Description: FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
METHOD AND SYSTEM FOR REAL TIME PREDICTION OF RELATIVE ANTENNA POSITION IN MULTI-TENANT TOWER

Applicant

Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India

Preamble to the description:
The following specification particularly describes the invention and the manner in which it is to be performed.
TECHNICAL FIELD
The disclosure herein generally relates to the field of network analytics and automation and, more particularly, to a method and system for real time prediction of relative antenna position in multi-tenant tower.
BACKGROUND
The volume of wireless traffic is increasing day by day and the demand for providing better Quality of Service (QoS) is also increasing due to increase in number of competitors. Nowadays, wireless network towers are shared by more than one service providers leading to multi-tenant towers. Antenna parameters like azimuth angle plays an important role in the operation and maintenance of mobile networks, and it is adjusted frequently to guarantee the high quality of coverage and low interference among neighboring cells. The antenna parameters of all the base stations need to be precisely managed by the operator such that the geographical coverage area (GEO) of the antenna can serve more users with better radio conditions.
Conventionally, antenna parameters like antenna azimuth angle are not open for third party network analysis. Hence it is required to collect those parameters from the multi-tenant towers for analysis. Conventionally, the antenna parameters are collected from the multi-tenant tower manually and further updated manually after analysis, which is a time consuming and risky process. Some conventional methods collected antenna parameters as a batch in a predefined time interval and not obtained in real time data. Further, the conventional methods are relying only on antenna azimuth angle and hence the prediction output is ineffective. Hence there is a challenge in predicting antenna position from one or more antenna parameters based on real time data in multi-tenant environment.

SUMMARY
Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a method for Real time prediction of relative antenna position in multi-tenant tower is provided. The method includes receiving by one or more hardware processors, a real time data pertaining to a plurality of antennas associated with a multi-tenant tower, wherein the real time data comprises a) a field data corresponding to each of the plurality of antennas, b) a plurality of network complaints, and c) a plurality of clutter images, associated with a multi-tenant tower environment comprising the multi-tenant tower. Further, the method includes generating by the one or more hardware processors, a grievance information based on the plurality of network complaints using a Natural Language Processing (NLP) technique. Furthermore, the method includes simultaneously identifying by the one or more hardware processors, a plurality of obstructions based on the plurality of clutter images associated with the multi-tenant tower environment using a Deep Learning (DL) technique. Furthermore, the method includes computing by the one or more hardware processors, a deviation value based on a comparison between the field data corresponding to each of a plurality of antennas and a reference data stored in a Geographic Information system (GIS) database, wherein the deviation value is set to one if there is any deviation and zero otherwise. Furthermore, the method includes predicting, by the one or more hardware processors, a plurality of relative antenna positions for a current antenna from the plurality of antennas based on the field data corresponding to each of a plurality of antennas, the grievance information and the plurality of obstructions, using a multi-output regression model if the deviation value is set to one, wherein each of the plurality of relative antenna positions comprises an antenna azimuth angle and an antenna height. Finally, the method includes identifying, by the one or more hardware processors, an optimal relative antenna position for the current antenna from the predicted plurality of relative antenna positions using an optimal relative antenna position selection technique.
In another aspect, a system for Real time prediction of relative antenna position in multi-tenant tower is provided. The system includes at least one memory storing programmed instructions, one or more Input /Output (I/O) interfaces, and one or more hardware processors operatively coupled to the at least one memory, wherein the one or more hardware processors are configured by the programmed instructions to receive a real time data pertaining to a plurality of antennas associated with a multi-tenant tower, wherein the real time data comprises a) a field data corresponding to each of the plurality of antennas, b) a plurality of network complaints, and c) a plurality of clutter images, associated with a multi-tenant tower environment comprising the multi-tenant tower. Further, the one or more hardware processors are configured by the programmed instructions to generate, a grievance information based on the plurality of network complaints using a Natural Language Processing (NLP) technique. Furthermore, the one or more hardware processors are configured by the programmed instructions to simultaneously identify, a plurality of obstructions based on the plurality of clutter images associated with the multi-tenant tower environment using a Deep Learning (DL) technique. Furthermore, the one or more hardware processors are configured by the programmed instructions to compute, a deviation value based on a comparison between the field data corresponding to each of a plurality of antennas and a reference data stored in a Geographic Information system (GIS) database, wherein the deviation value is set to one if there is any deviation and zero otherwise. Furthermore, the one or more hardware processors are configured by the programmed instructions to predict a plurality of relative antenna positions for a current antenna from the plurality of antennas based on the field data corresponding to each of a plurality of antennas, the grievance information and the plurality of obstructions, using a multi-output regression model if the deviation value is set to one, wherein each of the plurality of relative antenna positions comprises an antenna azimuth angle and an antenna height. Finally, the one or more hardware processors are configured by the programmed instructions to identify an optimal relative antenna position for the current antenna from the predicted plurality of relative antenna positions using an optimal relative antenna position selection technique.
