Transmit power control and data rate enhancement in cognitive radio network using computational intelligence

Underutilized radio frequencies are the chief apprehension in advance radio communication. The radio recourses are sparse and costly and their efficient allocation has become a challenge. Cognitive radio networks are the ray of hope. Cognitive radio networks use dynamic spectrum access technique to opportunistically retrieve and share the licensed spectrum. The licensed users are called primary users and the users that opportunistically access the licensed spectrum all called secondary users. The proposed system is a feedback system that work on demand and supply concept, in which secondary receivers senses the vacant spectrum and shares the information with the secondary transmitters. The secondary transmitters adjust their transmission parameters of transmit power and data rate in such a way that date rate is maximized. Two methods of spectrum access using frequency division multiple access (FDMA) and Time division multiple access (TDMA) are discussed. Interference temperature limit and maximum achievable capacity are the constraints that regulate the entire technique. The aim of the technique is to control the transmitter power according to the data requirements of each secondary user and optimizing the resources like bandwidth, transmit power using machine learning and feed forward back propagation deep neural networks making full use of the network capacity without hampering the operation of primary network.

Design of a robot for inspection and diagnostic operations of rail track facilities

PurposeThe need to examine the integrity of infrastructure in the rail industry in order to improve its reliability and reduce the chances of breakdown due to defects has brought about development of an inspection and diagnostic robot.Design/methodology/approachIn this study, an inspection robot was designed for detecting crack, corrosion, missing clips and wear on rail track facilities. The robot is designed to use infrared and ultrasonic sensors for obstacles avoidance and crack detection, two 3D-profilometer for wear detection as well as cameras with high resolution to capture real time images and colour sensors for corrosion detection. The robot is also designed with cameras placed in front of it with colour sensors at each side to assist in the detection of corrosion in the rail track. The image processing capability of the robot will permit the analysis of the type and depth of the crack and corrosion captured in the track. The computer aided design and modeling of the robot was carried out using the Solidworks software version 2018 while the simulation of the proposed system was carried out in the MATLAB 2020b environment.FindingsThe results obtained present three frameworks for wear, corrosion and missing clips as well as crack detection. In addition, the design data for the development of the integrated robotic system is also presented in the work. The confusion matrix resulting from the simulation of the proposed system indicates significant sensitivity and accuracy of the system to the presence and detection of fault respectively. Hence, the work provides a design framework for detecting and analysing the presence of defects on the rail track.Practical implicationsThe development and the implementation of the designed robot will bring about a more proactive way to monitor rail track conditions and detect rail track defects so that effort can be geared towards its restoration before it becomes a major problem thus increasing the rail network capacity and availability.Originality/valueThe novelty of this work is based on the fact that the system is designed to work autonomously to avoid obstacles and check for cracks, missing clips, wear and corrosion in the rail tracks with a system of integrated and coordinated components.

How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks

As the rapid growth of mobile users and Internet-of-Everything devices will continue in the upcoming decade, more and more network capacity will be needed to accommodate such a constant increase in data volumes (DVs). To satisfy such a vast DV increase, the implementation of the fifth-generation (5G) and future sixth-generation (6G) mobile networks will be based on heterogeneous networks (HetNets) composed of macro base stations (BSs) dedicated to ensuring basic signal coverage and capacity, and small BSs dedicated to satisfying capacity for increased DVs at locations of traffic hotspots. An approach that can accommodate constantly increasing DVs is based on adding additional capacity in the network through the deployment of new BSs as DV increases. Such an approach represents an implementation challenge to mobile network operators (MNOs), which is reflected in the increased power consumption of the radio access part of the mobile network and degradation of network energy efficiency (EE). In this study, the impact of the expected increase of DVs through the 2020s on the EE of the 5G radio access network (RAN) was analyzed by using standardized data and coverage EE metrics. An analysis was performed for five different macro and small 5G BS implementation and operation scenarios and for rural, urban, dense-urban and indoor-hotspot device density classes (areas). The results of analyses reveal a strong influence of increasing DV trends on standardized data and coverage EE metrics of 5G HetNets. For every device density class characterized with increased DVs, we here elaborate on the process of achieving the best and worse combination of data and coverage EE metrics for each of the analyzed 5G BSs deployment and operation approaches. This elaboration is further extended on the analyses of the impact of 5G RAN instant power consumption and 5G RAN yearly energy consumption on values of standardized EE metrics. The presented analyses can serve as a reference in the selection of the most appropriate 5G BS deployment and operation approach, which will simultaneously ensure the transfer of permanently increasing DVs in a specific device density class and the highest possible levels of data and coverage EE metrics.

