GB2566793A

GB2566793A - Improvements in or relating to radio propagation modelling.

Info

Publication number: GB2566793A
Application number: GB1811600.4A
Authority: GB
Inventors: Mizutani Tom
Original assignee: Jaguar Land Rover Ltd
Current assignee: Jaguar Land Rover Ltd
Priority date: 2017-08-23
Filing date: 2018-07-16
Publication date: 2019-03-27
Anticipated expiration: 2038-07-16
Also published as: GB201713498D0; GB201811600D0; GB2566793B; DE102018213744A1

Abstract

There is provided a system and a method for determining a performance metric of a wireless system in a virtual environment, the wireless system comprising two or more antennas (104, 108) that are adapted to transmit and/or receive signals there between, at least one of the two or more antennas being adapted to occupy two or more positions within the virtual environment. A plurality of routes through the virtual environment is generated, each route comprising a plurality of points in the virtual environment. A channel modelling module obtains both geographical topography data and Long-term Evolution (LTE) base station data relating to each of the plurality of points. At each of the plurality of points a characteristic of a signal produced by at least one of the antennas and received by another is emulated using data outputted from the channel modelling module. A plurality of performance parameters of the wireless system is determined in dependence on the emulated characteristics andan evaluation of each performance parameter is performed to determine the performance metric for the wireless system. The advantage of the invention is the saving of time and resources saved by not having to perform physical vehicle drive testing.

Description

IMPROVEMENTS IN OR RELATING TO RADIO PROPAGATION MODELLING

TECHNICAL FIELD

The present disclosure relates to improvements in or relating to virtual drive testing and particularly, but not exclusively, to radio propagation modelling to simulate a real environment in such testing. Aspects of the invention also relate to a virtual drive testing system, to a vehicle and to a method.

BACKGROUND

Drive testing is a method of measuring and assessing the coverage, capacity and Quality of Service (QoS) of a mobile radio or wireless network as perceived in a vehicle. The technique makes use of a motor vehicle carrying mobile radio network air interface measurement equipment that can detect and record a wide variety of the physical and virtual parameters of mobile cellular service in a given geographical area or on a predetermined route through that environment. The measurements can be used by a network provider or vehicle manufacturer to make changes that improve the network coverage and/or provide a better service to their customers.

Drive testing is not ideal as there is no way the same conditions can be guaranteed from one test to the next. This fundamental lack of control conditions means that it is difficult to detect trends and consistent problems within the environment. To mitigate the vagaries of drive testing, virtual drive testing is becoming an interesting solution.

Virtual drive testing makes use of radio propagation modelling and emulation to build laboratory-based tests to evaluate and predict the likely real-world performance of the wireless system. This generally takes place before vehicular systems and methods are ever deployed or measured in the field. One problem with the modelling and emulation approach is ensuring that the radio conditions which it creates accurately reflect those of the target operating environment. It is difficult to build an emulation which directly maps onto one specific real environment, such as a particular region in London. This is due to the fact that very accurate geographical mapping data, and wireless transmitter data (e.g. cellular network base station) are required. In addition, changing aspects of the environment (e.g. building sites, vegetation) cannot always be accurately accounted for in a model based on static data.

Vehicular communications demand high data rates with a minimum level of latency in dynamic environments in order to serve high mobility applications. Due to these characteristics, accurate propagation and channel modeling in vehicular communication is important. In addition, vehicles will also need to be able to communicate with other vehicles and infrastructure using a number of different wireless technologies, including, communications standards such as Long-term Evolution (LTE) and mmWave. LTE is a wireless broadband technology that can reliably provide high data rate to mobile users. It benefits from a large coverage area, high penetration rate, and high-speed terminal support. Extending its use to also support vehicular applications is opening new market opportunities to telco operators and service providers. LTE particularly fits the high-bandwidth demands and QoSsensitive requirements of a category of vehicular applications known as infotainment (information and entertainment), which includes traditional and emerging Internet applications either for driver or passengers (e.g., content download, media streaming, VoIP, web browsing, social networking, blog uploading, cloud access).

Due to the increasing amount of mobile data applications, LTE enabled Vehicle-toInfrastructure (V2I) communications is ideally suited to the development of vehicular wireless systems. Compared to other main vehicular communication Wi-Fi based standards the coverage of LTE cellular network systems is significantly higher per base station (several kilometers) compared to Wi-Fi based communication (up to 200m). Cellular LTE systems using multiple-input multiple-output (ΜΙΜΟ) antennas techniques can reach data rates from 50Mbit/s up to 1 GBit/s in LTE-A depending on the modulation and coding schemes (MCS) used.

To overcome the typical fading of the received signal at moving receivers, most services require multiple antennas for diversity or ΜΙΜΟ operation, which multiplies the number of antennas by a factor of 2 to 4. This presents a further problem as the design, placement on the vehicle and test of these antennas requires enormous efforts in manpower, time and cost.

A need thus exists to improve radio propagation modelling systems and methods to embrace the forthcoming technology advancements.

Embodiments of the present invention have been devised to mitigate or overcome at least some of the problems and disadvantages associated with the prior art.

SUMMARY OF THE INVENTION

Aspects and embodiments of the invention provide a method, a system, an antenna, a method of testing an antenna, and a vehicle as claimed in the appended claims.

According to an aspect of the present invention there is provided a system for determining a performance metric for a wireless system in a virtual environment, the wireless system comprising two or more antennas that are adapted to transmit and/or receive signals there between, at least one of the two or more antennas being adapted to occupy two or more positions within the virtual environment, the system comprising: a module for generating a plurality of routes through the virtual environment, the or each route comprising a plurality of points in the virtual environment; a channel modelling module configured to obtain geographical topography data relating to each of the plurality of points in the virtual environment and configured to obtain LTE base station data for an environment class at each of the points in the virtual environment; an emulator for receiving an output from the channel modelling module and emulating, based on the output at each of the plurality of points, a characteristic of a signal produced by at least one of the antennas; and a module for determining the plurality of performance parameters of the wireless system in dependence on the emulated characteristics of the signal; and an evaluation module for evaluating each performance parameter to determine the performance metric for the wireless system.

