The low Earth orbit (LEO) satellite megaconstellation can provide ubiquitous coverage and high-performance connectivity, supporting multi-slice applications with various key performance indicator (KPI) requirements. However, due to the dynamic nature of LEO satellites, limited resources, and the diverse demands of different slices, managing user association (UA) and resource allocation becomes an increasingly challenging task in areas with overlapping satellite coverage. This paper proposes a joint optimization model for UA and resource allocation in satellite networks (SLSNs). Based on mixed-integer non-linear programming (MILP), our model minimizes the total propagation delay and optimizes the demand satisfaction ratio (DSR) using a Max-Min approach to ensure each slice meets its unique throughput requirements. In addition, a visibility-aware component is incorporated to prioritize longer satellite visibility, reduce handovers, and improve network stability. Due to the computational complexity of the MILP model, we propose a heuristic-based balanced association with delay-aware bandwidth distribution (B-DAD) approach. B-DAD operates in two phases: the initial UA phase selects satellites based on a combined metric of delay, load, and visibility duration, while the residual bandwidth distribution phase reallocates unused bandwidth among associated users proportionally. Extensive simulations demonstrate that our approaches significantly improve DSR, propagation delays, transmission delays, and network stability compared to the widely adopted benchmark maximum sum of data rate (Max-SR) and Greedy methods under varying elevation angles. Our findings highlight the effectiveness of the MILP model in achieving optimal solutions and the efficiency of B-DAD as a scalable alternative for large-scale scenarios.

Cell-free massive multiple-input multiple-output (CF-mMIMO) has emerged as a new paradigm shift for future wireless communication networks to cope with the limitations of traditional cellular architectures. The deployment of CF-mMIMO networks comes with challenges, such as enabling reliable and low-latency fronthaul connections, particularly for isolated geographical areas where the fiber optics are costly to deploy or wireless fronthaul links are inaccessible. To address this, high altitude platform station (HAPS) emerges as a promising solution for fronthauling and ensuring cooperation tasks as a central processing unit (CPU) in the CF-mMIMO networks. The HAPS located in the stratosphere provides a wide coverage area, low latency, and cost-efficient fronthauling. Therefore, cell-free networks can achieve seamless connectivity between the distributed access points (APs) at a higher effective data rate by leveraging the HAPS. This paper investigates the sum rate maximization problem in the HAPS-assisted CF-mMIMO systems. Performance evaluations demonstrate that the proposed system design increases users’ data rates by leveraging both the high-capacity fronthauling supported by HAPS and the superior spatial multiplexing gain provided by CF-mMIMO. Furthermore, the study highlights the potential of HAPS-assisted architectures in providing services for users in underserved areas.

The modern Internet supports diverse applications with heterogeneous quality of service (QoS) requirements. Low Earth orbit (LEO) satellite constellations offer a promising solution to meet these needs, enhancing coverage in rural areas and complementing terrestrial networks in urban regions. Ensuring QoS in such networks requires joint optimization of routing, bandwidth allocation, and dynamic queue scheduling, as traffic handling is critical for maintaining service performance. This paper formulates a joint routing and bandwidth allocation problem where QoS requirements are treated as soft constraints, aiming to maximize user experience. An adaptive scheduling approach is introduced to prioritize flow-specific QoS needs. We propose a Monte Carlo tree search (MCTS)-inspired method to solve the NP-hard route and bandwidth allocation problem, with Lyapunov optimization-based scheduling applied during reward evaluation. Using the Starlink Phase 1 Version 2 constellation, we compare end-user experience and fairness between our proposed DSROQ algorithm and a benchmark scheme. Results show that DSROQ improves both performance metrics and demonstrates the advantage of joint routing and bandwidth decisions. Furthermore, we observe that the dominant performance factor shifts from scheduling to routing and bandwidth allocation as traffic sensitivity changes from latency-driven to bandwidth-driven.

Large-scale low-Earth-orbit (LEO) constellations demand routing that simultaneously minimizes energy, guarantees delivery under congestion, and meets latency requirements for time-critical flows. We present a segment routing over IPv6 (SRv6) flexible algorithm (Flex-Algo) framework that consists of three logical slices: an energy-efficient slice (Algo 130), a high-reliability slice (Algo 129), and a latency-sensitive slice (Algo 128). The framework provides a unified mixed-integer linear program (MILP) that combines satellite CPU power, packet delivery rate (PDR), and end-to-end latency into a single objective, allowing a lightweight software-defined network (SDN) controller to steer traffic from the source node. Emulation of Telesat’s Lightspeed constellation shows that, compared with different routing schemes, the proposed design reduces the average CPU usage by 73%, maintains a PDR above 91% during traffic bursts, and decreases urgent flow delay by 18 ms between Ottawa and Vancouver. The results confirm Flex-Algo’s value as a slice-based traffic engineering (TE) tool for resource-constrained satellite networks.

Internet of Things (IoT) devices have become increasingly ubiquitous with applications not only in urban areas but remote areas as well. These devices support industries such as agriculture, forestry, and resource extraction. Due to the device location being in remote areas, satellites are frequently used to collect and deliver IoT device data to customers. As these devices become increasingly advanced and numerous, the amount of data produced has rapidly increased potentially straining the ability for radio frequency (RF) downlink capacity. Free space optical communications with their wide available bandwidths and high data rates are a potential solution, but these communication systems are highly vulnerable to weather-related disruptions. This results in certain communication opportunities being inefficient in terms of the amount of data received versus the power expended. In this paper, we propose a deep reinforcement learning (DRL) method using Deep Q-Networks that takes advantage of weather condition forecasts to improve energy efficiency while delivering the same number of packets as schemes that don't factor weather into routing decisions. We compare this method with simple approaches that utilize simple cloud cover thresholds to improve energy efficiency. In testing the DRL approach provides improved median energy efficiency without a significant reduction in median delivery ratio. Simple cloud cover thresholds were also found to be effective but the thresholds with the highest energy efficiency had reduced median delivery ratio values.

This paper studies the effect of beam-squint on the coverage performance in wideband sub-Terahertz (sub-THz) multi-carrier systems from a system-level analysis perspective. Beam-squint, a frequency-dependent beam misalignment, intensifies in wideband systems, reducing beamforming accuracy and overall network performance. To address this issue, we use tools from stochastic geometry and provide an analytical framework to investigate the coverage probability performance of sub-THz networks under the effect of beam-squint. Our framework integrates important system parameters, such as the spatial deployment of base stations (BSs), system transmission bandwidth, transmit and receive antenna array sizes, channel propagation conditions, and blockage impacts. Using numerical and Monte Carlo simulations, we validate our framework's accuracy and highlight the critical impact of beam-squint in constraining the performance of wideband sub-THz networks. The findings reveal that in wideband multi-carrier systems, coverage performance declines as subcarrier frequencies diverge further from the center frequency due to the beam-squint effect. Additionally, the results highlight that although larger antenna arrays improve the coverage performance, their benefits diminish at higher subcarrier frequencies. This is due to reduced beamwidth, which makes the communication link more susceptible to beam-squint effects, ultimately degrading system performance. These insights are valuable for optimizing sub- THz network parameters to mitigate beam-squint's adverse effects and enhance overall network performance.

The increasing congestion of Earth’s orbit due to growing satellite deployments and space debris poses a significant challenge to sustainable space operations. Traditional space surveillance systems rely on centralized architectures, which introduce single points of failure and scalability constraints. This paper proposes a blockchain-based solution, where satellites function as nodes with distinct roles to validate and securely store debristracking data. Simulation results indicate that optimal network performance is achieved with approximately 30 nodes, balancing throughput and response time, representing an approximately $9 \times$ improvement over traditional consensus mechanisms.

The integration of low Earth orbit (LEO) satellite communication into 6G networks promises a transformative impact on global connectivity by expanding coverage to remote regions and enhancing service reliability. However, this new infrastructure also introduces significant security challenges due to its expansive attack surface. To address this concern, we propose a dynamic security functions allocation (SFA) model that optimizes the allocation of security functions (SFs) across satellites while considering computational resource limitations, dynamic topology changes, and the visibility constraints of satellite constellations. Our model leverages the flexibility of 6G network slicing (NS) to share non-critical SFs between slices, reducing resource overhead while maintaining essential security demands. To minimize the risk of sharing highly sensitive SFs between slices, our model employs a nonlinear penalty, which prioritizes minimizing risk by aggressively penalizing high-risk SFs sharing. This dynamic risk management framework assesses the probability and impact of security breaches, ensuring that SFs are shared only when the security risk is acceptable, balancing resource efficiency and security. By dynamically adapting to the network’s operational conditions, our approach provides a robust framework for efficient and secure satellite communication in 6G networks. Simulation results demonstrate the model’s flexibility in managing trade-offs across key network performance metrics.

Uncrewed Aerial Vehicles (UAVs), commonly known as drones, are widely deployed and often transmit sensitive data via cellular networks, Wi-Fi, or device-to-device (D2D) communication frameworks. Due to their aerial mobility, UAVs are well-suited for a wide range of applications such as military missions, cargo transport, mapping, and agricultural monitoring. However, securing their communications remains a significant challenge. Their continuous movement and frequent data transmissions make secure authentication difficult to maintain, especially as UAVs often need to switch between different network cells during flight. For example, a drone initially connected to cell A may need to handover to cell B as it progresses along its route. This study is designed to present new authentication and handover processes for multiple nodes within a predefined group. The proposed method makes the group handover process simple and low-cost, while also being resistant to various security threats. Compared to a previous handover scheme based on elliptic curves and Lagrange interpolation, the proposed method, leveraging inner product space, demonstrates significantly improved performance. The corresponding test results are also presented.

Arctic regions, such as northern Canada, face significant challenges in achieving consistent connectivity and low-latency computing services due to the sparse coverage of Low Earth Orbit (LEO) satellites. To enhance service reliability in remote areas, this paper proposes a hybrid satellite architecture for task offloading that combines Medium Earth Orbit (MEO) and LEO satellites. We develop an optimization framework to maximize task offloading admission rate while balancing the energy consumption and delay requirements. Accounting for satellite visibility and limited computing resources, our approach integrates dynamic path selection with frequency and computational resource allocation. Because the formulated problem is NP-hard, we reformulate it into a mixed-integer convex form using disjunctive constraints and convex relaxation techniques, enabling efficient use of off-the-shelf optimization solvers. Simulation results show that, compared to a standalone LEO network, the proposed hybrid LEO-MEO architecture improves the task admission rate by 15% and reduces the average delay by 12%. These findings highlight the architecture’s potential to enhance connectivity and user experience in remote Arctic areas.

