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.

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Positioning in 5G and 6G Networks—A Survey

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.

/pdf/effective-resource-allocation-technique-to-improve-qos-in-5g-3g5m4f2f.pdf

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

Accurate indoor positioning is a highly requested feature in a wide range of applications, and an increasing demand is expected with the rollout of the fifth-generation (5G) cellular communication system. Although intensive research has been conducted on this topic since the 1990s, the global market still lacks a reliable, affordable, flexible, and sufficiently accurate indoor positioning system. Indoor localization over the cellular 5G network is a promising alternative for multiple reasons: no additional infrastructure costs, high availability, good control over security, and easy integration with other services due to standardization. The only questionable aspect is meeting the accuracy requirements for certain applications. However, 5G has numerous beneficial modifications and new features concerning positioning that can be exploited in the future. Our paper investigates the realization of the first TDOA-based indoor positioning system on existing 5G small cell networks, focusing primarily on the challenging effects of indoor signal propagation and possible ways to overcome them. To achieve this, we created a channel model based on real 5G measurements taken in an open-office building, and we used the obtained novel model to create a realistic simulation framework. This simulation framework was utilized to investigate the positioning performance of several algorithms. In addition to signal propagation issues, we investigate and highlight several other crucial aspects (e.g., synchronization and installation errors) that must be considered when deploying an industry-grade TDOA-based 5G positioning system.

TDoA based indoor positioning over small cell 5G networks

With the advent of 6G and its mission-critical and tactile Internet applications running in a virtualized environment on the same physical infrastructure, even the shortest service disruptions have severe consequences for thousands of users. Therefore, the network hypervisors, which enable such virtualization, should tolerate failures or be able to adapt to sudden traffic fluctuations instantaneously, i.e., should be well-prepared for such unpredictable environmental changes. In this paper, we propose a latency-aware dual hypervisor placement and control path design method, which protects against single-link and hypervisor failures and is ready for unknown future changes. We prove that finding the minimum number of hypervisors is not only NP-hard, but also hard to approximate. We propose optimal and heuristic algorithms to solve the problem. We conduct thorough simulations to demonstrate the efficiency of our method on real-world optical topologies, and show that with an appropriately selected representative set of possible future requests, we are not only able to approach the maximum possible acceptance ratio but also able to mitigate the need of frequent hypervisor migrations for most realistic latency constraints.

Resilient Control Plane Design for Virtualized 6G Core Networks

The manifold impacts of the current pandemic have highlighted the importance of reliable communication networks and services. As more and more people and services rely on this critical infrastructure, single link failure resilience is not sufficient anymore; networks must be disaster resilient. In this paper, we analyze the effects of disasters from a connectivity perspective and focus on reducing the likelihood of network disconnection in the event of a disaster through targeted link upgrades.In particular, we formalize the generalized Minimum Cost Disaster Resilient Network Upgrade Problem (DNP) (based on the previously published eFRADIR framework). We prove that this problem is NP-hard and as hard to approximate as the Knapsack Problem (KP). We present several methods for solving the DNP, in particular an ILP and two heuristics. We evaluate their performance on real networks and earthquake data and show that the upgrade cost of our disconnection probability based heuristic is only 3.5% higher than the optimum, while its resource consumption is negligible compared to the ILP.

Disaster-Resilient Network Upgrade

Indoor localization is one of the most requested applications for 5G networks. With the continuous deployment of new 5G networks, the implementation of a reliable, robust, and accurate indoor positioning algorithm has become a widely and thoroughly researched topic. However, the solutions realized for real-world environments still do not meet all performance expectations. In this paper, a real-world 5G environment is investigated and the feasibility of the measurements collected for positioning is discussed. Based on these findings, a framework for indoor positioning is proposed and evaluated using Time Difference of Arrival (TDOA) measurements. Within the framework, an iterative algorithm is implemented to minimize a non-linear least squares function extended with four additional methods to mitigate the measurement errors caused mainly by non-line-of sight propagation. The best parameter settings are determined with hyperparameter optimization, and it is shown that with these settings, sub-meter positioning error is a viable goal in certain scenarios.

Ferenc Mogyorósi

Papers

Positioning in 5G and 6G Networks—A Survey

TDoA based indoor positioning over small cell 5G networks

Resilient Control Plane Design for Virtualized 6G Core Networks

Disaster-Resilient Network Upgrade

Performance of a TDOA indoor positioning solution in real-world 5G network