Network planning tool

Abstract

A computer-implemented method of determining placement for integrated access backhaul (IAB) nodes for connecting a target location to a core network, comprising: receiving first location data of the target location to connect to the core network; receiving second location data of an IAB donor connected to the core network; receiving topography data representative of terrain between the target location and the IAB donor; receiving transmission and reception properties of a candidate networking hardware device capable of serving as an IAB node; applying a multi objective optimization technique to determine a placement of one or more IAB nodes to connect the target location to the IAB donor, based on the topography data and the transmission and reception properties of the candidate networking hardware device capable of serving as an IAB node.

Description

NETWORK PLANNING TOOLTECHNICAL EIELD

[0001] The present disclosure pertains to systems, methods, and computer programs for a telecommunications network planning tool.BACKGROUND

[0002] Last mile broadband refers to the portion of a telecommunications network that reaches user equipment at end-user's premises. The reference to “last mile” is figurative rather than literal - i.e. the actual length of this final portion of the network is not material. In rural and remote areas, last-mile broadband is the most difficult and expensive part of the telecommunications network to install. This may be in part due to the terrain and environment in rural areas, which may be difficult to work in, increasing the time and cost involved in installing telecommunications equipment. Lor example, such rural environments may have significant elevation changes, inhospitable weather conditions or other environmental features that render the installation of equipment difficult. Furthermore, the lack of suitable existing infrastructure such as power and accessible road networks adds to such difficulties.

[0003] In such environments, the installation of wired (e.g. optical fibre) last-mile broadband connections may be particularly difficult. It may not be practical or desirable to dig trenches and lay cables over extreme terrain. Consequently, it may be preferable to adopt (at least in part) wireless solutions. One way to provide wireless last-mile broadband connection is using an integrated access backhaul (IAB) network, which operates on mmWave technology. An IAB network is formed of an IAB donor node connected to a core network (also referred to as a backbone network) and intermediate IAB nodes that are used to provide broadband connection to user equipment at the end-user’s premises. mmWave transmissions are typically unable to penetrate significant obstacles such as buildings and trees, and so the technology relies on “line-of- sight” between the transmitter and receiver.SUMMARY

[0004] One problem in using an IAB network to provide last-mile network connectivity is determining where to place the IAB nodes to most efficiently provide broadband connection.In rural areas, terrain with significant elevation changes such as mountains and valleys can provide obstacles to wireless IAB solutions by obscuring or obstructing line of sight. Designing a suitable network topology which takes account the topography of the terrain and is efficient in terms of minimising the use of expensive IAB nodes, requires significant expertise and skill.

[0005] Various examples herein pertain to a network planning tool for use in planning an IAB network for last-mile broadband connectivity. The tool has particular applicability in rural settings and is therefore referred to herein as the rural network planning tool, though it will be understood that its use is not limited in this regard. The rural network planning tool receives location data of a target location (e.g. one or more premises to be connected to the network) and a nearest available base station and uses a multi-objective optimisation approach to provide an optimal placement and number of IAB nodes required for providing broadband connectivity to the target location. The optimal placement takes into account the topography of the area between the target location and the nearest available base station.

[0006] According to a first aspect of the disclosure, there is provided: a computer-implemented method of determining a topology placement for integrated access backhaul (IAB) nodes for connecting a target location to a core network via one or more integrated access backhaul (IAB) nodes, comprising: receiving first location data of the target location to connect to the core network; receiving second location data of an IAB donor connected to the core network; receiving topography data representative of terrain between the target location and the IAB donor; receiving transmission and reception properties of at least one candidate networking hardware device capable of serving as an IAB node; applying a multi objective optimization technique to determine a placement of one or more IAB nodes to connect the target location to the IAB donor, based on the topography data and the transmission and reception properties of the at least one candidate networking hardware device capable of serving as an IAB node.

[0007] The method may comprise accessing a base station database to determine the second location data based on the first location data. The second location data may correspond to an IAB donor most proximate to the target location.

[0008] The method may comprise receiving address data of the target location. The method may comprise determining the first location data based on the address data. The address datamay comprise a postcode. The method may comprise rendering a user interface and receiving the address data via the user interface.

[0009] The method may comprise receiving first location data of a plurality of target locations. The method may comprise applying the multi objective optimization technique to determine the placement of one or more IAB nodes to connect each of the plurality of target locations to the IAB donor. The method may comprise receiving address data and determining each of the target locations from the address data.

[0010] The method may comprise receiving user input via a user interface selecting the candidate networking hardware device. The method may comprise retrieving the transmission and reception characteristics of the selected networking hardware from a candidate hardware database. The transmission and reception characteristics may comprise a data rate and / or a range. The method may comprise storing power consumption characteristics in the candidate hardware database. The method may comprise applying the multi objective optimization technique based on the power consumption characteristics.

[0011] The method may comprise determining an altitude profile between the first location data and the second location data. The method may comprise rendering the altitude profile on a user interface.

[0012] The multi objective optimization technique may be a genetic algorithm.

[0013] The multi objective optimization technique may seek to minimize a first objective function that represents a cost of deploying a placement of one or more IAB nodes.

[0014] The multi objective optimization technique may seek to minimize a second objective function that represents a power consumption associated with a placement of one or more IAB nodes.

[0015] The multi objective optimization technique may seek to maximize a third objective function that represents a coverage of the target location associated with a placement of one or more IAB nodes. The third objective function may penalize a placement with restricted line-of-sight. The coverage may represent a fraction of the plurality of target locations covered or a coverage of an area comprising each of the plurality of target locations.

[0016] First, second and third are merely labels. The method may comprise the use of any combination of the three abovementioned objective functions.

[0017] The method may comprise determining a plurality of placements by applying the multi objective optimization technique. The method may comprise displaying the plurality of placements on a user interface; and receiving user input via the user interface comprising a selection of one of the plurality of placements. The method may comprise selecting one of the plurality of placements based on a score in one of the objective functions.

[0018] The method may comprise determining a plurality of candidate sites for installation of an IAB node; and applying the multi objective optimization technique to determine the placement by selecting one or more of the candidate sites. Determining the plurality of candidate sites may comprise determining an area encompassing the target location and the IAB donor. The method may comprise distributing the candidate sites throughout the area. The method may comprise evenly spacing the candidate sites throughout the area. The area may be rectangular. Extremes of the area may comprise the first location data and second location data. The selection of candidate sites may be represented as a vector.

[0019] The method may comprise randomly generating a population of candidate IAB placements; evaluating the objective functions in respect of each member of the population; selecting a set of optimal placements of the candidate placements based on the evaluation of the objective functions. The method may comprise performing crossover and / or mutation of the selected set of optimal placements to generate new candidate placements, and adding the candidate placements to the population. The method may comprise repeating the generating, evaluating and selecting steps. The method may terminate after a predetermined number of iterations or when convergence criteria are met.

[0020] The method may comprise receiving transmission and reception properties of a plurality of candidate networking hardware devices. The method may comprise applying the multi objective optimization technique to determine, along with the placement of the one or moreIAB nodes, a selected networking hardware device of the plurality of candidate networking hardware devices for each of the one or more IAB nodes.

[0021] The candidate networking hardware device capable of serving as an IAB node may be configured to transmit and receive network signals at mmWave frequencies, suitably according to the 3GPP for 5G New Radio standard (particularly in bands above 24 GHz) and IEEE 802.1 lad / ay standards for ultra-fast Wi-Fi (WiGig).

[0022] The method may comprise receiving a map of an area comprising the target location and the IAB donor location and rendering, on the map, the determined placement of the one or more IAB nodes.

[0023] According to another aspect of the disclosure, there is provided a computer system comprising: at least one memory storing computer-readable instructions; and at least one processor coupled to the at least one memory and configured to execute the computer readable instructions, which upon execution cause the at least one processor to perform any of the methods disclosed herein.

[0024] According to another aspect of the disclosure, there is provided non-transitory computer readable medium embodying computer program instructions, the computer program instructions configured so as, when executed on one or more hardware processors, to implement any of the methods disclosed herein.BRIEF DESCRIPTION OF THE DRAWINGS

[0025] For a better understanding of the present subject matter, certain embodiments will now be described by way of example only with reference to the following figures, in which:

[0026] Figure 1 shows an example IAB network.

[0027] Figure 2 schematically shows an example computing system that can enact the methods or processes described herein.

