Maximizing Detection of Target with Multiple Direction Possibilities to Support Immersive Communications in Metaverse

The rise of immersive communication due to virtual reality (VR), augmented reality (AR) and mixed reality (MR) has imposed stringent requirements on the wireless communication systems. A basic requirement imposed by VR/AR/MR environments (in a Metaverse) on the communication system is the sensing ability. Therefore, integrated sensing and communication (ISAC) systems are considered an integral part of the Metaverse. In order to improve the sensing functionality of the ISAC system within the Metaverse, this paper proposes an iterative optimization algorithm to solve non-convex signal to clutter plus noise ratio (SCNR) maximization problem when the target direction is uncertain. Simulation results demonstrate that the effectiveness of the proposed algorithm as compared to the existing schemes which assume apriori information about the target direction.Peer reviewe

Performance Assessment of an ITU-T Compliant Machine Learning Enhancements for 5 G RAN Network Slicing

Network slicing is a technique introduced by 3GPP to enable multi-tenant operation in 5 G systems. However, the support of slicing at the air interface requires not only efficient optimization algorithms operating in real time but also its tight integration into the 5 G control plane. In this paper, we first present a priority-based mechanism enabling defined performance isolation among slices competing for resources. Then, to speed up the resource arbitration process, we propose and compare several supervised machine learning (ML) techniques. We show how to embed the proposed approach into the ITU-T standardized ML architecture. The proposed ML enhancement is evaluated under realistic traffic conditions with respect to the performance criteria defined by GSMA while explicitly accounting for 5 G millimeter wave channel conditions. Our results show that ML techniques are able to provide suitable approximations for the resource allocation process ensuring slice performance isolation, efficient resource use, and fairness. Among the considered algorithms, polynomial regressions show the best results outperforming the exact solution algorithm by 5–6 orders of magnitude in terms of execution time and both neural network and random forest algorithms in terms of accuracy (by 20–40 %), sensitiveness to workload variations and training sample size. Finally, ML algorithms are generally prone to service level agreements (SLA) violation under high load and time-varying channel conditions, implying that an SLA enforcement system is needed in ITU-T's 5 G ML framework.publishedVersionPeer reviewe

Comparison of Machine Learning Algorithms for Priority-Based Network Slicing in 5G Systems

Network slicing is a technique to enable multi-tenant operation in future 5G systems. Efficient implementation of slicing at the air interface requires comprehensive optimization algorithms characterized by high execution complexity. To address this issue in the paper, we first present a priority-based mechanism enabling performance isolation between slices competing for resources. Then, to speed up the resource arbitration process under high traffic conditions, when resource shares need to be re-calculated in sub-second timescales, we propose and compare several machine learning techniques: linear regression, polynomial regression, a random forest regressor, and a two-layer artificial neural network. The techniques' performance is assessed by utilizing the mean squared error. Our results show that a high order polynomial regression provides the desired balance between computational complexity and accuracy, outperforming both the simpler linear regression and the more complex random forest and neural network algorithms.acceptedVersionPeer reviewe

Dynamic Topology Organization and Maintenance Algorithms for Autonomous UAV Swarms

The swarms of unmanned aerial vehicles (UAV) are nowadays finding numerous applications in different fields. While performing their missions, UAVs have to rely on external positioning information to maintain connectivity and communications between units in a swarm. However, some of the critical applications such as rescue missions are performed in locations, where this information is partially or fully not available, e.g., deep woods, mountains, indoors. In this paper, we propose a method for dynamic topology organization and maintenance in UAV swarms. In addition to the baseline functionality, we also design advanced features required for dynamic swarms merging and disjoining, making it suitable for practical applications. Specifically, the proposal is based on the virtual coordinates system allowing for the utilization of conventional geographical routing algorithms. We test the proposed algorithm in different swarm conditions to illustrate that: (i) it is insensitive to distance estimates up to at least 30% allowing for simple estimation techniques, (ii) the accuracy of the topology inference is at least 90% even under impairments caused by mobility and temporal loss of connectivity, and (iii) the impact of the developed merging algorithm for swarms lasts for multiple tens of time steps that correspond to just few seconds in practice. The set of developed algorithms can be utilized to ensure always connected topology in conditions where positioning information is partially or fully unavailable.acceptedVersionPeer reviewe