Software-defined networking (SDN) has emerged as a promising approach for managing traditional satellite communication. This enhances opportunities for future services, including integrating satellite and terrestrial networks. In this paper, we have developed an SDN-enabled framework for Low Earth Orbit (LEO) satellite networks, incorporating the Open-Flow protocol, all within an OMNeT++ simulation environment. Dynamic controller assignment is one of the most significant challenges for large LEO constellations. Due to the movement of LEO satellites, satellite-controller assignments must be updated frequently to maintain low propagation delays. To address this issue, we present a dynamic satellite-to-controller assignment (DSCA) optimization problem that continuously adjusts these assignments. Our optimal DSCA (Opt-DSCA) approach minimizes propagation delay and optimizes the number of active controllers. Our preliminary results demonstrate that the DSCA approach significantly outperforms the static satellite-to-controller assignment (SSCA) approach. While SSCA may perform better with more controllers, this scheme fails to adapt to satellite movements. Our DSCA approach consistently improves network efficiency by dynamically reassigning satellites based on propagation delays. Further, we found diminishing returns when the number of controllers is increased beyond a certain point, suggesting optimal performance with a limited number of controllers. Opt-DSCA lowers propagation delays and improves network performance by optimizing satellite assignments and reducing active controllers.

Space Domain Awareness (SDA) system has different major aspects including continuous and robust awareness from the network that is crucial for efficient control over all actors in space. The observability of the space assets, on the other hand, requires efficient analysis of when and how observed space objects can be controlled. This becomes crucial when real-world spatial dynamics are taken into account, as it introduces complexities into the system. The real-world dynamics can reveal the structure of the network, including isolated and dominant stations. We propose a Temporal Spectrum Analysis (TSA) scheme that takes into account a set of real-world parameters, including actual dynamics of the objects in space, to analyze the structure of a ground-space network that inherits temporal spectrum as the key element of design. We study the potential interactions between multiple constellations using TSA and conduct comprehensive real-world simulations to quantify the structure of the network. Numerical results show how the temporal spectrum of each satellite affects the intra- and inter-constellation network structure, including interactions between ground stations and constellations.

The proliferation of space debris in low Earth orbit (LEO) presents critical challenges for orbital safety, particularly for satellite constellations. Integrated sensing and communication (ISAC) systems provide a promising dual-function solution by enabling both environmental sensing and data communication. This study explores the use of space shift keying (SSK) modulation within ISAC frameworks, evaluating its performance when combined with sinusoidal and chirp radar waveforms. SSK is particularly attractive due to its low hardware complexity and robust communication performance. Our results demonstrate that both waveforms achieve comparable bit error rate (BER) performance under SSK, validating its effectiveness for ISAC applications. However, waveform selection significantly affects sensing capability: while the sinusoidal waveform supports simpler implementation, its high ambiguity limits range detection. In contrast, the chirp waveform enables range estimation and provides a modest improvement in velocity detection accuracy. These findings highlight the strength of SSK as a modulation scheme for ISAC and emphasize the importance of selecting appropriate waveforms to optimize sensing accuracy without compromising communication performance. This insight supports the design of efficient and scalable ISAC systems for space applications, particularly in the context of orbital debris monitoring.

The increasing demand for data usage in wireless communications requires using wider bands in the spectrum, especially for backhaul links. Yet, allocations in the spectrum for non-communication systems inhibit merging bands to achieve wider bandwidth. To overcome this issue, spectrum-sharing or opportunistic spectrum utilization by secondary users stands out as a promising solution. However, both approaches must minimize interference to primary users. Therefore, spectrum sensing becomes vital for such opportunistic usage, ensuring the proper operation of the primary users. Although this problem has been investigated for two-dimensional networks, unmanned aerial vehicle (UAV) networks need different approaches concerning three-dimensional space, its challenges, and opportunities. For this purpose, we propose a federated learning (FL)-based method for spectrum sensing in UAV networks to account for their distributed nature and limited computational capacity. FL enables local training without sharing raw data while guaranteeing the privacy of local users, lowering communication overhead, and increasing data diversity. Furthermore, we develop a federated aggregation method, namely FedSNR, that considers the signal-to-noise ratio observed by UAVs to acquire a global model. The numerical results show that the proposed architecture and the aggregation method outperform traditional methods.

The rapid expansion of Vehicle-to-Everything (V2X) networks within the Internet of Vehicles (IoV) demands secure and efficient authentication to support high-speed, high-density and mobility-challenged environments. This paper presents a privacypreserving authentication scheme that incorporates batch authentication, mutual authentication, and secure key establishment, enabling users to authenticate one another without a central authority. Our proposed scheme facilitates simultaneous multi-user authentication, significantly enhancing scalability, robustness and security in dynamic IoV networks. Results from realistic implementations show that our method achieves average authentication and verification times of$\mathbf{1 0. 6 1 ~ m s}$and$\mathbf{1. 7 8 ~ m s}$, respectively, for a fleet of$\mathbf{1 0 0}$vehicles, outperforming existing methods. Scalability tests demonstrate efficient processing for larger groups of up to 500 vehicles, where average authentication times remain low, establishing our scheme as a robust solution for secure communication in IoV systems.

This study investigates the anti-jamming space-air-ground integrated network (SAGIN) scenario wherein a reconfigurable intelligent surface (RIS) is deployed on a fixed Unmanned Aerial Vehicle (UAV) to counteract malevolent jamming attacks. In contrast to existing research, in this paper, we consider that a Low Earth Orbit (LEO) satellite is sending the signal to the user on the ground in the presence of jamming from a Geostationary Equatorial Orbit (GEO) satellite side. We aim to maximize the signal-to-jamming plus noise ratio (SJNR) by optimizing the RIS beamforming and transmit power of the LEO satellite. Assuming the availability of global channel state information (CSI) at the RIS, we propose alternating optimization (AO) and semidefinite relaxation (SDR) techniques to address the complexity. Simulation results show that the optimization schemes lead to considerable performance improvements. The results also indicate that, given the high jamming power and the relatively small number of RIS elements, deploying the RIS on UAVs near the user is more effective in mitigating the impact of jamming interferers.

Full-array (FA) hybrid beamforming, integrating analog and digital components, is employed in millimeter wave (mmWave) systems to achieve directional signal transmission with enhanced gains. The main hardware challenge in this hybrid configuration resides in the analog segment, where each antenna at the transmitters and receivers is linked to an entire network of phase shifters (PSs). To address this, researchers have investigated sub-array (SA) hybrid designs that use fewer PSs to reduce hardware complexity; however, the number of PSs scales linearly with the number of antenna subarrays. This paper introduces innovative adaptive phase shifters (APSs) designed for hybrid beamforming that feature low hardware complexity and operate independently of the number of antenna arrays and radio frequency (RF) chains. The proposed APSs hybrid scheme is designed to achieve performance comparable to the FA iterative hybrid design while minimizing the number of required PSs. Specifically, the number of PSs can vary from two per RF chain to the entire network of PSs, allowing for a more efficient and high-performance design of hybrid systems. Furthermore, the full network of PSs per RF chain, corresponding to the total number of antenna elements, is reduced using modified K-means algorithms. Simulation results demonstrate that the spectral efficiency performance of APSs hybrid designs using only two PSs surpasses that of conventional FA hybrid design and SA hybrid design and exhibits similar performance to FA iterative hybrid designs with a smaller number of PSs. The proposed APSs hybrid design represents promising technology for 6G systems and beyond, owing to its innovative design with low hardware complexity and high-gain spectral efficiency.

In this paper, we address optimum design of uncrewed aerial vehicle (UAV)–based wireless networks with a focus on computation offloading in the presence of an active aerial attacker. Our design aims to minimize the maximum computation time among the tasks of ground users while satisfying the energy consumption requirements. To this end, we propose a joint optimization problem of partial computation offloading, ground user association, multiple UAVs trajectory control, computation resource, and sub-channel assignment. To tackle the underlying non-convex mixed-integer nonlinear optimization problem, we use the alternating optimization approach to iteratively solve the five sub-problems, namely, user-UAV association, user scheduling, partial offloading control and bit allocation over time slots, computation resource and sub-channel assignment, and UAV trajectory control until convergence. Moreover, the successive convex approximation method is employed to solve the non-convex sub-problems and improve the resilience of the system against jammer attacks. Additionally, we propose low-complexity algorithms to solve the involved sub-problems. Via extensive numerical studies, we illustrate the effectiveness of our proposed design compared to baselines under the impact of an aerial attacker.

Machine unlearning (MUL) is introduced as a means to achieve interference cancellation within artificial intelligence (AI)-enabled wireless systems. It is observed that interference cancellation with MUL demonstrates 30% improvement in a classification task accuracy in the presence of a corrupted AI model. Accordingly, the necessity for instantaneous channel state information for existing interference source is eliminated and a corrupted latent space with interference noise is cleansed with MUL algorithm, achieving this without the necessity for either retraining or dataset cleansing. A Membership Inference Attack (MIA) served as a benchmark for assessing the efficacy of MUL in mitigating interference within a neural network model. The advantage of the MUL algorithm was determined by evaluating both the probability of interference and the quantity of samples requiring retraining. In a simple signal-to-noise ratio classification task, the comprehensive improvement across various test cases in terms of accuracy demonstrates that MUL exhibits extensive capabilities and limitations, particularly in native AI applications.

Federated learning is a promising approach to training deep learning models over distributed devices without sharing their local datasets. Low Earth orbit (LEO) satellites have the potential to use the massive amount of collected Earth imageries and sensor data to train artificial intelligence (AI) models and provide global services such as disaster detection. However, the structure of LEO networks is different from that of the terrestrial networks, which makes the traditional (star-based or hierarchical-based FL) inefficient. This paper proposes a new distributed FL approach that is customized to the LEO structure. The approach is based on parallelizing the FL operations and decentralizing the aggregations over several satellites, aiming at reducing the convergence time at a given energy constraint. Simulation results show that the proposed algorithm converges significantly faster than traditional FL-LEO approaches proposed in the literature under the same energy consumption.

This study investigates an experimental software defined radio (SDR) implementation on 180 GHz. The system model is presented to evaluate the performance and unveil hidden opportunities. Accordingly, rate scarcity and frequency sparsity are discussed as hardware bottlenecks. Multiple error metrics for the terahertz (THz) signal are acquired, and various case scenarios are subsequently compared, revealing that the SDR-THz testbed reaches 3.2 Mbps with$< 1^\circ$skew error. It is observed that the use of a reflector plate can fine-tune the frequency error and gain imbalance in the expense of at least 14.91 dB signal-to-noise ratio. The results demonstrate the complete feasibility of SDR-based baseband signal generation in THz communication, revealing abundant opportunities to overcome hardware limitations in experimental research.

