US-12627996-B2 - Method and apparatus for designing a radio access network

US12627996B2US 12627996 B2US12627996 B2US 12627996B2US-12627996-B2

Abstract

According to an aspect, there is provided a computer-implemented method for designing a first radio access network, RAN, that is to operate according to a first radio access technology, RAT, in a first frequency range. The method comprises (i) obtaining ( 901 ) second RAN radio signal measurements of a second RAN. The second RAN operates according to a second RAT that is different to the first RAT, and/or with a second frequency range that is different to the first frequency range. The second RAN comprises a plurality of second RAN base stations that operate a plurality of second RAN cells, and the second RAN radio signal measurements comprise measurements by a plurality of wireless devices of radio signals on one or more frequencies from one or more of the plurality of second RAN base stations. The method further comprises (ii) processing ( 902 ) the second RAN radio signal measurements to estimate corresponding first RAN radio signal measurements that could be measured by said wireless devices if each of said second RAN cells were respective first RAN cells operating according to the first RAT and in the first frequency range; (iii) forming ( 903 ) an initial cell deployment for the first RAN based on an estimate of which wireless devices each first RAN cell provides coverage for according to said first RAN radio signal measurements, wherein the initial cell deployment comprises a subset of the first RAN cells; (iv) determining ( 904 ) a best serving first RAN cell in the initial cell deployment for each of the wireless devices based on the first RAN radio signal measurements; (v) for each first RAN cell in the initial cell deployment, estimating ( 905 ) the first RAN cell resource utilisation based on the wireless devices for which said first RAN cell is determined to be the best serving first RAN cell; and (vi) determining ( 906 ) an updated cell deployment based on the initial cell deployment and the estimated first RAN cell resource utilisation for each first RAN cell in the initial cell deployment.

Inventors

Thomas Bourgeois
Takayuki Takatori
Yak NG MOLINA

Assignees

TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)

Dates

Publication Date: 20260512
Application Date: 20201030

Claims (20)

