Predictive Maintenance (PdM) in industrial systems has evolved from reactive, time-based approaches to AI-driven strategies leveraging sensor and big data for failure prediction. However, existing architectures often rigidly separate streaming and batch processing, leading to inefficiencies in resource utilization and delayed responses. This paper proposes a novel Adaptive Data Processing Framework (ADPF) that dynamically switches between streaming and micro-batching modes based on real-time contextual factors such as data criticality, network latency, and computational load. By integrating a reinforcement learning (RL)-based decision algorithm, ADPF optimizes the trade-off between latency, cost, and prediction accuracy. Implemented within an Industrial IoT (IIoT) and Big Data ecosystem, the framework improves maintenance scheduling, reduces operational costs by 30%, and ensures timely interventions. The experimental results demonstrate a 25% improvement in the accuracy of failure prediction and a 40% reduction in maximum CPU load compared to static architectures.

Reconfigurable Intelligent Surface (RIS)-based Machine Learning (ML) methods have been extensively employed to enhance the performance of wireless communications networks. RIS has garnered significant interest due to its capability to both reflect signals and adjust their propagation directions in a greatly intelligent and efficient way, hence fostering the development of smart radio settings. The performance of the system may be considerably enhanced by investigating the ML-aided RIS in wireless communication networks, which can be a useful substitute to reduce the signal worsening in wireless networks. It is indispensable to identify the most effective solution in practice because there are so many different challenges such as system configurations and channel circumstances. This work provides a comprehensive study of RIS-based ML communications, an overview of several reviews that used RISs and ML to improve the overall performance of wireless communication networks as well as the most important future directions.

Low Power Wide Area Networks (LPWANs) have emerged as critical enablers for the rapidly expanding Internet of Things ecosystem, offering long-range communication and low energy consumption. Among LPWAN technologies, LoRaWAN has established itself as a preferred solution due to its cost-effectiveness and scalability. However, as IoT deployments scale, the impact of increasing device density on network performance remains a critical area of study. This paper evaluates the impact of device density on key performance indicators, such as uplink delivery rate and energy efficiency, in LoRaWAN networks. Additionally, we assess the performance of the no-acknowledgment mode as a potential solution for reducing network overhead in dense deployments. Using a Matlab-based simulation framework, we analyze LoRaWAN’s behavior under various network configurations and device densities. The results highlight significant performance degradation as device numbers increase, particularly at longer communication ranges, and demonstrate the trade-offs introduced by the no-acknowledgment mode. These findings provide insights into managing scalability challenges in LoRaWAN networks. )

Sixth-generation (6G) wireless networks are designed to deliver superior performance by enhancing spectral efficiency and minimizing latency. It enables next-generation applications such as virtual reality, autonomous transportation, and the Internet of Things (IoT). To achieve this, 6G integrates multiple advanced technologies, including satellite systems, unmanned aerial vehicles (UAVs), and stratospheric platforms, forming vertical heterogeneous networks (VHetNets). Despite these advancements, challenges persist in resource allocation, interference control, and capacity optimization. This paper proposes an intelligent spectral efficiency algorithm tailored for 6G VHetNets to enhance network resilience and high-speed connectivity. Simulation results indicate that the proposed approach not only improves overall network capacity but also ensures low computational complexity and rapid convergence.

Vehicle-to-Vehicle communication plays an important role in the development of intelligent transportation systems, enhancing road safety and traffic efficiency. Path loss represents a key factor that affects the reliability and performance of vehicular communication, as it determines signal attenuation over distance. In this paper, we characterize path loss for vehicle-to-vehicle communication within a rectangular tunnel, addressing both line-of-sight and non line-of-sight conditions. We present general path loss expressions and conduct numerical simulations that account for variables such as propagation distance, number of reflections, and tunnel surface material. The accuracy of the model is validated by comparing the simulation results with empirical path loss measurements. The simulations demonstrate that the model performs effectively, providing a reliable prediction of path loss in tunnel environments.

Conventional clustering algorithms in wireless sensor networks (WSNs) often face several challenges, including energy imbalance, routing, and the hot-spot problem, especially in networks with varying node densities and energy levels. To address these challenges, we propose the adaptive type-2 fuzzy unequal clustering algorithm with energy-aware routing (AT2UCAR). It employs a fuzzy inference mechanism to dynamically establish cluster sizes and select optimal cluster heads based on residual energy, node density, and distance from the base station. By creating a set of fuzzy rules, this algorithm enables the development of unequal clusters, allowing nodes situated farther from the BS to form larger clusters, while those nearer to the BS maintain smaller clusters to minimize overall energy consumption. AT2UCAR is proposed to optimize data transmission paths based on residual energy and further optimizes multi-hop routing to improve energy efficiency and load balancing. Simulation results indicate that AT2UCAR significantly improves load balancing and enhances energy efficiency in comparison to other clustering algorithms.

Agricultural sectors worldwide grapple with substantial economic losses and food insecurity, largely driven by pest infestations. The limitations of traditional manual inspection methods-marked by inefficiency and a lack of scalability-are particularly pronounced across extensive agricultural landscapes. In response, this study explores the cutting-edge integration of remote sensing techniques and artificial intelligence (AI) in pest detection, positioning these technologies as transformative tools in modern agriculture. Advanced remote sensing systems, including satellite imagery, aerial drones, and ground-based sensors, equipped with multispectral, hyperspectral, and thermal imaging capabilities, provide a nuanced understanding of crop health and pest vulnerability, enabling early and precise detection of infestations. Concurrently, AI's capacity for rapid and accurate analysis of vast datasets facilitates real-time monitoring and predictive analytics, offering a proactive approach to pest management. The synergistic application of these technologies promises to revolutionize pest control, significantly reducing reliance on chemical pesticides and mitigating environmental impacts. However, challenges persist, particularly in ensuring the robustness of data acquisition and the adaptability of AI models across diverse agro-ecological zones. This study underscores the strategic potential of integrating remote sensing technologies with AI to advance sustainable and resilient agricultural practices.

In this paper, we propose to analyze the coverage probability for a given Network Flying Platform (NFP) with an Aerial Base Station (ABS) as an Unmanned Aerial Vehicle (UAV) at its served receiver. Stochastic geometry is used to describe the (UAV) location, with a given density, and in a specific environment. Based on that, we derive the coverage probability expression for a standard aerial communication network with its serving user. Then, we evaluate the results with different (UAV) densities and higher.Monte Carlo simulations are conducted to confirm and discuss the analytical results.

Human Activity Recognition plays a crucial role in various applications extending from healthcare to smart environments. In this paper, we present a novel approach for HAR using edge computing resources. Our method combines spectral analysis with deep learning techniques to efficiently extract relevant features from raw sensor data. By employing spectral analysis, we successfully reduce the feature space from 561 to 39, while maintaining high accuracy, low loss, and improved performance metrics such as F1 score and recall. Furthermore, we demonstrate the adaptability of our approach by deploying a quantized neural network model onto a resource-constrained edge device, specifically the NodeMCU microcontroller. This enables real-time HAR inference at the edge, making our solution suitable for applications where computational resources are limited. Experimental results on the UCU HAR dataset validate the effectiveness and efficiency of our proposed method for edge-based human activity recognition.

The effective management of services based on Quality of Experience (QoE) is crucial for the successful deployment of multimedia services in advanced networks like 5G/6G. This necessitates the use of appropriate tools for monitoring, predicting, and managing quality, with Machine Learning (ML) playing a pivotal role. However, predicting the QoE of multimedia streams poses a challenge due to its dependence on numerous influencing factors, and it must adapt to dynamic environments with large-scale data. Machine learning approaches provide a method to quantify the complex relationships between these influencing factors and QoE. In this paper, we propose an innovative improved mixture of classification for assessing the QoE of video streaming. Our approach combines the predictive capabilities of ensemble learning models rather than single models, as experts. Also, we have employed a robust gating network trained by minimizing an improved error function that combines the Classification Loss and the gating loss. We assess our method through a comprehensive simulation model encompassing diverse wireless network environments and various video sequences. The experimental results reveal substantial enhancements in QoE, ensuring users have stable, high-quality video streaming sessions.

Vehicle-to-Vehicle (V2V) communications are revolutionizing the connectivity of transportation systems, ensuring safe and efficient road mobility. To meet the growing bandwidth needs of V2V services, the use of millimeter-wave (mmWave) and WiFi frequency bands has been proposed as a promising solution. However, communications at these frequencies are a challenging task due to the severe path-loss especially in a high-blocked environment. In this paper, we present a comparative study between mmWave and WiFi communication systems. In particular, realistic stochastic geometry tools have been used to accurately model and describe a typical V2V communication systems, where vehicles are randomly located. Accordingly, we derive the corresponding Successful Transmission Probability (STP) and Average Throughput (AT) expressions for both communication systems. In addition, Monte Carlo simulations have been performed and presented to confirm the analytical results, and to investigate the advantages and disadvantages of the mmWaves and WiFi systems, in different scenarios.

In this paper, we study the impact of the prototype filter length on FBMC-OQAM equalization. This waveform is still considered as a promising candidate for beyond 5G and Narrow Band Internet of things systems thanks to the well localized prototype filter providing high spectral efficiency, low side-lobes radiations and better robustness to users asynchronism, when compared to OFDM. As far as the prototype filter length gets shorter, the Inter-Carrier-Interferences level increases. In this context, we will demonstrate how the FBMC-OQAM prototype filter length impacts the performances of linear and widely linear processing. This work confirms the outstanding capability of widely linear processing in mitigating interference effects for non second order circular signals.

Abstract Multi‐user (MU) massive multiple input multiple output (mMIMO) is considered a potential technology for fifth generation (5G) and sixth‐generation (6G) wireless systems. The presence of the antenna arrays at the base station level to communicate with the users or to serve tens of single antenna users leads to excessively high system costs and power consumption. The deployment 1‐bit digital‐to‐analogue converters (DACs) in the base station can solve these problems. This paper starts by presenting an analytical study centered on the effects of 1‐bit DACs on the system envisaged for a Rayleigh‐type fading channel. Compact‐form expressions are derived for the symbol error rate. Afterwards, an efficient end‐to‐end deep learning technique to compensate for the joint effect of 1‐bit DAC and imperfect channel state information in downlink mMIMO systems. Moreover, to improve the performance of the considered system, a DAC mixed architecture is proposed, where a number of antennas use 1 bit DACs while the others do not. The simulations results showed the improvement in transmission quality of the downlink of the MU‐mMIMO system in the presence of hardware imperfections using the considered end‐to‐end compensation technique.

This paper investigates a novel high-performance autoencoder based deep learning approach for Multi-User massive MIMO uplink systems assisted by a Reconfigurable Intelligent Surface (RIS) in which the users are equipped by Power Amplifiers (PA) and aim to communicate with the base station. Indeed, the communication process is formulated in the form of a Deep Neuronal Network (DNN). To handle these scenarios, we have designed a DNN network that includes two components. the first is an encoder intended to process the nonlinear distortions of the PA. The second is a decoder consisting of two fundamental steps. 1) A classic Minimum Mean Square Error linear decoder to decode the information transmitted by the users. 2) The neural network decoder to minimize interference. The results of the numerical simulation illustrate that the proposed method offers a significant improvement in error performance in comparison with the different basic schemes.