[0028] Figures 3 A and 3B show an example user interface displaying a Eocalization tab.

[0029] Figure 4 shows an example user interface displaying a Topography tab.

[0030] Figures 5A and 5B show an example user interface displaying a Backhaul tab.

[0031] Figures 6A and 6B show an example user interface displaying an Antenna tab.

[0032] Figures 7A to 7E show an example user interface displaying a Topology tab.

[0033] Figure 8 shows an example user interface displaying a Power tab.

[0034] Figure 9 shows an example user interface displaying a Decision tab.

[0035] Figure 10 is a flowchart of an example method implemented by a rural network planning tool.

[0036] Figure 11 is a block diagram of an example method for generating an initial population.

[0037] Figure 12 is a flowchart of an example multi-objective optimization approach.DETAILED DESCRIPTION

[0038] In overview, examples of the disclosure provide a network planning tool that receives location data of a target location (e.g. one or more premises to be connected to a network) and a nearest available base station and uses a multi-objective optimisation approach to provide an optimal placement and number of IAB nodes required for providing broadband connectivity to the target location. The optimal placement of the IAB nodes takes into account the topography of the terrain between the target location and the location of the nearest available base station.

[0039] By way of context, there now follows a discussion of integrated access backhaul (IAB) networks for last-mile broadband. An IAB network connects IAB donors and IAB nodes to provide broadband connectivity to user equipment at a target location, e.g. in buildings at business and / or residential addresses. The IAB donors and IAB nodes are base stations. A base station is a transmitter and receiver that relays a wireless signal, for example a signal between a core network and user equipment. Both IAB donors and IAB nodes are types of base stations.

[0040] An IAB donor is directly connected to a core network. The core network (or backbone network) is a central conduit designed to transfer network traffic. The core network routes network traffic to and from IAB donors (amongst other networking devices) in different locations. An IAB donor comprises hardware configured to connect to the core network (e.g. via wired connection) as well as provide access and backhaul to user equipment and / or IAB nodes.

[0041] The IAB donors can be connected to the core network via wired or wireless connections, for example via fibre optic cables or mmWave technologies. The IAB donors connect to additional IAB nodes via a wireless connection in an IAB network, for example using mmWave technologies.

[0042] mmWave technologies refers to technology that can transmit and receive electromagnetic waves in a frequency range of 24GHz to 300GHz when routing data. The mmWave technologies discussed herein may be 5G Frequency Range 2 (FR2) frequencies. Networking hardware, for example an antenna, may transmit and receive data in this frequency range as part of an IAB network. Example standards governing mmWave technology include 3GPP for 5G New Radio (particularly in bands above 24 GHz) and IEEE 802.1 lad / ay standards for ultra-fast Wi-Fi (WiGig).

[0043] An IAB node, as defined herein, is considered to be hardware configured to receive and transmit data wirelessly and is not directly connected to the core network. An IAB node is connected to an IAB donor however the IAB node is not connected directly to the core network. In other words, the term IAB node herein may be generally used to refer to an IAB node connected to one or more other IAB nodes (including the IAB donor), whereas the term IAB donor refers to an IAB device that is specifically connected directly to the core network.

[0044] IAB refers to integrated access and backhaul. In this context, it has been conventional to deploy separate networks / devices for providing access (i.e. connectivity to user equipment) and backhaul (i.e. connectivity between the access network and the core network). IAB provides both - i.e. devices that can be accessed by user equipment and provide backhaul to other IAB nodes.

[0045] Figure 1 shows an example Integrated Access Backhaul (IAB) network to illustrate the context discussed above.

[0046] IAB network 100 comprises an IAB donor 102 that is connected to the core network 112, and connected to a first IAB node 104. The first IAB node 104 provides broadband connectivity to a user device 108 (i.e. providing access) and is connected to a second IAB node 106 (i.e. providing backhaul). The second IAB node 106 provides broadband connectivity to user device 110.

[0047] Although the user devices 108, 110 are shown as mobile devices, it will be understood that in reality the user devices 108, 110 may be other suitable customer premises equipment,such as suitable gateway or router which in turn provides connectivity (e.g. via a local area network) to user equipment such as smartphones, tablets, computers, loT devices and the like.

[0048] Figure 2 schematically shows an example computing system 200 that can enact the methods or processes described herein.

[0049] The computing system 200 comprises a processor 202, a user interface 204, a mapping services interface 206, a memory 208, and a base station database 210 and a candidate hardware database 212 stored in the memory 208.

[0050] The processor 202 is configured to execute instructions stored in the memory 208 to implement a rural network planning tool 214 as described herein. The memory 208 may store, transiently or permanently, any suitable data required for operation of the system 200. It can include volatile and non-volatile memory. The databases 210, 212 may for example reside in the memory.

[0051] The rural network planning tool 214 may be a software module (or modules) implemented by processor 202. The processor 202 may execute logic configured to implement the rural network planning techniques described herein. The logic may be configured to perform a multi-objective optimization process to determine an optimal location for an IAB node.

[0052] The user interface 204 is configurable to render a visual representation of the rural network planning tool such that it can be viewed on a display. The user interface 204 prompts the user to input information to be used by the rural network planning tool and outputs the results of the tool. As will be described in more detail later, the user interface 204 receives data from a user via tabs of a guided rural network planning tool and outputs an optimal location and number of an IAB node(s) based on the data received from the user.

[0053] The user interface 204 is configurable to present a visual representation of data such as data held by the memory 208. The visual representation takes the form of a graphical user interface (GUI) in some examples. The user interface 204 includes one or more display devices utilising any suitable type of technology. Such display devices are combined with processor 202 and memory 208 in a shared enclosure in some examples. In other examples, such display devices are peripheral display devices. An input subsystem 205b may be part of the user interface 204 which comprises or interfaces with one or more input devices such as user-input devices such as a keyboard, mouse, touch screen, or the like. In some embodiments, the userinterface 204 comprises or interfaces with selected natural user input (NUI) componentry. Such componentry may be integrated or peripheral, and the transduction and / or processing of input actions may be handled on-board or off-board. Examples of NUI componentry include without limitation a microphone for speech and / or voice recognition; an infrared, color, stereoscopic, and / or depth camera for machine vision and / or gesture recognition; a head tracker, eye tracker, accelerometer, and / or gyroscope for motion detection and / or intent recognition; as well as electric-field sensing componentry for assessing brain activity; and / or any other suitable sensor.

[0054] The mapping service interface 206 provides the system 200 with access to mapping services, such as Google Earth ®, Esri ®, TomTom ®, Garmin ®, Foursquare ®, GeoTechnologies ®, etc. In some embodiments, the mapping services are accessible via the internet. The mapping service may be a software component configured to make a call to an application programming interface (API) of the mapping services. The mapping service interface 206 may retrieve elevation profiles, maps to render on a display and satellite images of a given area, for example. In other examples, the mapping service interface 206 may access map data stored locally in memory 208.

[0055] The base station database 210 stores information related to IAB donors, as described in relation to Figure 1. The base station database 210 stores base station properties such as the base station locations, the connection type of the base station, the base station transmission range, the base station reception range, etc. The base station database 210 may also store properties of previously installed IAB nodes, for example in a previous iteration of the process described herein. Whilst referred to herein as a database, the base station database 210 need not be a relational database, and for example may comprise a non-relational database (e.g. a NoSQL database), flat files, a spreadsheet or any other suitable storage mechanism. Although shown as part of the memory 208 in Figure 2, in some embodiments, the base station database 210 is remotely located, and thus the system 200 accesses the database 210 via a suitable interface or API, in a similar manner to the way that the mapping services are provided by interface 206. For example, the database 210 may be an open access database hosted online such as cellmapper (https: / / www.cellmapper.net).

[0056] A candidate hardware database 212 stores properties of candidate networking hardware, such as antennas, for IAB nodes. For example, the candidate hardware database 212 stores the hardware type, the hardware model, the transmission range for the hardware, thechannel bandwidth of the hardware, the power consumption of the hardware, etc. Whilst referred to herein as a database, the candidate hardware database 212 need not be a relational database, and for example may comprise a non-relational database (e.g. a NoSQL database), flat files, a spreadsheet or any other suitable storage mechanism. Although shown as part of the memory 208 in Figure 2, in some embodiments, the candidate hardware database 212 is remotely located, and thus the system 200 accesses the database 212 via a suitable interface or API, in a similar manner to the way that the mapping services are provided by interface 206.