Terahertz (THz) frequencies play a crucial role in the advancement of next-generation wireless systems, primarily owing to their substantial available bandwidths. The inherent limitation of limited range, attributed to high attenuation in these frequencies, can be effectively addressed by implementing densely deployed heterogeneous networks, complemented by Unmanned Aerial Vehicles (UAVs) within a three-dimensional hyperspace. Yet, the success of THz communications relies on the precise alignment of beams. Inadequate handling of beam alignment can lead to diminished signal strength at the receiver, significantly affecting THz signals more than their conventional counter-parts. This research underscores the paramount importance of meticulous alignment in THz communication systems. The profound impact of proper alignment is substantiated through comprehensive measurements conducted using a state-of-the-art measurement setup, facilitating accurate data collection across the 240 GHz to 300 GHz spectrum. These measurements encompass varying angles and distances within an anechoic chamber to eliminate reflections. Through a meticulous analysis of the channel frequency and impulse responses derived from these extensive measurements, this study pioneers quantifiable results, providing an assessment of the effects of beam misalignment in THz frequencies.

In this paper, we develop an accurate closed-form analytic expression of the outage probability for each source-destination (S-D) pair in the two S-D pairs two relays wireless network, where cooperative network coding is applied over Nakagami-m fading channels. For different values of the shape parameter m, our analytic relation is validated with Monte-Carlo simulations of the outage probability using the overall equivalent signal-to-noise ratio (SNR) within each source-destination pair. The outage probability is also provided for the extended versions of the network consisting of multiple relays and multiple S-D pairs. Furthermore, we investigate the role of varying the fading factor (m values) on different links in the end-to-end outage probability performance, which yields interesting results that may be crucial for relay selection and power allocation procedures. Moreover, we derive the diversity order for the generalized extended version of cooperative network coded wireless networks.

For decades, satellites have facilitated remote internet of things (IoT) services. However, the recent proliferation of increasingly capable sensors and a surge in the number deployed, has led to a substantial growth in the volume of data that needs to be transmitted via satellites. In response to this growing demand, free space optical communication systems have been proposed, as they allow for the use of large bandwidths of unlicensed spectrum, enabling high data rates. However, optical communications are highly vulnerable to weather-induced disruptions, thereby limiting their high potential. This paper proposes the use of high altitude platform station (HAPS) systems in conjunction with delay-tolerant networking techniques to increase the amount of data that can be transmitted to the ground from satellites when compared to the use of traditional ground station network architectures. The architectural proposal is evaluated in terms of delivery ratio and buffer occupancy, and the subsequent discussion analyzes the advantages, challenges and potential areas for future research.

Clustering, as a technique for grouping nodes in geographical proximity together, in vehicular communication networks, is a key technique to enhance network robustness and scalability despite challenges such as mobility and routing. This paper presents a robust clustering scheme based on cluster head backup list algorithm for unmanned aerial vehicles (UAVs)-assisted vehicular communication network, where multiple UAVs act as communication base stations for a vehicular network. To tackle the high mobility issues in vehicular communications, instead of allowing direct communication between all vehicles to the UAV, clustering methods will potentially be efficient in overcoming delay limitations, excessive power consumption and resource issues. Using the clustering technique, neighboring vehicles are grouped into clusters with a specific vehicle selected as the cluster head (CH) in each cluster. The selected CH connects directly to the UAV through an infrastructure-to-vehicle (I2V) link, subsequently establishing vehicle-to-vehicle (V2V) communications with vehicles in the same cluster. To increase cluster connectivity period, the proposed clustering scheme is developed based on considering the vehicle behavior for efficient selection of CHs and providing a CH backup list to maintain the stability of the cluster structure. Numerical evaluations show that the proposed system outperforms benchmark schemes in terms of clustering stability and reliability. It is also shown that the performance of the proposed scheme is not much affected by the increase in the number of vehicles. This indicates that the proposed scheme can be efficient in dense vehicular networks where resource constraints pose significant challenges.

In this paper, we study the computation offloading problem against an active attacker in space-air-ground integrated networks (SAGIN), where joint optimization of partial computation offloading, unmanned aerial vehicle (UAV) trajectory control, computation and resource allocation is performed. Our design aims to minimize the maximum computation time of individual tasks among ground users while satisfying energy consumption constraints. To tackle the underlying non-convex optimization problem, we use the alternating optimization approach to iteratively solve three sub-problems, namely, partial offloading control and bit allocation over time slots, computation resource and bandwidth allocation, and UAV trajectory control, until convergence. Furthermore, the successive convex approximation method is employed to solve the non-convex sub-problems and improve the resilience of the SAGIN against active attacks. Via extensive numerical studies, we illustrate the effectiveness of our proposed design compared to baselines under the effect of an active attacker.

Drawing from the latest breakthroughs in swarm robotics and smart control systems, our study explores frequency synchronization and phase alignment in oscillatory networks. The core of our work is a novel distributed consensus protocol paired with a reinforcement learning algorithm tailored for a leader-follower multi-agent oscillatory network. We present a critic-based neuro-fuzzy learning strategy designed to unify phase and frequency tracking while reducing errors. Each agent is tasked with maintaining its assigned phase and frequency. Our method utilizes a fuzzy critic to assess situations and adjust the controller’s parameters accordingly, aiming to diminish the stress signal. Our versatile design adapts to various networks, countering dynamic agent behaviors and network parameter uncertainties, ensuring reliable controller operations. Our scalable solution supports numerous autonomous agents. We demonstrate our learning strategy’s efficacy through simulations on a network of ten oscillating vehicles. Furthermore, we conducted a comparative analysis of the results obtained from the implementation of the proposed approach against those derived from the use of a conventional PI controller.

Motivated by the great potential of multi-antenna, multi-user non-orthogonal multiple access (NOMA) systems for 5G+, this paper presents an outage probability (OP) analysis of a down-link multi-user NOMA network with Alamoutilmaximum ratio combining (AlamoutiIMRC) antenna diversity. Herein, a base station using Alamouti coding simultaneously serves mul-tiple users with MRC. We derive the exact and asymptotic OP expressions for users over the Rayleigh fading channel. In addition, we also constitute a software-defined radio-based three-users 2x2 AlamoutiIMRC test-bed to investigate the feasibility of the considered network in a real-time manner. Finally, the analytical expressions are verified through the test-bed results and the Monte Carlo simulations.

Group authentication is a technique that verifies the group membership of multiple users and establishes a shared secret key among them. Unlike the conventional authentication schemes that rely on a central authority to authenticate each user individually, group authentication can perform the authentication process simultaneously for all the members who participate. Group authentication has been found to be a suitable candidate for various applications in crowded in Internet of Things (IoT) environments, such as swarms of drones for agriculture, military, and surveillance, where a group of devices need to establish a secure authenticated communication channel among themselves. The recently presented group authentication algorithms mainly exploit Lagrange polynomial interpolation along with elliptic curve groups over finite fields. A polynomial interpolation-based group authentication scheme (GAS) has a vulnerability that allows malicious interruption by any single entity in the process. Moreover, this scheme requires each entity to obtain the tokens of all other entities, which is impractical in a large-scale setting. The cost of authentication and key establishment also depends on the number of users, creating a scalability issue. As a fresh approach to eliminate these issues, this work suggests the use of inner product spaces for group authentication and key establishment. The approach with linear spaces introduces a reduced computation and communication load to establish a common shared key among the group members. In addition to providing lightweight authentication and key agreement, this approach allows any user in a group to make a nonmember a member, which is expected to be useful for autonomous systems in the future. The scheme is designed in a way that the sponsors of such members can easily be recognized by anyone in the group. Unlike the other GASs based on Lagrange’s polynomial interpolation, the proposed scheme does not provide a tool for adversaries to compromise the whole group’s secrets by using only a few members’ shares as well as it allows to recognize a nonmember easily, which prevents the denial-of-service attacks from which the former group authentication algorithms suffer.

A precise incident wave angle estimation in aerial communication is a key enabler in sixth-generation wireless communication network. With this goal, a generic 3-dimensional (3D) channel model is analyzed for air-to-air (A2A) networks under antenna misalignment, radio frequency impairments and polarization loss. The unique aspects of each aerial node are highlighted and the few-shot learning as a model agnostic meta-learning (MAML) classifier is proposed for learning-to-learn (L2L) incident wave angle estimation by utilizing the received signal strength (RSS). Additionally, a more computationally efficient technique, first order model agnostic meta-learning (FOMAML) is implemented. It has been observed that the proposed approach reaches up to 85% training accuracy and 75.4% evaluation accuracy with MAML. Regarding this, a convergence rate and accuracy trade-off have been established for several cases of MAML and FOMAML. For different L2L models trained with limited data, heuristic accuracy performance is determined by an upper bound of the probability of confidence.

Ensuring the security of the Internet of Vehicles (IoV) has been a challenging task, particularly due to the high mobility rate of the many elements in the network. With vehicles constantly moving and frequently exchanging data, authentication is crucial for maintaining security. We propose a new approach based on group authentication to handle authentication of several units simultaneously while protecting privacy in an IoV environment. The proposed protocol is designed to perform an authentication and handover process of multiple users within a predefined group. The group authentication based approach ensures privacy of users as the group members’ identification is systematically redefined after each group exchange process. Thus, the proposed scheme is designed to offer a promising solution to security challenges within the IoV environment by simultaneously addressing scalability and protecting the privacy of user data. Additionally, our scheme is resistant to various types of attacks, including offline ID attacks, replay attacks, location spoofing attacks, and replay attacks. Comprehensive tests have been conducted and the results are presented in the analysis section, both in terms of the computational cost and the running times, which reveal that the proposed method demonstrates remarkable practicality when compared to the existing approaches.

In this work, extensive propagation characteristics of short-range 240 to 300 GHz terahertz (THz) channels are mapped based on a measurement campaign conducted utilizing a novel, task-specific measurement system. The measurement system allows collecting measurements from different distances and orientations in a very-fine grained resolution, which is a particular issue in achieving realistic THz channel estimation. After the accurate measurement results are obtained, they are investigated in terms of channel impulse and channel frequency response. Furthermore, the fading channel amplitude histograms are modeled with the Gamma mixture model (GMM). The expectation-maximization (EM) algorithm is utilized to determine the corresponding mixture parameters. Also, to demonstrate the flexibility of the GMM, the Dirichlet process Gamma mixture model (DPGMM) is utilized in cases where the EM algorithm fails to represent histograms. Moreover, the suitability of the GMM is evaluated utilizing Kolmogorov–Smirnov tests. Results verify that the GMMs can simulate the fading channel of micro-scale THz wireless communication in a realistic way, providing important implications regarding the achievable capacity in these channels. Finally, the average channel capacity of each link is evaluated using the probability density function of GMMs to gain deeper insight into the potential of micro-scale THz communications.

