Improved channel estimation

Abstract

The present disclosure is related to a receiving node and a method for improved channel estimation. A method at a receiving node for channel estimation for one or more transmitting nodes comprises: determining a first number of array response candidates for the one or more transmitting nodes; determining a second number of array responses for each of the one or more transmitting nodes based on at least the first number of array response candidates; and determining a channel estimate for each of the one or more transmitting nodes based on at least the second number of array responses associated with the corresponding transmitting node.

Description

[0001]IMPROVED CHANNEL ESTIMATION Technical Field The present disclosure is related to the field of telecommunication, and in particular, to a receiving node and a method for improved channel estimation. Background With the development of the electronic and telecommunication technologies, mobile devices, such as mobile phones, smart phones, laptops, tablets, vehicle mounted devices, become an important part of our daily lives. To support a numerous number of mobile devices, a highly efficient radio access network (RAN), such as a 3rdGeneration Partnership Project (3GPP) 5thGeneration (5G) New Radio (NR) RAN, will be required. Currently, the 3GPP RAN technology is kept evolving from 5G to 6G. To achieve higher spectral efficiency for future 6G, it is required to exploit broadband spectral resources and Multiple-Input-Multiple-Output (MIMO) spatial multiplexing. For broadband communication, it is necessary to utilize high-frequency band ranging from about 30 GHz to 300 GHz, such as millimeter wave and sub-terahertz band. However, despite their rich bandwidth, millimeter wave and sub-terahertz band have severe propagation loss. Therefore, beamforming techniques with a massive number of antenna elements at a base station (BS) is a critical solution to overcome this severe propagation loss. On the other hand, to increase the number of MIMO spatial multiplexing, the spatial degree of freedom after beamforming must be larger than the total number of transmitted streams to all served User Equipments (UEs). Therefore, in terms of both beamforming and spatial multiplexing, extremely large-scale-MIMO (XL- MIMO) with a large number of antennas has been widely considered as a key technology. Summary Compared to the conventional massive MIMO technology, the extreme increase in the antenna aperture of XL-MIMO results in extension of the corresponding Rayleigh distance, which defines the limit between the near field propagation and the far field propagation. In an XL-MIMO system, the area coverage can be in the near-field region due to the extremely large antenna aperture. For example, when the carrier frequency is 100 GHz and the BS is equipped with a half-wavelength uniform linear array (ULA) of 200 antenna elements, the Rayleigh distance which is the boundary between the far- field region and the near-field region is about 59 meters (m). Therefore, the far-field assumption where propagating waves can be considered as a planar-wave does not hold within this near-field region, and the spherical-wave front in the near-field must be considered. A near-field channel consists of multiple proximal paths composed of a Line-of- Sight (LoS) path component from the UE and Non-Line-of-Sight (NLoS) path components from scatterers (e.g., vehicles, buildings, etc.) around the UE. Since almost all UEs and scatterers might exist in the near-field region, the array response at the BS side depends on not only Angles of Arrivals (AoAs) but also distances between the BS and the UE / scatterers due to spherical-wave effect. Therefore, a classical compressed sensing-based channel estimation algorithm, which relies on the angular sparsity under the assumption of a planar wavefront in the far-field region, degrades channel estimation performance in the case of near-field regions. For near-field channel estimation, a novel compressed sensing-based channel estimation algorithm, Polar- domain Simultaneous Orthogonal Matching Pursuit (P-SOMP), has been proposed. Unlike the classical far-field channel estimation algorithm that only considers the angular sparsity, P-SOMP algorithm simultaneously exploits both the angular and distance sparsity in the near-field region. It is worth mentioning that XL-MIMO was mentioned in 3GPP when companies identified the study / work items for Release 19. Although some companies proposed to study channel models for XL-MIMO, studies of XL-MIMO have been left for future investigations and not been included in Release 19. In a multiuser XL-MIMO system, the P-SOMP algorithm requires orthogonal pilot sequences whose length must be equal to or larger than the number of UEs to cancel pilot contaminations between UEs. These orthogonal pilot sequences lead to an increased pilot overhead, which scales as the number of UEs increases. Especially in XL- MIMO systems that have a potential for extreme degree of spatial multiplexing, the pilot overhead could be one of the major problems because large pilot overhead significantly reduces spectral efficiency as the number of UEs increases. Therefore, it is necessary to overcome pilot overhead problems in multiuser XL-MIMO systems. If non-orthogonal pilot sequences are used in the P-SOMP algorithm to reduce pilot overhead, the channel estimation performance deteriorates due to pilot contamination among UEs since P- SOMP does not estimate the channels of multiple UEs and relies on the orthogonality of pilots to distinguish them. The data detection with the estimated channel including pilot contaminations results in a high error floor, which leads to degradation of spectral efficiency. Therefore, it is essential to realize the accurate channel estimation with non- orthogonal pilot sequences for the overhead reduction and the accurate data detection for higher spectral efficiency in near-field multiuser XL-MIMO systems. Therefore, to address or at least partially alleviate one or more of the above issues, some embodiments of the present disclosure are provided. According to a first