[0057] In some embodiments, the computing system 200 is hosted on one or more applications in a virtual system implemented on a cloud computing platform. In other embodiments, the computing system 200 may be physical hardware as part of a desktop computer or laptop. In embodiments, components of the computing system 200 may be distributed such that some components are implemented locally as hardware components of a physical computer and other components are accessible via the cloud.

[0058] Figures 3A-8 are all examples of screens rendered on a user interface such as user interface 204 of system 200. The rural network planning tool is described with reference to these figures with each screen guiding the user to input information required by the rural network planning tool. In the examples described below, the screens are displayed to the user as tabs in which information may be input or selected. Each tab may be selected by the user, so as to display that tab. In alternative embodiments, the information required from the user may be input into an electronic form or other suitable format to be processed by the rural network planning tool.

[0059] Figure 3A shows an example user interface displaying a localization tab 300. In overview, the localization tab 300 is used to determine an IAB donor location, based on a target location to connect to a network.

[0060] The target location is a location that requires connection to a core network via an IAB donor or an IAB node. The target location may be a customer location or another location input by a user. In examples, the target location is a fixed location or set of fixed locations that require connection to the core network. That is to say, in examples the purpose of the system is not to provide coverage over an area (thus connecting to mobile devices in that area), but instead to provide broadband connectivity to a fixed target location, such as a user’s premises.

[0061] The user is prompted to input their postcode into an input customer postcode box 301. In this example, the input customer postcode corresponds to the target location. In the example shown in this figure, only one postcode is entered into box 301, however multiple postcodes may be input by the user in other examples. The multiple postcodes may be separated by a suitable separator character or character string, such as a semi-colon.

[0062] The user enters the number of residence addresses within the area covered by the postcode(s) and the number of business addresses under the postcode(s) into boxes 302a and 302b respectively.

[0063] The user can then select box 303 to check their eligibility for the rural network planning tool described herein based on the postcode(s) and number of addresses. Eligibility is determined based on predefined user criteria. For example, a user’s budget or eligibility for a government grant may determine whether they are eligible to use the tool described herein.

[0064] In the localization tab 300, the user selects base station type 304 from a list of available base station types. The base station types 304 are retrieved from base station database 210 described with reference to Figure 2. In the example shown in Figure 3A, the base station type 304 can be selected from 2G (E.g. GSM), 3G (e.g., UMTS), 4G (e.g. ETE) or 5G (e.g.NR).

[0065] The user also selects a base station location format 305. The base station location format 305 may be postcode, points in a coordinate system, e.g. latitude and longitude, or both.

[0066] Once the user has provided the information described above via the user interface displaying the localization tab 300, the user selects find the nearest base station 306. Upon selecting the nearest base station 306, the screen shown in Figure 3B is presented to the user.

[0067] Figure 3B shows the localization tab 300 displaying a location of the nearest base station 307. In this example, the nearest base station corresponds to the nearest IAB donor or the nearest IAB node that has already been installed, for example as a result of a previous iteration of the process described herein.

[0068] The location data of the nearest IAB donor, or base station, 307 is output as a postcode and latitude and longitude values in this example. This is because the user selected both postcode and latitude and longitude values when selecting the base station location format 305.In examples where only one of these values is selected, the corresponding information is displayed.

[0069] In one example, to determine the location of an IAB donor (i.e. the nearest base station location 307), the postcode 301 is used to determine a relevant set of co-ordinates in a suitable co-ordinate system. For example, the postcode is used to determine a latitude and longitude or co-ordinates representative thereof such as global positioning system (GPS) co-ordinates. The co-ordinates may correspond to the midpoint of the postcode 301. The determined co-ordinates then are used to determine the most proximate base station by searching the base station database 210. Alternatively, the nearest available base station may be determined by accessing Cellmapper or another suitable resource using a suitable API and inputting the target location data.

[0070] If a user selects ‘postcode’ when selecting the base station location format 305, the postcode that covers the area containing the coordinates of the nearest available base station in the global coordinate system is output to the user. This is determined via the mapping service interface 206. The mapping service interface 206 receives the coordinates of the location in the coordinate system and queries a mapping service to identify the postcode of the area that contains the coordinates.

[0071] In other examples, additional constraints may be employed in the search, such that the returned base station is not strictly the nearest. For example, the nearest base station provided or managed by a certain supplier / operator may be returned, or base stations that are technically most proximate but over a relevant border may be excluded. Once the location of the nearest base station has been displayed to the user, the user proceeds to the next tab described with reference to Figure 4.

[0072] Figure 4 shows an example user interface displaying a topography tab. In overview, the topography tab 400 is used to display the elevation profiles of the terrain between a nearest IAB donor and target locations. In the example shown in this figure, there are multiple target locations each corresponding to a customer location.

[0073] Topography data refers to data representative of terrain, including the shape and physical features of the surface of the Earth, including elevation, slope, and landforms. For example, topography data includes data representative mountains, valleys, plains, and bodiesof water. The topography data that is considered herein may refer to the terrain between two well-defined, in some examples fixed, locations.

[0074] The left side of topography tab 400 shows a map 401 of latitude against longitude for an area. The map 401 displays locations of addresses input by the user 402a and the location of the nearest available IAB donor 402b. Mapping service interface 206, as described with reference to Figure 2, retrieves the map 401 from a mapping service, for example based on the co-ordinates of the donor and target locations.

[0075] The right side of the topography tab 400 shows altitude profiles. The user selects between showing altitude profiles for a single customer 406a or multiple customers 406b. In the example shown in this figure, the user selects multiple customers 406b.

[0076] The user then selects generate 407. Upon selecting generate 407, the mapping service interface 206 retrieves altitude profiles 405a-e for multiple customers from a mapping service. The customers with altitude profiles 405a-e correspond to the customers at location 402b on map 401.

[0077] Since the coordinates of the locations of the customers and the coordinates of the nearest IAB donor in the coordinate system are known, an altitude profile for terrain between the two points can be retrieved for each customer location. The altitude profiles are displayed on a graph 408 of height variation (m) against distance away from IAB donor (m). The user then proceeds to the next tab described with reference to Figure 5A.

[0078] Figure 5A shows an example user interface displaying a backhaul tab. In overview, the backhaul tab 500 is used to select properties of candidate hardware for IAB nodes.

[0079] The types of candidate hardware presented to the user via the backhaul tab 500 are stored in candidate hardware database 212.

[0080] Wired backhaul solutions 501 are displayed to the user via backhaul tab 500. In the example shown in Figure 5A, two wired solutions are presented to the user. Each solution has selectable options for providers of that solution. For example, a wired solution is optic fibre 502 and a selectable provider is Sky 503. Some wired backhaul solutions 502 are not selectable by the user. For example, TV White Space 504 is not a selectable option in this example.

[0081] The selectable options for wired backhaul solutions 501 are determined based on whether the solutions are available in the area covered by the postcode(s) entered by the user as described with reference to Figure 3A.

[0082] Wireless backhaul solutions 505 are also presented to the user. In the example shown in Figure 5A, Microwave (e.g. 3-30GHz frequency) solutions 506a and Millimeter wave (e.g.30-300 GHz frequency) solutions 506b are presented as selectable options to the user.

[0083] In this example, the microwave solutions 506a are not presented as selectable options to the user. However, in other examples it will be appreciated that such solutions may be selectable.

[0084] Millimeter wave solutions 506b are presented as selectable options to the user. In this example, light licensed spectrum (e.g. 70 / 80 GHz) is displayed with a selectable provider. An unlicensed spectrum (e.g. 60 GHz) option is also provided to the user with a selectable provider. The providers in this example are Siklu - 70 / 80 and Siklu - 60.

[0085] After selecting options for wired and wireless backhaul solutions and corresponding providers, the user then selects Done 507.

[0086] Figure 5B shows a popup window displayed on a backhaul tab. In overview, the popup window 510 on the backhaul tab 500 is used to enable mix and match of the types of candidate hardware.

[0087] A popup window 510 is displayed in front of backhaul tab 500 described with reference to Figure 5 A. The popup window 510 reads:3 solutions have been selectedPlease decide if Mix and Match is allowed.Then proceed to the Antenna Tab for further specifications.

