The latest ITU IMT-2030 recommendations for sixth generation (6G) networks have imposed strict specifications on communication systems. Some of these requirements include increased throughput, ultra-low delay and jitter, differentiated services, and energy efficiency. Current dynamic bandwidth allocation (DBA) schemes for passive optical networks (PON) may meet some of these requirements, yet they fail to fulfill other recommendations, especially energy efficiency. Therefore, we propose a new PON architecture, whose objective is to offer flexibility in meeting the IMT-2030 recommendations. It uses a multiclass power efficient dynamic bandwidth allocation (MPE-DBA) scheme that helps achieving both differentiated services and sustainability in terms of energy and cost. For computational efficiency, we propose a two stages Mamdani fuzzy inference system (FIS). The inputs of the first FIS are the latency and cost of the 6G traffic, whereas the latency and cost of the non-6G (N6G) traffic are the inputs of the second FIS stage. Both FISs use the variation in the number of channels as output. The proposed algorithm achieves less than 100 μs delay, less than 10μs jitter and high aggregate throughput for the 6G packets. In addition, it reduces the power consumption by three times and the cost of traffic transmission by four times as compared to the state-of-the art solution.

Recently, the use of Internet of Things (IoT) with 5G and beyond networks had led to a revolution in telecommunication systems. It improves device interconnectivity and ensures real-time data exchange, allowing for predictive maintenance, efficient resource management, and enhanced user experiences. However, such revolution also drives the emergence of sophisticated cyberattacks, outpacing traditional defense mechanisms and demanding more advanced security solutions. In order to detect such emerging threats, researchers have focused on cutting-edge technologies to strength the security and maintain the performance of IoT-enabled 5G and beyond networks. This paper presents a comparative study of recent advances on attack detection in IoT-enabled 5G and beyond networks, focusing on the integration of Artificial Intelligence (AI), Federated Learning (FL), and Edge Computing (EC) technologies. Based on specific criteria, recent works were selected and implemented, followed by a Friedman test to identify the most effective approach. The paper also highlights the key challenges in designing a secure IoT-enabled 5G networks in future work, including resource restriction, data and network heterogeneity, system expandability and adaptability, and emerging security and privacy threats.

Visible light communication (VLC) has emerged as a promising technology for 5G and beyond networks, offering a solution for indoor wireless connectivity. This paper implements non-orthogonal multiple access (NOMA) scheme alongside multiple-input multiple-output (MIMO) technique in VLC systems to enhance the overall performance. We evaluate the impact of various power allocation techniques through three efficient and low-complexity techniques such as normalized gain difference power allocation (NGDPA), fixed power allocation (FPA) and gain ratio power allocation (GRPA) within an indoor MIMO-VLC with NOMA systems. Our investigation includes the application of the most used diversity combining techniques to improve the reliability of the received signal. Numerous simulations have been conducted to assess the system’s performance.

The rapid growth of Internet of Things (IoT) devices has increased the demand on cellular networks, particularly within Narrowband Internet of Things (NB-IoT) technology. To address the challenge of limited cellular capacity, this paper proposes an optimization method that groups devices and allocates specific time slots for each group to engage with the base station. The aim is to minimize interference and packet collisions, ensuring efficient device connectivity. We explore various clustering algorithms, including K-means, uniform repetition, and genetic algorithms (GA), with a focus on GA for its superior performance in reducing interference and improving connection success rates. Simulations confirm that the GA-based approach effectively manages device groups, enhances connectivity, and optimizes cell capacity in NB-IoT networks.

Localization of transmitters has been gaining interest increasingly. Due to the emergence of directional transmitters in future technologies, localization in this manner is particularly considered. However, limited studies consider the localization of multiple directional transmitters. Our proposed system model describes multiple directional emitters by the means of a Uniform Linear Array (ULA). To localize these transmitters, we adapt the classical Direction of Arrival (DoA) localization techniques with our model. The localization accuracy is studied for multiple affecting parameters and compared to the theoretical limit of Cramer Rao Bound (CRB). The results show that a directional transmitter can be localized by the means of its high sidelobe effects. Root MUSIC outperforms other techniques in localizing the users.