aspect of the present disclosure, a method at a receiving node for channel estimation for one or more transmitting nodes is provided. The method comprises: determining a first number of array response candidates for the one or more transmitting nodes; determining a second number of array responses for each of the one or more transmitting nodes based on at least the first number of array response candidates; and determining a channel estimate for each of the one or more transmitting nodes based on at least the second number of array responses associated with the corresponding transmitting node. According to a second aspect of the present disclosure, a receiving node for channel estimation for one or more transmitting nodes is provided. The receiving node comprises: a processor; a memory storing instructions which, when executed by the processor, cause the receiving node to: determine a first number of array response candidates for the one or more transmitting nodes; determine a second number of array responses for each of the one or more transmitting nodes based on at least the first number of array response candidates; and determine a channel estimate for each of the one or more transmitting nodes based on at least the second number of array responses associated with the corresponding transmitting node. In some embodiments, the instructions, when executed by the processor, further cause the receiving node to perform any of the methods of the first aspect. According to a third aspect of the present disclosure, a receiving node for channel estimation for one or more transmitting nodes is provided. The receiving node comprises: a first determining module configured to determine a first number of array response candidates for the one or more transmitting nodes; a second determining module configured to determine a second number of array responses for each of the one or more transmitting nodes based on at least the first number of array response candidates; and a third determining module configured to determine a channel estimate for each of the one or more transmitting nodes based on at least the second number of array responses associated with the corresponding transmitting node. In some embodiments, the receiving node comprises one or more further modules, each of which performs any of the steps of any of the methods of the first aspect. According to a fourth aspect of the present disclosure, a computer program comprising instructions is provided. The instructions, when executed by at least one processor, cause the at least one processor to carry out any of the methods of the first aspect. According to a fifth aspect of the present disclosure, a carrier containing the computer program of the fourth aspect is provided. In some embodiments, the carrier is one of an electronic signal, optical signal, radio signal, or computer readable storage medium. With some embodiments of the present disclosure, channel estimation accuracy can be improved. Actually, the disclosed solution proves to be superior to the state-of- the-art methods in terms of the Normalized Mean-Squared Error (NMSE) performance. Further, Reference Signal (RS) overhead reduction can be achieved by utilizing the sparsity in the AoA-distance domain (or the polar domain). In other words, the disclosed solution demonstrates its capability to reduce the RS overhead in multiuser uplink channels with a reasonable channel estimation performance. Further, the disclosed solution is widely applicable in various scenarios. For example, the disclosed solution works not only with orthogonal pilots but also with non- orthogonal pilots and / or under pilot contamination. Therefore, the method is applicable to not only conventional use cases with orthogonal pilots or no pilot contamination but also situations such as dense networks with severe pilot contamination and / or non- orthogonal multiple access. Furthermore, the method is applicable to not only the near field region, but also to the far field region, resulting in a more generally applicable solution than other channel estimation solutions. Brief Description of the Drawings Fig. 1 is a diagram illustrating an exemplary telecommunication network in which improved channel estimation is applicable according to an embodiment of the present disclosure. Fig. 2 is a diagram illustrating an exemplary near field and an exemplary far field in which improved channel estimation is applicable according to an embodiment of the present disclosure. Fig. 3 is a diagram illustrating an exemplary near field channel model according to an embodiment of the present disclosure. Fig. 4 is a diagram illustrating an exemplary overall procedure for improved channel estimation according to an embodiment of the present disclosure. Fig. 5 is a diagram illustrating an exemplary specific implementation of the improved channel estimation according to an embodiment of the present disclosure. Fig. 6 and Fig. 7 are diagrams illustrating exemplary comparisons between simulation results of channel estimations in the related art and the improved channel estimation according to an embodiment of the present disclosure. Fig. 8 is a flow chart illustrating an exemplary method for improved channel estimation according to an embodiment of the present disclosure. Fig. 9 schematically shows an embodiment of an arrangement which may be used in a receiving node according to an embodiment of the present disclosure. Fig. 10 is a block diagram of an exemplary receiving node according to an embodiment of the present disclosure. Fig. 11 shows an example of a communication system in accordance with some embodiments of the present disclosure. Fig. 12 shows an exemplary User Equipment (UE) in accordance with some embodiments of the present disclosure. Fig. 13 shows an exemplary network node in accordance with some embodiments of the present disclosure. Fig. 14 is a block diagram of an exemplary host, which may be an embodiment of the host of Fig. 11, in accordance with various aspects described herein. Fig. 15 is a block diagram illustrating an exemplary virtualization environment in which functions implemented by some embodiments may