[0088] After the user selects ‘Okay’ on the popup window 510, the user selects an ‘Allow wireless backhaul solution Mix and Match’ 512 option. Once the user has selected whether to allow mix and match, the user proceeds to the next tab.

[0089] Figures 6A and 6B both show examples of an antenna tab for different backhaul solutions. In these examples, the antenna tab is used to gather further information regarding theproperties of candidate hardware for IAB nodes. The properties selected using the tabs described in these figures are used to determine transmission and reception characteristics of the candidate networking hardware. Candidate networking hardware may refer to a collection or combination of different devices (e.g. a particular piece of transmission hardware and an antenna). The transmission and reception characteristics of the candidate networking hardware may correspond to the efficiency and range capabilities of the candidate networking hardware.

[0090] The antenna tabs of these figures enable a user to customize an antenna type, the candidate hardware in this example, by varying configurable parameters through selecting selectable options. Each antenna type, for example Siklu-70 / 80 and Siklu-60 has transmitter hardware and receiver hardware.

[0091] The selectable options presented to the user via the antenna tab described in relation to Figures 6A and 6B are stored in candidate hardware database 212.

[0092] Figure 6 A shows an example user interface displaying an Antenna tab. Antenna tab 600 is used to collect specification data for an antenna.

[0093] Backhaul selection 601 options are provided to the user. The backhaul selection 601 options correspond to backhaul solutions selected by the user as described with reference to Figures 5 A and 5B.

[0094] In the example shown in Figure 6 A, three backhaul selection 601 options are presented to the user, Sky, Siklu-70 / 80 and Siklu 60. In this example, Siklu-70 / 80602 has been selected by the user. The Sky option cannot be selected by a user on antenna tab 600. This is because the Sky option is a wired backhaul solution and therefore does not require an antenna for connection.

[0095] An antenna pattern 603 refers to the directional or angular dependence of the strength of the waves from the antenna. In the example shown in Figure 6A, only an omnidirectional antenna pattern may be selected by the user. A directional antenna pattern option is not selectable by the user because the Siklu 70 / 80602 antenna does not support this functionality.

[0096] Selectable options for channel bandwidth [MHz] 604 are presented to the user on the antenna tab 600. The channel bandwidth 604 is defined as the range of frequencies that can beprocessed by the antenna. In this example, the selectable options for the channel bandwidth of this antenna are 250, 500, 750 and 1250.

[0097] Selectable options for maximum antenna gain 605 are also presented to the user. Maximum antenna gain 605 describes how well the antenna converts input power into waves, e.g. microwave or millimeter waves, headed in a specified direction. In this example, the maximum antenna gain 605 is selected from 43, 44 or 50.

[0098] The user also selects an antenna model 606. The antenna model 606 represents characteristics of the hardware of the antenna. Each antenna model 606 option has different hardware characteristics such as transmission range, price, lossy bandwidth etc. In the example shown in this figure, only one antenna model 606 is selectable by the user.

[0099] Once the user has selected an antenna model 606, the user selects ‘Finalize’ 607.

[0100] In response to the user selecting Finalize 607, the transmission and reception properties of the candidate hardware are determined. In this example, the transmission and reception properties of the candidate hardware is a maximum expected data rate 608 which is stored in the candidate hardware database. In this example, the maximum expected data rate 608 is 2 Gbps within 14209m from IAB donor. It will be understood that the data rate 608, any other suitable transmission and reception characteristics and indeed any other relevant specifications of the candidate hardware may be provided by the vendor or manufacturer of the candidate hardware device. Such details may be stored in the candidate hardware database 212.

[0101] Figure 6B illustrates another view of the antenna tab, in which a different type of antenna is selected.

[0102] Figure 6B shows an example user interface displaying an antenna tab. In overview, the antenna tab 620 is used to collect properties for an antenna. The definitions of configurable parameters described above in relation to Figure 6A apply correspondingly to the corresponding configurable parameters below.

[0103] In this example, backhaul selection 621 options are presented to the user and the user selects Siklu-60622.

[0104] The user selects an antenna pattern 623. In this example, the user selectable options are omnidirectional and directional.

[0105] The user selects a channel bandwidth 624. The channel bandwidth 624 only has one selectable option, 2160, in this example.

[0106] The user is unable to select a maximum antenna gain 625 in this example as this is not a configurable parameter for this antenna type.

[0107] The user also selects an antenna model 626. In this example, only one antenna model is available, based on the selections made by the user, and therefore it is presented to the user as the only selectable option.

[0108] After the user has made their selections, the user selects Finalize 627 and the transmission and reception properties of the candidate hardware are determined. In this example, the transmission and reception properties of the candidate hardware is a maximum expected data rate 628. The maximum expected data rate 628 in this example is 2.05 Gbps within 230m from IAB donor. Once the user has selected the available options in the antenna tabs described with reference to Figures 6A and 6B, the user proceeds to a topology tab.

[0109] Figure 7 A shows an example user interface displaying a topology tab. In overview, the topology tab 700 is used trigger the generation of, and then to subsequently display an IAB network topology that takes into account the candidate hardware properties selected with reference to the previous figures as well as the topography of the terrain between the target location and the IAB donor location.

[0110] Selectable options for a backhaul selection 701 are presented to the user. In the figure, the user selects between Siklu - 70 / 80, Siklu-60 and Both. The selectable options presented to the user may consider the receiver part of the candidate hardware however, this necessarily determines the corresponding transmitter properties. These options are presented to the user based on the selections made as described in relation to the backhaul and antenna tabs in Figures 5A and 5B and Figures 6A and 6B respectively. The Both option is selectable by the user as ‘Mix and Match’ was enabled as described in relation to the backhaul tab previously. In this example, the user selects Both 702.

[0111] Although only one combination option, Both 702, is presented to the user in this example, in other examples the user may select more than two backhaul selections and all possible combinations may be presented as selectable options to the user.

[0112] The user then selects a show the final topology button 703. Upon selecting the show the final topology button 703, a topology is generated using the process discussed in detail below with respect to Figure 10. Subsequently, a map 705 is displayed on the left side of topology tab 700, which displays the generated topology. The map 705 is retrieved by the mapping service interface 206. On the map 705, locations of an IAB donor 706a, an IAB node 706b and customers 706c are represented as dots. Horizontal stripes correspond to the IAB donor 706a, diagonal stripes correspond to the IAB node 706b and vertical stripes correspond to the customers 706c. The map 705 may be retrieved on the basis of the maximum and minimum co-ordinates of the donor 706a and customers 706c.

[0113] On the right side of the topology tab 700 is a number of IAB nodes required 704. In this example, the rural network planning tool has determined that 1 IAB node is required. A list 710 of the customers 706c, serving base station 712 and max data rate 714 is displayed to the user. The customers 706c correspond to the customers 706c shown on the map 705 on the left side of the topology tab 700. The serving base station 712 column shows the IAB node that the customers 706c are connected to. In this example, the serving base station 712 may be either the IAB node 706b or the IAB donor 706a.

[0114] The number of IAB nodes required 704 and the location of the IAB node 706b is determined using a multi-objective optimization approach performed by a rural network planning tool. This approach, as performed by the rural network planning tool, is described with reference to Figure 12 below.

[0115] The user then selects a check box 715 to trigger the display of a satellite image displaying line-of-sight connections between the customers 706c, the IAB node 706b and the IAB donor 706a.

[0116] Figure 7B shows a satellite image displayed over the topology tab described with reference to Figure 7A. In overview, the satellite image 720 is used to show line-of-sight connections between an IAB donor, IAB node and customer locations

[0117] A satellite image 720 corresponding to the area shown on map 705 of Figure 7 A is displayed to a user. The satellite image 720 is retrieved by the mapping service interface 206 from a mapping service.

[0118] On the satellite image 720, locations of customers are represented by icons 722a-e, an IAB node is represented by icon 726 and an IAB donor is represented by icon 724. The number of IAB nodes displayed on the satellite image 720 corresponds to the number of IAB nodes required shown in Figure 7A. The connection between the IAB donor and the IAB node is represented by a line with an arrow showing the direction the network extends away from the IAB donor. The connection between the IAB node and the customers are represented by lines with an arrow showing the direction extending away from the node.