This paper proposes an autoencoder-based deep learning approach for multiuser massive multiple-input multiple-output (mMIMO) downlink systems assisted by a reconfigurable intelligent surface (RIS) whose base station is equipped with an antenna array with 1-bit digital-to-analog converters (DACs) to serve multiple user terminals. RIS has introduced today one of the most revolutionary techniques to improve spectrum and energy efficiency for the 6G of wireless networks. First, we present an analytical study on the effects of 1bit DAC on the system under consideration for a Rician fading channel. Then, the transmission system assisted by the proposed RIS design is presented, which allows network operators to control the signal propagation environment. To further improve our system, we propose the deep learning technique to compensate for the signal degradation caused by 1-bit DACs. Numerical simulations demonstrate that the compensation technique considered with the RIS presence achieves competitive performance compared to the existing literature.

Agriculture is a vital sector in Tunisia, serving as the country’s economic backbone and providing subsistence and employment to a large number of people. However, climate change, pesticide use, water scarcity, and other factors have resulted in decreased agricultural productivity and quality. In this work, we investigate the potential of deep learning techniques in combination with IoT systems to address these challenges and enhance agricultural practices. Specifically, we conduct an in-depth review of recent research on the application of deep learning algorithms with IoT devices in various agricultural applications. According to our results, this integration has the potential to significantly improve crop yields, reduce waste, and promote more sustainable farming practices. In addition, we highlight the synergistic relationship between IoT and DL in smart agriculture. Overall, this study provides valuable insights for farmers, researchers, and policymakers on how to employ DL and IoT to increase agricultural productivity and sustainability in Tunisia as well as globally.

Plant diseases are one of the most significant threats to the yield and quality of agriculture in the world. Conventional plant disease detection techniques rely heavily on expert diagnosis, which can easily lead to delays in crop disease control and field management. Therefore, early implementation of protective measures is the most effective way to control these diseases and minimize crop damage. To this end, a wheat leaf disease detection and classification method based on a Convolutional Neural Network model is proposed to increase the speed and accuracy of disease classification. The presented work used various image augmentation approaches to eliminate the overfitting problem. We achieved a testing accuracy of 94%. We also demonstrated the viability of our method for real-time detection of wheat disease in resource-constrained environments, where prompt disease detection and management are essential for sustainable agriculture.

In large cities, road traffic congestion is a big concern especially with its negative effects on the economy and the environment. Hence, with the advance of the IoT and AI many smart solutions are proposed to deal with this problem. Yet, those solutions enclosed traffic congestion detection and light street manipulation. However, road jams still occur far away from street lights especially in rush hours and in unexpected situations such as accidents and extreme weather conditions. The aim of this study is to propose a solution that helps vehicles avoid traffic jams and congestions based on data analysis gathered from traffic sensors and from a publish–subscribe application integrated with a distributed system to give vehicle driver a real-time updates about the situation of his road traffic.

The integration of multiple Radio Access Technology (RAT) in vehicular communication has gained interest in recent years. Indeed, a single RAT might be unable to satisfy all the requirements of vehicular communication networks. Particularly with the increasing amount of exchanged messages in vehicular communication networks in addition to the increasing number of connected autonomous vehicles. Although Multi-RAT Heterogeneous Vehicular Communication Networks (HVCNets) can provide increased network capacity to vehicle networks, they face several challenges. For instance, how to integrate multiple technologies that use different PHY and MAC protocols and techniques. Furthermore, how to select the most suitable RAT. This paper presents an in-depth study of what is currently the state of the art in HVCNets as well as RAT selection schemes in such networks.

Abstract In this paper, the authors study the compensation of high‐power amplifier (HPA) non‐linear distortion in the multi‐user (MU) massive multiple‐input multiple‐output (MIMO) systems and focus on uplink transmission, where the base station (BS) uses a large antenna array. First, the authors present a non‐linear distortion iterative cancellation (NDIC) algorithm‐based MMSE and approximate message passing (AMP) at the receiver level, in order to estimate and mitigate jointly a non‐linear distortion and the channel noise. Second, the authors propose a novel distortion cancellation technique based on deep learning. At this level, the authors first introduce a multilayer neural network, trained in the Levenberg–Marquardt algorithm by eliminating HPA non‐linearities on the ‘Pre distortion’ transmitter and ‘Post distortion’ receiver side. Next, the authors developed a novel end‐to‐end (E2E) learning approach for the joint transmitter and non‐coherent receiver in the Rayleigh fading channel. The basic idea lies in the use of deep neural networks (DNNs), auto encoder (AE) for unknown channels, where DNNs are applied to perform several functions and modules existing in the transmission chain. The simulation results demonstrate the strong potential of the proposed approach E2E in terms of improving the link quality and symbol error rate (SER) compared to other compensation techniques presented in this work.

Multi-User (MU) Massive Multiple Input Multiple Output (MIMO) is considered a potential and disruptive technology for fifth-generation (5G) and sixth-generation (6G) wireless systems. The presence of antennas networks at the base station level to communicate with single antenna users leads to in excessively high system costs and power consumption. The deployment of the analog-digital converters (ADC) 1 bit in the BS can solve these problems. In this work, we study the compensation of the impact of 1 bit ADC in MU massive MIMO systems on the uplink transmission accompanied with Minimum Mean Square Error (MMSE) detector. First, we propose a new end-to-end (E2E) learning approach based on Deep neural network (DNN). The objective is to design the communication system as a set of layers for the transmitter and the receiver in unknown channels. Then we present a mixed ADC architecture, where some antennas use 1-bit ADCs while the rest does not exploit ADCs using the end-to-end approach. The simulation results demonstrate the high potential of the proposed E2E approach in terms of uplink quality improvement. It is also demonstrated that the performance of massive MIMO systems with a mixed ADC architecture approaches those of ideal systems.

Efficient surveillance system is required to be able to sense the whole monitored area in order to detect and track the target. In addition it should alert the controller as soon as possible for further actions. In this paper we discussed the WSN network deployment strategies and the efficient network deployment for a high level performance. In addition, we developed a dashboard that allows the responsible party to keep an eye on the border and on all the sensors that have been deployed. And we implemented an loT based platform as well.

Abstract The hybrid beamforming can significantly improve the spectral and energy efficiencies of the millimeter‐wave (mmWave) massive multiple input multiple output (MIMO) systems without obvious performance loss. However, the fundamental limitation of existing hybrid beamforming is that they include high‐resolution analog‐to‐digital converters (ADCs) which consume high power. To overcome this fundamental limitation, in this paper, spectrum and energy efficient mmWave massive MIMO transmission scheme is proposed that jointly optimizes the hybrid combiner and the number of ADC bits. Particularly, the hybrid combiner is presented as a combination of three parts: the analog combiner matrix, the quantization matrix, and the digital baseband matrix which is based on the Kalman filter. Unlike most existing works that rely on sumrate optimization to design the hybrid combiner, in this work, the mean square error (MSE) is chosen as the main performance metric to describe the reliability of the system. Simulations results confirm that the proposed Kalman based hybrid combiner exhibits the best performance in terms of spectral and energy efficiencies compared to minimum mean square error (MMSE) and zero forcing (ZF) receivers.

As Sparse code multiple access (SCMA) has proved to be a fascinating research in order to meet the requirements of future wireless communication systems. To reach high power efficiency, wireless communication systems are equipped with high power amplifiers (HPAs). In this paper, we investigate the effects of distortions due to high power amplifiers (HPA) nonlinearities. We study the performance of amplified SCMA systems, in terms of bit error rate (BER). Message passing algorithm (MPA) is considered for SCMA detectors. BER performance is derived and evaluated for Additive White Gaussian Noise (AWGN) and Rayleigh fading channels. Numerical results and comparisons are provided for several system parameters, such as the input back-off (IBO). Indeed, we propose a new distortion cancellation technique based on feed-forwarded neural networks (FNNs) to restore the system performance via eliminating the HPA nonlinearities at transmitter and receiver sides. It is confirmed that the proposed pre-distorter and post-distorter with neural network exhibit a good performance improvement of quality of the transmission. Specifically, post-distortion based on NNs shows a better BER performance, which is almost close to the one of the linear system.

The Joint Radar Communications (RadCom) system uses a shared waveform to sense the environment and transmit information simultaneously. In this paper, we propose a combined transmit waveforms processing based on the Filter Bank Multicarrier using Offset Quadrature Amplitude Modulation (OQAM-FBM C). The processing offers better spectral efficiency, which improves the RadCom system performance. The identification of the target parameters and communication symbols was discussed, and it demonstrated that the unknown parameters are deduced from the back-scattered signal. This analysis was verified with numerical simulations. The results show that OQAM-FBMC RadCom presents a much higher spectral efficiency, as shown this article, better performance in radar targeting with improving the bit error rate (BER) performance in multipath channels.

Wireless Sensor Network (WSN) is considered as the backbone of Internet of Things (IoT) networks. Authentication is the most important phase that guarantees secure access to such networks but it is more critical than that in traditional Internet because the communications are established between constrained devices that could not compute heavy cryptographic primitives. In this paper, we are studying with real experimentation the efficiency of HIP Diet EXchange header (HIP DEX) protocol over IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN) in IoT. The adopted application layer protocol is Constrained Application Protocol (CoAP) and as a routing protocol, the Routing Protocol for Low power and lossy networks (RPL). The evaluation concerns the total End-to-End transmission delays during the authentication process between the communicating peers regarding the processing, propagation, and queuing times' overheads results. Most importantly, we propose an efficient handshake packets' compression header, and we detailed a comparison of the above evaluation's criteria before and after the proposed compression. Obtained results are very encouraging and reinforce the efficiency of HIP DEX in IoT networks during the handshake process of constrained nodes.

Recently, the deployment of Wireless Sensor Networks (WSNs) increases in various applications. These networks are dispersed in an inaccessible area and have to operate on small batteries. Energy is one of the most critical resources since sensor nodes sense, collect, process and send data to its base station. Many researches have demonstrated the importance of clustering to extend the lifetime of wireless sensor networks. Clustering algorithms consider several parameters like the density, the residual energy, and the distance separating the base station and the nodes to select the appropriate cluster heads. In this paper, K-means algorithm is first used to select a set of Cluster Heads (CHs) from all the nodes. Then we propose the Kruskal algorithm to define the optimal path between CHs and BS. Simulation results show that the proposed combination allows improved lifetime extension compared with existing algorithms.

Traditionally, the communication and radar are separately designed. Recently, and with 5G technology, the Millimeter Wave (mmWave) spectrum becomes paramount for the exploitation of the large bandwidths. However, the interference problems have negative impacts on both radar and communication systems. To overcome these problems, the joint radar and communication (JRC) systems has been proposed as a promising technique that improves the spectrum utilization, allowing an optimal spectrum sharing and an efficient cooperative detection.In this paper, we propose an accurate performance analysis of JRC-enabled cooperative detection systems, where stochastic geometry is used to model the different vehicle positions in a given JRC systems. We derive closed form expressions of the average cooperative detection range (CDR) for different scenarios, with a different number of vehicles. Based on that, the general average CDR expression for a general number of vehicles has been derived. The results confirm the derived analytical expressions, which present efficient metrics to evaluate the cooperative detection in JRC systems.

