In this paper, we investigate the performance of wireless-powered communication (WPC) systems operating under the harvest-then-transmit protocol with a three-piecewise nonlinear energy harvesting (EH) model, which explicitly captures the sensitivity and saturation behavior of practical EH circuits. In parallel, we adopt a versatile fading characterization by assuming Nakagami-mchannels in the EH phase and generalized Gamma channels in the information transfer phase, the latter being of practical interest as it unifies several standard fading distributions and accurately approximates fading environments in next-generation networks. Based on this setting, we develop a novel analytical framework that yields, for the first time, exact analytical expressions for the average throughput in the delay-limited, delay-tolerant, and Quality of Service (QoS) delay-constrained transmission modes. We then apply the developed framework to study the throughput of WPC-enabled reconfigurable intelligent surface-assisted systems as a direct application example. We conduct extensive Monte-Carlo simulations under various system configurations to confirm the accuracy of the analytical results. Our results show that nonlinear EH effects yield a more realistic benchmark for the average throughput compared to the conventional linear EH model, particularly under stringent sensitivity and saturation constraints.

Thermal noise modulation (TNM) encodes information in thevarianceof Johnson noise, enabling ultra-low/zeropower operation with intrinsic physical-layer security. While recent demonstrations have focused on binary TNM, its twolevel alphabet fundamentally limits spectral efficiency. This paper develops anM-ary TNM (M-TNM) framework that maps symbols to multiple variance levels, thereby conveying (log2M) bits per channel use. We first establish a structural property of the optimal detector: for zero-mean Gaussian classes with ordered variances, the maximum-likelihood (ML) decision regions are contiguous in the energy statistic and each symbol’s boundaries depend only on its two adjacent variance levels. Leveraging this result, we derive closed-form expressions for theoptimalML detection thresholds and the associatedexactaverage symbol error rate (SER) forM-TNM. We then formulatevariance-constellation optimization—the selection and spacing of symbol variances—as a SER-minimization problem under practical constraints. Analytical results are validated via extensive Monte Carlo simulations, which show near-perfect agreement with theory and demonstrate substantial BES reductions for optimized constellations compared with naïve (e.g., uniformly spaced) variance levels. The proposedM-TNM framework thus improves spectral efficiency while preserving TNM’s energy and security promise, positioning variance-domain modulation as a viable physical-layer technique for bandwidth-limited, large-scale Internet of Things (IoT) deployments.

This paper introduces a unified framework for activity detection in IRS-assisted IoT networks that simultaneously addresses three critical practical challenges: asynchronous transmissions, heterogeneous power levels used by devices to report their local observations, and signal blockage in dynamic environments. The system leverages an intelligent reflecting surface (IRS) to enhance detection reliability, with optional incorporation of a direct line-of-sight (LoS) path. Departing from conventional approaches that handle these challenges in isolation, we formulate a comprehensive detection problem as a binary hypothesis test and develop a structured hierarchy of four detectors: an optimal detector alongside three computationally efficient detectors designed for practical scenarios with different levels of prior knowledge about noise variance, channel state information, and device transmit powers. This hierarchical approach systematically bridges the gap between theoretical optimality and implementation practicality. For each detector, we derive closed-form expressions for both detection and false alarm probabilities, establishing theoretical performance benchmarks and revealing fundamental scaling laws. Extensive simulations validate our analytical results and extract actionable design guidelines by systematically evaluating the impact of key system parameters including the number of antennas, samples, users, and IRS elements on detection performance, providing valuable insights for 6G IoT system designers. The proposed framework effectively bridges theoretical optimality with implementation practicality, providing a scalable solution for IRS-assisted IoT networks in emerging 6G systems.

This paper introduces a unified learning framework for Byzantine-resilient spectrum sensing and secure transmission in intelligent reflecting surface (IRS)-assisted networks under channel state information (CSI) uncertainty. The sensing module employs robust Bayesian belief updates with adversary-resistant aggregation and consensus, guaranteeing reliable primary user (PU) detection even when a bounded fraction of users are malicious. Based on the sensing outcome, the transmission module formulates the downlink design as a sum mean-squared error (MSE) minimization problem under transmit-power and signal-leakage constraints, jointly optimizing the base station (BS) precoder, IRS configuration, and user equalizers. For partial or known CSI, we develop a lightweight alternating optimization algorithm with provable sublinear convergence. For unknown CSI, we integrate constrained Bayesian optimization (BO) within a geometry-aware, low-dimensional latent space. Simulations demonstrate that the proposed framework achieves a higher probability of detection at a fixed false-alarm rate under adversarial attacks compared to state-of-the-art schemes. It also yields substantial reductions in user MSE, strong suppression of eavesdropper signal power, and fast convergence. This work provides a practical, resilient solution for coherent sensing–communication coordination in emerging sixth-generation (6G) applications such as vehicular, unmanned aerial vehicle (UAV), and Internet of Things (IoT) networks.

We propose a novel clustered random beamforming (RBF) scheme for multi-user multiple-input multiple-output (MIMO) downlink systems. The objective of this scheme is to maximize the number of served users while ensuring that each user meets a minimum quality of service (QoS) requirement. The system model comprises a base station (BS) equipped with multiple transmit antennas, serving multiple users. Hierarchical modulation (HM) is employed for each beam to improve spectral efficiency. To analyze the performance of the proposed scheme, we develop an analytical model by mapping the symbols of all users within a cluster onto different layers of a HM constellation. We demonstrate that the proposed scheme not only increases the number of served users but also enhances the sum-rate capacity of the system. Additionally, we discuss the selection of the optimal HM parameter based on predefined performance requirements, illustrated through a sample case. Simulation results indicate that the proposed hierarchical random beamforming (HRBF) scheme is highly promising for multi-user massive MIMO downlink systems, particularly in the context of 5G New Radio (NR) and future 6G networks and beyond.

CubeSats have gained significant attention due to their low cost and versatility, yet their low inertia makes them particularly vulnerable to disturbances, such as magnetic torques and residual atmospheric drag, that induce strong nonlinearities in their rotational dynamics. While nonlinear controllers can address these challenges, their high computational burden and intricate stability analyses often forces practitioners to rely on linearization-based techniques, which fail to capture the full extent of the system’s nonlinear behavior. Moreover, existing Koopman-based approaches for attitude control have not exploited the quaternion representation intrinsic to CubeSat dynamics, nor have they adequately addressed the impact of environmental disturbances and sensor noise, an important gap in current research. In this paper, we propose a neural Koopman framework that lifts the nonlinear dynamics, represented in quaternion form, into a higher-dimensional linear space. By training deep neural networks as lifting functions using a control-aware loss function, our method enables the design of efficient linear controllers such as LQR. Comparative results demonstrates that the Koopman model preserves the unit-norm property of quaternions under sensor noise and disturbances and delivers superior reference tracking performance compared to the conventional linearized model, which suffers from renormalization issues. These findings highlight the potential of the proposed framework for achieving robust and computationally efficient CubeSat attitude control.

This paper proposes an efficient path-planning scheme for unmanned aerial vehicles (UAVs) aimed at addressing coverage holes in wireless networks. Coverage holes can undermine the quality of service (QoS) of terrestrial cellular networks where they cause outage times longer than a threshold value dictated by the different application requirements. The proposed approach leverages the self-organizing map (SOM), an unsupervised machine learning technique, to design a UAV trajectory that minimizes the flight path length, while ensuring a coverage hole-free cell or guaranteeing a maximum outage time across the existing holes. The designed path also satisfies constraints on minimum and maximum UAV velocity. Simulation results show that for realistic scenarios, we can practically eliminate all coverage holes when one UAV travels over the designed path. For more extreme scenarios, we show that we need to deploy multiple UAVs to satisfy the QoS requirements where each UAV covers a partition of the holes. To achieve optimal partitioning, we utilize the ant colony algorithm.

High-altitude platform stations (HAPS) have gained significant attention for their role in supporting intelligent transportation systems (ITS) due to their wide coverage and cost-effectiveness. Positioned at 20 km altitude, HAPS serve as aerial base stations, where terrestrial networks are unavailable or damaged due to disasters. However, wind disturbances can cause HAPS to drift, leading to coverage hole area and reduced reliability in ITS operations. To address this challenge, we propose the use of networked flying platforms (NFPs), specifically unmanned aerial vehicles (UAVs) as a backup system to dynamically restore coverage and ensure continuity and stability in ITS services during HAPS displacement. The study uses the ERA5 wind dataset for the year 2023 to analyze stratospheric wind behavior in Doha, Ottawa, and New York, confirming the global need for backup solutions during high-wind events.

This work investigates the bit error probability (BEP) of thermal noise modulation (TNM) in Rayleigh fading channels. TNM has emerged as a promising ultra-low-power modulation scheme, where information is conveyed through variations in thermal noise variance. Existing studies provide only approximate BEP expressions and assume perfect channel state information (CSI) at the receiver. Furthermore, current detection strategies require per-symbol threshold adjustments, which, along with CSI estimation, introduce significant computational overhead for power-constrained devices. To address these limitations, we propose a fixed-threshold detection strategy that eliminates the need for channel estimation and threshold adaptation. Additionally, we derive an exact closed-form BEP expression for TNM under Rayleigh fading and formulate the problem of optimal threshold selection as an optimization task, solvable using efficient line-search techniques such as gradient descent. The accuracy of our analytical results is validated through Monte Carlo simulations, demonstrating strong agreement with theoretical predictions. These contributions provide a more precise characterization of TNM performance in fading environments, paving the way for its practical implementation in energy-efficient wireless systems.

Millimeter-wave (mmWave) beam selection in MIMO systems presents significant challenges in dynamic environments due to computational constraints, data heterogeneity, and privacy concerns. In this paper, we propose a novel Maturing Federated Transfer Learning (MFTL) framework that integrates radar and image data to enhance beam prediction accuracy while ensuring user data privacy. The proposed approach utilizes ResNet-50 as a pre-trained model, fine-tuned locally at distributed Antenna Units (AUs) to adapt to diverse scenarios. To mitigate the effects of data heterogeneity, we evaluate multiple aggregation strategies, with FedMedian demonstrating superior robustness compared to FedAvg and weighted averaging. Our optimized MFTL configuration, utilizing a learning rate of 0.005, a batch size of 32, and five aggregation rounds, significantly improves model performance. Experimental results on the DeepSense 6G dataset indicate that our approach achieves a Top-1 accuracy of 57.58% and a Top-5 accuracy of${9 4. 7 \%}$, outperforming state-of-the-art methods. These findings highlight the effectiveness of federated learning, transfer learning, and robust aggregation techniques in improving beam selection accuracy for next-generation mmWave communication systems.

Power Amplifiers (PAs) are an integral part of any wireless communication chain. They increase the power of the transmitted signal, albeit non-linearly at certain power levels, to enable long-range transmission. This paper showcases a novel approach based on Reinforcement Learning (RL) that performs Digital Predistortion (DPD) for Radio Frequenccy (RF) PAs. The RL-agent predistorts the desired signal by manipulating the complex form of the signal to proactively compensate for the non-linearity effect on the PA output. The proposed model accounts for the memory effect of the PA. The PA in this work was modeled after a GMP whose coefficients were derived from an actual PA. Results show that the RL-DPD achieves a significant performance boost, reducing Error Vector Magnitude (EVM) by nearly 80% compared to Generalized Memory Polynomial (GMP) DPD, and improving Normalized Mean Square Error (NMSE) by over 13 dB. Additionally, RL-DPD outperformed the existing machine learning based ARVTDNN and RVTDCNN in NMSE by approximately 4 dB, while also delivering a 0.6 dB and 0.34 dB improvement in Adjacent Channel Power Ratio (ACPR) respectively. With these results, the effectiveness of the RL-DPD technique was thoroughly evaluated, positioning this work as a base for further investigation.

Federated learning (FL) has gained attention for enabling efficient distributed learning while maintaining data privacy. However, the data privacy constraint reduces the transparency in the agents’ model update making the learning process vulnerable to Byzantine attacks. In this paper, a mathematical proof is provided to show that when the traditional model-combining scheme is used, the model will eventually diverge to non-useful solutions in the presence of Byzantine agents independently from their number or their contributions. A low complexity norm-control based aggregation approach is also proposed and shown to converge to the optimal and sub-optimal solutions in the absence or presence of Byzantine nodes, respectively. Monte-Carlo simulations are also conducted to verify and validate the mathematical derivations and the efficiency of the proposed approach in protecting the FL model.

Coverage holes directly affect the quality of service (QoS) and reliability of wireless networks and should be avoided as much as possible. In this paper we address this issue through the deployment of unmanned aerial vehicles (UAVs) as mobile base stations and we propose proper UAV path planning. While most of the works in the literature define holes based on statistical sense, i.e., when the coverage probability for a point on cell is below a certain threshold, e.g., 95%, in this paper, we target applications that allow only for short time disconnections, and define a location that is not covered for a certain amount of time to be in a coverage hole. To minimize such holes, we use optimal UAV path planning based on the two families of trajectories, namely, spiral and oval curves. We show that the proposed oval curves will result in a better performance in addressing the coverage holes and can guarantee a minimum signal to noise ratio over the whole coverage area, and over a guaranteed amount of time.

This study leverages the YOLOv8m computer vision algorithm with Ground Penetrating Radar (GPR) for enhanced aquifer detection in simulated Qatari terrains. Using image processing techniques like the Canny operator, a detection accuracy of 72% was achieved. Different radar resolutions were tested, highlighting the future potential of Transfer Learning for improvement. While simulations show promise, real-world deployment remains an essential next step.