The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR Systems

Multicasting is a key feature of cellular systems, which provides an efficient way to simultaneously disseminate a large amount of traffic to multiple subscribers. However, the efficient use of multicast services in fifth-generation (5G) New Radio (NR) is complicated by several factors, including inherent base station (BS) antenna directivity as well as the exploitation of antenna arrays capable of creating multiple beams concurrently. In this work, we first demonstrate that the problem of efficient multicasting in 5G NR systems can be formalized as a special case of multi-period variable cost and size bin packing problem (BPP). However, the problem is known to be NP-hard, and the solution time is practically unacceptable for large multicast group sizes. To this aim, we further develop and test several machine learning alternatives to address this issue. The numerical analysis shows that there is a trade-off between accuracy and computational complexity for multicast grouping when using decision tree-based algorithms. A higher number of splits offers better performance at the cost of an increased computational time. We also show that the nature of the cell coverage brings three possible solutions to the multicast grouping problem: (i) small-range radii are characterized by a single multicast subgroup with wide beamwidth, (ii) middle-range deployments have to be solved by employing the proposed algorithms, and (iii) BS at long-range radii sweeps narrow unicast beams to serve multicast users.acceptedVersionPeer reviewe

Optimal Multicasting in Dual mmWave/ μ Wave 5G NR Deployments With Multi-Beam Directional Antennas

The design of multicast services in the fifth-generation (5G) New Radio (NR) deployments is hampered by the directional nature of antenna radiation patterns. This complexity is further compounded by the emergence of new deployment options, such as dual millimeter wave (mmWave) and microwave (μ Wave) base station (BS) deployments, as well as new antenna design solutions. In this paper, the resource allocation task for multicast services in dual mmWave/ μ Wave deployments with multi-beam directional antennas is addressed as a multi-period variable cost and size bin packing problem. We solve this problem and characterize the globally optimal solution. To decrease complexity, we then propose and test the simulated annealing approximation and relaxation techniques, i.e., local branching and relaxation-induced neighborhood search heuristic. Our results show that for the considered system parameters, the properties of the optimal solution depend on the density of dual-mode BS deployment and BS deployment type. We observe a transition point at which the system shifts from primarily utilizing mmWave resources to exclusively using μ Wave BS. Furthermore, the optimal number of beams is upper limited by 3 for mmWave and by 2 for μ Wave BSs. The efficiency of resource utilization is also affected by the utilized numerology and technology selection priority. Finally, we show that the simulated annealing technique allows for decreasing the solution complexity at the expense of slightly overestimating the amount of resources.Peer reviewe

Poverty Research Based on Small Area Model-Based Estimators and Big Data Sources

The presented thesis is an interdisciplinary study in the field of official statistics and big data. The main purpose of the thesis is to investigate the problem of poverty in small areas by analyzing population data obtained from official surveys and big data sources. To achieve this goal, the following tasks were completed. Analysis of methods of poverty estimation indicators in small areas has been conducted. The big data analysis methods for extracting poverty-related metrics from small area data was studied. The software tools for analyzing big data, small territories and calculating poverty metrics were developed. A case study on small area territories in Chile was designed and conducted

GAR : Gradient assisted routing for topology self-organization in dynamic mesh networks

Modern mobile handheld devices, such as smartphones and tablets, feature multiple wireless interfaces, some of which can support device-to-device communications, which enable mesh networks on even when the infrastructure is unavailable. One of the key technological challenges hampering the use of multi-hop mesh networks is the extremely high communication overhead of route discovery and maintenance algorithms. The problem is especially pronounced under dynamic network conditions caused by user mobility and nodes joining and leaving the network. In this paper, we propose a fully distributed algorithm for constructing a virtual coordinate system used for geo-like routing by approximating the physical network nodes coordinate. The proposed algorithm, called gradient assisted routing (GAR), builds upon two-hop neighbors’ information exchanged in beacons in contrast to conventional geographic routing protocols which rely on external positioning information. We evaluate the proposed solution using algorithmic, topological, and routing-related metrics of interest. We further numerically quantify how the node mobility increases the time needed for topology stabilization, and how network size affects the route discovery success rate. Our comparison also shows that for small to mid-size mesh networks (up to 60 nodes), the performance of the proposed routing procedure is similar to the conventional geographic routing protocols that exploit external positioning information. The proposed solution may efficiently supplement the traditional on-demand routing in small to mid-size mesh systems by independently establishing 50 to 70% of paths and thereby reducing the discovery overheads.publishedVersionPeer reviewe