Unmanned Aerial Vehicle (UAV) is a booming trend in major civil and military applications such, but not limited to, transportation, delivery, and surveillance missions. In order to accomplish the mission’s objective, trajectory planning must be optimally achieved. The communication link established between the UAV and the ground/aerial stations is the main factor to account for designing the trajectory. However, this link is highly affected by the shape of the topography, especially when the UAV must fly at a low altitude between mountains of variable elevations. Therefore, this paper addresses the challenge of three-dimensional trajectory optimization for low/mid-altitude flying UAVs in complex propagation environments. To tackle this challenge, we propose a system model for the trajectory using the diffraction phenomenon with Multiple Knife Edge (MKE) to model the channel between the UAV and the station when the Line of Sight (LoS) is absent. Then, we propose a joint optimization to minimize the trajectory and maximize the communication quality via the Mixed Integer Linear Programming (MILP) problem design and solution. We validate the proposed approach by using real terrain profiles in the simulations with a rough topography; where the LoS propagation aspect is barely present. Our approach is able to jointly find, when physically achievable, the UAV trajectory with the shortest path and the "best feasible" communication quality.

In this paper, we propose a discrete time transfer function approximation for the reduced complexity parallel least mean square (RC-pLMS) adaptive beamforming algorithm. The RC-pLMS is built using a single least mean square (LMS) stage whose inputs are obtained as a linear combination of the present and past sample. Thus, in order to numerically assess the RC-pLMS stability and to determine the approximate maximum parametric value of the step size for which it remains stable, we derive its discrete time transfer function approximate. In this approximation, the input uniform linear antenna array is remodeled as a finite impulse response (FIR) fractional delay Farrow filter. Computer simulations, presented by the mean square error and beam radiation pattern, demonstrates the validity of the transfer function approximate. Additionally, the RC-pLMS stability is evaluated, with respect to the pole-zero plot, for different step sizes and the approximate upper bound value of the step size is determined.

We consider a cognitive radio network with a primary user (PU) and secondary users (SU), all equipped with multiple antennas to exploit the spatial characteristics for transmission. The SUs cooperate to estimate the beam of the PU's signal. Thus, the available space is divided into two parts. The first is occupied by the PU and should be restricted on the SUs, whereas the second is highly accessible by the SUs transmissions. The beam-estimation accuracy is studied based on two metrics, namely, the angular missed and false detection. The results showed that the accuracy increases for a Rician channel rather than a Rayleigh fading channel. Additionally, no matter how far the SUs' range of distribution is, a high number of SUs can accurately estimate the beam. A zero angular missed detection occurs for many SUs, with a tax of slightly increasing the angular false detection.

In Wireless Sensor Networks (WSN), the nodes may be randomly deployed over a harsh geographical zone. Usually, these nodes are battery powered with limited transmission and processing capabilities. Managing the residual energy is very crucial in such network, since replacing a battery may not be always feasible. Energy is essentially consumed during packet transmission phase. Therefore, routing protocols become of a high importance since they impact on energy consumption during data transmissions. In this paper, we present a new routing protocol based on the geographical location information in an uplink multi-hop WSN. The proposed protocol assumes that the geographical zone and the transmitted data are more precious than the sender node authentication. Our proposed protocol guarantees the delivery of data packet in a dense network without any knowledge of the topology nor the path nodes between the source and the destination. We analytically derive the average consumed energy, the probability of packet loss, and the mean delivery time. Numerical results corroborate the superiority of our protocol over the state-of-the-art Distance Routing Protocol (DIR) in terms of connectivity, lifespan, memory, and latency.

Satisfying the users’ increasing demand and reducing the networks’ energy consumption are among the most critical requirements of future cellular networks. In this paper, we exploit Intelligent Reflecting Surfaces (IRSs) to reduce the bandwidth required by users, which will allow more users to be served and/or reduce the energy footprint of cellular base stations. In contrast to most of the existing studies that focus on configuring the phase shifts of IRSs and/or the active beamforming of the base station, we consider that the IRS consists of blocks of resources that can be shared by several users. We formulate the problem of managing these resources as nonlinear integer problem. Then, we solve the optimization problem using exhaustive search, and propose two low complexity heuristic algorithms. The performance of the system is evaluated considering variable number of users, position of IRS, required bit rate and radius of the cell. Results show that using IRS can achieve significant bandwidth savings and important energy demand reduction when the IRS resources are well managed.

In this paper, we present a new Cognitive Radio (CR) paradigm based on the Full-Duplex mechanism. In the classical Full-Duplex CR (FD-CR) system, the Secondary User (SU) can examine the availability of a channel while transmitting. This fact leads to enhance the SU transmission rate. However, in this classical secondary network, SU is assumed to adopt frequency or time division duplex. In the proposed work, we analyse the CR performance with an In-Band Full-Duplex communication, i.e. SU can simultaneously receive and transmit at the same band. This scenario is accompanied with a Full-Duplex sensing, i.e. SU performs the Spectrum Sensing while transmitting. The Spectrum Sensing performance is analysed as well as proposing an adaptable detection mechanism that can perform well under such situation. Further, a study on the SU throughputs is developed in order to show the spectral efficiency of the proposed CR paradigm.