1 . A method of designing a first Radio Access Network (RAN) that is to operate according to a first Radio Access Technology (RAT) in a first frequency range, the method comprising: obtaining second RAN radio signal measurements of a second RAN that operates according to a second RAT different from the first RAT and/or in a second frequency range that is different from the first frequency range, wherein the second RAN comprises a plurality of second RAN base stations that operate a plurality of second RAN cells and the second RAN radio signal measurements comprise measurements by a plurality of wireless devices of radio signals on one or more frequencies from one or more of the plurality of second RAN base stations; processing the second RAN radio signal measurements to estimate corresponding first RAN radio signal measurements that could be measured by said wireless devices if each of said second RAN cells were respective first RAN cells operating according to the first RAT and in the first frequency range; forming an initial cell deployment for the first RAN based on an estimate of which wireless devices each first RAN cell provides coverage for according to said first RAN radio signal measurements, wherein the initial cell deployment comprises a subset of the first RAN cells; determining a best serving first RAN cell in the initial cell deployment for each of the wireless devices based on the first RAN radio signal measurements; estimating the first RAN cell resource utilization, for each first RAN cell in the initial cell deployment, based on the wireless devices for which said first RAN cell is determined to be the best serving first RAN cell; and determining an updated cell deployment based on the initial cell deployment and the estimated first RAN cell resource utilization for each first RAN cell in the initial cell deployment.
2 . The method of claim 1 , further comprising performing one or more additional iterations of determining the best serving first RAN cell, estimating the first RAN cell resource utilization, and determining the updated cell deployment to determine a final cell deployment.
3 . The method of claim 2 , wherein performing the one or more additional iterations comprises performing the one or more additional iterations until one or more performance targets for the first RAN are obtained.
4 . The method of claim 1 , wherein determining the updated cell deployment comprises: adding one or more first RAN cells to the cell deployment that are not yet included in the initial cell deployment based on the estimated first RAN cell resource utilization for each first RAN cell; or removing one or more first RAN cells from the initial cell deployment based on the estimated first RAN cell resource utilization for each first RAN cell.
5 . The method of claim 1 , wherein forming the initial cell deployment for the first RAN comprises: estimating how many wireless devices each first RAN cell provides coverage for based on the estimated first RAN radio signal measurements; including in the initial cell deployment the first RAN cell that provides coverage to the highest number of wireless devices; and performing one or more process iterations until a criterion is satisfied, each process iteration comprising: for each remaining first RAN cell not yet included in the initial cell deployment, re-estimating how many wireless devices in a subset of the wireless devices each of the remaining first RAN cells provides coverage to, wherein the subset of wireless devices comprises wireless devices that do not have coverage from a first RAN cell already included in the initial cell deployment; including in the initial cell deployment the remaining first RAN cell that provides coverage to the highest number of wireless devices in the subset of wireless devices.
6 . The method of claim 5 , wherein the criterion is satisfied if the subset of wireless devices is empty.
7 . The method of claim 5 , wherein the criterion is satisfied if the subset of wireless devices comprises less than a threshold number of wireless devices.
8 . The method of claim 1 , wherein estimating the first RAN cell resource utilization for each first RAN cell in the initial cell deployment comprises: for each first RAN cell in the initial cell deployment, estimating a first RAN cell load based on the wireless devices for which said first RAN cell is determined to be the best serving first RAN cell; estimating a respective first RAN downlink signal quality for each wireless device based on the estimated first RAN cell loads; and estimating the first RAN cell resource utilization for each first RAN cell using the estimated first RAN downlink signal quality for each wireless device and the estimated first RAN cell load.
9 . The method of claim 1 , wherein the first RAT is New Radio (NR) and the second RAT is Long Term Evolution (LTE).
10 . The method of claim 9 , wherein a wireless device is to use the second RAN for user plane signaling and the first RAN for control plane signaling.
11 . The method of claim 10 , further comprising determining whether the wireless device has coverage from the second RAN according to the second RAN radio signal measurements.
12 . An apparatus for designing a first Radio Access Network (RAN) that is to operate according to a first Radio Access Technology (RAT) in a first frequency range, the apparatus comprising: processing circuitry and a memory storing instructions executable by the processing circuitry whereby the apparatus is configured to: obtain second RAN radio signal measurements of a second RAN that operates according to a second RAT different from the first RAT and/or in a second frequency range that is different from the first frequency range, wherein the second RAN comprises a plurality of second RAN base stations that operate a plurality of second RAN cells and the second RAN radio signal measurements comprise measurements by a plurality of wireless devices of radio signals on one or more frequencies from one or more of the plurality of second RAN base stations; process the second RAN radio signal measurements to estimate corresponding first RAN radio signal measurements that could be measured by said wireless devices if each of said second RAN cells were respective first RAN cells operating according to the first RAT and in the first frequency range; form an initial cell deployment for the first RAN based on an estimate of which wireless devices each first RAN cell provides coverage for according to said first RAN radio signal measurements, wherein the initial cell deployment comprises a subset of the first RAN cells; determine a best serving first RAN cell in the initial cell deployment for each of the wireless devices based on the first RAN radio signal measurements; estimate the first RAN cell resource utilization, for each first RAN cell in the initial cell deployment, based on the wireless devices for which said first RAN cell is determined to be the best serving first RAN cell; and determine an updated cell deployment based on the initial cell deployment and the estimated first RAN cell resource utilization for each first RAN cell in the initial cell deployment.
13 . The apparatus of claim 12 , wherein the apparatus is further configured to perform one or more additional iterations of determining the best serving first RAN cell, estimating the first RAN cell resource utilization, and determining the updated cell deployment to determine a final cell deployment.
14 . The apparatus of claim 13 , wherein to perform the one or more additional iterations, the apparatus is configured to perform the one or more additional iterations until one or more performance targets for the first RAN are obtained.
15 . The apparatus of claim 13 , wherein to determine the updated cell deployment, the apparatus is configured to: add one or more first RAN cells to the cell deployment that are not yet included in the initial cell deployment based on the estimated first RAN cell resource utilization for each first RAN cell; or remove one or more first RAN cells from the initial cell deployment based on the estimated first RAN cell resource utilization for each first RAN cell.
16 . The apparatus of claim 13 , wherein to form the initial cell deployment for the first RAN the apparatus is configured to: estimate how many wireless devices each first RAN cell provides coverage for based on the estimated first RAN radio signal measurements; include in the initial cell deployment the first RAN cell that provides coverage to the highest number of wireless devices; and perform one or more process iterations until a criterion is satisfied, each process iteration comprising: for each remaining first RAN cell not yet included in the initial cell deployment, re-estimating how many wireless devices in a subset of the wireless devices each of the remaining first RAN cells provides coverage to, wherein the subset of wireless devices comprises wireless devices that do not have coverage from a first RAN cell already included in the initial cell deployment; and including in the initial cell deployment the remaining first RAN cell that provides coverage to the highest number of wireless devices in the subset of wireless devices.
17 . The apparatus of claim 16 , wherein the criterion is satisfied if the subset of wireless devices is empty.
18 . The apparatus of claim 16 , wherein the criterion is satisfied if the subset of wireless devices comprises less than a threshold number of wireless devices.
19 . The apparatus of claim 13 , wherein to estimate the first RAN cell resource utilization for each first RAN cell in the initial cell deployment the apparatus is configured to: for each first RAN cell in the initial cell deployment, estimate a first RAN cell load based on the wireless devices for which said first RAN cell is determined to be the best serving first RAN cell; estimate a respective first RAN downlink signal quality for each wireless device based on the estimated first RAN cell loads; and estimate the first RAN cell resource utilization for each first RAN cell using the estimated first RAN downlink signal quality for each wireless device and the estimated first RAN cell load.
20 . The apparatus of claim 13 , wherein the first RAT is New Radio (NR) and the second RAT is Long Term Evolution (LTE).