In an embodiment the system may comprise a determination module configured to determine a radio propagation channel between the two or more antennas at any of the plurality of points in the environment to produce a radio propagation channel dataset; and wherein the emulator is configured to emulate fading of any signal which is adapted to pass from one antenna to another of the two or more antennas at one or more points on the or each route to produce an output signal in dependence on the radio propagation channel dataset.

In another embodiment the determination module may comprise a ray tracing module configured to predict principal radio frequency propagation phenomena based on the geographical topography data and the LTE base station data.

In one embodiment the emulator is configured to emulate the fading based on at least one of: antenna propagation characteristics; antenna patterns and directional considerations of transmissions between antennas.

In an embodiment the system may be configured to run a virtual drive test based on one or more route datasets and the corresponding emulated signals to determine said one or more performance parameters of the wireless system.

The virtual drive test may comprise: determining first and second antenna patterns by either measurement of the antennas or by a simulation; incorporating the antenna patterns into the radio propagation channel dataset; connecting a device under test, for testing, bypassing the or each antenna to be emulated; operating the device under test in a predetermined manner; emulating via a channel emulator an emulation of a scenario in which the device under test is to be tested; and capturing the results of the emulation of the device under test in the scenario to determine if the device under test performs within predetermined performance characteristics.

In another embodiment the step of emulating via the channel emulator comprises: stepping through a predetermined channel model for each successive location on the route, simulating a vehicle moving along the route and measuring the performance of at least one of the device under test at each successive location.

In one embodiment the step of emulating via the channel emulator may comprise: at least one of using steps of 20cm for a vehicle moving along the modelled route; and varying the speed with which the emulation proceeds in dependence on changes in vehicle speed on the modelled route.

In another embodiment the system may be configured to determine an antenna pattern for the or each antenna of the two or more antennas. Determining an antenna pattern for the or each antenna of the two or more antennas may comprise determining a 3d spatial polarimetric antenna pattern.

In an embodiment the system may be configured to determine at least one of: testing the viability of an antenna; analyzing the effects of rotation or translation on a moving antenna; analyzing the effects on an antenna of being in a moving vehicle; to select an antenna for use in a predetermined situation.

In another embodiment the system may be configured to generate routes in dependence on one or more classes of environment or classes of scenario. In an embodiment the system may be configured to generate routes in dependence on one or more of a signal strength model or a frequency model.

In an embodiment the system may be configured to generate routes in dependence on one or more different scenarios, such as for example urban, semi-urban or rural environment. In another embodiment the system may be configured to calibrate the emulations using real data relating to the route.

In an embodiment each antenna may relate to an antenna located in or on one of a base station, device under test and a device on a vehicle.

According to another aspect of the present invention there is provided a method for determining a performance metric of a wireless system in a virtual environment, the wireless system comprising two or more antennas that are adapted to transmit and/or receive signals there between, at least one of the two or more antennas being adapted to occupy two or more positions within the virtual environment, the method comprising: generating a plurality of routes through the virtual environment, the or each route comprising a plurality of points in the virtual environment; performing channel modelling in dependence on obtained geographical topography data relating to each of the plurality of points in the virtual environment and on obtained LTE base station data for an environment class for each of the points in the virtual environment; emulating, based on channel modelling at each of the plurality of points, a characteristic of a signal produced by at least one of the antennas and received by another of the antennas; determining a plurality of performance parameters of the wireless system in dependence on the emulated characteristics of the signal; and performing an evaluation of each performance parameter to determine the performance metric for the wireless system.

According to an aspect of the present invention there is provided an antenna, configured on the basis of an evaluation carried out in accordance with the method of another aspect of the present invention or using the system of another aspect of the present invention.

According to an aspect of the present invention there is provided a method of testing an antenna according to another aspect of the present invention.

According to an aspect of the present invention there is provided a vehicle including the antenna according to another aspect of the present invention.

Within the scope of this application it is expressly intended that the various aspects, embodiments, examples and alternatives set out in the preceding paragraphs, in the claims and/or in the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination. That is, all embodiments and/or features of any embodiment can be combined in any way and/or combination, unless such features are incompatible. The applicant reserves the right to change any originally filed claim or file any new claim accordingly, including the right to amend any originally filed claim to depend from and/or incorporate any feature of any other claim although not originally claimed in that manner.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

Figure 1 is a schematic drawing of a route for a drive test, according to one embodiment of the present invention;

Figure 2 is a point to point ray tracing of an environment, according to one embodiment of the present invention;

Figure 3 is a block diagram of the overall system, according to an aspect of the present invention.

Figure 4 shows a flow diagram of an implementation of the post-processing of the generated channels, according to one embodiment of the present invention;

Figure 5 is a schematic drawing of an emulator conductive testing phase, according to one embodiment of the present invention; and

Figure 6 shows graphs of a comparison of simulation results with real world results for a drive test, according to one embodiment of the present invention.

