Direction of arrival estimation using non-beamforming antennas

Abstract

The disclosure provides a method for estimating a direction of arrival by using non-beamforming antennas by a computing device (700) The method includes determining (502) that signals from two or more directional antennas (102-2, 102-4, 102-6) correspond to a transmission (108). The method includes estimating (504), based on a cross-correlation function, for the signals received by the two or more directional antennas (102), relative received signal powers and estimating (506) a direction of arrival of the transmission (108) based on determining a minimum value of an error function between vectors of the relative received signal powers and vectors of expected relative received signal powers, wherein the expected relative received signal powers are based on antenna manifolds and orientations of the two or more directional antennas (102).

Description

DIRECTION OF ARRIVAL ESTIMATION USING NON-BEAMFORMING ANTENNASTECHNICAL FIELD

[0001] The present disclosure relates to a method for performing direction of arrival estimation using non-beamforming antennas and a computing device and a computer program configured to perform the same. A carrier containing the computer program is also disclosed.BACKGROUND

[0002] Positioning of wireless devices is an attractive use-case for next-generation 5G and 6G radio-access networks (RAN) that can allow performance optimization as well as enabling new applications and services. Most positioning methods focus on internal devices, i.e., devices communicating with the RAN, and use system-specific 3GPP standardized signals for positioning. However, other applications and services could benefit from positioning of external signals.

[0003] External and 3GPP non-compliant signals can originate from multiple different sources ranging from malfunctioning electronic equipment to deliberate jamming signals aimed at disrupting RAN services. Moreover, due to misconfigurations or aging equipment, a RAN site may be subject to passive intermodulation (PIM) interference signals. Ultimately, such interference signals degrade system performance or disrupt the availability entirely.

[0004] Direction-of-arrival (DoA) estimation for such external interference is the first step in positioning the signal source, which can be useful for quickly identifying and mitigating the rootcause of the problem. In addition, positioning of jamming transmitters can enable automatic detection solutions that notify police or authorities of the location of the unlawful activity.

[0005] In a paper titled “Applying Correlation Method To The Problem Of Passive Amplitude Monopulse Direction Finding” A. A. Loginov; M Yu. Semenova (2011 IEEE 3rd International Conference on Communication Software and Networks), a two-step method for preprocessing signals in the problem of passive amplitude monopulse direction finding is described. The first step includes the preliminary radio bearing estimation using the Gaussian models of the antenna patterns, while the second step is based on monopulse correlation schema. In the disclosed paper, however, the antenna receivers are in phase sync with each other. At most cellular sites, however, the deployed antennas are not necessarily time / phase coherent array antennas that can perform the methods disclosed in this paper.SUMMARY

[0006] An object of the present disclosure is to enable the estimation of direction of arrival using non-beamforming antennas that are not in time or phase synchronization with each other.

[0007] The present disclosure provides methods for estimating a direction of arrival by using non-beamforming antennas by a computing device. The method includes determining that signals from two or more directional antennas correspond to a transmission. The method includes estimating, based on a cross-correlation function, for the signals received by the two or more directional antennas, relative received signal powers and estimating a direction of arrival of the transmission based on determining a minimum value of an error function between vectors of the relative received signal powers and vectors of expected relative received signal powers, wherein the expected relative received signal powers are based on antenna manifolds and orientations of the two or more directional antennas.

[0008] In an embodiment, the cross-correlation function is in a time domain for a periodic transmission.

[0009] In another embodiment, the cross-correlation function is in a frequency domain for a constant transmission.

[0010] In another embodiment, the determining that the signals correspond to the transmission (108) is based on an auto-correlation function.

[0011] In another embodiment, the error function is a mean-squared error (MSE) function.

[0012] In another embodiment, the vectors of the relative received signal powers correspond to an estimated direction of arrival of the transmission.

[0013] In another embodiment, the computing device is located at a radio access network node, a core network node, or a cloud computing node.

[0014] In another embodiment, the cloud computing node is operating an Uplink Spectrum Analyzer.

[0015] In another embodiment, the two or more directional antennas are located at a single radio access network node.

[0016] In another embodiment, the two or more directional antennas are located at separate radio access network nodes.