The identification of malicious users within a large set of participants poses a significant challenge in the domains of cybersecurity, data integrity, user management, and particularly within federated learning (FL) environments. FL, a distributed machine learning approach, necessitates rigorous mechanisms for safeguarding data integrity, model accuracy by effectively managing and identifying malicious participants. Traditional methods require the sequential removal and evaluation of users to determine their impact on the system’s overall error rate or loss function, fall short in terms of efficiency and scalability, especially in FL contexts where data is distributed across multiple clients. To address these limitations, we propose the Refine and Identify Algorithm, a two-phased approach that efficiently narrows the search space for identifying malicious users by initially evaluating users in groups rather than individually and iteratively focusing on those groups with the highest potential for containing malicious users. A rigorous mathematical framework, including a proof of convergence and a detailed analysis of iteration necessities, underpins the algorithm’s efficacy. The convergence proof and analysis of iteration requirements provide a solid mathematical foundation for the proposed method’s effectiveness, paving the way for further optimization and application-specific tuning. Simulation results depict the efficiency of the proposed technique and show a significant reduction in computational resources and time required for identifying malicious users.

Locating sources of hazardous phenomena (or undesired behaviour such as satellite jamming) accurately and timely is key to emergency response operations (or operation under hostile environment). Classically, odor-based localization is performed using trained animals with a sharp sense of smell (e.g. dogs). Instead, the localization of a toxic gas leakage source (or nuclear radiation source) can be performed by robot searchers (agents) equipped with proper sensors to avoid endangering animals. Typically, the leakage distribution encounters severe randomness and sparsity induced by the diffusion physical model and the environmental aspects (e.g. varying flow currents in the medium). As a consequence, classical gradient-based search algorithms become inefficient. Infotaxis has been proposed as an effective non-gradient-based method for source localization. In this paper, we study the case of two altruistic search agents leveraging Infotaxis assuming non-ideal (erroneous) conditions for the communication channel used for cooperation. The performance of the altruistic agents is examined from accuracy and efficiency points of view. The results show that although having multiple agents improves the average search time, a severely degraded communication channel causes the rate of successfully finding the source to drop drastically, suggesting a non-cooperative approach in such scenarios.

Heterogeneous wireless networks (HetNets), where networks are deployed with ultra-dense small cells (SCs), is one of the main enabling technologies for future wireless networks. In such networks, signals are vulnerable to severe blockage, interference, and intermittent connectivity. This can be largely overcome using the emerging Reconfigurable Intelligent Surface (RIS) technology that can enhance HetNets performance by controlling the propagation environment. However, jointly optimizing the parameters of base stations’ (BSs’) active beamforming and RISs’ passive beamforming is a major challenge in RIS-empowered HetNets. In this paper, we investigate the issue of rate control in RIS-empowered multi-cell multiple-input single-output (MISO) HetNets via joint users’ equipment (UEs) rate fairness and SCs rate load balancing. We assume RIS-assisted SC BSs at mmWave underlying a RIS-assisted macrocell (MC) BS at sub-6GHz serving dual-connectivity UEs that can concurrently connect to the MC BS and a single SC BS. Then, we formulate an optimization problem whose objective is to jointly optimize the active transmit beamforming vectors of the MC and SCs BSs on the one hand and the passive beamforming vectors of the MC and SCs RISs on the other hand. Due to the high non-convexity and complexity of the formulated problem, we propose a novel distributed Deep Deterministic Policy Gradient (DDPG)-based multi-task deep reinforcement learning (MTDRL) scheme to solve the problem and learn network dynamics. Through deliberate definitions of MTDRL agent’s tasks and their corresponding main elements, we demonstrate via simulations that our proposed scheme guarantees a fair distribution of rates within UEs and SCs. In addition, we quantify the robustness of our proposed MTDRL scheme compared with some benchmarks in terms of convergence speed and utility values.

This paper introduces a database of multi-script (Arabic and English) for indoor scene text detection, taken from different angle-of-view. This database can be used in a variety of real-world applications, such as image search, robot navigation, and assisting the visually impaired. The database contains 944 images taken with smartphones in an indoor environment at Qatar University. These images were taken from at least three angles, making the database even more challenging. To evaluate the database, an OCR method based on multiple language detection is considered. The results show that multi-language detection should be given more attention in practice. The database is publicly available11https:/www.dropbox.com/s/7s7f936y4etzsu7/QU_door_dataset%20%282%29.zip?dl=0.

The main subject of this work is to make a detailed comparison between the performance of free-space optical (FSO) and terahertz (THz) links under different conditions. To this end, we first perform a detailed modeling of the FSO and THz channels, taking into account all real channel parameters. Then we compare the performance of THz and FSO links using two important wireless communication performance metrics, i.e., channel capacity and outage probability. We mathematically show that, unlike FSO links, the pointing errors of THz links are not function of linklength. Finally, by examining the strengths and weaknesses of both technologies, we show that increasing the linklength has a more destructive effect on FSO links compared to the THz links. For shorter linklength, FSO links have better performance, however, with the increase in linklength, THz has the ability to reach higher capacity than FSO links.

Localization of sources of hazardous phenomena such as gas leakages in air and oil spells in seawater is of high importance for environmental and civil protection. In this paper, we study variations of bio-inspired search and man-developed search methods that can be used to find (localize) the sources of the hazardous phenomena. We use two main quality measure factors for our comparison; namely, the success rate, which is the probability of finding the source location correctly and the number of steps which is steps (time) taken by the searcher till it declares the source found. This work aims to be the basis of future research in this important domain. The results show that the gradient-climbing and biased random walk algorithms had the highest success rate at 81.1% and 85.4% respectively, followed by the random walk algorithm at a success rate of 70.9%.

Despite its potential benefits, Federated learning (FL) is vulnerable to various types of attacks that can compromise the accuracy and security of the trained model. While several defense mechanisms have been proposed to protect FL against such attacks, attackers are continuously developing more advanced techniques to bypass these protection mechanisms.In this context, this paper proposes a novel attack mechanism that allows malicious users to optimize their crafted reports, maximizing potential damage while limiting the chances of being detected. Our proposed attack technique is a robust approach designed to bypass existing defense mechanisms in FL. Our contributions are mainly investigating the FL model attack from the attacker’s perspective, proposing a model relaxation approach to optimize a single poisoning ratio variable, and formulating a compromise between the chances of being detected and the amount of damage that the attack could cause. Additionally, we introduce three new attack designs, namely DTA, ATA, and NEA, which maximize the effect of the attack. The proposed Distance Target Attack (DTA) minimizes the distance from the target attack model, while the Accuracy Target Attack (ATA) deteriorates the accuracy of the global model. Furthermore, the Number Estimation Attack (NEA) aims to maximize the expected number of attackers that could bypass the aggregation detection mechanisms.The numerical results based on the KDD dataset confirm the ability of the proposed approach to deteriorate the global model accuracy. The experiments showed that the proposed DTA, ATA, and NEA attacks can significantly reduce the accuracy of the global model. These results demonstrate also the effectiveness and robustness of the proposed attack mechanism in compromising the accuracy and security of FL models.

THz technology is considered a key element in 6G wireless communication because it provides ultra-high bandwidths, considerable capacities, and significant gains. However, wireless systems operating at high frequencies are faced with uncertainty and highly dynamic channels. Reflecting intelligent surfaces (RISs) can increase the range of the THz communication links and boost the rate at the receiver. In contrast to the existing literature, we investigate the scenario of multiple access multi-hop (cascaded) RISs uplink THz networks in a correlated channel environment. We show that our inspected cascaded RIS system is over-determined and that the rate maximization optimization problem is non-convex. To this end, we derive a closed-form expression of the received power and derive an analytical solution based on pseudo-inverse to obtain optimum RISs’ phase shifts that maximize the received signal power and hence increase the rate. In addition, we utilize deep reinforcement learning (DRL), which is capable of solving non-convex optimization problems, to obtain the optimum cascaded RISs’ phase shifts at the receiver taking into account the situation of the spatially correlated channels. Simulation results demonstrate that the DRL algorithm achieves higher rates than the mathematical sub-optimal method and the case of randomized phases.

In the CubeSATs ecosystem, flight-launching and resilient communication are two major challenges for Low Earth Orbit (LEO), Near Earth Orbit (NEO), and Medium Earth Orbit (MEO) satellites. Dynamic beamforming, satellite Guidance, Navigation and Control (GNC) operations, and channel-tolerant communication are core and common problems faced by all satellite mission designs. In this work, a UAV-assisted SkyCube Testbench is proposed that can be used to demonstrate a) Orthogonal Frequency Division Multiplexing (OFDM) in GNURadioOR over 6 GHz Universal Software Radio Peripheral (USRP); b) GNC operations over 923 MHz-2 W Long Range (LoRA) wireless node; c) beam-forming using 2x2 phased-array antenna; d) energy-harvesting via 25 W mono-crystalline PV modules and two 3.7 V/6000mAh batteries driven by 50 W Maximum Power Point Tracking (MPPT) converter; and e) energy-optimized thrust control nozzle for 30+ min airborne flight-time with 881 g payload. This design is expected to serve 35 km altitude with 73.298 km orbital curvature.

The gigantic increase in population, social isolation, and mobility constraint lifestyle since the COVID-19 era has resulted in challenges like remote availability of critical bio-instrumentation like magnetocardiography (MCG) and electrocardiography (ECG). This availability can only be made possible through portable hand-held bio-instrumentation systems. Unmanned aerial vehicles (UAVs) can be used to deliver these systems to remote areas. In cardiological bioinstrumentation, MCG and ECG are two major innovations-based field effect techno-scientific approaches. The MCG systems face several major challenges that have hampered their applications and utility for cardio patients and their inspection labs. In this work, the main challenges were addressed by using a novel ML-based probabilistic interpolation algorithm over a dual ECG probing system-on-chip (SoC) with IoT capabilities to generate the identical MCG signal from two ECG signals with a segmented translation of PR, QRS, ST, and QT characteristic patches at real-time. The implementation findings provided a rich resource for approximating wave-shaping filters, frequencies, mean, and variance whilst addressing redundancy.

CubeSats present a promising opportunity to meet the rapidly increasing demand on high-speed connectivity. The SkyCubesNet project aims to establish a CubeSat constellation over Qatar and Turkey that provides high-speed connectivity over urban and rural areas to serve individual users and support smart cities infrastructure. Nevertheless, establishing high-speed connectivity with CubeSats requires groundbreaking solutions to overcome the obstacles associated with designing low-power high-speed transceivers at the size and mass requirements of CubeSats. Demodulation and channel decoding are known to be two of the most computationally extensive tasks of any transceiver. In this paper, we develop a low-complexity channel coding and modulation scheme suitable for CubeSats based on Low Density Parity Check (LDPC) coding and Differential Binary Phase Shift Keying (DBPSK). We propose a low-complexity LDPC decoding algorithm that can support high-throughput applications at low implementation cost. It is found that at a bit error rate of $10^{-6}$ the coded system signal to noise ratio (SNR) requirement is $4\mathrm{~dB}$ lower than that of the uncoded system. Moreover, the performance improvement of the coded system increases with increasing SNR. The paper presents evaluation of the system’s performance and implementation cost. Compared with similar works on CubeSat transceivers, our system has the highest data rate, highest code rate, and lowest number of decoding iterations.

Recently, developing automated video surveillance systems (VSSs) has become crucial to ensure the security and safety of the population, especially during events involving large crowds, such as sporting events. While artificial intelligence (AI) smooths the path of computers to think like humans, machine learning (ML) and deep learning (DL) pave the way more, even by adding training and learning components. DL algorithms require data labeling and high-performance computers to effectively analyze and understand surveillance data recorded from fixed or mobile cameras installed in indoor or outdoor environments. However, they might not perform as expected, take much time in training, or not have enough input data to generalize well. To that end, deep transfer learning (DTL) and deep domain adaptation (DDA) have recently been proposed as promising solutions to alleviate these issues. Typically, they can (i) ease the training process, (ii) improve the generalizability of ML and DL models, and (iii) overcome data scarcity problems by transferring knowledge from one domain to another or from one task to another. Although the increasing number of articles proposed to develop DTL- and DDA-based VSSs, a thorough review that summarizes and criticizes the state-of-the-art is still missing. To that end, this paper introduces, to the best of the authors’ knowledge, the first overview of existing DTL- and DDA-based video surveillance to (i) shed light on their benefits, (ii) discuss their challenges, and (iii) highlight their future perspectives.

Federated learning (FL) enables an effective and private distributed learning process. However, it is vulnerable against several types of attacks, such as Byzantine behaviors. The first purpose of this work is to demonstrate mathematically that traditional arithmetic-averaging model-combining approach will ultimately diverge to an unstable solution in the presence of Byzantine agents. This article also proposes a low-complexity, decentralized Byzantine resilient training mechanism. The proposed technique identifies and isolates hostile nodes rather than just mitigating their impact on the global model. In addition, the suggested approach may be used alone or in conjunction with other protection techniques to provide an additional layer of security in the event of misdetection. The suggested solution is decentralized, allowing all participating nodes to jointly identify harmful individuals using a novel cross-check mechanism. To prevent biased assessments, the identification procedure is done blindly and is incorporated into the regular training process. A smart activation mechanism based on flag activation is also proposed to reduce the network overhead. Finally, general mathematical proofs combined with extensive experimental results applied in a healthcare electrocardiogram (ECG) monitoring scenario show that the proposed techniques are very efficient at accurately predicting heart problems.

We propose a fixed-wing unmanned aerial vehicles (UAV)-based millimeter wave (mmWave) backhaul architecture that is offered as a cost effective and easy to deploy solution, to connect a disaster or remote area to the nearest core network. First, we fully characterize the single relay fixed-wing UAV-based communication system by taking into account the effects of realistic physical parameters, such as the UAV’s circular path, critical points of the flight path, heights and positions of obstacles, flight altitude, tracking error, the severity of UAV’s vibrations, the real 3D antenna pattern, mmWave atmospheric channel loss, temperature and air pressure. Second, we derive the distribution of the signal-to-noise ratio (SNR) metric, which is based on the sum of a series of Dirac delta functions. Using the SNR distribution, we derive analytical expressions for the outage probability and the ergodic capacity of the considered system as a function of all system parameters. To provide an acceptable quality of service for longer link lengths, we extend the analytical expressions to a multi-relay system. The accuracy of the analytical expressions are verified by Monte-Carlo simulations. Finally, by providing sufficient simulation results, we investigate the effects of key channel parameters such as antenna pattern gain and flight path on the performance of the considered system; and we carefully analyze the relationships between those parameters in order to maximize the average channel capacity.