In yet another aspect, a computer program product including a non-transitory computer-readable medium having embodied therein a computer program for Real time prediction of relative antenna position in multi-tenant tower is provided. The computer readable program, when executed on a computing device, causes the computing device to receive a real time data pertaining to a plurality of antennas associated with a multi-tenant tower, wherein the real time data comprises a) a field data corresponding to each of the plurality of antennas, b) a plurality of network complaints, and c) a plurality of clutter images, associated with a multi-tenant tower environment comprising the multi-tenant tower. Further, the computer readable program, when executed on a computing device, causes the computing device to generate, a grievance information based on the plurality of network complaints using a Natural Language Processing (NLP) technique. Furthermore, the computer readable program, when executed on a computing device, causes the computing device to simultaneously identify, a plurality of obstructions based on the plurality of clutter images associated with the multi-tenant tower environment using a Deep Learning (DL) technique. Furthermore, the computer readable program, when executed on a computing device, causes the computing device to compute, a deviation value based on a comparison between the field data corresponding to each of a plurality of antennas and a reference data stored in a Geographic Information system (GIS) database, wherein the deviation value is set to one if there is any deviation and zero otherwise. Furthermore, the computer readable program, when executed on a computing device, causes the computing device to predict a plurality of relative antenna positions for a current antenna from the plurality of antennas based on the field data corresponding to each of a plurality of antennas, the grievance information and the plurality of obstructions, using a multi-output regression model if the deviation value is set to one, wherein each of the plurality of relative antenna positions comprises an antenna azimuth angle and an antenna height. Finally, the computer readable program, when executed on a computing device, causes the computing device to identify an optimal relative antenna position for the current antenna from the predicted plurality of relative antenna positions using an optimal relative antenna position selection technique.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
FIG. 1 is a functional block diagram of a system for real time prediction of relative antenna position in multi-tenant tower, in accordance with some embodiments of the present disclosure.
FIGS. 2A and 2B are exemplary flow diagrams illustrating a processor implemented method for real time prediction of relative antenna position in multi-tenant tower, implemented by the system of FIG. 1, in accordance with some embodiments of the present disclosure.
FIG. 3 illustrates a functional architecture of the system of FIG. 1, for real time prediction of relative antenna position in multi-tenant tower, in accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments.
The antenna parameters are not open for third party network analysis and the conventional methods are unable to obtain antenna parameters in real time. Further, the conventional methods are focusing only on antenna azimuth angle and hence the prediction output is ineffective. Furthermore, after prediction, the antenna azimuth angle is changed manually which is time consuming and risky. The term “antenna azimuth angle” and the term “antenna azimuth” are used interchangeably throughout the document.
Embodiments herein provide a method and system for real time prediction of relative antenna position in multi-tenant tower. The present disclosure provides a regression model based real time prediction of relative antenna position. Initially, the system receives a real time data pertaining to a plurality of antennas associated with a multi-tenant tower. The real time data includes a field data corresponding to each of the plurality of antennas, a plurality of network complaints and a plurality of clutter images associated with the multi-tenant tower environment. Further, a grievance information is generated based on the plurality of network complaints using a Natural Language Processing (NLP) technique. Simultaneously a plurality of obstructions are identified based on the plurality of clutter images associated with the multi-tenant tower environment using a Deep Learning (DL) technique. Further, a deviation value is computed based on a comparison between the field data corresponding to each of a plurality of antennas and a reference data stored in a Geographic Information system (GIS) database. The deviation value is set to one if there is any deviation and zero otherwise. Further, a plurality of relative antenna positions are predicted for a current antenna from the plurality of antennas based on the field data corresponding to each of a plurality of antennas, the grievance information and the plurality of obstructions using a multi-output regression model if the deviation value is set to one. Each of the plurality of relative antenna positioning parameters includes an antenna azimuth angle and an antenna height. Finally, an optimal relative antenna position is identified for the current antenna from the predicted plurality of relative antenna positions using an optimal relative antenna position selection technique.