Abstract With the ever‐increasing number of intelligent devices, extensive research efforts have been spent towards developing efficient resource allocation in underlying device to device communications. Centralised and distributed approaches have been proposed to improve the network performances through uplink and downlink schemes. However, the full‐duplex resource allocation remains challenging. Here, a joint uplink–downlink resource block assignment for device to device communications is proposed. The device's geometric distribution via the Poisson point process is formulated and the optimization problem as a mixed strategy non‐cooperative game is modelled. The proposed resource allocation scheme allows many device to device communication pairs to share the same cellular resource block but does not restrict their number in advance; it is determined dynamically according to the channel's capacity and interference condition. Inversely, a device to device communication transmitter can simultaneously be allocated many resource blocks according to its bandwidth requirement and the number of its antennas. Furthermore, the optimal amount of data and power transmitted by each device is calculated. The efficiency of this algorithm is evaluated through computer simulations, and the results demonstrate good performances in the spectral and energy efficiency with relatively low complexity and convergence time.

Abstract The expansion of smart devices and throughput requirements have triggered the integration of multi‐hop Device to Device (D2D) underlay the fifth generation (5G) network. This new paradigm has extended the coverage area and enabled ultra‐low latency and cost‐per‐bit communications. While the previous 3GPP releases address only the coverage problem via two‐hop D2D, this paper focuses on multi‐hop D2D defined in 3GPP release 15 and beyond. It proposes a distributed relay selection and resource allocation algorithm based on a mixed strategy game and a novel amplify and forward factor. The proposed schema maximizes the joint spectral and energy efficiency. Besides, it determines the optimal amount of data and energy transferred by each device. The efficiency of this approach against other state‐of‐the‐art solutions is evaluated and verified through computer simulations.

In next-generation communication systems, higher transmission capacity through a wireless channel and high data rate, are essential for researchers. The growing demand for higher data rates, more dependable quality of services is leading to the use of Multiple Input Multiple Output (MIMO) transmission, and phased array techniques in wireless communication. We present a comprehensive investigation of these two techniques applied on the filter bank-based multicarrier using offset quadrature amplitude modulation (FBMC-OQAM), where the Bit Error Rate (BER) and capacity performance results are provided. In this paper, we propose the use of a lattice reduction (LR) equalization to enhance the performance of both MIMO and phased array techniques.

Recently the MIMO technique has been considered as a promising approach for WSN. Due to the miniature size and the limited power processing of sensor node, the direct application of multiple antennas to this latter is not feasible. Nevertheless, some nodes can cooperate between them to get a Cooperative MIMO system. In this paper, we study the importance of node selection for Cooperative MIMO system in WSN. When the source is surrounded by multiple nodes, we choose some of them to cooperate and send the source data to the destination. The nodes are selected according to the maximum capacity criteria. We consider a rich scattering environment and two scenarios to determine the capacity of the system. When the Channel State Information is known at the receiver, we use singular value decomposition to get the system capacity. In the case of Channel State Information known at the receiver and transmitter, Water Filling technique is used to calculate the capacity. Simulation results prove that cooperative node selection can improve the capacity of the system compared to that one without selection and random selection. These results are proved with singular value decomposition and Water Filling techniques.

Sparse code multiple access (SCMA) has attracted growing research interests in order to meet the targets of the next generation of wireless communication networks. Since it relies on non-orthogonal multiple access (NOMA) techniques, it is considered as a promising candidate for future systems that can improve the spectral efficiency and solve the problem of massive user connections. In this paper, the basic concept of SCMA is introduced, including SCMA encoding, codebook mapping, and SCMA decoding. The major challenge of SCMA is the very high detection complexity. Then, a novel strategy for blind decoding based on convolutional neural networks is proposed. Through simulations, we showed that our proposed scheme outperforms conventional schemes in terms of both BER and computational complexity, where 0.9 dB improvements can be achieved.

Sparse code multiple access (SCMA) and polar codes (PC) are two promising candidates for Future communication systems since they are capable of achieving high system capacity. In this paper, we develop a novel detection and decoding scheme for SCMA systems combined with channel coding candidate polar codes. First, we propose a separate detection and decoding (SDD) receiver for uplink communications. Then, we introduce a joint detection and decoding (JDD) receiver scheme. The investigation of system receiver is decomposed on message passing algorithm (MPA) based SCMA multiuser detection and soft cancellation (SCAN) algorithm based polar codes decoder. The separate and joint schemes are studied over additive white gaussian noise (AWGN) channels. JDD scheme yields a better performance gain. Moreover, the joint scheme has a lower computational complexity compared to the separate one. Numerical results show that when polar code length polarN= 1024 and R = 1/2, under system loading 150%, JDD outperforms the SDD 1.8dB at BER = 10-2and 3.3dB at BER = 10-6over AWGN channels.

Wireless Sensor Networks attract nowadays a great deal of not only research but also the industrial. It is deployed in a variety of area such as military, health care, monitoring. Energy is the main challenge of this network. When providing fountain codes with the assistance of training machine learning models, their ability to accurately determine the needed number of encoded packets significantly improves. In this paper, we discussed and compared our proposed distributed estimation scheme with some machine learning based methods for data classification. Simulations show that our proposed scheme which is based on the Bayesian model looks advantageous over other methods. Consequently, we can determine the needed number of encoded packets to recover initial data with appreciable accuracy and error rate notably low.

The Multi User-Multiple Input Multiple Output (MU-MIMO) technique offers big advantages over traditional MIMO solutions. Nevertheless, Nonlinearity of High Power Amplifier (HPA) presents a crucial role in the performance of MU-MIMO systems.This paper investigates the performance of Massive MIMO system in the presence of nonlinear HPA over Rayleigh fading channel. Different receivers and algorithms are used in order to illustrate this fact, such as LS, MMSE, ZF and AMP, are evaluated and mutually compared. Specifically, we assess the effect of the nonlinear distortion on the BER. Numerical results are provided and show the impacts of several system parameters, such as type of receivers, number of antennas, number of users and values of IBO, on system performance.

The dynamic development of information and communication technology has led to a considerable need for energy. It also contributes to an important part of Carbon dioxide emissions (CO2). These facts have created an urgent need for wireless communication. In this paper, we have developed an analytical modeling performance analysis in order to reduce the overall energy consumption of the femtocell Base stations (BSs) according to the number of handovers in two-tier FemtoBS-MacroBS network. Our work has been based on analyzing the simulation results to present a more performance solution for a monthly energy saving.

Mobile operators around the world have been caught off guard by the popularity of mobile data services and with the massive deployment of macro base stations, and mobile data traffic continues to increase at an exponential speed. This increases overall energy consumption, which translates into a much higher rejection of carbon dioxide into the atmosphere. By the integration of small cells, i.e., femtocells, in the new generation networks, important questions are raised about the energy consumption in heterogeneous networks. In this paper, a new proposed Dynamic transmit power (DTP) based scheme is proposed to decrease the network power consumption. A stochastic geometry model is used to describe the different femtocells position in the network with realistic constraints and to evaluate the performance of our solution. Performance has been studied in terms of energy consumption and energy gains. Simulation results are used to confirm the analytical results and show that the DTP based scheme can reach an energy gain up to 35% compared to the constant transmit power (CTP) based scheme.

Massive Multiple-Input Multiple-Output (Massive MIMO) is expected to be one of the key feature technology for the next cellular communication networks due to its significant benefits in terms of energy efficiency and increased spectral. To fully exploit these advantages, an accurate estimation of the Channel Impulse Response (CIR) between each transmit-receive antenna pair is crucial. In literature, it has been demonstrated that wireless communication tends to have a sparse structured CIR in delay and/or spatial domains. Several channel estimation strategies have exploited the sparsity in the massive MIMO channel based on additional assumptions on shared common support between uplink channels. In this paper, we propose a general approach which highlights the individual sparsity in the different uplink channels without any other additional assumption. To achieve this purpose, a training sequence, also known as pilot sequence and compressed sensing based CIR estimation techniques have been proposed for the massive MIMO sparse uplink channels. In order to underline the performance of proposed techniques, a comparison in terms of Bit Error Rate (BER) and Normalized Mean Square Error (NMSE) is performed with other CS algorithms. The simulation results have shown that proposed algorithm presents a performance gain between 5 dB and 10 dB in terms of NMSE, and a gain of 1dB to 9dB in terms of BER.

Nowadays, the world of communication is experiencing significant technological progress from generation to another, which leads to an increase in the number of devices and traffic. This increase leads to a sharp increase in energy consumption. Therefore, reducing energy consumption in wireless mobile networks is becoming a major concern; not only because of the rising cost of energy of mobile network operators but also because of its adverse environmental effects due to Global warming. In this paper, to reduce energy consumption, we propose to switch on/off the eNodeB (eNB) according to their amount of traffic and SINR (Signal to Interferences Noise Rate) values of their Users Equipment (UEs). The switch on/off is performed in LTE-Advanced (LTE-A) based Heterogeneous Network (HetNet). In order de guarantee Quality of Service (QoS) and maintain network performances after the switch on/off procedure, we apply a tilt variation and Coordinated Multipoint (CoMP) techniques.

Wireless communications systems are the most prosperous technologies of this decade. Driven by newfangled innovative applications such as internet of things and augmented reality, this trend certainly continues exponentially. Massive MIMO technique use almost infinite number of antennas at the base station are expected to play a key role in future communication systems as part of horizon 2020 project. However, the large number of antenna elements in massive MIMO also pose a major challenge, due to the interference is the main impairment to successful communication. In this paper, we propose an original massive MIMO system. Adaptive Beamforming algorithm's used since it is a promising solution in order to avoid interference problems where conventional beamformers are susceptible to interference signals on the contrary the algorithm can account for interference signals, the simulate results evaluate the interference inter-beams by giving the coherence between the different beams which each beam is appropriate to a specific terminal in communication system and demonstrates the effectiveness of our solution to mitigate interference, thereby contributing to the increase of network capacity. Our research affords a suitable solution for the design of next-generation wireless communication systems.

This paper presents a localization algorithm based on the identification of a linear expression connecting a vector of mean received powers to a vector of Cartesian coordinates. The linear expression is based on a Taylor expansion of the received power collected on a set of measurement points. The terms of the Taylor expansion are then integrated in a transfer matrix able to predict a vector of received power from a composite vector of Cartesian coordinates. Using the pseudo inverse of this matrix it is then possible to find the Cartesian coordinates of any unknown reception point, from the vector of mean received powers at this point.

Today with the expansion of mobile devices, the increase of high data rate applications and services, the integration of Device to Device communication underlay cellular networks became an urgent task. It brings numerous advantages to the cellular environment, by allowing proximate devices to communicate directly, without relying on eNodeB to route data. Hence, D2D can greatly improve network's spectral efficiency, coverage extension and QoS. However its main issue is that successful scenarios are closely related to the available spectrum, User equipments (UEs) power consumption, position and density in the cell.For this purpose, the main objective of this paper is to review and further develop models and computing techniques to provide a spectral and energy efficient D2D communication. Thus, scarce resources such as spectrum and UEs consumed power must be well managed. To achieve this goal, we will design and implement an interference and power management D2D resource allocation algorithm. Firstly, we propose a mathematical model for our problem as a mixed strategy non cooperative game, and then we design a distributed spectral and energy efficient resource allocation algorithm, by minimizing interferences and power consumption. This leads to performances maximizing not only for D2D communicating pairs, but also for other cellular network communications. Finally we analyze and compare our simulation results and such system parameters to other previous solutions.