People counting is one of the computer vision tasks that can be useful for crowd management. In addition, estimating the crowdedness of a surveilled scene for crowd behavior analysis is one of the prominent challenges in video surveillance systems. With the introduction of deep learning, this operation has become doable with a convincing performance. However, this task still represents a challenge for these methods. In this regard, we propose a combination of deep reinforcement learning (DRL) networks and deep learning architecture for crowd counting. DRL network used the Context-Aware Attention (CAA) module for segmenting the crowd region, Then, on the segmented results, the crowd density estimation is performed using an encoder-decoder. The proposed method is evaluated and compared with and without the segmentation parts on the existing datasets including UCF_QNRF, UCF_CC_50, ShangaiTech_(A, B), while the obtained results in terms of MAE metric achieved 84,8, 179.2, 44.6, and 8.2 respectively.

With the emergence of three new paradigms, namely the Internet of Things (IoT), cloud/edge computing and mobile social networks; Mobile Crowd Sensing (MCS) has emerged as a potential approach for data collecting in numerous applications, such as traffic management, infotainment, disaster management or public safety. MCS mechanisms are receiving a lot of attention, both from research and development areas, showing their impact and benefit. But their optimization is still under development, mainly due to the large number of involved parameters. A major field within MCS relates to crowd management for emergency situations, where the management and optimization mechanisms become crucial to local authorities. To tackle this problem, in this work, we propose an MCS hybrid worker selection scheme that operated various modes depending on the delay-cost requirements. Our scheme exploits the user behavior to achieve an optimal bi-objective for any delay-cost requirement. We use simulations to evaluate the performance of our proposal, and we show the optimal and different sub-optimal solutions that can match the delay-cost requirements.

Reconfigurable Intelligent Surface (RIS) has recently emerged as an enabling technology to enhance reliability and overcome blockage in future heterogeneous wireless networks (HetNets). Adjusting amplitudes and phases of the RIS elements to achieve such goals is a major challenge. In this paper, we study the problem of network rate control to achieve users (UEs) fairness and smallcells (SCs) load balancing in multi-cell RIS-assisted multiple-input single-output (MISO) HetNets. We consider dual-connectivity UEs that can simultaneously connect to mmWave-operating SCs and sub-6GHz-operating RIS-assisted macrocell (MC), where RISs are mainly deployed to enhance sub-6GHz signal reception and mitigate interference. Then, we formulate an optimization problem whose objective is to jointly control the active beamforming vectors of SCs and MC on the one hand and the passive beamforming vectors of RISs on the other hand to maximize UEs fairness and network load balancing. Due to the high complexity of the formulated problem, we propose a novel multi-task deep reinforcement learning (MTDRL) model based on the Deep Deterministic Policy Gradient (DDPG) algorithm to solve the problem and learn system dynamics. Through proper definitions of network tasks and their main elements, we show via simulations that our proposed MTDRL-based model ensures fair distribution of rates within UEs and SCs and that it outperforms key benchmarks.

The main contribution of this paper is to analyze a long networked flying platform (NFP)-based millimeter wave (mmWave) backhaul link that is offered as a cost effective and easy to deploy solution to connect a disaster or remote area to the nearest core network. The same network model can be considered for interlinking Low Earth Orbit (LEO) satellite constellations with different geometry and channel characteristics. For this aim, we characterize the backhaul channel as a function of realistic physical parameters such as heights and distances of obstacles along the route, flight altitude and the intensity of NFPs' vibrations, the actual 3D antenna pattern, etc. For the characterized channel, we derive an analytical closed-form expression for the outage probability. Finally, using the obtained results, we provide a fast algorithm for the optimal parameter design of the considered system that minimizes the cost.

Federated Learning (FL) has attracted a lot of attention in numerous applications due to recent data privacy regulations and increased awareness about data handling issues, combined with the ever-increasing big-data sizes. This paper proposes a server-less, robust FL training mechanism that allows any set of participating data-owners to train a neural network (NN) model collaboratively without the assistance of any central node and while being resilient to Byzantine attacks. The proposed approach makes use of a dual-way update mechanism to allow each node to take a model forwarding decision towards a global collaborative decision of isolating any malicious updates. The efficiency of the proposed approach in detecting cardiac irregularities is verified using simulation results conducted based on the Physikalisch-Technische Bundesanstalt Database electro-cardiogram (PTBDB ECG) dataset.

In this work, we study the energy saving merits of proactive scheduling for downlink multimedia streaming towards a finite-buffer receiver under Rayleigh fading. Three different threshold-based proactive scheduling policies are proposed, each with a different threshold structure on the queue/channel state space. The first two policies consider a single channel gain threshold per queue state, either with a fixed or variable cache amount, whereas the last policy imposes a set of thresholds on the channel gain, fixed on all queue states. We consider a time-causal system in which only the current and previous environment states are known. Required transmit power/signal-to-noise ratio (SNR) for the proposed policies is analytically derived, and numerically benchmarked against a non-proactive (reactive) transmission upper bound, as well as to a non-causal genie-aided lower bound with fully-observable future channel values. Numerical results show that proactive scheduling could save more than 50% of the transmission energy on average.

Federated Learning (FL) is a machine learning approach that enables private and decentralized model training. Although FL has been shown to be very useful in several applications, its privacy constraints cause a lack of model update transparency which makes it vulnerable to several types of attacks. In particular, based on detailed convergence analyses, we show in this paper that when the traditional model-combining scheme is used, even a single Byzantine node that keeps sending random reports will cause the whole FL model to diverge to non-useful solutions. A low complexity model combining approach is also proposed to stabilize the FL system and make it converge to a suboptimal solution just by controlling the model norm. The Physikalisch-Technische Bundesanstalt extra-large electrocardiogram (PTB-XL ECG) dataset is used to validate the findings of this paper and show the efficiency of the proposed approach in identifying heart anomalies.

We propose a framework that enables the cluster head (CH) to harvest energy from uplink transmission by Internet of Things (IoT) nodes employing data compression under nonorthogonal multiple access (NOMA) scheme. Our framework enables the CH to maximize the harvested energy while meeting constraints on outage probability, consumed energies by the transmitting IoT nodes and compression and distortion ratios. We provide necessary analysis for our framework and derive an expression for the outage probability and average harvested energy under the NOMA scheme. We formulate an optimization problem with NOMA factors, simultaneous wireless information and power transfer (SWIPT) factors, and NOMA user distances as optimization parameters. We first solve the optimization problem and find the optimized values using a grid-based search. Then, we exploit a deep reinforcement learning algorithm to solve the optimization problem more efficiently. Throughout this work, we prove the feasibility of such framework and deliver key observation that will help the CH scheduling different IoT nodes such that the harvested energy is maximized while the constraints are met.

The Internet of Things (IoT) is used to interconnect a massive number of heterogeneous resource-constrained smart devices. This makes such networks exposed to various types of malicious attacks. In particular, jamming attacks are among the most common harmful attacks to IoT networks. Therefore, an anti-jamming power allocation (PA) strategy is first proposed in this article for health monitoring IoT networks by exploiting the game theory to minimize the worst case jamming effect under multichannel fading. This strategy uses an iterative algorithm based on gradient descent to identify the Nash Equilibrium (NE) of the game. An artificial neural network (ANN) model is also proposed to accelerate the convergence of the algorithm making it more suitable for IoT networks. Furthermore, novel data population (DP), extension, and balancing techniques are proposed to enhance the efficiency of the proposed strategy in combating jamming attacks even for network configurations that were never used in the training phase. In addition, time and spatial diversities are exploited using a heterogeneous iterative algorithm to enhance the security of the network.

Proactive caching shows great potential to minimize peak download rates by caching popular data, in advance, at the edge. Fast-changing file features, such as fast-changing file popularities and fast-changing file contents (data freshness), represent a challenge for proactive caching if cache content update is much slower, which decreases the efficiency and usability of caching. We present a dynamic caching scheme that updates local user caches and optimizes the use of caching resources. The developed scheme index-code the updates with the delivery messages. The developed scheme is presented for a network with one cache-enabled server, that has a pool of files, communicating with$K$cache-enabled receivers with requests limited to the server’s file pool. The developed scheme assumes partial knowledge of features variation. Asynchronous file delivery is assumed as a result of non-flexible receivers’ request timing. We show that the file delivery messages can be used to proactively and constantly update the receivers’ finite caches by index-coding the update messages with delivery messages at no additional rate-cost. We also show that this mechanism reduces the downloaded traffic and can be used to reduce other QoS metrics.

A secure energy efficient approach is proposed to connect Internet of Things (IoT) sensors that operate with limited power resources. This is done by optimizing simultaneously the energy efficiency, the communication rate and the network security while limiting the potential data leakage and tracking the finite battery status evolution. The proposed model uses spatial diversity in addition to artificial jamming introduced by an intermediate device to forward the data from the sensors to the destination and to secure the communication links without draining the rechargeable batteries. The energy harvested by the source is also maximized without affecting the security level of the network. The outage secrecy capacity is derived to evaluate the security level. Furthermore, the system power stability is analyzed using Markov chains and statistical approaches to validate the efficiency of the proposed technique in maintaining the system in a self-sufficient mode and making it operate without the assistance of external power resources.

A jamming combating power allocation strategy is proposed to secure the data communication in IoT networks. The proposed strategy aims to minimize the worst case jamming effect on the intended transmission under multi channel fading and total power constraints by modelling the problem as a Colonel Blotto game Nash Equibrium (NE). Both Logistic Regression as well as a specifically designed algorithm are used to iteratively and rapidly obtain the equilibrium strategy. The conducted theoretical derivations and Monte Carlo simulations confirm that the proposed approach can secure the IoT network with a limited amount of power and with a number of iterations that is much reduced compared to state-of-the-art techniques.

In spite of the wide spread of practical crypto- systems and ciphers nowadays, they still lack a unique metric to measure the secrecy level they provide. Their strength is measured in an ad-hoc way by exposing them to different kinds of attacks. In addition, their ability to hold secure against these attacks is evaluated in time and computations. In this paper, we introduce an approach for calculating the equivocation of the secret key used in these ciphers. In addition, we prove that it can be used as an indicator for the work required to break the cipher. This will help in unifying the metrics used in evaluating the strength of the ciphers and in comparing them with the classical information theoretic secreacy measures.

Telemedicine is a technique that allows patients to have health-related consultations without the need to be physically present in the hospital through phone and video calling technologies. In recent years, researchers have made many contributions to reform and facilitate better telemedicine services through the use of body area networks or wireless body area networks. This paper presents a testbed where we implement a lightweight physical layer security scheme, using gray code, on an IoT-based health monitoring system to secure transmitted patient readings while preserving its clinical features. We address several existing adversarial scenarios, where an adversary can eavesdrop on the packets and infer their content using some of the existing packet inspection techniques. We prove that the introduced physical layer security scheme effectively protects the patient transmitted data, even if read by an adversary.

The open nature of the wireless communication medium renders it vulnerable to jamming attacks by malicious users. To detect their presence and to avoid such attacks, several techniques are present in the literature. Most of these techniques aim to reduce the effect of the jamming signals by increasing the transmission power or by using complex coordination schemes. However, the implementation of such power consuming techniques might be challenging or not feasible in limited resources Internet-of-Things (IoT) devices. Therefore, a defending strategy against jamming attacks in health monitoring IoT networks is proposed in this article. This strategy operates in orthogonal frequency-division multiplexing channels and takes into consideration the effect of slow fading channels in the strategy design. Specifically, the jamming combating problem is formulated as a Colonel Blotto game where the equilibrium defines the minimization of the worst case jamming effect on the IoT sensors communications. Then, the optimal power allocation strategy for all the potential jammer power ranges is derived by investigating the Nash equilibrium of the game. This proposed strategy is shown to be efficient in combating jamming attacks while minimizing the IoT sensors power consumption.

Radio frequency identification (RFID) systems are gaining more attention in new frontiers of applications due to their simplicity, low size factor and low cost of deployment. One of the emerging frontiers of applications of RFID is the healthcare system. In this paper, we propose a novel physical layer security technique (PLS) to secure radio frequency identification (RFID) tags used in healthcare systems. To achieve secure communications for RFID systems, directional modulation (DM) scheme is exploited at the reader side. The proposed system maintains the simple circuity and processing nature of passive tags. The results obtained demonstrate that DM techniques can achieve physical layer security for RFID systems.

Electrocardiogram (ECG) is an electrical activity of the heart, which can be recorded by placing electrodes near heart or on the limbs. ECG is a vital body signal, which reflects the heart health condition. This paper presents a new wearable ECG system, which can be used for long-term rhythm monitoring with the potential of increased sensitivity to detect intermittent or subclinical arrhythmia. This study presents the design and development of a wearable pervasive healthcare monitoring system by ECG measurement systems and internet of things (IoT) platform. In this design, non-intrusive healthcare system was designed based on wireless body area network (WBAN) for wide area coverage with minimum battery power to support wireless transmission. Data were transmitted via Wi-Fi to the personalized mobile system. These were integrated into a comfortable, easy to wear, and ergonomically designed armband ECG sensor system, which can acquire an ECG signal from the upper arm of the user over a period of 72 hours.