[0017] In an embodiment, a computing device for estimating a direction of arrival using non-beamforming antennas is provided, where the computing device comprises processing circuitry configured to cause a network node to determine that signals from two or more directional antennas correspond to a transmission. The processing circuitry further causes the network node to estimate, based on a cross-correlation function, for the signals received by thetwo or more directional antennas, relative received signal powers and estimate a direction of arrival of the transmission based on determining a minimum value of an error function between vectors of the estimated relative received signal powers and vectors of expected relative received signal powers, wherein the expected relative received signal powers are based on antenna manifolds and orientations of the two or more directional antennas.

[0018] In an embodiment, a computer program is provided that comprises instructions which, when executed on processing circuitry, causes the processing circuitry to carry out the method according to any one of the above embodiments.

[0019] In an embodiment, a carrier is also provided that contains the computer program, wherein the carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium.

[0020] In an embodiment, an advantage provided by the methods, computing devices, and computer programs disclosed herein is that the methods do not require any time or phase coherence between received samples from respective antenna receivers, and hence it can be useful for allowing direction of arrival (DoA) estimation in traditional three-sector Radio Access Network (RAN) macro sites that do not support synchronization between the sectors or advanced massive Multiple Input Multiple Output (MIMO) antennas. Another advantage is that the methods are suitable for DoA estimation for non Third Generation Partnership Program (3 GPP) signals such as external periodic interference or jamming signals, and that the methods can be implemented using already available measurements in current products.BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.

[0022] Figure 1 shows an example system deployment in accordance with some embodiments of the present disclosure;

[0023] Figure 2 shows an example of components of a system architecture in accordance with some embodiments of the present disclosure;

[0024] Figure 3 shows an example of antenna direction sensitivity for a three sector site in accordance with some embodiments of the present disclosure;

[0025] Figure 4 shows an example of I / Q sample snapshots from three radio units in accordance with some embodiments of the present disclosure;

[0026] Figure 5 shows a flowchart of a method for estimating a direction of arrival by using non-beamforming antennas by a computing device in accordance with some embodiments of the present disclosure;

[0027] Figure 6 shows an example of a communication system in accordance with some embodiments of the present disclosure; and

[0028] Figure 7 shows a computing device, which may be configured to operate in the communication system of Figure 6.DETAILED DESCRIPTION

[0029] The embodiments set forth below represent information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.

[0030] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0031] There currently exist certain challenge(s). Positioning via triangulation requires determining the direction of arrival (DoA) at two or more known locations. Common DoA methods assume time / phase-coherent array antennas as described in the background, but most deployed antennas at cellular sites are not such array antennas. Furthermore, for non 3GPP signals, standardized positioning reference signals (RS) cannot be used, as the devices emitting the non-3GPP signals do not communicate with the Radio Access Network (RAN). DoA by amplitude comparison has been performed, but the gains of the antennas and amplifying chains must be closely matched, which is not always the case in cellular deployments.

[0032] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. The proposed solution is a low-complexity method for estimating the DoA for periodic signals impinging on a multi-antenna RAN site. The solution is based on matching the relative received power with the respective antenna manifolds on the site, where the relative power is estimated through correlation of time-domain signals and / or frequency domain signals from the respective antennas.

[0033] Since the cross-correlation method focuses on periodic signals, it does not require time-coherent synchronization between samples from the respective antenna arrays, which issuitable in three-sector macro deployments that cannot leverage MIMO for direction finding. The most apparent application is DoA estimation for non-3GPP periodic signals like external interference (e.g., jamming transmitters).

[0034] One of the problems addressed is how to determine the azimuth DoA for a periodic signal, based on received time-domain samples at multiple directional antennas deployed in a RAN site. Fundamentally, the solution disclosed herein consists of two steps:1) Estimate the relative received signal power (RRSP) at the antennas deployed in the site.2) Find the azimuth DoA angle that minimizes the error (e.g., mean-squared error) between the estimated RRSP and the expected relative powers obtained from the antenna array manifolds and orientations.

[0035] The main inventive step in this disclosure is that we present a cross-correlation method for estimating the RRSP in step (1) with respect to the periodic signal which DoA is to be determined.

