Secure and Energy Efficient Transmission for RSMA-Based Cognitive Satellite-Terrestrial Networks

In this paper, we propose a novel robust downlink beamforming (BF) design for satellite-terrestrial integrated networks. Under a realistic assumption that the angular information of eavesdroppers is not perfectly known, we establish an optimization framework for hybrid BF at the terrestrial base station and digital BF at the satellite to maximize the secrecy-energy efficiency of the system, while satisfying the quality-of-service constraints of both earth station and cellular user. Since the formulated optimization problem is mathematically intractable, we present an iterative algorithm based on the Charnes-Cooper approach to optimize the BF weight vectors. The effectiveness and superiority of the proposed robust hybrid BF scheme are validated via computer simulations.

Low earth orbit (LEO) multi-beam satellites have the characteristics of high capacity and high spectral efficiency, providing geographically independent services to ground users. However, the use of frequency reuse technologies leads to increasing inter-beam interference. Rate-splitting multiple access (RSMA), a novel multiple access technology, decomposes messages into common and private parts, effectively reducing interference. This makes RSMA highly promising for multibeam satellite scenarios. Therefore, this paper investigates the resource allocation problem of RSMA in the beam-hopping multicast LEO satellite network. The aim is to minimize the gap between traffic demand and achievable rates while meeting user latency requirements. To solve this problem, it is decomposed into two subproblems. First, considering user latency constraints, the beam scheduling problem is modeled as a potential game to determine the beam hopping pattern (BHP). Then, for the power allocation and rate matching problem, auxiliary variables are introduced to convert it into a convex problem, which is solved using the successive convex approximation (SCA) method. The simulation results confirm that this method performs better than the baseline approaches of Spatial Division Multiple Access (SDMA) and Orthogonal Frequency Division Multiple Access (OFDMA).

This paper investigates a dual-layer heterogeneous communication network assisted by multiple autonomous aerial vehicles (AAVs) based on rate-splitting multiple access (RSMA). Considering the repulsion characteristics between AAVs' positions and terrestrial base stations (BSs), AAVs are deployed in non-hole areas outside the BSs. The positions of AAVs are modeled by Poisson hole process (PHP). Due to the difficulty of directly solving the user association problem under PHP, the nearest neighbor hole approximation method is introduced. Due to the complex interference caused by multiple communication links in the dual-layer heterogeneous network, we adopt the conditional Laplace transform method to represent interference, deriving expressions for coverage probability and area spectral efficiency (ASE). Considering the energy constraints of the AAV network, this paper defines the system-level network energy efficiency (EE) as the ratio of the ASE to the average energy consumption per unit area. Utilizing this framework, we jointly optimize the AAV network density and the RSMA power allocation coefficients to maximize EE. The numerical results ultimately validated the accuracy of the theoretical analysis. Through simulation analysis, this paper explores the impact of network deployment parameters on system performance and provided the AAV network density and RSMA power allocation scheme that maximizes EE.

Background: Manmade disasters like explosion, toxic wastes, chemical spills etc. have become an imperative concern for our society. Manmade disasters are not everyday phenomenon. Thus, the allocation of separate resources is not realistic. The idea is to use existing cellular infrastructure to implement single hop cognitive radio sensor network for protecting human being from manmade disasters. Objective: The main objectives of this paper are as follows: (a) design of an efficient iterative power regulation algorithm based on the Firefly Algorithm for the proposed network, (b) computation of sensor nodes’ optimal power for different position of the cellular user from the base station assuming that cellular user compromises power for its own sensor node, (c) computation of the maximum number of sensor nodes coupled with single cellular user for different distances from the base station in worst channel condition, and (d) comparative performance analysis with state-of-the-art algorithms. Method: In the presence of explosive and toxic gases, cognitive radio in sensor node establishes a connection with the nearest base station to send pre-disaster alert signal utilizing cellular user’s resources. The power is distributed among sensor nodes maintaining the fundamental requirements of cellular users. Here, an iterative power regulation mechanism is employed for distributing the power between sensor node and cellular user to achieve reliable utility of the network. The fitness function is designed under the constraints of interference and the designed algorithm is implemented in MATLAB platform towards the search of optimal power of sensor nodes by maximizing the fitness function. Results: Comparative performance analysis demonstrates the effectiveness of the proposed algorithm in terms of speed of convergence, the position of mobile phone user from the base station, number of coupled sensor nodes with single cellular user, and Jain’s fairness factor. Conclusion: The proposed network controls the occurrence of manmade disasters and achieves reliable transmission of emergency information prior to disaster without disrupting cellular phone users. Simulation results validate that the proposed IPRFA algorithm outperforms with respect to state-ofthe- art methods in terms of sharing power and Jain’s fairness factor.

