This paper investigates a movable-antenna-assisted dual-hop space-air-ground non-terrestrial network (NTN) operating under an underlay spectrum sharing paradigm. A satellite communicates with multiple unmanned aerial vehicles (UAVs) over free-space optical (FSO) links, while each UAV simultaneously serves a cluster of ground users over radio-frequency (RF) channels subject to interference constraints imposed by a primary receiver. To efficiently exploit the complementary advantages of FSO and RF transmission and the additional spatial degrees of freedom offered by movable antennas, a joint optimization framework is developed to maximize the system sum rate. The proposed framework jointly optimizes satellite and UAV power allocation, multi-antenna precoding at the UAVs, and the positions of multiple movable antennas mounted on each UAV. An alternating-optimization algorithm is employed, where minimum mean square error (MMSE) based precoding accounts for both communication and interference channels, power allocation is convexified using auxiliary rate variables and successive convex approximation, and antenna locations are optimized via Taylor-based convex surrogates. Simulation results demonstrate that the proposed approach significantly outperforms benchmark schemes with fixed antenna locations, heuristic optimization and reinforcement learning methods, while providing robust performance across different system parameters.

This work explores the integration of rate-splitting multiple access (RSMA), simultaneous wireless information and power transfer (SWIPT), and beyond-diagonal reconfigurable intelligent surface (BD-RIS) to enhance the spectral-efficiency, energy efficiency, coverage, and connectivity of future sixth-generation (6G) communication networks. Specifically, with a multiuser BD-RIS-empowered RSMA-SWIPT system, we jointly optimize the transmit precoding vectors, the common rate proportion of users, the power-splitting ratios, and scattering matrix of the BD-RIS, under the assumption of imperfect channel state information (CSI). Additionally, to better capture practical hardware behavior, we incorporate a nonlinear energy harvesting model and ensure that the resulting system satisfies all energy harvesting constraints. In the considered system, we design a robust optimization framework to maximize the system sum-rate, while explicitly accounting for the worst-case impact of CSI uncertainties. To tackle the inherent non-convexity of the problem, we introduce an alternating optimization framework that partitions the problem into several blocks, which are optimized in an iterative manner. More specifically, the transmit precoding vectors are optimized by reformulating the problem as a convex semidefinite programming problem through successive-convex approximation (SCA), whereas the inherently convex power-splitting problem is solved using the MOSEK-enabled CVX toolbox. Subsequently, to optimize the scattering matrix of the BD-RIS, we first employ SCA to reformulate the problem into a convex form, and then design a manifold optimization strategy based on the conjugate-gradient method. Finally, numerical simulations are conducted to evaluate the performance of the proposed scheme, revealing significant performance improvements over existing benchmarks and demonstrating rapid convergence within a reasonable number of iterations.

Non-terrestrial networks (NTNs) using unmanned aerial vehicles (UAVs) as temporary base stations enable flexible coverage in disaster-stricken areas and congested regions. This paper presents a beamforming design for rate splitting multiple access (RSMA) enabled UAV underlay networks that addresses untrusted users (potential eavesdroppers), enforces max–min fairness in achievable user rates, accommodates imperfect CSI, and limits interference to a licensed primary receiver. We transform the non-convex optimization problem via auxiliary-variable-based reformulation and successive convex approximation (SCA), obtaining a tractable convex semi-definite program (SDP). Simulations demonstrate that the proposed framework achieves perfect rate fairness (Jain’s fairness index = 1.0) across all users while delivering robust sum-rate performance under varying numbers of users, UAV transmit antennas, and secrecy requirements.

The next generation of wireless communication networks must offer robust connectivity to serve users in remote or disaster-stricken regions where terrestrial infrastructure is unavailable or compromised. High-altitude platforms (HAPs), functioning as non-terrestrial network (NTN) nodes, can rapidly restore coverage and extend service reach by transmitting directly to ground users. To further enhance communication performance, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) can be deployed alongside HAPs, intelligently shaping the wireless channel to improve channel reliability. In this work, we investigate a HAP-assisted NTN in which a STAR-RIS aids downlink transmission to multiple ground users under a rate-splitting multiple access (RSMA) protocol with imperfect successive interference cancellation (SIC). The joint design of power allocation at the HAP and STAR-RIS beamforming presents a challenging non-convex problem because of the coupled rate expressions and rank-one constraints on the STAR-RIS matrices. To address this, we introduce auxiliary variables and apply successive convex approximation (SCA) to convexify rate functions, while employing a difference-of-convex (DC) programming approach to handle the rank-one requirement. An alternating optimization framework is then developed to iteratively solve a convex power allocation subproblem and a penalized semi-definite program for STAR-RIS beamforming. Simulation results show the efficacy of the proposed framework, showing excellent performance even under imperfect SIC and with discretized phase shifts at the STAR-RIS.

Low Earth orbiting (LEO) satellite-assisted wireless communication is increasingly vital for future communication networks due to the significant spectrum scarcity in radio frequency channels, presenting a critical bottleneck. Thus, optimizing the utilization of available radio frequency spectrum has become imperative. Advanced techniques like underlay communication and Rate Split Multiple Access (RSMA) have proven effective in enhancing spectrum utilization. When LEO satellites are applied to tasks such as agricultural assistance, search and rescue operations, and military defense, LEO-to-ground communication can leverage underlay fashion using RSMA to transmit messages to multiple users simultaneously on the same channel. However, conventional underlay communication setups necessitate transmitter cooperation to manage system interference. Enabling non-cooperative systems to communicate in an underlay fashion unlocks the untapped potential of these advanced transmission techniques. This study addresses the challenge of maximizing the RSMA rate of the LEO-to-ground communication system (secondary system) operating in an underlay mode without cooperation with the ground-to-ground communication system (primary system), where the primary network operates in a time-division multiple-access fashion. We propose a dueling-based double deep Q-learning solution to optimize the allowed transmission power at the LEO satellite, ensuring no outage in the primary system. Additionally, we introduce an optimal solution framework to distribute the allowed transmission power among all signals of the secondary devices, maximizing the RSMA rate while meeting the rate requirements of all underlay secondary devices. Simulation results demonstrate that this hybrid solution framework provides excellent performance while ensuring no outage at the primary network.

