Robust Secure Beamforming for Wireless Powered Cognitive Satellite-Terrestrial Networks

Rate-splitting multiple access (RSMA) has recently received considerable attention due to its high efficiency in both spectral utilization and energy consumption. Inspired by this emerging technique, this letter presents a secure beamforming (BF) scheme for RSMA-based cognitive satellite terrestrial networks in the presence of multiple eavesdroppers. Assuming that the system operates in the millimeter wave band and only imperfect wiretap channel state information is available at the satellite and terrestrial base station, our objective is to maximize the secrecy-energy efficiency of the earth station (ES) while meeting the constraints on the ES secrecy rate, the cellular users' rate requirements, and transmit power budgets of the satellite and base station. As the formulated optimization problem is mathematically intractable, by applying successive convex approximation and Taylor expansion methods, we propose a robust BF scheme to convert the nonconvex objective and constraints into convex ones, which can be iteratively solved. The effectiveness and superiority of the proposed scheme are confirmed through simulation results.

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.

In this paper, a robust transmission scheme for simultaneous wireless information and power transfer (SWIPT) in the presence of an eavesdropper is proposed. With imperfect channel state information (CSI) at the transmitter, the optimal transmit covariance matrix is obtained based on the maximization of the worst case harvested energy for the energy receiver (ER) while guaranteeing the achievable secrecy rate constraint for the information receiver (IR). To solve such a challenging nonconvex problem, in this paper, a two-stage optimization approach is proposed. In the first stage, the original problem is transformed into a robust design problem, which can be further converted into a convex semidefinite program (SDP) problem by using S-procedure as a tool. In the second stage, the optimal transmit covariance matrix is obtained via the aid of one-dimensional search algorithm. Finally, simulation results are provided to illustrate the robustness and effectiveness of the proposed method.

Hybrid energy supply composed of batteries and radio frequency (RF) signals has been anticipated to be a prominent solution for balancing the reliability and self-sustainability of future IoT networks. The newly emerging reconfigurable intelligent surface (RIS) is also capable of greatly enhancing spectral and energy efficiencies. In this paper, by considering an asynchronous transmission protocol among all energy receivers (ERs) and assuming the perfect self-interference cancellation (SIC) at the hybrid access point (HAP), we aim to maximize sum throughput in the RIS-aided hybrid powered communication networks (HPCNs) by jointly optimizing the transmit covariance matrices of the HAP and all ERs, the RIS reflection matrix and the downlink/uplink (DL/UL) time allocation. Generally, this optimization problem is intractable to solve due to strongly coupled variables and nonconvex unit-modulus constraints. To draw more insights into this joint design, we firstly carry out feasibility analysis on this problem, and then develop a 2-block alternating optimization algorithm, which consists of the semi-closed-form solution based iterative algorithm for deriving the optimal MIMO transceivers together with the DL/UL time allocation and the alternating direction method of multipliers (ADMM) based algorithm for the RIS design. To avoid the potential high complexity of alternating optimization, we also propose a two-stage scheme, where the RIS design is independent of the others and aims to create favorable DL/UL channels. The extension of our proposed algorithms to the practical imperfect SIC case is then discussed. Numerical results illustrate the superior performance of our proposed algorithms over the baselines in terms of the achievable sum throughput, and their time effectiveness in solving large-scale problems.

In this paper, we propose a beamforming design for dual-functional radar-communication (DFRC) systems at the millimeter wave (mmWave) band, where hybrid beamforming and sub-arrayed MIMO radar techniques are jointly exploited. We assume that a base station (BS) is serving a user equipment (UE) located in a Non-Line-of-Sight (NLoS) channel, which in the meantime actively detects multiple targets located in a Line-of-Sight (LoS) channel. Given the optimal communication beamformer and the desired radar beampattern, we propose to design the analog and digital beamformers under non-convex constant-modulus (CM) and power constraints, such that the weighted summation of the communication and radar beamforming errors is minimized. The formulated optimization problem can be decomposed into three subproblems, and is solved by the alternating minimization approach. Numerical simulations verify the feasibility of the proposed beamforming design, and show that our approach offers a favorable performance tradeoff between sensing and communication.