[0036] The method is assumed to process I / Q signal samples in a centralized analysis node (e.g., an rApp or part of the Service Management and Orchestration (SMO)). It is assumed that the periodic signal common to all sector s / antenna manifolds have been identified, e.g., by using an auto-correlation function (acf), before the RRSPs are determined using the cross-correlation method. This may be considered a step 0.

[0037] Certain embodiments may provide one or more of the following technical advantage(s). In an embodiment, an advantage provided by the methods, computing devices, and computer programs disclosed herein is that the methods do not require any time or phase coherence between received samples from respective antenna receivers, and hence it can be useful for allowing direction of arrival (DoA) estimation in traditional three-sector Radio Access Network (RAN) macro sites that do not support synchronization between the sectors or advanced massive Multiple Input Multiple Output (MIMO) antennas. Another advantage is that the methods are suitable for DoA estimation for non Third Generation Partnership Program (3GPP) signals such as external periodic interference or jamming signals, and that the methods can be implemented using already available measurements in current products.

[0038] Figure 1 shows an example system deployment in accordance with some embodiments of the present disclosure.

[0039] The present disclosure considers a RAN site 106 with N antennas 102-2, 102-4, and 102-6 (e.g., sector antennas) and a transmitter (Tx) 104 sending a periodic signal 108 in the uplink frequency band. The goal is to estimate the DoA for the periodic signal with respect to the RAN site. In addition, there may be multiple 3GPP UEs (e.g., 110-2, 110-4) communicating with theRAN site 106 on the same frequency, which are considered interference in the DoA estimation problem. The embodiment shown in Figure 1 is a three sector site and the dashed lines show the coverage areas for each of antennas 102-2, 102-4, and 102-6. It is to be appreciated that in a real world implementation of a three sector site, the coverage areas of each of the antennas would overlap each other so that RAN site 106 would have coverage to provide service to all UEs within the cell.

[0040] Figure 2 shows an example of components of a system architecture in accordance with some embodiments of the present disclosure.

[0041] Antennas 102-2, 102-4, and 102-6 from RAN site 106 can be communicably coupled to Radio Units (RU) 202-2, 202-4, and 202-6, which are in turn communicably coupled to a distributed unit (DU) such as a Baseband Unit (BBU) 204 which can serve as a base station for the RAN site, and which may also be connected to a Central Unit (CU) 206, which is in turn communicably coupled to the cloud 208. The computing device (e.g., computing device 700) that performs the DoA estimation as described herein can be located at or implemented by any of the cloud 208, or the radio access network nodes such as the CU 206, or the BBU 204. In other embodiments, the computing device 700 could also be implemented by a core network node. In various embodiments, the cloud 208 is operating an application from Ericsson entitled Uplink Spectrum Analyzer that performs the DoA estimation.

[0042] For the following description of the proposed methods disclosed herein, it is assumed that (i) the transmitter 104 remains stationary during the data collection period and (ii) the angular dependent antenna array manifolds of the M antenna arrays (e.g., antennas 102-2, 102-4, and 102-6) and their respective orientations are known. It is noted that for a given type of antenna installed at the RAN site 106, there can be individual manufacturing variations between antennas 102-2, 102-4, and 102-6 and these may be measurable, but in general these variations are assumed to be negligible compared to other error sources. It is further assumed that there are no reflectors in the vicinity of the receive antennas, i.e., the received power depends only on the sensitivity of the antennas and is not affected by any reflections. An example of the direction sensitivity for a three-sector site is given in Figure 3.

[0043] The antenna manifold can be represented by a matrix with entries corresponding to the directivity in the vertical and horizontal directions. In various embodiments disclosed herein, the DoA that is relevant is the azimuth, and so the manifold is a vector with the directivity in each azimuth angle.

[0044] For ease of notation, the approach described herein is for M = 3 (e.g., a traditional three-sector RAN site), but it can be straightforwardly extended to M > 3 antenna arrays.Technically, this can also be performed when M = 2, but there will be an angular ambiguity as there would be two possible solutions.

[0045] The received time-domain complex I / Q samples from antenna i = 1,2,3 is denoted by:sx[n] — y / Pic[n — ki] + xjn]52[n] = y / ~P^c[n - k2] + x2[n]53[n] = JP^c[n - k3] + x3[n],

[0046] where n = 0,..., IV — 1, c[n] denotes the transmitted periodic waveform, Ptrepresent the RRSP levels, and x n] represent the background noise (i.e., thermal noise and potential background traffic signals) received at antenna array i.