DETAILED DESCRIPTION

At least in certain embodiments, the present invention is intended to overcome or ameliorate the problems of ensuring that an emulation maps directly onto a specific real-world case. This is achieved by using a statistical approach based on analyzing a large number of automatically generated scenarios and/or routes, which taken together are representative of a typical class of operating environment (e.g. dense urban, urban, suburban, rural). Based on a set of static maps for the class of environment in question, a large number of alternative virtual drive routes through the same map areas can be plotted in software. In addition, based on a set of rules describing typical deployment patterns of cellular network base stations (or equally TV or radio transmitter masts, etc.) a large number of potential network deployment scenarios can also be plotted in software. By using the set of random drive routes and transmitter maps, propagation modelling can be completed between the transmitters and a moving vehicle on the drive routes, using techniques such as for example, ray tracing. A set of propagation models can be generated from N routes and M transmitter maps thereby allowing N*M propagation models to be created per scenario class. None of these models need map directly onto reality, but together will reflect a range of operating scenarios for a wireless device within for example, a dense urban, urban, suburban or rural environment. A key strength of virtual drive testing is that running such a large number of scenarios and/or environments is feasible, whereas it would not be practicable for real-world drive testing

Embodiments of the present invention relate to LTE for Vehicle-to-lnfrastructure (V2I) applications using ΜΙΜΟ techniques and includes a unique and generic antenna test and radio performance analysis process based on one or more of a 3D ray traced channel model, theoretic or measured antenna patterns, RF channel emulation and hardware-in-the-loop radiofrequency measurements. This generic process has been validated against real-world drive measurement data for an urban route in Bristol. The process is shown to be more reliable, cost efficient and repeatable than actual drive tests, providing the opportunity to replicate different propagation scenarios as often as required to confirm the performance parameters of the vehicle antenna installation and LTE terminal.

In this disclosure, a method of virtual drive testing (VDT) for Vehicle-to-lnfrastructure (V2I) applications over ΜΙΜΟ LTE vehicular urban scenarios is described using a 3D ray-tracing channel modelling tool. Thus, an appropriate drive route is selected through an available geographic database of for example, Bristol and then a virtual car is “driven” around the chosen test route. A detailed channel structure is modelled between two LTE base stations and the vehicle. Predicted channel matrices are generated using measured 3D antenna data for the base station and vehicle respectively. The resulting channels were streamed into a channel emulator, which is programmed to communicate with a multi-channel LTE base station emulator and a mobile client.

At least in certain embodiments, the present invention also aims to demonstrate reliable laboratory-based handovers between base station adjacent sectors and cells. Therefore, as the virtual vehicle moves between two adjacent cells, one of the base station emulators is configured to perform a handover since it supports the bidirectional signalling necessary for automated handover between adjacent base stations. Embodiments of the present invention are thus capable of assessing a real8 world performance for any on-vehicle LTE client without the need to perform costly, unreliable and resource intensive real life drive tests.

Specific embodiments of the present invention seek to provide a Virtual Drive test which also takes into account the possible design, configuration and implementation of the vehicular antennas and antenna systems which are integrated in a vehicles. Accordingly, the systems may be tested in scenarios indicative of different environments like urban, rural, motorway, and so on. If this is carried out in a nonvirtual manner, it is time and cost intensive and results are subject to influence from variation in environmental factors. The Virtual Drive test allows the test and optimization of communication antennas before the vehicle is finally built.

There are three main ways of conducting virtual drive tests: the first is using Physical layer propagation simulator including a traffic model to generate realistic mobility of vehicles and a 3D ray-optical model to calculate the multipath propagation channel between transmitter and receiver; a second way is to use a channel emulator; a third is to take recordings of radio signals on a particular test route and then replay these in the lab. The first two of these form part of the present invention. The second may require the use of a telematics unit or a similar vehicle control unit, incorporated in the vehicle. Indeed, this may be one of the devices under test (DUT) in the present invention. The third may have issues associated with the fact that the recordings will reflect the characteristics of the antenna used to take them, so it does not lend itself well to then evaluating different vehicle antenna installations in the lab. However, the third way may be used in other tests.

The above mentioned broad concepts of the present invention will now be described in greater detail.

Figure 1 shows a schematic drawing of a route 100 being travelled by a vehicle 102. The vehicle is fitted with an antenna 104 and associated antenna system (not shown in figure 1 and which may include a telematics unit or the like). The route 100 is along a road 101 which passes through an environment shown generally as 106. The environment may include one or more base stations 108, each of which has a footprint or coverage area over which it can transmit and receive signals to a network (not shown). The base stations 108 can communicate with the network and the antenna 104 on the vehicle 102. As the vehicle travels between the coverage areas of the respective base stations 108, the base stations can carry out a hand over so that the vehicle continues to receive signals from the network in a seamless manner.

The environment 106 may include other features, such as for example buildings 110, trees 112, street furniture 114, and many other features. The features may be of any size or shape and may, for one reason or another, have an effect on the propagation of any signals between the vehicle antenna 104 and the base station 108. This may also occur with inter-vehicular communications. For example, one of the buildings 110 may block signals from one of the base stations 108, when the building is between the vehicle antenna 104 and the base station in question 108 giving rise to so called “shadowing” or black spots. Shadowing can detrimentally affect signal quality between the vehicle antenna and the base station and as a result user satisfaction in the quality of operation of the network. Accordingly, it is important to identify shadowing between the vehicle antenna and the base station throughout the environment to ensure optimal signal quality and user satisfaction. It will be appreciated that shadowing is one detrimental effect to signal propagation but there may also be others. These include: attenuation, delay, phase shift, interference, and Doppler shift. In addition, atmospheric conditions such as rain, snow mist or cloud, may also cause detrimental effects on signal propagation.

There are many different ways black spots and other detrimental effects to the network can be identified. One particular methodology will now be described with reference to figure 2. Figure 2 shows a point to point ray-tracing of a particular environment. This tracing is generated by means of a 3D ray-tracing tool, which is described in greater detail below and not shown in Figure 2. The 3D ray-tracing tool can be used to identify black spots and other detrimental effects. It is assumed that the vehicle (not shown in figure 2) is moving within the environment on one or more predetermined routes.