Advanced Optimization Models For Bandwidth Provisioning And Routing In Fixed Microwave Backhaul Networks

This paper addresses the problem of determining bandwidth allocation and traffic routes in fixed microwave networks such that overall bandwidth cost is minimized while traffic demands are satisfied with a required reliability level. These networks exhibit high variability in link throughput as modulations schemes are adapted dynamically to ensure acceptable bit-error rate at the receivers according to external conditions such as the weather. First, we formulate an optimal optimization approach based on mixed-integer linear programming, which is subsequently reinforced by inserting problem-specific valid inequalities based on global network capacity and feasible bandwidth/modulation combinations. Then, we introduce a Lagrangian-based heuristic that provides near optimal solutions while reducing drastically the computation time. In comparison to previous work, our experimental results show that our approaches are capable to solve large real-world network instances in an effective manner. Furthermore, the results evaluate the impact of reliability and transported traffic demands on bandwidth cost.

Automated Contextual Anomaly Detection for Network Interface Bandwidth Utilisation: A Case Study in Network Capacity Management

We present a contextual anomaly detection methodology utilised for the capacity management process of a managed service provider that administers networks for large enterprises. We employ an ensemble of forecasts to identify anomalous network traffic. Stream of observations, upon their arrival, are compared against these baseline forecasts and alerts generated only if the anomalies are sustained. The results confirm that our approach significantly reduces false alerts, triggering rather more accurate and meaningful alerts to a level that could be proactively consumed by a small team. We believe our methodology makes a useful contribution to the applications enabling proactive capacity management.

Relay-Based Clustering Method for Interference Management in Heterogeneous Wireless Cellular Network

Femtocell is one of solutions to improve quality of services and network capacity for users in indoor areas. Radio resources used by femtocells are shared from macrocell network, thus it saves the use of frequency spectrum. However, one of problems in deploying femtocells within coverage area of macrocells is interference due to radio resources sharing between femtocells and macrocells. It creates interferences called as cross-tier (macrocell-femtocell/femtocell-macrocell) and co-tier (macrocell-macrocell/femtocell-femtocell) interferences. This paper proposes a relay-based clustering method to mitigate interference in femtocells located in the whole edge area of macrocell and the cell edge area of sectorized macrocells. Relay nodes are deployed statically (fixed location) in the neighboring macrocell area. Relay node will recruit their members based on the shortest distance. Certain relay node’s members do not need to transmit large amounts of power to enhanced Node B (eNB), such that interference from Macrocell User Equipment (MUE) to Home enhanced Node B (HeNB) can be minimized. Simulation experiments has been carried out and optimistic results for the sectorized macrocells scenario show that Signal-to-Interference-plus-Noise-Ratio (SINR) of femtocells for the conventional system that does not reach the targeted SINR of 20 dB is 87%. Meanwhile, after applying the relay-based clustering method, SINR value of femtocells below or equal to 20 dB reaches 72%. Optimistic results for throughput and Bit Error Rate (BER) show improvement of 15% and 14%, respectively. It has been shown that the relay-based clustering method can provide better performance compared to the conventional system even for femtocells densely deployed.

Vector Analysis of Electrical Networks for Temperature Measurement of MOS Power Transistors

The article presents the concept of using VNA (Vector Network Analyzer) to measure the temperature of the MOS transistor junction. The method assumes that the scattering parameters of the network consisting of the transistor depend on the temperature. The tests confirmed the influence of temperature on the S11 parameter and the input network capacity during ambient temperature changes in the range of 35–70 °C. Measurements were made for the gate-source (G-S) input of the system. The measurements were carried-out with the transistor in the ON/OFF states. In order to validate the measurements, the temperature of the tested element was recorded with the MWIR Cedip-Titanium thermal imaging camera.