[0047] Note that it is assumed that the received signals are not sampled with time coherence which means that the periodic waveforms c[n — k will have a different time-lag k for each antenna array. This assumption is consistent with the current implementation of Ericsson’s Uplink Spectrum Analyzer (ULSA) feature. Moreover, it means that background signals X / fzi] can be considered independent and zero-mean stochastic signals. An illustration of the non-synchronized snapshots of I / Q samples from three sector RUs 202-2, 202-4, and 202-6 can be seen in Figure 4.

[0048] Figure 5 shows a flowchart of a method for estimating a direction of arrival by using non-beamforming antennas by a computing device in accordance with some embodiments of the present disclosure.

[0049] At step 502, the method includes determining that signals from two or more directional antennas (e.g., two or more of antennas 102-2, 102-4, or 102-6) correspond to transmission 108. This can be performed by detecting the presence of the periodic signal in each RU 202-2, 202-4, and 202-6 by computing the auto-correlation function, e.g., m = max |(si *si)[k] | and invoking the algorithm if two or more sectors report a power over a given threshold.

[0050] At step 504, the method includes estimating, based on a cross-correlation function, for the signals received by the two or more directional antennas, relative received signal powers (e.g., RRSP). The cross-correlation function compares the signal powers as received by each pair of directional antennas of the two or more directional antennas of the periodic signal. Thus, for example, if a periodic signal is received by antennas 102-2, 102-4, and 102-6; for each pair of antennas, 102-2 and 102-4, 102-4 and 102-6, and 102-2 and 102-6, the received signal power at each antenna is estimated relative to all the other antennas. In at least one embodiment, this is performed by computing the maximum of the cross-correlation, denoted by Sj * Sy, between each pair of received signals:r12= maxKsi * s2)[fc]|fcr13= max\(s1* s3)[k]|kr23= max\(s2* s3)[k]|.k

[0051] The estimated RRSP levelscan then be obtained by solving the following system of equations:p(dB)201og10(r12) 1 1 O’rl201og10(r13) p(dB)1 0 1r2201og10(r23).0 1 1. p(dB)3 -1

[0052] This system of equations is obtained by using the independence of the background signals [n] and Xj[n]. Then it follows that Tj ~for i = j and, hence, 201og10(ri;) » paB)+ pfaB

[0053] In an embodiment, the cross-correlation function is in a time domain for a periodic transmission, whilst in another embodiment, the cross-correlation function is in a frequency domain for a constant transmission.

[0054] At step 506, the method includes estimating a direction of arrival of the transmission 108 based on determining a minimum value of an error function between vectors of the relative received signal powers and vectors of expected relative received signal powers, wherein the expected relative received signal powers are based on antenna manifolds and orientations of the two or more directional antennas 102-2, 102-4, and / or 102-6.

[0055] Step 506 of the proposed method uses the receive antenna array manifolds (<>) to find the DoA that best matches the estimated RRSP levels.

[0056] Let 0t denote the azimuth orientation of antenna array i and define the vectors:a(<p) = [A2{(p - 02) - A^cp - ) A3((p - 03) - Ar(<p - )]T,

[0057] where A (p) is assumed to be measured in decibels, and:

[0058] The final estimated DoA (p is obtained by minimizing a value of the error function, which in an embodiment is the mean-squared error (MSE) between the expected and estimated RRSP vectors:(p = min MSE (a(<p), b)<p

[0059] While the present formulation uses MSE, it is noted that other error metrics could be used in the same way.

[0060] Given that the proposed approach has loose requirements on the synchronization between I / Q samples from the different antenna arrays, there are multiple options for where to implement the algorithm logic performed by computing device 700.

[0061] Local processing in DU: With multiple sector antennas connected to a single DU, the proposed algorithm can run locally with the advantage of low-latency access to the PHY layer I / Q samples.

[0062] xApp in near-realtime RIC: Data can be merged from multiple RUs in the network which can coordinate DoA estimation between multiple sites for triangulation.

