Design of an adaptive neural predictive nonlinear controller for nonholonomic mobile robot system based on posture identifier in the presence of disturbance

This paper proposes an adaptive neural predictive nonlinear controller to guide a nonholonomic wheeled mobile robot during continuous and non-continuous gradients trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and a feedforward neural controller. The models are modified Elman neural network and feedforward multi-layer perceptron respectively. The modified Elman neural network model is trained off-line and on-line stages to guarantee the outputs of the model accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The feedforward neural controller is trained off-line and adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index optimization algorithm to find the optimal torque action in the transient state for N-step-ahead prediction. General back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. Simulation results show the effectiveness of the proposed adaptive neural predictive control algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained with bounded external disturbances

Analysis of energy detection with diversity receivers over non-identically distributed κ − μ shadowed fading channels

The performance of energy detection (ED) over shadowed fading channel is analysed. The analysis is then extended to include the maximal ratio combining (MRC) and square law combining (SLC) schemes with non-identically distributed branches. Moreover, the analysis over extreme shadowed fading channel, that is utilised to model the wireless communications scenarios in enclosed areas, is also investigated. To this end, exact closed-form analytic expressions of the average area under the receiver operating characteristics curve (AUC) are derived

Evaluating the energy efficiency of software defined-based cloud radio access networks

Densifying the communications network and integrating innovative technologies leads to increased Power Consumption (PC), along with increased signalling and degraded scalability. The latter can be mitigated by using Software Defined Networks (SDN), while Cloud Radio Access Network (C-RAN) reduces the PC. Since evaluating and improving the PC is an important key success factor for the upcoming 5G generations, a reliable Power Model (PM) is required. This paper proposes a componentised, linear and parameterised PM, and explores the individual components relevant for PC analysis, particularly for Software Defined Cloud-Radio Access Network (SDC-RAN) architecture. The model quantifies the Energy Efficiency (EE) by capturing the PC of individual components, and measures the amount of PC in the network. Cooling and total PC of C-RAN and SDC-RAN for different parameters such as varying numbers of antennas and different system’s bandwidth share has also been considered. The results show that SDC-RAN increases the total PC by about 20% compared to C-RAN. Additionally, the paper shows the results of modelling the participating Core Network’s (CN) control plane unit’s PC along with establishing the accuracy of the components and the parameterised models

64-GHz millimeter-wave photonic generation with a feasible radio over fiber system

Copyright 2017 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.A full-duplex radio over fiber (RoF) link with the generation of a 64-GHz millimeter wave (mm-wave) is investigated. This system is proposed as a solution to cope with the demands of a multi-Gb/s data transmission in the fifth generation (5G) and beyond for small cell networks. Cost reduction and performance improvement are achieved by simplifying the mm-wave generation method with an RoF technique. High-frequency radio signals are considered challenging in the electrical generation domain; therefore, our photonic generation method is introduced and examined. RoF design is proposed for mm-wave generation using both phase modulation and the effect of stimulated Brillouin scattering in the optical fiber for the first time. RoF system with transmission rates of 5 Gb/s is successfully achieved. In our scheme, one laser source is utilized and a fiber Bragg grating is used for wavelength reuse for the uplink connection. Stable mm-wave RoF link is successfully achieved in up to a 100-km fiber link length with high quality carrier. Simulation results show a reduction in fiber nonlinearity effects and the mm-wave signal has low noise equal to -75 dBm. This study ensures a practical mm-wave RoF link, and it could be appropriate for small cell 5G networks by reducing the installation cost

Unified approaches based effective capacity analysis over composite /gamma fading channels

This letter analyses the effective capacity of communications system using unified models. In order to obtain a simple closed-form mathematically tractable expression, two different unified approximate models have been used. The mixture gamma (MG) distribution which is highly accurate approximation approach has been firstly employed to represent the signal-to-noise-ratio (SNR) of fading channel. In the second approach, the mixture of Gaussian (MoG) distribution which is another unified representation approach has been utilised. A comparison between the simulated and numerical results using both distributions over composite α − η − µ/gamma fading channels has been provided

Ratio of Products of Mixture Gamma Variates with Applications to Wireless Communications Systems

