To design and plan wireless communication systems, an accurate propagation estimate is required of a deployment region. Propagation prediction models consist of two types of fading: large-scale and small-scale fading. With large-scale fading, the path loss information is crucial for cell planning, coverage estimation, and optimization. With small-scale fading, the statistical fluctuation on the local variations of the average signal level can have a dramatic effect on protocol decisions and resulting performance. To obtain accurate estimates of both types of fading, typically field measurements are needed that use drive testing, which is expensive in terms of time and cost. Recently, LTE release 10 in 3GPP TS 37.320 has developed a Minimization of Drive Test (MDT) specification to monitor the network Key Performance Indicators (KPIs) via crowdsourcing.
In this approach, each User Equipment (UE) will be used as a measurement tool to provide the required performance measurement for the operators. MDT is a crowdsourced approach that does not increase the processing load of a UE, and the UE does little more than its regular network monitoring already required for cellular operation. The additional step required by the UE is to share these measurements periodically with the base station and network infrastructure. MDT requests the location information of the UE along with the KPI information in case that the GPS receiver is enabled. Many use cases have been defined for MDT such as coverage optimization, mobility optimization, capacity optimization, parametrization for common channels, and Quality of Service (QoS) verification. In this thesis, we study the capability of the MDT to infer wireless channel characteristics.
However, mobile phones are not designed to function as a measurement tool. Namely, there are various imperfections induced by user equipment when sampling signal quality. To confidently use the MDT approach, we first need to understand the role that mobile phone imperfections have on wireless characterizations when compared to drive testing equipment. In particular, our focuses in this work consists of three fundamental concepts. First, we evaluate the perceived channel quality in terms of the average loss from crowdsourced data using state of the art phones versus professional RF measurement tools. Specifically, we perform extensive experimentation across different mobile phone types, two pieces of software, and a channel scanner in three representative geographical regions: single-family, multi-family, and downtown areas. With these devices and in-field measurements, we evaluate the effects of averaging over multiple samples, uniform and non-uniform downsampling (in time and space), quantization, and crowdsourcing on the path loss exponent estimation.
Then, we design a model to use the crowdsourced data efficiently. We build a regional analysis framework to infer KPIs by establishing a relationship between geographical data and crowdsourced measurements. To do so, we use a neural network and crowdsourced data obtained by a UE to predict the KPIs in terms of the reference signal's received power (RSRP) and path loss estimation. Since these KPIs are a function of terrain type, we provide a two-layer coverage map by overlaying a performance layer on a 3-dimensional geographical map. As a result, we can efficiently use crowdsourced data (to not overextend user bandwidth and battery) and infer KPIs in areas where measurements have not or can not be performed.
Finally we study the capability of the MDT approach to estimate the fast fluctuations of the wireless channel which has rarely been addressed in prior studies. Estimating the multipath and fading characterization would help in different real-life scenarios such as channel characterization, link budget calculations, adaptive modulation, and geolocation applications, to enhance the network performance for the end user. However, currently this information is only achievable in a lab environment, and under controlled conditions.
A UE in an LTE network can measure the rapid fluctuations of the wireless channel condition using reference signals. MDT enables the UE to periodically send additional information to the transmitter according to the base station and infrastructure requirements. There is, however, concerns over battery consumption if the MDT reporting becomes too frequent, memory concerns if the reporting becomes too infrequent (and yet the recording level stays high), and privacy concerns over providing location information.
Also, a mobile phone may average over multiple samples of received signal quality, which might affect the instantaneous observations of the channel variations. In this work, we study the capability of MDT measurements to estimate the channel fluctuation characteristics in the presence of phone measurements shortcomings include averaging over multiple samples, imprecise quantization, and non-uniform and/or less frequent channel sampling. We use outage probability as the performance metric, which is a function of the wireless channel variation. Outage probability defines as the point at which the receiver power value falls below a threshold. This threshold is the minimum signal-to-noise ratio within a channel to have a certain QoS.
Dr. Joseph Camp
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Enami, Rita, "Wireless Channel Characterization Based on Crowdsourced Data and Geographical Features" (2019). Electrical Engineering Theses and Dissertations. 23.