This letter investigates secure hybrid beamforming (HB) design for an extremely large-scale antenna array-aided simultaneous wireless information and power transfer (SWIPT) system operating in a mixed near-field (NF)/far-field (FF) environment. A base station (BS) employs HB to transmit information and artificial noise (AN) signals simultaneously to multiple FF information receivers (IRs) and NF energy receivers (ERs). The objective is to maximize the weighted sum secrecy rate for the IRs, considering both Type-I (unable to cancel AN) and Type-II (capable of canceling AN) IRs, subject to minimum energy harvesting requirements at the ERs and a BS transmit power constraint. We formulate optimization problems for both IR types and develop an efficient iterative algorithm based on successive convex approximation. Simulation results validate the proposed scheme and provide crucial insights into the security performance of mixed-field SWIPT systems, highlighting the influence of visibility regions and angular user separation.

We investigate energy-efficient cooperation for secrecy in cognitive radio networks. In particular, we consider a four-node cognitive scenario where the secondary receiver is treated as a potential eavesdropper with respect to the primary transmission. The cognitive transmitter should ensure that the primary message is not leaked to the secondary user by using cooperative jamming. We investigate the optimal power allocation and power splitting at the secondary transmitter for our cognitive model to maximize the secondary energy efficiency (EE) under secrecy constraints. We formulate and analyze an important EE Stackelberg game between the two transmitters aiming at maximizing their utilities. We illustrate the analytical results through our geometrical model, highlighting the EE performance of the system and the impact of the Stackelberg game.

In this paper, we address the problem of beamforming (BF) design in a cognitive relay network (CRN), where the cognitive network not only shares the spectrum with the primary one but also acts as a relay to assist the primary signal transmission. Considering that all of the wireless channels are subject to the correlated Rayleigh fading distribution, we first formulate a constrained optimization problem to minimize the total transmit power of the cognitive base station (CBS) with multiple antennas, while guaranteeing the quality-of-service (QoS) of the primary and secondary users and keeping the mutual interference below an acceptable level. Then, by adopting the available partial channel state information (CSI), and proposing an approximation method to relax the constraints, a support vector machine (SVM) based algorithm is presented to solve the optimization problem for the BF weight vectors. The benefit of the new approach is that the slow change of the partial CSI can be taken into account. Finally, computer simulation results are provided to confirm the superiority of the proposed BF design strategy.

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.

