to a signaloptimized signal predistortion suited for satellite communications. Then we present the data-optimized signal predistorsion scheme [4]. In order to determine whether it is better to define the optimization error from the data sequence or from the modulated signal, we compare both schemes in terms of normalized minimum mean square error (NMSE), adjacent channel interference (ACI) and total degradation (TD).…”