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.

Double Media-Based Modulation Scheme for High-Rate Wireless Communication Systems

Current wireless communication technologies are insufficient in the face of ever-increasing demands. Therefore, novel and high-performance communication systems are needed. In this paper, a novel high data rate and high-performance index modulation scheme called double media-based modulation (DMBM) is proposed. The DMBM system doubles the number of mirror activation patterns (MAPs) and the number of transmitted symbols compared to the traditional MBM system during the same symbol period. In this way, the spectral efficiency of the DMBM is doubled and the error performance improves as the number of bits carried in the indices increases. Performance analysis of the DMBM scheme is evaluated for $M$-ary quadrature amplitude modulation ($M$-QAM) on Rayleigh fading channels. The error performance of the proposed DMBM system is compared with spatial modulation (SM), quadrature SM (QSM), MBM, and double SM (DSM) techniques. Also, the throughput, complexity, energy efficiency, spectral efficiency, and capacity analyses for the proposed DMBM system and SM, QSM, MBM, and DSM systems are presented. All analysis results show that the proposed DMBM system is superior to the compared systems.

Antenna Selection For Receive Spatial Modulation System Empowered By Reconfigurable Intelligent Surface

Reconfigurable intelligent surface (RIS) enhances signal quality by adjusting the phase of electromagnetic waves in wireless communication. Spatial modulation (SM), a prominent index modulation (IM) technique, provides high spectral efficiency and low energy consumption. In this article, a new wireless communication system is proposed by combining capacity-optimized antenna selection (COAS), antenna correlation antenna selection (ACAS), and Euclidean distance-optimized antenna selection (EDAS)-supported RIS-empowered receive SM (RIS-RSM) system (AS-RIS-RSM) in a single-input multiple-output (SIMO) structure. The proposed AS-RIS-RSM schemes (COAS-RIS-RSM, ACAS-RIS-RSM, and EDAS-RIS-RSM) have superior features such as high spectral efficiency, high energy efficiency, and low error data transmission. Integrating COAS, ACAS, and EDAS techniques into the system enables the selection of the channel with the best conditions, thus increasing the error performance of the proposed system. Also, using RIS increases the error performance of the system by controlling the transmitted signal to a certain extent. The analytical ABER results of the proposed AS-RIS-RSM systems are derived and shown to overlap with simulation results. For the proposed systems, an optimal maximum likelihood (ML) detector and a sub-optimal low-complexity greedy detector (GD) are offered. Also, capacity analyses of the proposed AS-RIS-RSM systems are derived and it is observed that they have higher capacity compared to RIS-QAM/PSK and RIS-RSM systems. Then, computational complexity analyses of the proposed COAS-RIS-RSM, ACAS-RIS-RSM, and EDAS-RIS-RSM systems are presented. The proposed systems have been compared to counterpart wireless communication systems including RIS-RSM, RIS-QAM, and RIS-PSK under equivalent conditions, demonstrating that the proposed systems achieve better error performance.

Deep Learning Based Detection on RIS Assisted RSM and RSSK Techniques

The reconfigurable intelligent surface (RIS) is considered a crucial technology for the future of wireless communication. Recently, there has been significant interest in combining RIS with spatial modulation (SM) or space shift keying (SSK) to achieve a balance between spectral and energy efficiency. In this paper, we have investigated the use of deep learning techniques for detection in RIS-aided received SM (RSM)/received-SSK (RSSK) systems over Weibull fading channels, specifically by extending the RIS-aided SM/SSK system to a specific case of the conventional SM system. By employing the concept of neural networks, the study focuses on model-driven deep learning detection namely block deep neural networks (B-DNN) for RIS-aided SM systems and compares its performance against maximum likelihood (ML) and greedy detectors. Finally, it has been demonstrated by Monte Carlo simulation that while B-DNN achieved a bit error rate (BER) performance close to that of ML, it gave better results than the Greedy detector.

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.

Reconfigurable Intelligent Surface-Empowered Code Index Modulation for High-Rate SISO Systems

In this study, a novel index modulation based communication system is proposed by combining the recently popular code index modulation-spread spectrum (CIM-SS) and reconfigurable intelligent surface (RIS) techniques. This technique is called CIM-RIS in short. In this proposed system, in addition to the traditional modulated symbols, the spreading code indices also carry data by being embedded in the signal, and the reflection/scattering properties of the signals are voluntarily controlled via the RIS technique. Consequently, the proposed system consumes little energy while transmitting extra bits of information compared to the traditional RIS. Average bit-error error (ABER) analysis of the proposed system is carried out and the system complexity, energy efficiency, and throughput analyses are obtained. Performance analysis of the system is carried out on Rayleigh fading channels for the M-ary quadrature amplitude modulation (QAM) technique. It has been shown by computer simulations that the CIM-RIS scheme has better error performance, faster data transmission speed, and lower transmission energy, compared to traditional RIS, transmit spatial modulation aided RIS (TSM-RIS) and transmit quadrature spatial modulation based RIS (TQSM-RIS) techniques.

Reconfigurable Intelligent Surface Aided Spatial Media-Based Modulation

The demands for high data rate, reliability, high energy efficiency, high spectral efficiency, and low latency communication have been increasing rapidly. For this reason, communication models that use limited resources in the best way, allow fast data transmission, and increase performance has become very important. In this work, a novel high energy and spectral efficient reconfigurable intelligent surface aided spatial media-based modulation system, called RIS-SMBM, is proposed for Rayleigh fading channels. In addition to the bits carried in the M-QAM symbol, while media-based modulation (MBM) provides data bits to be carried in the indices of different channels according to the radio frequency (RF) mirrors are on or off, spatial modulation (SM) provides data bits to be carried in the indices of the transmit antennas. By combining these two modulation schemes, the spectral efficiency increases considerably since the amount of information transmitted in the same time interval is substantially increased. The optimal maximum-likelihood (ML) detector and the enhanced low-complexity (ELC) detector for the RIS-SMBM system are proposed. The ELC detector achieves near ML performance while reducing the complexity of the optimal ML detector for the proposed RIS-SMBM system. We analyze the average bit error rate (ABER), throughput, complexity, and energy efficiency for the RIS-SMBM scheme and verify the analytical results with Monte Carlo simulations. It has been observed that the proposed system provides better error performance as well as providing higher spectral and energy efficiency than benchmark systems.

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.