A 5G wireless network requires an efficient approach to effectively manage and segment the resource. A Centralized Radio Access Network (CRAN) is used to handle complex distributed networks. Specific to network infrastructure, multicast communication is considered in the performance of data storage and information-based network connectivity. This paper proposes a modified Resource Allocation (RA) scheme for effectively handling the RA problem using a learning-based Resource Segmentation (RS) technique. It uses a modified Random Forest Algorithm (RFA) with Signal Interference and Noise Ratio (SINR) and position coordinates to obtain the position coordinates of end-users. Further, it predicts Modulation and Coding Schemes (MCS) for establishing a connection between the end-user device and the Remote Radio Head (RRH). The proposed algorithm depends on the accuracy of positional coordinates for the correctness of the input parameters, such as SINR, based on the position and orientation of the antenna. The simulation analysis renders the efficiency of the proposed technique in terms of throughput and energy efficiency.

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Effective Resource Allocation Technique to Improve QoS in 5G Wireless Network

New Sixth-generation (6G) networks rely heavily on the Intelligence Internet of Things (IIoT) to store and process data more efficiently. 6G is desired to offer ultra-low latency, high bandwidth, and improvised quality of service that can effectively handle the communication among the nodes. All the healthcare facilities must be outfitted with cutting-edge technology to assist the individual with intelligent diagnosis, patient-centric treatment, and a range of other healthcare services both in the hospital and remotely. To make the system ready and adaptable to the technology and provide services to divergent applications ranging from robotic surgeries to remote monitoring of the patients through wearable technologies in an Ambient Assistive Living (AAL) environment over the intelligent networking platform. Various networking nodes and terminal devices provide the services for applications in the healthcare domain, which needs a backbone framework to deliberate the time-intensive services. This paper proposes a reference layered communication framework for the nodes and devices in real-time communication. The feature perspective aspects of 6G technology present the futuristic healthcare application for effective treatment and smart integration of services. 

/pdf/6g-driven-fast-computational-networking-framework-for-1j990bgw.pdf

6G Driven Fast Computational Networking Framework for Healthcare Applications

New Sixth-generation (6G) networks rely heavily on the Intelligence Internet of Things (IIoT) to store and process data more efficiently. 6G is desired to offer ultra-low latency, high bandwidth, and improvised quality of service that can effectively handle the communication among the nodes. All the healthcare facilities must be outfitted with cutting-edge technology to assist the individual with intelligent diagnosis, patient-centric treatment, and a range of other healthcare services both in the hospital and remotely. To make the system ready and adaptable to the technology and provide services to divergent applications ranging from robotic surgeries to remote monitoring of the patients through wearable technologies in an Ambient Assistive Living (AAL) environment over the intelligent networking platform. Various networking nodes and terminal devices provide the services for applications in the healthcare domain, which needs a backbone framework to deliberate the time-intensive services. This paper proposes a reference layered communication framework for the nodes and devices in real-time communication. The feature perspective aspects of 6G technology present the futuristic healthcare application for effective treatment and smart integration of services.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09870788.pdf

5G NR mm Wave promises accurate positioning down to the centimeter level. However, mmWave signals endure prismatic propagation, making them prone to signal blockages and non-line of sight (NLoS) communications. To achieve a precise positioning solution, it is rather essential to filter out NLoS gNBs as they yield erroneous pose estimation of IoT vehicular applications. Previous works have attempted to address this issue, however, they are either based on impractical or invalid assumptions about the operation scenario. In this paper, a novel, yet, simple and realistic NLoS detection algorithm is developed. The proposed method measures the discrepancy between Received Signal Strength (RSS)-based and time-based ranges as means to detect NLoS operation. To validate the proposed NLoS detection method, it was incorporated into a Kalman Filter (KF) that fuses the angle of departure (AoD) and round trip time (RTT) measurements from multiple gNBs in a loosely coupled fashion. The proposed method was evaluated on quasi-real measurements acquired from a highly validated 5G simulation tool that simulates the cores of downtown Toronto. The proposed method demonstrates superior results, as it sustains a sub-1m level of accuracy for around 95% of the time, as compared to merely 29% of the time without the NLoS detection.