Referring now to the drawings, and more particularly to FIGS. 1 through 3, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
FIG. 1 is a functional block diagram of a real time prediction of relative antenna position in multi-tenant tower, in accordance with some embodiments of the present disclosure. The system 100 includes or is otherwise in communication with hardware processors 102, at least one memory such as a memory 104, an I/O interface 112. The hardware processors 102, memory 104, and the Input /Output (I/O) interface 112 may be coupled by a system bus such as a system bus 108 or a similar mechanism. In an embodiment, the hardware processors 102 can be one or more hardware processors.
The I/O interface 112 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 112 may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, a printer and the like. Further, the I/O interface 112 may enable the system 100 to communicate with other devices, such as web servers, and external databases. For example, other devices comprises a plurality of sensors and a plurality of camera.
The I/O interface 112 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. For the purpose, the I/O interface 112 may include one or more ports for connecting several computing systems with one another or to another server computer. The I/O interface 112 may include one or more ports for connecting several devices to one another or to another server.
The one or more hardware processors 102 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, node machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the one or more hardware processors 102 is configured to fetch and execute computer-readable instructions stored in the memory 104.
The memory 104 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the memory 104 includes a plurality of modules 106. The memory 104 also includes a data repository (or repository) 110 for storing data processed, received, and generated by the plurality of modules 106.
The plurality of modules 106 include programs or coded instructions that supplement applications or functions performed by the system 100 for real time prediction of relative antenna position in the multi-tenant tower. The plurality of modules 106, amongst other things, can include routines, programs, objects, components, and data structures, which performs particular tasks or implement particular abstract data types. The plurality of modules 106 may also be used as, signal processor(s), node machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions. Further, the plurality of modules 106 can be used by hardware, by computer-readable instructions executed by the one or more hardware processors 102, or by a combination thereof. The plurality of modules 106 can include various sub-modules (not shown). The plurality of modules 106 may include computer-readable instructions that supplement applications or functions performed by the system 100 for the semantic navigation using spatial graph and trajectory history. In an embodiment, the modules 106 includes a deviation identification module (shown in FIG. 3), an NLP module (shown in FIG. 3), an obstruction identification module (shown in FIG. 3), a prediction module (shown in FIG. 3), an optimal relative antenna motion identification module (shown in FIG. 3), and a trigger module (shown in FIG. 3). In an embodiment, FIG. 3 illustrates a functional architecture of the system of FIG. 1, for real time prediction of relative antenna position in multi-tenant tower, in accordance with some embodiments of the present disclosure.
The data repository (or repository) 110 may include a plurality of abstracted piece of code for refinement and data that is processed, received, or generated as a result of the execution of the plurality of modules in the module(s) 106.
Although the data repository 110 is shown internal to the system 100, it will be noted that, in alternate embodiments, the data repository 110 can also be implemented external to the system 100, where the data repository 110 may be stored within a database (repository 110) communicatively coupled to the system 100. The data contained within such external database may be periodically updated. For example, new data may be added into the database (not shown in FIG. 1) and/or existing data may be modified and/or non-useful data may be deleted from the database. In one example, the data may be stored in an external system, such as a Lightweight Directory Access Protocol (LDAP) directory and a Relational Database Management System (RDBMS). Working of the components of the system 100 are explained with reference to the method steps depicted in FIGS. 2A and 2B and the components depicted in FIG. 3.