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks due to its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The proposed scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. We formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation-based methods are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner but also it improves the delay performance and maintains the delay stability.

In this paper, the impact of the acquisition, tracking, and pointing (ATP) module utilization on inter-satellite energy harvesting is investigated for 1U (0.1 × 0.1 × 0.1 m) and 12U (0.2 × 0.2 × 0.3 m) satellites for adaptive beam divergence and the corresponding distances while maintaining the spot diameters. Random elevation and azimuth misalignment error angles at both the transmitter and the receiver are modeled with Gaussian distribution hence the radial pointing error angle is modeled with Rayleigh distribution. The Monte Carlo approach is used to determine mean radial error angles for both transmitter and receiver in the non-ATP and ATP cases. The average harvested powers are analyzed as a function of the transmit powers and inter-satellite distances for both 1U and 12U satellites while considering the minimum power requirements. Our simulation results show that in the non-ATP case, the minimum required average harvested power cannot be achieved beyond 680 and 1360 km distances for 1U and 12U satellites, respectively, with a maximum transmit power of 1 kW. However, 2 W of average harvested power can be achieved at around 750 and 1500 km for 1U and 12U satellites, respectively, with a transmit power of 27 W in the presence of an ATP mechanism.

Space has been reforming and this evolution brings new threats that, together with technological developments and malicious intent, can pose a major challenge. Space domain awareness (SDA), a new conceptual idea, has come to the forefront. It aims sensing, detection, identification and countermeasures by providing autonomy, intelligence and flexibility against potential threats in space. In this study, we first present an insightful and clear view of the new space. Secondly, we propose an integrated SDA and communication (ISDAC) system for attacker detection. We assume that the attacker has advanced communication capabilities to vary attack scenarios, such as random attacks on some receiver antennas. To track random patterns and meet SDA requirements, a lightweight convolutional neural network architecture is developed. The proposed ISDAC system shows superior and robust performance under 12 different super-attacker configurations with a detection accuracy of over 97.8%.

Due to the unprecedented advances in satellite fabrication and deployment, innovative communications and networking technologies, ambitious space projects and programs, and the resurgence of interest in satellite networks, there is a need to redefine space networks (SpaceNets) to incorporate all of these evolutions. This paper introduces a vision for future SpaceNets that considers advances in several related domains. First, we present a reference architecture that captures the various network entities and terminals in a holistic manner. Based on this, space, air, and ground use cases are studied. Then, the architectures and technologies that enable the envisaged SpaceNets are investigated. In so doing, we highlight the activities and projects of different standardization bodies, satellite operators, and national organizations towards the envisioned SpaceNets. Finally, the challenges, potential solutions, and open issues from communications and networking perspectives are discussed.

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks because of its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. On this basis, we formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation–based method are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner, it also takes delay performance into account and maintains the delay stability.

There is no doubt that we need to keep our eyes on the sky as satellite networks aim to address the demands of 6G and beyond communications systems. On the other hand, the existence of millions of space debris pieces, large or small, poses a threat to the new space communications systems which consist of large number of small satellites, especially in the low-orbit. In this study, a dual-functioning pulsed linear frequency modulated (LFM) waveform at Terahertz (THz) bands is proposed for both wireless communications and space debris sensing over low-orbit inter-satellite links (ISLs). Initially, the ambiguity function of the proposed waveform is derived. Then, velocity and range estimation performance for the radar function and bit error rate performance for the communications function are investigated. Simulation results indicate significant performance gains in the THz-bands compared to the legacy LFM systems.

Low Earth orbit (LEO) satellite mega-constellation networks aim to address the high connectivity demands with a projected 50,000 satellites in less than a decade. To fully utilize such a large-scale dynamic network, an air network composed of stratospheric nodes, specifically high altitude platform station (HAPS), can help significantly with a number of aspects including mobility management. HAPS-LEO network will be subject to time-varying conditions, and in this paper, we introduce an artificial intelligence (AI)-based approach for the unique channel estimation and synchronization problems. First, channel equalization and carrier frequency offset with residual Doppler effects are minimized by using the proposed convolutional neural networks based estimator. Then, the data rate is compounded by increasing spectral efficiency using non-orthogonal multiple access method. We observed that the proposed AI-empowered HAPS-LEO network provides not only a high data throughput per second but also higher service quality thanks to the agile signal reconstruction process.

Automatic modulation classification (AMC) with deep learning (DL) based methods has been studied in recent years and improvements have been shown in many studies; however, it has been difficult to design a classifier that can distinguish modulation orders such as 16-QAM and 64-QAM, with high accuracy. In this study, the distinction performance of 16-QAM and 64-QAM modulation orders increased by feeding the features obtained during the preprocessing stage to the multi-channel convolutional long short-term deep neural network (MCLDNN). Simulation results indicate performance improvements, particularly at the low SNR region. Furthermore, the proposed method can be extended for the separation of other orders of QAM and other digital modulations.

Physical layer authentication (PLA) is the process of claiming identity of a node based on its physical layer characteristics such as channel fading or hardware imperfections. In this work, we propose a novel PLA method for the intersatellite communication links (ISLs) of the LEO satellites. In the proposed PLA method, multiple receiving satellites validate the identity of the transmitter by comparing the Doppler frequency measurements with the reference mobility information of the legitimate transmitter and then fuse their decision considering the selected decision rule. Analytical expressions are obtained for the spoofing detection probability and false alarm probability of the fusion methods. Numerically obtained high authentication performance results pave the way to a novel and easily integrable authentication mechanism for the LEO satellite networks.

Caching and distributing of network packets among devices has been accepted by the research community as a cost-effective method of transferring especially audio-video data among mobile users. Another newly established technology, blockchain, has been successfully applied to decentralized financial systems like Bitcoin. The research on caching and distributing methods has not focused on the security aspect so far. In this article, we present an effective scheme to provide security and integrity of network packets in caching and distribution systems by employing the blockchain technology. Unlike the existing blockchain applications, the proposed protocol requires a lightweight consensus algorithm. Our scheme also allows peers in the network to make correctly addressed inquiries as the packets’ locations are kept in the blocks which are available to any peer in the network. Considering that cryptographic primitives alone are not adequate to prevent malicious network packets among the peers, the proposed blockchain application presents an invaluable tool to prevent malicious packets from leaking into the system and to address which peers own the desired packets. We demonstrate the efficiency of the proposed method by implementing it for Android OS devices and conduct real-time testing on different settings.

The use of satellites to provide ubiquitous coverage and connectivity for densely deployed Internet of Things (IoT) networks is expected to be a reality in emerging 6G networks. Yet the low battery capacity of IoT nodes constitutes a problem for their direct connectivity to satellites, which are located at altitudes of up to 2000 km. In this article, we propose a novel architecture involving the use of reconfigurable intelligent surface (RIS) units to mitigate the path loss associated with long transmission distances. These RIS units can be placed on satellite reflectarrays, and, when used in broadcasting and beamforming, they can provide significant gains in signal transmission. This study shows that RIS-assisted satellites can provide up to 105times higher downlink and achievable uplink rates for IoT networks.

This paper presents a unified outage probability (OP) performance analysis of two hybrid antenna selection (AS) schemes, transmit antenna selection (TAS) and maximal ratio combining (MRC), and joint transmit and receive antenna selection (JTRAS) in multiple-input multiple-output non-orthogonal multiple access based downlink amplify-and-forward (AF) relaying network with channel estimation error (CEE) and feedback delay (FD). Since the communications in the first and second hops are kinds of single-user and multi-user communications, respectively the AS is done as optimal TAS/MRC or JTRAS is applied in the first hop while the suboptimal majority-based TAS/MRC or JTRAS is employed in the second hop. For both TAS/MRC and JTRAS schemes, the OP expressions are derived in single closed-form over Nakagami-${m}$fading channels in the practical and ideal cases. Moreover, in the practical case, the lower bound OP expressions are found and at high signal-to-noise ratio (SNR) values, the OP reaches an error floor value, which means zero-diversity order. In the ideal case, asymptotic OP expressions are obtained in high SNR regime and demonstrate achievable non-zero diversity and array gains. Finally, through simulations and software-defined radio-based real-time tests, the accuracy of theoretical analysis is validated.

Edge intelligence, a new paradigm to accelerate artificial intelligence (AI) applications by leveraging computing resources on the network edge, can be used to improve intelligent transportation systems (ITS). However, due to physical limitations and energy-supply constraints, the computing powers of edge equipment are usually limited. High altitude platform station (HAPS) computing can be considered to be a promising extension of edge computing. HAPS is deployed in the stratosphere to provide wide coverage and strong computational capabilities. It is suitable to coordinate terrestrial resources and store the fundamental data associated with ITS-based applications. In this work, three computing layers, i.e., vehicles, terrestrial network edges, and HAPS, are integrated to build a computation framework for ITS, where the HAPS data library stores the fundamental data needed for the applications. In addition, the caching technique is introduced for network edges to store some of the fundamental data from the HAPS so that large transmission delays can be reduced. We aim to minimize the delay of the system by optimizing computation offloading and caching decisions as well as bandwidth and computing resource allocations. The simulation results highlight the benefits of HAPS computing for mitigating delays and the significance of caching at network edges.

High altitude platform station (HAPS) computing can be considered as a promising extension of edge computing to improve intelligent transportation systems (ITS). HAPS is deployed in the stratosphere to provide wide coverage and strong computational capabilities, which is suitable to coordinate terrestrial resources and store the fundamental data associated with ITS-based applications. In this work, three computing layers, i.e., vehicles, terrestrial network edges, and HAPS, are integrated to build a computation framework for ITS, where the HAPS data library stores the fundamental data needed for the applications. In addition, the caching technique is introduced for network edges to store some of the fundamental data from the HAPS so that large propagation delays can be reduced. We aim to minimize the delay of the system by optimizing computation offloading and caching decisions as well as bandwidth and computing resource allocations. The simulation results highlight the benefits of HAPS computing for mitigating delays and the significance of caching at network edges.

In this paper, graph attention network (GAT) is firstly utilized for the channel estimation. In accordance with the 6G expectations, we consider a high-altitude platform station (HAPS) mounted reconfigurable intelligent surface-assisted two-way communications and obtain a low overhead and a high normalized mean square error performance. The performance of the proposed method is investigated on the two-way backhauling link over the RIS-integrated HAPS. The simulation results denote that the GAT estimator overperforms the least square in full-duplex channel estimation. Contrary to the previously introduced methods, GAT at one of the nodes can separately estimate the cascaded channel coefficients. Thus, there is no need to use time division duplex mode during pilot signaling in full-duplex communication. Moreover, it is shown that the GAT estimator is robust to hardware imperfections and changes in small scale fading characteristics even if the training data do not include all these variations.