The work reports the developed planning algorithms for optimum resource allocation over a dynamic 5G infrastructure, addressing all involved types of system resources, from the radio access domain to the MEC domain through the Optical Distribution Network (ODN). A modular modelling design is adopted considering these three domains. In that manner, allocation is composed of three optimization phases, which are executed sequentially and ensure optimal end-to-end allocation of resources. The first allocation phase focuses on the over-the-air resources, which are directly assigned to the 5G mobile users. The key entities for radio access are the Remote Radio Heads (RRHs), which are placed at the edge of the network fronthaul and provide wireless communication slots to the User Equipment (UE). Beamforming capabilities are also assumed allowing each RRH to support simultaneous and independent transmission of multiple directed beams to provide high quality targeted coverage. The resource allocation scheme is responsible for assigning subchannels within specific beams to address the bandwidth requirements of the requested services. The translation from the requested data-rate and the allocated bandwidth is based on the characteristics of 3GPP New Radio (NR) standard. Furthermore, the radio resource allocation process is responsible for assigning different bands for optimal frequency reuse within the available spectrum and avoids interference. The formulated ILP optimization problem, which is solvable in close to real-time due to its low computational complexity, allows dynamic adaptation to requests and resources, with a target to minimize energy consumption. The latter is achievable through the aggregation of wireless resources into individual beams and RRHs. In order to further enhance this process, an RRH prioritization algorithm is introduced for weighting the candidate allocations. With the completion of the first allocation phase, the optical resource allocation phase is initiated. The considered architecture follows that of the EU funded blueSPACE project and consists of a fully flexible ODN exploiting the cutting-edge capabilities of Spectrally Spatially Flexible Optical Networks (SS-FONs). In this context, the allocator considers the spectral and/or spatial multiplexing features of the optical nodes for performing elastic switching. The aim is to establish complete optical routes (lightpaths) from the radio access domain to the Central Office (CO). In particular, the output of this allocation phase is utilized to determine end-to-end fiber-based connections between BBUs and beams. The formulated optimization problem (a low complexity ILP) is able to model any type of network topology and provide optimal paths to dynamic demands according to the available optical domain resources. Power saving is achieved by minimizing the number of the optical elements used for building the required lightpaths. The last allocation phase assigns MEC resources within the blueSPACE virtual Content Delivery Network (vCDN) and Evolved Packet Core (vEPC). The services request MEC resources to offload computationally demanding tasks (such as real-time multimedia processing) and the allocator is responsible to make optimal decisions for the hosting of the required VM instances. The formulated ILP problem provides such a fast decision-making mechanism, while averting the scattering of allocated resources to numerous physical servers in order to conserve energy. With the termination of this third phase, all required network and computing resources are fully allocated for serving dedicated enhanced Mobile Broadband (eMBB) slices. The aforementioned resource allocation algorithms are implemented and simulated in MATLAB. For the evaluation process, two real-world simulation scenarios are considered to assess the allocation effectiveness and energy conservation capabilities for changing traffic requests. One scenario simulates a 5G deployment over a football stadium and the second scenario a deployment over a city park. Due to their coverage requirements the two scenarios lead to different RRH location and connectivity properties. Results show that in the “Stadium” scenario we achieve 50% savings in the number of activated RRHs and beams, while in the “City Park” scenario this metric reaches 67%. With respect to the allocation of optical networking resources, our optimization approach achieves conservation of up to 11% of optical ports and 14% of optical cores for the stadium scenario, while the corresponding results for the “City Park” scenario are 9% and 16%, respectively. Lastly, regarding the MEC resource allocation, the conducted simulations in the context of the “Stadium” scenario have shown 22% average reduction of the activated physical MEC servers (reaching up to 38%), while in the “City Park” scenario these savings average even higher to 68% (reaching up to 91%).

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.

In this article, we investigate optimal beamforming at a multiantenna primary base station (PBS) and a multiantenna cognitive base station (CBS) for energy-efficient (EE) secure downlink communication in cognitive radio networks with one single-antenna primary user (PU), one single-antenna cognitive user (CU), and multiple single-antenna eavesdropping nodes. An artificial noise transmission scheme is used by CBS to protect the data against the eavesdropping security attacks at the cost of extra power consumption. To improve the secrecy energy efficiency (SEE), we propose a SEE maximization (SEEM) scheme by exploiting the instantaneous channel state information (CSI) of the eavesdroppers under the secrecy rate (SR) constraints of the PBS-PU and CBS-CU channels, the quality-of-service requirement of the PU, and the transmit power constraint of the CBS. When the eavesdropping links' instantaneous CSI are unknown at the legitimate transmitters (i.e., PBS and CBS), we propose another SEEM scheme based on the statistical CSI of the eavesdropping links. Since the formulated optimization problems with fractional objective functions are nonconvex and mathematically intractable, we first transform them into equivalent subtractive problems, and then, employ the difference of two-convex functions approximation method to arrive at approximate convex problems. In addition, new two-tier optimal BF algorithms are proposed. Finally, simulation results are presented to illustrate the effectiveness and performance gains of our proposed SEEM schemes over conventional SR-only maximization and EE-only maximization schemes.