Millimetre‐wave (mmWave) communication is a key technology for future cellular networks. However, mmWave signals may be limited in coverage since they are susceptible to blockages. In order to mitigate blockages effect and enhance the coverage probability, device‐to‐device relaying can be used to improve the direct link reliability. In this study, the authors propose a new communication scheme to enhance the successful transmission probability. This scheme is based on a selected user equipment relay that can assist the transmission from the base station (BS) to the target user. The selected relay is an available user that provides the minimum distance among the retained maximum distances (the distance between this relay and the served user, and that between the same relay and the serving BS). Based on the stochastic geometry and random shape theory, they derive the expressions of the probability density function, and the successful transmission probability within the proposed scheme under the Nakagami‐m fading assumption. Using numerical results, the derived expressions are evaluated, and the advantages of the proposed scheme are investigated. The results confirm the analytical derived expression. In addition, the performance of their proposed scheme in terms of coverage probability outperform the existing schemes.

The fifth generation upcoming applications require an alternative multicarrier modulation (MCM) scheme rather than the well‐known orthogonal frequency division multiplexing scheme. Filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) is the most suitable scheme to meet this requirement. However, as an MCM, the high peak to average power ratio (PAPR) is a significant problem which upsets the FBMC/OQAM signal. In the presence of a non‐linear high power amplifier, FBMC/OQAM signals with high PAPR can be strongly distorted. To date, the PAPR reduction technique called tone reservation (TR) has been revised and adapted to the overlapping nature of the FBMC/OQAM signal in many research works. In view of that, the authors (i) present two refined TR methods deemed relevant for reducing the PAPR of the FBMC/OQAM and (ii) compare their performances with those of some existing versions of TR PAPR reduction methods.

Multiple‐input multiple‐output is being considered as a highly interesting topic for the new generation of wireless communication systems. The major challenge is how to enhance the link reliability while neither affecting the spectral efficiency nor the quality of service. To reduce the complexity of the maximum likelihood (ML) receiver, efforts have been focused on zero forcing (ZF) decoder using lattice reduction‐aided (LRA) techniques. In this study, the authors propose a method that improves performance using the LRA‐based precoding technique without increasing the decoder complexity. Their approach is to exploit the channel matrix to generate matrices for both the precoding and LRA detection technique. This makes it possible to achieve maximum orthogonality for the generated channel matrices while reducing considerably the noise effect. They focus on the use of the LRA‐ZF for the ease of its implementation and low complexity. The proposed method outperforms LRA‐minimum mean square error‐based receiver and gets closer to ML receiver performance. The performance and complexity of the proposed scheme are discussed.

Opportunistic routing (OR) is a new paradigm in routing protocol for wireless sensor network (WSN). The OR has aroused wide concern because it can improve the energy consumption and reliability in wireless sensor networks (WSNs), especially if the most sensor nodes are equipped with a limited norechargeable battery power. In this context, performing optimal routing strategy is a crucial task at the network layer of WSN. In this paper, we focus on minimizing energy consumption and enhance the network lifetime. Indeed, we proposed a new opportunistic routing algorithm called ECS-OR (Energy Candidate Set-opportunistic Routing) that permit to balance energy consumption and extend the lifespan of wireless sensor network. Extensive simulations show that our proposed routing strategy ECS-OR can significantly improve the network performance on energy saving and wireless connectivity in comparison with other existing WSN routing schemes.

Industrial wireless networks are now used in many applications, and require to fulfill a certain set of requirements to operate properly. IEEE802.15.4-TSCH is considered a suitable solution to provide real time multihop transmissions in noisy and harsh environments. The standard relies on a strict schedule of the transmissions to reduce the radio duty cycle ratio. While constructing a schedule for periodic traffic has been widely studied in the past, we focus here on the aperiodic, sporadic case. We have to multiplex the transmissions in the schedule to reduce the energy consumption while limiting the number of collisions to provide still high reliability. We propose here to study experimentally the performance of TSCH with shared access and bursty arrivals. Then, we demonstrate how to re-adapt the scheduler to better deal with unpredictable traffic. By performing experiments, we can predict the optimal number of transmitters in a shared cell. Thus, we can overcome collisions and packets drops in complex scenarios where bursty traffic is required.

Practical channel estimation algorithms for millimeter wave massive multiple-input multiple-output (mmWave massive MIMO) systems with huge number of antennas at the base station must achieve high spectrum efficiency. Furthermore, the uplink channel estimation becomes very challenging since the required pilot overhead used for channel estimation and feedback can be prohibitively large. In conventional channel estimation algorithms, the channel model is approximated using a virtual channel model with quantized angles of arrival/departure (AoA/AoD). In this paper, we consider the continually distributed AoA/AoD, we show that the mmWave massive MIMO channels share common cosparsity properties, and we propose a structured analysis compressive sensing (SACS) based algorithm which exploits those common cosparsity properties for channel estimation with low overhead. Simulation results show that our proposal can accurately estimate the channel with low overhead, and is capable of attaining the optimal Cramer-Rao Lower Bound (CRLB).

This paper introduces an energy harvesting based scheme for 3-D cellular networks in order to enhance communications system performance. The proposed scheme is based on the energy harvesting to reduce the transmission delay and to enhance the ergodic capacity. The stochastic geometry is used to describe the different cells' positions in the network with realistic constraints. Based on that, the average transmission delay and the ergodic capacity expressions are detailed and derived. Using numerical results, the analytical derived expressions are evaluated, and the advantages of the proposed scheme are investigated.

When speech is transmitted over non reliable channel, encryption techniques are needed to secure the communication. BSS-based speech encryption aims to encrypt speech signal by linearly combining it with secret key signal. In the current framework, we discuss the robustness of the speech encryption technique based on blind source separation. Human person have a capability to decipher the content of the speech signal even in the presence of a large amount of noise. In the current study, we show that it is possible to extract an intelligible version from BSS based encrypted speech using modified speech denoising scheme. Such study, allow us to adjust the key signal in order to divert the noise estimation algorithms. A first set of experimental results shows the effectiveness of the denoising attack. A second set shows the validity of the proposed method to reinforce the security of transmitting encrypted signal.

High Power Amplifier (HPA) non-linearity has received utmost attention in the current study of MultiCarrier Modulation (MCMs) systems (Orthogonal Frequency Division Multiplexing (OFDM), and Filter Bank Multicarrier Modulations/Offset Quadrature Amplitude Modulations (FBMC/OQAM)) characterized by a high Peak to Average Power Ratio (PAPR). With high PAPR, an MCM signal suffers from distortions when it is amplified with a non-linear HPA. The linearization and the PAPR reduction approaches were separately proposed to deal with the effects of HPA non-linearity and moderate the expensive cost of high performance linear HPA. To date, research has not considered the idea of combining both approaches for the FBMC/OQAM system. In this paper, we propose and compare two schemes based on the joint use of the PAPR reduction and the linearization process, for both the OFDM and FBMC/OQAM systems. Numerical results showed that the two suggested combinations of the Tone Reservation (TR) PAPR reduction scheme and the Neural Networks Predistortion(NNPD) schemes outperform the separate use of TR and NNPD for both OFDM and FBMC/OQAM systems.

Many applications for the Internet of Things require strict guarantees to operate properly. IEEE802.15.4-TSCH has been proposed to design a real-time access for industrial wireless networks. The standard relies on a strict schedule of the transmissions., and combines frequency hopping to provide high-reliability. While most of the propositions focus on a periodic traffic, we propose here to address the non-periodic case. In particular, shared access would be more efficient to reduce the power duty cycle by multiplexing the transmissions through the same cells. We propose here a Markov model for a shared access and sporadic traffic. By formulating the collision probability, the scheduler can decide how many transmitters to assign to a given shared cell according to the amount of traffic each node generates on average. The simulations validate the accuracy of our model to capture the collision rate for non-periodic traffic.

In this paper, the error performance of linear (MMSE-LE) and widely linear (MMSE-WLE) equalizers is analyzed in terms of mean square error (MSE) and bit error rate (BER). This analysis is done in a system using improper modulations of type Pulse Amplitude Modulations (PAM) having inter-symbol-interferences (ISI) and/or inter-user-interferences (IUI). For this scenario, we derive theoretical expressions of both MSE and BER for the two equalizers. Analytical expressions developed in this paper are verified by computer simulations. It is worth mentioning that these expressions are valid for any number of interferers and any channel delay spread.

This study introduces a three‐dimensional (3D) model for K ‐tier heterogeneous networks (HetNets) in order to evaluate the performance of using millimetre wave (mmWave) and radio‐frequency (RF) bands. The model is based on 3D stochastic geometry that describes the different cells' positions in the network with realistic constraints. Based on the 3D model, the successful transmission probability, the average throughput, and the average bit error rate expressions of down‐link HetNets are detailed and derived for both considering bands. Using numerical results, the analytical derived expressions are evaluated, and the advantages of using mmWave and RF bands for HetNets are investigated.

In wireless sensor networks (WSNs) nodes are often powered by batteries, which are difficult to replace or recharge. Hence, it is crucial to use energy-efficient techniques to reduce the energy consumption of such transmission. Mainly the multi -input multi-output (MIMO) which is a proven technique to decreases the energy consumption of a wireless network. However, the direct application of multiple antennas in one sensor node is impossible since the latter is characterized by a minimal physique size. Neighbors nodes at transmitter and receiver cooperate with the source and destination nodes to form a cooperative MIMO system. In this paper, the energy efficiency of cooperative MISO (multi-input single-output) and MIMO in WSNs are investigated over a Rayleigh fading channel. This comparison helps us to choose, according to the distance between the source and the destination, the appropriate cooperative technique.

When non second order circular (NSOC) signals are transmitted over time/frequency varying channels, classical linear equalization (LE) suffers from performance loss due to the signal's impropriety. For such signals, and signals corrupted by one single improper interference, it has been demonstrated that widely linear (WL) processing can enhance equalization performance. In this paper, we will study the limit of WL processing as a function of the number of interferers. We will carry simulations corroborated by analytical analysis and show that the performance of WL processing, in terms of bit error rate, decreases as the number of improper interferences increases.

In recent years, there has been a growing interest in the wireless sensor networks (WSN) for a variety of applications such as the localization and real time positioning. Different approaches based on artificial intelligence are applied to solve common issues in WSN and improve network performance. This paper addresses a survey on machine learning techniques for localization in WSNs using Received Signal Strength Indicator.

One of the most important challenges in Wireless Sensor Networks is their stringent energy contraint. Sensors are basically battery power devices where the replacement of these batteries can be costly and complicated. Node mobility is the main cause of energy over consumption, the disconnection between nodes and by the way the instability of the whole network. Motivated by this, we investigate clustering approach based on the Weighted Clustering Algorithm (WCA) that outperforms the state-art in terms of energy consumption and possesses the advantages of resilience against node mobility. The proposed method aims to elect the most stable and powerful node in term of energy, connectivity and link quality by controlling the network stability in order to avoid the disconnection between nodes.

The next mobile generation is featured by extreme densities of cell stations to overcome the higher future demand. Hence, interference management is a crucial challenge in ultra-dense cellular networks. In contrast to the classical inter-cell interference coordination approach, which is no longer fit for the high density of cell-tiers, this paper proposes a novel virtual coordination based on the dynamic common cognitive monitor channel protocol to deal with the inter-cell interference issue. A tractable and flexible model for the coverage probability of a typical user is developed through the use of the stochastic geometry model. The analyses of the performance of the suggested protocol are illustrated both analytically and numerically in terms of coverage probability.