Spatial and temporal multipath ray components of a radio propagation channel between a LTE base station (BS) and a vehicle are modelled using a 3D outdoor raytracing tool. The ray-tracing engine identifies all possible ray paths between the transmitter and the receiver in 3D space, up to a predetermined cut-off threshold of signal strength at the receiver, for example, 140 dBm. The database builds up a view of the environment which includes terrain, buildings and foliage, all represented at a predetermined resolution, such as for example 10m. Figure 2 shows all the traced rays for a point-to-point link example which may be used to generate corresponding power delay profile. The rays may be coded according to the received power at the virtual vehicle antenna.

The ray-tracing deterministic model is validated for cellular and microcellular applications, where the transmitter is located above or well below the rooftop level at frequencies from 200MHz up to 6GHz. This will be described in greater detail with reference to the channel modelling step discussed below. A validation of the model may be performed in an appropriate location. It should be noted that the model can be a 2D version but a 3D version is preferred as it is more accurate.

Figure 3 is a block diagram of the overall system according to an aspect of the present invention. In broad terms the overall system 500 includes at least the following modules: route generation 502; channel modelling 504; antenna modelling 506; emulator programming 508 and emulator operation 510.

In a first instance a plurality of different routes are generated by the route generation module. The journey made is different for each route and the start and end may also vary. The route generation module plots an appropriate drive route between randomly selected start and end points within a geographical dataset. One or more Long Term Evolution (LTE) base stations are also plotted within the geographical dataset. The data related to the base stations includes, location coordinates and antenna parameters. The route between the start and end points may be determined using routing such as for example “HERE” maps. The resulting co-ordinates are then used to plot the route within the geographical dataset to be used for the virtual drive testing.

The routes may be generated by a number of different methodologies, including for example signal strength models frequency models or any other appropriate methodology. The routes may be based on different scenarios, such as for example urban, semi-urban or rural environments.

An example of an appropriate geographic dataset is a LIDAR dataset. LIDAR is a surveying method that measures distance to a target by illuminating the target with a laser light. The name LIDAR is considered an acronym of light detection and ranging. The present invention also provides for a manner of validating the virtual drive testing against one or more real-world tests. The or each LTE base station may be plotted within the geographical dataset using data including: location, height, sector orientation, antenna down-tilt, antenna type, transmit power and operating frequency. This data may be obtained directly from a relevant mobile network operator or regulator in the region, or, optionally, may be plotted using representative data for the above parameters. For validating against a real-world test, precise data obtained from the mobile network operator or regulator is essential. In an embodiment of the invention, the emulation may be calibrated by comparing the results from the random set of generated routes with a known and relevant real driving route in the same vicinity and having the same geographical features and handovers.

The channel modelling module 504 carries out a number of operations. A set of geographical topography data relating to the locations in which the drive routes have been obtained is input in module 512. This includes data such as terrain profile (the underlying ground surface), elevation profile (including buildings and vegetation) and ground clutter data, which identifies whether an area contains buildings, foliage, water, is open, or contains other features. The drive route locations may be classified based on the environment. Typical classification include dense urban, suburban and rural. Other classifications may be used as required. For example, the classifications may be further sub-divided according to the radio technology modelled and its operating frequency.

A module 514 may obtain LTE base station data for the above mentioned class of environment. The LTE base station data may be generated by module 514 or may be input to module 514. For example, LTE base station data may be generated based on physical and/or electrical characteristics of LTE base stations by module 514. The physical and electrical characteristics of the LTE base station data may include, for example, height, power output, frequency, bandwidth, deployment topology etc. Alternatively, LTE base station may be obtained remotely and input to module 514.

Typically, LTE base station data obtained remotely is indicative of real world LTE base station data provided by a mobile network operator or regulator in the region.

Data from modules 512 and 514 may be combined and supplied to a ray tracing module 516. The ray tracing module 516 may be used to predict the principal radio frequency (RF) propagation phenomena which occur on signal links between the LTE base station and the device under test (DUT). The resultant prediction gives rise to spatio-temporal profile which is graphical representation of the signal characteristics (geometry, power level, time-of-flight) both in terms of location and time for every individual signal path from transmitter to receiver, as described below in relation to Equation 1.

The antenna modelling module 506 carries out a number of different operations. A module 518 may be used to determine a 3d spatial and polarimetric antenna pattern for the or each LTE base station in the desired environmental class. The pattern may be obtained by measurement or by an appropriate computer simulation. Similarly a module 520 may be used to determine a 3d spatial and polarimetric antenna pattern for the DUT in its desired deployment configuration or position on the vehicle. The patterns from the base station and the DUT may then be processed by a post processing module 522. The post processing module can thus use specific and realistic antenna patterns to act as an input for further modelling steps and modules as will be described below. This step provides for the generic results of the ray tracing module to be modified to reflect the properties of a specific antenna type as used on the vehicle or at the base station. The output from the post processing module may be in the form of channel matrices or power delay profiles. It will be appreciated that other outputs may be generated as required, depending on which further processing steps are envisaged.

Typically the outputs from the ray tracing module 516 are included in the processing which occurs in the post processing module 522. The ray tracing is carried out assuming isotropic antennas at the base station and the car. These results are postprocessed to make them reflect the actual antenna types that are to be tested. The post processing uses spatial and polarimetric convolution to incorporate synthetic and/or measured BS and DUT antennas patterns with the predicted channel data. The original ray tracing results can be post-processed in this way for many different vehicle antenna types, while allows VDT comparisons between different antenna installation options during the design and development of the vehicle. The step size between two points for which ray tracing is performed may be approximately 1m.

