Context-Aware Holographic Communication Based on Semantic Knowledge Extraction

Augmented, mixed and virtual reality are changing the way people interact and communicate. Five dimensional communications and services, integrating information from all human senses are expected to emerge, together with holographic communications (HC), providing a truly immersive experience. HC presents a lot of challenges in terms of data gathering and transmission, demanding Artificial Intelligence empowered communication technologies such as 5G. The goal of the paper is to present a model of a context-aware holographic architecture for real time communication based on semantic knowledge extraction. This architecture will require analyzing, combining and developing methods and algorithms for: 3D human body model acquisition; semantic knowledge extraction with deep neural networks to predict human behaviour; analysis of biometric modalities; context-aware optimization of network resource allocation for the purpose of creating a multi-party, from-capturing-to-rendering HC framework. We illustrate its practical deployment in a scenario that can open new opportunities in user experience and business model innovation.

     

Implementation of Machine Learning for Autonomic Capabilities in Self-Organizing Heterogeneous Networks

The 3GPP’s self-organizing networks (SONs) standards are a huge step towards the autonomic networking concept. They are the response to the increasing complexity and size of the mobile networks. This paper proposes a novel scheme for SONs. This scheme is based on machine learning techniques and additionally adopting the concept of abstraction and modularity. The implementation of these concepts in a machine learning scheme allows the usage of independent vendor and technology algorithms and reusability of the proposed approach for different optimization tasks in a network. The scheme is tested for solving an energy saving optimization problem in a heterogeneous network. The results from simulation experiments show that such an approach could be an appropriate solution for developing a full self-managing future network.     

Multilayered Distributed Routing for Power Efficient MANET Performance

In this paper a new power efficient routing algorithm for MANETs with self-organizing and self-routing features is described and its performance analyzed in different simulation scenarios. The algorithm has the logic of a non-cooperative routing algorithm based on the evaluation of a weight parameter, the latter being a function of properties of the MANET nodes related to the nominal available power and the transmission range. A self-estimation of this weight parameter for each node is introduced in the routing process based on the status and functional history of the node. The routing is based on network layering, formation of service areas in each layer and choice of nodes from these areas to have the functionality of default gateways. The proposed algorithm, named service zone gateway prediction (SZGP), is a hybrid type of routing mechanism, incorporating pre-computed multipath hop-by-hop distributed routing, with a periodically updated hierarchical multilayered structure. The results from the simulation experiments show that the performance of the proposed SZGP algorithm in relation to the basic performance parameters such as packet delivery ratio, delay and throughput are similar to those of the well-known AODV algorithm, but in relation to power efficiency the proposed algorithm outperforms AODV significantly. This is due to the fact that such an approach reduces the overall number of broadcasts in the network and ensures a reliable and energy efficient connection by balancing the load among the nodes.     

Resource Management Based on Dynamic Users Association for Future Heterogeneous Telecommunication Access Infrastructures

In this paper an approach for resource management for Heterogeneous Networks (HetNets) based on the dynamic association of users in sets is proposed. The approach is based on the consideration that in a telecommunication network every access point has the property of limited performance, capacity and service potential. Through the definition of two generalized parameters of the servicing cell a characteristic model of the servicing properties of a HetNet access point is developed. An optimization task is defined for a resource management approach with the goal of “providing user service with the required quality of Service while minimizing servicing costs”. The task is solved via the introduction of a modified ADD/DROP heuristic algorithm. A comparison with two other heuristic algorithms is performed showing some of the advantages of the proposed algorithm such as its low computational complexity, fast convergence and effectiveness of the heuristic procedure. A sample application for transmission power control is presented.     

Long-Term Spectrum Monitoring with Big Data Analysis and Machine Learning for Cloud-Based Radio Access Networks

Spectrum monitoring is important for efficient spectrum sharing and resource management in cloud-based radio access networks (C-RAN). In this paper we show how data obtained from long-term spectrum monitoring together with machine learning (ML) operating on big data (BD) can be used in a C-RAN scenario for spectrum management purposes. We propose an approach for spectrum occupancy forecasting which can be used to reduce the delay in making dynamic spectrum allocation decisions and improve the cognitive and management functionalities of cloud-based architectures such as C-RAN. The spectrum occupancy and usage activity in a predefined frequency band is based on the statistical processing of a large amount of collected data and the introduction of a frequency–time resources indicator as a measure of spectrum usage. Furthermore, we apply ML algorithms to predict spectrum usage and compare the predicted with actual measured data. Taking into consideration that the accuracy of the prediction depends on the volume of collected data and the time of prediction on the BD and ML approach, we propose the development of a cloud-based generic processing architecture to solve the “accuracy versus latency” trade-off problem. The proposed architecture is appropriate for deployment in cognitive C-RAN.     

eWALL: An Open-Source Cloud-Based eHealth Platform for Creating Home Caring Environments for Older Adults Living with Chronic Diseases or Frailty

Independent living of older adults is one of the main challenges linked to the ageing population. Especially those living with diseases like COPD, MCI or frailty, need more support in everyday life and this is by itself a big societal challenge with impact in multiple sectors. In this paper we present eWALL, an innovative open-source eHealth platform that aims to address these challenges by means of an advanced cloud-based infrastructure. eWALL is designed in an innovative manner and achieved technical breakthroughs in eHealth platforms, while prioritizing user and market needs that are often abandoned and are the major reason for technically sound solutions that fail. We consider this as an opportunity and we aim to change the eHealth systems’ experience for older adults and break the barriers for the penetration of ICT solutions.     

Digitizing Human Behavior with Wireless Sensors in Biogas 2020 Technological Business Model Innovation Challenges

Wireless Personal Communications - The ability to develop innovative Business Models (BMs) with high speed and good Return of Investment has become the cornerstone for the competitiveness of many...     

Disruptive Innovations as a Driving Force for the Change of Wireless Telecommunication Infrastructures

Innovations are the driving force for fundamental changes and development of future generation telecommunication networks. When considering innovations as drivers for the development of next generation telecommunication networks an important question is whether an innovative emerging technology would cause a disruption requiring fundamental change of the infrastructure or will it will only catalyze its evolution. This paper describes the major characteristics of disruptive innovations as a driving force for fundamental changes in existing telecommunications infrastructures. By analyzing the current trends in mobile communications, we reason that the emergence of new telecommunication architectures and infrastructures is inevitable. An important consideration in the analysis is the driving role of disruptive technologies for the future of telecommunications. Based on a model for evaluating the potential disruptiveness of a technology, we provide examples of some new emerging technologies in telecommunications that have the potentials to alter the telecommunication infrastructure landscape.     

Dynamic Self-Optimization of the Antenna Tilt for Best Trade-off Between Coverage and Capacity in Mobile Networks

One major factor influencing the coverage and capacity in mobile networks is related to the configuration of the antennas and especially the antenna tilt angle. By utilizing antenna tilt, signal reception within a cell can be improved and interference radiation towards other cells can be effectively reduced, which leads to a higher signal-to-interference-plus-noise ratio received by the users and increased sum data rate in the network. In this work, a method for capacity and coverage optimization using base station antenna electrical tilt in mobile networks is proposed. It has the potential to improve network performance while reducing operational costs and complexity, and to offer better quality of experience for the mobile users. Our solution is based on the application of reinforcement learning and the simulation results show that the algorithm improves significantly the overall data rate of the network, as compared to no antenna tilt optimization. The analysis in this paper focuses on the downlink of the cellular system. For the simulation experiments a multicellular and sectorized mobile network in an urban environment and randomly distributed user terminals are considered. The main contribution in this work is related to the development of a learning algorithm for automated antenna tilting.