Wireless Sensor network (WSN) can be defined as a collection of battery powered wireless sensor nodes, which can sense events in the environment and share the data gathered from sensing node to a sink node. However, this type of network is seriously constrained by energy supply. Therefore, how to enhance the network lifetime is a prominent and challenging issue. In this context, the choice of the suitable routing protocol is a vital task in the network layer of WSN. This paper describes the EEOR (Energy Efficient Opportunistic Routing), which focuses on the selection of the forwarding list to minimize energy consumption. Then, we define a new strategy of opportunistic routing, denoted ROR (Range-based Opportunistic Routing), which is an improvement of EEOR. Simulation results demonstrate that ROR is the most efficient to minimize the energy and the number of dropped paquets.

Wireless Sensor Network(WSN) has been widely used for monitoring and control applications in our daily lives due to its promising features; such as low cost, low power, and ease of implementation. The choice of energy efficient routing protocol is a vital task in the network layer of wireless sensor networks to improve their lifetime. A performance analysis of energy consumption referring to opportunistic routing algorithms is presented in this paper. The particularly analysis is evaluated in terms of energy consumption, number of candidate groups, flow rate, number of packets lost, and lifetime. Based on the simulation results, we observe that opportunistic routing protocol perform better characteristic than traditionnal protocl (TR). Futhermore, our findings disclose that opportunistic routing algorithm yields in a significant improvement of power consumption.

This paper compares the energy consumption of non-cooperative schemes and the Selective Decode and Forward (SDF) cooperative protocol in Wireless Sensor Networks. This Analysis was performed over Nakagami-m channel, under Outage probability and system end-to-end throughput constraints. Under these constraint, we assume that in multi-hop and cooperative schemes scenarios, the nodes operate at a spectral efficiency L greater than that of the single-hop. As the energy consumption of the RF circuitry cannot be neglected, it is noteworthy that cooperation may be considerably more energy efficient than non-cooperative schemes even in small range distance. We study then the effect of pathloss component and the target outage probability in the energy consumption.

Communication protocols have been designed according to a layered stack architecture, but with the evolution of networks, this layered stack system cannot meet the wireless network requirements. In sensor networks, and specifically the routing issue, given the need to conserve sensor energy and maximize application performance, cooperation between several layers in the protocol stack is needed. This can be reached in a cross-layer architecture. Cross-layer design allows protocols belonging to different layers to cooperate and share network status information; this will ensure that the best route is chosen by taking into consideration energy consumption and performance requirements. This paper presents a routing protocol for Wireless sensor networks using Cross-layer approach for energy optimization and lifetime raise.

Coupled with large multiple-input multiple-output (MIMO) techniques, Millimeter Wave (mmWave) communication systems are able to boost capacity and deliver a huge data rate. Hence, a fully digital processing becomes impractical with mmWave MIMO systems. Unfortunately, these systems require a prohibitively high hardware cost and power consumption. In order to overcome these limitations, various hybrid precoding architectures have been recently proposed accomplishing the MIMO processing in an analog and digital domain mixture based on different array mappings. In this paper, we introduce the sub-array structure that efficiently designs hybrid precoders and combiners. We propose a hybrid precoding design based on power factorization (HP-PF) algorithm which provides a trade-off between complexity and system performance. When our proposal is compared with previous methods in the literature, simulation results show that the proposed solution provides an important reduction in terms of computational complexity while maintaining performance gains in terms of spectral efficiency slightly comparable to that of the semidefinite relaxation (SDR) based algorithm.

This paper presents a comparison study of different similarity metrics used for RSSI fingerprint based indoor localization. These metrics are used for nearest neighbor search which is a crucial step in fingerprint localization system. Including Euclidean distance, Manhattan distance and Gauss distance, the present study compares the localization error respect to a proposed parameter named “error density”. This latter is related to the location error and the size of the studied area. In addition, different methods of combining the locations of neighbors have been introduced to estimate the current position and their performances have been compared. Extensive implementation details are discussed and simulation is conducted to compare them. Obtained results show that the Kernel method combined with the weighted average exhibits the localization accuracy on the studied dataset.

Positioning and tracking of wireless devices in indoor environment is a challenging research problem. Accurate localization of a moving target is a fundamental requirement in Wireless Sensor Networks monitoring applications. In this paper, a novel location tracking algorithm which combines learning methods is proposed. In previous work, regression tree using received signal strength method is proposed to localize a static sensor node. This approach is employed in this paper to solve the complex relation between the received signal strength and the target position. Then, an ensemble of trees are applied leading to more accurate position of the moving target. The proposed algorithm has been experimentally evaluated using real measurement of a moving target in an office room. The performance results have been analyzed through a comparison with the standard regression tree and ordinary Kalman filter.

Wireless communications are currently undergoing very rapid development, and as a result, the demand for wireless spectrum has been growing dramatically. In this regard, future wireless solutions have to meet multiple needs in terms of spectrum efficiency (SE). Both 5G, the next generation of mobile networks, and Cognitive Radio (CR) came as promising new technologies dealing with those requirements. In the process of molding a future for those systems, it is becoming increasingly clear that energy efficiency (EE) is a desirable design. In this paper, an innovative architecture will be conceived by combining those two technologies. In addition, a novel approach of technical sensing will be implemented in order to ensure a compromise between SE-EE.

Due to the exploding evolution of wireless communication systems, energy efficiency (EE) becomes the concern of many researches. In this context, we examine this metric into multiband orthogonal frequency division multiplexing ultra wideband (MB-OFDM UWB) systems. Indeed, the focus is about investigating a cross-layer design based on the joint combination between data link layer through the hybrid automatic repeat request (HARQ) protocol and the physical layer across modulation and coding scheme (MCS) towards the MB-OFDM UWB (MB-UWB) systems. The objective is to find the suitable MCS allowing energy minimization. Nevertheless, the idea is about determining the most energy efficient mode selection providing total energy consumption per throughput diminishing. Next, a theoretical framework displaying the throughput of different MCS derivation is introduced following a specific energy consumption model adoption. Numerical results reveal the most energy efficient MCS scheme ensuring energy expenditure diminishing.

Indoor localization methods based on the received signal strength indicator are widely used in the literature since no additional hardware is required for data acquisition. In this paper, a novel localization algorithm which combines both classification and regression methods is proposed to enhance the localization accuracy of previous methods based on regression tree. The proposed approach is based on the selection of the three anchors nearest to the target for the generation of the training set and during the testing phase. The performances are evaluated using real measurements acquired in office rooms. The experimental results show that the anchor selection procedure provides an increased accuracy if compared to the standard regression tree localization algorithm.

The wireless sensor network for rotating electrical machine is beneficial for both real time monitoring and diagnosis purposes. In this paper, our contribution is to realize a wireless sensor network node based on IEEE 802.11 for detecting the temperature value of electrical machine's rotating component (shaft). The IEEE 802.11 has been chosen for its low power, low cost and high flexibility. As for the choice of the temperature value, it is mainly due to the fact that it defines the rotating electrical machine's lifetime. The experimental result, illustrated in this paper, shows a temperature variation. This variation represents one of the signal fading effects of the transmitter rotation. First, the proposed network system design is presented. Then, the communication protocol, the software and hardware design based on the transmitting chip RN-171 (Roving Networks) used for the system are illustrated and finally, the experimental results of the proposed wireless sensor network node are investigated.

The QoS (Quality of Service) requirements of real time (RT) applications are within the most important challenges that LTE (Long Term Evolution) must provide. However, due to the limited radio resources, it is important to take advantage of these resources efficiently. User traffic can be classified into two main classes which are RT (Real Time) and NRT traffic. To respond to RTs QoS requirements, resource allocation steps must be as optimal as possible. This paper proposes a scheduling algorithm in two levels based on cooperative game theory, aiming at improving performance and justice in the distribution of radio resources. Simulations demonstrate that the proposed algorithm improve the level of the system's QoS more effectively than other algorithms under the circumstance that guarantees users minimum QoS requirement.

With the large deployment of smart and heterogeneous devices, interest of researchers to define new protocols to meet Internet of Things (IoT) requirements is growing. A particular interest was accorded to define a robust MAC layer for wireless sensor networks, in order to reduce interference caused by other co-located networks and applications using the ISM band. This paper gives a comprehensive study of the Time Slotted channel Hopping IEEE802.15.4, part of the 6TiSCH stack and explains how its TDMA approach improves the reliability with performance guarantees. We also investigate analytically and experimentally the impact of the scheduling algorithm on the reliability. Then, we provide an experimental evaluation of co-located WSN using the FiT-IoT LAB testbed and the OpenWSN Stack. Performance analysis of IEEE802.15.4e-TSCH is achieved with a variable number of co-located synchronized or unsynchronized instances. While this standard is robust for lightly loaded networks, new mechanisms have to be proposed when we have too much traffic or too many interfering networks.

Electric distribution companies have been facing a strong trend towards substation automation. In this paper, we present an intelligent type-2 fuzzy agent approach in substation automation. The proposed concept is based on learning approach for intelligent agents that are embedded in intelligent infrastructure. The substation network is considered as a multi-agent system (MAS). MAS is used allowing the existing distribution grid to operate with maximum autonomy by adding as much self-control decentralized intelligence as possible. A fuzzy technique is proposed for extracting fuzzy membership functions and rules that represent the maximum of electric threshold to each end-user. Our contribution consist on using Interval-Type 2 (IT2) fuzzification process rather Type 1 fuzzy to reduce uncertainty of measurements. Simulation results has approved our choice of IT2 Fuzzy System on fixing threshold of consumption.

Wireless communication had a significant evolution over the last few years especially with the progress of 5G networks. In our paper, we investigate the impact of the asynchronism on the performance of a Cognitive Radio (CR) network, using the underlay paradigm. We provide a new theoretical aspect of interference analysis in the context of Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM). Such asynchronous interference can be very harmful on the system performance and as a result on the Quality of Service (QoS). Previous works were evaluating the performance analytically either in terms of power allocation or spectrum sensing, and if the interference evaluation is done, it is not in the context of a CR network. In our work, we perform a theoretical analysis of the interference within a simple and a generalized case. Finally, simulation results confirmed the accuracy of the proposed theoretical model.

The point process theory and stochastic geometry are usually used to model cellular networks in two-dimensional (2-D) space. However, the well known 2-D models are appropriate for rural or suburban areas but are not suitable for dense urban environments, where a large number of heterogeneous small cells are deployed in the new generation networks. By the integration of the millimeter wave (mmWave) bands into the new generation networks, accurate modeling and performance analysis of mmWave based heterogeneous networks (HetNets) become important issues. In this paper, a new 3D model of next generation HetNets is proposed, where a 3-D Matérn Hard-Core Process (MHCP) is used to describe the different cells positions in the network with realistic constraints. The average coverage probability expressions of down-link HetNets are detailed and derived. MatLab software simulations are conducted to evaluate the analytical results and the advantage of the proposed 3-D model when compared to the traditional 2-D model. The results confirm the accuracy of the proposed model which presents an efficient description and tractable model of dense small cells deployment in next generation HetNets.

Fountain codes are a type of erasure codes which are characterized by their global acknowledgement and used as a solution to reduce the use of feedback channel as well as the number of transmissions and then minimize the energy consumption. With the multi-hop transmission, fountain codes raise the problem of overflow leading to a waste of energy, the most critical issue and the big challenge in the WSN. Combining fountain codes and clustering technique aim to reduce the impact of this drawback. In this paper, an improved and adapted algorithm based on the weighted clustering algorithm is proposed with additional constraints for the election of cluster heads in wireless sensor networks. The principal goal of our algorithm is to minimize, balance the energy consumption among all nodes and reduce the impact of the fountain codes' drawback, the overflow. By comparing the proposed algorithm with LEACH, simulation results have shown its effectiveness in saving energy.