The unbreakable security and higher data rates offered by quantum communication networks have made quantum communication an inevitable necessity of the future. Many quantum communication frameworks, such as quantum key distribution and super dense coding, require entangled pairs to be shared between the source and destination nodes. Communication nodes in quantum networks have a minimum fidelity requirement for the entangled pairs. If the fidelity requirement is not satisfied, the entangled pairs may not be used for the desired operations. Successful sharing of the entangled pairs between the nodes is a crucial step in making quantum communication practical. In this work, we propose a framework to maximize entanglement sharing between a source and multiple destination nodes in a fair manner. We consider a system that takes advantage of the purification process to improve the fidelity of the entangled pairs when necessary. The proposed solution framework provides the optimal number of entangled pairs required for the purification process and the optimal transmission rate at the source node to achieve the desired system objective. The non-convex problem is first transformed into a convex form, and then a Lagrangian dual-based framework is proposed to find the optimal solution values. Moreover, to optimize the number of entangled pairs for purification at each link, we derive a closed-form expression that provides the optimal value in a single step. Selected simulation results demonstrate that the proposed framework exhibits excellent performance.

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.

Low Earth orbit (LEO) satellite communication has drawn particular attention recently due to its high data rate services and low round-trip latency. It is low-cost to launch and can provide global coverage. However, the spectrum scarcity might be one of the critical challenges in the growth of LEO satellites, impacting severe restrictions on the development of ground-space integrated networks. To address this issue, we propose rate splitting multiple access (RSMA) for cognitive radio (CR) enabled nongeostationary orbit (GEO)-LEO coexisting satellite network. In particular, this work aims to maximize the system's sum rate by simultaneously optimizing the power allocation and sub carrier beam assignment of LEO satellite communication while restricting the interference temperature to GEO satellite users. The problem of sum rate maximization is formulated as non-convex and a Global optimal solution is challenging to obtain. Therefore, we first employ the successive convex approximation technique to reduce the complexity and make the problem more tractable. Then for the power allocation, we exploit Karush-Kuhn-Tucker (KKT) condition and adopt an efficient algorithm based on the greedy approach for subcarrier beam assignment. We also propose two suboptimal schemes with fixed power allocation and random sub carrier beam assignment as the benchmark. Results demonstrate the benefits of the proposed scheme compared to the benchmark schemes.

This work presents a deep-Q reinforcement learning (DQ-RL) framework to achieve fairness in multi-access cognitive radio (CR) systems. The proposed framework provides fast solution and is robust to channel dynamics. Further, to remove the computational overhead and the burden to feedback thousands of weights from the secondary receiver (SR), we propose a solution where the process of learning is carried out at the secondary transmitters (STs). The simulations show that by using the proposed technique, a good level of fairness is achievable with an outage probability of the primary system less than 0.04. We also provide the comparison of the proposed technique with a brute-forcing optimization method, and show the fairness gain of the proposed framework compared to the rate maximization model.

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.

The issue of spectrum scarcity in wireless networks is becoming prominent and critical with each passing year. Although several promising solutions have been proposed to provide a solution to spectrum scarcity, most of them have many associated tradeoffs. In this context, one of the emerging ideas relates to the utilization of cognitive radios (CR) for future heterogeneous networks (HetNets). This paper provides a marriage of two promising candidates (i.e., CR and HetNets) for beyond fifth generation (5G) wireless networks. More specifically, a joint power allocation and user assignment solution for the multiuser underlay CR-based HetNets has been proposed and evaluated. To counter the limiting factors in these networks, the individual power of transmitting nodes and interference temperature protection constraints of the primary networks have been considered. An efficient solution is designed from the dual decomposition approach, where the optimal user assignment is obtained for the optimized power allocation at each node. The simulation results validate the superiority of the proposed optimization scheme against conventional baseline techniques.

Smart grid (SG) has been recognized as a promising solution to manage and distribute electricity in the next generation power systems. The large number of devices in power systems make the wired communication a non feasible solution, thus wireless transmission becomes necessary. However this demands a huge portion of radio spectrum. In this work, we aim to provide the secure and reliable SG communication through an efficient spectrum sharing mechanism. Specifically, we consider an underlay transmission and seek to minimize the interference under complete spectrum reuse while guaranteeing the required secrecy rate as well as end to end delay. A mathematical optimization problem is formulated and convex optimization techniques are exploited to find the near optimal solution. Further a suboptimal solution is also proposed to reduce the computational complexity. Simulation results are presented to verify the effectiveness of the proposed schemes.

This paper presents energy efficient resource allocation schemes for multi-user cognitive radio networks. The aim is to achieve the fairness in power consumption of different secondary users. A binary integer programming problem is formulated to jointly allocate sub-carriers to users and power loading over different sub-carriers. Energy fairness is achieved subject to individual power constraints at secondary nodes, interference constraint of primary network, and minimum required rate of the secondary system. Dual decomposition based approach is used to find a joint optimization solution. Further, a sub-optimal scheme is also designed where the power is optimized for fixed sub-carrier allocation. Simulation results are presented to evaluate the performance of proposed schemes.