FIG. 2A is an exemplary flow diagram illustrating a method 200 for real time prediction of relative antenna position in multi-tenant tower implemented by the system of FIG. 1 according to some embodiments of the present disclosure. In an embodiment, the system 100 includes one or more data storage devices or the memory 104 operatively coupled to the one or more hardware processor(s) 102 and is configured to store instructions for execution of steps of the method 200 by the one or more hardware processors 102. The steps of the method 200 of the present disclosure will now be explained with reference to the components or blocks of the system 100 as depicted in FIG. 1 and the steps of flow diagram as depicted in FIG. 2A. The method 200 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 200 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network. The order in which the method 200 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 200, or an alternative method. Furthermore, the method 200 can be implemented in any suitable hardware, software, firmware, or combination thereof.
At step 202 of the method 200, the one or more hardware processors 102 are configured by the programmed instructions to receive the real time data pertaining to the plurality of antennas associated with the multi-tenant tower. The multi-tenant tower holds one or more antennas associated with one or more service providers. The real time data includes the field data corresponding to each of the plurality of antennas, the plurality of network complaints and the plurality of clutter images associated with the multi-tenant tower environment. In an embodiment, the field data includes a site ID (for example “ABC1234”), a cell ID (for example, “123412”), the antenna azimuth angle (between 0 to 360 degrees), an antenna height (between 0 to 100 meters), a latitude (for example “18.5463”), and a longitude (for example, “72.5674”). In an embodiment, the plurality network complaints include a plurality of voice complaints, and a plurality of text complains obtained from a plurality of network users.
In an embodiment, the field data is obtained in real time using a Supervisory Control And Data Acquisition (SCADA) system. SCADA is a software application to control industrial processes locally or at remote locations in real time. In the present disclosure SCADA is used to collect field data associated with the plurality of antennas on the multi-tenant tower with the help of sensors and relays. The collected field data is communicated to the system 100 in real time with the help of Programmable Logic Controllers (PLC) associated with the SCADA software.
At step 204 of the method 200, the NLP module 306 executed by one or more hardware processors 102 is configured by the programmed instructions to generate a grievance information based on the plurality of network complaints using a Natural Language Processing (NLP) technique. The plurality of network complaints includes a plurality of voice complaints, and a plurality of text complains obtained from a plurality of network users. For example, the text complaint associated with the user can be “there is lack of network in my locality”, “no coverage at my place”, “my calls are not getting through”, “I am unable to receive any call” and the like.
In an embodiment, the NLP technique identifies a location of problematic towers based on the plurality of complaints. For example, the plurality of complaints specific to a tower location and particularly a serving cell are mapped with nearby cell Key Performance Indicators (KPIs) to validate the network path followed. Further, Machine Learning (ML) algorithms like Principal Component Analysis (PCA) are used to identify any deviation from the original path. These anomalies/outliers form the grievance information.
At step 206 of the method 200, the obstruction identification module 308 executed by the one or more hardware processors 102 is configured by the programmed instructions to simultaneously identify a plurality of obstructions based on the plurality of clutter images associated with the multi-tenant tower environment using the Deep Learning (DL) technique. The plurality of obstructions are identified for a plurality of directions pertaining to the multi-tenant tower. In another embodiment, the plurality of obstructions are identified sequentially after step 204.
For example, new constructions, buildings, or any other similar obstructions can be introduced in any area which might block the antenna radiations. Such obstructions are identified using DL techniques like Region based Convolutional Neural Network (R-CNN). Similar other DL techniques can also be used for obstruction identification. A plurality of image acquisition devices associated with the multi-tenant towers captures the plurality of clutter images on periodic basis and share these images to the DL model. Any obstruction in line of sight of antenna firing angle, any new construction, big trees, module damage etc are identified and notified. This information is the plurality of obstructions.
In an embodiment, the DL technique receives the plurality of clutter images captured periodically by the plurality of image acquisition devices attached to the multi-tenant tower. These images are preprocessed and passed to the R-CNN. The R-CNN includes a plurality of convolution layers and pooling layers. The R-CNN outputs a class of obstructing object in the line of antenna.
In an embodiment, the R-CNN is trained as follows. Initially, a pretrained CNN is obtained and the last layer of the pretrained CNN is retrained for identifying a plurality of object classes pertaining to the present disclosure. The CNN is retrained using a plurality of training images. While training, a Region of Interest (ROI) is obtained for each of the plurality of training images. Further, each of the plurality of training images are reshaped corresponding to an input size of the CNN. Further, a Support Vector Machine (SVM) is trained to classify the objects. One binary SVM is trained for each of the plurality of object classes. Finally, a linear regression model is trained to generate bounding boxes for each of the plurality of identified objects in each of the plurality of training images. The objects in the bounding boxes are identified as obstruction or not based on a comparison between the object in the bounding box and the plurality of object classes. The object is identified as an obstruction if there is a match between the object in the bounding box and the plurality of object classes.
