Reconfigurable Intelligent Surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by dynamically controlling the propagation environment. Recently, a non-diagonal RIS architecture has been proposed, enabling more advanced signal manipulation by allowing signals impinging on one element to be reflected from another element after appropriate phase-shift adjustment. This paper analyzes the energy efficiency of non-diagonal RIS-assisted wireless communication systems in high- and low-signal-to-noise-ratio (SNR) regime. We derive closed form expressions of the spectral and energy efficiency for both the non-diagonal and its diagonal counterpart, which is used as a benchmark for comparison. Simulation results reveal that non-diagonal RIS systems are the preferred choice for communication systems that prioritize spectral efficiency. Interestingly, for energy efficiency, the selection between diagonal and non-diagonal RIS architectures depends on the received SNR conditions, with diagonal RIS systems excelling at high SNR and non-diagonal RIS systems performing better at low SNR scenarios.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is an emerging technology that enables full-space ($\mathbf{3 6 0}$ degrees) coverage on both sides of the surface. To harness the benefits of the dynamic configuration of STAR-RIS while avoiding co-channel interference, we investigate the performance of a multi-user network assisted by STARRIS. In this setup, users are divided into multiple groups, each comprising two users located on opposite sides of the STARRIS. Orthogonal time resources are allocated to each group such that the groups are served sequentially. Based on the Gamma moment matching method, we introduce a Gamma distribution to model the product of Rician, Rayleigh and mixed fading STAR-RIS channels. We then derive exact closed-form expressions for the outage probability and diversity order per user in the proposed system model. Moreover, simulation results are provided to substantiate the analytical derived expressions. Our findings highlight a reliability trade-off associated with the number of grouped users per time slot, STAR-RIS elements, and the user targeted data rates. This balance is crucial for optimizing network performance.

Distributed Denial of Service (DDoS) attacks continue to cause a substantial threat to network infrastructure and services. In this paper, we propose an approach called DDoS Layered Model Stacking (DDoS_LMS) to improve DDoS detection accuracy. Our model uses advanced ensemble machine-learning techniques to enhance the robustness and reliability of detection systems. We evaluate our model using a dataset of network traffic, including both legitimate and attack traffic. Multiple machine learning models are employed, such as Logistic Regression, k-nearest Neighbors (k-NN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Naive Bayes. Our proposed model, which combines the strengths of these individual classifiers, achieves exceptional results with 0.9872 accuracy, 0.9829 precision, 0.9847 recall, and 0.9837 F1 score. The DDoS_LMS notably outperforms individual models and proves its efficiency in detecting DDoS attacks.

This paper explores multi-connectivity (MC) techniques to enhance spectral efficiency (SE) of multi-tiered non-terrestrial networks (NTNs) in a pay-as-you-go (PAYG) service model, in which subscribers pay based on the volume of data consumed. The key objective is to maximize SE, and a resource allocation architecture is proposed to incorporate a multi-tier NTN with a hybrid gateway station (HGS) that manages the orbital satellites through co-located gateway antennas. This architecture operates with two different waveforms: 5G New Radio (NR) and DVB-S2X, both adapted into the 3rd Generation Partnership Project (3GPP) protocol stack, allowing for carrier capacity merging from all links with varying waveforms at the receiving user. To this end, a non-convex combinatorial opti-mization problem is formulated with inequality constraints and solved using a multi-agent reinforcement learning (MARL) aided resource allocation algorithm. This algorithm functions using the channel quality indicator (CQI) obtained for the different waveforms and under two channel conditions of clear sky (CS) and rain fading (RFD), to intelligently configure a resource allocation pattern which maximizes SE. The proposed algorithm is compared to proportional fairness (PF) and bottleneck max fairness (BMF) algorithms, and it outperforms in terms of SE by 11.16% and 24.15%, respectively.