[0119] In the example described in relation to Figures 7 A and 7B, the Siklu-70 / 80 transmitter hardware is attached to the IAB donor and the Siklu-70 / 80 receiver hardware is attached to the IAB node. The Siklu-60 transmitter hardware is attached to the IAB node and the Siklu-60 receiver hardware is attached to the customer locations. This combination has been determined as optimal by the multi-objective optimisation process as it provides the best maximum data rate at the customer locations whilst using the minimum amount of hardware required.

[0120] The user then selects a different option from the backhaul selection 701 to display a different topology tab, as described with reference to Figure 7C below.

[0121] Figure 7C shows an example user interface displaying a topology tab. In this example, different options are selected on the user interface, which causes a different topology to be generated.

[0122] Selectable options for a backhaul selection 751 are presented to the user. In the Figure, the user selects between Siklu - 70 / 80, Siklu-60 and Both. These options are presented to the user based on the selections made as described in relation to the backhaul and antenna tab in Figures 5 A and 5B and Figures 6A and 6B respectively. In this example, the Siklu-70 / 80 option is selected by the user and therefore only Siklu 70 / 80 transmitter and receiver hardware will be used.

[0123] The user then selects a show the final topology button 753. Upon selecting the show the final topology button 753 a topology is generated using the process discussed in detail below with respect to Figure 10. Subsequently, a map 755 is displayed on the left side of the topology tab 750. On the map 755, locations of an IAB donor 756a, an IAB node 756b and customers 756c are represented as dots. Horizontal stripes correspond to the IAB donor 756a, diagonal stripes correspond to the IAB node 756b and vertical stripes correspond to thecustomers 756c. In this example, there are no IAB nodes 756b (diagonal striped dots) shown on the map 755.

[0124] On the right side of the topology tab 750 a number of IAB nodes required 754 is displayed to the user. In this example, a rural network planning tool determines that no IAB nodes are required. A list 760 of the customers 756c, serving base station 762 and max data rate 764 are displayed to the user. The customers 756c correspond to the customers 756c shown on the map 755 on the left side of the topology tab 750. The serving base station 762 column shows the IAB node that the customers 756c are connected to. In this example, the serving base station 752 is the IAB donor 756a.

[0125] The number of IAB nodes required 754 and the location of the IAB node 756b, which in this example is the IAB donor 756a, is determined using a multi-objective optimization approach performed by a rural network planning tool. This approach is described with reference to Figure 12 below.

[0126] The user then selects a check box 765 to trigger the display of a satellite image displaying line-of-sight connections between the customers 756c and the IAB donor 756a.

[0127] Figure 7D shows a satellite image displayed over the topology tab described with reference to Figure 7C. A satellite image 770 corresponding to the area shown on map 755 of Figure 7C showing line-of-sight connections is displayed to a user. The satellite image 770 is retrieved by the mapping service interface 206 from a mapping service.

[0128] On the satellite image 770, locations of customers are represented by icons 772a-e. The number of IAB nodes displayed on the satellite image 770 corresponds to the number of IAB nodes required shown in Figure 7C, which in this example is zero. An IAB donor, which acts as the IAB node in this example, is represented by icon 776. The connection between the IAB donor 776 and the customers 772a-e are represented by lines with an arrow showing the direction the network extends away from the IAB donor.

[0129] The user then selects a different option from the backhaul selection 751 to display a different topology tab, as described with reference to Figure 7E below.

[0130] Figure 7E shows an example user interface displaying a topology tab. In this example, different options are selected on the user interface, which causes a different topology to be generated.

[0131] Selectable options for a backhaul selection 781 are presented to the user. In the Figure, the user selects between Siklu - 70 / 80, Siklu-60 and Both. These options are presented to the user based on the selections made as described in relation to the antenna tab in Figures 6A and 6B. In this example, the user selects Siklu-60782 and therefore only Siklu 60 transmitter and receiver hardware will be considered.

[0132] The user then selects a show the final topology button 783. Upon selecting the show the final topology button 783, a topology is generated using the process discussed in detail below with respect to Figure 10. Subsequently, a map 785 is displayed on the left side of the topology tab 780. On the map 785, locations of an IAB donor 786a, an IAB node 786b and customers 786c are represented as dots. Horizontal stripes correspond to the IAB donor 786a, diagonal stripes correspond to the IAB node 786b and vertical stripes correspond to the customers 786c.

[0133] On the right side of the topology tab 780 a number of IAB nodes required 784 is displayed to the user. In this example, a rural network planning tool determines that 4 IAB nodes are required. A list 790 of the customers 786c, serving base station 792 and max data rate 794 is displayed to the user. The customers 786c correspond to the customers 786c shown on the map 785 on the left side of the topology tab 780. The serving base station 782 column shows the IAB node that the customers 786c are connected to. In this example, the serving base station 792 may be IAB nodel, IAB node 2, IAB node3, IAB node 4 or the IAB donor 786a.

[0134] The number of IAB nodes required 784 and the location of the IAB nodes 786b is determined using a multi-objective optimization approach performed by a rural network planning tool. This approach is described with reference to Figure 12 below.

[0135] Figure 8 shows an example user interface displaying a power tab 800. In overview, the power tab 800 is used to select a power source for the number of IAB nodes determined by the rural network planning tool.

[0136] Power source type selection 801 is displayed to a user with selectable power options on power tab 800. Two selectable power options are displayed to the user to be used to power the IAB nodes required, as determined by the rural network planning tool.

[0137] New electricity connection(s) 802 may be selected. This option considers the cost of establishing new connections and annual electricity bill estimation.

[0138] Off-grid source(s) 803 may alternatively be selected by the user. This option considers the cost of hardware, charge controller, storage battery and inverter.

[0139] The user then proceeds to a decision tab, after having selected the options presented via the power tab 800.

[0140] Figure 9 shows an example user interface displaying a decision tab. In overview, the decision tab 900 is used by a user to compare and select a backhaul solution.

[0141] In decision tab 900, a user is prompted to ‘rank your preference’ 901. Selectable options for maximum data rate 902 are provided to the user. The user can select from high, medium and low. In this example, the user selects high.

[0142] Selectable options for power supply 903 are provided to the user. The user can select from high, medium and low. In this example, the user selects high.

[0143] Selectable options for energy efficiency 904 are provided shown on the decision tab 900 however the selectable options are not able to selected by the user.

[0144] Selectable options for cost 905 are provided to the user. The user can select from high, medium and low. In this example, the user selects high.

[0145] Once the user has selected options for maximum data rate 902, power supply 903 and cost 905, the user selects make final decision 906.

[0146] A table 910 is presented to the user with columns for solution, max data rate, power supply and total cost.

[0147] Backhaul selection described with reference to Figures 5 and 6 are presented as rows in the table 910. For example, wired solutions Sky 911 and wireless solutions Siklu -70 / 80913. Siklu-60912 and both 914 are in the solution column of table 910.

[0148] The max data rate for each solution is provided to the user in the table 910 to enable the user to compare solutions.

[0149] The power supply for each solution is also provided to the user. This information may be stored in candidate hardware database 212 described with reference to Figure 2.

[0150] The total cost for each solution is provided to the user for comparison. The total cost calculation is described in more detail in relation to Figure 12.

[0151] Figure 10 is a flowchart of an example method which may be implemented by a rural network planning tool.

[0152] A rural network planning tool is used to determine the optimal location and number of IAB nodes required to connect a desired location to a base station, or IAB donor, connected to a core network. The rural network planning tool uses the information received by system 200 from a user via user interface 204, the information having been input to tabs described in relation to figures 3A to 7E.

[0153] The rural network planning tool uses a multi-objective optimization approach to determine the optimal location and number of IAB nodes required for connection. The multiobjective optimization approach considers the topography of the terrain between a target location, e.g. a customer location, and a location of a nearest available base station connected to a core network.

[0154] At step S1002, the rural network planning tool receives first location data of at least one target location to connect to the core network. In the examples described in relation to Figures 3A-7E, the at least one target location are the customer locations (e.g. in co-ordinates), derived from the user inputting the postcode(s) and number of customer addresses.

[0155] At step S1004, the rural network planning tool receives second location data of an IAB donor, or nearest available base station as described in relation to Figures 3 A and 3B, to the target location received at step S1002. In the examples described herein, the second location data, or in other words the location of the nearest available base station, is retrieved from base station database 210 based on the first location data of the target location.