The Internet of things (IoT) is one of the most exposed networks to attackers due to its widespread and its heterogeneity. In such networks, jamming attacks are widely used by malicious users to compromise the private and secure communications. Many techniques are proposed in the literature to secure the network from malicious jamming attacks. However, most of these techniques require either the implementation of complex coordination schemes or the use of high transmission power and are therefore challenging to implement in limited resources IoT networks. In this paper, a low complexity anti-jamming defending strategy using smart power allocation under limited power constraints is proposed for health monitoring IoT networks. This strategy is designed by formulating the worst case jamming effect minimization problem as a Colonel Blotto game while considering the slow channel fading effect. By analyzing the Nash Equilibrium (NE) of the game, making use of efficient and fast equilibrium approximation techniques, designing a fast numerical solving approach, training an artificial neural network (ANN) to enhance the accuracy of the estimation, an anti-jamming power allocating strategy is proposed and is shown to be effective in reducing the power consumption and in combating jamming attacks with less resources. A data population scheme is also proposed to make the proposed ANN exploit as much possible the available data to provide accurate NE estimation.

Employing reconfigurable intelligent surfaces (RIS) is emerging as a game-changer candidate, thanks to their unique capabilities in improving the power efficiency and supporting the ubiquity of future wireless communication systems. Conventionally, a wireless network design has been limited to the communicating end points, i.e., the transmitter and the receiver. In general, we take advantage of the imposed channel state knowledge to manipulate the transmitted signal and to improve the detection quality at the receiver. With the aid of RISs, and to some extent, the propagation channel has become a part of the design problem. In this paper, we consider a single-input single-output cooperative network and investigate the effect of using RISs in enhancing the physical layer security of the system. Specifically, we formulate an optimization problem to study the effectiveness of the RIS in improving the system secrecy by introducing a weighted variant of the secrecy capacity definition. Numerical simulations are provided to show the design trade-offs and to present the superiority of RIS-assisted networks over the conventional ones in terms of the system’s secrecy performance.

This paper addresses the problem of the detection of the presence of a pre-classified (i.e., pulse shape is known) signals with IQ imbalance considering free-space path-loss. We study the effect of IQ distortion on the probability of detection and false alarm and find the degradation in the probability of detection compared with the case of ideal IQ branches. We propose closed form expressions for the probability of detection and false alarm for signals suffering from IQ imbalances.

In this paper, we propose a novel PHY layer security technique in radio frequency identification (RFID) backscatter communications system. In order to protect the RFID tag information confidentiality from the eavesdroppers attacks, the proposed technique deploys beam steering (BS) using a one dimensional (1-D) antenna array in the tag side in addition to noise injection from the reader side. The performance analysis and simulation results show that the new technique outperforms the already-existing noise injection security technique and overcomes its design limitations.

Channel-aware encryption is investigated as a physical layer security technique in internet of things (IoT) scenarios. Clustering algorithms for grouping sensor nodes into cooperative clusters are proposed, with the purpose of decreasing energy consumption and reducing the transmission time of sensor data. Bit flipping is implemented with the clustering method in order to "encrypt" the transmitted data based on channel state information. The simulation results validate the performance of the proposed approach in terms of reducing energy consumption, reducing transmission time, and of confusing the eavesdropper from guessing the correct transmissions of sensor nodes.

Although small cells (SC) densification approach plays a prominent role in achieving the data rate and coverage requirements in 5G networks, it poses serious challenges concerning the flexible and cost efficient backhauling solutions. The traditional terrestrial backhauling hubs are subject to limited line of sight probabilities in such dense networks. Having this challenge in hand, and recognizing the increasing interest in the unmanned aerial vehicle (UAV) enabled communication systems, we address the problem of wireless multihop backhauling of SCs using UAV hubs. We present two linear optimization programs that optimize the SC-UAV association and the SC-SC formation in order to maximize the total backhaul flow. Some practical constraints are considered, such as backhaul reliability, association criteria, SC relaying capacity and the number of available links at each SC. Numerical simulations show that the approach allowing partial demand fulfillment of the SCs outperforms the binary one in terms of accumulated rate and the percentage of associated SCs, even at low number of maximum hops and links allowed.

Network densification is promising to achieve higher data rates and higher network capacity, while causing new backhauling challenges especially when the network is highly dense. The flexible and cost efficient backhauling solutions are among the most concerning issues. Due to the high density of the next generation networks, the terrestrial wired backhauling approaches are not cost efficient. Having this challenge in hand, and recognizing the advantages of the networked flying platform (NFP)-enabled communications, we address in this paper the problem of wireless multi-hop backhauling of small cells (SC) using NFP hubs. We propose an efficient iterative heuristic algorithm that jointly optimize the SCNFP association and the SC-SC formation in order to maximize the total backhaul flow, with practical constraints in consideration, such as backhaul reliability, hardware and processing limitations in the SCs and the NFPs. Numerical simulations show that the proposed algorithm can achieve close to optimal solution at a much faster convergence rate.

Physical layer security has attracted research attention as a means to achieve secure communication without the need for complicated upper layer encryption techniques. The secure degrees of freedom (SDoF) of various networks in the absence of instantaneous eavesdropper channel state information is still unknown. In this work, we study the SDoF of a multiple access network composed of two transmitters and a single receiver in the presence of an eavesdropper. All parties are equipped with multiple antennas and are subject to Gaussian noise in addition to fading channel conditions. A realistic, worst case scenario, where the channel state information (CSI) for the channels between the trusted parties is known to everyone, while the trusted parties can only estimate the channel statistics (environment based) of the eavesdropper is considered. The asymptotic secure network sum capacity (aka sum SDoF) is provided utilizing a novel proposed comprehensive upperbound along with a novel achievable scheme based on exploiting jamming.

The evolution of Internet of Things (IoT) as a new application in wireless networks mandates the utilization of wireless cooperative relaying to overcome the energy limitations of IoT devices. Multi-hop relaying is a communication scheme, where packets are forwarded from source to destination through intermediate relay nodes. All these relay nodes are assumed to have buffers for temporarily storing their received packets. During each time-slot, one node can be selected among all nodes to transmit and forward a single packet to the consequent relay node towards the final destination. Based on the nature of the data and its sensitivity to the delay, different schemes can be used to control the movement of packets in the multi-hop networks. This paper presents a framework for the delay analysis of buffered multi-hop networks based on a recently proposed packet-forwarding scheme that uses the best hop for transmission. Based on the channel, the best hop, having the highest signal-to-noise ratio (SNR), is selected. This hop selection procedure produces selection diversity, which minimizes the error and outage probability. The network delay is studied analytically, based on a finite-state Markov chain model. Also, we derive analytical closed form expressions for the average queue length for each relay buffer in the network and the end-to-end network delay. Finally, we compare the delay and outage of the best hop scheme with the conventional multi-hop transmission scheme. The results show how the number of intermediate relays and the buffer size of each one can affect the network delay.

The omnipresence of unmanned aerial vehicles, or drones, among civilians can lead to technical, security, and public safety issues that need to be addressed, regulated and prevented. Security agencies are in continuous search for technologies and intelligent systems that are capable of detecting drones. Unfortunately, breakthroughs in relevant technologies are hindered by the lack of open source databases for drone’s Radio Frequency (RF) signals, which are remotely sensed and stored to enable developing the most effective way for detecting and identifying these drones. This paper presents a stepping stone initiative towards the goal of building a database for the RF signals of various drones under different flight modes. We systematically collect, analyze, and record raw RF signals of different drones under different flight modes such as: off, on and connected, hovering, flying, and video recording. In addition, we design intelligent algorithms to detect and identify intruding drones using the developed RF database. Three deep neural networks (DNN) are used to detect the presence of a drone, the presence of a drone and its type, and lastly, the presence of a drone, its type, and flight mode. Performance of each DNN is validated through a 10-fold cross-validation process and evaluated using various metrics. Classification results show a general decline in performance when increasing the number of classes. Averaged accuracy has decreased from 99.7% for the first DNN (2-classes), to 84.5% for the second DNN (4-classes), and lastly, to 46.8% for the third DNN (10-classes). Nevertheless, results of the designed methods confirm the feasibility of the developed drone RF database to be used for detection and identification. The developed drone RF database along with our implementations are made publicly available for students and researchers alike.

We investigate the model of a single primary-transceiver pair with multiple secondary-transceiver pairs. The secondary pairs can act as relays for the primary transmitter enabling access to its channel resources. Each secondary user (SU) is assumed to be a radio-frequency energy-harvester node. We formulate a framework that aims at specifying the optimal SU set that operates as relay nodes for the primary user (PU) data message. The set of the SUs is selected such that the SUs total throughput is maximized under a certain quality-of-service (QoS) requirement constraint on the PU target data rate. We propose both centralized and distributed approaches for solving the formulated optimization problems. The centralized approach is based on solving a convex optimization problem at the PU. On the other hand, the distributed approach leverages a Sackelberg game where all users interact to achieve the best relay-selection scheme and PU’s transmit power. We prove the uniqueness and Nash equilibrium of the considered Stackelberg game, and develop a game-theoretic relay and PU’s transmit power selection algorithm. We also introduce a fairness optimization-based scheme (FOBS) that aims at enhancing the fairness among the SUs under our proposed centralized approach. Our simulation results show the efficiency of our proposed schemes in terms of SUs total throughput.

The increased interest in full-duplex communication combined with spectrum utilization enhancements stemming from cognitive radio motivate research activities in full-duplex cognitive radio. Energy detection is among the simplest and most practical methods for spectrum sensing in cognitive radio networks. Energy detection spectrum sensing in full-duplex cognitive radio has been addressed in the literature; however, either due to the lack of experimental verification or for the sake of mathematical simplicity, the residual self-interference (RSI) signal has been conventionally considered as a zero-mean Gaussian signal. In this paper, we establish for the first time an accurate (realistic) mathematical framework and performance analysis for energy detection spectrum sensing in full-duplex cognitive radio under the presence of RSI signal exhibiting Rician statistical nature, as recently proven experimentally. The performance analysis results are derived in the presence of RSI after passive self-interference cancellation (SIC) scenario and after combining passive SIC with active SIC using active analog and digital cancellation (ACDC). Exact expressions for the probabilities of detection and false alarm are derived for the considered RSI cancellation scenarios and verified through simulations. The results show that the previously used conventional zero-mean Gaussian self-interference model was producing pessimistic (lower bound) performance estimates.

Epileptic seizure detection could be detected through investigating the electroencephalography (EEG), which is deemed to be very important for IoT wearable sensor-based health systems. EEG-based classification is crucial for a wide-range of applications to analyze real-time vital signs using features concerning predefined set of data classes. The aim of this paper is to conduct a comparative study for several classification techniques and demonstrate the effect of uncertainty in the EEG data on the classification accuracy. We define a model for decomposing the EEG using various transformation such as discrete cosine transform, discrete wavelet transform into several sub-bands. After feature extraction, a comparative study to assess the classification algorithms' performance is conducted. In addition, we evaluate their overall accuracy and complexity as performance measures. For this purpose, we use the support vector machine (SVM) and the Artificial Neural Network (ANN). These are chosen as classifier models to study the performance of the obtained features. The discussion will include the evaluation of the classifiers' performance using the EEG-based epileptic seizure data in two categories, noiseless and noisy. In addition, there are some statistical features extracted to characterize the complete EEG data feeding to these two classifiers. A publically available EEG dataset is employed for both normal and epileptic seizure for automatic epileptic seizure detection as a benchmark.

In this paper, we investigate the effect of a relay on the Degrees of Freedom (DoF) of a single input single output (SISO) X-channel with no channel state information at transmitters (CSIT). In contrast to previous work, which focused on two antennas at the relay to achieve the optimal 4/3 DoF, we focus on the case of a single antenna half duplex relay. We show that with a single antenna relay and delayed output feedback, the upper bound of 4/3 DoF for the X-channel is achievable and we propose the achievability scheme. Moreover, we study the alternating CSIT availability distribution for the SISO X-channel and provide few remarks on the achievability of the optimal DoF.

In an mHealth remote patient monitoring scenario, usually control units/data aggregators receive data from the body area network (BAN) sensors then send it to the network or “cloud”. The control unit would have to transmit the measurement data to the home access point (AP) using WiFi for example, or directly to a cellular base station (BS), e.g., using the long-term evolution (LTE) technology, or both (e.g., using multi- homing to transmit over multiple radio access technologies (Multi-RATs). Fast encryption or physical layer security techniques are needed to secure the data. In fact, during normal conditions, monitoring data can be transmitted using best effort transmission. However, when real-time processing detects an emergency situation, the current monitoring data should be transmitted real-time to the appropriate medical personnel in emergency response teams. In this paper, a fast and secure approach for transmitting monitoring data over multi-RATs is proposed. The presented approach consists of benefiting of the presence of multi-RATs in order to exchange the secrecy information more efficiently while optimizing the transmission time.

We study a number of wireless networks with secrecy constraints and No CSI. The wiretap and MIMO broadcast channels in a presence of an external eavesdropper and the MIMO interference channel with secret messages and no external eavesdroppers are considered. We assume that the transmitters' information is restricted to the channels coherence times and second order statistics rather than the actual channel states. We provide the achievable secure degrees of freedom for each network.

In this paper, the problem of sub-channel selection for carrier aggregation (CA) systems is examined. CA enables the achievement of high data rate links via simultaneous transmissions over multiple component carriers. A CA system usually occupies only a limited number of sub-channels M of these components due to limitations on the maximum permitted number of sub-channels per system. From an information theoretic point of view, a CA system should detect and employ the M-best sub-channels out of the N available ones. To that end, such a system probes a subset of sub-channels during each coherence time via pilot transmission. Then, for the best M sub-channels, one-bit feedback information is transmitted in order to prohibit the transmission through sub-channels with gain below a threshold. The aim is to derive tractable forms via employing the extreme value theory for the sum rate (lower bound on ergodic capacity) achieved by the system under Rayleigh fading and then, to optimize jointly the training length and power, the number of the probed sub-channels (probing bandwidth size) and the feedback threshold such that the sum rate is maximized by considering the sub-channel estimation error. The accuracy of the theoretical analysis is verified by numerical results.