At step 208 of the method 200, the deviation identification module 304 executed by the one or more hardware processors 102 is configured by the programmed instructions to compute the deviation value based on the comparison between the field data corresponding to each of a plurality of antennas and the reference data stored in the GIS database. The deviation value is set to one if there is deviation and zero otherwise In an embodiment, the reference data includes a reference site ID, a reference cell ID, a reference antenna azimuth angle, a reference antenna height, a reference latitude and a reference longitude.
At step 210 of the method 200, the prediction module 310 executed by the one or more hardware processors 102 is configured by the programmed instructions to predict the plurality of relative antenna positions for the current antenna from the plurality of antennas based on the field data corresponding to each of a plurality of antennas, the grievance information and the plurality of obstructions using a multi-output regression model if the deviation value is set to one. Each of the plurality of relative antenna positions comprises an antenna azimuth angle and an antenna height. For example, three configurations are predicted like (40,50), (35,50) and (40,40). Here, first value is the antenna azimuth, and the second value is the antenna height.
At step 210 of the method 200, the optimal relative antenna position identification module 312 executed by the one or more hardware processors 102 is configured by the programmed instructions to identify the optimal relative antenna position for the current antenna from the predicted plurality of relative antenna positions using an optimal relative antenna position selection technique.
In an embodiment, the method of identifying the optimal relative antenna position for the current antenna from the predicted plurality of relative antenna positions using the optimal relative antenna position selection technique includes the following steps. Initially, the predicted plurality of relative antenna positions for the current antenna are received. Further, a plurality of compatible configurations are identified by repeatedly performing the following steps until a compatible configuration without any obstruction is obtained. The compatible configuration includes an antenna azimuth angle and the corresponding antenna height. In the repetitive process, a first compatible configuration for the current antenna is identified based on a comparison between each of the predicted plurality of relative antenna positions of the current antenna and a plurality of configurations corresponding to the plurality of antennas excluding the current antenna. Further an obstruction pertaining to the first compatible configuration is identified based on the plurality of obstructions and a direction associated with the first compatible configuration. For example, the method checks whether there are any obstructions available in the plurality of obstructions for the direction associated with the first compatible configuration. Further, a second compatible configuration for the current antenna is identified based on a comparison between each of the predicted plurality of relative antenna positions excluding the first compatible configuration and a plurality of configurations corresponding to the plurality of antennas excluding the current antenna if there is an obstruction identified pertaining to the identified first compatible configuration. Finally, an obstruction pertaining to the identified second compatible configuration based on the plurality of obstructions and a direction associated with the second compatible configuration. For example, the method checks whether there are any obstructions available in the plurality of obstructions for the direction associated with the second compatible configuration. A third compatible configuration is identified if there is an obstruction identified pertaining to the second compatible configuration. After identifying the plurality of compatible configurations, one optimal configuration is selected from the plurality of compatible configuration without any obstruction as the optimal relative antenna position for the current antenna. In an embodiment, the obstruction is selected based on an obstruction threshold. For example, the compatible configuration with an obstruction less than the obstruction threshold is selected.
FIG. 2B is an exemplary flow diagram 220 illustrating a processor implemented method for identifying the optimal relative antenna position using the optimal relative antenna position selection technique, implemented by the system of FIG. 1, in accordance with some embodiments of the present disclosure. Now referring to FIG. 2B, the method receives ‘n’ number of relative antenna positions (the plurality predicted plurality of relative antenna positions) at step 222. At step 224, a variable ‘i’ is initialized to zero. The iterative process begins at step 226. The value of ‘i’ is incremented inside the loop at step 228 and then it checked whether the value of ‘i’ is less than or equal to ‘n’. If the condition is true, ith relative antenna position of the current antenna is compared with the antenna positions of the plurality of antennas excluding the current antenna at step 232. At step 234, it is checked whether the ith relative antenna position is overlapping with the antenna positions of other antennas. If no overlap, an obstruction associated with the ith relative antenna position is identified at step 236. If there is no obstruction identified, the current relative antenna position is selected as the compatible relative antenna position. If the ith relative antenna position is overlapping with other antenna positions, the ‘i’ value is incremented, and the next relative antenna position is selected and the process in repeated. The process is stopped at step 238 after identifying the compatible configuration.