To harness the benefits of non-orthogonal multiple access (NOMA) and reconfigurable intelligent surfaces (RISs), we propose a novel RIS-empowered decode-and-forward (RIS-DF) relaying scheme tailored to cooperative NOMA transmissions. This integration is a promising direction for improving communication reliability, extending coverage, and enhancing the overall performance in sixth-generation (6G) wireless networks. This paper focuses on enhancing signal reception of the cell-edge users with weak channel conditions by deploying multiple RISs within cooperative NOMA systems. In this setting, we investigate system outage performance and derive closed-form analytical expressions for both cooperative NOMA and its orthogonal mul-tiple access (OMA) counterpart, which is used as a benchmark for comparison. To validate the proposed solutions, numerical results are provided to demonstrate the performance gains of the proposed scheme compared to the existing conventional DF relays developed for cooperative NOMA. Further, the impact of RIS placement within the system on performance is also examined, offering useful practical design insights.

We consider the problem of estimating time-varying quantum noise. Specifically, we focus on Pauli qubit noise with time-variations and attempt to construct the most accurate instantaneous channel description. To this end, we propose an adaptive framework of simultaneous communication and parameter estimation (SCAPE) that efficiently and accurately estimates the time-varying Pauli channel while communicating reliably over the channel being estimated. This adaptive framework gives the informed control of communication rate–parameter estimation tradeoff to communicating parties. Interestingly, this adaptive SCAPE requires post-processing entirely on the receiver’s end and minimal feedback to the sender to increase, decrease, or continue with the same code rate of employed error correcting code. This procedure can be particularly useful in time-varying quantum channels with natural periodic deviations in channel conditions, e.g., in satellite communication channels.

This paper proposes a robust beamforming algorithm for massive multiple-input multiple-output (MIMO) low earth-orbit (LEO) satellite communications under uncertain channel conditions. Specifically, a Conditional Value at Risk (CVaR)-based stochastic optimization problem is formulated to optimize the hybrid digital and analog precoding aiming at maximizing the network data rate while considering the required Quality-of-Service (QoS) by each ground user. In particular, the CVaR is used as a risk measure of the downlink data rate to capture the high dynamic and random channel variations of the satellite network, achieving the required QoS under the worst-case scenario. Utilizing the decomposition and relaxation optimization techniques, an alternating optimization algorithm is developed to solve the formulated problem. Simulation results demonstrate the efficacy of the proposed approach in achieving the QoS requirements under uncertain satellite channel conditions.

This paper proposes a novel transmission scheme that leverages the synergistic benefits of multiple reconfigurable intelligent surfaces (RISs) and cooperative non-orthogonal multi-ple access (NOMA) to improve both spectral and energy efficiency in 5G and beyond wireless systems. The proposed scheme involves selecting one of cell-center users to an access point (AP), which then relays the data to a cell-edge user without a direct connection to the AP with assistance of multiple distributed RISs. In this respect, we propose a cooperative NOMA scheme and develop a user selection strategy for two RIS exploitation scenarios: (i) the RIS selection scheme where the transmission between the selected cell-center and cell-edge users is performed via a selected RIS, and (ii) the distributed RIS scheme where all RISs assist in the end-to-end communications. The system outage performance is statistically characterized and its closed-form expressions are derived. Additionally, simulations are carried out to validate the analytical expressions and compare the performance of these two schemes to a single RIS-assisted network under different numbers of RISs, reflecting elements, and cell-center users. The results show that combining multiple RIS in cooperative NOMA can yield high spectral efficiency gains and improved outage performance, even with a low number of RIS elements.

Quantum technologies have recently scaled up from laboratories into commercial applications thanks to the rapid technical developments and the growing investments in quantum computing. These developments open up the way for the emergence of the so-called noisy intermediate-scale quantum (NISQ) devices, where the quantum approximation optimization algorithms (QAOAs) represent a class of algorithms tailored for the NISQera computing for provisioning tangible quantum advantages. Meanwhile, wireless communications networks have become more complex over time and the pressure to conquer communications complexity is intense for both researchers and system designers. Specifically, a major optimization problem in this context is the resource allocation in modern communications where typically appears as an intricate 0/1 knapsack (0/1-KP) problem and finding its optimal solution using classical computers is prohibitively difficult. Thus, a parallel QAOA framework for optimizing the 0/1- KP problems is proposed in this paper. The proposal has the space complexity of$\mathcal{O} (n)$and pseudopolynomial time complexity of$\mathcal{O} (nW)$, where$W$is the knapsack's total capacity and$n$is the total number of items. However, the proposed QAOA solution is highly parallel and can be implemented on$M$NISQ devices of n-qubits each to obtain$\mathcal{O}(n W / M)$time complexity and$\mathcal{O} (nM)$space complexity. Numerical experiments show high approximation ratios even for shallow depth QAOA instances.