The fading scenario of many realistic wireless communication transmission systems, such as, multi-hop communications and spectrum sharing in cognitive radio networks (CRNs), can be modelled by the product and the ratio of the product of the random variables (RVs) of the channel distribution. However, there is no work has been investigated in the literature to provide unified statistics of the product and the ratio of the products that can be used for a wide range of non-composite and composite fading conditions. Accordingly, in this paper, the statistical properties, namely, probability density function (PDF), cumulative distribution function (CDF), and moment generating function (MGF) of the product and the ratio of the product of independent and non-identically distributed (i.n.d.) mixture Gamma (MG) RVs are derived. A MG distribution has been widely employed to approximate with high accuracy most of the conventional fading models, for example, Rayleigh, Nakagami-m, Nakagami-q (Hoyt), and Nakagami-n (Rician) as well as the generalised composite fading channels, such as, generalised- (),− /gamma, − /gamma, and − /gamma. Hence, the derived PDF, CDF, and MGF are utilized for the Beaulieu–Xie and −−− shadowed fading channels that have not been yet presented by the previous works due to mathematical intractability of their statistics. Thus, the equivalent parameters of a MG distribution for these channels are given. To this end, simple closed-form mathematically tractable expressions of the performance metrics are obtained. The derived statistics are applied to analyse the outage probability (OP), the average error probability for different modulation schemes, the effective rate (ER) of wireless communication systems and the average area under the receiver operating characteristics (AUC) curve of energy detection over cascaded fading channels. Moreover, the OP of the multi-hop communications systems with co-channel interference (CCI), both the lower bound of secure OP (SOPL) and probability of non-zero secrecy capacity (PNSC) of the physical layer security (PLS), and the outage and delay-limited capacities of CRNs are studied via using the statistics of the ratio of the product of MG variates. A comparison between the numerical results and the Monte Carlo simulations is presented to verify the validation of our analysis

Smart IoT Network Based Convolutional Recurrent Neural Network with Element-Wise Prediction System

© Copyright 2021, The Author(s). An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to change its affiliation with other clusters based on a deep learning modified Element-wise Attention Gate. The modified Element-wise Attention Gate has the ability to handle the buffer capacity in all the network, thereby enriching the Quality of Service. A deep learning modified training algorithm is proposed to learn the artificial intelligent system allowing the neurons to have greater concentration ability. The simulation results demonstrate that the Root Mean Square error is minimized by 37.14% when using modified Element-wise Attention Gate when compared with a Deep Learning Recurrent Neural Network. Also, the Quality of Service of the network is improved, for example, the network lifetime is enhanced by 12.7% more than with Deep Learning Recurrent Neural Network

Optimized Artificial Intelligence Model for DDoS Detection in SDN Environment

© Copyright 2023 The Authors. Distributed denial of service (DDoS) attacks continue to be a major security concern, threatening the availability and reliability of network services. Software-defined networking (SDN) has emerged as a promising solution to address this issue, enabling centralized network control and management. However, conventional SDN-based DDoS mitigation techniques often struggle to detect and mitigate sophisticated attacks due to their limited ability to analyze complex traffic patterns. This paper proposes an innovative and optimized approach that effectively combines mininet, Ryu controller, and one dimensional-convolutional neural network (1D-CNN) to detect and mitigate DDoS attacks in SDN environments. The proposed approach involves training the 1D-CNN model with labeled network traffic data to effectively identify abnormal patterns associated with DDoS attacks. Furthermore, seven hyperparameters of the trained 1D-CNN model were tuned using non-dominated sorting genetic algorithm II (NSGA-II) to achieve the best accuracy with minimum training time. Once the optimized 1D-CNN model detects an attack, the Ryu controller dynamically adapts the network policies and employs appropriate mitigation techniques to protect the network infrastructure. To evaluate the effectiveness of the optimized 1D-CNN model, extensive experiments were conducted using a simulated SDN environment with a realistic DDoS attack dataset. The experimental results demonstrate that the developed approach achieves significantly improved detection accuracy of 99.99% compared to other machine learning (ML) models. The NSGA-II enhances the optimized model accuracy with an improvement rate of 9.5%, 8%, 5.4%, and 2.6% when it is compared to logistic regression (LR), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) optimized models respectively. This research paves the way for future developments in leveraging deep learning (DL) driven techniques and SDN architectures to address evolving cybersecurity challenges