Channel modelling for MIMO systems

Systems equipped with multiple antennas at the transmitter and at the receiver, known as MIMO (Multiple Input Multiple Output) systems, offer higher capacities, allowing an efficient exploitation of the available spectrum and/or the employment of more demanding applications. It is well known that the radio channel is characterized by multipath propagation, a phenomenon deemed problematic and whose mitigation has been achieved through techniques such as diversity, beamforming or adaptive antennas. By exploring conveniently the spatial domain MIMO systems turn the characteristics of the multipath channel into an advantage and allow creating multiple parallel and independent virtual channels. However, the achievable benefits are constrained by the propagation channel’s characteristics, which may not always be ideal. This work focuses on the characterization of the MIMO radio channel. It begins with the presentation of the fundamental results from information theory that triggered the interest on these systems, including the discussion of some of their potential benefits and a review of the existing channel models for MIMO systems. The characterization of the MIMO channel developed in this work is based on experimental measurements of the double-directional channel. The measurement system is based on a vector network analyzer and a two-dimensional positioning platform, both controlled by a computer, allowing the measurement of the channel’s frequency response at the locations of a synthetic array. Data is then processed using the SAGE (Space-Alternating Expectation-Maximization) algorithm to obtain the parameters (delay, direction of arrival and complex amplitude) of the channel’s most relevant multipath components. Afterwards, using a clustering algorithm these data are grouped into clusters. Finally, statistical information is extracted allowing the characterization of the channel’s multipath components. The information about the multipath characteristics of the channel, induced by existing scatterers in the propagation scenario, enables the characterization of MIMO channel and thus to evaluate its performance. The method was finally validated using MIMO measurements.

Monitoring sandy shores morphologies by DGPS: a practical tool to generate digital elevation models

The highly dynamic nature of some sandy shores with continuous morphological changes require the development of efficient and accurate methodological strategies for coastal hazard assessment and morphodynamic characterisation. During the past decades, the general methodological approach for the establishment of coastal monitoring programmes was based on photogrammetry or classical geodetic techniques. With the advent of new geodetic techniques, space-based and airborne-based, new methodologies were introduced in coastal monitoring programmes. This paper describes the development of a monitoring prototype that is based on the use of global positioning system (GPS). The prototype has a GPS multiantenna mounted on a fast surveying platform, a land vehicle appropriate for driving in the sand (four-wheel quad). This system was conceived to perform a network of shore profiles in sandy shores stretches (subaerial beach) that extend for several kilometres from which high-precision digital elevation models can be generated. An analysis of the accuracy and precision of some differential GPS kinematic methodologies is presented. The development of an adequate survey methodology is the first step in morphodynamic shore characterisation or in coastal hazard assessment. The sample method and the computational interpolation procedures are important steps for producing reliable three-dimensional surface maps that are real as possible. The quality of several interpolation methods used to generate grids was tested in areas where there were data gaps. The results obtained allow us to conclude that with the developed survey methodology, it is possible to Survey sandy shores stretches, under spatial scales of kilometers, with a vertical accuracy of greater than 0.10 m in the final digital elevation models.

Vaness: DNS for nomadic users in vehicular networks

Vehicular networks, also known as VANETs, are an ad-hoc network formed by vehicles and road-side units. Nowadays they have been attracting big interest both from researchers as from the automotive industry. With the upcoming of automotive specific operating systems and self-driving cars, the use of applications on vehicles and the integration with common mobile devices is becoming a big part of VANETs. Although many advances have been made on this field, there is still a big discrepancy between the communication layer services provided by VANETs and the user level services, namely those accessible through mobile applications on other networks and technologies. Users and developers are accustomed to user-to-user or user-tobusiness communication without explicit concerns related with the available communication transport layer. Such is not possible in VANETs since people may use more than one vehicle. However, to send a message to a specific user in these networks, there is a need to know the ID of the vehicle where the user is, meaning that there is a lack of services that map each individual user to VANETs endpoint (vehicle identification). This dissertation work proposes VANESS, a naming service as a resource to support user-to-user communication within a heterogeneous scenario comprising typical ISP scenario and VANETs focused on mobile devices. The proposed system is able to map the user to an end point either locally (i.e. there is not internet connection at all), online (i.e. system is not in a vehicular network but has direct internet connection) and using a gateway (i.e. the system is in a vehicular network where some of the nodes have internet access and will act as a gateway). VANESS was fully implemented on android OS with results proving his viability, and partially on iOS showing its multiplatform capabilities.

Structural Time Series Modeling: an Application to Environmental Variables

A structural time series model is one which is set up in terms of components which have a direct interpretation. In this paper, the discussion focuses on the dynamic modeling procedure based on the state space approach (associated to the Kalman filter), in the context of surface water quality monitoring, in order to analyze and evaluate the temporal evolution of the environmental variables, and thus identify trends or possible changes in water quality (change point detection). The approach is applied to environmental time series: time series of surface water quality variables in a river basin. The statistical modeling procedure is applied to monthly values of physico- chemical variables measured in a network of 8 water monitoring sites over a 15-year period (1999-2014) in the River Ave hydrological basin located in the Northwest region of Portugal.