[0063] Edge cloud implementation: Improves scalability by moving processing closer to the site.

[0064] Cloud implementation: Provides flexibility and allows use of compute clusters.

[0065] Figure 6 shows an example of a communication system 600 in accordance with some embodiments.

[0066] In the example, the communication system 600 includes a telecommunications network 602 that includes an access network 604, such as a radio access network (RAN), and a core network 606, which includes one or more core network nodes 608. The one or more core network nodes 608 can in some embodiments implement the computing device 700 as described herein. The access network 604 includes one or more access network nodes or base stations of various types, access network nodes 610A and 610B are depicted (which may be collectively referred to as network nodes 610), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points (APs). In an embodiment, the access network nodes 610A and 610B are radio access network nodes and can in various embodiments be CU 206 or BBU 204, and thus can also implement the computing device 700 in various embodiments. The antennas 102-2, 102-4, and 102-6 can be part of a RAN site 106 associated with one or more of access network nodes 610A or 610B. Some embodiments of the access network 604 may include more than one access network technology. The network nodes 610 of access network 604 facilitate direct or indirect connection of wireless devices, also referred to as user equipments (UEs), such as by connecting UEs 612A, 612B, 612C, and 612D (one or more of which may be generally referred to as UEs 612) to the core network 606 over one or more wireless connections.

[0067] Moreover, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. Forexample, in some embodiments, the telecommunications network 602 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a network node in the telecommunications network 602 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other network nodes to implement one or more functionalities of any network node in the telecommunications network 602, including one or more access network nodes 610 and / or core network nodes 608.

[0068] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). An ORAN network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN network node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies.

[0069] The network nodes 610 facilitate direct or indirect connection of one or more UEs 612 to the core network 606 over one or more wireless connections. Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 600 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 600 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0070] The UEs 612 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with thenetwork nodes 610 and other communication devices. Similarly, the network nodes 608, 610 are arranged, capable, configured, and / or operable to communicate directly or indirectly (e.g., via other devices of telecommunications network 602) with the UEs 612 and / or with other network nodes or equipment in the telecommunications network 602 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunications network 602. More specifically, UEs 612 may send messages, data, and / or other signals to network nodes 608, 610 or other elements of the telecommunications network 602 by transmitting such signals to the relevant device directly without the signals passing through any intervening devices or by transmitting such signals to the relevant device indirectly through an intervening device (or multiple intervening devices) that then transmit the signal to the relevant device. Similarly, network nodes 608, 610 may send messages, data, and other signals to UEs 6122, other network nodes 608, 610, and other devices in telecommunications network 602 directly or indirectly. As one specific example, a core network node 608 may transmit a particular message to a UE 612 by transmitting the message to an access network node 610 that will then transmit the message to the intended UE 612. Similarly, a core network node 608 may receive a particular message from a UE 612 by receiving the message from an access network node 610 that itself received the message from the UE 612.

[0071] In the depicted example, the core network 606 connects elements of the access network 604 (e.g., one or more of the network nodes 610) to one or more host computing systems, such as host 616. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 606 includes one or more core network nodes (e.g., core network node 608) of various types, one or more of which may be generally referred to as network nodes 608. Network nodes 608 are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, access network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 608. Example core network nodes provide functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), Network Data Analytics Function (NWDAF) and / or a User Plane Function (UPF).

[0072] The host 616 may be under the ownership or control of a service provider other than an operator or provider of the access network 604 and / or the telecommunications network 602. The host 616 may be operated by the service provider or on behalf of the service provider. The host 616 may host a variety of applications to provide one or more services. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0073] As a whole, the communication system 600 of Figure 6 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 600 may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (Wi-Fi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (Wi-Max), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, Li-Fi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox. Moreover, the communication system 600 may be configured to support multiple different standards, protocols, or other rule sets, with individual components supporting all of the relevant rule sets or with different components or sub-systems within the communication system 600 supporting different standards, protocols, or rule sets.