A rotor temperature monitoring system corresponds to a wireless communication between a transmitter module, fixed inside the electrical machine, and a fixed receiver located outside the electrical machine. Nowadays wireless communication for rotating electrical machines is interesting for both real time monitoring and diagnosis objectives. However, applying this technology can lead to some problems. One of them is that the material components of the electrical machine have a deep impact on the electromagnetic wave propagation between the transmitter and the receiver. This paper deals with the electrical machine effect on the rotor sensor transmission and propagation through the characterization of the radiation pattern. The simulation results obtained via HFSS have shown that the presence of the electrical machine components has a significant effect on the propagation characteristics.

Cognitive Radio (CR) Technology has emerged for the future of wireless communications, its key parameters of interest are frequency, power and modulation techniques. The Spectrum Sensing is an important task to determine the availability of the vacant channels. In Multicarrier Communication using Digital Signal Processing Techniques, Filter Bank Multi Carrier (FBMC) has an edge over other technologies in terms of Bandwidth and Spectral Efficiency. This multi-carrier modulation technique, and like its counterparts, suffers from high Peak to Average Power Ratio (PAPR). In this paper we study, the effect of nonlinear distortions due to high-power amplifier (HPA) on the performance of the FBMC-CR system. According to the Bussgang's theorem, the in-band distortion induced by a nonlinear HPA can be modeled with a complex gain and an independent additive noise term. However, based on these two conditions, we introduce an iterative Nonlinear Distortion cancellation (NDC) technique at the receiver level in order to compensate the impact of HPA nonlinearities. The Bit Error Rate (BER) performance of the FBMC-CR systemin presence of HPA and nonlinear distortion cancellation receiver will beevaluated over Rayleigh fading channel.

In this paper, we introduce the impact of non-linear high-power amplifier (NL HPA) on the bit error rate (BER) and on the spectrum efficiency of multicarrier techniques. A comparison between two multicarrier transmission techniques based on cognitive radio (CR) is presented: the classical orthogonal frequency division multiplexing (OFDM), based on cyclic prefix (CP), and the filter bank-based multicarrier (FBMC), based on filter bank architecture, including HPA model. One of the key issues is the large peak-to-average power ratio (PAPR) resulting in signal OFDM, what becomes, the performance of the transceiver is very sensitive to nonlinear distortions caused by HPA. In this work, we treat the impact of the nonlinear distortion for both OFDM-CR and FBMC-CR systems over Rayleigh fading channel.

Resource allocation for an uplink cooperative OFDMA system is considered in this paper. Relays are simple mobile users with advantageous positions to relay other users with bad channel conditions. Having their own data to transmit to the Base Station (BS), the relays multiplex their own data to relayed data before transmitting to BS using Decode and Forward (DF) scheme. The resource allocation is formulated as an optimization problem to minimize the transmit system power while ensuring a target data rate per user. The considered features to optimize are relay selection, power and Resource Blocks (RB) allocation. Dual decomposition is used for resolution. Both half and full-duplex users cases are studied and compared to non-cooperative system. Simulation results show that our proposed algorithm reduces system's power consumption while ensuring required QoS.

We investigate in this paper the resource allocation problem in an uplink cooperative MIMO OFDMA multiuser system. This system may be used in 5G in the context of green communications since we aim to save energy by reducing the system transmit power. We focus on multiplexing Decode and Forward (DF) relays that have their own data to transmit in addition to data to relay. Aiming to minimize the system transmit power with a required target data rate per user, the resource allocation problem is formulated as an optimization problem. The feature to optimize are relay selection, power allocation and resource block (RB) allocation. We propose a novel resolution algorithm based on dual method and Lagrangian decomposition. Moreover, at each user, Equal Power Allocation (EPA) and beamforming are studied and compared to allocate power to antennas. Simulation results show that the cooperative proposed algorithm leads to an important improvement comparing to a non-cooperative system.

Abstract In this paper, we review a previous research paper presenting a scheme for quantum key distribution that performs authentication using only quantum means. The authentication mechanism in the protocol runs along with an eavesdropping checking approach. To upgrade security properties, especially against man‐in‐the‐middle attack, we propose a revised and enhanced scheme, so that the probability of detecting eavesdropping is distinctly improved. We use prism tool as a model checker to analyze the security of the quantum system and to emphasize the extent of our contribution. We particularly focus on two security features: (i) the capability of the participants in the protocol to detect any eavesdropping attempt and (ii) any valid knowledge gained on the shared key must be minimized. Copyright © 2016 John Wiley & Sons, Ltd.

By relying upon the laws of quantum physics, quantum cryptography is reaching an extremely high level of security guarantee. It allows establishing a secret and perfectly random key material between two network nodes. It improves the contemporary cryptographic methods based only on computational security by integrating a quantum cryptographic solution. Since quantum technology becomes an attractive prospect, our overriding objective is to provide a quantum key agreement scheme that properly authenticates users in 802.11i networks. In this paper, we introduce a quantum scheme, for mutual authentication and key establishment for the integration within IEEE 802.11 networks towards absolute communication security. The key issued from the quantum algorithm authenticates the two endpoints and will be used for building the pairwise key and the group key hierarchies. To analyze the security of our approach, a formal verification of some security properties is performed using PRISM as a model checker tool.

The expanded use of wireless networks has led to significant improvement in security approaches that aim to protect information. Providing secure communication for wireless local area network (WLAN) has become one of the prime concerns. The current standard for exchanging a secret key to authenticate users and ensure secure data transmission within EAP-TLS protocol relies on Public Key Infrastructure. This technique remains secure only in case of limited computational power of eavesdroppers. Thus, we propose a quantum extension of EAP-TLS which allows exchanging a cryptographic key and authenticating a remote client peer with absolute security, ensured by the laws of quantum laws.

In a security context, to render a cryptosystem more resistant against man-in-the-middle and impersonation attacks, an authentication mechanism has to be considered. CHAP protocol (Challenge Handshake Authentication Protocol) is among the most widely spread authentication technique used within Local Area Networks. To offer greater robustness and privacy, we intend through our present paper to introduce a new quantum extension of CHAP that integrates quantum cryptography and takes advantage of its benefits such as eavesdropping detection, random secret key generation and unconditional security. A new scheme using a previously shared entangled qubits pairs with quantum unitary operators to ensure key agreement and mutual authentication is performed. To analyze the security of the proposed protocol, we use the PRISM tool as a model checker. We particularly focus on the security property of minimizing the quantity of information about the key that an enemy can obtain from applying a “man in the middle” attack. If we act on the parameters of the channel and the eavesdropper, we prove that this security property is affected by varying the efficiency of the channel as well as the power of the enemy. More interestingly, we show that the enemy could never be able to retrieve the value of the key.

Link reliability and power consumption are a prime concern in the design of wireless sensor networks. The data exchanged between nodes are vulnerable to corruption by errors induced by random noise, signal fading and other factors. Error control codes (ECC) is an efficient technique for increasing reliability of links and minimizing power transmitted. In this context, the choice of energy efficient ECC with a suitable modulation scheme is a vital task at the physical layer of wireless sensor networks to improve their lifetime. A performance analysis of energy consumption referring to MSK modulation with suitable error control codes approach is presented in this paper. The particularly analysis is evaluated in terms of energy consumption and probability of Bit Error Rate (BER) for various error control codes combined with optimal modulation scheme through a Gaussian channel (AWGN). Based on simulations results, we observe that the benefit of error correcting approach varies with the transmission distance. The combination MSK modulation and Reed Solomon code yields a significant improvement power consumption.

Filter Bank based Multicarrier communication with Offset Quadrature Amplitude Modulation (FBMC/OQAM) has been gaining considerable interest as an important competitor to Orthogonal Frequency Division Multiplexing system (OFDM). However, FBMC/OQAM signal inherits the same drawback of High Peak to Average Power Ratio as OFDM signal. Because of the overlapping structure of FBMC/OQAM symbols, the direct application of the conventional PAPR reduction scheme originally proposed for OFDM is not effective. In this paper, we investigate the performance of Smart Gradient Project Active Constellation Extension (ACE-SGP) and Tone Reservation (TR) approaches in PAPR reduction for FBMC/OQAM signals. Indeed, we introduce three PAPR reduction schemes for FBMC/OQAM system based on ACE-SGP, TR and a hybrid one. The performance of the three schemes are presented and compared with those of the OFDM system.

Wireless sensor networks are different from other networks; therefore it is necessary to use innovative techniques to solve some issues. Localization is a significant area of research in wireless sensor networks due to its various applications. This paper proposes and evaluates a Received Signal Strength-based localization algorithm using Regression Tree by comparing its performance with Least Squares Support Vector Regression and Multi Layers Perceptron Neural Network. The evaluation considers the localization error and the complexity of the algorithm. Simulations show that Regression Tree method is simple and efficient, even when using a small number of anchor nodes.

In recent years, there has been a growing interest in localization for wireless sensor networks. Since the complex behavior of such network, various machine learning-based methods are proposed in order to improve localization goals. The objective of this paper is to compare three well known learning-based localization techniques using Received Signal Strength Indicator (RSSI): the Support Vector regression, Naïve Bayes and Artificial Neural Network. We take into consideration two performance keys: the localization error and the computation complexity.

Improving energy consumption in transmission mechanism and reliability of links are two important and challenging issues which are the focus of designing the physical layer of wireless sensor networks (WSN) in order to enhance their lifetime. Energy consumption of wireless sensor devices depends on various parameters but especially on transmission distance, channel conditions, modulation techniques and channel coding strategy. Therefore, this paper investigates the appropriate combination of modulation scheme with error control codes (ECCs) for wireless sensor data transmission. The performance of Gaussian Minimum Shift Keying (GMSK) modulation with channel coding is evaluated and compared to Minimum Shift Keying (MSK) technique based on the BER (Bit Error Rate) and power consumption under AWGN (Additive White Gaussian Noise) and Rayleigh fading channels. A deep campaign of simulations reveals that GMSK scheme with Golay coding strategy under AWGN channel condition outperforms the other combination of coding method from the energy consumption minimization perspective.

Researches in techniques and technologies of indoor geo-localization are submerging location-based services. In this paper, we present a new fuzzy process for localization in Wireless Sensor Networks (WSN). The proposed concept is based on creating a Fuzzy Location Indicator (FLI) to characterize the map of the target environment. The main contribution in this work consists on proposing a Fuzzy Localization System (FLS) that collects the Received Radio Signal Strength (RSS) from the anchors and proceeds to Interval-Type2 (IT2) Fuzzification process to reduce uncertainty of measurements. The proposed method is range-based and uses computational intelligence (CI) through fuzzy fingerprinting. The system proceeds to pattern matching between the RSS IT2 fuzzy sets and the FLI. Simulation results has proved higher success rate on linguistic localization using IT2 Fuzzy sets (FSs) than using Type1 fuzzy sets.

Due to the great evolution of high data rate applications in current wireless standard, energy efficiency (EE) is becoming an attractive metric that needs to be well investigated according to the increase of the consumed power. In this paper, we analyze the EE (bits/Joule/Hertz) of multiband orthogonal frequency division multiplex (MB-OFDM) system over IEEE 802.15.3a ultra wide band (UWB) channel. Here we focus on capacity maximization subject to power constraint. Traditionally we think about improving the data rate in terms of spectral efficiency (SE) criterion but recently the purpose is to minimize the consumed power which needs a tradeoff. Hence, the goal is to do a theoretical framework showing the relation between maximization of data rate and power control of the system. The challenge following the improvement of data rate is solved by convex optimization tools. Then, we characterize the relation between EE and the consumed energy through numerical analysis.