be virtualized. Fig. 16 shows a communication diagram of an exemplary host communicating via an exemplary network node with an exemplary UE over a partially wireless connection in accordance with some embodiments of the present disclosure. Detailed Description Hereinafter, the present disclosure is described with reference to embodiments shown in the attached drawings. However, it is to be understood that those descriptions are just provided for illustrative purpose, rather than limiting the present disclosure. Further, in the following, descriptions of known structures and techniques are omitted so as not to unnecessarily obscure the concept of the present disclosure. Those skilled in the art will appreciate that the term “exemplary” is used herein to mean “illustrative,” or “serving as an example,” and is not intended to imply that a particular embodiment is preferred over another or that a particular feature is essential. Likewise, the terms “first”, “second”, “third”, “fourth,” and similar terms, are used simply to distinguish one particular instance of an item or feature from another, and do not indicate a particular order or arrangement, unless the context clearly indicates otherwise. Further, the term “step,” as used herein, is meant to be synonymous with “operation” or “action.” Any description herein of a sequence of steps does not imply that these operations must be carried out in a particular order, or even that these operations are carried out in any order at all, unless the context or the details of the described operation clearly indicates otherwise. Conditional language used herein, such as "can," "might," "may," "e.g.," and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and / or states. Thus, such conditional language is not generally intended to imply that features, elements and / or states are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without author input or prompting, whether these features, elements and / or states are included or are to be performed in any particular embodiment. Also, the term "or" is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term "or" means one, some, or all of the elements in the list. Further, the term "each," as used herein, in addition to having its ordinary meaning, can mean any subset of a set of elements to which the term "each" is applied. The term “based on” is to be read as “based at least in part on.” The term “one embodiment” and “an embodiment” are to be read as “at least one embodiment.” The term “another embodiment” is to be read as “at least one other embodiment.” Other definitions, explicit and implicit, may be included below. In addition, language such as the phrase "at least one of X, Y and Z," unless specifically stated otherwise, is to be understood with the context as used in general to convey that an item, term, etc. may be either X, Y, or Z, or a combination thereof. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limitation of example embodiments. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”, “comprising”, “has”, “having”, “includes” and / or “including”, when used herein, specify the presence of stated features, elements, and / or components etc., but do not preclude the presence or addition of one or more other features, elements, components and / or combinations thereof. It will be also understood that the terms “connect(s),” “connecting”, “connected”, etc. when used herein, just mean that there is an electrical or communicative connection between two elements and they can be connected either directly or indirectly, unless explicitly stated to the contrary. Of course, the present disclosure may be carried out in other specific ways than those set forth herein without departing from the scope and essential characteristics of the disclosure. One or more of the specific processes discussed below may be carried out in any electronic device comprising one or more appropriately configured processing circuits, which may in some embodiments be embodied in one or more application- specific integrated circuits (ASICs). In some embodiments, these processing circuits may comprise one or more microprocessors, microcontrollers, and / or digital signal processors programmed with appropriate software and / or firmware to carry out one or more of the operations described above, or variants thereof. In some embodiments, these processing circuits may comprise customized hardware to carry out one or more of the functions described above. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive. Although multiple embodiments of the present disclosure will be illustrated in the accompanying Drawings and described in the following Detailed Description, it should be understood that the disclosure is not limited to the disclosed embodiments, but instead is also capable of numerous rearrangements, modifications, and substitutions without departing from the present disclosure that as will be set forth and defined within the claims. Further, please note that although the following description of some embodiments of the present disclosure is given in the context of 5G NR, the present disclosure is not limited thereto. In fact, as long as channel estimation is involved, the inventive concept of the present disclosure may be applicable to any appropriate communication architecture, for example, to Global System for Mobile Communications (GSM) / General Packet Radio Service (GPRS), Enhanced Data Rates for GSM Evolution (EDGE), Code Division Multiple Access (CDMA), Wideband CDMA (WCDMA), Time Division - Synchronous CDMA (TD-SCDMA), CDMA2000, Worldwide Interoperability for Microwave Access (WiMAX), Wireless Fidelity (Wi-Fi), 4thGeneration Long Term Evolution (LTE), LTE-Advance (LTE-A), or 5G NR, 6th generation (6G) mobile system standard, etc. Therefore, one skilled in the arts could readily understand that the terms used herein may also refer to their equivalents in any other infrastructure. For example, the term “receiving node” used herein may refer to a UE, a terminal device, a mobile device, a mobile terminal, a mobile station, a user device, a user terminal, a wireless device, a wireless terminal, a transmission