In this paper, we study distributed power allocation for multiflow carrier aggregation in cognitive cellular networks. Our approach differs from the conventional water filling (WF) algorithm since we deal with heterogeneous fading channels, wherein all of the Lagrange multipliers are not handled equally over the heterogeneous cells. The distributed power control solution is carried out over heterogeneous fading channels that are considered nonidentically distributed Nakagami-m fading channels. We first formulate the optimization problem, and we then solve it using the alternating direction method of multipliers (ADMM), which allows the required decomposition for each channel and the required statistical learning among the different subproblem solutions. We also provide comparison with the dual decomposition method and WF solutions considered as solutions without cognition. Simulation results highlight the performance gain of ADMM in terms of the number of iterations. Furthermore, we provide a multiuser application scenario, where the analysis on the fading channel model with Nakagami-m distribution is carried out. We distinguish into the synchronous and the asynchronous ADMM implementations tackling the asynchronous user updates that can be found in a heterogeneous network deployment. The simulation results are obtained to highlight the impact of the partial barrier and the bounded delay in the asynchronous use case.

In this paper, we introduce a secure multiple access scheme, which exploits the multipath structure of the channel to create a multi-user interference environment. The generated interference enables legitimate users to share time and frequency resources over spatially secure communication links. Utilizing directional modulation, we ensure secrecy for legitimate users against eavesdropping while preserving mutual confidentiality between the legitimate users themselves. Moreover, we introduce a complementary scheme for covering the non-selective channel case. The scheme uses directional modulation in coordinated multi-point transmission to provide location-specific secure communication to legitimate users. We characterize the achievable performance using a newly defined metric called vulnerable region. We provide analysis for the achievable secrecy rate, secrecy outage probability, and channel correlation effect on the secrecy performance for the proposed scheme. Furthermore, the effect of the channel spatial diversity, channel estimation error, and the number of legitimate users on the secrecy performance is studied.

The densification of small cell base stations in a 5G architecture is a promising approach to enhance the coverage area and facilitate the ever increasing capacity demand of end users. However, the bottleneck is an intelligent management of a backhaul/fronthaul network for these small cell base stations. This involves efficient association and placement of the backhaul hubs that connects these small-cells with the core network. Terrestrial hubs suffer from an inefficient non line of sight link limitations and unavailability of a proper infrastructure in an urban area. Realizing the popularity of flying platforms, we employ here an idea of using networked flying platform (NFP) such as unmanned aerial vehicles (UAVs), drones, unmanned balloons flying at different altitudes, as aerial backhaul hubs. The association problem of these NFP-hubs and small- cell base stations is formulated considering backhaul link and NFP related limitations such as maximum number of supported links and bandwidth. We then present an efficient and distributed solution of the designed problem, which performs a greedy search in order to maximize the sum rate of the overall network. A favorable performance is observed via a numerical comparison of our proposed method with optimal exhaustive search algorithm in terms of sum rate and run-time speed.

In full-duplex systems, the local oscillator phase noise (PN) problem is one of the bottleneck challenges that may face the self-interference cancellation (SIC) stage especially when orthogonal frequency division multiplexing (OFDM) transmission scheme is deployed. Phase noise degrades the used SIC technique performance significantly, if not mitigated before or during the SIC stage. PN can be estimated and mitigated digitally in either time or frequency domain. Through this work, we propose a novel time-domain self-interference phase noise estimation and mitigation algorithm that is based on Wiener filter and enhances the performance of the SIC stage in full-duplex communication systems. Simulation results show that the proposed algorithm has a better performance than some current time-domain or frequency-domain PN mitigation solutions with a noticeable reduction in the computational complexity.

Proactive caching is a promising technique used to minimize peak traffic rates by storing popular data, in advance, at different nodes in the network. We study a cellular network with one base station (BS) communicating with multiple mobile units (MUs). The BS has a number of cached files to be delivered to the MUs upon demand, and the popularities of these files are changing over time. We show that proactively and constantly updating the MU finite caches and jointly encoding the delivery of different demanded files to the MUs over different time slots minimize the delivery sum rate. We propose two different schemes for a two different scenarios, where the file popularities over time can be either arbitrary increasing or decreasing for the first scheme and decreases with demand for the second scheme. Numerical results show the benefits of the proposed schemes, over conventional caching schemes, in terms of reducing the delivery sum rate.

5G+ systems expect enhancement in data rate and coverage area under limited power constraint. Such requirements can be fulfilled by the densification of small cells (SCs). However, a major challenge is the management of fronthaul links connected to an ultra dense network of SCs. A cost effective and scalable idea of using network flying platforms (NFPs) is employed here, where the NFPs are used as fronthaul hubs that connect the SCs to the core network. The association problem of NFPs and SCs is formulated considering a number of practical constraints such as backhaul data rate limit, maximum supported links and bandwidth by NFPs and quality of service requirement of the system. The network centric case of the system is considered that aims to maximize the number of associated SCs without any biasing, i.e., no preference for high priority SCs. Then, two new efficient greedy algorithms are designed to solve the presented association problem. Numerical results show a favorable performance of our proposed methods in comparison to exhaustive search.

We investigate the secure degrees of freedom (SDoF) of the two-transmitter multiple access channel (MAC) with multiple antennas transmitters and multiple antennas legitimate receiver in the vicinity of an eavesdropper that has NE antennas. The SDoF is derived for two cases of the legitimate nodes knowledge about eavesdroppers' channels, instantaneous CSI and statistical CSI. Comparing the SDoF of the two cases, the effect of eavesdropper CSI knowledge on the SDoF is developed. We derive the exact sum SDoF for both cases and provide matching respective upperbounds. We show that obtaining statistical CSI can achieve even more SDoF in some cases than obtaining the instantaneous eavesdropper's channels coefficients if some time slots were dedicated to channel estimation.

We investigate the secure degrees of freedom (SDoF) of the -user MIMO multiple access channel (MAC) in the vicinity of an eavesdropper which is trying to decode the messages sent by the transmitters. The eavesdropper is equipped with antennas and has fading channels. The transmitters and the legitimate receiver are assumed to be oblivious of the eavesdropper channels values. We derive an upperbound on the sum SDoF of such -user MAC channel and present an achievable scheme that partially meets the derived upperbound.

We propose a practical, simple and hardware friendly, yet novel and very efficient, angle of arrival (AoA) estimation system. Our intuitive, two-phases cross-correlation based system requires a switched beam antenna array with a single radio frequency chain. Our system cross correlates a reference omni-directional signal with a set of received directed signals to determine the AoA. Practicality and high efficiency of our system are demonstrated through performance and complexity comparisons with multiple signal classification algorithm.

Sharing a relay in cognitive radio (CR) networks in order to enhance the secondary user's (SU's) performance in an interference‐limited scheme is investigated. It is assumed, by utilising hierarchical modulation, a shared relay is used simultaneously between both primary user (PU) and SU. The proposed scheme relies on both power adaptation and rate scheduling. Based on channel side information and tolerable interference limitation at the PU, the SU adapts its power and the transmission data rates are regulated by the PU's status and channels condition. Theoretical analyses and simulation results are provided to evaluate the performance of the proposed dynamic spectrum sharing scheme. It is shown that rate adaptation leads to significant improvement of the throughput of the SU, at the cost of negligible degradation in performance of the PU. The results also demonstrate that sharing CR relay with the PU can compensate the side effects of spectrum sharing and interference caused by the SU, which for instance is equivalent to ∼8 dB (low signal‐to‐noise ratio (low‐SNR) regime) and to 15 dB (high‐SNR regime) SNR improvement.

Estimating the number of sources is a key task in many array signal processing applications. Conventional algorithms such as Akaike's information criterion (AIC) and minimum description length (MDL) suffer from underestimation and overestimation errors. In this study, the authors propose four algorithms to estimate the number of sources in white Gaussian noise. The authors’ proposed algorithms are categorised into two main categories; namely, sample correlation matrix (CorrM) based and correlation coefficient matrix (CoefM) based. Their proposed algorithms are applied on the CorrM and CoefM eigenvalues. They propose to use two decision statistics, which are the moving increment and the moving standard deviation of the estimated eigenvalues as metrics to estimate the number of sources. For their two CorrM based algorithms, the decision statistics are compared to thresholds to decide on the number of sources. They show that the conventional process to estimate the threshold is mathematically tedious with high computational complexity. Alternatively, they define two threshold formulas through linear regression fitting. For their two CoefM based algorithms, they re‐define the problem as a simple maximum value search problem. Results show that the proposed algorithms perform on par or better than AIC and MDL as well as recently modified algorithms at medium and high signal‐to‐noise ratio (SNR) levels and better at low SNR levels and low number of samples, while using a lower complexity criterion function.

In this paper, we characterize the total degrees of freedom (DoFs) of the full-duplex asymmetric multiple-input multiple- output (MIMO) three-way channel. Each node has a separate-antenna full-duplex MIMO transceiver with a different number of antennas, where each antenna can be configured for either signal transmission or reception. We study this system under two message configurations; the first configuration is when each node has two unicast messages to be delivered to the two other nodes, while the second configuration is when each node has two unicast messages as well as one broadcast message to be delivered to the two other nodes. For each configuration, we first derive upper bounds on the total DoF of the system. Cut-set bounds in conjunction with genie-aided bounds are derived to characterize the achievable total DoF. Afterward, we analytically derive the optimal number of transmit and receive antennas at each node to maximize the total DoF of the system, subject to the total number of antennas at each node. Finally, the achievable schemes for each configuration are constructed. The proposed schemes are mainly based on zero-forcing and null-space transmit beamforming. We show that the derived outer and inner bounds on the total DoF are tight for each message configuration.

This paper considers the problem of analytically assessing the maximum achievable rates (capacity) of orthogonal frequency division multiplexing (OFDM) transmissions when the receiver, for complexity reasons, only accounts for the common phase error (CPE) effect due to phase noise impairments with inter-carrier interference (ICI) treated as noise. By recognizing that the functional form of the CPE with respect to the phase noise realization is actually a free design parameter, determination of the capacity is posed as a functional optimization problem with respect to the, so-called, CPE function. A simple lower bound of the capacity is obtained, revealing the performance degradation due to the unknown CPE at the receiver, as well as the suboptimal performance achieved in severe phase noise conditions by the conventional CPE function that is routinely employed in previous works. The existence of an optimal number of subcarriers that balances the effects of the (unknown) CPE and ICI is highlighted and critical system design/operation issues, such as selection of the CPE function and effect of unknown channel on the achievable rate, are discussed. The analysis in this paper can be employed for determining the suitability of OFDM in phase noise channels and provides a tractable utility function for resource allocation purposes.

The problem of multiple antenna spectrum sensing in cognitive radio (CR) networks is studied in this paper. We propose two new invariant constant false-alarm rate eigenvalue-based (EVB) detectors, using the higher order moments of the sample covariance matrix eigenvalues, by exploiting the separating function estimation test framework. We find closed-form expressions for the false-alarm and detection probabilities of the proposed detectors by providing moment-based approximations of their statistical distributions. The accuracy of the obtained closed-form expressions is validated by Monte Carlo simulations. In addition, we compare the performance of the proposed detectors with that of their two counterparts, i.e., John's and the arithmetic to geometric mean (AGM) detectors, in terms of the asymptotic relative efficiency. This comparison enables us to demonstrate the superiority of our proposed detectors over those detectors within the typical range of signal-to-noise ratio in CR application. The comparative simulation results also illustrate the superiority of the proposed detectors over John's and the AGM detectors as well as some other state-of-the-art EVB algorithms given in the literature.

This work introduces a physical-layer secure multiple-users communication scheme. Our scheme employs the multi-path nature of the wireless channel to provide a different secure communication link for each of the legitimate users. We show that the proposed scheme highly degrades the eavesdroppers channel even for the worst case scenarios. We also provide the secrecy capacity and secrecy outage probability for the proposed scheme. We analyze the effect of the number of users, channel paths, and antenna elements on the secrecy performance of the scheme.

Performance analysis of selection combining (SC) for decode-and-forward (DF) relaying in case of general time-varying Rayleigh fading channels is investigated. While auto-regressive (AR) time series model is applied in channels, differential binary phase shift keying (DBPSK) modulation and non-coherent detection at relay and destination are used together. Due to SC scheme, DF relaying and DBPSK modulation no channel side information (CSI) is required to provide average bit-error-rate (BER), which is exactly calculated. Simulation results in different channel scenarios and fading rates, verifies the presented analysis. It is shown that SC is very close to semi-maximum-ratio-combining (semi-MRC) which is difficult to analyze and error floors occur at high signal-to-noise ratio (SNR) in both methods. The error floor in SC is obtained.

In this paper, we study a general practical stochastic energy harvesting (EH) model where in, an EH relay is able to harvest energy from the radio frequency (RF) radiation in a super capacitor. The different aspects of this stochastic energy harvesting model (SEHM) are discussed and the performance of a point-to-point (P2P) wireless link is evaluated in the presence of such an EH model and decode-and-forward (DF) relaying in terms of average error and outage probabilities. The performance limitations and practical applicability of the SEHM compared to the DF scenario in multi-relay case are studied. The simulation results show the performance improvement of this EH cooperative relaying compared to the traditional relay-based cooperative communications.

In this paper, a new watermarking method based on spread spectrum technique, which we call capacious spread spectrum (CSS) is proposed. In this method, unlike the traditional spread spectrum watermarking (TSS), more than one pseudo noise (PN) sequence will be deployed. Also, a novel method to produce orthogonal PN-sequences is achieved. Moreover, it is shown that with utilizing the new approach, for the same watermarking capacity, the reconstruction error probability would be less than that of the TSS method.