With the growth of the scale of antenna arrays, the full digital beamformer, which requires one dedicated radio frequency (RF) chain per antenna element, is costly and of power consumption. The hybrid beamformer, which offloads part of the beamformer to the analog domain via phase shifters, is an alternative to the full digital beamformer for large-scale antenna arrays. The whole hybrid beamformer is equivalent to the product of a reduced digital beamformer and an analog beamformer. Whereas, the analog beamformer subjects to a constant modulus constraint, because the phase shifters can only control the phase of signals. Due to the non-convex constant modulus constraint, the global optimal hybrid beamformer is infeasible. Therefore, a novel alternating iterative hybrid beamforming algorithm is proposed in this paper. The algorithm divided the analog beamforming matrix and digital beamforming matrix into column blocks and row blocks respectively, only one column of the analog beamforming matrix and one row of the digital beamforming matrix are updated in each processing cycle. The optimal column of the analog beamforming matrix is obtained by the partial derivative of the phase angle of the entries of the analog beamforming matrix, and the corresponding row of the reduced digital beamforming matrix is computed by the least square method. All of the columns of the analog beamforming matrix and rows of the digital beamforming matrix are updated one by one, meanwhile, the residual error of the hybrid beamformer is reduced gradually. Simulation results show that the proposed approach achieves higher achievable data rate compared with the previous works.

This paper studies cooperative spectrum sensing based on crowdsourcing in cognitive radio networks. Since intelligent mobile users such as smartphones and tablets can sense the wireless spectrum, channel sensing tasks can be assigned to these mobile users. This is referred to as the crowdsourcing method. However, there may be some malicious mobile users that send false sensing reports deliberately, for their own purposes. False sensing reports will influence decisions about channel state. Therefore, it is necessary to classify mobile users in order to distinguish malicious users. According to the sensing reports, mobile users should not just be divided into two classes (honest and malicious). There are two reasons for this: on the one hand, honest users in different positions may have different sensing outcomes, as shadowing, multi-path fading, and other issues may influence the sensing results; on the other hand, there may be more than one type of malicious users, acting differently in the network. Therefore, it is necessary to classify mobile users into more than two classes. Due to the lack of prior information of the number of user classes, this paper casts the problem of mobile user classification as a dynamic clustering problem that is NP-hard. The paper uses the interdistance-to-intradistance ratio of clusters as the fitness function, and aims to maximize the fitness function. To cast this optimization problem, this paper proposes a distributed algorithm for user classification in order to obtain bounded close-to-optimal solutions, and analyzes the approximation ratio of the proposed algorithm. Simulations show the distributed algorithm achieves higher performance than other algorithms.

Multiple simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) aided cell-free massive multiple-input multiple-output (CF mMIMO) network is investigated. A long-term sum-rate maximization problem is formulated to jointly optimize the active beamforming at each access point (AP) and the passive beamforming at each STAR-RIS while satisfying the quality of service (QoS) requirements. To address this non-convex problem with challenges caused by user mobility and high-dimensional optimization variables, two deep reinforcement learning (DRL)-based beamforming algorithms are proposed. Firstly, a soft actor-critic (SAC)-based centralized joint beamforming algorithm is proposed, which adds maximum entropy term to the objective function and provides an exceptional exploration-exploitation trade-off. However, since the centralized scheme might suffer from high communication loads and latency, we generalize the proposed approach into a distributed control. A multi-agent SAC (MASAC)-based distributed beamforming algorithm is further proposed, which employs the centralized training and decentralized execution (CTDE) framework, where each AP plays a role of an agent and makes decisions based on local observations. Simulation results demonstrate that: 1) The multiple STAR-RISs can effectively improve the performance of the CF mMIMO networks compared to other baseline schemes; 2) Both SAC-based and MASAC-based beamforming algorithms can maximize the sum-rate and guarantee the QoS of users in the long term; and 3) The MASAC-based beamforming algorithm performs better and converges faster than the SAC-based beamforming algorithm as it reduces the overall complexity and alleviates pressure on the fronthaul link transmission by making decentralized decisions.