This paper investigates the energy efficiency maximization problem in a resource-constrained visible light communication (VLC)-based probabilistic semantic communication (PSCom) system. In the considered model, light-emitting diode (LED) transmitters perform semantic compression, reducing data size at the cost of computation overhead. The compressed semantic information is transmitted to the users for semantic inference based on a shared knowledge base, which requires regular updates to maintain synchronization. Rate splitting multiple access (RSMA) is used to transmit both knowledge base and information data simultaneously. The goal is to maximize the energy efficiency of the system through optimizing transmit beamforming, direct current (DC) bias, rate allocation, and semantic compression ratio, considering both communication and computation costs. An alternating optimization algorithm, utilizing successive convex approximation and Dinkelbach method, is proposed to solve the problem. Simulation results validate the effectiveness of the proposed approach.

Unmanned aerial vehicles (UAVs) have been applied to various tasks in the low-altitude economy (LAE) with the advantages of high mobility, low costs, and flexible deployment. However, due to the broadcast nature of wireless channels and the increasing number of UAVs, the security of UAV control information and the spectrum resource utilization face significant challenges and threats. Therefore, in this paper, we investigate the secrecy performance of UAV short-packet control information transmission networks based on rate-splitting multiple access (RSMA) with the presence of multiple eavesdroppers. Moreover, we consider and analyze the impacts of both imperfect channel state information (CSI) and successive interference cancellation (SIC) in a more realistic scenario. Considering both large-scale fading and Nakagami-<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> small-scale fading, the closed-form expression of the average effective secrecy sum rate is derived utilizing stochastic geometry and the Gauss-Chebyshev quadrature. Considering that the private stream can be concealed within the high-power common stream, an optimization problem is formulated to maximize the common rate by jointly optimizing the blocklength and power allocation coefficients to enhance security. The block coordinate descent (BCD) algorithm is adopted to solve this problem. Finally, simulation results demonstrate the accuracy of the analysis and the effectiveness of the proposed scheme.

Hybrid satellite-terrestrial relay networks (HSTRNs) which exploit relay cooperative technology in the integrated system of satellites and terrestrial base stations (BSs) are expected to offer enhanced transmission performance benefited from the spatial gain of independent fading paths. To enable multi-user accessing and resource sharing among users, non-orthogonal multiple access (NOMA) technique can be applied in HSTRNs. In this paper, we consider a NOMA-based HSTRN where a satellite transmits signals to a relay base station (RBS) and a destination ground station (DGS), and RBS also serves a cellular user (CU) and DGS simultaneously by leveraging NOMA scheme. We assume that the satellite shares spectrum with a number of cellular small BSs (SBSs), hence SBSs will cause co-channel interference (CCI) to DGS, RBS and CU. We examine the transmission characteristics of the satellite and terrestrial links, and derive the closed-form expressions of the outage probability (OP) and average symbol error rate (ASER) of DGS and CU, respectively. Specially, we consider the effect of perfect and imperfect successive interference cancellation (SIC) on the ASER performance of CU. Then, considering the stochastic characteristics of satellite links and CCI, we analyze the mean energy efficiency (MEE) of the satellite and propose an optimal power allocation scheme which maximizes the MEE of the satellite and achieves system performance optimization. Simulation results demonstrate the effectiveness of the theoretical analysis and the proposed power allocation scheme.

The design problem associated with robust downlink beamforming in multicast, multigroup, multicell wireless systems is addressed. The channel state information (CSI) of users is assumed to be imperfect and the uncertainty of CSI is modelled using the Frobenius norm. The objective is to optimise the signal-to-interference-plus-noise ratio over all users with a constraint on the maximum total transmitted power. This was achieved through a robust solution using the successive convex approximation (SCA) method. The beamforming problem is treated as a bi-convex problem, which is solved using the iterate-alternative convex technique. Here, the CSI uncertainty is addressed using a convex package through the non-monotone spectral projected gradient method and the beamforming vector is extracted using the SCA method. Also, the authors offer the required condition to extract the beamform vector using the SCA method through a suboptimal solution that always addressed before using different beamforming methods. Their simulation results examine all proposed system parameters in order to show convergence and feasibility of the solution. They also compare the solution with a suboptimal solution and the quality of service method for imperfect CSI in downlink beamforming. Numerical results show that the robust solution achieves the best power efficiency for practical solutions.