In an embodiment, Table I illustrates a plurality of example scenarios. The Table I includes antenna height, parameters of 3 antennas and top 3 predictions (relative antenna positions) for the current antenna.
Table I
Scenario Tower height Operator 1 parameters Operator 1 parameters Operator 1 parameters Top 3 predictions for operator 1

Scenario 1
50
(30,10)
(40,90)
- (40,50); (35,50); (40,40)

Scenario 2 40 (35,120) (35,150) (28,170) (37,160); (35,160); (40,150)
Scenario 3 50 (30,240) (35,270) (30,290) (35,270); (40,270); (35,280)
For example, in an embodiment, tower height is 50m and operator’s current parameters are as per the scenario 1 of Table I. Top three predictions are (40,50); (35,50); (40,40). The first choice is feasible since the predicted height is 40m which is less than tower height (50 m). Similarly, the predicted antenna azimuth (50 degrees) is best to cover the targeted clutter as per the DL model. There is no other equipment or antenna of other operator at 40m and 50 degrees. As there is space available at best predicted option 1 and no obstruction in vertical and horizontal movement of antenna, the first prediction (40,50) is chosen as the optimal relative antenna position.
In another embodiment, the tower height is 40m and operator’s current parameters are as per the scenario 2 of the Table I. Top three predictions are (37,160); (35,160); (40,150). Selecting the first choice as 37m is less than tower height. Antenna azimuth 160 degrees is best to cover the targeted clutter as per the DL model. There is no other equipment or antenna of other operator at 37m and 160 degrees. As there is space available at best predicted option 1 but for horizontal movement of operator 1 is obstructed by antenna positioning of operator 2 at 150 degrees. To avoid this horizontal movement collision, the antenna should be moved vertically first before rotating horizontally.
In another embodiment, tower height is 50m and operator’s current parameters are as per the scenario 3 of the table I. Top three predictions are (35,270); (40,270); (35,280). Here, first predicted option (35,270) is not considered as at same location operator 2 has their antenna. In second predicted option antenna azimuth 270 degrees is same as per second operator’s azimuth but here antenna height is different and less than the tower height which is feasible to implement on field. There is no other equipment or antenna of other operator at 40m and 270 degrees. As there is space available at best predicted option 2 (40,270) is selected to avoid the vertical movement collision. Here, the antenna should be moved vertically to 40m before rotating horizontally.
The trigger module 314 executed by the one or more hardware processors 102 is configured by the programmed instructions to actuate the optimal relative antenna position using a firmware and a server motor. The firmware supports either direct keyboard control, or commands from programs written specifically to support it. In addition to reading and setting antenna angle and rotation speed, the firmware includes clocked positioning routines to automatically step the antenna.
Once the azimuth angle and height is predicted, the input will be given to the trigger program. The trigger program will take the azimuth angle and height as input and trigger a command to Rotor Serial Computer Interface which in turn will activate the Servo Motor. In an embodiment, the servo motor redirects the antenna directions vertically and horizontally as per the command received from Rotor Serial Computer Interface.
In an embodiment, the pseudocode for implementing the present disclosure is given in table II.

Table II
Steps Description Phase
1 Get the field data for antennas of all the operators on Tower Input
2 Check it with GIS database
3 Mapping of complaints and clutter/obstruction data against sector/site
4 Using collected data, predict the top 3 antenna azimuth (a_1,a_2,a_3) through multi-ML regression (ML algorithm stack with 3 best validation result). Process
5 Parse the database to find out any conflict exists with azimuth and height combination (a_1,h_1) of other operator.
5a Check the availability of (a_1,h_1) where h_1 is the current height of antenna.
5b If conflict exists, check the availability of (a_1,h_1) where h_2=h_1+x, x is configurable and h_2<=H (Height of the tower).