In Multi-Band Orthogonal Frequency Division Multiplexing Ultra WideBand (MB-OFDM UWB) systems, the used modulation schemes generate circular complex signals. A complex random variable is called circular if its probability distribution is invariant under rotation. Recently, it has been shown that non-circular signals provide significant performance gains. In this paper, we propose a modified Dual Carrier Modulation (DCM) scheme, namely rhombic-DCM, generating non-circular complex signals for MB-OFDM UWB systems. However, the particular nature of the generated signals requires Widely Linear (WL) processing to take advantage of all the available second-order statistics. For this reason, we derive a novel channel estimator based on WL processing technique. Via an illustrative example, we demonstrate the non-circularity of the proposed modulation scheme. We prove also that, under the rhombic deformation, the WL channel estimator achieves significant performance gains.

In this paper, we investigate the robustness of the Unique Word Orthogonal Frequency Division Multiplexing (UW-OFDM) technique for channel estimation issue. We consider the UW-OFDM technique instead of Zero-Padding (ZP) OFDM one to construct the OFDM symbols for MultiBand Orthogonal Frequency Division Multiplexing Ultra WideBand (MB-OFDM UWB) systems. Then, we propose a two-step LMMSE channel estimator. In the first step, by using the knowledge of UW sequence in the receiver side, the Channel Frequency Response (CFR) is estimated. In the second step, the Linear Minimum Mean-Squared Error (LMMSE) channel estimation is performed using the estimated CFR of the first step and the Channel Estimation Sequence (CES) included in the frame preamble. We evaluate the performance of the proposals in terms of Bit Error Rate (BER) by computer simulations. We show that the consideration of UW-OFDM technique provides, not only reduction of complexity, but also improvement in BER performance. Moreover, we show that the two-step LMMSE channel estimation does not affect the system performance mainly for low Signal-to-Noise Ratio (SNR).

In this paper, we propose an analytical framework for uplink effective Signal-to-Interference-plus-Noise (SINR) analysis in LTE networks. Uplink Inter-cell Interference (ICI) is approximated with a lognormal distribution with statistical parameters being determined analytically. Then, we rely on log skew normal approximation to model the single-carrier SINR distribution. For effective SINR computation, the exponential effective SINR mapping (EESM) method is used. Closed-form expression for the link outage probability is achieved assuming a log normal approximation for exponential effective SINR. Simulations show that our approximations provide results with accuracy within 0.5 dB for any defined outage probability. The proposed framework is used to calibrate the power control compensation factor.

This paper proposes an analytical framework for LTE and LTE-Advanced network dimensioning. Our main focus is to provide an efficient analytical tool for downlink coverage analysis in LTE based networks. For effective Signalto-Interference-plus-Noise Ratio (SINR) computation, the exponential effective SINR mapping (EESM) method is used. We propose to use the log skew normal approximation for single carrier SINR evaluation. Then we rely on the lognormal approximation to find the spatial distributions of effective signal to interference-plus-noise ratio for different values of shadowing standard deviation and correlation coefficients. Using Monte Carlo simulations, we show that lognormal distribution provides a good fit for the effective SINR spatial distribution in LTE based networks.

Filter Bank Multicarrier with Offset Quadrature Amplitude Modulation (FBMC/OQAM) is recognized as an appropriate modulation scheme for 5G wireless technologies. With an extremely low Adjacent Channel Leakage Ratio (ACLR), FBMC/OQAM scheme is a suitable candidate for cognitive radio (CR) applications. Like all multicarrier systems, FBMC/OQAM suffers from the high Peak to Average Power Ratio (PAPR). Because of its overlapping signal structure, the direct application of the PAPR reduction schemes proposed for the Orthogonal Frequency Division Multiplexing (OFDM) signal to FBMC/OQAM one is not effective. In this investigation, the main contribution is a novel PAPR reduction scheme based on the extension of the current Tone Reservation (TR) scheme as used in OFDM to FBMC/OQAM. Based on simulation results, the proposed scheme shows almost the same PAPR reduction performance when compared with that of the conventional TR method originally proposed for OFDM.

In this paper, we investigate receiver techniques for nonlinear high power amplifier distortion compensation in Orthogonal Frequency Division Multiplexing (OFDM) systems. We introduce a new method for the detection of the nonlinearly distorted symbols received by the OFDM transmission system receiver. The proposed approach allows the determination of the position of the symbol in error and based on the Bussgang theorem application and the fast fourier transform (FFT), we create a set of a simultaneous equations which, when solved, return the true symbols before they were distorted. The numerical performance evaluation shows that the proposed method enables significant performance improvement compared to conventional receivers.

In this paper, we propose a very low complexity Linear Minimum Mean Square Error (LMMSE) channel estimation technique for Orthogonal Frequency Division Multiplexing (OFDM) systems. The conventional LMMSE channel estimation technique suffers from a high computational complexity with an order of O(N_P^3) operations (N_P represents the total number pilot in one OFDM symbol) due to an inversion matrix operation. By exploiting the structure of the channel autocorrelation matrix, we propose a very low complexity LMMSE channel estimator based on a FFT operation, a circle shift operation and a fast Toeplitz matrix-vector multiplication. The complexity of the proposed algorithm is reduced to O(N_P logN_P) operations. Simulation results show that the proposed technique has the same performances as well as the classical LMMSE in terms of Bit Error Rate (BER) and Mean Square Error (MSE).

The Filter Bank Multi-Carrier (FBMC) along with Offset Quadrature Amplitude Modulation (OQAM), is emerging to be one of the serious candidates for the next generation radio access technology. This multi-carrier modulation technique, and like its counterparts, suffers from high Peak to Average Power Ratio (PAPR). When a FBMC/OQAM modulated signal crosses a nonlinear Power Amplifier (PA), the transmitted signal exhibits in-band as well as out-of-band distortions. In such a scenario, it's mandatory to add dedicated signal processing, at the transmitter or at the receiver side, in order to reduce the impact of the PA nonlinear distortion. According to the Bussgang's theorem, the in-band distortion induced by a nonlinear PA can be modeled with a complex gain and an independent additive noise term. The complex gain can be computed from analytical formula or estimated jointly with the channel response. However, for the additive noise we will investigate, in this paper, the use of an iterative technique for PA Nonlinearity Cancellation (PANC), at the receiver side. The Bit Error Rate (BER) performance of the FBMC/OQAM system in presence nonlinear PA and iterative PANC receiver will be evaluated over Rayleigh fading channel.

Outage probability computation has been extensively studied in cellular radio systems where the interference is modeled by a sum of lognormal random variables. Since the sum of correlated lognormal random variables distribution does not have a closed-form expression, many approximation methods and bounds were proposed in the literature. However the accuracy of each method relies highly on the region of the resulting distribution being examined and the individual lognormal parameters, i.e., mean and variance. There is no such method which can provide the needed accuracy for all cases. This paper proposes a universal yet very simple approximation method for the sum of correlated lognormal random variables based on log skew normal approximation. Hence, the outage probability is accurately computed over the whole range of dB spreads for any correlation coefficient. We show that our method provides same results as Monte Carlo simulation for all cases.

Relay assisted cognitive radio is a promising technology that allows wireless systems to sense the environment, learn from previous experience and improves spectrum utilization by allowing unlicensed users to access the licensed spectrum. Studies of cognitive radio were mostly assuming a perfect channel state information (CSI). This assumption is usually unrealistic in practice because of the feedback errors or scheduling delays, where transmitting node can have outdated channel state information. This aspect leads to a performance degradation of the secondary and primary systems. To quantify the impact of imperfect CSI on the secondary users (SUs) as well as the primary user (PU), we derive in this paper the exact closed form expression for the outage probability, the bit error rate and the ergodic capacity of the SUs for two different relay selection schemes proposed herein. Additionally, we investigate how harmful imperfect CSI can be on the PU's side. The theoretical results obtained in this work are confirmed through simulations.

In this contribution we study the challenging issue of WiMedia solution which the ability to support high data rate applications but is limited to three piconets simultaneously communicating in the same area. For this reason we analyze the suitability of Channel Division Multiple Access (ChDMA) approach for Multiband Orthogonal Frequency Division Multiplexing (MBOFDM) Ultra Wide Band (UWB) (MB-UWB) system over IEEE 802.15.3a (UWB) channel model. ChDMA approach exploits the characteristics of MB-UWB system to separate the piconets. Each piconet employs its own Channel Frequency Response (CFR) as a signature code which introduces diversity. We have presented analytical analysis in terms of spectral efficiency (bits/s/Hz) assuming that the receiver knows the channel. Numerical analysis is run over IEEE 802.15.3a high data rate channel justified the importance of ChDMA solution to improve the spectral efficiency of MB-UWB system. Then, performance evaluation is investigated when minimum mean square error (MMSE) and matched filter (MF) receiver is applied. Simulations results show the importance of MMSE receiver to improve the performance of MB-UWB ChDMA system.

Algebraic Space-Time Codes (ASTC) for MIMO systems are based on quaternion algebras. Thanks to their algebraic construction, the ASTC codes, called the golden codes, are full-rank, full-rate and have the non-vanishing determinant property. Proposed for MIMO flat fading channels, these codes can increase the spectral efficiency and enhance the coding gain. ASTC codes maintain their proprieties for a frequency-selective channel. The purpose of this work is to investigate the suitability and analyze the performance of the ASTC codes under Ultra-Wideband (UWB) channels as an example of a frequency and temporal-selective channels, and for a Multi-Band Orthogonal Frequency Division Multiplexing (MB-OFDM) system. The Alamouti code, which has recently been proposed in the literature for MB-OFDM UWB systems, can improve the system capacity, error performance and wireless communication range. It will be shown that ASTC code can provide significantly better error performance, compared to the conventional MB-OFDM UWB system as well as to the Alamouti MB-OFDM system, at the same data rate.

To support vast range of multimedia and internet services at high data rates; LTE need to be addressed by radio resource allocation schemes. Furthermore scheduling play a principal role in sharing out radio resources between different stations to be able to meet the Quality of Service (QoS) requirements for various applications. Especially in this work we study and compare scheduling algorithms for real time (RT) multimedia traffics in LTE system. The performance of resource allocation techniques have been evaluated by varying the number of UEs and the target delay imposed to real time traffic also considering the inter-cell interference scenario in a vehicular environment. The particularly study is evaluated in terms of delay and packet loss ratio (PLR) in different number of UEs.

The Filter Bank Multicarrier with Offset Quadrature Amplitude Modulation (FBMC-OQAM) is emerging as one of the alternatives to Orthogonal Frequency Division Multiplexing (OFDM) for next generation broadband wireless access systems. In this paper, we focus on the nonlinear distortion effects in a FBMC-OQAM system when the signal is passed through a nonlinear High Power Amplifier (HPA) which is modeled as Saleh's one exhibiting amplitude and phase distortions. First, we develop an analysis showing that the FBMC-OQAM signals are more sensitive to the phase distortion than OFDM signals. Then, we address the problem of the compensation of these distortions in FBMC-OQAM systems using digital predistortion (DPD) at the transmitter. Therefore, two DPD schemes are considered in this investigation and their performance are compared in OFDM and FBMC-OQAM systems.