Data collection from field sensors by using Unmanned Aerial Vehicle (UAV) is the application taken in consideration in this paper. All the sensor nodes are kept location unaware to reduce their cost and energy utilization. The issue addressed in this paper is localization of sensor nodes by UAV to collect data in efficient way. ULA of multiple antennas are used to measure the Angle of Arrival (AoA) of incoming signals. However, the drawbacks of mounting such multiple antennas on an Unmanned Aerial Vehicle (UAV) outweigh the benefits. The challenge is to affix multiple antennas and receivers on an UAV, increase its weight which ultimately decrease its payload capacity, flight time, speed and agility. In this paper, we are proposing a new method to estimate the AoA, called Virtual Phase Array (VPA) antenna system. A single moving antenna installed over an UAV taking snapshots every fixed time periods forms an antenna array virtually. This VPA has enable us to introduce two new concepts of adaptive staring precision and multiple frequency use. All these became reality only because number and spacing between antenna elements can be adjusted, which is not easy to implement in physical antenna array especially when antenna is onboard. The proposed system is evaluated by simulation model. Suggested modifications and additions in classical MUSIC algorithm make it possible to operate the virtual antenna system with the same precision as the physical antenna may have, but adding more flexibility, ease of use, cost economy, more reliability and better throughput.

The next generation of mobile systems based on LTE-A (Long Term Evolution-Advanced) networks are expected to support the new promising technology M2M (Machine-to-Machine) communications while keeping an eye on its previous H2H (Human-to-Human) communications not to be affected especially when it comes to the expected exponential growth of the number of M2M in the coming years, in particular, with the advance of IoT (Internet of Things) deployment and the expected ubiquity of such objects in the near future. In this article, we review the M2M communication technology from the LTE-A perspective and we outline the random access challenges in high dense areas where the LTE-A network is striving to fulfill the massive number of M2M devices. Moreover, we compare the most common mechanisms found in the literature that deal with the RACH (Random Access Channel) procedure issues and challenges by analyzing the existing solutions and approaches to avoid RACH overload congestion in the M2M communications. To this end, we have developed different M2M scenarios using SimuLTE Modeler to investigate the impact of M2M communications on LTE-A networks in emergency events.

Nowadays, many wireless communication devices should share the precious radio-frequency bandwidths. In order to get the most of the available spectrum, Cognitive Radio (CR) are recently introduced to allow better spectrum management for newly standardized radio communication systems. An advanced multistandard terminal device may cover several communication standards. The system of such device should be highly flexible to dynamically and autonomously adjust its radio operating parameters. Moreover, the reduction of interference among radio communication systems is needed to allow the coexistence of different standards in the same device. In this paper, a new concept and system architecture to reach our goal are proposed and discussed. Our ultimate goal aims to ensure the coexistence of different standards in a CR mobile terminal.

This paper investigates the bit error rate (BER) performance of spatial diversity free-space optical (FSO) communication systems using on-off keying modulation. The study considers correlated log-normal FSO channels as well as path losses due to weather effects using intensity modulation and direct detection schemes. An approximated moment generating functions (MGF) for the joint probability density function of correlated log-normal channels are considered. Using MGF approximation, BER expressions for repetition codes (RCs) and orthogonal space time block codes (OSTBCs) in correlated lognormal channels are derived. Results show that RCs outperform OSTBCs in correlated channel conditions. In addition, the effect of different weather conditions (e.g., haze, rain and fog) on the BER performance of the FSO links are studied. Monte Carlo simulation results are further provided to demonstrate the validity of the proposed mathematical analysis.

Facial features’ localization is a crucial step for many systems of face detection and facial expression recognition. It plays an essential role in human face analysis especially in searching for facial features (mouth, nose and eyes) when the face region is included within the image. The fundamental technique used in facial analysis is to detect the face and subsequently the associated salient features. In this paper, a new Algorithm is based on morphological properties of the face region for the extraction of salient features is proposed. A morphological operation is used to locate the pupils of the eyes and estimate the mouth position according to them. The boundaries of the allocated features are computed as a result when the features are allocated. This algorithm is applied to individual images subsequently application to video sequences. The experimental results achieved from this work indicate that the algorithm has been very successful in recognizing different types of facial expressions.