The outputs from the ray tracing module 516 and the post processing module 522 are then passed to the emulator programming module 508. Module 508 includes a further post processing module 524. Module 524 may convert the channel model into a required format which is appropriate for the channel emulation hardware, such as for example Anite F8, which is to be used. This formatting may give rise to an output comprising channel matrices and/or power delay profiles. If the channel model is already in an appropriate format, this step may not be required.

The emulator operation module 510 then carries out the emulation processes. The emulation is described in greater detail below. In general the emulation processes include a channel emulator module 528 which may emulate the RF fading of any transmitted or received signals. These signal are the traffic to and from the base station and/or the DUT. The resultant output is an RF signal from the channel emulator which reflects the signal between the base station and the DUT, degraded according to the channel modelling carried out earlier for the VDT scenario. Module 510 may include a base station emulator module 530. Module 530 may emulate a real world LTE network and signalling resulting in an RF output signal or output data set. The module 510 may also include a module to conduct the VDT 532 that may include a recording device which can capture and/or evaluate performance parameters. A module 534 can extract data from the log files generated by module 532.

The emulator may be directly connected to the antenna port of the DUT by for example a cable, thereby bypassing the device antenna. The antenna may be bypassed since its operating characteristics have already been accounted for in the simulation which is used to configure the emulator.

Channel emulation is typically used when evaluating product performance in realistic situations. With the aid of a channel emulator the equipment manufacturers avoid unintended variation in the test conditions due to influences from external factors (e.g.

weather conditions, road traffic conditions and other possible defects to signal propagation), hence the simulation environment can be controlled. Furthermore, the tiresome task of performing successive field measurements is limited to the minimum (to obtain the channel model, if there is none already available) and the rest of the experiments can be carried out inside a testing lab. It should be noted that calibration may be required if the transmission power and antennas type are not known, however this is needed to a lesser extent as the quantity of base station data which is known increases. The evaluation of the performance parameters will be described in greater detail elsewhere and may include one or more of statistical analysis, comparison of performance or any other appropriate method.

The emulation processes are controlled by a control module. The control software makes use of the output data from the emulations and enables the virtual drive test to be conducted. The control module activates the base station emulation and causes the DUT to connect to the base station emulator. The control module then commences the desired operation on the DUT (e.g. performing a data download, making a voice call etc.). The control module then causes the channel emulator to commence emulation of the scenario. The channel emulator steps through the channel model it has been preprogrammed with for each successive location on the route, simulating the vehicle moving along the route. The step size between locations may be about 20cm in the emulation for a vehicle moving along the route. The speed with which the emulation proceeds may vary in dependence on changes in vehicle speed on the modelled route. The control module may act to vary the speed with which the emulation proceeds, in keeping with varying vehicle speed on the modelled journey. The control module carries out the logging and the step size and speed is set directly through the emulator module. It should be noted that in some tests antennas are tested in similar locations in order to determine the best antenna for a specific scenario or type of locations, for example a rural location or a city. A similar location is thus one in which the antennas being tested are exposed to the same or similar conditions. In addition, where there are other types of test the antenna may be exposed to similar situations or conditions that are not based on location but some other parameters.

Throughout the test the control module will receive various parameters relating the RF signal and the performance of the DUT. Parameters may include the reference signal received power (RSRP) of each antenna, signal to interference plus noise ratio for each antenna and the Physical Downlink Shared Channel (PDSCH) throughput. Such parameters are available to the radio chipset of the DUT, and may be logged from the DUT by the control module provided that the DUT contains software to allow this. Optionally the parameters may be recorded from the base station emulator. The control module thus generates a parameter log file which includes all the recorded device performances during the drive test. The performances parameters are mentioned above and elsewhere.

A software analysis module 534 is then used to analyse the log file and extract the desired data from a combination of a number of the log files. For example a cumulative distribution function of signal strength or data throughput may be generated and used to represent the virtual drive test results.

The channel modelling step will now be described in more detail. In order to compute a set of wideband channel matrices suitable for orthogonal frequency division multiplexing (OFDM) modelling, the following procedure is followed. A point-source 3D ray-tracing is performed from the base station to each vehicle location as described above. This provides information on the amplitude, phase, time delay, azimuth and elevation Angle of Departure (AoD) and Angle of Arrival (AoA) for each multipath component (MPC) of the radio signal between the base station antenna and the vehicle antenna. The complex gain of each MPC is adjusted according to transmitting (Tx) and receiving (Rx) antenna electric field (E) pattern responses for the corresponding AoD/AoA and polarisation. The double-directional time-variant channel impulse response h for a link is given by the following equation:

£

f.=i

AoQ ' ^ΑβΑ,ί.)

Equation 1

where: £)(t) =

7	Γ , ii;' L'A		ί U^,r fi-:
			pSf

In the above equation, 5(-) is the Dirac delta function, t is the variable of time, τ is the time-of-flight, represents the departure/arrival solid angle, and L is the total number of MPCs. The i^th MPC has double-directional time-variant channel impulse response k_ls a complex amplitude a^e’^ (2x2 matrix for all four polarisation combinations), a time-of-flight τ_?, a Doppler frequency vj, and departure/arrival solid angles represent the vertical/horizontal polarisation components of the transmitting and the receiving antenna electric field radiation pattern. The Doppler frequency shift is given by:

^_ A where p is the car velocity, is the azimuth AoA of the /^th MPC, is the elevation AoA of the l-th MPC, 6¾ is the car direction of travel in azimuth, ζ,, is the car direction of travel in elevation, and 2 is the carrier wavelength. In the system level simulations that follow, the time-variant nature of the channel is taken into consideration by assuming that within a packet transmission the propagation channel alternates between successive OFDM symbols, due to the Doppler shifts introduced by the vehicle motion.