NLoS Detection for Enhanced 5G mmWave-based Positioning for Vehicular IoT Applications

The Industry 4.0 initiative has been showing the way for industrial production to optimize operations based on collecting, processing, and sharing data. There are new requirements on the production floor: flexible but ultra-reliable, low latency wireless communications through interoperable systems can share data. Further challenges of data sharing and storage arise when diverse systems come into play at the Manufacturing Operations Management and Business Planning & Logistics levels. The emerging complex cyber-physical systems of systems need to be engineered with care. Regarding industrial requirements, the telecommunication industry has many similarities to production—including ultra-reliability, high complexity, and having humans “in-the-loop”. The current paper aims to provide an overview of converging telco-grade solutions that can be successfully applied in the wide sense of industrial production. These toolsets range from model-driven engineering through system interoperability frameworks, 5G- and 6G-supported manufacturing, and the telco-cloud to speech recognition in noisy environments.

/pdf/converging-telco-grade-solutions-5g-and-beyond-to-support-1kr0zkr7.pdf

Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0

Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning—indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios.

/pdf/positioning-in-5g-and-6g-networks-a-survey-1wi6haky.pdf

Positioning in 5G and 6G Networks—A Survey

The HERe2Cure project, which involved a group of breast cancer experts, members of multidisciplinary tumor boards from healthcare institutions in Bosnia and Herzegovina, was initiated with the aim of defining an optimal approach to the diagnosis and treatment of HER2 positive breast cancer. After individual multidisciplinary consensus meetings were held in all oncology centers in Bosnia and Herzegovina, a final consensus meeting was held in order to reconcile the final conclusions discussed in individual meetings. Guidelines were adopted by consensus, based on the presentations and suggestions of experts, which were first discussed in a panel discussion and then agreed electronically between all the authors mentioned. The conclusions of the panel discussion represent the consensus of experts in the field of breast cancer diagnosis and treatment in Bosnia and Herzegovina. The objectives of the guidelines include the standardization, harmonization and optimization of the procedures for the diagnosis, treatment and monitoring of patients with HER2-positive breast cancer, all of which should lead to an improvement in the quality of health care of mentioned patients. The initial treatment plan for patients with HER2-positive breast cancer must be made by a multidisciplinary tumor board comprised of at least: a medical oncologist, a pathologist, a radiologist, a surgeon, and a radiation oncologist/radiotherapist.

/pdf/2020-consensus-guideline-for-optimal-approach-to-the-lfn9roomo9.pdf

2020 consensus guideline for optimal approach to the diagnosis and treatment of HER2-positive breast cancer in Bosnia and Herzegovina.

In this work we introduce the concept and method of so-called cooperative solar generation forecasting, where geographically close data sources are utilized in order to improve forecasting accuracy. We devised and examined various largescale one-hour-ahead artificial neural networks based solar generation forecasting scenarios to prove the benefits of cooperation. The introduced cooperative solar generation forecasting method showed significant improvement in forecasting accuracy, especially when combined with previous generation data, where a root mean square error reduction of at least 50% could be achieved in the majority of cases. We believe these results point to a scientific and economical benefit of international cooperation in solar generation forecasting.

/pdf/ann-based-large-scale-cooperative-solar-generation-6zvd7x2r.pdf

ANN-Based Large-Scale Cooperative Solar Generation Forecasting

Even though interest in Machine Learning Based Clinical Decision Support Systems (ML-CDSS) has been rapidly growing in recent years, most research and development is exclusively focused on secondary and tertiary care – even though effective diagnostic support in primary care could significantly improve both the circumstances, the process and the outcome of general practice. In this paper, we study the suitability of five supervised machine learning algorithms to the problem of multiclass classification with sparse Boolean features on a primary care data set, and we examine the robustness of the algorithms to incomplete data. We introduce our own classification algorithm, the Artificial Intelligence Based Diagnostic Assistant (AIDA), which is capable of incorporating both symptoms and contextual information into its diagnostic process, thus modeling the decision-making of physicians in a novel and accurate manner. Through our experimental results we show that AIDA is by far the most suitable classification algorithm for ML-CDSS applications in primary care, owing to its high accuracy and outstanding robustness to missing, sparse information.

Azra Pašić

Papers

Positioning in 5G and 6G Networks—A Survey

2020 consensus guideline for optimal approach to the diagnosis and treatment of HER2-positive breast cancer in Bosnia and Herzegovina.

ANN-Based Large-Scale Cooperative Solar Generation Forecasting

The Artificial Intelligence Based Diagnostic Assistant – AIDA