This article addresses the security problem for wireless powered cognitive satellite-terrestrial network, where a multibeam satellite sub-network shares the portion of millimeter wave bands with multiple cellular networks, each consisting of a base station, several mobile users (MUs) and energy receivers (ERs). Considering that the ERs are potential eavesdroppers of the MUs, and only imperfect knowledge of the angles of departure for the wiretap channels is available, we aim at maximizing aggregated rate of the considered network while guaranteeing the signal-to-interference-plus-noise ratio requirements of the MUs, the energy harvesting thresholds and the secrecy constraints at ERs. Since the formulated optimization problem is mathematically intractable, we exploit a discretization method and the Taylor expansion method to transform the non-convex objective and constraints into convex ones, and then propose an iterative beamforming (BF) algorithm to solve the problem. Furthermore, we present a combined multibeam scheme to obtain suboptimal BF weight vectors with low computational burden. Finally, simulation results reveal that the proposed BF schemes can efficiently improve the aggregated rate with fast convergence compared to the benchmark schemes.

It is well known that unmanned aerial vehicles (UAVs) can help terrestrial base stations (BSs) offload data traffic from crowded areas to improve coverage and boost throughput. However, the limited backhaul capacity cannot cope with the ever-increasing data demands, for which caching is introduced to relieve the backhaul bottleneck. In this paper, we focus on a multi-UAV assisted wireless network, and target to fully utilize the benefits of wireless caching and UAV mobility for multiuser content delivery. By taking into account the limited storage, our goal is to maximize the minimum throughput among UAV-served users by jointly optimizing cache placement, UAV trajectory, and transmission power in a finite period. The resultant problem is a mixed-integer non-convex optimization problem. To facilitate solving this problem, an alternating iterative algorithm is proposed by adopting the block alternating descent and successive convex approximation methods. Specifically, this problem is split into three subproblems, namely cache placement optimization, trajectory optimization, and power allocation optimization. Then these subproblems are solved alternately in an iterative manner. We show that the proposed algorithm can converge to the set of stationary solutions of this problem. Besides, we further analyze the computational complexity of this algorithm. Numerical results show that great throughput enhancement is achieved by applying our proposed joint design in comparison with other benchmarks without trajectory design and power control.

Stackelberg game based relay selection for physical layer security and energy efficiency enhancement in cognitive radio networks

&lt;p&gt;Device-to-device (D2D) communication can effectively use cognitive radio network approach to coexist with cellular users. For such a cognitive D2D system, two approaches (underlay and overlay) are considered to manage the spectrum sharing among the cellular (primary) users and the D2D (secondary) users. Energy efficiency (EE) is crucial in both these cases due to limited battery capacity and quality of service requirements of the D2D users. This study effectively models the power allocation problem of such a cognitive D2D system by maximising the EE of the D2D users subject to a minimum rate requirement for both the D2D users and the cellular users. This leads to a non-linear fractional optimisation problem which is more complicated and computationally intractable. Alternatively, geometric water-filling approach have been utilised for power allocation to solve this optimisation problem which results in an ‘exact’ and ‘low complexity’ solution. Simulation results reveal the benefits of the proposed algorithm.&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt;

In this paper, we investigate joint optimization of secure AirComp and reliable multicasting assisted by a multiple-input multiple-output untrusted two-way relay, where artificial noise is employed at the access point (AP) to interfere the relay for ensuring secure AirComp. We aim to minimize the computation distortion at the AP by jointly designing the transmit variables of all the nodes and the aggregation variables at the AP and relay, under the secure AirComp constraint, the reliable multicasting constraint, and the transmit power constraints of all the nodes. We consider two scenarios that perfect and imperfect channel state information (CSI) are available. For the former, the formulated optimization problem is highly nonconvex, and we propose an effective block coordinate descent (BCD)-penalty successive convex approximation (penaltySCA) method to solve the nonconvex problem. For the latter, we model the imperfect CSI by using worst-case criterion and the formulated robust optimization problem is much more challenging than its counterpart with perfect CSI. To solve the robust problem effectively, we first transform it to a deterministic optimization problem by employing some powerful mathematical lemmas, and then apply the proposed BCD-penaltySCA method to solve the reformulated deterministic problem. The proposed methods are shown by simulations to significantly reduce the computation distortion compared with other benchmarks under considering secure AirComp and reliable multicasting.

