International audienceThis paper proposes a multiple-input multiple-output (MIMO) transmission scheme for M-ary modulations, called Spatially Modulated Orthogonal Space-Time Block Coding (SM-OSTBC), based on the concept of Spatial Constellation (SC) codewords introduced by Le at el. . In the proposed scheme, transmit codeword matrices are generated by multiplying SC matrices with codewords constructed from Orthogonal Space-time Block Codes (O-STBC). The maximum spectral efficiency of the proposed scheme is equal to (n_T-2+ log_2 M) bpcu, where n_T is the number of transmit antennas and M is the modulation order. The SC matrices provide a means of carrying information bits together with the O-STBC codewords and allow the SM-OSTBC scheme to achieve second-order transmit diversity by satisfying the non-vanishing determinant property. A systematic method to design the SC codewords for an even number of transmit antennas greater than 3 is presented. A single-stream maximum-likelihood (ML) decoder, which requires a low computational complexity thanks to the structure of the SM-OSTBC codewords and to the orthogonality of the O-STBCs, and a sphere decoder with further reduced signal processing complexity are developed. The bit error rate (BER) performance of the proposed scheme is studied by using both theoretical union bound analysis and computer simulations. Finally, simulation results are presented in order to compare BER performance, energy efficiency and decoding complexity of the proposed scheme with those of several existing MIMO transmission schemes

This paper proposes a deep learning (DL)-aided multicarrier (MC) system operating on fading channels, where both modulation and demodulation blocks are modeled by deep neural networks (DNNs), regarded as the encoder and decoder of an autoencoder (AE) architecture, respectively. Unlike existing AE-based systems, which incorporate domain knowledge of a channel equalizer to suppress the effects of wireless channels, the proposed scheme, termed as MC-AE, directly feeds the decoder with the channel state information and received signal, which are then processed in a fully data-driven manner. This new approach enables MC-AE to jointly learn the encoder and decoder to optimize the diversity and coding gains over fading channels. In particular, the block error rate of MC-AE is analyzed to show its higher performance gains than existing hand-crafted baselines, such as various recent index modulation-based MC schemes. We then extend MC-AE to multiuser scenarios, wherein the resultant system is termed as MU-MC-AE. Accordingly, two novel DNN structures for uplink and downlink MU-MC-AE transmissions are proposed, along with a novel cost function that ensures a fast training convergence and fairness among users. Finally, simulation results are provided to show the superiority of the proposed DL-based schemes over current baselines, in terms of both the error performance and receiver complexity.

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In this paper, a low-complexity linear precoding algorithm based on the principal component analysis technique in combination with the conventional linear precoders, called Principal Component Analysis Linear Precoder (PCA-LP), is proposed for massive MIMO systems. The proposed precoder consists of two components: the first one minimizes the interferences among neighboring users and the second one improves the system performance by utilizing the Principal Component Analysis (PCA) technique. Numerical and simulation results show that the proposed precoder has remarkably lower computational complexity than its low-complexity lattice reduction-aided regularized block diagonalization using zero forcing precoding (LC-RBD-LR-ZF) and lower computational complexity than the PCA-aided Minimum Mean Square Error combination with Block Diagonalization (PCA-MMSE-BD) counterparts while its bit error rate (BER) performance is comparable to those of the LC-RBD-LR-ZF and PCA-MMSE-BD ones.

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