The produced rays are assumed to be equivalent to the MPCs of the channel impulse response. Therefore, time binning was applied to the captured rays with time resolution equal to the inverse of the signal bandwidth. The wideband channel frequency response G(f) = [gi, g₂,..., gn], where g_k represents the frequency domain channel for the kth subcarrier, was computed using Discrete Fourier Transform (DFT)

G</) = F{fc}

The channel model can be easily extended to arrays with multiple antenna elements, accounting for a relative phase shift of each element with respect to a zero-phase reference point within the array as shown in the following equation:

kd ί-5 -5·

where x°, /, z° is the position of the antenna element with respect to the zero-phase reference point at the receiving array (similar for the transmitting side), k is the wave vector and §, φ are the elevation and azimuth angles in the spherical coordinate system of reference.

Embodiments of the present invention also include a system and method for processing the 3D ray-tracing channels that are required to be loaded onto the channel emulator. In addition, a comprehensive overview analysis of the antenna radiation patterns employed in the emulations is determined. The simulation parameter settings are determined, together with the proposed configuration setup of the conductive system. These and other features of the present invention will now be described.

Figure 4 shows a flow diagram which is an outline of an implementation of the postprocessing of the generated channels, incorporating the specific vehicle and base station antenna patterns and converting the output into the format accepted by an RF fading emulator.

In a first step 700, the original antenna patterns are determined for the base station or stations and the DUT. For example, the type of antenna may be determined and the patterns these produce are collected, by measurement, simulation or from where they are stored. Data is collected for both the uplink and downlink transmissions.

Step 702 extracts patterns from the measurements and converts the DUT antenna pattern to a required format. For example, this may be required to convert the antenna simulation file from 2deg steps to 1deg steps as required by the channel modelling. This step may be omitted if no interpolation procedure is required. The output 704 is a version of the antenna pattern is derived which is of the required format, for example Mat Lab compatible.

At step 706 a rotate and translate pattern step is carried out. This step sets the orientation of the antenna pattern correctly according to the driving direction at each ray tracing point along the virtual drive test route. A rotated and translated antenna pattern is thus produced as the output 708. At step 710 the original ray tracing file is accessed.

The rotated and translated antenna pattern and the original ray tracing are the processed in step 712 to create channel impulse response (CIR) files. These files contain information of post-processed channels with antenna patterns embedded for the links between each ray traced point to associated (relevant) BS. The process of step 712 is performed in a loop for each sequential point on the virtual test route for which the ray tracing is performed. The number of iterations therefore varies with the length of the VDT scenario. The function produces a corresponding number of individual base station-to-DUT CIR files, for a single base station and all possible DUT positions, at a single central frequency. For a scenario involving two base stations, the same process must be repeated four times in total, for the uplink and downlink central frequencies of both the first (BS1) and the second (BS2) base stations. The final result of such processing is accordingly the CIR files 716.

Also in step 712, in order to prevent sudden changes in the channels between any two points for which ray tracing was performed n interpolated points are computed using equation below:

= c * H (MS_id) -Hl - H -F I) , where c represents a n-step coefficient to ensure that each update is a time-correlated channel taking into consideration both anchor points at MSJd and (MS_id+1). As a result, the number of points is extended by n times and they become time correlated. At step 718 the CIR files are compiled into an appropriate format, such as a Propsim configuration file. This is required to produce a set of CIR files in the correct format to be accepted by the emulator. This function could be modified accordingly for other equivalent types of emulator hardware. The resulting final output 720 is then sent to the emulator for further processing.

Referring to figure 5, the conductive testing phase with the channel emulator is described in greater detail. The circuit includes a channel emulator 800 connected to the DUT 802 and an LTE base station emulator 804. The resulting channel data (from figure 4) is loaded into the channel emulator with the device under test, such as for 19 example, a telematics controller or the like, is connected as described. The telematics controller is an electronic controller in the vehicle responsible for most functionality involving the vehicle communicating with the internet, e.g. eCall, stolen vehicle tracking, Wi-Fi hotspot, internet radio, etc.

The device under test (in this case the telematics controller of the car) communicates with the LTE base station emulator over the emulated radio channel and performance parameters are recorded. These may include examples such as, reference signal received power (RSRP), signal to interference plus noise ratio (SINR), and applicationlevel throughput. The parameters may be recorded either through logging at the device itself through software capable of querying the parameters from the device chipset, or at a base station emulator.

The resulting performance parameters can be used to determine if the given DUT works sufficiently well in the tested environment or to compare its performance with that of similar devices, or to compare its performance with different on-car antenna variants. If the performance parameters are insufficient further tests may be required or the DUT may need to be redesigned to meet the required performance parameters.

In an embodiment test results from a Hardware-ln-the-Loop (HIL) emulation (the setup as shown in figure 5 for example) are compared with test results from on-road testing, in order to verify that the simulation and emulation tools accurately reflect a real world scenario and hence can be relied upon for predicting the performance of a telematics controller without resorting to extensive drive testing. In figure 5, as mentioned above, the DUT 802 on which the VDT HIL test are carried out may comprise the telematics controller. Other DUT may equally be connected for the test.

After the test results have been collected from the HIL emulation step, the procedure is repeated again beginning with a new route being generated for the same classification of environment (or selected from a library of previously generated routes). Once sufficient data has been collected for the chosen class of environment, the VDT is complete. The results of various drive tests can be compared to validate the viability of the DUT to carry out the functions required of it in whatever environment.

While Virtual Drive Testing is much less resource intensive to run than on-road testing once the test scenarios have been built, the present invention provides a Virtual Drive Testing scenario which matches a given real world case, and is calibrated to be correct. The present invention avoids effort-intensive configuration of the scenarios associated with prior art systems by instead configuring the VDT with generic classes of scenario and running enough tests within each scenario class that the results can be analyzed statistically. As a result there is less need for each individual test to actually match a particular real-world case.