reception point (TRP), a base station, a base transceiver station, an access point, a hot spot, a NodeB, an Evolved NodeB (eNB), a gNB, a network element, a satellite, an aircraft, or any other equivalents. Further, following documents are incorporated herein by reference in their entireties: - [1] E. D. Carvalho, A. Ali, A. Amiri, M. Angjelichinoski, and R. W. Heath, “Non- stationarities in extra-large-scale massive MIMO,” IEEE Wireless Commun., vol. 27, no. 4, pp. 74–80, Aug. 2020. - [2] J. Rodr ́ıguez-Fern ́andez, N. Gonz ́alez-Prelcic, K. Venugopal, and R. W. Heath, “Frequency-domain compressive channel estimation for frequency-selective hybrid millimeter wave MIMO systems,” IEEE Trans. Wireless Commun., vol. 17, no. 5, pp. 2946–2960, 2018. - [3] M. Cui and L. Dai, “Channel estimation for extremely large-scale MIMO: Far- field or near-field?” IEEE Trans. Commun., vol. 70, no. 4, pp. 2663– 2677, 2022. - [4] C. Rusu, and N. Gonzalez-Prelcic, “Designing incoherent frames through convex techniques for optimized sensing”, IEEE Trans. Signal Process., vol 64, no. 9, pp. 2334-2344, May 2016. Fig. 1 is a diagram illustrating an exemplary telecommunication network 10 in which improved channel estimation is applicable according to an embodiment of the present disclosure. As shown in Fig. 1, the network 10 may comprise one or more UEs 100-1 and 100-2 (collectively, UE(s) 100) and a RAN node 105, which could be a base station, a Node B, an evolved NodeB (eNB), a gNB, or an AN node which provides the UEs 100 with access to the network. Further, the network 10 may comprise its core network portion that is not shown in Fig. 1. However, the present disclosure is not limited thereto. In some other embodiments, the network 10 may comprise additional nodes, less nodes, or some variants of the existing nodes shown in Fig. 1. Further, although two UEs 100 and one gNB 105 are shown in Fig. 1, the present disclosure is not limited thereto. In some other embodiments, any number of UEs and / or any number of gNBs may be comprised in the network 10. As shown in Fig. 1, the UEs 100 may be communicatively connected to the gNB 105 which in turn may be communicatively connected to a corresponding Core Network (CN) and then the Internet, such that the UEs 100 may finally communicate its user plane data with other devices outside the network 10, for example, via the RAN node 105. As mentioned earlier, in a multiuser XL-MIMO system, the algorithms in the related art typically require orthogonal pilot sequences whose length must be equal to or larger than the number of UEs to cancel pilot contaminations between UEs. These orthogonal pilot sequences lead to an increased pilot overhead, which scales as the number of UEs increases. Especially in XL-MIMO systems that have a potential for extreme degree of spatial multiplexing, the pilot overhead could be one of the major problems because large pilot overhead significantly reduces spectral efficiency as the number of UEs increases. Therefore, it is necessary to overcome pilot overhead problems in multiuser XL-MIMO systems. If non-orthogonal pilot sequences are used in P-SOMP algorithm to reduce pilot overhead, the channel estimation performance deteriorates due to pilot contamination among UEs since P-SOMP does not estimate the channels of multiple UEs and relies on the orthogonality of pilots to distinguish them. The data detection with the estimated channel including pilot contaminations results in a high error floor, which lead to degradation of spectral efficiency. Therefore, it is essential to realize the accurate channel estimation with non-orthogonal pilot sequences for the overhead reduction and the accurate data detection for higher spectral efficiency in near-field multiuser XL-MIMO systems. Therefore, to address or at least partially alleviate the above issues, some embodiments of the present disclosure propose a method for channel estimation of multi-user MIMO channels with large aperture large arrays. Due to the large aperture of the arrays, it experiences “near-field” effects, where the array responses are defined by both angles and distance due to the spherical wavefront. The disclosed methodology proposes to utilize the sparsity induced by the “near-field” effect by leveraging a compressive sensing technique, which leads to a better complexity-performance trade- off. Further, some embodiments of the present disclosure propose a new method to identify which estimated path(s) from the sparse estimation are associated with which UE, which has not been addressed by the state-of-the-art methods. As will be described in detail later, the disclosed methodology is evaluated through simulations and found to be more effective in terms of the channel estimation performance when compared to other state-of-the-art algorithms, such as Least Squares (LS) and the P-SOMP. In some embodiments, a method in a network node (or a Central Processing Unit (CPU)) for performing estimation of uplink channel(s) from at least one UE(s) is provided. The network node may be equipped with large arrays with a large aperture. The method may comprise: 1. Identify a first set of UEs for uplink channel estimation; 2. Perform measurements on the identified set of UEs: a. Configure the first set of UEs to transmit uplink Reference Signals (RSs); b. Receive RSs from the first set of UEs at RS time-frequency resources; 3. Perform angle and distance estimation based on the received RSs, wherein the estimation is based on a certain set of array response candidates: a. Determine a set of array response candidates, wherein each array response candidate corresponds to a certain pair of AoA and distance; b. Determine the number of prospective path candidates: i. The number of prospective path candidates is based on measurements on the first set of UEs; ii. The number of prospective path candidates is based on statistics of propagation of the intended channel; c. Perform angle and distance estimation based on the determined set of array response candidates by leveraging the sparsity in the AoA and distance domain; 4. Identify which estimated AoAs and distances are associated with each UE in