This paper studies the influence of tolerances in inter-element spacing and element gain on the operation of the LLMS adaptive beamforming algorithm. Both random and worst case scenarios of inter-element spacing and element gain variations have been considered. Computer simulations show that these practical tolerances have greater influence on the beam pattern than the error vector magnitude (EVM). Simulated results also confirm the superior performance of the LLMS algorithm over the recursive least square (RLS) and the constrained stability LMS (CSLMS) algorithms when operating in the presence of multiple sources of co-channel interference.

This paper proposes a RSSI-aware (rETT) routing metric for WMN. The embedded RSSI information from the mesh nodes are extracted, processed, transformed and incorporated into the routing process. The performance of the rETT metric is then optimised and the results compared with the fundamental hop count metric and the link quality metric by Expected Transmission Count (ETX). The implementation results on OLSR show that rETT improvement on network throughput is more than double (120%) compared to hop count and a 21% improvement compared to ETX. Also, an improvement of 33% was achieved in network delay compared to hop count and 28% better than ETX. This work provides a valuable insight into adapting RSSI information in mesh network routing and the implementation method ensures that the results are realistic.

This paper describes a new blind channel estimation algorithm for Space-Time Block Coded (STBC) systems. The proposed method exploits the statistical independence of sources before space-time encoding. The channel matrix is estimated by minimizing a kurtosis-based cost function after Zero-Forcing equalization. In contrast to subspace or Second-Order Statistics (SOS) approaches, the proposed method is more general since it can be employed for the general class of linear STBCs including Spatial Multiplexing, Orthogonal, quasi-Orthogonal and Non-Orthogonal STBCs. Furthermore, unlike other approaches, the method does not require any modification of the transmitter and, consequently, is well-suited for non-cooperative context. Numerical examples corroborate the performance of the proposed algorithm.

A new adaptive algorithm, called LLMS, which employs an array image factor, AI, sandwiched in between two Least Mean Square (LMS) sections, is proposed for different applications of array beamforming. The convergence of LLMS algorithm is analyzed, in terms of mean square error, in the presence of Additive White Gaussian Noise (AWGN) for two different modes of operation; namely with either an external reference or self-referencing. Unlike earlier LMS based schemes, which make use of step size adaptation to enhance their performance, the proposed algorithm derives its overall error signal by feeding back the error signal from the second LMS stage to combine with that of the first LMS section. This results in LLMS being less sensitive to variations in input signal-to-noise ratio as well as the step sizes used. Computer simulation results show that the proposed LLMS algorithm is superior in convergence performance over the conventional LMS algorithm as well some of the more recent LMS based algorithms, such as constrained-stability LMS (CSLMS), and Modified Robust Variable Step Size LMS (MRVSS) algorithms. Also, the operation of LLMS remains stable even when its reference signal is corrupted by AWGN. It is also shown that LLMS performs well in the presence of Rayleigh fading.

Amongst the advantages of Multicarrier on-off keying (MOOK) is the simplicity of its transmitter-receiver pair and inherent good performance in noisy channels which make it a popular choice for some communication systems. However the MOOK technique is widely considered as bandwidth inefficient relative to other existing transmission schemes. The aim of this paper is to investigate methods for improving the spectral efficiency of this technique. Two strategies are proposed and analysed here. The methods investigated show that significant bandwidth efficiency enhancement can be achieved especially when the number of subcarriers is large.

In this paper, we propose a new iterative algorithm to solve the blind deconvolution problem of MIMO-FIR channels driven by source signals which are temporally second-order uncorrelated but fourth-order correlated and spatially second- and fourth-order uncorrelated. In our new approach, to solve the blind deconvolution problem, we consider two stages: First, filtered source signals are extracted from the mixtures of source signals. Second, the source signals are recovered from the filtered source signals.

In this work we develop a system for enhancement of the speech signal with highest energy from a linear convolutive mixture of n statistically independent sound sources recorded by m microphones, where m

In this work, we point out some important properties of the normalized fourth-order cumulant (i.e., the kurtosis). In addition, we emphasize the relation between the signal distribution and the sign of the kurtosis. One should mention that in many situations, authors claim that the sign of the kurtosis depends on the nature of the signal (i.e., over- or sub-Gaussian). For a unimodal probability density function, that claim is true and is clearly proved in the letter. But for more complex distributions, it has been shown that the kurtosis sign may change with parameters and does not depend only on the asymptotic behavior of the distributions. Finally, these results give theoretical explanation to techniques, like nonpermanent adaptation, used in nonstationary situations.