This paper discusses the resource allocation problem in an uplink multi-cell OFDMA system. The classical LTE model considers two areas in the cell: the cell center and the cell border and divides the resources in four sets. The users in the cells center zones use the same subset of subcarriers whereas the users of the border zones use the remaining resources with a Fractional Frequency Reuse (FFR) equal to 3. Considering the same cell zones, we present in this work a different FFR model and prove with a theoretical analysis that the system throughput can be improved. The resource allocation is then performed considering the Weighted Proportional Fair (WPF) algorithm. Simulation results illustrate the performance improvements of the proposed solution in comparison to the classical model. The fairness for the different models is also studied and compared.

Nowadays, the development of e-health concept is offering various aspects. In this paper, we present a novel website architecture. Designed for an e-health platform based on a wireless sensor network (WSN), this website offers the ergonomic and multi-functions opportunity for an ambient intelligent hospital. It enables the management of patients' records, real time patient's state monitoring based on remote sensing of biometric signals as well as indoor geo-localization for either in or out patient and professionals. Using architecture based on combined approach, this website presents a part of a big e-health project development.

The nonlinearity of High Power Amplifiers (HPAs) has crucial effects on the performance of Multiple-Input Multiple-Output (MIMO). This paper analyzes a theoretical characterization of nonlinear distortion effects introduced HPA which is modeled as a nonlinear polynomial model in MIMO Output Orthogonal Frequency Division Multiplexing (OFDM) systems accompanied with Minimum Mean Square Error (MMSE) receiver. The aim of this work is to study the effect of nonlinearity on the capacity of systems and is to present an iterative estimation and compensation of the nonlinear distortion due to HPA in MIMO OFDM systems under Rayleigh fading channel. The numerical performance evaluation shows that the proposed method of compensation reduces significantly BER of MIMO OFDM systems.

We investigate in this paper the resource allocation problem in an uplink OFDMA multiuser system. We focus on the Proportional Fairness approach which aims to maximize the system throughput and while ensuring fairness between users. In this paper, we propose a Weighted Proportional Fair algorithm for resource allocation that introduces priorities between users to better exploit good channel conditions in the cell center. We also perform a theoretical analysis to compare both standard and weighted proportional fair algorithms. Simulation results, compared to existing algorithms, show that the proposed algorithm approaches the max sum rate upper bound but still maintains sufficient fairness between users.

In order to provide high data rate over wireless channels and improve the system capacity, Multiple-Input Multiple-Output (MIMO) wireless communication systems exploit spatial diversity by using multiple transmit and receive antennas. Moreover, to achieve high date rate and fulfill the power, MIMO systems are equipped with High Power Amplifiers (HPAs). However, HPAs cause nonlinear distortions and affect the receiver's performance. In this paper, we investigate the joint effects of HPA nonlinearity and frequency selective channel on the performance of MIMO receiver. Then, we propose two equalization schemes to compensate simultaneously nonlinear distortions and frequency selective channel effects. The first one is based on a feedforward Neural Network (NN) named (NN-MIMO-Receiver) and the second uses NN technique and LMS equalizer (LMS-NN-MIMO). The Levenberg-Marquardt algorithm (LM) is used for neural network training, which has proven [1] to exhibit a very good performance with lower computation complexity and faster convergence than other algorithms used in literature. These proposed methods are compared in term of Symbol Error Rate (SER) running under nonlinear frequency selective channel.

The standard Alamouti space-frequency block code (SFBC) suffers from performance degradation when used over highly frequency-selective channels because the channel frequency response is not necessarily flat over the Alamouti block. In this paper, we present an enhanced Alamouti space frequency block decoding scheme for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems over highly frequency selective channels. The enhanced Alamouti scheme uses the channel frequency variations in consecutive subcarriers to adapt the Alamouti decoder. Simulation results of DVB-T2 system confirm that the proposed method has substantial performance improvement in terms of bit error rate when compared to standard Alamouti decoder mainly over highly frequency-selective channels such as single frequency networks (SFN).

Algebraic Space-Time Codes (ASTC) for MIMO systems are based on quaternion algebras. Thanks to their algebraic construction, the ASTC codes are full-rank, full-rate and have the non-vanishing determinant property. These codes have been proposed for MIMO flat fading channels in order to increase the spectral efficiency and to maximize the coding gain. The purpose of this work is to analyze the performance of the ASTC in a frequency selective Rayleigh channel. To deal with the frequency selectivity, we use the OFDM modulation. The capacity performances of an ASTC-MIMO-OFDM system, under correlated Rayleigh frequency-selective channel, have been evaluated. Index Terms-

Mobile ad hoc networks are characterized by their highly dynamic, multi-hop, and infrastructure-less nature. Due to limited computing power, scarce bandwidth, high mobility and the lack of a central coordinating entity, service discovery in these network is a challenging task. The great majority of service discovery protocols developed for MANETs deal with the above issues at the application layer. In this paper, we discuss the fact of implementing service discovery at the routing layer instead of the application layer, in order to reduce Service Discovery (SD) overhead and to limit resources consumption. We develop an integrated service discovery protocol, called BF-SD-ZRP, utilizing a combination of different optimization techniques: piggi backing of SD information on the routing messages, compact description of SD using Bloom filter (BF) and service caching. Our simulation results allows to validate our scheme and demonstrate its efficiency in terms of overhead reduction and service discoverability.

MANETs are highly dynamic and suffer from frequent and unpredictable changes in the network topology due to the fact that devices are mobile and operate with low battery power. Due to limited computing power, high mobility and the lack of a central coordinating entity, service discovery in MANETs has been an attractive area of research. However, proposed solutions are broadcast-based and result in overloading the constrained network. The issue addressed in this paper is therefore to provide a cross-layer Service Discovery in order to reduce energy consumption. In this work we propose an efficient Energy-Aware Service And Route Discovery scheme called EASARD. We extended the Zone Routing Protocol in order to encapsulate service information in its routing messages. We present a performance comparison of EASARD versus another integrated protocol in terms of energy consumption. We evaluate how the different approaches affect the energy usage of mobile devices. Extensive simulation demonstrate the superiority of our proposed approach over other cross-layer schemes.

This paper presents a novel mobile localization algorithm under non-line-of-sight (NLoS) conditions in a Gaussian scattering channel model involving two base stations (BS) at most. By using such a small number of BSs, the proposed technique avoids problems relative to weak mobile hearability, network overload and linear BSs layout. Based on the known scattering distribution, the Gaussian NLoS hybrid approximate maximum likelihood (G-NLoS HAML) approach presented herein estimates the distances between the mobile station (MS) and the scatterers through the measured hybrid time-of-arrival/angle-of-arrival (ToA/AoA) statistics. Then, it matches the calculated distances to the Gaussian scatterers' distribution in a maximum likelihood (ML) sense. Numerical results prove that the proposed algorithm gives better performance compared to previous approaches.

We consider a multiple access MAC fading channel with two users communicating with a common destination, where each user mutually acts as a relay for the other one as well as wishes to transmit his own information as opposed to having dedicated relays. We wish to evaluate the usefulness of relaying from the point of view of the system's throughput (sum rate) rather than from the sole point of view of the user benefiting from the cooperation as is typically done. We do this by allowing a trade-off between relaying and fresh data transmission through a resource allocation framework. We develop capacity expressions for our scheme and derive the rate-optimum power allocation in closed form for distributed frameworks. In the distributed scenario, partially statistical and partially instantaneous channel information is exploited. Our results indicate that the sum rate is maximized when both mobiles act selfishly.

Users of today's networks expect certain services from the network. Indeed one of the purposes of the network is to enable nodes to access remote services and resources not locally available. Given the significance and potential benefits of MANETs, it is necessary to design robust protocols which ensure that users of such networks are able to find the services they require. Due to limited computing power, scarce bandwidth, high mobility and the lack of a central coordinating entity, service discovery in MANETs is a challenging task. The great majority of service discovery protocols developed for MANETs deal with the above issues at the application layer. In this paper, we focus on integrated approaches of service and route discovery. We develop an integrated service discovery protocol, called BF-SD-ZRP combining several optimization techniques: the use of Bloom Filter compression scheme providing an optimized description of service information. The subsequent piggybacking of service information into ZRP routing messages, taking advantage of its intelligent bordercasting mechanism to propagate information to remote nodes. A Caching scheme is used to obtain further reductions of both overhead and latency. We present and examine our simulation results for both BF-SD-ZRP and SD-OLSR cross-layer protocols in order to demonstrate the efficiency of our scheme in terms of overhead reduction and service discoverability using the scalable network simulator Jist/Swans.

The combination of Multiple-Input Multiple-Output (MIMO) with Orthogonal Frequency Division Multiplexing (OFDM) is a promising technique for achieving high performance with very high data rates in 4G broadband wireless communications. Nevertheless, one major disadvantage of MIMO-OFDM systems lies in a prohibitively large Peak-to-Average Power Ratio (PAPR) of the transmitted signal on each antenna. Indeed, the performance of the receivers is very sensitive to nonlinear distortions caused by the High Power Amplifier (HPA). Furthermore, Crosstalk can take place before or after the power amplifiers designated herein as nonlinear and linear crosstalk, respectively. In this paper, we extend the efficient Neural Network Predistorter (NNPD) proposed in [1] to MIMO-OFDM systems and equally demonstrate that nonlinear crosstalk significantly affects the performance of NNPD. Along, we propose a new Crossover Neural Network Predistorter (CO-NNPD) model to compensate simultaneously for crosstalk and HPA nonlinearity in MIMO-OFDM systems. The Levenberg-Marquardt algorithm (LM) is used for neural network training, which has proven [3] to exhibit a very good performance with lower computation complexity and faster convergence than other algorithms used in literature. This paper is supported with simulation results for the Alamouti STBC MIMO OFDM system in terms of Bit Error Rate (BER) in Rayleigh fading channel.

A novel single base station based localization technique is proposed. Starting from the probability density function of the scatterers' distribution around the mobile station, we apply the maximum likelihood estimator. The mobile location estimate is then approximated iteratively starting from a set of nonlinear equations. Simulation results show that the technique herein can enhance mobile positioning accuracy by as much as 40% (for certain scattering environments). In addition, the performance of the proposed approximate maximum likelihood technique can be further improved when accurate angle-of-arrival (AOA) data is used.

In this paper, we consider optimizing the performance of 16-QAM orthogonal frequency division multiplexing (OFDM) signals over the non-linear, satellite transmitters. A high power amplifier produces non-linear distortions; a predistorsion technique will reduce theses distortions. The pre-distorter is based on a feed-forward neural network. In addition, the performance of the new pre-distortion scheme for OFDM systems is evaluated in terms of spectrum and total degradation BER. From simulation results, it is confirmed that the proposed pre- distorter with neural network consisting with one hidden layer and nine neurons gives a good performance improvement of quality of the transmission.

With the blossoming demand for efficiently storing and transmitting digital information, image compression has been increasingly vital. On the other hand, at very low bitrates, lossy compression is supposed to be a strong attack to watermarking process. In this work, we attempt to integrate watermarking in wavelet-based image coding. In our study, we used three different wavelet-based compression techniques. The last two coders are respectively included in part I and part II of JPEG2000 standard. Taking advantage of the coding architecture, a watermark embedding is directly performed on the wavelet coefficients before the quantization phase. On the recovery stage, the hide information may be retrieved during the decoding or where ever we decompose the reconstructed image on its wavelet domain. Experimental results have shown that blind watermarking coupled with JPEG2000-based image coders exhibits a high robustness to compression attacks with respect to the quality of the reconstructed images. Moreover, its time consuming is negligible compared to coding and decoding duration