In this paper, we tackle the problem of joint symbol level precoding (SLP) and reconfigurable intelligent surface (RIS) phase shift design with constellation rotation in the finite block length regime. We aim to increase energy efficiency by minimizing the total transmit power while satisfying the quality of service constraints. The total power consumption can be significantly minimized through the exploitation of multiuser interference by symbol level precoding and by the intelligent manipulation of the propagation environment using reconfigurable intelligent surfaces. In addition, the constellation rotation per user contributes to energy efficiency by aligning the symbol phases of the users, thus improving the utilization of constructive interference. The formulated power minimization problem is non-convex and correspondingly difficult to solve directly. Hence, we employ an alternating optimization algorithm to tackle the joint optimization of SLP and RIS phase shift design. The optimal phase of each user’s constellation rotation is obtained via an exhaustive search algorithm. Through Monte-Carlo simulation results, we demonstrate that the proposed solution yields substantial power minimization as compared to conventional SLP, zero forcing precoding with RIS as well as the benchmark schemes without RIS.

Location-based services are witnessing a rise in popularity owing to their key features of delivering personalized digital experience. The recent developments in wireless sensing techniques make the realization of device-free localization (DFL) feasible within wireless sensor network (WSN) architectures. The DFL is an emerging technology that utilizes radio signal information for detecting and positioning a passive movable target without attached devices. However, determining the characteristics of the massive raw signals and extracting meaningful discriminative features relevant to the localization are highly intricate tasks due to the different patterns associated with different locations. To overcome these issues, deep learning (DL) techniques can be utilized here owing to their remarkable performance gains in similar practical problems. In this direction, we propose a DFL framework consists of multiple convolutional neural network (CNN) layers along with deep autoencoders based on the restricted Boltzmann machines (RBM) to construct a convolutional deep belief network (CDBN) for features recognition and extracting. Each CNN layer has stochastic pooling to sample down the feature map and reduced the dimensions of the required data without losing important information. This dimensionality reduction can alleviate the heavy computation while ensuring precise localization. The proposed framework is validated using real experimental dataset. The results show that the proposed model is able to achieve a high accuracy of 98% with reduced data dimensions and low signal-to-noise ratios (SNRs).

Satellite communications industry is currently going through a rapid and profound transformation to adapt to the recent innovations and developments in the realm of non-geostationary orbit (NGSO) satellites. The growing popularity of NGSO systems, with cheap manufacturing and launching costs, has set to revolutionize the internet market. In this context, accurate channel characterization is crucial for the performance optimization and designing efficient NGSO communications, especially considering the dynamic propagation environment. While the Third Generation Partnership Project (3GPP) has provided some guidelines in Release 15, we observed certain divergence on the channel models considered in the literature, each with different assumptions and peculiarities. This paper provides an extensive review of the existing methods proposed for NGSO channel modeling that consider different orbits, frequency bands, user equipment, use-case and scenario peculiarities. The provided review discusses the channel modeling efforts from a contemporary perspective through trade-off analyses, classifications, and highlighting their advantages and pitfalls. The main goal is to provide a comprehensive overview of NGSO channel models to facilitate the selection of the most appropriate channel based on the scenario requirements to be evaluated and/or analysed.

In this paper, the performance of an RF energy harvesting scheme for multi-user massive multiple-input multiple-output (MIMO) is investigated in the presence of multiple active jammers. The key idea is to exploit the jamming transmissions as an energy source to be harvested at the legitimate users. To this end, the achievable uplink sum rate expressions are derived in closed-form for two different antenna configurations. An optimal time-switching policy is also proposed to ensure user-fairness in terms of both harvested energy and achievable rate. Besides, the essential trade-off between the harvested energy and achievable sum rate are quantified in closed-form. Our analysis reveals that the massive MIMO systems can make use of RF signals of the jamming attacks for boosting the amount of harvested energy at the served users. Numerical results illustrate the effectiveness of the derived closed-form expressions over Monte-Carlo simulations.