the first set: a. Compute a set of UE-specific codewords for all UEs in the first set, wherein the codewords are composed of prospective AoAs and distances; b. Determine the number of prospective paths for each UE; c. Perform identification of which estimated angles and distances are associated with which UE in the first set by leveraging the sparsity in the AoA and distance domain; d. Estimate the path gains corresponding to the identified set of angles and distances for all UEs; 5. Compute a channel estimate from the estimated AoAs, distances, and path gains for each UE in the first set. Although the embodiment described above is related to uplink channel estimation, the present disclosure is not limited thereto. In some other embodiments, the method is also applicable to channel estimation in other directions, such as, downlink, sidelink, or Non-Terrestrial Network (NTN) link, or the like. Further, although the method described above is performed by a network node (e.g., a BS), the present disclosure is not limited thereto. In some embodiments, the method is able to be performed by a UE, a terminal device, a mobile device, a mobile terminal, a mobile station, a user device, a user terminal, a wireless device, a wireless terminal, a TRP, a base station, a base transceiver station, an access point, a hot spot, a NodeB, an eNB, a gNB, a network element, a drone, a satellite, an aircraft, or any other equivalents. Further, the steps in the above method may be performed by multiple nodes in a distributed manner. For example, the step 1 may be performed by a first node (e.g., a Central Unit (CU) of a gNB), the step 2 may be performed by a second node (e.g., a Distributed Unit (DU) of the gNB), and the steps 3 through 5 may be performed by a third node (e.g., a virtualized node in a cloud). However, the present disclosure is not limited thereto. With some embodiments of the present disclosure, channel estimation accuracy can be improved. Actually, the disclosed solution proves to be superior to the state-of- the-art methods in terms of the NMSE performance. Further, RS overhead reduction can be achieved by utilizing the sparsity in the AoA-distance domain (or the polar domain). In other words, the disclosed solution demonstrates its capability to reduce the RS overhead in multiuser uplink channels with a reasonable channel estimation performance. Further, the disclosed solution is widely applicable in various scenarios. For example, the disclosed solution works not only with orthogonal pilots but also with non- orthogonal pilots and / or under pilot contamination. Therefore, the method is applicable to not only conventional use cases with orthogonal pilots or no pilot contamination but also situations such as dense networks with severe pilot contamination and / or non- orthogonal multiple access. Furthermore, the method is applicable to not only the near field region, but also the far field region, resulting in a more generally applicable solution than other channel estimation solutions. Fig. 2 is a diagram illustrating an exemplary near field and an exemplary far field in which improved channel estimation is applicable according to an embodiment of the present disclosure. As shown in (a) of Fig. 2, when a UE 100 is located in the near-field of an antenna array 200 (e.g., the antenna array associated with the RAN node 105 shown in Fig. 1) and transmits its uplink radio signals to the antenna array 200, the transmitted signals may have a spherical wavefront when the signals arrive at the antenna array 200. As shown in (b) of Fig. 2, when the UE 100 is located in the far-field of the antenna array 200 and transmits its uplink radio signals to the antenna array 200, the transmitted signals may have an approximately planar wavefront when the signals arrive at the antenna array 200. For the far-field channel estimation, some compressive sensing (CS) based algorithms have been studied to accurately estimate the high-dimensional channels with low pilot overhead. For example, by utilizing the angular-domain sparsity, the classical Orthogonal Matching Pursuit (OMP) algorithm was used to recover the angular-domain channel, where the channel was transformed into its angular-domain representation through the standard spatial Fourier transform. However, this channel sparsity in the angular domain may no longer be achievable in XL-MIMO systems. The change from massive MIMO to XL-MIMO not only means the increase in antenna number, but also leads to the fundamental transformation of electromagnetic field structure. As shown in Fig. 2, the radiation field of electromagnetic wave can be divided into two regions, i.e., the near-field region shown in (a) and the far-field region shown in (b). The widely adopted boundary ^between these two fields is the Rayleigh distance, ^ = ^^^ , where ^ and ^ denote thearray aperture and wavelength, respectively. As the antenna spacing is ^ = and thenumber of antennas is ^, the array aperture of a uniform linear array is ^ = ^^ = Therefore, the Rayleigh distance is ^ =^ which is proportional to ^ . The physical meaning of the Rayleigh distance ^ is that, when the distance between the source (e.g., a transmitter or a scatterer) and receiver is larger than ^, the radiation field is far-field, and the wavefronts can be approximated as planar waves. Otherwise, if the distance between the radiating source and the receiver is less than ^, the radiation field is near- field, and the wavefronts are spherical waves. Next, an exemplary of the near-field channel model will be described in detail with reference to Fig. 3. Let’s consider an uplink XL-MIMO system in the near-field region, where ^ single-antenna UEs may communicate to a BS 105 with ^-antenna uniform linear array (ULA) placed on the y-axis as shown in Fig. 3. Although only one of the UEs, the ^-th UE 100, is shown in Fig. 3, the present disclosure is not limited thereto. As shown in Fig. 3, the near-field channel between the BS 105 and the ^-th UE100, which consists of one LoS path component and ^^ − 1 NLoS path components fromscatterers around the ^-th UE, can be modelled as where ^^,^and ^^,^are the AoA and the complex