The widespread use of new technologies, such as the Internet of Things and machine-type communication (MTC) forces an increase on the number of user equipments (UEs) and MTC devices that are connecting to mobile networks. Inherently, as the number of UEs inside a base station’s (BSs) coverage area surges, the quality of service tends to decline. The use of drone-BS (UxNB) is a solution in places where UEs are densely populated, such as stadiums. UxNB emerges as a promising technology that can be used for capacity injection purposes in the future due to its fast deployment. However, this emerging technology introduces a new security issue. Mutual authentication, creating a communication channel between terrestrial BS and UxNB, and fast handover operations may cause security issues in the use of UxNB for capacity injection. This new protocol also suggests performing UE handover from terrestrial to UxNB as a group. To the best of our knowledge, there is no authentication solution between BSs according to LTE and 5G standards. The proposed scheme provides a solution for the authentication of UxNB by the terrestrial BS. Additionally, a credential sharing phase for each UE in handover is not required in the proposed method. The absence of a credential sharing step saves resources by reducing the number of communications between BSs. Moreover, many UE handover operations are completed in concise time within the proposed group handover method.

To cope with the unprecedented surge in demand for data computing for the applications, the promising concept of multiaccess edge computing (MEC) has been proposed to enable the network edges to provide closer data processing for mobile devices (MDs). Since enormous workloads need to be migrated, and MDs always remain resource-constrained, data offloading from devices to the MEC server will inevitably require more efficient transmission designs. The integration of nonorthogonal multiple access (NOMA) technique with MEC has been shown to provide applications with lower latency and higher energy efficiency. However, the existing designs of this type have mainly focused on the transmission technique, which is still insufficient. To further advance offloading performance, in this work, we propose an application-driven NOMA-enabled computation offloading scheme by exploring the characteristics of applications, where the common data of the application is offloaded through multidevice cooperation. Under the premise of successfully offloading the common data, we formulate the problem as the maximization of individual offloading throughput, where the time allocation and power control are jointly optimized. By using the successive convex approximation (SCA) method, the formulated problem can be iteratively solved. Simulation results demonstrate the convergence of our method and the effectiveness of the proposed scheme.

Automatic modulation classification (AMC) has been studied for more than a quarter of a century; however, it has been difficult to design a classifier that operates successfully under changing multipath fading conditions and other impairments. Recently, deep learning (DL)-based methods are adopted by AMC systems and major improvements are reported. In this paper, a novel convolutional neural network (CNN) classifier model is proposed to classify modulation classes in terms of their families, i.e., types. The proposed classifier is robust against realistic wireless channel impairments and in relation to that, when the data sets that are utilized for testing and evaluating the proposed methods are considered, it is seen that RadioML2016.10a is the main dataset utilized for testing and evaluation of the proposed methods. However, the channel effects incorporated in this dataset and some others may lack the appropriate modeling of the real-world conditions since it only considers two distributions for channel models for a single tap configuration. Therefore, in this paper, a more comprehensive dataset, named as HisarMod2019.1, is also introduced, considering real-life applicability. HisarMod2019.1 includes 26 modulation classes passing through the channels with 5 different fading types and several number of taps for classification. It is shown that the proposed model performs better than the existing models in terms of both accuracy and training time under more realistic conditions. Even more, surpassed their performance when the RadioML2016.10a dataset is utilized.

Internet-of-Things (IoT) networks are becoming a part of our daily lives, as the number of IoT devices around us are surging. The authentication of millions of connected things and the distribution and management of secret keys between these devices pose challenging research problems. Current one-to-one authentication schemes do not take the resource limitations of IoT devices into consideration. Nor do they address the scalability problem of massive machine type communication (mMTC) networks. Group authentication schemes (GASs), on the other hand, have emerged as novel approaches for many-to-many authentication problems. They can be used to simultaneously authenticate numerous resource-constrained devices. However, existing GAS is not energy efficient and they do not provide enough security for widespread use. In this article, we propose a lightweight GAS that significantly reduces energy consumption on devices, providing almost 80% energy savings when compared to the state-of-the-art solutions. Our approach is also resistant to the replay and man-in-the-middle attacks. The proposed approach also includes a solution for key agreement and key distribution problems in mMTC environments. Moreover, this approach can be used in both centralized and decentralized group authentication scenarios. The proposed approach has the potential to address the fast authentication requirements of the envisioned agile 6G networks, supported through aerial networking nodes.

In this article, we propose a spatial sampling approach to reduce energy consumption for over-the-air (function) computation (AirComp) scheme by utilizing the cross-correlations among sensor readings. Since the conventional AirComp scheme leads to a reduction in total transmission time and latency thanks to the joint communication and computation processes, it is especially well-suited to Internet of Things (IoT) monitoring systems. AirComp lets simultaneous transmissions of all nodes by exploiting the superposition property of wireless channel; however, it does not overcome the high energy consumption paradigm, which is a fundamental problem of densely deployed IoT monitoring systems. We present a minimum mean square error (MMSE) estimation scheme while a small number of observations are available for densely deployed networks. The proposed MMSE estimator provides a significant mean squared error improvement with reducing energy consumption compared to the conventional estimator. Since the network lifetime of IoT monitoring systems can be almost doubled, the proposed estimator provides flexibility for the dense deployment of nodes. The simulation results verify the theoretical expressions.

By introducing high randomness with reduced computational cost, physical layer (PHY) key generation is one of the candidate tools that can be used for the security of the Internet of Things applications. Nonidentical secret keys are one of the main problems of the physical layer (PHY) key generation schemes. In order to address this problem, key verification schemes, which are based on information reconciliation, are used in this article. In the current key verification techniques, the legitimate nodes reveal some information related to their secret keys to eliminate nonidentical bits. In this article, by jointly using PHY key generation with an embedded key, we propose a hybrid key generation and key verification scheme, where the revealed information during the key verification process is negligible, and the verified keys are identical. Numerical results and software-defined radio-based tests show that the proposed verification scheme achieves the requirements of the Industrial Internet of Things systems.

Underutilized spectrum constitutes a major concern in wireless communications especially in the presence of legacy systems and the prolific need for high-capacity applications as well as consumer expectations. From this perspective, Terahertz frequencies provide a new paradigm shift in wireless communications since they have been left unexplored until recently. Such a vast frequency spectrum region extending all the way up to visible light and beyond points out significant opportunities from dramatic data rates on the order of tens of Gbps to a variety of inherent security and privacy mechanisms, and techniques that are not available in the traditional systems. Thus, in this paper, we investigate statistical parameters for short-range line- of-sight channels of Terahertz communication. Short-range measurement campaign within the interval of [3cm, 20cm] are carried out between 275GHz to 325GHz range. Path loss model is examined for different frequencies and distances to provide the insight regarding the effect of the operating frequency. Measurement results are provided with relevant discussions and future directions.

This paper considers a network of source nodes that transmit data packets to a destination node via relay nodes over erasure channels by using random linear network coding. The probability that the destination node will fail to recover the packets of all source nodes has been bounded and approximated in the literature for the case of relay nodes that randomly assign only nonzero values to the coefficients of linear combinations of data packets. The paper argues for the necessity of giving relay nodes the choice to also assign the zero value to coefficients when arithmetic operations are over finite fields of small size, e.g. GF(2). Alternative probability mass functions for the coefficients are considered, and expressions for the decoding failure probability are re-derived.

Signal identification is an important notion that leads to significant performance improvements for adaptive wireless spectrum access techniques. Besides identifying the modulation types and other features, standard-based identification has also an important place in signal identification domain. In this paper, a generalized identification method which utilizes the outputs of spectral correlation function as the training inputs for the support vector machines to distinguish wireless signals is introduced. The proposed method eliminates the dependence on the distinct features to identify different signals. The method's performance is tested using the measurements taken in the laboratory environment and various wireless signals are successfully distinguished from each other. The comparative performance of the proposed method is also quantified by the classification confusion matrix.

Cyber-physical systems (CPS) are state-of-the-art communication environments that offer various applications with distinct requirements. However, security in CPS is a nonnegotiable concept, since without a proper security mechanism the applications of CPS may risk human lives, the privacy of individuals, and system operations. In this paper, we focus on PHY-layer security approaches in CPS to prevent passive eavesdropping attacks, and we propose an integration of physical layer operations to enhance security. Thanks to the McEliece cryptosystem, error injection is firstly applied to information bits, which are encoded with the forward error correction (FEC) schemes. Golay and Hamming codes are selected as FEC schemes to satisfy power and computational efficiency. Then obtained codewords are transmitted across reliable intermediate relays to the legitimate receiver. As a performance metric, the decoding frame error rate of the eavesdropper is analytically obtained for the fragmentary existence of significant noise between relays and Eve. The simulation results validate the analytical calculations, and the obtained results show that the number of low-quality channels and the selected FEC scheme affects the performance of the proposed model.

In this article, we present probabilistic energy models for hybrid energy harvesting (HEH) Internet of Things (IoT) nodes. We aim to model the energy obtained from multiple energy sources in a single form with mixture models and to obtain energy characteristics at every stage of HEH. Multiple energy sources are classified into energy source clusters (ESCs), and harvested energies at the output of energy harvesting modules are modeled by using Gaussian mixture models (GMMs). The joint distributions of harvested energies with GMMs are expressed as the sum of the joint densities produced by the possible combinations of component densities. A generalized expression is presented for both independent and correlated ESCs. The distributions of energy levels are obtained based on the joint densities at the output of the energy combiner unit as well as at the energy storage unit. This approach provides a single form and an accurate probabilistic model for each cluster including multiple sources, taking the randomness of ambient energy into account in HEH. Depending on the energy requirements, the characteristics of an energy storage unit can be determined for IoT applications.

In this paper, we develop a framework to estimate network flow length distributions in terms of the number of packets. We model the network flow length data as a three-way array with day-of-week, hour-of-day, and flow length as entities where we observe a count. In a high-speed network, only a sampled version of such an array can be observed and reconstructing the true flow statistics from fewer observations becomes a computational problem. We formulate the sampling process as matrix multiplication so that any sampling method can be used in our framework as long as its sampling probabilities are written in matrix form. We demonstrate our framework on a high-volume real-world data set collected from a mobile network provider with a random packet sampling and a flow-based packet sampling methods. We show that modeling the network data as a tensor improves estimations of the true flow length histogram in both sampling methods.