To demonstrate this, using predetermined map data, a plurality of routes have been created. The plurality of routes may be created manually or in an automated manner. The plurality of routes may be created in an automated manner, by using route planning algorithms or tools to automatically determine a route between two randomly plotted points within the geographical data set in which the VDT should be carried out. These include a plurality of urban test routes, dense urban routes, suburban routes and rural routes. This may avoid the need to obtain specific (and possibly confidential) data from one or more mobile network operators in the specific test region. Each route includes representative assumptions or real data for base station deployment, for example, at both 800MHz and 2600MHz, representing the upper and lower extents of the frequencies which are generally used for LTE network deployment.

Figure 6 shows a comparison of virtual drive test data for the 800MHz band and the 2600MHz band. Based on the assumptions made about the deployment of 800MHz and 2600MHz base stations within the test scenario, figure 6 shows the 800MHz case offers a median throughput level of nearly twice that of the 2600MHz case. This is due to improved propagation characteristics at the lower frequency. Although the virtual drive test results will not map directly onto one particular real world case, the invention shows that the scenarios allow comparison of different system configurations under controlled conditions. For example, the comparison could equally be between an antenna system from a first manufacturer with one from a second manufacturer. Alternatively a comparison could be made between two systems used by different vehicle OEMs. Any appropriate comparison can be selected and the result will be reliable. It should be noted that the comparison can be achieved by comparing RSRP or throughput graphs over the course of the route. In the case of the graphs the comparison is made by analysis of the Cumulative Distribution Function (CDF) of the parameters. Alternatively comparative measurements between different configurations may be performed using a given class of scenarios. The example given in the graph in figure 6 is for 800MHz operation vs 2600MHz, although equally it could have been one telematics controller against another, or one vehicle antenna installation against another. For example a 2-element LTE antenna mounted in a roof pod may be compared against the same antenna mounted in an alternative location such as the rear bumper of the vehicle. Any other comparisons can be made.

The scenario classes of dense urban, urban, etc. could additionally be sub-divided into specific classes according to the radio technology modelled, and its operating frequency, because the rules on the typical installation of transmitters will vary by technology type. For example, in a cellular network, a cell size at 2600MHz will be considerably smaller than at 800MHz, due to the propagation characteristics, and this should be reflected in the VDT scenario

Rules regarding typical network deployments may include aspects such as base station height, power level, spacing between cells, overlay of different frequency bands, cellular technology variants (e.g. Frequency Division Duplexing (FDD) I Time Division Duplexing (TDD) LTE), use of carrier aggregation, etc. For example, in the UK, OFCOM currently mandate a limit of 61 dBm/ (5MHz) Equivalent Isotropically Radiated Power (EIRP) maximum transmit power for 900MHz and 1800MHz LTE base stations. Data indicates that historically in the UK most base stations did not tend to use the maximum EIRP permitted under their licenses. These rules may vary in different regions of the world and may be factored into the present invention accordingly. For example, in the US LTE is deployed in the 1700MHz frequency band, but not in Europe. Relevant network deployment rules could in practice be identified and applied as required. By reflecting these rules in the creation of VDT scenarios, the VDT user may have confidence that the scenarios will reflect the operating environment of interest.

Module 514 may utilise the aforementioned rules to generate the physical and electrical characteristics of the LTE base station data. The generated LTE base station data is representative of LTE base station data in a real world environment at a given location. The LTE base station data generated by module 514 may be input to module 516, along with geographical data obtained by module 512 and used to predict principal radio frequency propagation phenomena.

The present invention includes a method of configuring a VDT scenario using parameters such as base station transmit power and operating frequency, etc., assuming this data is available for the environment which is to be modeled.

A technical effect of embodiments of the present invention is to configure RF channel emulation and base station emulation hardware units to alter and/or control the output signal of those devices in accordance with the scenario or virtual drive route in question. The output signal is shown at the 4 RF OUT ports of the emulator 800 in figure 5. It is used to drive the DUT 802 in a way which reflects the device being operated in the modelled scenario. The output, will also affect the behavior and performance of the device under test, allowing measurements to be taken which correspond to the scenario.

For the avoidance of any doubt an output data set as a result of the emulation comprises the performance results which are measured from the DUT during the emulation. The emulation itself involves generating radio signals based on the channel model; the radio signals are fed into the DUT; the output data set is the performance results which are measured from the DUT during the emulation.

Because simulation and emulation allow testing of wireless devices to be completed within a lab (as opposed to in real-world drive testing) the many test runs needed to complete all scenarios within an environment class need not require significant operator effort and time. The savings in terms of time and costs is potentially enormous. Other advantages of the various features of the present invention give rise to other technical solutions to the various problems addressed by the present invention.

The present invention is described with reference to a combination of hardware and software elements. It will be appreciated that the hardware may be implemented in software and vice versa. Any combination of hardware and software is intended to be encompassed in the present invention. Where method steps are carried out in the software or hard ware there may be an equivalent module that carries out each step.

In general, the routines executed to implement the embodiments of the invention may be referred to as computer program code. Program code typically comprises computer readable instructions that are resident at various times in various memory and storage devices in the computer and that may cause that computer to perform the operations necessary to execute operations or functions and/or elements embodying the various aspects of the embodiments of the invention.

The program code embodied in any of the applications/modules described herein is capable of being individually or collectively distributed as a program product in a variety of different forms. In particular, the program code may be distributed using a computer readable storage medium having computer readable program instructions thereon for causing a processor to carry out aspects of the embodiments of the invention.