Carrier Aggregation is one of the essential approaches to achieve several orders of magnitude increase in peak data rates. While carrier aggregation benefits have been extensively studied in terrestrial wireless systems, its application to satellite has not been substantially explored. Carrier aggregation can be a prominent solution to address the issue of the spatially-heterogeneous satellite data traffic demand. This paper studies introducing carrier aggregation into satellite systems from a link layer perspective. The proposed modifications at the link layer have been carefully designed to make carrier aggregation transparent to the other layers. However, deployment of carrier aggregation in satellite systems with the combination of multiple carriers that have different characteristics requires effective scheduling schemes for reliable communications. Since channel awareness is indispensable for any efficient resource allocation schemes, we have proposed a perceptive scheduling algorithm that takes into account channel properties along with the instantaneous available resources to ensure that the received data packets are delivered without perturbing the original transmission order. Simulation results are given to validate our analysis and demonstrate the design tradeoffs, and thus, our results provide useful insights to practical scheduler design.

Carrier Aggregation (CA) is an integral part of current terrestrial networks. Its ability to enhance the peak data rate, to efficiently utilize the limited available spectrum resources and to satisfy the demand for data-hungry applications has drawn large attention from different wireless network communities. Given the benefits of CA in the terrestrial wireless environment, it is of great interest to analyze and evaluate the potential impact of CA in the satellite domain. In this paper, we study CA in multibeam high throughput satellite systems. We consider both inter-transponder and intra-transponder CA at the satellite payload level of the communication stack, and we address the problem of carrier-user assignment assuming that multiple users can be multiplexed in each carrier. The transmission parameters of different carriers are generated considering the transmission characteristics of carriers in different transponders. In particular, we propose a flexible carrier allocation approach for a CA-enabled multibeam satellite system targeting a proportionally fair user demand satisfaction. Simulation results and analysis shed some light on this rather unexplored scenario and demonstrate the feasibility of the CA in satellite communication systems.

In this paper, the detrimental effects of intra-cell pilot contamination for physical layer secure communication in cognitive multi-user massive multiple-input multiple-output (MIMO) systems with underlay spectrum sharing are investigated. The channel estimates at the primary base-station (PBS) and secondary base-station are obtained by using non-orthogonal pilot sequences transmitted by the primary user nodes and secondary user nodes, respectively. Hence, these channel estimates are affected by intra-cell pilot contamination. Furthermore, a passive multi-antenna eavesdropper is assumed to be eavesdropping upon either the primary or secondary confidential transmissions. In this context, a physical layer security strategy is provisioned for the primary and secondary transmissions via artificial noise generation at the PBS and zero-forcing precoders. For this system set-up, the average and asymptotic achievable secrecy rate expressions are derived in closed-form, and thereby, the secrecy rate degradation due to intra-cell pilot contamination is quantified. Our analysis reveals that a physical layer secure communication can be provisioned for both primary and secondary massive MIMO systems even with channel estimation errors and pilot contamination.

The asymptotic sum rate performance of multi-cell/multi-user cognitive massive multiple-input multiple-output (MIMO) systems with underlay spectrum sharing is investigated. Specifically, each cell consists of a licensed (primary) multi-user massive MIMO system and a cognitive (secondary) multi-user MIMO system which is allowed to utilize the licensed frequency spectrum provided that the intra-cell interference inflicted at the primary base-station due to the concurrent transmissions of the secondary user nodes is maintained below a predefined interference temperature. For the uplink transmission of the aforementioned system set-up, the signal-to-interference-plus-noise ratio and achievable sum rate expressions are derived for three specific antenna configurations; (i) infinitely many primary and secondary base-station antennas, (ii) infinitely many primary base-station antennas and finitely many secondary base-station antennas, and (iii) finitely many primary and secondary base-station antennas. Our asymptotic analysis reveals that the achievable sum rate expressions become independent of the primary interference threshold whenever the number of primary base-station antennas grows unbounded. Consequently, the secondary network can be operated at its maximum average transmit power level without degrading the asymptotic performance of the primary network. Therefore, the primary and secondary networks can be operated independent of each other as both intra-cell and inter-cell interference can be asymptotically mitigated by exploiting the zero-forcing detectors employed at the massive MIMO enabled base-stations.