6 Continue this process sequentially till we get a non-conflicting combination (a_i,h_j) where i=1,2,3 and j=1,2,3
7 Check whether the non-conflicting configuration out from step 6 has any obstruction found from the data collected in step 3
8 Continue the process steps 4, 6, 7 till we get a non-conflicting combination (a_i,h_j)where i=1,2,3 and j=1,2,3
9 Get the best possible combination (a_i,h_j) and send it to servo-motor trigger to adjust the vertical and horizontal position of the antenna. Output
The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
The embodiments of present disclosure herein address the unresolved problem of predicting an optimal relative antenna position. The optimal relative position is determined based on the positions of other antennas present in the same multi-tenant antenna. The optimal relative position provides a horizontal and a vertical movement without disturbing the positions of other antennas. This is achieved using a multi-output regression model and an optimal relative antenna position selection technique. This optimal relative antenna position helps the service providers to provide better QoS for the users with minimal effort.
It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein such computer-readable storage means contain program-code means for implementation of one or more steps of the method when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs, GPUs and edge computing devices.
The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e. non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims. , Claims:WE CLAIM:

1. A processor implemented method (200), the method comprising:
receiving (202), by one or more hardware processors, a real time data pertaining to a plurality of antennas associated with a multi-tenant tower, wherein the real time data comprises a) a field data corresponding to each of the plurality of antennas, b) a plurality of network complaints, and c) a plurality of clutter images, associated with a multi-tenant tower environment comprising the multi-tenant tower;
generating (204), by the one or more hardware processors, a grievance information based on the plurality of network complaints using a Natural Language Processing (NLP) technique;
simultaneously identifying (206), by the one or more hardware processors, a plurality of obstructions based on the plurality of clutter images associated with the multi-tenant tower environment using a Deep Learning (DL) technique;
computing (208), by the one or more hardware processors, a deviation value based on a comparison between the field data corresponding to each of a plurality of antennas and a reference data stored in a Geographic Information system (GIS) database, wherein the deviation value is set to one if there is any deviation and zero otherwise;
predicting (210), by the one or more hardware processors, a plurality of relative antenna positions for a current antenna from the plurality of antennas based on the field data corresponding to each of a plurality of antennas, the grievance information and the plurality of obstructions, using a multi-output regression model if the deviation value is set to one, wherein each of the plurality of relative antenna positions comprises an antenna azimuth angle and an antenna height; and
identifying (212), by the one or more hardware processors, an optimal relative antenna position for the current antenna from the predicted plurality of relative antenna positions using an optimal relative antenna position selection technique.
2. The method as claimed in claim 1, wherein the plurality of obstructions are identified for a plurality of directions pertaining to the multi-tenant tower.
3. The method as claimed in claim 1, wherein the field data comprises a site ID, a cell ID, the antenna azimuth angle and the antenna height, a latitude, and a longitude.
4. The method as claimed in claim 1, wherein the reference data comprises a reference site ID, a reference cell ID, a reference antenna azimuth angle, a reference antenna height, a reference latitude, and a reference longitude.
5. The method as claimed in claim 1, wherein the plurality network complaints comprise a plurality of voice complaints and a plurality of text complains obtained from a plurality of network users.
6. The method as claimed in claim 1, wherein the method of identifying the optimal relative antenna position for the current antenna from the predicted plurality of relative antenna positions using the optimal relative antenna position selection technique comprises:
receiving the predicted plurality of relative antenna positions for the current antenna;
iteratively performing until a compatible configuration comprising an antenna azimuth angle and the corresponding antenna height without any obstruction is obtained, wherein obtaining the compatible configuration comprises:
identifying a first compatible configuration for the current antenna based on a comparison between each of the predicted plurality of relative antenna positions of the current antenna and a plurality of configurations corresponding to the plurality of antennas excluding the current antenna;
identifying an obstruction pertaining to the first compatible configuration based on the plurality of obstructions and a direction pertaining to the first compatible configuration;
identifying a second compatible configuration for the current antenna based on a comparison between each of the predicted plurality of relative antenna positions excluding the first compatible configuration and a plurality of configurations corresponding to the plurality of antennas excluding the current antenna if there is an obstruction identified pertaining to the first compatible configuration; and
identifying an obstruction pertaining to the second compatible configuration based on the plurality of obstructions and a direction pertaining to the second compatible configuration, wherein a third compatible configuration is identified if there is an obstruction identified pertaining to the second compatible configuration; and
selecting one of, the first compatible configuration, the second compatible configuration, and the third compatible configuration as the optimal relative antenna position for the current antenna, based on absence of obstruction.