[0074] As one example, in certain embodiments, access network 604 may contain some access network nodes 610 that support 3 GPP radio access technologies (RAT), such as LTE or NR, while other access network nodes 610 support (or the same access network nodes 610 additionally support) non-3GPP RATs, such as Wi-Fi or a proprietary RAT. As another example, telecommunications network 602 may support multiple generations of related communication standards (e.g., 4G and 5G 3GPP communication standards) and, as a result, may include an access network 104 and / or a core network 106 that supports multiple different standard generations or may include multiple access networks 104 and / or multiple core networks 106 with individual networks 104, 106 supporting different standard generations.

[0075] Telecommunications network 602 may support network slicing to provide different logical networks to different devices that are connected to the telecommunications network 602.For example, the telecommunications network 602 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0076] In some examples, one or more of the UEs 612 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 604 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 604. Additionally, a UE may be configured for operating in single- or multi -RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0077] In the example, the hub 614 communicates with the access network 604 to facilitate indirect communication between one or more UEs (e.g., UE 612C and / or 612D) and network nodes (e.g., network node 610B). In some examples, the hub 614 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 614 may be a broadband router enabling access to the core network 606 for the UEs. As another example, the hub 614 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 610, or by executable code, script, process, or other instructions in the hub 614.

[0078] As another example, the hub 614 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 614 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 614 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 614 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 614 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0079] The hub 614 may have a constant / persistent or intermittent connection to the network node 610B. The hub 614 may also allow for a different communication scheme and / or schedule between the hub 614 and UEs (e.g., UE 612C and / or 612D), and between the hub 614 and the core network 606. In other examples, the hub 614 is connected to the core network 606 and / or one or more UEs via a wired connection. Moreover, the hub 614 may be configured to connect to anM2M service provider over the access network 604 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 610 while still connected via the hub 614 via a wired or wireless connection. In some embodiments, the hub 614 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 61 OB. In other embodiments, the hub 614 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 61 OB, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0080] Figure 7 shows a computing device 700, which may be configured to operate in communication system 600 of Figure 6 or in one of the BBU 204, CU 206, or cloud 208 of Figure 2. As used herein, a computing device refers to a device capable, configured, arranged and / or operable to communicate wired or wirelessly with cloud computing resources, core network nodes, radio access network nodes and / or other computing devices. The computing device 700 is in an embodiment a part of a satellite-based / aided communication system, and may even be a satellite in such a system, be it as a base station / access point in the form of a satellite, a satellite relay network node, or even a core network node embodied as a satellite.

[0081] In particular embodiments, computing device 700 includes processing circuitry 702 that is operatively coupled via a bus 704 to an input / output interface 706, a power source 708, a computer readable storage medium (CRSM) 710, and optionally a communication interface 712, and / or any other component, or any combination thereof. Certain embodiments of computing device 700 may include all or a subset of the components shown in Figure 7. The level of integration between the components may vary from one embodiment of computing device 700 to another. In general, in a particular embodiment of computing device 700, processing circuitry 702, input / output interface 706, power source 708, memory 710, and communication interface 712 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of computing device 700. Further, certain embodiments of computing devices 700 may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0082] The processing circuitry 702 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs 714 in the CRSM 710. The processing circuitry 702 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs,general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 702 may include multiple central processing units (CPUs).

[0083] In the example, the input / output interface 706 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into computing device 700. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0084] In some embodiments, the power source 708 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used to supply power to circuitry or to charge an associated battery. The power source 708 may further include power circuitry for delivering power from the power source 708 itself, and / or an external power source, to the various parts of computing device 700 via input circuitry or an interface such as an electrical power cable. Power source 708 may perform any formatting, converting, or other modification to make accessible power suitable for the respective components of the computing device 700 to which power is supplied.

[0085] The CRSM 710 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the CRSM 710 includes one or more computer programs 714, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 716. The CRSM 710 may store, for use by computing device 700, any of a variety of various operating systems or combinations of operating systems.

[0086] The CRSM 710 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The CRSM 710 may allow computing device 700 to access instructions, programs, and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data.

[0087] The processing circuitry 702 may be configured to communicate with an access network or other network via or using the communication interface 712. The communication interface 712 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 722. The communication interface 712 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication. Each transceiver may include a transmitter 718 and / or a receiver 720 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 718 and receiver 720 may be coupled to one or more antennas (e.g., antenna 722) and may share circuit components, software, or firmware, or alternatively be implemented separately.

