Upcoming modern communications are based on 5G specifications and aim at providing solutions for novel vertical industries. One of the major changes of the physical layer is the use of Low-Density Parity-Check (LDPC) code for channel coding. Although LDPC codes introduce additional computational complexity compared with the previous generation, where Turbocodes where used, LDPC codes provide a reasonable trade-off in terms of complexity-Bit Error Rate (BER). In parallel to this, Deep Learning algorithms are experiencing a new revolution, specially to image and video processing. In this context, there are some approaches that can be exploited in radio communications. In this paper we propose to use Autoencoding Neural Networks (ANN) jointly with a Deep Neural Network (DNN) to construct Autoencoding Deep Neural Networks (ADNN) for demapping and decoding. The results will unveil that, for a particular BER target, 3 dB less of Signal to Noise Ratio (SNR) is required, in Additive White Gaussian Noise (AWGN) channels.

Index Modulations, in the form of Spatial Modulation or Polarized Modulation, are gaining traction for both satellite and terrestrial next generation communication systems. Adaptive Spatial Modulation based links are needed to fully exploit the transmission capacity of time-variant channels. The adaptation of code and/or modulation requires a real-time evaluation of the channel achievable rates. Some existing results in the literature present a computational complexity which scales quadratically with the number of transmit antennas and the constellation order. Moreover, the accuracy of these approximations is low and it can lead to wrong Modulation and Coding Scheme selection. In this work we apply a Multilayer Feedforward Neural Network to compute the achievable rate of a generic Index Modulation link. The case of two antennas/polarizations is analyzed in depth, showing not only a one-hundred fold decrement of the Mean Square Error in the estimation of the capacity as compared with existing analytical approximations, but also a fifty times reduction of the computational complexity. Moreover, the extension to an arbitrary number of antennas is explained and supported with simulations. More generally, neural networks can be considered as promising candidates for the practical estimation of complex metrics in communication related settings.

Mutual Information is the metric that is used to perform link adaptation, which allows to achieve rates near capacity. The computation of adaptive transmission modes is achieved by employing the mapping between the Signal to Noise Ratio and the Mutual Information. Due to the high complexity of the computation of the Mutual Information, this process is performed off-line via Monte Carlo simulations, whose results are stored in look-up tables. However, in Index Modulations, such as Spatial Modulation or Polarized Modulation, this is not feasible since the constellation and the Mutual Information are channel dependent and it would require to compute this metric at each time instant if the channel is time varying. In this paper, we propose different approximations in order to obtain a simple closed-form expression that allows to compute the Mutual Information at each time instant and thus, making feasible the link adaptation.

In this paper, we present a novel modulation technique for dual-polarization communication systems, which reduces the error rate compared with the existent schemes. This modulation places the symbols in a 3-D constellation, rather than the classic approach of 2-D. Adjusting the phase of these symbols depending on the information bits, we are able to increase the bit rate. Hence, the proposed scheme conveys information by selecting both polarization state and the phase of radiated electromagnetic wave. We also analyses the performance of the 3-D polarized modulation (PMod) for different constellation sizes and we obtain a curve of rate adaptation. Finally, we compare the proposed 3-D PMod with the other existing schemes, such as single polarization phase shift keying and double polarization vertical Bell Laboratories layer space-time, both carrying the same number of information bits. The results show that the 3-D PMod always outperforms all other schemes, except for low-order modulation. Therefore, we can conclude that the 3-D PMod is an excellent candidate for medium- and high-modulation-order transmissions.

Determining the capacity of a modulation scheme is a fundamental topic of interest. Index modulations (IM), such as spatial modulation (SMod), polarized modulation (PMod), or frequency index modulation (FMod), are widely studied in the literature. However, finding a closed-form analytical expression for their capacity still remains an open topic. In this paper, we formulate closed-form expressions for the instantaneous capacity of IM, together with its second- and fourth-order approximations. We show that, in average, the second-approximation error tends to zero for low signal to noise ratio (SNR) and is o(SNR). Also, a detailed analysis of the ergodic capacity over Rayleigh, Rice, and Nakagami-m channel distributions is provided. As application of the capacity analysis, we leverage the proposed expressions to compute the ergodic capacities of SMod for different antenna configuration and correlations, PMod for different channel components and conditions, and FMod for different frequency separations.

This paper presents the novel application of polarized modulation (PMod) for increasing the throughput in mobile satellite transmissions. One of the major drawbacks in mobile satellite communications is the fact that the power budget is often restrictive, making it unaffordable to improve the spectral efficiency without an increment of transmitted power. By using dual polarized antennas in the transmitter and receiver, the PMod technique achieves an improvement in throughput of up to 100% with respect to existing deployments, with an increase of less than 1 dB at low Eb/N0regime. Additionally, the proposed scheme implies minimum hardware modifications with respect to the existing dual polarized systems and does not require additional channel state information at the transmitter; thus it can be used in current deployments. Demodulation (i.e., detection and decoding) alternatives, with different processing complexity and performance, are studied. The results are validated in a typical mobile interactive scenario, the newest version of TS 102 744 standard [Broadband Global Area Network (BGAN)], which aims to provide interactive mobile satellite communications.