Description

TECHNICAL FIELD This disclosure relates to designing a radio access network, RAN, that is to operate according to a radio access technology, RAT, in a first frequency range. BACKGROUND Radio frequency (RF) network design and optimisation activities require an accurate characterisation of the propagation environment for each cell in order to account for the coverage and interference levels expected in the network. Complex propagation models can be used to achieve this purpose, which requires extensive propagation model optimisation (PMO) campaigns to adapt them to the particular morphology under study. For example propagation models are described in “LTE, WIMAX and WLAN network design, optimization, and performance analysis” by L. Korowajczuk, John Wiley & Sons, 2011, and “The LTE-advanced deployment handbook: the planning guidelines for the fourth generation networks” by Jyrki T. J. Penttinen, John Wiley & Sons, 2016. Additionally, if the network performance needs to be addressed, detailed traffic maps, which can show the user density and spread areas, are used to run Monte Carlo simulations that estimate the network resource usage. Knowing the location of subscribers (users) within the network is a key component during the above procedure in order to detect high-traffic areas and to be able to reinforce the signal quality and the overall system capacity for them. Currently, the most accurate data sources to achieve that are Cell Traffic Recordings (CTR) and/or crowdsource information. However, the user location based on CTR is based on triangulation techniques (for example as described in “Geolocation of LTE subscriber stations based on the timing advance ranging parameter” by L. Jarvis, J. McEachen and H. Loomis, 2011—MILCOM 2011 Military Communications Conference, Baltimore, M D, 2011, pp. 180-187) and its accuracy is limited to the technology resolution of the measured time difference of arrival (TDOA) and the inter-site distance, achieving an accuracy error between 30 metres (m) and 400 m. On the other hand, the crowdsource information provides limited network information, dependent on the user terminal configuration and Global Positioning System (GPS) availability, which leads to a partial representation of the network, with a clear bias towards outdoor areas. Nowadays, network operators can depend on Third Party Publishing (3PP) planning tools to characterise the wireless network propagation environments. To design a good network, a deep technology knowledge and precise environment data are needed, which means that the results from a design procedure are highly dependent on the engineers running the services and the amount and type of input data they have access to. The availability of higher frequency bands for 5th Generation (5G) networks has evidenced the need to revisit the traditional tools and processes to account for the new physical effects that takes place in the higher part of the spectrum, adding an extra layer of complexity to the planning tools, such as introducing ray tracing models (as described in “Ray tracing for radio propagation modeling: Principles and applications” by Yun, Zhengqing, and Magdy F. Iskander, IEEE Access 3 (2015): 1089-1100), 3-dimensional (3D) cartography and other effects, such as rain (as described in “Influence of climate variability on performance of wireless microwave links” by Kantor, P., & Bitó, J., 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp. 891-895) and vegetation (as described in “Wideband 39 GHz Millimeter-Wave Channel Measurements under Diversified Vegetation” by Zhang, Chao, et al., 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), IEEE, 2018) to their modelling techniques. However, both the propagation model optimisation and the ray tracing techniques mentioned above represent a high investment due to license costs and time consumption required for their well-functioning. A propagation model optimisation requires significant measurement campaigns. Besides, the obtained results are only valid for the network topology where the measurements were taken, having little room for reusability. On the other hand, ray tracing models require very expensive inputs (e.g. detailed 3D databases and description of the materials), which usually require large computational times to run. Monte Carlo simulations for performance estimation are cumbersome to operate, relies on hypothesis about the user profiles and the actual user location, and requires several runs in order to achieve convergence and statistical relevance. Additionally, a low level of automation has been achieved so far in planning activities requiring a high level of expertise and local knowledge to provide a good network solution. Therefore there is a need for improved techniques for designing a radio access network that is to operate according to a radio access technology in a fr