We study the probability of error optimization problem in collaborative spectrum sensing (CSS) with limited time and energy resources in energy harvesting cognitive radio (CR) networks. The frame structures for sensing and data transmission, if the spectrum is identified as vacant, are both supposed to be fixed. This implies that the time resource dedicated for CSS is limited and shared between spectrum sensing time and results reporting time, which depends on the number of sensing users. We consider an optimization problem containing network constraints on time and energy resources for an energy harvesting secondary user (SU) which cooperates with other users using an `OR' fusion rule. It is assumed that the SU, with arbitrary signal to noise ratio, is in energy deficit regime and a predefined constraint on probability of collision is satisfied to protect the cooperative network performance quality. We analytically prove convexity of the considered optimization problem ensuring a global solution can be obtained. Analytical results in addition to simulation results are provided to prove our claims.

In cognitive radio networks, secondary users (SUs) utilize the unused spectrum slots in the assigned band for the primary users (PUs). Conventional cognitive radio networks operate in half-duplex (HD) mode. Recently, full-duplex (FD) communication has become feasible. SUs with full-duplex capabilities can sense the spectrum and transmit simultaneously, which improves the efficiency of cognitive radio networks. In this paper, we study the performance of spectrum sensing based on general likelihood ratio (GLR) when the SU is operating in FD mode. We compare our results to the HD GLR case. We present the effect of residual self interference on the performance of the spectrum sensing technique. Moreover, we consider uncertainty in estimating the variance of the combined residual self interference and noise and show its effect on the performance of the FD GLR.

In this paper, we investigate joint relay selection and optimal power allocation, as a means to maximize the achievable rate of an underlay cooperative cognitive radio with carrier aggregation, taking into account the availability of multiple carrier components in two different bands and primary users (PUs) with specific average outage probability requirements. For the acquisition of the interference thresholds, which are set by the PUs on the secondary user (SU), we incorporate a minimum feedback strategy into the problem formulation, based on the minimization of the PUs outage probabilities. The resulting non-convex optimization problem is transformed into a convex one and optimally solved using dual decomposition and an efficient iterative method with closed-form power policies. Simulation results illustrate that the proposed configuration exploits the available degrees of freedom in an efficient way which maximizes the SU throughput while the average outage probability of the PUs is kept at acceptable levels.

We study a collaborative spectrum sensing (CSS) problem with limited time and energy resources in cognitive radio (CR) network. Time duration of sensing and data transmission, if the spectrum is identified as vacant, are both supposed to be fixed. This implies that the time resource dedicated for CSS is limited and shared between results reporting time (which depends on the number of sensing users) and spectrum sensing time. We consider an optimization problem containing network constraints on time and energy resources for an energy harvesting secondary user (SU) which cooperates with other users by an OR fusion rule. It is assumed that the SU with arbitrary signal to noise ratio is in energy deficit regime and also predefined constraint on probability of collision is satisfied to protect the cooperative network performance quality. We analytically prove the convexity of the considered optimization problem to ensure we obtain a global solution. Analytical results in addition to simulation results are provided to prove the claims.

In this paper, we investigate the performance of energy harvesting relay-aided cooperative system wherein, the source node transmits hierarchical signal to the relay and destination nodes. But, since in cooperative networks relays are often nearer to the source than the destination, the relay node is able to decode multi-resolution signal whereas, the destination is only capable of detecting the most important layer of hierarchical modulation signal. Moreover, the relay node is subject to be in lack of energy with certain probability thus, not being able to cooperate at every communication instances. This, in turn degrades the overall system performance. We concentrate on the error probability analysis of such system and derive exact and upper bound expressions for it. We also provide the optimal selection of the hierarchical constellation parameter based on the link average SNR and the relay node availability chance. We will show how the energy harvesting node could vary the system performance from conventional point-to-point system to fixed-power relay-aided cooperative system.

Abstract The vast majority of existing secret key generation protocols exploit the inherent randomness of the wireless channel as a common source of randomness. However, independent noise added at the receivers of the legitimate nodes affects the reciprocity of the channel. In this paper, we propose a new simple technique to generate the secret key that mitigates the effect of noise. Specifically, we exploit the estimated channel to generate a secondary random process (SRP) that is common between the two legitimate nodes. We compare the estimated channel gain and phase to a preset threshold. The moving differences between the locations at which the estimated channel gain and phase exceed the threshold are the realization of our SRP. We study the properties of our generated SRP and derive a closed form expression for the probability mass function of the realizations of our SRP. We simulate an orthogonal frequency division multiplexing system and show that our proposed technique provides a drastic improvement in the key bit mismatch rate between the legitimate nodes when compared with the techniques that exploit the estimated channel gain or phase directly. In addition to that, the secret key generated through our technique is longer than that generated by conventional techniques. Moreover, we compute the conditional probabilities used to estimate the secret key capacity. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract In this paper, we consider a cognitive scenario where an energy harvesting secondary user shares the spectrum with a primary user. The secondary source helps the primary source in delivering its undelivered packets during periods of silence of the primary source. The primary source has a queue for storing its data packets, whereas the secondary source has two data queues: a queue for storing its own packets and the other for storing the fraction of the undelivered primary packets accepted for relaying. The secondary source is assumed to be a battery‐based node, which harvests energy packets from the environment. In addition to its data queues, the secondary user has an energy queue to store the harvested energy packets. The secondary energy packets are used for primary packets decoding and data packets transmission. More specifically, if the secondary energy queue is empty, the secondary source can neither help the primary source nor transmit a packet from the data queues. The energy queue is modeled as a discrete‐time queue with Markov arrival and service processes. Because of the interaction of the queues, we provide inner and outer bounds on the stability region of the proposed system. We investigate the impact of the energy arrival rate on the stability region. Numerical results show the significant gain of cooperation.Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, collaborative spectrum sensing to detect random signals corrupted by Gaussian noise in the presence of Primary User Emulation Attackers (PUEAs) is studied. We consider smart PUEAs which aims at increasing the false alarm probability and constitute a PUEA network on a Cognitive Radio (CR) network by impersonating Primary Users (PUs). In addition, we propose two security schemes in which sensing nodes get assistance from the Secondary Users (SUs) using two different approaches. In the first approach, the proposed scheme requires having some knowledge about the PUEA network similar to most of the schemes available in the literature. In our second proposed scheme, information about the PUEA network is not required yielding a scheme which is robust to the strategy of attackers. In both proposed approaches, we propose an algorithm to incorporate the SUs assistance in spectrum sensing. The final collaborative decision is made through solution of an optimization problem in order to achieve the best performance and protect the CR predefined requirements. Furthermore, in order to evaluate the performance of the proposed detector at the SUs and at the employed detector in the Fusion Center (FC), the closed form expressions for detection and false alarm probabilities are computed analytically. The provided closed-form analytical results in addition to simulation results show that the proposed schemes significantly outperform the existing secure spectrum sensing schemes.

In this paper, we propose a new detector for multiantenna spectrum sensing in cognitive radios (CR) by exploiting the Separating Function Estimation Test (SFET) framework. Specifically, we consider a blind scenario for multiantenna spectrum sensing in which both the channel gains and noise variance are assumed to be unknown. For such a scenario, we find an appropriate Separating Function (SF) whose Maximum Likelihood Estimate (MLE) leads us to a SFET-based detector which uses the second order moments of the eigenvalues of the Sample Covariance Matrix (SCM). We also find closed-form expressions for the detection and false-alarm probabilities of the proposed detector. The performance of the proposed detector asymptotically tends to that of the Uniformly Most Powerful Unbiased (UMPU) detector as the number of independent and identically distributed observations increases. In addition, simulation results show that the proposed detector outperforms the state-of-art eigenvalue- based detectors because of using the second order moments of the SCM eigenvalues.

In this paper, we propose a novel downlink system that is able to maximize the number of users which could be served with high correct decoding probability while, minimizing the total consumed energy for data reconstruction by whole users simultaneously. We deploy 2/4/…/2N-ASK hierarchical modulation that enables multi-resolution transmission along with an energy harvesting system to implement a wise and efficient downlink system. In our scheme, there is one information sender and two decode-and-forward access-points each of them equipped with one transmission antenna that wish to successfully transmit data to the users based on their link quality. In order to minimize the total consumed energy by the users, we prepare energy access-point which is an harvester with the capability of harvesting energy from radio frequency radiations. Each time the transmission commences, our scheme based on the weighted difference relay selection method selects the best access-point that mutually is able to maximize the number of serving users via a smart users number-defining algorithm and minimize the total consumed energy of the users. We investigate the performance of such a system in terms of average error probability and provide upper and lower bounds on them. We also discuss about careful hierarchical parameter selection that maximizes the number of serving users.

In this paper, we investigate the degrees of freedom (DoF) of the two-user single input single output (SISO) X channel with alternating channel state information at the transmitters (CSIT). Three cases are considered for the availability of CSIT; perfect, delayed and no-CSIT. Each state is associated with a fraction of time denoted by λP, λDand λN, respectively. We provide new results for the achievable DoF of the channel when the available CSIT alternates between these three cases under a certain distribution for Λ(λP, λD, λN). Specifically, we show that the two-user SISO X channel with alternating CSIT for Λ(1/8, 3/8, 1/2) can achieve 5/4 DoF. The achieved DoF in this case lie between the maximum DoF of the channel, i.e., 4/3 DoF for Λ(1, 0, 0), and the 6/5 DoF achieved for Λ(0, 1, 0).

The broadcast nature of wireless communications imposes the risk of information leakage to adversarial users or unauthorized receivers. Therefore, information security between intended users remains a challenging issue. Most of the current physical layer security techniques exploit channel randomness as a common source between two legitimate nodes to extract a secret key. In this paper, we propose a new simple technique to generate the secret key. Specifically, we exploit the estimated channel to generate a secondary random process (SRP) that is common between the two legitimate nodes. We compare the estimated channel gain and phase to a preset threshold. The moving differences between the locations at which the estimated channel gain and phase exceed the threshold are the realization of our SRP. We simulate an orthogonal frequency division multiplexing (OFDM) system and show that our proposed technique provides a drastic improvement in the key bit mismatch rate (BMR) between the legitimate nodes when compared to the techniques that exploit the estimated channel gain or phase directly. In addition to that, the secret key generated through our technique is longer than that generated by conventional techniques.

The paper cogitates about a comparative simulation for various distillation, reconciliation, and privacy amplification techniques that are used to generate secure symmetric physical layer keys. Elementary wireless model of two mobile nodes in the presence of a passive eavesdropper is used to perform the comparison process. Important modifications are proposed to some phases' techniques in order to increase the performance of the generation process as a whole. Different metrics were used for comparison in each phase, in the distillation phase, we use the Bit Mismatch Rate (BMR) for different SNR values to compare various extracted random strings of the two intended nodes. On the other hand, the messaging rate and process complexity is exploited to estimate the performance of the compared techniques in both reconciliation and privacy amplification phases. The randomness and entropy properties of the keys are verified using the NIST suite, all the generated keys are 128 bits, it is shown that the success rate of the keys passing the randomness tests depends strongly on the techniques that are used through the three generation phases.

Estimating the number of sources received by an antenna array have been well known and investigated since the starting of array signal processing. Accurate estimation of such parameter is critical in many applications that involve prior knowledge of the number of received signals. Information theoretic approaches such as Akaikes information criterion (AIC) and minimum description length (MDL) have been used extensively even though they are complex and show bad performance at some stages. In this paper, a new algorithm for estimating the number of sources is presented. This algorithm exploits the estimated eigenvalues of the auto correlation coefficient matrix rather than the auto covariance matrix, which is conventionally used, to estimate the number of sources. We propose to use either of a two simply estimated decision statistics, which are the moving increment and moving standard deviation as metric to estimate the number of sources. Then process a simple calculation of the increment or standard deviation of eigenvalues to find the number of sources at the location of the maximum value. Results showed that our proposed algorithms have a better performance in comparison to the popular and more computationally expensive AIC and MDL at low SNR values and low number of collected samples.

In the context of physical layer security, a physical layer characteristic is used as a common source of randomness to generate the secret key. Therefore an accurate estimation of this characteristic is the core for reliable secret key generation. Estimation of almost all the existing physical layer characteristic suffer dramatically at low signal to noise (SNR) levels. In this paper, we propose a novel secret key generation algorithm that is based on the estimated angle of arrival (AoA) between the two legitimate nodes. Our algorithm has an outstanding performance at very low SNR levels. Our algorithm can exploit either the Azimuth AoA to generate the secret key or both the Azimuth and Elevation angles to generate the secret key. Exploiting a second common source of randomness adds an extra degree of freedom to the performance of our algorithm. We compare the performance of our algorithm to the algorithm that uses the most commonly used characteristics of the physical layer which are channel amplitude and phase. We show that our algorithm has a very low bit mismatch rate (BMR) at very low SNR when both channel amplitude and phase based algorithm fail to achieve an acceptable BMR.

The use of N cognitive relays to assist primary and secondary transmissions in a time-slotted cognitive setting with one primary user (PU) and one secondary user (SU) is investigated. An overlapped spectrum sensing strategy is proposed for channel sensing, where the SU senses the channel for τ seconds from the beginning of the time slot and the cognitive relays sense the channel for 2τ seconds from the beginning of the time slot, thus providing the SU with an intrinsic priority over the relays. The relays sense the channel over the interval [0, τ] to detect primary activity and over the interval [τ, 2τ] to detect secondary activity. The relays help both the PU and SU to deliver their undelivered packets and transmit when both are idle. An optimization-based formulation with quality of service constraints involving queueing delay is studied. The results show the benefits of relaying and its ability to enhance both primary and secondary performance, especially in the case of no direct link between the PU and the SU transmitters and their respective receivers. Three packet decoding strategies at the relays are also investigated and their performance is compared.