[0088] In the illustrated embodiment, communication functions of the communication interface 712 may include cellular communication, Wi-Fi communication (e.g., according to an IEEE 802.11 family standard), LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0089] In particular embodiments, computing device 700 may provide an output of data captured via a sensor, through its communication interface 712, via a wireless connection to a network node, and / or in any appropriate manner. Data captured by sensors of a computing device 700 can be communicated through a wired or wireless connection to a network node via another computing device 700. In particular embodiments, such output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0090] As another example, computing device 700 comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, computing device 700 may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0091] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions, and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0092] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0093] Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.

Claims

CLAIMS1. A method for estimating a direction of arrival by using non-beamforming antennas by a computing device (700), the method comprising:determining (502) that signals from two or more directional antennas (102) correspond to a transmission (108);estimating (504), based on a cross-correlation function, for the signals (108) received by the two or more directional antennas (102), relative received signal powers; andestimating (506) a direction of arrival of the transmission (108) based on determining a minimum value of an error function between vectors of the relative received signal powers and vectors of expected relative received signal powers, wherein the expected relative received signal powers are based on antenna manifolds and orientations of the two or more directional antennas (102).

2. The method of claim 1, wherein the cross-correlation function is in a time domain for a periodic transmission (108).

3. The method of claim 1, wherein the cross-correlation function is in a frequency domain for a constant transmission (108).

4. The method of any of claims 1 to 3, wherein the determining that the signals correspond to the transmission (108) is based on an auto-correlation function.

5. The method of any of claims 1 to 4, wherein the error function is a mean-squared error, MSE, function.

6. The method of any of claims 1 to 5, wherein the vectors of the relative received signal powers correspond to an estimated direction of arrival of the transmission (108).

7. The method of any of claims 1 to 6, wherein the computing device (700) is located at a radio access network node (610), a core network node (608), or a cloud computing node (616).

8. The method of claim 7, wherein the cloud computing node (616) is operating an Uplink Spectrum Analyzer.

9. The method of any of claims 1 to 8, wherein the two or more directional antennas (102) are located at a single radio access network node (610).

10. The method of any of claims 1 to 8, wherein the two or more directional antennas (102) are located at separate radio access network nodes (610).

11. A computing device (700) for estimating a direction of arrival using non-beamforming antennas, comprising processing circuitry (702) configured to cause a network node to:determine (502) that signals from two or more directional antennas (102) correspond to a transmission (108);estimate (504), based on a cross-correlation function, for the signals (108) received by the two or more directional antennas (102), relative received signal powers; andestimate (506) a direction of arrival of the transmission (108) based on determining a minimum value of an error function between vectors of the estimated relative received signal powers and vectors of expected relative received signal powers, wherein the expected relative received signal powers are based on antenna manifolds and orientations of the two or more directional antennas (102).

12. The computing device (700) of claim 11, wherein the cross-correlation function is in a time domain for a periodic transmission (108).

13. The computing device (700) of claim 11, wherein the cross-correlation function is in a frequency domain for a constant transmission (108).

14. The computing device (700) of any of claims 11 to 13, wherein the determining that the signals correspond to the transmission (108) is based on an auto-correlation function.

15. The computing device (700) of any of claims 11 to 14, wherein the error function is a mean-squared error, MSE, function.

16. The computing device (700) of any of claims 11 to 15, wherein the vectors of the estimated relative received signal powers correspond to an estimated direction of arrival of the transmission (108).

17. The computing device (700) of any of claims 11 to 16, wherein the computing device (700) is located at a radio access network node (610), a core network node (608), or a cloud computing node (616).

18. The computing device (700) of claim 17, wherein the cloud computing node (616) is operating an Uplink Spectrum Analyzer.

19. The computing device (700) of any of claims 11 to 18, wherein the two or more directional antennas (102) are located at a single radio access network node (610).

20. The computing device (700) of any of claims 11 to 18, wherein the two or more directional antennas (102) are located at separate radio access network nodes (610).

21. A computer program (714) comprising instructions which, when executed on processing circuitry (702), causes the processing circuitry (702) to carry out the method according to any one of claims 1 to 10.

22. A carrier containing the computer program (714) of claim 21, wherein the carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium (710).

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