7. The method as claimed in claim 1, wherein the current antenna is actuated based on the optimal relative antenna position using a corresponding servo motor if there is any deviation.
8. A system (100) comprising:
at least one memory (104) storing programmed instructions; one or more Input /Output (I/O) interfaces (112); and one or more hardware processors (102) operatively coupled to the at least one memory (104), wherein the one or more hardware processors (102) are configured by the programmed instructions to:
receive a real time data pertaining to a plurality of antennas associated with a multi-tenant tower, wherein the real time data comprises a) a field data corresponding to each of the plurality of antennas, b) a plurality of network complaints, and c) a plurality of clutter images, associated with a multi-tenant tower environment comprising the multi-tenant tower;
generate a grievance information based on the plurality of network complaints using a Natural Language Processing (NLP) technique;
simultaneously identify a plurality of obstructions based on the plurality of clutter images associated with the multi-tenant tower environment using a Deep Learning (DL) technique;
compute a deviation value based on a comparison between the field data corresponding to each of a plurality of antennas and a reference data stored in a Geographic Information system (GIS) database, wherein the deviation value is set to one if there is any deviation and zero otherwise;
predict a plurality of relative antenna positions for a current antenna from the plurality of antennas based on the field data corresponding to each of a plurality of antennas, the grievance information and the plurality of obstructions, using a multi-output regression model if the deviation value is set to one, wherein each of the plurality of relative antenna positions comprises an antenna azimuth angle and an antenna height; and
identify an optimal relative antenna position for the current antenna from the predicted plurality of relative antenna positions using an optimal relative antenna position selection technique.
9. The system of claim 8, wherein the plurality of obstructions are identified for a plurality of directions pertaining to the multi-tenant tower.
10. The system of claim 8, wherein the field data comprises a site ID, a cell ID, the antenna azimuth angle and the antenna height, a latitude, and a longitude.
11. The system of claim 8, wherein the reference data comprises a reference site ID, a reference cell ID, a reference antenna azimuth angle, a reference antenna height, a reference latitude, and a reference longitude.
12. The system of claim 8, wherein the plurality network complaints comprise a plurality of voice complaints and a plurality of text complains obtained from a plurality of network users.
13. The system of claim 8, wherein the method of identifying the optimal relative antenna position for the current antenna from the predicted plurality of relative antenna positions using the optimal relative antenna position selection technique comprises:
receiving the predicted plurality of relative antenna positions for the current antenna;
iteratively performing until a compatible configuration comprising an antenna azimuth angle and the corresponding antenna height without any obstruction is obtained, wherein obtaining the compatible configuration comprises:
identifying a first compatible configuration for the current antenna based on a comparison between each of the predicted plurality of relative antenna positions of the current antenna and a plurality of configurations corresponding to the plurality of antennas excluding the current antenna;
identifying an obstruction pertaining to the first compatible configuration based on the plurality of obstructions and a direction pertaining to the first compatible configuration;
identifying a second compatible configuration for the current antenna based on a comparison between each of the predicted plurality of relative antenna positions excluding the first compatible configuration and a plurality of configurations corresponding to the plurality of antennas excluding the current antenna if there is an obstruction identified pertaining to the first compatible configuration; and
identifying an obstruction pertaining to the second compatible configuration based on the plurality of obstructions and a direction pertaining to the second compatible configuration, wherein a third compatible configuration is identified if there is an obstruction identified pertaining to the second compatible configuration; and
selecting one of, the first compatible configuration, the second compatible configuration, and the third compatible configuration as the optimal relative antenna position for the current antenna, based on absence of obstruction.
14. The system of claim 8, wherein the current antenna is actuated based on the optimal relative antenna position using a corresponding servo motor if there is any deviation.

Dated this 25th day of May 2022
Tata Consultancy Services Limited
By their Agent & Attorney

(Adheesh Nargolkar)
of Khaitan & Co
Reg No IN-PA-1086