Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.

Automotive-Industry 5.0 will use emerging 6G communications to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other intelligent transportation system entities. Nonorthogonal multiple access (NOMA) and backscatter communications are two key techniques of 6G communications for enhanced spectrum and energy efficiency. In this article, we provide an introduction to green transportation and also discuss the advantages of using backscatter communications and NOMA in Automotive Industry 5.0. We also briefly review the recent work in the area of NOMA empowered backscatter communications. We discuss different use cases of backscatter communications in NOMA-enabled 6G vehicular networks. We also propose a multicell optimization framework to maximize the energy efficiency of the backscatter-enabled NOMA vehicular network. In particular, we jointly optimize the transmit power of the roadside unit and the reflection coefficient of the backscatter device in each cell, where several practical constraints are also taken into account. The problem of energy efficiency is formulated as nonconvex, which is hard to solve directly. Thus, first, we adopt the Dinkelbach method to transform the objective function into a subtractive one, then we decouple the problem into two subproblems. Second, we employ dual theory and KKT conditions to obtain efficient solutions. Finally, we highlight some open issues and future research opportunities related to NOMA-enabled backscatter communications in 6G vehicular networks.

Communication at terahertz (THz) frequencies has emerged as a potential answer to the challenge of providing massive capacity and ultra-high data speeds for 6G wireless networks. However, THz waves are vulnerable to obstruction because of their attenuation during propagation and poor diffraction. The development of reconfigurable intelligent surfaces (RIS) technology has shown promise in addressing these issues. The RIS enables the development of smart radio settings with enhanced spectrum coverage and efficiency by regulating the phase shifts of passively reflecting elements. Amidst the vast potential of RIS-based THz communication, a noticeable void exists in comprehensive research, calling for a systematic survey to shed light on its principles, use cases, performance evaluation, challenges, and future trajectories. This review investigates the exciting field of RIS-based THz communication in fifth-generation (B5G), sixth-generation (6G), and beyond. Our study provides a comprehensive overview of this emerging technology, covering a variety of use cases, including CE, coverage, security, sum rate, and energy efficiency. We also investigate resource allocation strategies for THz communication using RIS. Additionally, the performance of RIS-based THz systems with multiple-input and multiple-output (MIMO) and massive MIMO and unmanned aerial vehicle (UAV) technologies is analyzed. By analyzing the existing literature, we illuminate the various approaches, methodologies, and future developments in this field. We also emphasize open issues and research opportunities in THz communication based on RIS. This survey is a valuable resource, contributing to understanding the emerging paradigms and research frontiers of RIS-based THz communication.

Reflecting intelligent surfaces (RIS) has gained significant attention due to its high energy and spectral efficiency in next-generation wireless networks. By using low-cost passive reflecting elements, RIS can smartly reconfigure the signal propagation to extend the wireless communication coverage. On the other hand, non-orthogonal multiple access (NOMA) has been proven as a key air interface technique for supporting massive connections over limited resources. Utilizing the superposition coding and successive interference cancellation (SIC) techniques, NOMA can multiplex multiple users over the same spectrum and time resources by allocating different power levels. This paper proposes a new optimization scheme in a multi-cell RIS-NOMA network to enhance the spectral efficiency under SIC decoding errors. In particular, the power budget of the base station and the transmit power of NOMA users while the passive beamforming of RIS is simultaneously optimized in each cell. Due to objective function and quality of service constraints, the joint problem is formulated as non-convex, which is very complex and challenging to obtain the optimal global solution. To reduce the complexity and make the problem tractable, we first decouple the original problem into two sub-problems for power allocation and passive beamforming. Then, the efficient solution of each sub-problem is obtained in two-steps. In the first-step of For power allocation sub-problem, we transform it to a convex problem by the inner approximation method and then solve it through a standard convex optimization solver in the second-step. Accordingly, in the first-step of passive beamforming, it is transformed into a standard semi-definite programming problem by successive convex approximation and different of convex programming methods. Then, penalty based method is used to achieve a Rank-1 solution for passive beamforming in second-step. Numerical results demonstrate the benefits of the proposed optimization scheme in the multi-cell RIS-NOMA network.

The future generation of wireless communication networks demands for high spectral efficiency to accommodate a large number of devices over the limited available frequency spectrum. Device to device (D2D) systems exploit channel reuse to offer high spectral efficiency and reduce the burden on the communication infrastructure by facilitating communication between devices without involving the base station. We can further enhance the efficiency of D2D systems by employing non-orthogonal multiple access (NOMA) for the transmission of the signals. In NOMA the signals of multiple users are transmitted on the same channel, simultaneously. Deployment of relays can assist the users that do not have a reliable link of communication. A combination of these advanced technologies may offer very high spectral efficiency and a robust communication system. This article aims to design efficient resource allocation techniques for the future communication systems. We consider sum rate maximization problem subject to limited power budget at different transmitting nodes and necessary transmit power gap among users for successful NOMA implementation. Under decode and forward relaying protocol, the problem turns out to be a unique joint uplink-downlink NOMA optimization. We then propose a deep neural networks (DNN) framework to acquire a joint power loading solution at source and relaying nodes. To obtain reliable data for DNN training and testing, we also derive an optimal solution of the problem through convex optimization paradigm, which is used later as a bench mark to verify the performance of proposed DNN based solution. It is observed that DNN provides promising results both in terms of sum rate and the computational complexity.