path gain of the ^-th path for (or otherwise associated with) the ^-th UE, where ^^,^is the distance between the BS 105 and the ^-th UE 100 (e.g., when^ = 1) or the scatterers (e.g., when ^ ≠ 1),∈ ℂ^×^ is the array response vector, whose ^-th elementpressed as (2) where ^ is wavelength, is the distancebetween the ^-th antenna of the BS 105 and the ^-th UE 100, and is the coordinate of the ^-th antenna element, ^ = ^^ ,^, ^^,^,^^… , ^^,^^^ AoAs, distances, and path gains for the associated with the ^-th UE 100, respectively, theresponse matrix at the ^-th UE 100. In some embodiments, for uplink channel estimation, it is assumed that the ^-thUE 100 transmits non-orthogonal pilot sequence ^ ^^,^ ∈ ℂ ^×^ and optionally datasymbols ^ ∈ ℂ^^×^^ , where ^^ and ^^ are the lengths of pilot and data symbols,respectively, and the pilot sequence may satisfy ^^ ^^,^ However, the present disclosure is not limited thereto. In some other embodiments, thepilot sequence may satisfy ^^ ^^,^^ = 0, ∀^, ^^ ∈ {1, 2, … , In some embodiments, the data symbol is generated at the ^-th UE 100 and the ^-th symbol ^^,^,^is generated by Quadrature Amplitude Modulation (QAM) ^constellation points set ^ = {^^, ^^, … , ^^}. Then, the received pilot ^ ^×^^ ∈ ℂ ^ andthe received data symbol ^^ ∈ ℂ^×^^ at the BS 105 may be expressed as:^^ = ^^^ + ^^ (3)^^ = ^^^ + ^^ (4)where ^^ = ^^^,^, ^^,^, … , ^^,^^^∈ ℂ^×^^ and ^^ = ^^^,^, ^^,^, … , ^^,^^^∈ ℂ^×^^are the transmitted pilot matrix and the transmitted data matrix, respectively, where ^^ ∈ ℂ^×^^ and ^^ ∈ ℂ^×^^ are additive Gaussian noise matrices withnoise variance ^^, respectively, where ^ = [^ , ] ^×^^ ^^, … , ^^ ∈ ℂ is the near-field channel matrix. By stacking the received pilot ^^and data ^^in the column direction, thereceived stacked signal ^ = [^^, ^^] ∈ ℂ^×(^^^^^) can be represented as^ = ^^ + ^ (5)where ^ = is the stacked transmittedsignal matrix. From the equations (1) and (5), the received signal ^ can be reformulated as: ^= ^(^, ^) ^ + ^ (6)where ^ = [^^, ^^, … , ^^^ ]^ ∈ ℝ^×^ a [ ^ ^ ^^ ^ ]^ ^×^^ ^ nd ^ = ^^, ^ , … , ^ ∈ ℝ are the AoAsand distances for all UEs, the array responsematrix, where ^ = is the matrix composed of pathgain, pilot symbol, and data symbol, where ^ = is the total number of path components for all UEs.Based on this near-field channel model, an exemplary channel estimation algorithm will be described in detail with reference to Fig. 4 and Fig. 5. Fig. 4 is a diagram illustrating an exemplary overall procedure 400 for channel estimation according to an embodiment of the present disclosure, and Fig. 5 is a diagram illustrating an exemplary specific implementation of the channel estimation shown in Fig. 4. As shown in Fig. 4, the inputs of the improved channel estimation algorithm arethe pilot sequence ^^ and the received signal ^ = [^^, ^^], and the output of theproposed algorithm is the estimated channel ^^ . Detailed descriptions for the proposedchannel estimation will be provided below with reference to the steps of S510 to S580 shown in Fig. 5. However, please note that the present disclosure is not limited thereto. In some other embodiments, the received signal ^ may comprise only the received pilotsignal ^^ without any received data ^^, that is, ^ = [^^]. For example, when the pilotsignal is a Demodulation Reference Signal (DMRS) that is transmitted together with data,then the received signal may be ^ = [^^, ^^]. For another example, when the pilotsignal is a Sounding Reference Signal (SRS) that is transmitted without data, then thereceived signal is ^ = [^^]. In other words, the data signal is optional for channelestimation as also clearly seen from the steps S510 through S580 described below. Further, additional attention may be directed to the steps S550 and S570 shown in Fig. 5. At step S550 (corresponding to step 4.a mentioned above), unlike the state-of- the-art method, the disclosed methodology in some embodiments may introduce this step to compute UE-specific codewords from the estimates from step S530, which is needed to estimate the channel per UE. At step S570 (corresponding to step 4.c mentioned above), given the UE-specific codewords from step S550, identification of which estimated angles and distances are associated with which UE is performed. Referring to Fig. 5, the procedure may begin with step S510 where a set of array response candidates with discrete AoA and distance candidates may be determined. To exploit the near-field channel sparsity in the angle and distance domain (or the polar domain) and enable the compressed sensing technique, initially, discretecandidates of AoAs ^^ = ^^^ , ^^ , … , ^^ ^ ^^×^^^^ ^^^ ∈ ℝ and distances ^ = ^^^̃, ^^̃, … , ^^̃^^∈ ℝ^^×^may be generated, which may be generated as discretizing the angle domain[^^^^, ^^^^] ⊂ [−^, ^] and distance domain [^^^^, ^^^^] ⊂ [0, ∞] into ^^ and ^^candidates, respectively. In some embodiments, the discrete candidates of AoAs may be uniformly sampled in a predetermined interval. For example, a sample point ^^^in the angle domain may be determined as: where ^^^^^ and ^^^^^ may be the predetermined minimum and maximum valuesof the AoAs, respectively. In some embodiments, the discrete candidates of AoAs may have their sine values uniformly sampled in a predetermined interval. For example, a sample point ^^^in the angle domain may be determined such that: where sin ^^^^^ and sin ^^^^^ are the predetermined minimum and maximum sinevalues of the AoAs, respectively. In some embodiments, the discrete candidates of distances may be non- uniformly sampled in a predetermined interval. Then, a set of array response candidates corresponding to the AoA candidates ^^and distance candidates ^ can be represented as From (6) and (7), the received signal ^ in (6) can be approximated as: ^≃ ^^^^^, ^^ ^^ + ^ (8)where ^^ ∈ ℂ^^^^×(^^^^^) is the row sparse matrix corresponding to thecandidates ^^ and ^. Only ^ rows of ^^ are nonzero, and the other ^^^^ − ^ rows are zerobecause the near-field channel is composed of the total ^ path components as defined in (1). At step S520, the number of prospective path candidates ^^may be determined. In some embodiments, if the number of