Modern process automation and the industrial evolution heading towards Industry 4.0 require a huge variety of information to be fused in a Cyber-Physical System. Important for many applications is the spatial position of an arbitrary object given directly or indirectly in terms of data that has to be processed to obtain position information. Starting point for the idea of the technical reflection-based sound localization system presented in this paper is the biological role model of humans being able to learn how to localize sound sources. Compared to other forms of sound localization, this nature-inspired method has no need for high spatial and temporal accuracy or big microphone arrays. Possible applications for this system are indoor robot localization or object tracking.

Due to its several implementation advantages, orthogonal frequency division multiplexing (OFDM) has become the most frequently used waveform in modern wireless communication technologies. However, OFDM has some vulnerabilities that may limit its advantages. In order to alleviate OFDM's vulnerabilities, several waveforms have been proposed within the context of 5G research activities. Universal-filtered OFDM (UF-OFDM) is one of the most effective waveforms due to its OFDM-like structure, easier implementation procedure and robustness against synchronization errors. Most of the recent studies on novel waveforms generally target filter optimization or performance improving techniques. In this paper, as a novel approach, UF-OFDM with filter shift keying (OFDM-FSK) is proposed. Here, we consider the filter realizations to carry information through index modulation mechanism. Accordingly, utilized filter of each UF-OFDM subband is selected from a filter set based on the incoming data bits, and an increase in data rate is obtained. At the receiver side, accumulated maximum-likelihood decision metric in each subband is computed. The filter realization, as well as data symbols, that provide the minimum decision metric in each subband are detected. Hence the throughput of the conventional waveform is increased in an efficient and practical manner. The performance of OFDM-FSK system is evaluated through extensive computer simulations. To assess its real-time performance, real-time experiments are also carried out by using software defined radio (SDR) nodes. In the created testbed, a wireless synchronization structure is provided and the considered waveforms are tested in a setup with imperfect synchronization. This experimentation provides realistic insights about the benefits of OFDM-FSK. As it will be demonstrated via both computer simulation and test results, OFDM-FSK provides significant throughput and error performance benefits over UF-OFDM.

While half and full duplex wireless enabled cyber physical systems (CPSs) are gaining more importance and research attention, maintaining the security of CPS systems remains a vital challenge. The broadcast nature of wireless channels makes it possible for unauthorized receivers, called eavesdroppers, to capture the information signal. Wireless physical layer security techniques aim to harden the secrecy characteristics of wireless systems, decreasing the signal quality of a prospective eavesdropper. This paper considers a joint optimization of artificial noise (AN) signal and transmit filter for the information signals in order to achieve a target secrecy level. As eavesdropping attacks are very critical threats in CPS networks, and the need for high secrecy techniques remains valid. The proposed framework includes a scenario with two multi-antenna legitimate parties and a multi-antenna eavesdropper node (or multiple cooperating eavesdroppers). The transmit filter and AN signal are jointly optimized by the transmitters, where receiver nodes can make use of optimal transmit filters. The problem is derived for the cases where the legitimate CPS nodes perform full duplex and half duplex transmissions. The impact of channel estimation errors and self interference are also discussed.

Today’s wireless networks allocate radio resources to users based on the orthogonal multiple access (OMA) principle. However, as the number of users increases, OMA based approaches may not meet the stringent emerging requirements including very high spectral efficiency, very low latency, and massive device connectivity. Nonorthogonal multiple access (NOMA) principle emerges as a solution to improve the spectral efficiency while allowing some degree of multiple access interference at receivers. In this tutorial style paper, we target providing a unified model for NOMA, including uplink and downlink transmissions, along with the extensions to multiple input multiple output and cooperative communication scenarios. Through numerical examples, we compare the performances of OMA and NOMA networks. Implementation aspects and open issues are also detailed.

https://doi.org/10.1155/2018/1907506

Network coded cooperative (NCC) systems have recently gained increasing attention due to their high power and spectral efficiency. For high speed data transmission over frequency-selective channels, NCC is further combined with orthogonal frequency division multiple access (OFDMA). In this paper, we derive outage probability and diversity gain of NCC-OFDMA systems in the presence of carrier frequency offset (CFO). Our results demonstrate that NCC-OFDMA system is capable to achieve full spatial and frequency diversity in noise limited system where the CFO variance σ2ε→ 0. However, in interference limited case where the CFO variance σ2ε> 0, error floor occurs leading zero diversity gain. Numerical results are further presented to validate our analytical findings.

As private information of individuals become more accessible, data privacy evolved to be an important aspect of communication technologies. Among existing privacy definitions, the utility privacy trade-off model is suitable for the assessment of privacy due to its relation with the information theory and the rate distortion theory. In this paper, the effects of the transmission errors due to the wireless channel are studied with respect to the utility privacy trade-off in a wireless communication network. First, the transmission error probability is embedded to the existing trade-off definitions. Then, it is shown via numerical examples that binary and normal distributed sources comply with the limits that are set by the updated rate distortion equivocation and equivocation distortion functions.

Heterogenous networks (HetNets) using different size cells and several different networks with multiple wireless access technologies can provide large capacities while also improving the localization accuracy. In this paper, we propose a novel received signal strength (RSS) based inter-network cooperative localization framework based on a Metropolis-Hastings (MH) algorithm for two-tier HetNets with unknown transmit powers. Through the MH based estimation methodology, the unknown position of user equipment and transmit powers of base stations (BSs) are jointly estimated. The validity of the proposed method is confirmed by simulation results.

RF energy harvesting (RFEH) is a promising technology for energy requirements of wireless communication nodes. However, providing sufficient amount of energy to ensure self-sufficient devices based on RFEH may be challenging. In this paper, the use of diversity combining in RFEH systems is proposed to increase the amount of harvested energy. The power consumption of diversity combining process is also taken into account to analyze the net benefit of diversity combining. Performances of RFEH systems are investigated for selection combining (SC), equal gain combining (EGC), and maximal ratio combining (MRC) techniques. Simulations are conducted to compare the numerical results of SC, EGC, and MRC, and the results show that although the diversity combining techniques can improve the energy harvesting performance, the power consumption parameters have a critical importance while determining the suitable technique.

The privacy risks for individuals are becoming more concrete in communication systems, and existing privacy protection methods may not be suitable in communication systems. Among existing privacy definitions, the utility privacy trade-off is proper evaluation model of privacy in communication systems due to its relation with information theory and rate distortion theory. In this paper, at first we theoretically investigate the utility privacy trade-off on noisy channels and derive the updated expressions for trade-off functions. Then, these functions are both simulated and tested with software defined radios (SDRs) for orthogonal frequency division multiplexing (OFDM) based wireless communication system.

This paper presents a framework for computing successful decoding probability of random network coding (RNC) in wireless networks. As cooperation emerges due to the naturally occurring broadcasting in wireless links, the application of RNC in wireless networks enables random network coded cooperation (RNCC). The theoretical successful decoding probability of RNCC systems is derived by obtaining the ratio of the full rank and the rank deficient matrices. The full rank condition of the global encoding matrix indicates the successful decoding of source symbols. The results of a single relay along with a relay selection scheme are also investigated. The validity of the presented theoretical expressions is demonstrated through identical simulation results. An implementation scenario is also presented to demonstrate the practical usage effectiveness of RNC in real-time applications, by using software-defined radio nodes.

Network-coded cooperation (NCC) is an effective method to improve the throughput efficiency in cooperative wireless networks. The combined use of orthogonal frequency division multiplexing (OFDM) with NCC has been further studied in the literature to exploit the multipath diversity gains. In this paper, we consider orthogonal frequency division multiple access (OFDMA), an extension of OFDM to a multiuser system where subsets of carriers are assigned to different users. We first derive a closed-form expression for the outage probability of NCC-OFDMA over Rician fading channels and then present the asymptotical and finite-SNR DMT expressions. Our results provide insight into the performance mechanisms under practical SNR regime of NCC-OFDMA systems and demonstrate that NCC-OFDMA is able to fully exploit both frequency and spatial diversity. We also show that the derived finite-SNR DMT converges to an asymptotical one as expected. Furthermore, special cases for our derived analytical expressions are presented, to show that the derived expression is a generalized case of the related the state of the art results. Simulation results are further presented to verify our theoretical analyses.

Network Function Virtualization (NFV) is utilized to simplfy the deployment and management of constantly evolving and highly complex networking services. In this paper, we propose a dynamic load management framework for IMS networks using NFV, where IMS functions are created within a single virtual machine (VM) instance and moved to the cloud environment. Requests coming from the IMS clients are first processed by a Load Balancer module to efficiently distribute the incoming load over a pool of IMS VMs. The decision of switching an IMS VM instance on or off is performed by the VM provisioning service using the resource utilization information periodically received from the IMS VMs. The proof-of-concept experiments using a realistic testbed demonstrate that the proposed framework increases the scalability of the IMS networks when there are sudden significant increases in traffic.

Physical layer security is a promising approach to provide secure communications by considering the characteristics of wireless channels. In this work, we propose a secure multiple input single output (MISO) multiuser system with a quantized feedback link. We assume that eavesdropper is passive and its channel state information (CSI) is not available at transmitter. In order to disrupt reception of a passive eavesdropper, we schedule more than one legitimate user. For the sake of ensuring secure communication, the CSI of legitimate users has great impact on overall performance of secrecy sum capacity. The proposed solution applies a semi-orthogonal selection with a specific codebook to reduce the quantization errors for legitimate user side while disrupting the reception of the eavesdropper. The proposed solution improved secrecy sum capacity while reducing the feedback overhead for secure MISO multiser system.

Monitoring network statistics is important for the maintenance and infrastructure planning for the network service providers. In this demonstration, we will showcase an initial analysis of a general purpose network monitoring platform for high speed mobile networks. The developed platform is the basis for performing complex real-time analysis such as application usage behaviour, security analysis, infrastructure planning. We have used the platform for real-time flow size and length monitoring with packet sampling.

A new frequency switching receiver structure is proposed for simultaneous wireless information and power transfer in multi-carrier communication systems. Each subcarrier is switched to either the energy harvesting unit or the information decoding unit, according to the optimal subcarrier allocation. To implement the system, one-bit feedback is required for each subcarrier. Two optimization problems are defined, converted to binary knapsack problems, and solved using dynamic programming approaches. Upper bounds are obtained using continuous relaxations. Power allocation is integrated to further increase the performance. Numerical studies show that the proposed frequency switching based model is better than existing models in a wide range of parameters.

Network coded cooperative (NCC) systems have recently attracted attention with their high spectral and power efficiency. In this paper, we investigate the information theoretical limits of NCC systems in the presence of imperfect channel state information (CSI). Specifically, we derive exact outage probability and finite-SNR diversity- multiplexing tradeoff (DMT) for general multisource multirelay NCC wireless networks over Rayleigh fading channels. Numerical results are presented to verify our analytical derivations and provide insight into the impact of imperfect CSI on the NCC system performance.

