Optimal 3D UAV BS Placement by Considering Autonomous Coverage Hole Detection, Wireless Backhaul and User Demand

The rising number of technological advanced devices making network coverage planning very challenging tasks for network operators. The transmission quality between the transmitter and the end users has to be optimum for the best performance out of any device. Besides, the presence of coverage hole is also an ongoing issue for operators which cannot be ignored throughout the whole operational stage. Any coverage hole in network operators' coverage region will hamper the communication applications and degrade the reputation of the operator's services. Presently, there are techniques to detect coverage holes such as drive test or minimization of drive test. However, these approaches have many limitations. The extreme costs, outdated information about the radio environment and high time consumption do not allow to meet the requirement competently. To overcome these problems, we take advantage of Unmanned aerial vehicle (UAV) and Q-learning to autonomously detect coverage hole in a given area and then deploy UAV based base station (UAV-BS) by considering wireless back-haul with the core network and users demand. This machine learning mechanism will help the UAV to eliminate human-in-the-loop (HiTL) model. Later, we formulate an optimisation problem for 3D UAV-BS placement at various angular positions to maximise the number of users associated with the UAV-BS. In summary, we have illustrated a cost-effective as well as time saving approach of detecting coverage hole and providing on-demand coverage in this article

Guest Editorial: Design and Analysis of Communication Interfaces for Industry 4.0

This special issue (SI) aims to present recent advances in the design and analysis of communication interfaces for Industry 4.0. The Industry 4.0 paradigm aims to integrate advanced manufacturing techniques with Industrial Internet-of-Things (IIoT) to create an agile digital manufacturing ecosystem. The main goal is to instrument production processes by embedding sensors, actuators and other control devices which autonomously communicate with each other throughout the value-chain [1]

On the bits per joule optimization in cellular cognitive radio networks

© 2014 IEEE. Cognitive radio has emerged as a promising paradigm to improve the spectrum usage efficiency and to cope with the spectrum scarcity problem through dynamically detecting and re-allocating white spaces in licensed radio band to unlicensed users. However, cognitive radio may cause extra energy consumption because it relies on new and extra technologies and algorithms. The main objective of this work is to enhance the energy efficiency of proposed cellular cognitive radio network (CRN), which is defined as bits/Joule/Hz. In this paper, a typical frame structure of a secondary user (SU) is considered, which consists of sensing and data transmission slots. We analyze and derive the expression for energy efficiency for the proposed CRN as a function of sensing and data transmission duration. The optimal frame structure for maximum bits per joule is investigated under practical network traffic environments. he impact of optimal sensing time and frame length on the achievable energy efficiency, throughput and interference are investigated and verified by simulation results compared with relevant state of art. Our analytical results are in perfect agreement with the empirical results and provide useful insights on how to select sensing length and frame length subject to network environment and required network performance

Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective

Indoor air quality typically encompasses the ambient conditions inside buildings and public facilities that may affect both the mental and respiratory health of an individual. Until the COVID-19 outbreak, indoor air quality monitoring was not a focus area for public facilities such as shopping complexes, hospitals, banks, restaurants, educational institutes, and so forth. However, the rapid spread of this virus and its consequent detrimental impacts have brought indoor air quality into the spotlight. In contrast to outdoor air, indoor air is recycled constantly causing it to trap and build up pollutants, which may facilitate the transmission of virus. There are several monitoring solutions which are available commercially, a typical system monitors the air quality using gas and particle sensors. These sensor readings are compared against well known thresholds, subsequently generating alarms when thresholds are violated. However, these systems do not predict the quality of air for future instances, which holds paramount importance for taking timely preemptive actions, especially for COVID-19 actual and potential patients as well as people suffering from acute pulmonary disorders and other health problems. In this regard, we have proposed an indoor air quality monitoring and prediction solution based on the latest Internet of Things (IoT) sensors and machine learning capabilities, providing a platform to measure numerous indoor contaminants. For this purpose, an IoT node consisting of several sensors for 8 pollutants including NH3, CO, NO2, CH4, CO2, PM 2.5 along with the ambient temperature & air humidity is developed. For proof of concept and research purposes, the IoT node is deployed inside a research lab to acquire indoor air data. The proposed system has the capability of reporting the air conditions in real-time to a web portal and mobile app through GSM/WiFi technology and generates alerts after detecting anomalies in the air quality. In order to classify the indoor air quality, several machine learning algorithms have been applied to the recorded data, where the Neural Network (NN) model outperformed all others with an accuracy of 99.1%. For predicting the concentration of each air pollutant and thereafter predicting the overall quality of an indoor environment, Long and Short Term Memory (LSTM) model is applied. This model has shown promising results for predicting the air pollutants’ concentration as well as the overall air quality with an accuracy of 99.37%, precision of 99%, recall of 98%, and F1-score of 99%. The proposed solution offers several advantages including remote monitoring, ease of scalability, real-time status of ambient conditions, and portable hardware, and so forth

Hadamard upper bound on optimum joint decoding capacity of Wyner Gaussian cellular MAC

This article presents an original analytical expression for an upper bound on the optimum joint decoding capacity of Wyner circular Gaussian cellular multiple access channel (C-GCMAC) for uniformly distributed mobile terminals (MTs). This upper bound is referred to as Hadamard upper bound (HUB) and is a novel application of the Hadamard inequality established by exploiting the Hadamard operation between the channel fading matrix G and the channel path gain matrix Ω. This article demonstrates that the actual capacity converges to the theoretical upper bound under the constraints like low signal-to-noise ratios and limiting channel path gain among the MTs and the respective base station of interest. In order to determine the usefulness of the HUB, the behavior of the theoretical upper bound is critically observed specially when the inter-cell and the intra-cell time sharing schemes are employed. In this context, we derive an analytical form of HUB by employing an approximation approach based on the estimation of probability density function of trace of Hadamard product of two matrices, i.e., G and Ω. A closed form of expression has been derived to capture the effect of the MT distribution on the optimum joint decoding capacity of C-GCMAC. This article demonstrates that the analytical HUB based on the proposed approximation approach converges to the theoretical upper bound results in the medium to high signal to noise ratio regime and shows a reasonably tighter bound on optimum joint decoding capacity of Wyner GCMAC

ICAR: endoscopic skull‐base surgery

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Distance based cooperation region for D2D pair

Device-to-device (D2D) communication is being considered an important traffic offloading mechanism for future cellular networks. Coupled with pro-active device caching, it offers huge potential for capacity and coverage enhancements. In order to ensure maximum capacity enhancement, number of nodes for direct communication needs to be identified. In this paper, we derive analytic expression that relates number of D2D nodes (i.e., D2D user density) and average coverage probability of reference D2D receiver. Using stochastic geometry and poisson point process, we introduce retention probability within cooperation region and shortest distance based selection criterion to precisely quantify interference due to D2D pairs in coverage area. The simulation setup and numerical evaluation validates the closed-form expression