We develop a secure communication scheme for an active intelligent reflecting surface (IRS) aided wireless network to maximize the mobile users' (MUs') transmission rate, where a multiple antenna base station (BS) transmits confidential information to MUs with the assistance of an active IRS. First, we formulate a rate maximization problem by jointly optimizing the transmission beamforming vectors, the IRS's phase-shifts and amplification-coefficients, and the system channel bandwidth allocation coefficients. Since the formulated optimization problem is non-convex with multiple coupled variables, we first adopt the block coordinate descending (BCD) method to decompose the formulated non-convex optimization problem into several subproblems, and then use the sequential convex approximation (SCA) method to transform the non-convex subproblems into the convex problems. Then, we can use CVX to solve them. Finally, the proposed scheme is verified by numerical analysis, which show that compared with the baseline scheme with passive IRS, when the noise power at the active IRS is much smaller than that at the MUs, our proposed scheme can achieve much larger transmission rates by using the active IRS even with a small number of reflecting elements.

Energy harvesting (EH), which refers to replenishing energy from the environment, is demonstrated to be a promising approach in reducing the operational expenses (OPEX) and increasing the network lifetime of emerging wireless communication technologies by eliminating the need for frequent battery replacing or recharging of wireless devices. Among various ambient energy sources, the focus of this dissertation is on wireless power transfer (WPT) considering the unique characteristic of radio frequency (RF) signals; that is, they inherently carry both information and energy. A particularly interesting scenario arises when sources support simultaneous wireless information and power transfer (SWIPT) to provide a remote, controllable, and on-demand energy source. In this context, one of the promising deployment approaches is SWIPT-enabled cognitive radio networks (CRNs), which offer significant gains in terms of spectral and energy efficiencies. One of the practical scenarios of RF-powered CRN is deploying a number of distributed cognitive sensor nodes, equipped with RF EH modules, to sense a specific area and send the sensed data to an access point while coexisting with a primary licensed network. However, exploiting the full potentials of SWIPT-enabled CRNs is subject to carefully aligning the requirements of the EH unlicensed secondary receivers (SRs) with those of the legitimate primary receivers (PRs). In light of this, the first contribution of this thesis focuses on investigating the problem of beamforming for the downlink of multi-user multiple-input single-output (MU-MISO) CRNs. With the objective of minimising the transmission power of the secondary base station (SBS), optimal and suboptimal solutions for the formulated optimisation problem are provided by jointly optimising the transmit beamforming vector at the SBS and adjusting the parameters of the energy harvesters at the SRs. It is shown that the obtained solutions are efficient in meeting the EH and quality-of-service (QoS) requirements of the SRs and the levels of interference accepted by the PRs. Apart from SWIPT-enabled CRNs, relay-assisted SWIPT networks are envisioned to be a promising framework offering extended coverage, diversity gains, and enhanced energy efficiency. In this case, the relay network itself can benefit from the relayed transmissions in terms of saving energy, and the harvested energy can be used to charge relay nodes and extend their lifetime as compensation for their role of data forwarding. Nonetheless, a concrete performance analysis on the impact of the involved system parameters such as, the energy conversion efficiency and the location of the EH relay terminal, on the trade-off between the achievable information transfer efficiency and the harvested energy level is crucial for the successful implementation of SWIPT in this context. This inspired the research work in the next two contributions with the main focus on developing novel comprehensive analytical frameworks for the investigation and evaluation of SWIPT relaying systems. Specifically, the second contribution is dedicated to examining the application of noncoherent modulation, which is recognised as an energy efficient modulation scheme for SWIPT, due to its ability to eliminate the need of instantaneous channel state information (CSI) estimation/tracking. Through adopting a moments-based approach, novel expressions are derived for the outage probability, achievable throughput, and average symbol error rate (ASER) of dual-hop SWIPT relaying systems. Furthermore, new asymptotic analytical results are derived for the high SNR regime and are then utilised to analytically quantify the achievable diversity order. The proposed mathematical tools are demonstrated to be an accurate and efficient means by which one can conduct a thorough analysis on the system performance without the burden of Monte Carlo simulations. Finally, the third contribution focuses on SWIPT relaying systems operating in the presence of impulsive non-Gaussian noise, which is typical in several practical scenarios. Several studies show sufficient evidences that impulse man-made noise is encountered in various metropolitan, indoor, and underwater wireless applications. Examples of these sources include automotive ignition, electronic devices, household appliances, medical equipment, and industrial equipment. To characterise the behavior of the system performance under this type of noise, an efficient analytical framework is developed where novel closed-form xpressions for the pairwise error probability (PEP) are derived for two relaying schemes, namely, blind relaying and CSI-assisted relaying, employed under two assumptions imposed on the deployed EH process; namely, instantaneous EH (IEH) and average EH (AEH). Apart from being accurate, the derived expressions are shown to be efficient in quantifying the diversity order of the system and providing a comprehensive study on the impact of the severity of impulsive noise on the behavior of the system.