Power and Channel Optimization for WiFi Networks Based on REM Data

The very high commercial exploitation of the WiFi based technologies in recent years and the absence of solutions for optimal WiFi orchestration, usually leads to suboptimal spectrum usage and user performances. The combination of WiFi based radio resource management (RRM) and the radio environmental maps (REMs) can provide an efficient solution for a Smart-WiFi technology, which improves the underlying spectrum usage as well as network performance. The REM facilitates efficient utilization of the radio environmental data, like device location, estimated channel models, real-time interference levels between the networks, WiFi channels occupancies etc. This information can be utilized for an intelligent and optimal RRM decision making in WiFi related scenarios. This paper proposes a novel REM based RRM approach for management and optimization of commercial WiFi devices that utilizes the available underlying radio environmental information. The paper demonstrates the proposed approach on a commercially available platform, conducting on-the-fly radio environmental data acquisition and optimized WiFi RRM allocation. The simulation analysis results also show that the proposed Smart-WiFi leverage noticeable performance gains for large scale scenarios, compared to conventional WiFi networks.     

Dynamic Virtual Resource Allocation in Virtualized multi-RAT Cellular Networks

The legacy wireless systems are designed to exploit static configuration and deployment, and cannot handle the discrepancies of the spatio-temporal traffic demand. Cloud RAN (C-RAN) is a novel flexible radio technology that utilizes the virtualization concepts and can efficiently address the static deployment of conventional wireless systems. The C-RAN also leverages high radio network flexibility by introducing the network function virtualization approach to wireless networks. This paper presents a novel C-RAN platform that virtualizes and operates with full GSM and LTE systems. The presented platform is solely based on open-source and off the shelf solutions, providing easy implementation, low cost and high scalability. The paper also introduces a novel dynamic resource allocation algorithm that facilitates the C-RAN’s optimal performance in dynamic scenarios. The proposed algorithm is analyzed and validated on the presented C-RAN platform. The results of the performance analysis clearly show the advantages of the proposed dynamic resource allocation algorithm. Moreover, they prove the applicability of the C-RAN platform for variety of different scenarios.     

Spectrum Sensing Framework for Cognitive Radio Networks

Spectrum sensing feature of cognitive radio devices represents a cornerstone characteristic facilitating real-time and accurate spectrum occupancy measurements in cognitive radio networks. It practically enables the cognitive radio devices to detect vacant spectrum holes and use them for their communication purposes. There are numerous spectrum sensing methods proposed in the literature ranging from local based ones to cooperative strategies among several devices increasing the confidence level of the detected spectrum. This paper gives a general spectrum sensing framework for cognitive radio networks, classifies and explores different spectrum sensing techniques and approaches and shows practical examples, from authors’ own experience, of realized spectrum sensing engines and strategies along with some obtained results.     

RSS-Based Self-Localization Framework for Future Wireless Networks

The localization is an important asset in all existing and emerging wireless networking solutions since it can extend the radio environmental awareness and assist in providing better network operation. The received signal strength (RSS) based non-Bayesian transmitter localization is especially interesting due to the inherent presence of the RSS observations in all commercial radio devices and the instantaneous estimations without the need for extensive training/learning phases. The existing RSS based localization solutions neglect the problems arising from the inherent sensing network topology uncertainty. The sensors, usually assumed to have a-priori known positions, are often a subject of a previous estimation which propagates errors in the transmitter localization procedure, and, hence, results in significant transmitter localization performance degradation. This paper presents a recently developed generic RSS based joint transmitter/sensors localization framework, founded on the assumption of uncertain topology information. The derived joint maximum likelihood (JML) algorithm simultaneously estimates the transmitter and uncertain sensor positions providing twofold gains: improving the transmitter localization and reducing the network topology uncertainty. The paper broadly evaluates the JML algorithm, emphasizing the substantial localization gains originating from the joint transmitter/sensor position estimation. The results prove up to 85 % sensor position uncertainty reduction with the general system model with multiple transmitters locations and multiple previous estimations of the sensors positions. The paper also derives the theoretical lower bounds of the joint estimation framework, and proves the convergence of the JML algorithm. The presented joint estimation framework is applicable to a variety of wireless networking applications. It can provide self-awareness in future wireless networks and cope with the environment and topology dynamism in wireless ad-hoc networks.     