total paths ^ is known, the number of prospective path candidates ^^can be set as ^. If the number of total paths ^ is unknown, ^^can be estimated using rough estimation with some prior information, for example, information about propagation environments or some classical estimation methods such as multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT). At step S530, a set of prospective array response candidates may be determined from a set of array response candidates determined at step S510 by a compressed sensing algorithm. In some embodiments, to extract ^^paths from ^^^^ paths candidates in (8), anestimation problem for ^^may be considererd. The AoAs and distances corresponding to the non-zero elements of the estimated ^^may be defined as the prospective AoAcandidates ^^ = ^^^^, ^^^, … , ^^^^ ^^∈ ℝ^^×^ and the prospective distance candidates ^ = ∈ ℝ^^×^. Thanks to the row sparsity of ^^, which means ^^has commonsupport in a row direction, the estimation problem for ^^can be formulated as: subject to ^^^^^,^= ^^ (9)where ^^^^^,^indicates the number of non-zero rows of ^^. This minimizationproblem in (9) can be approximately solved by the compressed sensing algorithms suchas Simultaneous Orthogonal Matching Pursuit (SOMP). Then, ^^prospective AoAcandidates ^^and prospective distance candidates ^ can be obtained, and a set ofprospective array response candidates ^^^^^, ^^ = can be constructed. At step S540, the vectorized received pilot ^^may be obtained. In some embodiments, from (3) and (6), the received pilot signal can be reformulated as: Therefore, the vectorized received pilot can be calculated as ^^ = vec(^^) ∈ℂ^^^×^. At step S550, a set of UE-specific codewords may be calculated based on the array response candidates obtained at step S530 and the pilot sequence. In some embodiments, for the prospective path candidate set obtained by a compressed sensing algorithm at step S530, the UE-path mapping cannot be ensured, which means that which path corresponds to which UE is not clear. Therefore, using user-specific pilot sequences the UE-path mapping frompath candidate set to ^ UEs may be performed. Using a set ofarray responses ^^^^^, ^^, the channel vector can be approximated as:^^ = ^(^^, ^^)^^ ≃ ^^^^^, ^^^^ , ∀^ ∈ {1, 2, … , ^} (11)where ^^ ∈ ℂ^^×^ is an ^^-sparse path gain vector whose only ^^ out of ^^elements are non-zero, and the remaining ^^ − ^^ elements are zero. From (10), (11),and the vectorization property vec(^^^) = (^^ ⊗ ^)vec(^), where ^, ^, and ^ are anymatrices and ⊗ is Kronecker product, the vectorized received pilot ^^obtained at step S540 can be approximated as: ^^ = vec(^^) a set of UE-specific codewords, ^ = ^^×^ ∈ ℂ is a sparse path gainand ^^ = vec(^^) is a noise vector. At step S560, the estimated number of paths of the ^-th UE ^^^may be determined. In some embodiments, through the estimation for sparse path gain vector ^ in(12), the UE-path mapping from the prospective path set to ^ UEs may be performed. If the number of paths of the ^-th UE ^^is known, the estimated number of paths ^^^can be determined as ^^. If the number of paths of the ^-th UE ^^is unknown, ^^^can be estimated using rough estimation with some prior information about propagation environments or some classical estimation methods such as MUSIC and ESPRIT. At step S570, a set of estimated array response ^^^^^^, ^^^ and estimated pathgain ^^may be determined from the vectorized received pilot ^^obtained at step S540 and a set of UE-specific codewords ^^obtained at step S550. The estimation problem for ^ in (12) can be formulated as: subject In some embodiments, this problem can be solved by the compressed sensing algorithms such as Orthogonal Matching Pursuit (OMP). The non-zero elements of ^^∈ ℂ^^×^obtained by solving (13) may be defined as the estimated path gain vector ^^∈ℂ^^^×^. Then, the estimated AoAs of the ^-th UE ^^ ^^^ ∈ ℝ ^×^ and the estimated distancesof the ^-th UE ^^ ∈ ℝ^^^×^ corresponding to the estimated path gain vector ^^ may beobtained. Using these estimated values ^^^ and ^^, a set of estimated array response ofthe ^-th UE can be constructed as: At step S580, an estimated channel ^^ may be determined from a set ofestimated array response ^^^^^^, ^^^ and the estimated path gain ^^ obtained at stepS570. In some embodiments, from the estimated array response ∈ ℂthe estimated path gain ^^, the channel vector of the ^-th UE and the channel matrix for all UEs can be estimated as: ^^^ = ^^^^^^, ^^^^^ ∈ ℂ^×^, ∀^ ∈ {1, 2, … , ^} (14)^^ = ^^^ ^×^^, ^^^, … , ^^^^ ∈ ℂ (15) With the improved channel estimation method described with reference to Fig. 4 and Fig. 5, channel estimation accuracy can be improved even for the near-field scenario. Further, RS overhead reduction can be achieved by utilizing the sparsity in the AoA-distance domain (or the polar domain). In other words, the improved channel estimation method demonstrates its capability to reduce the RS overhead in multiuser uplink channels with a reasonable channel estimation performance. Next, some numerical results will be provided to show the improved performance of the improved channel estimation algorithm over other channel estimation algorithms. Considering an uplink XL-MIMO OFDM communication system, where a BS with^ = 200 antenna elements, receives the pilot sequence and data symbol from ^ = 50single-antenna UEs. The length of pilot sequence and data symbol are ^^ = 25 and^ = 100. The non-orthogonal pilot sequence ^^×^^^ ^^ ∈ ℂ is generated by a quadraticcomplex successive iterative decorrelation by convex optimization (QC-SIDCO) [4]. Note that these parameters are chosen in order to highlight the fact that the disclosed method could work even in case the conventional ones cannot operate well. The near-field channel between the BS and the ^-th UE are composed of ^^ = 3 paths, i.e., 1 LoSpath and 2 NLoS path components from scatterers. Rician ^-factor, that is the energy ratio of the LoS path and NLoS path components, is 10 dB. Owing to the considered mmwave scenario with 100 GHz carrier frequency, the distances between the BS andUEs or scatterers are uniformly randomly generated in the range ^^,^ ∈ [3, 