In this work, we study the problem of band allocation of Msbuffered (i.e., with data queues capable of storing incoming traffic packets) secondary users (SUs) to Mpprimary frequency bands licensed to (owned by) Mpbuffered primary users. The bands are assigned to SUs in an orthogonal (one-to-one) fashion, such that neither band sharing nor multiband allocations are permitted. In order to study the stability region of the secondary network, the optimization problem used to obtain the stability region's envelope (closure) is established and is shown to be a linear program, which can be solved efficiently and reliably. We compare our orthogonal allocation system with two typical low-complexity and intuitive band allocation systems. In one system, each cognitive user chooses a band randomly, in each time slot, with some assignment probability designed such that the system maintained stable, while in the other system, fixed (deterministic) band assignment is adopted throughout the lifetime of the network. We derive the stability regions of these two systems. We prove mathematically, as well as through numerical results, the advantages of our proposed orthogonal system over the other two systems.

A blind (non-data-aided) SNR estimator using the statistical moments of the received signal is proposed. The proposed envelope-based non-data-aided estimator works for any time-domain Gaussian-distributed signal (e.g., OFDM signals). A closed-form expression for the estimated SNR as a function of the moments of the received signal and the Nakagami-m parameter, is derived. Interestingly, the obtained expression shows that the proposed estimator operation and performance is independent of the constellation of the received signal. Moreover, the existence of the closed-form expression results in lower implementation complexity. Furthermore, to enable theoretical performance analysis, a general mathematical expression is derived for the even moments of the received signal in terms of SNR and the Nakagami-m parameter. The performance of the proposed estimator is evaluated based on the mean-squared-error, under different conditions of the channel. An extension of our SNR estimation method into multiple antennas configurations is provided. Our results reveal that the proposed estimator works better in low SNR conditions, which is attractive to applications such as cognitive radio spectrum sharing scenarios.

We address the problem of spectrum sensing in cognitive radios (CRs) when the secondary user (SU) is equipped with a multiantenna receiver. We consider scenarios with correlation between the received channels at different antennas and unequal per-antenna noise variances to accommodate calibration errors. First, we derive the exact as well as the asymptotic performance of the genie-aided (benchmark) detector with perfect knowledge of the antenna correlation coefficients, the primary user (PU) signal power and the noise covariance matrix. Then, we consider the sensing problem in which the SU is non-cognizant of the per-antenna noise variances, the PU signal power and the correlation of channel gains, starting with a specific treatment of the two-antenna case. For a general multiantenna receiver, we propose combining the derived test statistics among all antenna pairs. The related optimization problem to obtain the optimum combination weights is analyzed, which requires the analytical performance characterization of the constituent two-antenna detector. Thus, we compute the exact performance of the proposed detector in a special case (a particular case of the Hadamard ratio test) in terms of its detection and false alarm probabilities. Performance analyses are verified with simulations, showing that the proposed detector outperforms several recently-proposed multiantenna detectors for CR in the scenarios considered.

In this paper, we investigate relay-sharing in cognitive radio (CR) networks to improve the secondary user's (SU's) performance in the presence of interference. It is assumed that the relay is shared simultaneously between both the primary user (PU) and the SU by using hierarchical modulation at the relay node while mapping the PU's and the SU's symbols onto two different layers of the hierarchical constellation. The proposed scheme relies on power adjusting, where the SU power adjustment is based on channel side information (CSI) and tolerable interference limit at the PU. Theoretical analyses along with simulation results are provided to evaluate the performance of the proposed dynamic spectrum sharing scheme. The results demonstrate that the proposed relay-sharing scheme may result in about 7dB and 15dB signal to noise ratio (SNR) improvement for the SU in low-SNR and high-SNR regimes, respectively.

In this paper, we study the problem of multiple antenna spectrum sensing by using cyclostationary features of Primary Users (PUs) signals in Cognitive Radios (CRs). We consider the general case of multiple antenna sensing in the presence of spatially and temporally correlated noise when the PU signal has more than one cyclic frequency. We model and formulate the multiple antenna sensing problem as a composite hypothesis testing problem and use the Generalized Likelihood Ratio Test (GLRT) to derive a detector for the general model mentioned above. Then, we also propose the GLRT-based detectors for the two special cases of: 1) spatially uncorrelated but colored noise; 2) spatially white noise. Moreover, in order to calculate the decision threshold, the asymptotic performance of the proposed detectors under the null hypothesis is given. The provided simulation results show the superiority of the performance of the proposed detectors compared to the recently-proposed cyclostationary-based detectors.

In this paper, we study the performance of hybrid cognitive radio (CR) system in the presence of channel estimation error (CER) in terms of bit-error-probability (BEP) and outage probability. We assume simultaneous switching between the underlay and overlay modes of CR where in, the secondary user (SU) probabilistically accesses the primary user's (PU) channel in both underlay and overlay modes to utilize the benefits of hybrid CR system. The PU uses two-layer 2/4 — ASK constellation with unequal level of bit protection against the interference imposed by the SU's transmitter. The SU wishes to opportunistically use the PU's channel to transmit its own data while maintaining the performance of the PU's layer with the worst level of protection at a desired value. We derive the SU's maximum allowable transmit power in the overlay mode to guarantee the PU reliable communication then, we discuss about the optimum value of the hybrid switching rate that minimizes the outage probability of the SU along with, deriving the exact BEP and outage probability expressions for the secondary system as well as that of the first and the second layers of the PU system.

In this paper, the degrees of freedom (DoF) of the two-user single input single output (SISO) X channel are investigated. Three cases are considered for the availability of channel state information at the transmitters (CSIT); perfect, delayed, and no-CSIT. A new achievable scheme is proposed to elucidate the potency of interference creation-resurrection (IRC) when the available CSIT alternates between these three cases. For some patterns of alternating CSIT, the proposed scheme achieves 4/3 DoF, and hence, coincides with the information theoretic upper bound on the DoF of the X channel with perfect and instantaneous CSIT. The CSIT alternation patterns are investigated where the patterns that provide extraordinary synergistic gain and dissociative ones are identified.

This paper proposes a new cooperative protocol which involves cooperation between primary and secondary users. We consider a cognitive setting with one primary user (PU) and multiple secondary users (SUs). The time resource is partitioned into discrete time slots. Each time slot, one of the SUs is scheduled for transmission according to time division multiple access scheme, and the remainder of the SUs, which we refer to as secondary relays, attempt to decode the primary packet. If more than one relay can decode the primary packet, the secondary relays then employ cooperative beamforming to forward the packet and to provide protection to the destination of the SU scheduled for transmission from interference. We characterize the diversity-multiplexing tradeoff of the primary source under the proposed protocol. We consider certain quality of service for each user specified by its required throughput. The optimization problem is stated under such condition. It is shown that the optimization formulation is linear and can be readily solved.

In this paper, we investigate a cognitive scenario with one secondary user and one primary user. Users are assumed to be buffered terminals. Each user has certain arrival rate with certain packet size. We propose a scheme where the cognitive radio user (secondary user) may combine some of the arrived packets into a single larger packet or split each of them into smaller packets to increase its maximum mean stable arrival rate. We consider sensing errors and study two channel models; namely, collision channel model, where concurrent transmissions cause definite packets loss, and multi-packet reception channel model, where packet could survive from interference if the received signal-to-interference-and-noise-ratio is greater than a certain threshold. When the channel is a collision channel, the objective function of the optimization problem which characterizes the stability region is shown to be log-concave. The optimization problem can be easily converted to a concave program that can be solved efficiently and reliably. When the channel is a multi-packet channel model, the problem is a simple grid search over the divisors and multiples of the original packets size. The results show the gains of the proposed technique and demonstrate its ability to alleviate the sensing errors' negative impact on the secondary stable throughput.

In this paper, we investigate the maximum throughput of a saturated rechargeable secondary user (SU) sharing the spectrum with a primary user (PU). The SU harvests energy packets (tokens) from the environment with a certain harvesting rate. All transmitters are assumed to have data buffers. In addition to its own traffic buffer, the SU has a buffer for storing the admitted primary packets for relaying; and a buffer for storing the energy tokens harvested from the environment. We propose a new cooperative cognitive relaying protocol that allows the SU to relay a fraction of the undelivered primary packets. We consider an interference channel model (or a multi-packet reception (MPR) channel model), where concurrent transmissions can survive with certain probability characterized by the complement of channel outages. The proposed protocol exploits the primary queue burstiness and receivers' MPR capability. In addition, it efficiently expends the secondary energy tokens. Our numerical results show the benefits of cooperation, receivers' MPR capability, and secondary energy queue arrival rate on the system performance from a network layer standpoint.

In this paper, we propose a cognitive protocol that involves cooperation between the primary and secondary users. In addition to its own queue, the secondary user (SU) has a queue to store, and then relay, the undelivered primary packets. When the primary queue is nonempty, the SU remains idle and attempts to decode the primary packet. When the primary queue is empty, the SU splits the total channel bandwidth into two orthogonal subbands and assigns each to a queue probabilistically. We show the advantage of the proposed protocol over the prioritized cognitive relaying (PCR) protocol in which the SU assigns a priority in transmission to the primary packets over its own packets. We present two problem formulations, one based on throughput and the other on delay. Both optimization problems are shown to be linear programs for a given bandwidth assignment. Numerical results demonstrate the benefits of the proposed protocol.

In this paper, we investigate the performance of multi-user diversity (MUD) with optimal power allocation (OPA) in spectrum sharing (SS) under average interference power (AIP) constraint. In particular, OPA through average transmit power constraint in conjunction with the AIP constraint is assumed to maximize the ergodic secondary capacity. The solution of this problem requires the calculation of two Lagrange multipliers instead of one as obtained for the peak interference power (PIP) constraint and calculated using the well known water-filling algorithm. To this end, an algorithm based on bisection method is devised in order to calculate both Lagrange multipliers iteratively. Moreover, Rayleigh and Nakagami-m fading channels with one and multiple primary users are considered to derive the required end-to-end SNR analysis. Numerical results are depicted to corroborate our performance analysis and compare it with the PIP case highlighting hence, the impact of the AIP constraint compared to the PIP constraint application.

We investigate a cognitive radio scenario involving a single cognitive transmitter equipped with K antennas sharing the spectrum with M primary users (PUs) transmitting over orthogonal bands. Each terminal has a queue to store its incoming traffic. We propose a novel protocol where the cognitive user transmits its packet over a channel formed by the aggregate of the inactive primary bands. We study the impact of the number of PUs, sensing errors, and the number of antennas on the maximum secondary stable throughput.

Spectrum sensing is a key task in any cognitive radio network. On the other hand, a literature survey in this topic shows a lack of implementation and testbeds for spectrum sensing techniques. In this paper, we plot the ROC curves for the cyclostationary detection through an extensive Monte Carlo simulation for different detection times. Then we implement the cyclostationary feature detection algorithm on an FPGA based WARP kit. We compare its implementation complexity to the conventional energy detection technique as well as our newly developed and implemented quickest detection algorithm. We then propose a peak search based FAM algorithm that speeds up the detection time.

In this paper, we consider an underlay secondary network comprising of K transmitter-receiver pairs sharing the spectrum of a primary network, which is divided into n non-overlapping frequency bands (subchannels). Each secondary pair can communicate over only one subchannel and should keep the interference generated to the primary network below a threshold. A decentralized multiple access algorithm is used by the secondary pairs to select a proper subchannel. We study the performance of a modified version of the medium access control (MAC) protocol proposed by Elkashlan et al., referred to as the general order selection algorithm. We obtain the statistics of the output signal-to-noise ratio of the algorithm, and the statistics of the achievable capacity per secondary pair. Closed-form expressions for the outage probability, average bit error rate of the secondary users, and the aggregate throughput of the secondary network are derived. Assessment of the analytical work of this paper is done through comparisons with simulation.

A single cognitive radio transmitter-receiver pair shares the spectrum with two primary users communicating with their respective receivers. Each primary user has a local traffic queue, whereas the cognitive user has three queues; one storing its own traffic while the other two are relaying queues used to store primary relayed packets admitted from the two primary users. A new cooperative cognitive medium access control protocol for the described network is proposed, where the cognitive user exploits the idle periods of the primary spectrum bands. Traffic arrival to each relaying queue is controlled using a tuneable admittance factor, while relaying queues service scheduling is controlled via channel access probabilities assigned to each queue based on the band of operation. The stability region of the proposed protocol is characterized shedding light on its maximum expected throughput. Numerical results demonstrate the performance gains of the proposed cooperative cognitive protocol.

In this paper, we study band allocation of Msbuffered secondary users (SUs) to Mporthogonal primary licensed bands, where each primary band is assigned to one primary user (PU). Each SU is assigned to one of the available primary bands with a certain probability designed to satisfy some specified quality of service (QoS) requirements for the SUs. In the proposed system, only one SU is assigned to a particular band. The optimization problem used to obtain the stability region's envelope (closure) is shown to be a linear program. We compare the stability region of the proposed system with that of a system where each SU chooses a band randomly with some assignment probability. We also compare with a fixed (deterministic) assignment system, where only one SU is assigned to one of the primary bands all the time. We prove the advantage of the proposed system over the other systems.

Signal-to-noise ratio (SNR) estimation is an important parameter that is required in any receiver or communication systems. It can be computed either by a pilot signal data-aided approach in which the transmitted signal would be known to the receiver, or without any knowledge of the transmitted signal, which is a non-data-aided (NDA) estimation approach. In this paper, a NDA SNR estimation algorithm for QPSK signal is proposed. The proposed algorithm modifies the existing Signal-to-Variation Ratio (SVR) SNR estimation algorithm in the aim to reduce its bias and mean square error in case of negative SNR values at low number of samples of it. We first present the existing SVR algorithm and then show the mathematical derivation of the new NDA algorithm. In addition, we compare our algorithm to two baselines estimation methods, namely the M2M4 and SVR algorithms, using different test cases. Those test cases include low SNR values, extremely high SNR values and low number of samples. Results showed that our algorithm had a better performance compared to second and fourth moment estimation (M2M4) and original SVR algorithms in terms of normalized mean square error (NMSE) and bias estimation while keeping almost the same complexity as the original algorithms.

