Orthogonal Time-Frequency Space (OTFS) Aided Media-Based Modulation System For 6G and Beyond Wireless Communications Networks

This paper proposes a new orthogonal time frequency space (OTFS)-based index modulation system called OTFS-aided media-based modulation (MBM) scheme (OTFS-MBM), which is a promising technique for high-mobility wireless communication systems. The OTFS technique transforms information into the delay-Doppler domain, providing robustness against channel variations, while the MBM system utilizes controllable radio frequency (RF) mirrors to enhance spectral efficiency. The combination of these two techniques offers improved bit error rate (BER) performance compared to conventional OTFS and OTFS-based spatial modulation (OTFS-SM) systems. The proposed system is evaluated through Monte Carlo simulations over high-mobility Rayleigh channels for various system parameters. Comparative throughput, spectral efficiency, and energy efficiency analyses are presented, and it is shown that OTFS-MBM outperforms traditional OTFS and OTFS-SM techniques. The proposed OTFS-MBM scheme stands out as a viable solution for sixth generation (6G) and next-generation wireless networks, enabling reliable communication in dynamic wireless environments.

A New OTFS-Based Index Modulation System for 6G and Beyond: OTFS-Based Code Index Modulation

This paper proposes the orthogonal time frequency space-based code index modulation (OTFS-CIM) scheme, a novel wireless communication system that combines OTFS modulation, which enhances error performance in high-mobility Rayleigh channels, with CIM technique, which improves spectral and energy efficiency, within a single-input multiple-output (SIMO) architecture. The proposed system is evaluated through Monte Carlo simulations for various system parameters. Results show that increasing the modulation order degrades performance, while more receive antennas enhance it. Comparative analyses of error performance, throughput, spectral efficiency, and energy saving demonstrate that OTFS-CIM outperforms traditional OTFS and OTFS-based spatial modulation (OTFS-SM) systems. Also, the proposed OTFS-CIM system outperforms benchmark systems in many performance metrics under high-mobility scenarios, making it a strong candidate for sixth generation (6G) and beyond.

Deep-Learning Based Reconfigurable Intelligent Surfaces for Intervehicular Communication

This letter proposes a novel deep neural network (DNN) assisted cooperative reconfigurable intelligent surface (RIS) scheme and a DNN-based symbol detection model for intervehicular communication over cascaded Nakagami-m fading channels. In the considered realistic channel model, the channel links between moving nodes are modeled as cascaded Nakagami-m channels, and the links involving any stationary node are modeled as Nakagami-m fading channels, where all nodes between source and destination are realized with RIS-based relays. The performances of the proposed models are evaluated and compared with the conventional methods in terms of bit error rates (BER). It is exhibited that the DNN-based systems show near-identical performance with low system complexity.

Space-Time Block Coded Reconfigurable Intelligent Surface-Based Received Spatial Modulation

Reconfigurable intelligent surface (RIS) structures reflect the incident signals by adjusting phase adaptively according to the channel condition where doing transmission in order to increase signal quality at the receiver. Besides, the spatial modulation (SM) technique is a possible candidate for future energy-efficient wireless communications due to providing better throughput, low-cost implementation and good error performance. Also, Alamouti's space-time block coding (ASBC) is an important space and time coding technique in terms of diversity gain and simplified ML detection. In this paper, we proposed the RIS assisted received spatial modulation (RSM) scheme with ASBC, namely RIS-RSM-ASBC. The termed RIS is portioned by two parts in the proposed system model. Each one is utilized as an access point (AP) to transmit its Alamouti coded information while reflecting passive signals to the selected received antenna. The optimal maximum likelihood (ML) detector is designed for the proposed RIS-RSM-ASBC scheme. Extensive computer simulations are conducted to corroborate theoretical derivations. Results show that RIS-RSM-ASBC system is highly reliable and provides data rate enhancement in contrast to conventional RIS assisted transmit SM (RIS-TSM), RIS assisted transmit quadrature SM (RIS-TQSM), RIS assisted received SM (RIS-RSM), RIS assisted transmit space shift keying with ASBC (RIS-TSSK-ASBC) and RIS-TSSK-VBLAST schemes.

Deep-Learning Assisted Reconfigurable Intelligent Surfaces for Cooperative Communications

Reconfigurable intelligent surfaces (RISs) are software-controlled passive devices to reflect incoming signals from the source ($S$) to destination ($D$), just like a relay ($R$) with optimum signal strength, improving the performance of wireless communication networks. The configurable nature of the RIS can provide network designers the flexibility to use in a stand-alone or cooperative configuration with many advantages over conventional networks. In this paper, two new deep neural networks (DNN) assisted cooperative RIS models, namely DNN$_R$\:-\:CRIS and DNN$_{R, D}$\:-\:CRIS, are proposed for cooperative communications. In these two models, the potential of RIS deployment as a relaying element in a next-generation cooperative network is investigated using deep learning (DL) techniques as a tool for optimizing the RIS. To reduce maximum likelihood (ML) complexity at the $D$, unlike the DNN$_R$\:-\:CRIS, in the DNN$_{R, D}$\:-\:CRIS model, a new DNN based symbol detection method is presented for the same network model. For a different number of relays and receiver configurations, bit error rate (BER) performance results of the proposed DNN$_R$\:-\:CRIS, DNN$_{R, D}$\:-\:CRIS models and traditional cooperative RIS (CRIS) scheme (without DNN) are presented for a multi-relay cooperative communication scenario with path loss effects.