Abstract In this study, analysis and modeling of arrival and service processes are presented in a comprehensive fashion in order to determine statistical properties of voice traffic from end‐user perspective in accordance with the queueing theory. For the first time in the literature, we introduce a user centric approach and examine these services considering both flow directions of voice traffic, the uplink and the downlink as opposed to existing studies with the network centric approach. In our study, we use experimental data composed of actual phone calls collected from 2G/3G networks. To achieve this, we designed and implemented a data collection system for mobile users and compared the results by using data from an operational cellular network. In order to determine the time correlation of voice calls, Hurst parameter estimation methods are used. On the basis of the outcomes, independency of call arrivals is shown. Additionally, it is shown that calls acquired from user and network centric approaches are both Poisson distributed. Next, looking at the problem from service process perspective, thorough analyses are performed to determine mathematical models that can best characterize call holding times. Maximum likelihood estimation and expectation maximization algorithm are used, and it is shown that the optimum mathematical model for the characterization of call holding times is the lognormal distribution family. Copyright © 2014 John Wiley & Sons, Ltd.

Network coded cooperation (NCC) is an effective method to improve the throughput efficiency in cooperative wireless networks. In an effort to have further gains over the initial works, which build upon the assumption of time division multiple access, the combined use of orthogonal frequency division multiplexing (OFDM) with NCC has been proposed in the literature. In this paper, we consider orthogonal frequency division multiple access (OFDMA), an extension of the OFDM to a multiuser system where subsets of carriers are assigned to different users. We derive a closed-form expression for the outage probability of the system under consideration and present the diversity-multiplexing tradeoff (DMT) analysis. Our results demonstrate that NCC-OFDMA system is able to fully exploit both frequency and spatial diversity. Simulation results are presented to verify our theoretical analysis.

In received signal strength (RSS) based localization problems, the accuracy of the position information obtained is closely associated with the RSS model used. Therefore, positioning success can be improved with a more accurate RSS model. In this study, to analyze the effect of RSS model in localization performance, lognormal mixture shadowing model is used that provides a more accurate RSS model than the classical lognormal shadowing model. For the corresponding mixture model, a tight upper bound for Cramer-Rao lower bound (CRLB) based on Jensen's inequality is derived. The obtained expressions are used in CRLB analyses that are employed in determination of the best estimates in localization. The improved localization accuracy with lognormal mixture shadowing model is demonstrated by means of examining various numerical analyses.

Maintaining security in wireless systems has been a challenge due to the broadcast nature of wireless communication channels. Many methods have been proposed as countermeasures to eavesdropping attacks, which stand as one of the most important secrecy attack types that use the vulnerabilities of wireless channels. The majority of these methods come with the cost of additional power consumption for maintaining secrecy. In order to provide a framework that can enable a tradeoff between energy efficiency and secrecy, we provide a flexible adaptive physical layer security approach that enables tunable security levels for distinct bit streams of a single user. Our main motivation stems from the fact that not all data bits within a single bit steam require the same amount of security. The proposed adaptive power optimization framework allows the assignment of different secrecy rate constraints for distinct data streams. Via simulations, we show that the proposed approach can increase the power efficiency compared to the non-adaptive physical layer security solution, while satisfying the required secrecy constraints.

Physical layer security in the presence of passive eavesdroppers (i.e., the eavesdroppers' channels are unknown to the transmitter) in correlated channels is addressed. The majority of the literature with passive eavesdroppers assumption focus on uncorrelated channels and random artificial noise (AN) generation in the null space of the legitimate channel. This paper targets joint optimization of the AN vector and the power fraction used for beamforming in correlated channels. These two variables are obtained by maximizing the minimum secure probability of attaining a pre-determined secrecy level for a certain number of the most dangerous potential Eves, which are obtained by the correlation map. The correlation map contains long-term channel correlation matrices for all the potential Eves' locations, which are captured by legitimate users passing through these areas in the past. We use a secure probability map to illustrate the secrecy levels for all the possible positions of the legitimate user. The results show the significant secrecy enhancement of our proposed algorithm when compared to the exiting AN generation methods.

This study addresses the problem of the determining network performance metrics, the outage probability (pout) and the coverage area (C(φ)) more accurately, based on the distribution of shadow fading. Shadow fading characteristics are crucial in order to calculate of pout and C(φ) more properly. We investigate existing shadow fading models, lognormal, Gamma and Weibull distributions on empirical data and also propose a new lognormal mixtures model. We calculate pout and C(φ) according to the considered distributions and verify that shadow fading distribution needs to be accurately determined in order to obtain correct values of outage probability and coverage area.

This paper presents a novel physical layer secrecy enhancement technique for multicarrier communications based on dynamic filter hopping. Using the Filter Bank Multicarrier (FBMC) waveform, an efficient eavesdropping mitigation technique is developed using time- and frequency-varying prototype filters. Without knowledge of the filter assignment pattern, an eavesdropper will experience a high level of inter-carrier (ICI) and inter-symbol interference (ISI). With this severe receive signal-to-interference-plus-noise ratio (SINR) degradation for an illegitimate receiver, the secrecy capacity of the communication system is increased. At the same time, the interference at the legitimate receiver is designed to be negligible in comparison to the channel noise.

This paper targets the joint optimization of the signal-to-interference-plus-noise ratio (SINR) and secrecy in wireless networks. Although the optimization of the SINR with beamforming in wireless networks is well known, the joint optimization of the secrecy and the SINR of the users is a new problem which gets a high relevance recently. The optimization problems investigated in this paper are based on the joint optimization of the beamforming vectors and transmit powers. This paper presents closed form solutions for two optimization approaches for a simple power control scenario with a single user and a single eavesdropper. Two approaches are distinguished: beamforming without artificial interference (AI) and beamforming with AI. For both approaches, this paper investigates a max-min based beamforming problem and a minimum eavesdropper SINR problem with an SINR constraint for the legitimate receiver.

This paper proposes a novel matched filter optimization based approach to improve secrecy in a communication among two legitimate users. The presented optimization scheme, named as QoS-based filter design, minimizes the stop-band attenuation and uses quality-of- service constraints on the legitimate receiver and an eavesdropper. The resulting problem is relaxed to a convex problem. The filter coefficients are optimal regarding the legitimate receiver's matched filter auto-correlation function, however, it results in a sub-optimal filter for the eavesdropper secrecy constraints. Therefore, an additional post-processing is developed to match the secrecy constraints.

We propose a practical network code for the wireless two-way relay channel where all nodes communicate in full duplex (FD) mode. The physical layer network coding (PNC) operation is applied with the FD operating nodes, reducing the transmission time to a single time slot, hence doubling the spectral efficiency when compared to classical PNC systems. In our system, binary phase shift keying modulated signals are transmitted over Rayleigh fading channels. We derive the theoretical error rates at the relay and end nodes according to the maximum likelihood detection rule, in case of non-ideal self-interference cancellation. Theoretical results are also verified via simulations.

This paper investigates the usage of radio frequency (RF) signal as a source in energy harvesting systems. The open issue in the related literature is the characterization of battery recharging time of an energy harvesting receiver node. RF energy harvesting has challenges due to the wireless propagation environment and conversion efficiency. On the propagation side, the different channel models between source and harvesting node should be taken into account in order to obtain realistic results. The main goal of this paper is to propose statistical models for battery recharging time for the Nakagami-m and the generalized-K fading channels. We also include the effects of lognormal shadowing. We derive the associated closed form probability density function, cumulative distribution function, moment generation function, mean and variance expressions for battery recharging time. The simulations are used to verify the theoretical results.

Increasing data demand started to become a common challenge in mobile cellular networks. Various techniques, intelligent approaches and network architectures are being developed to tackle this difficulty, in order to provide high quality of service and to allocate mobile resources effectively. Adaptive modulation and coding is one of those techniques, that provides high quality of service while adapting modulation and transmission power with respect to erratic channel conditions. In this work, sufficient modulation and coding technique will be proposed to provide the necessary quality of service level while minimizing mobiles' consumed power by foreseeing actual data need of UMTS mobile users through digital divide analyses. As a consequence, with prior information on potential data requirements, it is shown here that power and energy efficiency can be improved by making use of convex optimization methods.

Requiring a single frequency band for transmission, full-duplex (FD) systems can ideally double the spectral efficiency. However, in addition to the information signal, radiated from user's own transmit antenna generates a strong self-interference (SI). To eliminate effects of SI, active and passive cancellation techniques are used in the literature. However, omitted in these studies is the fact that the time dispersion in the SI channel may cause an error floor. In this paper, it is verified that the SI channel is time dispersive via measurements. Later, we present an analysis in order to determine the error performance. Following these results, a fractionally spaced SI canceler is proposed to eliminate the error floor. We verify our analytical results via simulations, showing that the proposed SI canceler can critically enhance the performance of FD systems.

Effective capacity model defines a delay bound violation probability and quality of service (QoS) exponent that help determine QoS in a system as a wireless link model for time varying channels. In this paper, we examine the effective capacity of an adaptive cooperative multihop wireless network using adaptive modulation and coding, under Nakagami-m fading. We extend the effective capacity derivations to four hops in cooperative networks employing relays with no queues. Then, we examine the effective capacity performance via numerical analysis in direct transmission and relay-aided cooperative communication networks under various fading conditions. We demonstrate by numerical analysis that relay participation in cooperative communication considerably increases the capacity at stringent QoS demands. However, it is also shown that effective capacity is lower at loose QoS demands when more than one relay is used, due to increased total average frame lengths with more time slot resource usage.

Beamforming and artificial noise (AN) are two major techniques to maintain physical (PHY) layer security. Using AN to degrade eavesdroppers' signal reception enables data secrecy with less power compared to no-AN scenarios. Existing AN approaches make use of the spatial selectivity of the wireless channel however these techniques do not consider the associated signal structure. In this paper we extend the existing AN techniques to MIMO-OFDM, where we prove that the optimization problem can be inverted to a convex form in multi-antenna, multi-carrier systems, hence show that convex optimization can still be applied to preserve privacy. Furthermore, inspired by the fact that pilot symbols are critical in error-free the reception of the information symbols, we propose a spatiotemporal selective AN approach that can make use of both the space and time selectivity, as our main contribution. We also show that our proposed method can cause extensive channel estimation error for eavesdroppers and achieves significant transmission power savings at the same time.