[0156] At step S1006, the rural network planning tool receives topography data of terrain between the target location and the location of the IAB donor. In embodiments, the topography data of the terrain is retrieved by mapping service interface 206 from a mapping service.

[0157] At step S1008, the rural network planning tool determines transmission and reception properties of a candidate networking hardware device, also referred to more generally as candidate networking hardware herein, from properties of the candidate networking hardware device. The properties of the candidate networking hardware device may be specification data selected by the user for an antenna, as described with reference to figures 6A and 6B. The transmission and reception properties of a candidate networking hardware device corresponds to data representative of performance of the candidate networking hardware device. In the example described with reference to figures 6 A and 6B, the specification data of an antenna is used to determine a maximum data rate within a range for the antenna.

[0158] At step S1010, the rural networking planning tool (RNPT) performs a multi-objective optimization to determine an optimal location and number of IAB nodes. In one embodiment, an optimization approach called Multi-Objective Optimization Evolutionary Algorithm (MOEA) is used. For example, the multi-objective optimization approach is a genetic algorithm.

[0159] The core objectives of the multi-objective optimisation approach described herein include minimising cost (which may in turn minimise the number of IAB nodes deployed and thus maximise the efficiency of the solution), minimising power consumption, and maximising coverage / topology (i.e., ensuring sufficient coverage and connectivity) provided by IAB nodes.

[0160] Figures 3A-9 describe a user interface that enables a user to input information required by the RNPT to perform the multi-objective optimisation described below. However, in some embodiments, the information may be input directly into the RNPT without requiring the user interface. In this example, the location data of the target location, the location data of the IAB donor, and the candidate networking hardware transmission and reception properties are input directly to the RNPT.

[0161] In a genetic algorithm, a population of candidate solutions to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties which can be mutated and altered.

[0162] In general, evolution in a genetic algorithm starts from a population of randomly generated candidate solutions, and is an iterative process, with the population in each iteration called a generation. In each generation, the fitness of every candidate solution in the population is evaluated. The fitness is the value of an objective function in the optimization problem being solved. The most fit candidate solutions are selected (e.g. stochastically) from the current population, and the properties of each candidate solution are modified (recombined and possibly mutated) to form a new generation. The new generation of candidate solutions is then used in the next iteration of the algorithm. The algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population.

[0163] In the present disclosure, an initial population of vectors representing candidate IAB placements is determined by the RNPT. Figure 11 shows an example diagram of a process for determining an initial population. This figure shows one method that may be used to generate an initial population by the RNPT however alternative methods may be used in other embodiments.

[0164] A number of placements 1120, also referred to as sites herein, are identified in an area 1100 between the first location data of at least one target location to connect to a core network 1110 and second location data of an IAB donor 1105. The sites 1120 are available candidate locations, or placements, where IAB nodes may be placed in the area 1100. In the example shown in Figure 11, a rectangular area 1110 is created such that the first location data 1110 and the second location data 1105 are at the endmost points of the diagonal of the rectangular area 1100.

[0165] Candidate IAB sites 1120 are arranged within the rectangular area 1100 in a matrix array. That is to say that the candidate IAB sites are distributed across the rectangular area 1100 in an evenly-spaced manner. It will be appreciated that in other examples the area may not be rectangular and / or could be larger to encompass more candidate sites.

[0166] The number of available sites within the area is represented by a set of candidate sites S = {si, S2, ..., sn}. In the example shown in Figure 11, si is denoted by reference numeral 1120-1, S2 is denoted by reference numeral 1120-2 and snis denoted by reference numeral 1120-n.

[0167] To generate an initial population, the sites 1120, {si, S2, ..., sn], are randomly assigned a value of 1 or 0. A site is assigned a value of 1 if it is deployed, and 0 otherwise. This can berepresented by a vector of binary digits of size n, such that x = (xi, X2, ... , xn) where Xi = 1 if a site is deployed, 0 otherwise.

[0168] In the example shown in Figure 11, m binary vectors 1150 have been generated by randomly assigning sites with values of 1 or 0. The m binary vectors in this example represent the candidate solutions that are described later.

[0169] Each site 1120, Si, has a coverage radius that is at least in part based on the transmission and reception range of the candidate hardware. A propagation model may be used to determine service areas provided by the candidate hardware at the sites. The propagation model predicts the coverage area of a transmitter by characterizing wave propagation as a function of frequency and distance. In other words, the propagation model models the trade-off between performance (e.g. data rate) and distance, to provide an estimate of the likely data rate at a given distance from the candidate hardware. The propagation model may also take the environmental terrain into account, for example if there is a hill or other object obscuring line-of-sight transmission. That is to say, in some examples the propagation model takes into account topography data.

[0170] In some examples, the coverage radius may be used to determine the spacing of the candidate sites (i.e. each site is spaced a distance apart that reflects its radius of coverage), though in other examples the spacing of the candidate sites may be arbitrary (e.g. every 50m, 100m, 200m etc).

[0171] In the present disclosure, three objective functions are defined in relation to the multiobjective optimization. A first objective function is considered to minimize the total cost of the candidate hardware:

[0172] A second objective function is considered to minimize power consumption of the candidate hardware:

[0173] A third objective function, / 3(x), is considered by the RNPT in the multi-objective optimization to maximize coverage provided by an IAB node at a candidate IAB node placement.

[0174] The third objective function may include a term that is expressed as a fraction of the target locations, or the area served, for example the area described with reference to Figure 11 (or a subset thereof such as one defined by the boundaries of a target postcode or some other predefined area).

[0175] In some embodiments, the line-of-sight transmission between a placement and proximate other placements may be represented as a term in the third objective function. The line-of-sight transmission range may consider the elevation or topography of terrain between two candidate sites or between a candidate site and the IAB donor and / or the target locations. That is to say, the calculation of the third objective function may be based on topography data. The line-of-sight transmission range may also consider immovable objects that would occlude the line-of-sight transmission, such as a hill, valley, building, etc. The term may penalize a placement that has a restricted line-of-sight to other placements. For example, the system may retrieve an elevation profile and determine based on the profile that line-of-sight is restricted, and penalize the solution accordingly.

[0176] It will be understood that whilst the objective function f3(x) is discussed as a value to be maximized, it may straightforwardly be converted to a cost to be minimized, for example by defining the coverage shortfall such that coverage shortfall = (1 - coverage).

[0177] Reference is now made to Figure 12. Figure 12 is an example flowchart of a multiobjective optimization approach according to the present disclosure.

[0178] At step S1202, an initial population of candidate solutions, also referred to simply as solutions herein, is created. In this example, the candidate solutions are binary vectors each representing a set of candidate IAB placements, or sites. One method for generating an initial population is described with reference to Figure 11 above.

[0179] A feasibility check may be performed on the initial population such that the binary vectors containing sites that violate one or more predefined hard constraints imposed on the sites by the RNPT may be discarded (or alternatively penalized in the ranking discussed below). For example, if the cost associated with a binary vector is above a threshold value or if themaximum coverage is below a threshold value, the binary vector may be discarded from the population.

[0180] At step S1204, the RNPT evaluates the first, second and third objective functions to obtain objective values in respect of each candidate solution.

[0181] At step S1206, a set of the most fit solutions are selected. In other words, once the objective functions have been evaluated, the RNPT selects a subset of the populations that corresponds to the ‘best’ solutions.

[0182] In one example, the selection of the subset is accomplished using non-dominated sorting. Non-dominated sorting is used to classify a population of solutions into different levels of Pareto fronts. A solution is said to be non-dominated if no other solution in the population is better in all objectives. The first front contains the non-dominated solutions, and subsequent fronts are formed by removing the previous fronts' solutions and finding the next set of nondominated solutions. This sorting enables the identification and preservation of high-quality solutions in multi-objective optimization.

[0183] A Pareto Front is defined to be a set of all Pareto optimal solutions. The highest ranking solution when all the solutions are sorted by Pareto dominance is a Pareto optimal solution. A solution is Pareto optimal if it is as least as good as all the other solutions when comparing objective values.

[0184] In this example, three objective functions are evaluated for each vector and compared. If a vector has objective values that are at least as good as the objective values of all the other vectors, it is defined to be the Pareto optimal solution. In this context, a good solution is considered to be a solution that provides the best trade-off of cost and power consumption and coverage.

