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The intelligent reflecting surface (IRS) is a novel and innovative communication technology that aims at the control of the wireless environment. The IRS is considered as a promising technology for sixth-generation wireless communication. In the last few years, machine learning has emerged as a powerful tool for solving complex problems in diverse application areas. In this paper, we propose a convolutional neural network (CNN)-based demodulation technique called Demod-CNN in IRS-based wireless communication for multiple users. A multiple-input multiple-output based orthogonal multiple frequency division multiplexing system is considered for channel modeling. The received signal data are used for training and testing the model. The simulation results show that the proposed model performs better than the conventional demodulation technique. [ABSTRACT FROM AUTHOR]

The reconfigurable intelligent surface (RIS) and rate-splitting multiple access (RSMA) are considered as promising technologies for the beyond Fifth-Generation (B5G) and Sixth-Generation (6G) wireless systems by controlling the propagation environment, which attenuates the transmitted signal, and by managing the interference by splitting the user message into common and private messages. Because conventional RIS elements have each impedance connected to the ground, the sum-rate performance improvement of the RIS is limited. Therefore, the new RISs, which have impedance elements connected to each other, have been proposed recently. To be more adaptive to each channel, the optimization of the grouping of the RIS elements is required. Furthermore, since the solution of the optimal rate-splitting (RS) power-splitting ratio is complex, the value should be simply optimized to be more practical in the wireless system. In this paper, the grouping scheme of the RIS elements according to the user scheduling and the solution of the RS power-splitting ratio based on fractional programming (FP) are proposed. The simulation results showed that the proposed RIS-assisted RSMA system achieved a high sum-rate performance compared to the conventional RIS-assisted spatial-division multiple access (SDMA) system. Therefore, the proposed scheme can perform adaptively for the channel and has a flexible interference management. Furthermore, it can be a more suitable technique for B5G and 6G. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid beamforming and relay selection scheme for spectral efficiency maximization in a non-regenerative multi-relay multi-input multi-output (MIMO) system. The analog beamforming component in the radio-frequency (RF) domain must follow an element-wise constant modulus constraint, which makes the underlying design problem mathematically intractable and therefore, it is quite challenging to obtain the global optimal solution. To address this problem, phase-only precoding/combining matrices are derived by maximizing the end-to-end received signal-to-noise ratio (SNR) under transmit power constraint at the source and each relay node. This task is achieved by decomposing the original complicated optimization problem into two independent components. The first component designs the RF precoder/combiner at source and relay nodes by maximizing the received SNR at relay nodes. While the second component attempts to derive the analog precoder/combiner at relay nodes and destination by maximizing the received SNR at the destination. Digital baseband processing matrices are obtained by deriving the closed-form expression, which minimizes interference among different sub-channels. Finally, the relay selection is made by maximizing the overall SNR from the source to the destination. Computer simulations reveal that the performance of the proposed algorithm is close to its fully digital counterpart and approximately 6% higher than the specified relay-assisted hybrid beamforming techniques. Moreover, the proposed method achieves more than 15% higher performance in a sparse scattering environment when compared with the given relay selection techniques. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) has contributed new unused spectrum in addition to the traditional radio frequency communication and can play a significant role in wireless communication. The adaptation of VLC technology enhances wireless connectivity both in indoor and outdoor environments. Multiple-input multiple-output (MIMO) communication has been an efficient technique for increasing wireless communications system capacity and performance. With the advantages of MIMO techniques, VLC can achieve an additional degree of freedom. In this paper, we systematically perform a survey of the existing work based on MIMO VLC. We categorize the types of different MIMO techniques, and a brief description is given. Different problem-solving approaches are given in the subsequent sections. In addition, machine learning approaches are also discussed in sufficient detail. Finally, we identify the future study direction for MIMO-based communication in VLC. [ABSTRACT FROM AUTHOR]

For a low complexity signal detector to reduce the power consumption for multiple input and multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems, the depth-first sphere decoding (DFSD) detection scheme was proposed. However, the DFSD detection scheme still has high complexity in the hardware implementation. The complexity is especially high when the signal-to-noise ratio (SNR) is low. Therefore, this paper proposes an adaptive DFSD detection scheme. The proposed detection scheme arrays nodes, sorting by ascending order of squared Euclidean distance (ED) at the top layer of tree structure. Then, the proposed detection scheme uses the different number of nodes according to thresholds based on channel condition. In the simulation results, the proposed detection scheme has similar error performance and low complexity compared with the conventional DFSD detection scheme. Therefore, the proposed detection scheme reduces the power consumption in the signal detector. [ABSTRACT FROM AUTHOR]