Computer readable program instructions stored in a computer readable medium may be used to direct a computer, other types of programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions that implement the functions, acts, and/or operations specified in the flowcharts, sequence diagrams, and/or block diagrams. The computer program instructions may be provided to one or more processors of any computer or other programmable data processing apparatus to produce a machine, which execute and thus cause a series of computations to be performed to implement the functions, acts, and/or operations specified in the flowcharts, sequence diagrams, and/or block diagrams.

In certain alternative embodiments, the functions, acts, and/or operations specified in the flowcharts, block diagrams and the like may be re-ordered or processed in a different manner.

Many modifications may be made to the above examples without departing from the scope of the present invention as defined in the accompanying claims.

Claims

1. A system for determining a performance metric for a wireless system in a virtual environment, the wireless system comprising two or more antennas that are adapted to transmit and/or receive signals there between, at least one of the two or more antennas being adapted to occupy two or more positions within the virtual environment, the system comprising:

a module for generating a plurality of routes through the virtual environment, the or each route comprising a plurality of points in the virtual environment;

a channel modelling module configured to obtain geographical topography data relating to each of the plurality of points in the virtual environment and configured to obtain LTE base station data for an environment class at each of the points in the virtual environment;

an emulator for receiving an output from the channel modelling module and emulating, based on the output at each of the plurality of points, a characteristic of a signal produced by at least one of the antennas; and a module for determining the plurality of performance parameters of the wireless system in dependence on the emulated characteristics of the signal; and an evaluation module for evaluating each performance parameter to determine the performance metric for the wireless system.

2. The system as claimed in claim 1, wherein the system comprises a determination module configured to determine a radio propagation channel between the two or more antennas at any of the plurality of points in the environment to produce a radio propagation channel dataset; and wherein the emulator is configured to emulate fading of any signal which is adapted to pass from one antenna to another of the two or more antennas at one or more points on the or each route to produce an output signal in dependence on the radio propagation channel dataset.

3. The system as claimed in claim 2, wherein the determination module comprises a ray tracing module configured to predict principal radio frequency propagation phenomena based on the geographical topography data and the LTE base station data.

4. The system as claimed in claim 2 or claim 3, wherein the emulator is configured to emulate the fading based on at least one of: antenna propagation characteristics; antenna patterns and directional considerations of transmissions between antennas.

5. The system as claimed in claim 3 or claim 4, wherein the system is configured to run a virtual drive test based on one or more route datasets and the corresponding emulated signals to determine said one or more performance parameters of the wireless system.

6. The system as claimed in claim 5, wherein the virtual drive test comprises: determining first and second antenna patterns by either measurement of the antennas or by a simulation;

incorporating the antenna patterns into the radio propagation channel dataset; connecting a device under test, for testing, bypassing the or each antenna to be emulated;

operating the device under test in a predetermined manner;

emulating via a channel emulator an emulation of a scenario in which the device under test is to be tested; and capturing the results of the emulation of the device under test in the scenario to determine if the device under test performs within predetermined performance characteristics.

7. The system as claimed in claim 6, wherein the step of emulating via the channel emulator comprises:

stepping through a predetermined channel model for each successive location on the route, simulating a vehicle moving along the route and measuring the performance of at least one of the device under test at each successive location.

8. The system as claimed in claim 7, wherein the step of emulating via the channel emulator comprises at least one of using steps of 20cm for a vehicle moving along the modelled route; and varying the speed with which the emulation proceeds in dependence on changes in vehicle speed on the modelled route.

9. The system of any one of the preceding claims, wherein the system is configured to determine an antenna pattern for the or each antenna of the two or more antennas.

10. The system as claimed in claim 9, wherein determining an antenna pattern for the or each antenna of the two or more antennas comprises determining a 3d spatial polarimetric antenna pattern.

11. The system of any one of the preceding claims, wherein the system is configured to determine at least one of: testing the viability of an antenna; analyzing the effects of rotation or translation on a moving antenna; analyzing the effects on an antenna of being in a moving vehicle; to select an antenna for use in a predetermined situation.

12. The system of any one of the preceding claims, wherein the system is configured to generate routes in dependence on one or more classes of environment or classes of scenario.

13. The system of any one of the preceding claims, wherein the system is configured to generate routes in dependence on one or more of a signal strength model or a frequency model.

14. The system of any one of the preceding claims, wherein the system is configured to generate routes in dependence on one or more different scenarios, such as for example urban, semi-urban or rural environment.

15. The system of any one of the preceding claims, wherein the system is configured to calibrate the emulations using real data relating to the route.

16. The system of any one of the preceding claims, wherein each antenna relates to an antenna located in or on one of a base station, device under test and a device on a vehicle.

17. A method for determining a performance metric of a wireless system in a virtual environment, the wireless system comprising two or more antennas that are adapted to transmit and/or receive signals there between, at least one of the two or more antennas being adapted to occupy two or more positions within the virtual environment, the method comprising:

generating a plurality of routes through the virtual environment, the or each route comprising a plurality of points in the virtual environment;

performing channel modelling in dependence on obtained geographical topography data relating to each of the plurality of points in the virtual environment and on determined LTE base station data for an environment class for each of the points in the virtual environment;

emulating, based on the channel modelling at each of the plurality of points, a characteristic of a signal produced by at least one of the antennas and received by another of the antennas;

determining a plurality of performance parameters of the wireless system in dependence on the emulated characteristics of the signal; and performing an evaluation of each performance parameter to determine the performance metric for the wireless system.

18. An antenna, configured on the basis of the evaluation of each performance parameter carried out in accordance with the method of claim 17 or using the system according to any of claims 1 to 16.

19. A method of testing an antenna according to claim 18.

20. A vehicle including the antenna of claim 18.