[0185] In one example, techniques may be applied to maintain diversity within the subset of solutions. For example, a crowding distance is calculated for each solution within each front. The crowding distance is a measure used in techniques that maintain diversity among solutions within a Pareto front. This helps to ensure solutions are well-distributed across the objective space by favouring those in less crowded regions which helps to avoid clustering of solutions.

[0186] Initially, the crowding distance of all solutions is set to zero. For each objective function the solutions are sorted based on their objective values. Subsequently, an infinite crowding distance is assigned to boundary solutions. For each solution, the crowding distance is calculated based on the normalized difference in objective values of neighbouring solutions. The subset is then selected based on both the Pareto rank (i.e. which front it belongs to) and the crowding distance.

[0187] Put generally, the highest ranking vectors are selected from the previous population. These vectors represent the optimal placements of the IAB nodes in this population. A percentage of the population may be selected for crossover and mutation described below. In one example, the top 40% of the ranked vectors may be selected. Alternatively, a predetermined number, P, of vectors may be selected. For example, the top 200 vectors may be selected from the ranked vectors.

[0188] At step S1208, the solutions selected in the previous step undergo crossover and / or mutation.

[0189] Crossover refers to combining genetic material of two selected solutions to generate a new solution. For example, binary vectors xi and X2 may be combined to form a new vector to be included in a population in the next iteration. Each solution, or vector, is crossed over with probability pc. Common options for crossover include single point crossover, multipoint crossover and uniform crossover.

[0190] Mutation is flipping elements of the selected solutions to generate new solutions. For example, a 1 may be changed to a 0, or vice versa, in a binary vector that was selected in the previous step. In some examples, more than one element within a solution is flipped such that multiple Is are changed to 0s and vice versa for a selected binary vector. A selected solution is mutated with probability pm.

[0191] Steps S1204, S1206 and S1208 are repeated for a predetermined number of iterations or until convergence criteria are met. A solution is then selected from amongst the highest ranking solutions across the various generations of evolution.

[0192] In some embodiments, the number of solutions in a population is the same throughout all iterations. In other embodiments, as populations are refined by each iteration, the number of solutions becomes iteratively smaller.

[0193] The multi-objective optimization approach described above considers the case when only one type of candidate networking hardware is considered for the IAB nodes. However, in other examples multiple candidate networking hardware devices are considered by the RNPT. There are a number of ways in which this could be implemented. In one example, the vectors forming each candidate placement are changed from being binary to being capable at each element of representing multiple options. In one example, m candidate solutions are represented by vectors of the form:y = (y™ y™ y^

[0194] Where yi = 0 in location where there are no IAB nodes, yi = 1 for a first candidate networking hardware and yi = 2 for a second candidate networking hardware.

[0195] In other examples, there may be more than two types of candidate networking hardware such that any number of candidate networking hardware devices may be considered for the IAB nodes in the multi-objective optimization.

[0196] In this example the first objective function returns a cost that depends on the value yi, such that different pieces of networking hardware have different costs. This could be accomplished by using a suitable lookup table. The same is true of the other cost functions.

[0197] Steps S1202-S1210, as described above, are then performed using the vectors and objective functions for yi.

[0198] The multi-objective optimization approach implemented by the RNPT, as described herein, outputs a vector representative of an optimum number and placement of IAB nodes to connect the target location to the IAB donor. As each element in the vector corresponds to a known site in the real world having respective coordinates, the coordinates for sites of the IAB nodes can be readily determined, such that the system outputs the locations in coordinate form. In examples where more than one candidate networking hardware devices are considered, the vector again encodes the selection, which can be readily used to look up the networking hardware to which the values correspond and then output them.

[0199] There are a number of ways in which the links between the nodes at optimal placements may be determined. In this context, links refer to the connections from one IAB node at one site to another site (and / or to the donor as applicable). In one example, the links are inferredfrom the output vector. Since each element in the vector corresponds to a known site in the real world, the transmission and reception properties of the networking hardware may be used to determine which sites will transmit to other sites, including the IAB donor. In other words, one site will be linked to whichever sites are within range. Alternatively, in other examples an optimization may be used to set the links between the nodes, the target location and the IAB donor.

[0200] It will be understood that the multiple objective optimization algorithm outlined above may result in the output of a plurality of acceptable solutions. In one example, multiple solutions, each representative of an optimum number and placement of IAB nodes are presented to a user for selection. In one example, a predetermined number of the top ranked solutions are output to the user. In another example, the top ranked solutions with the highest objective value for each objective function, e.g. the top solution with the minimum cost, the top solution with the minimum power consumption and the top solution with the maximum coverage, are all output to the user. Alternatively, rather than user selection the top ranked solution according to one of the objective functions (e.g. the lowest cost or maximum coverage) may be selected automatically.

[0201] Using the multi-objective optimization approach described herein improves scalability and flexibility. Large search spaces are well handled due to the use of meta heuristics whilst incorporating real-world constraints such as topography, budgets and user distribution, for example. The objectives considered by the optimization approach are also adaptable such that additional objectives (e.g., latency, reliability, etc.) may be added or removed without changing the overall structure. Pareto efficiency is also achieved as decision-makers can see multiple optimal solutions, each offering different trade-offs among cost, power consumption, and coverage.

[0202] As described with reference to Figure 9, the total cost of the optimal solution may be displayed to a user. The total cost of the optimal solution is calculated by multiplying the binary vector corresponding to the optimal solution by the cost of the candidate networking hardware that forms part of the IAB nodes.

[0203] Returning now to Figure 2 in more detail, the computer system 200 shown in Figure 2 is shown in simplified form. The computer system 200 comprises a processor 202 and a memory 208. In this example, the memory 208 may comprise volatile memory and a non-volatile storage. In this example, the computer system 200 may include a display subsystem 205a, an input subsystem 205b, and a communication subsystem. In other examples, one, some or all of these components are omitted. The processor 202 comprises one or more hardware processing units configured to carry out processing operations. A hardware processing unit may be programmable or non-programmable. Certain hardware processing units are configured to execute computer-readable instructions based on an instruction set architecture. Examples of such a hardware processing unit include a central processing unit (CPU), graphics processing unit (GPU), tensor processing unit (TPU), neural processing unit (NPU), intelligence processing unit (IPU) or other form of accelerator processing unit. Such hardware processing units may be single-core or multi-core, and instructions executed thereon may be configured for sequential, parallel, and / or distributed processing. Other examples of such hardware processing units include a field-programmable gate array (FPGAs) or a nonprogrammable fixed-logic circuit, such as an application- specific integrated circuit (ASIC). The processor 202 is contained in a single device in some examples. Individual components of the processor 202 are distributed among two or more separate devices in other examples. In some such examples, such devices are remotely located from each other and / or configured for coordinated processing. The non-volatile storage includes one or more physical devices configured to hold data and / or computer-readable instructions executable by the processor 202. Examples of non-volatile storages include optical memory (e.g., CD, DVD, HD-DVD, Blu-Ray Disc, etc.), semiconductor memory (e.g., ROM, EPROM, EEPROM, FLASH memory, etc.), magnetic memory (e.g., hard-disk drive), or other mass storage device technology. The volatile memory includes one or more physical devices that include random access memory in some examples. The volatile memory is typically utilized by processor 202 to temporarily store data and / or instructions during processing. The terms “module,” “program,” and “engine” are used to describe particular functionality of the computer system 200 implemented in hardware or software. In some examples, a software module, program, or engine is instantiated via the processor 202 executing instructions held by non-volatile storage, using portions of the volatile memory. Different modules, programs, and / or engines are instantiated from the same application, service, code block, object, library, routine, API, function, etc. in some examples. In other examples, the same module, program, and / or engine are instantiated by different applications, services, code blocks, objects, routines, APIs, functions, etc. The terms “module,” “program,” and “engine” encompass among other things individual or groups of executable files, data files, libraries, drivers, scripts, database records, etc. The display subsystem 205a, as part of the user interface 204, is configurable to present a visualrepresentation of data such as data held by the non-volatile storage. The visual representation takes the form of a graphical user interface (GUI) in some examples. The display subsystem 205a includes one or more display devices utilizing virtually any type of technology. Such display devices are combined with processor 202, volatile memory, and / or non-volatile storage in a shared enclosure in some examples. In other examples, such display devices are peripheral display devices. The input subsystem 205b comprises or interfaces with one or more input devices such as user-input devices such as a keyboard, mouse, touch screen, or game controller. In some embodiments, the input subsystem 205b comprises or interfaces with selected natural user input (NUI) componentry. Such componentry may be integrated or peripheral, and the transduction and / or processing of input actions may be handled on-board or off-board. Examples of NUI componentry include without limitation a microphone for speech and / or voice recognition; an infrared, color, stereoscopic, and / or depth camera for machine vision and / or gesture recognition; a head tracker, eye tracker, accelerometer, and / or gyroscope for motion detection and / or intent recognition; as well as electric-field sensing componentry for assessing brain activity; and / or any other suitable sensor. The communication subsystem is configured to communicatively couple the computer system 200 to another device or system. The communication subsystem may include wired and / or wireless communication devices compatible with one or more different communication protocols. In some examples, the communication subsystem allows computer system 200 to send and / or receive messages to and / or from other devices via a communication network such as the internet. The term computer readable media as used herein includes for example computer storage media. Computer storage media includes for example volatile and non-volatile, removable and nonremovable media (e.g., volatile memory or non-volatile storage). Computer storage media includes for example solid-state storage, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other article of manufacture which can be used to store information, and which can be accessed by a computing device (e.g., the computer system 200 or a component device thereof). Computer storage media does not include a carrier wave or other propagated or modulated data signal. Communication media is embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” describes a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal.Examples of communication media include without limitation wired media such as a wired network or direct wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0204] Various alterations or modifications may be made to the examples discussed herein. For example, the rural network planning tool may perform a multi-objective optimization to determine the optimal number and locations of IAB without necessarily requiring a user interface. In this example, the information required to perform the optimization is directly input to the rural network planning tool, which could be hosted on a server or cloud computing platform. Alternatively, a different sequence of screens, or screens formatted differently, may be displayed on a user interface in order to obtain the information required by the rural network planning tool.