Blockchain Paradigm and Internet of Things

Wireless Personal Communications - Wireless sensor network (WSN) with mobile sink serves a lot of industrial and agricultural monitoring applications. The data collection with WSN has been...     

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.     

Finding Near-Optimal Regularization Parameter for Indoor Device-free Localization

Device-free Localization (DfL) systems offer real-time indoor localization of people without any electronic devices attached on their bodies. The human body influences the radio wave propagation between wireless links and changes the Received Signal Strength (RSS). Wireless Sensor Networks (WSNs) nodes easily measure these RSS changes and appropriate Radio Tomographic Imaging (RTI) algorithms can then process the RSS data and allow human localization. This paper investigates how to choose near-optimal regularization parameter during the regularization process for indoor DfL and describes an experimental indoor DfL setup realized with a Sun SPOT based WSN. The work elaborates on the numerical calculation of the near-optimal regularization parameter by usage of the trade-off curve criterion. The calculated parameter enables conclusive RTI image with sufficient localization precision for eHealth or other ambient-assisted-living applications where the error tolerance is at a scale of several tens of centimeters. The value for the regularization parameter matches the empirical derived value obtained in the authors’previous work.     

Optimal Cooperative Spectrum Sensing Under Faded and Bandwidth Limited Control Channels

Most of the cooperative spectrum sensing related research assumes system models with perfect control channel (i.e. with unlimited channel bandwidth and no channel errors). However, the assumption is not realistic and can lead to incorrect conclusions regarding the performance analysis of the cooperative spectrum sensing detection capabilities. This paper proposes a novel cooperative spectrum sensing framework that mitigates the imperfect control channel features, like the limited control channel bandwidth and error proneness, and achieves the detection performances of cooperative spectrum sensing under ideal control channel. It utilizes node clustering and multi-antenna spatial multiplexing (i.e. beamforming) and provides a generic framework that can be exploited by any cooperative spectrum sensing and fusion technique. The performance analysis shows that the proposed framework alleviates the control channel bandwidth limitation and significantly decreases the control channel error rate. The performance evaluation results also show that the proposed framework achieves the upper bound detection performances, i.e. achieves the same detection performances as the conventional cooperative spectrum sensing with ideal control channel.     

Potentials for Application of Millimeter Wave Communications in Cellular Networks

Future 5G cellular networks will need to deliver significantly increased system capacity and user data rates. This expected growth along with today’s shortage of spectrum raises the need for new frequency allocations. Millimeter wave spectrum is emerging as a suitable candidate with a vast amount of available bandwidth (around 60 GHz). Extending cellular networks communications on millimeter wave frequencies requires extensive measurement campaigns and analysis of signals propagation characteristics. This paper gives an overview of recent measurement studies and results used for modeling millimeter wave channel behavior in different propagation environments. Also , the paper provides a preliminary simulation analysis of a hybrid LTE-millimeter wave heterogeneous network, which suggests that Gbps user data rates are achievable with sufficient beamforming gains. However, the millimeter wave cellular extensions will require architectural changes to address the technical issues spanning from the transceivers design to the operational procedures in both access and backhaul network parts.     

Visions Towards 5G: Technical Requirements and Potential Enablers

Compared to the previous generations of mobile networks, 5G will provide a significant paradigm shift by including beyond state of the art technical solutions, like very high carrier frequencies with massive bandwidths, extreme base station and device densities, and very high number of transceiver antennas. However, unlike the previous generations, it will also be highly integrative and backward compatible: combining the novel 5G air interface and spectrum together with legacy wireless systems like LTE/LTE-A and WiFi, in order to facilitate an umbrella of high-rate coverage and a seamless user experience. In order to support this advances in the radio interface, the core network will also have to reach unprecedented levels of elasticity and intelligence. Spectrum regulation will need to be rethought and significantly improved, whereas energy and cost efficiencies will become one of the key parameters that will steer the 5G design and development. This paper elaborates on the 5G related topics, identifying the key challenges for future research and preliminary 5G standardization activities, as well as providing a comprehensive survey of the current literature.