ABSTRACT Proximity‐based services (PBS) are a subclass of location‐based services that aim to detect the closest point of interest by comparing relative position of a mobile user with a set of entities to be detected. Traditionally, the performances of PBS are measured on the basis of the norm of the estimation error. Although this performance criterion is suitable for location‐based services that aim tracking applications, it does not give enough information about the performance of PBS. This paper provides a novel framework quantifying the system performance of PBS by making use of spatially quantized decision regions that are determined according to service properties. The detection problem in PBS is modeled by an M‐ary hypothesis test, and analytical expressions for correct detection, false alarm, and missed detection rates are derived. A relation between location estimation accuracy requirements that are mandated by regulatory organizations and the performance metrics of PBS is given. Additionally, a flexible cost expression that can be used to design high‐performance PBS is provided. A system deployment scenario is considered to demonstrate the results. By using this framework, PBS designers can improve their command on the services’ behavior and estimate service performance before deployment. Copyright © 2011 John Wiley & Sons, Ltd.

This paper focuses on two different signal combining and detection methods for multi-antenna spectrum sensing in cognitive radio networks. These methods were previously introduced, and their performances are analyzed in the presence of additive white Gaussian noise (AWGN). Noting the severe fading conditions that may be encountered in cognitive radio networks, considering solely AWGN as channel impairment might not be realistic. Moreover the SNR gain obtained by receiver diversity using multiple antennas at the receiver may well be different than the single antenna case when Rayleigh fading is added to the channel model. Therefore, this paper evaluates the combining and detection methods in the presence of Rayleigh fading channel conditions and concludes that evaluating multi-antenna spectrum detection methods and algorithms with AWGN channels does not provide an accurate indication about their performances. As expected equal gain combining (EGC) method, which gives the best performance for the AWGN case, gives the worst performance with uncorrelated Rayleigh fading channels. Thus, we conclude that the performance comparison of the combining and detection algorithms may differ with different channel conditions.

ABSTRACT Error performance of a cooperative system can be enhanced by using transmit and receive diversity techniques at transmission links. The number of transmit/receive RF chain pairs required to achieve full diversity can be decreased to one for each link by using transmit antenna selection (TAS) method at the transmitter and selection combining (SC) method at the receiver. Thus, hardware complexity of a multiple input multiple output (MIMO) cooperative scheme can be significantly reduced when compared to systems that use TAS and maximum ratio combining (MRC). In this paper, we investigate the performance of an amplify‐and‐forward cooperative system where TAS/SC is utilized. We derive the probability density function (pdf) of end‐to‐end SNR of the system for Rayleigh fading channels. By using this pdf, we obtain the exact symbol error rate expressions for M‐PSK and M‐QAM modulations and the exact outage probability expression. We also obtain the asymptotical diversity order using upper and lower bounds of the outage probability expression and show that our system provides the same diversity order as the cooperative system where TAS/MRC is utilized. We verify our results via computer simulations. Copyright © 2010 John Wiley & Sons, Ltd.

We present a novel system for audio emotion recognition based on the Perceptual Evaluation of Audio Quality (PEAQ) model as described by the standard, ITU-R BS.1387-1 which provides a mathematical model resembling the human auditory system. The introduced feature set performs perceptual analysis in time, spectral and Bark domains thus enabling us to represent the statistics of emotional audio for arousal and valence modes with a small number of features. Unlike the existing systems, the proposed feature set learns statistical characteristic of emotional differences hence does not require data normalization to eliminate speaker or corpus dependency. Recognition performance obtained for the well known VAM and EMO-DB corpora show that the classification accuracy achieved by the proposed feature set outperforms the reported benchmarking results particularly for valence both for natural and acted emotional data.

Researchers have shown that extended Kalman based interacting multiple model (EK-IMM) can significantly mitigate location estimation errors. In this study, we propose an EK-IMM smoother generalized for multi carrier systems. In our system, we use COST 231 Walfisch — Ikegami path loss model instead of street microcell path loss model in order to improve the applicability of the method. By implementing the new path loss model, we do not need to know distances from base stations to building corners, hence a database to hold building coordinates is not required. Via simulation results, we have shown that by implementing the new EK-IMM smoother in multi-carrier systems, location estimation accuracy can be improved by approximately 60%, making use of 48 subcarriers.

Communication channels in wireless networks are always subject to fading. Ignoring fading characteristics leads to overly optimistic quality expectations for the offered services. In this paper, we examine the behavior of IEEE 802.11s wireless mesh networks considering Nakagami-m fading channels, which is a more generalized fading model than Rayleigh and Rician distributions. We quantify the effects of fading on the quality of experience (QoE) performance of VoIP services. Via simulation results, we demonstrate that in order to obtain acceptable VoIP QoE levels, at least 20 dB additional signal to noise ratio (SNR) is required by severe fading channels when compared to non-fading channels.

Performance of cooperative communication systems can be improved effectively by using multiple input multiple output (MIMO) techniques at transmission links. We make use of amplify and forward cooperative relaying techniques with transmit antenna selection (TAS) and selection combining (SC) methods to present an effective transmission scheme. Applying TAS method at the transmitter and SC method at the receiver reduces hardware complexity at both sides as the required number of radio frequency chains is decreased. We assume that Rayleigh fading takes place at all links. We derive the probability density function of end-to-end SNR. Using this, we obtain the exact symbol error rate expression analytically for M-PSK modulation and verify our results via computer simulations for BPSK. We also derive a bound for error probability, assuming the link between relay and destination has sufficiently larger SNR than the other links. Using this bound, we analyze asymptotic diversity orders for several system configurations.

GSM networks consist of cells that can support mobile users. Due to this mobility, it is expected that the network model can be different than that of wired networks which do not support mobile users. In our study, using empirical data provided by a GSM operator, TURKCELL, we analyze the associated network model from the mobility perspective. We first show that the handover rate of a regular cell is not negligible. Therefore, call arrivals (answered calls or new calls) in a cell have to be modeled by taking into account the handover calls, which terminate in a different cell than it originated from. However in the related literature this handover rate is usually ignored. We demonstrate that the call arrivals of both handover calls, and inner calls (which terminate in the originated cell) can be separately modeled as Poisson variables with unity fraction of variance and mean by using χ-square test evaluation. We use Wavelet Transformation (WT), autocorrelation function and Modified Allan Variance (MAVAR) methods to investigate the correlation between call arrivals to indicate the relevance of Poisson model. We show that call arrivals in a second is found perfectly uncorrelated with call arrivals in other seconds and Poisson distributed for both mobile and fixed cases regardless the time of the day. The result presented here are useful to evaluate performance of GSM network and to utilize network resources efficiently.

In this paper, a methodology for densely packing subcarriers for multicarrier systems is considered. A unit density Weyl-Heisenberg frame is proposed to achieve pulse shaping in multi-carrier communication systems. Hermite pulses are utilized as mother functions to be orthonormalized in pulse shape generation process. The proposed orthonormalized pulse shape, termed as UDALOP, is the first localized pulse shape for unit density multi-carrier communication systems. By using this pulse shape, the need for offset phase value of the O-QAM systems can be eliminated, without losing robustness to frequency dispersion.

A methodology for doubling the throughput of multi-carrier systems is proposed. Throughput enhancement method is introduced by using pulse shaping in multi-carrier systems. By employing more than one Weyl-Heisenberg frames that are orthonormal both within the frame and in between the frames, the throughput of multi-carrier systems is increased. Different lattice density scenarios are investigated for throughput enhancement. It is shown that by employing the proposed methodology, throughput of a multi-carrier system can be doubled. Theoretical analysis quantifying throughput enhancements are presented and results are supported by simulation studies. A system level comparison is presented for IEEE 802.16 standards. The effects of various impairments are investigated.

In this paper, we propose a novel system architecture for channel estimation and multi-user detection in CDMA systems. Estimation and detection are performed jointly by sequential basis selection algorithms; namely the basic matching pursuit (BMP) algorithm and the orthogonal matching pursuit (OMP) algorithm. We start by demonstrating that the iterations of the BMP algorithm are equivalent to that of the well known detection method, successive interference cancelation (SIC) in code domain. We then introduce the OMP algorithm as a multi-user detector. This novel detection structure orthogonalizes the SIC process hence improves the detection performance. By using simulation results, we demonstrate that its performance is equivalent to decorrelating detector. Inspired from these results, we propose a joint blind channel estimation and detection scheme employing OMP. This novel scheme is less complex than subspace based techniques and gives better performance.

The orthogonal matching pursuit (OMP) algorithm is an adaptive nonlinear algorithm for signal decomposition using an overcomplete dictionary. A tree-search based orthogonal matching pursuit (TB-OMP) has been proposed (Cotter et al. (2001)). Although the TB-OMP algorithm improves the approximation performance, its computation time requirement increases exponentially making the algorithm impractical for certain applications. In this paper, we propose the flexible tree-search based orthogonal matching pursuit (FTB-OMP). The algorithm provides design parameters that give flexibility to establish a tradeoff between approximation performance and experimental time complexity. Sparse signal representations are frequently required in problems related to signal processing and communication areas. The proposed FTB-OMP algorithm is a promising solution for such problems.

In this paper, we present a novel optical CDMA multi-user detector employing the orthogonal matching pursuit algorithm. The proposed system is compared with most of the receiver structures in the literature. It is shown by simulation results that the proposed detection architecture is a very promising candidate with its low computational complexity, and high detection performance. It is also shown to be more robust to low SNR and near-far effect when compared to most of the well-known optical CDMA receiver architectures.

In communications system design, it is frequently assumed that source symbols are equiprobable. However, in real life applications, this is not the case since most sources produce Gaussian samples. In this paper, we introduce a Karhunen Loeve transform (KLT) based integer to integer transform, I/sub 2/I KLT, over GF(q) that forces the symbols to uniform distribution. This transform can be used as an interface between sources with different distributions and communication systems designed according to uniform distributions.

Our objective is to design efficient coded modulation techniques with multi-stage decoding (MSD) for Rayleigh fading channels. The well-known rate design rule is conventionally used for AWGN channels. We extend the application of the rate design rule for multi-level coding by considering MSD for Rayleigh fading channels with interleaving. We model the fading channel as a time invariant additive non-Gaussian noise channels by assuming that the perfect channel state information is available at the receiver. The equivalent channel capacity curves that can be used for the multi-level code design for Rayleigh channels, and a code design example are presented. We demonstrate that a very good coding gain can be achieved if code rates are selected correctly.

A recent work explicitly models the discontinuous motion estimation problem in the frequency domain where the motion parameters are estimated using a harmonic retrieval approach. The vertical and horizontal components of the motion are independently estimated from the locations of the peaks of respective periodogram analyses and they are paired to obtain the motion vectors using a procedure proposed. In this paper, we present a more efficient method that replaces the motion component pairing task and hence eliminates the problems of the pairing method described. The method described in this paper uses the fuzzy c-planes (FCP) clustering approach to fit planes to three-dimensional (3-D) frequency domain data obtained from the peaks of the periodograms. Experimental results are provided to demonstrate the effectiveness of the proposed method.