[0205] Although a postcode search is described with reference to Figure 3A, in some embodiments the coordinates of the target location and the nearest available IAB donor are known and may be input directly. In other embodiments, a user inputs a location however the location of the IAB donor is not subsequently explicitly displayed to the user. In yet further embodiments, a map may be displayed to the user from which suitable locations can be selected.

[0206] In some embodiments, the topography tab described with reference to Figure 4 is omitted from the user interface. In some embodiments, there is only one target location in contrast to the multiple customer locations described herein.

[0207] In some embodiments, candidate networking hardware is not configurable such that the user is not able to customize the hardware by selecting from options for different properties. Alternatively, the selectable options for the properties described with reference to Figures 6A and 6B are different to those described herein.

[0208] In some embodiments, there are no wired solutions available. In other embodiments, enabling mix and match is automatic and therefore does not require the screen described with reference to Figure 5B. In some embodiments, the satellite images described with reference to Figures 7B and 7D are not displayed via a user interface.

[0209] In some embodiments, the selected options of the power source tab, described with reference to Figure 8, are received by the rural network planning tool to be used in the multi-objective optimization. In some embodiments, the decision tab described with reference to Figure 9 is omitted from the screens of the user interface.

[0210] Advantageously, examples of the disclosure described herein provide a solution for last-mile network connectivity by determining where to place IAB nodes to most efficiently provide broadband connection. In rural areas, terrain with significant elevation changes such as mountains and valleys can provide obstacles to wireless IAB solutions based on line of sight. The rural network planning tool described herein takes into account the topography of the terrain and is efficient in terms of minimising the use of expensive IAB nodes.

[0211] Although at least some aspects of the embodiments described herein with reference to the drawings comprise computer processes performed in processing systems or processors, the invention also extends to computer programs, particularly computer programs on or in a carrier, adapted for putting the invention into practice. The program may be in the form of non-transitory source code, object code, a code intermediate source and object code such as in partially compiled form, or in any other non-transitory form suitable for use in the implementation of processes according to the invention. The carrier may be any entity or device capable of carrying the program. For example, the carrier may comprise a storage medium, such as a solid-state drive (SSD) or other semiconductor-based RAM; a ROM, for example a CD ROM or a semiconductor ROM; a magnetic recording medium, for example a floppy disk or hard disk; optical memory devices in general; etc.

[0212] The examples described herein are to be understood as illustrative examples of embodiments of the invention. Further embodiments and examples are envisaged. Any feature described in relation to any one example or embodiment may be used alone or in combination with other features. In addition, any feature described in relation to any one example or embodiment may also be used in combination with one or more features of any other of the examples or embodiments, or any combination of any other of the examples or embodiments. Furthermore, equivalents and modifications not described herein may also be employed within the scope of the invention, which is defined in the claims.

Claims

CLAIMS:

1. A computer-implemented method of determining placement for integrated access backhaul (IAB) nodes for connecting a target location to a core network, comprising:receiving first location data of the target location to connect to the core network;receiving second location data of an IAB donor connected to the core network;receiving topography data representative of terrain between the target location and the IAB donor;receiving transmission and reception properties of a candidate networking hardware device capable of serving as an IAB node;applying a multi objective optimization technique to determine a placement of one or more IAB nodes to connect the target location to the IAB donor, based on the topography data and the transmission and reception properties of the candidate networking hardware device capable of serving as an IAB node.

2. The method of claim 1, comprising:accessing a base station database to determine the second location data based on the first location data.

3. The method of claim 1 or 2, comprising:receiving address data of the target location; anddetermining the first location data based on the address data.

4. The method of any preceding claim, comprising:receiving first location data of a plurality of target locations; and36applying the multi objective optimization technique to determine the placement of one or more IAB nodes to connect each of the plurality of target locations to the IAB donor.

5. The method of any preceding claim, comprising:receiving user input via a user interface selecting the candidate networking hardware device; andretrieving the transmission and reception characteristics of the selected networking hardware device from a candidate hardware database.

6. The method of any preceding claim, comprising:determining an altitude profile between the first location data and the second location data; andrendering the altitude profile on a user interface.

7. The method of any preceding claim, wherein the multi objective optimization technique is a genetic algorithm.

8. The method of any preceding claim, wherein the multi objective optimization technique seeks to minimize a first objective function that represents a cost of deploying a placement of one or more IAB nodes.

9. The method of any preceding claim, wherein the multi objective optimization technique seeks to minimize a second objective function that represents a power consumption associated with a placement of one or more IAB nodes.

10. The method of any preceding claim, wherein the multi objective optimization technique seeks to maximize a third objective function that represents a coverage of the target location associated with a placement of one or more IAB nodes.

11. The method of claim 10, wherein the third objective function penalizes a placement with restricted line-of-sight.

12. The method of claim 10 when dependent directly or indirectly on claim 4, wherein the coverage represents a fraction of the plurality of target locations covered or a coverage of an area comprising each of the plurality of target locations.

13. The method of any preceding claim, comprising:determining a plurality of placements by applying the multi objective optimization technique;displaying the plurality of placements on a user interface;receiving user input via the user interface comprising a selection of one of the plurality of placements.

14. The method of any preceding claim, comprising:determining a plurality of candidate sites for installation of an IAB node; andapplying the multi objective optimization technique to determine the placement by selecting one or more of the candidate sites.

15. The method of claim 14, wherein determining the plurality of candidate sites comprises :determining an area encompassing the target location and the IAB donor; anddistributing the candidate sites throughout the area.

16. The method of any preceding claim, comprising:receiving transmission and reception properties of a plurality of candidate networking hardware devices; andapplying the multi objective optimization technique to determine, along with the placement of the one or more IAB nodes, a selected networking hardware device of the plurality of candidate networking hardware devices for each of the one or more IAB nodes.

17. The method of any preceding claim, wherein the candidate networking hardware device capable of serving as an IAB node is configured to transmit and receive network signals at mmWave frequencies.

18. The method of any preceding claim, comprising:receiving a map of an area comprising the target location and the IAB donor;rendering, on the map, the determined placement of the one or more IAB nodes.

19. A computer system comprising:at least one memory storing computer-readable instructions; andat least one processor coupled to the at least one memory and configured to execute the computer readable instructions, which upon execution cause the at least one processor to perform the method of any preceding claim.

20. A non-transitory computer readable medium embodying computer program instructions, the computer program instructions configured so as, when executed on one or more hardware processors, to implement the method of any preceding claim.

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