This paper proposes a cooperation protocol between a secondary user (SU) and a primary user (PU) which dedicates a free frequency subband for the SU if cooperation results in energy saving. Time is slotted and users are equipped with buffers. Under the proposed protocol, the PU releases portion of its bandwidth for secondary transmission. Moreover, it assigns a portion of the time slot duration for the SU to relay primary packets and achieve a higher successful packet reception probability at the primary receiver. We assume that the PU has three states: idle, forward, and retransmission states. At each of these states, the SU accesses the channel with adaptive transmission parameters. The PU cooperates with the SU if and only if the achievable average number of transmitted primary packets per joule is higher than the number of transmitted packets per joule when it operates alone. The numerical results show the beneficial gains of the proposed cooperative cognitive protocol.

In this paper, we investigate the use of time-frequency analysis for improvement of spectrum sensing in cognitive radios and exploit compressed sensing (sampling) to reduce the extremely high sampling rate of signal in time-frequency plane. We suggest a non-parametric spectrum sensing technique similar to energy detection utilizing time-frequency analysis to generally compromise between accuracy and sensing time, though the computational cost is significantly increased. As the representation of signals on the time-frequency plane is intrinsically sparse, thus we use the compressed sensing to achieve a significant reduction in the number of measurements. We propose using of different time-frequency representation such as short time Fourier transform, wavelet, Wigner-Ville and pseudo Wigner-Ville distribution in conduction of compressed sampling technique. The simulation results evaluate the performance of the proposed time-frequency compressed detectors compared to other time-frequency and energy detectors using basis pursuit and Bayesian compressive sensing reconstruction algorithms for AWGN and also Rayleigh and Rician fading channels.

Cognitive relaying has been introduced for opportunistic spectrum access systems by which a secondary node forwards primary packets whenever the primary link faces an outage condition. For spectrum sharing systems, cognitive relaying is parametrized by an interference power constraint level imposed on the transmit power of the secondary user. For sensing-based spectrum sharing, the probability of detection is also involved in packet relaying control. This paper considers the choice of these two parameters so as to maximize the secondary nodes' throughput under certain constraints. The analysis leads to a Markov decision process using dynamic programming approach. The problem is solved using value iteration. Finally, the structural properties of the resulting optimal control are highlighted.

Spectrum sensing is the core for any cognitive radio network. Quick detection of the primary user signal allows for higher spectrum efficiency. In this paper, we introduce and implement a new hybrid spectrum sensing technique that is based on combining GLRT with energy detection and utilizing multiple antennas. Our system introduces a compromise between speed and complexity. When the SNR goes below the SNR wall for the low complexity energy detection, our system switches to the more expensive GLRT algorithm. A practical prototype of our system is implemented on the WARP FPGA-based nodes to study its efficiency and complexity. In addition to practical experimental results, we derive theoretical closed form expressions for the probability of false alarm and the probability of detection for our new approach when using multiple antennas at the secondary users. We also compare the multiple antenna approach to the collaborative detection approach.

In this paper, we consider the problem of multiple antenna spectrum sensing in Cognitive Radios (CR) when some or all parameters are unknown. The Generalized Likelihood Ratio (GLR) test is the convectional method to solve the composite hypothesis testing problem in which the detection and estimation sub-problems are considered separately. In this paper, the multiple spectrum sensing problem is solved using a novel approach in which the the detection and estimation sub-problems considered jointly and the resulted detectors are optimal under finite number of samples. We assume some additional side statistical information is available for unknown parameters and as theoretical results of the novel GLR detector imply, the optimal way of using this additional side information, is to use them in the Maximum A-Posteriori (MAP) or Minimum Mean Square Error (MMSE) estimation of unknown parameters for constructing the GLR tests. The simulation results show that the newly derived GLR detectors outperform traditional GLR detectors significantly. Also for the situation that all parameters are unknown the proposed detectors are compared with the Energy Detector (ED), where the simulation results indicate that the proposed detectors not only have significantly better performance, but also are robust to practical noise mismatch.

In this paper, we investigate the use of multiple antennas at the cognitive radio receiver for improved wideband spectrum sensing and exploit compressed sensing to reduce the extremely high sampling rate of wideband signal. Using a priori statistical knowledge about the non-zero coefficients of the sparse primary user signal, we propose two novel multiband compressive detectors to detect the presence of primary user signal within frequency subbands while the occupancies of these are correlated. Therefore, we model the occupancy of each subband as a binary random variable and assume subband energies are linearly combined. The simulation results evaluate the performance of the proposed compressive detectors compared to existing compressive detectors and the traditional energy detectors.

Cognitive broadcast channel, where two multiantenna transmitters communicate with their respective receivers, is considered. One of the transmitters is said to be cognitive (secondary) as it is assumed to know the messages of the other (primary) transmitter non-causally. The goal is to design cooperative schemes between the two transmitters, which impose only minimal changes to the primary broadcast channel (compared to the non-cognitive scenario). Towards this end, an achievable scheme is provided under which both intra cell and inter cell interferences at the primary receivers are aligned. The interference at the secondary receivers, on the other hand, is canceled by dirty paper coding. The corresponding achievable region and an outer bound region are provided in terms of the degrees of freedom (DoF) metric. Special cases shows the optimality of the proposed scheme in the high SNR regime for those cases. We also illustrate the advantage of the cognitive cooperation over the non-cognitive system by proving that the achieved sum DoF is strictly larger than the non-cognitive case.

Downlink communication in a cellular network with a cognitive (secondary) cell is considered. In our model, the base station of the cognitive cell knows the messages of the other cell non-causally. We propose a new interference cancellation technique that zero forces the intra-cell interference in the primary cell by the help of the cognitive base station. In addition, as the primary messages are known at the cognitive base station, the interference caused by the primary base station on the secondary users are canceled using dirty paper coding (DPC). Moreover, we provide an outer bound on the achievable degrees of freedom (DoF) region and show that for some special cases the proposed signaling scheme is sum DoF optimal for the considered system when the cognitive cell operates at its maximum sum DoF. The benefit of the cognitive paradigm is also established using the derived outer-bound and showing that the achieved sum DoF is strictly larger than the case when cognitive message sharing is unavailable.

The anticipated increase in the density of the deployed wireless sensor networks calls for spectrum sharing through unlicensed access to licensed spectrum. The key technology for spectrum sharing in this scenario is cognitive radio networks. Cognitive relaying scenarios, where a cognitive (unlicensed) user provides relaying services to a licensed (primary) user, have been proposed before as a method to increase chances of spectrum white spaces. In this paper, we develop a wireless sensor network framework containing a cognitive user (sensor node), with delay sensitive data and limited power budget. The cognitive user offers relaying capability to the primary traffic when the primary connection fails to deliver. The cognitive user utilizes a scheduling mechanism that grants priority to relayed traffic over its own traffic. In our framework, the cognitive user is allowed to control the volume of the relayed traffic through an admission control parameter. The objective of the sensor node (cognitive user) is to minimize its traffic delay, subject to certain power budget allowed for relaying the primary traffic. Our key contributions in this work are the development of the aforementioned framework, the establishment of a mathematical formalization for this problem, the derivation of mathematical expressions for average power consumption and average packet delay, and the solution for the developed optimization problem by finding a value for the admission control parameter that minimizes delay while satisfying power budget constraints.

In a wireless sensor network with relaying capability, intermediate relay nodes are with limited energy budget. To maximize lifetime of relay nodes, selective relay strategies, requiring full channel state information (CSI), are used to utilizes the best relaying channel. Pilot overhead on relay nodes lifetime is reduced by assuming pilot transmission from destination instead of relay nodes. This is only valid if channel reciprocity is assumed. It is evident that channel reciprocity is not valid for low cost simple transceivers, such as sensor node transceivers. In this paper a novel sequential random (SR) selective cooperative relay strategy is proposed in amplify and forward (AF) relay networks for applications with non reciprocal links. The outage probability of SR strategy is derived and its average lifetime, average number of pilot signals per transmitted message, and average transmit energy per message are simulated. SR strategy uses less number of pilots than selective cooperative relay strategies (S-CRSs). A dynamic transmit power threshold is adopted for the proposed SR strategy to improve its performances. In contrast to S-CRSs, in which each relay knows its CSI to the destination before every transmission, SR acquires CSI on the need- to-know basis and significantly outperforms the lifetime of S-CRSs for large number of relays and relatively good channel conditions. The proposed SR selects relay nodes in a distributed manner which makes it suitable for heterogeneous ad-hoc sensor networks in which some relays have processing capabilities and less restricted energy supply, i.e. these relays can act as the intermediate destinations.

Chip-Level Modulated Binary Pulse Position Modulation (CLM-BPPM) is proposed as a modulation scheme for Fiber-Optic Code Division Multiple Access (FO-CDMA) systems using Optical Orthogonal Code (OOC) for time domain signal spreading. The proposed scheme provides better synchronization and source activity detection at the receiver side as compared to On-Off Keying (OOK). A mathematical expression is derived for the BER of CLM-BPPM using a combinatorial interference pattern analysis approach. The mathematical model is verified using simulation. Numerical results demonstrate that CLM-BPPM has a BER that is very close to OOK. Moreover, increasing the average source activity causes the performance of the CLM-BPPM to approach that of the OOK system with an asymptotic BER equal to the BER of OOK at full user activity.

A diversity scheme which exploits the existence of a source-destination path to improve the lifetime and outage probability of a wireless selective relay network with Amplify-and-Forward (AF) relays is proposed and studied. The destination obtains a portion of the required signal to noise ratio (SNR) during the broadcast phase (Phase I) from the source and the remainder of the required SNR from a selected relay in the second phase (Phase II) of the transmission protocol. An algorithm based on an energy conserving dynamic transmit power threshold is proposed to improve the network lifetime. It is shown that the proposed scheme improves network lifetimes for the following four different relay selection strategies: Minimum Transmit Power (MTP), Maximum Residual Energy (MRE), Maximum residual-Energy Index (MEI), and Minimum Outage Probability (MOP).

In this letter, the cumulative distribution function (and hence outage probability) of the r-th order signal-to-noise ratio from a set of n correlated Nakagami fading branches is studied. Numerical results are presented to illustrate the effect of fading correlation and the fading severity parameter. The accuracy of a simple exchangeable approximation is also examined.

A new adaptive multiple access scheme based on the theory of order statistics is introduced. Because the proposed low-complexity method requires relatively small channel information overhead and processing delays, it can be feasible in fast-fading environments and systems with large number of users. Numerical results reveal significant system performance improvement over conventional approaches.

In this paper, we propose a novel simplified mathematical analysis technique for modeling and calculating the effect of multiple access interference on bit error rate in optical code division multiple access (OCDMA) systems. Our technique applies to OCDMA systems using optical orthogonal codes (OOC) with optical time-domain spreading. The proposed analysis uses combinatorial methods on the combined signal at the output of the optical correlator decoder to derive a mathematical expression for the bit error rate.

Generalized multi-protocol label switching (GMPLS) is a multipurpose control-plane paradigm that extends the MPLS scheme allowing switching without recognizing packet boundaries. In this paper, we present a novel extension that exploits a new physical layer for switching in optical GMPLS. The proposed extension is achieved through adding an optical code switching layer, or code switch capable (CSC) layer, to the existing label mapping layers. Our proposal enables finer granularity at sub-wavelength level in all-optical GMPLS core switches, resulting in significant enhancements to traffic isolation capabilities for all-optical GMPLS core switches. We employ mathematical analysis to derive performance bounds for the proposed scheme, from both the labeling capacity and network throughput points of view. We use our analytical model to derive several optimum operating points for the network, and show that our techniques significantly improve the overall performance of all-optical core networks

The use of optical CDMA as a labeling mechanism in generalized multi-protocol label switching (GMPLS) optical networks significantly increases the traffic isolation capabilities. These networks, referred to as optical code labeled GMPLS (OC-GMPLS), have higher resource utilization due to the finer flow granularity introduced into the network. In this paper we present a cross-layer mathematical model for the throughput of OC-GMPLS networks, which provides a quantitative measure for the performance of optical networks throughput taking into consideration the effect of the physical layer. The proposed mathematical model incorporates the physical layer effects on the network layer performance by expressing the throughput as a function of the physical layer bit error rate, as well as the network traffic parameters such as the number of users and the packet length. Using the developed analytical model we were able to demonstrate the significant enhancement in the network performance due to the use of OC-GMPLS. We also used our analytical model to derive several optimum network operating points, which are of great importance to network designers and researchers.

This paper proposes a novel algorithm for constructing optical orthogonal codes (OOC). The proposed algorithm is a modified element-by-element greedy algorithm based on the extended set representation of optical orthogonal codes. The algorithm employs a technique that reuses previously rejected delay elements during the construction process. We call this method the rejected delays reuse (RDR) greedy algorithm. We show that employing the RDR method leads to code lengths that are significantly shorter than those achieved for OOCs constructed using the classical greedy algorithm for the same code weight and the same number of simultaneous codes constraints. To quantify the effect of the reduction in the code length on sub-wavelength multiplexing, we introduce a factor called the expansion efficiency factor and use it to show that the RDR generated codes have higher efficiency

We propose a novel extension to the label mapping space in GMPLS networks by exploiting further physical layer properties. The proposed extension, OC-GMPLS (optical code enabled GMPLS), is a modified version of the standard GMPLS. OC-GMPLS relies on adding an optical CDMA code switching layer called code switch capable (CSC) layer to the existing label mapping layers. OC-GMPLS provides a larger label mapping space and enhances the QoS capabilities for optical networks through increasing the granularity of traffic identification. In our extension, we use Manchester signaling to allow for more robust clock recovery while providing enhanced performance over conventional on-off keying (OOK) schemes. We also provide a reference architecture for the label switching layers in our OC-GMPLS architecture capable of performing labelling through optical CDMA. We analyze the performance of the proposed scheme and show that our proposal significantly enhances the overall network performance.