10] m. Notethat the carrier frequency was determined to align with the setup of the state-of-the-art [3]. The AoAs of LoS and NLoS path components are also uniformly randomly generatedin the range ∈ [−60∘, 60∘]. The number of candidates of AoAs and distances are^^ = 400 and ^^ = 50. To perform the channel estimation algorithm in Fig. 4 and Fig. 5,the SOMP [2] algorithm is used at step S530 shown in Fig. 5 and the OMP algorithm is used at step S570 shown in Fig. 5. The number of prospective path candidates may beset as ^^ = 150 at step S520 and the number of estimated paths of the ^-th UE ^^^ = 3,∀^ at step S570. Fig. 6 shows CDF of NMSE between the actual channel and estimated channel atSNR = 14 dB and ^^ = 25. Fig. 7 shows NMSE between the actual channel andestimated channel versus SNR at ^^ = 25. As shown in Fig. 6 and Fig. 7, channelestimation error is evaluated as NMSE, which is defined as: ^ (16) where E[⋅] means the expectation with respect to the AoAs, distances, and path gains. The signal-to-noise ratio (SNR) is defined as: SNR = ^^‖^^‖^^^^^‖^‖^^^(17) As benchmarks, the classical channel estimation algorithms, least square (LS) and conventional near-field channel estimation algorithm, P-SOMP [2], are employed. Fig. 6 shows the cumulative distribution function (CDF) of NMSE(^) at SNR =14 dB and ^^ = 25. As shown in Fig. 6, the proposed method outperforms in NMSEperformance compared to the conventional methods. Evaluated at the CDF 50% value, the proposed method achieves an improvement by about 10.8 dB and 6.7 dB comparedto LS and P-SOMP. Fig. 7 shows the NMSE(^) versus SNR at ^^ = 25. As shown in Fig.7, it is can also confirmed that the proposed method outperforms the conventional methods in all SNR regions. With the embodiments described above, channel estimation accuracy can be improved. Actually, the disclosed solution proves to be superior to the state-of-the-art methods in terms of the NMSE performance. Further, RS overhead reduction can be achieved by utilizing the sparsity in the AoA-distance domain (or the polar domain). In other words, the disclosed solution demonstrates its capability to reduce the RS overhead in multiuser uplink channels with a reasonable channel estimation performance. Further, the disclosed solution is widely applicable in various scenarios. For example, the disclosed solution works not only with orthogonal pilots but also with non- orthogonal pilots and / or under pilot contamination. Therefore, the method is applicable to not only conventional use cases with orthogonal pilots or no pilot contamination but also situations such as dense networks with severe pilot contamination and / or non- orthogonal multiple access. Furthermore, the method is applicable to not only the near field region, but also the far field region, resulting in a more generally applicable solution than other channel estimation solutions. Fig. 8 is a flow chart illustrating an exemplary method 800 for channel estimation for one or more transmitting nodes according to an embodiment of the present disclosure. The method 800 may be performed at a receiving node (e.g., the gNB 105 or the UE 100). The method 800 may comprise steps S810, S820, and S830. However, the present disclosure is not limited thereto. In some other embodiments, the method 800 may comprise more steps, less steps, different steps, or any combination thereof. Further the steps of the method 800 may be performed in a different order than that described herein when multiple steps are involved. Further, in some embodiments, a step in the method 800 may be split into multiple sub-steps and performed by different entities, and / or multiple steps in the method 800 may be combined into a single step. The method 800 may begin at step S810 where the receiving node may determine a first number of array response candidates for the one or more transmitting nodes. At step S820, the receiving node may determine a second number of array responses for each of the one or more transmitting nodes based on at least the first number of array response candidates. At step S830, the receiving node may determine a channel estimate for each of the one or more transmitting nodes based on at least the second number of array responses associated with the corresponding transmitting node. In some embodiments, the receiving node may be a network node, and the one or more transmitting nodes may be one or more User Equipments (UEs). In some embodiments, one or more radio signals transmitted from the one or more transmitting nodes may be received by the receiving node via an antenna array. In some embodiments, the one or more transmitting nodes may be located in the near field region associated with the antenna array. In some embodiments, before the step of determining the first number of array response candidates, the method 800 may further comprise: identifying the one or more transmitting nodes for channel estimation; and performing one or more measurements on one or more pilot signals transmitted from the one or more transmitting nodes. In some embodiments, the one or more pilot signals may be associated with one or more pilot sequences, respectively. In some embodiments, the one or more pilot sequences may be non-orthogonal to each other. In some embodiments, before the step of determining the first number of array response candidates, the method 800 may further comprise: generating a set of a third number of array response candidates comprising the first number of array response candidates. In some embodiments, each of the array response candidates in the set may correspond to a path that is defined in the Angle of Arrival (AoA) and distance domain. In some embodiments, the set of the array response candidates may be defined as: where ^^^^^, ^^ is an ^-by-^^^^ complex matrix for the set of the array responsecandidates, ^ is the number of antennas in the antenna array, ^^is the total number of unique distances defined for all the paths, and ^^is the total number of unique AoAsdefined for all the paths, where is an ^-by-1 complex vector corresponding to apath having an AoA of ^^^ and a distance of ^^̃ with respect to the antenna array for all^ ∈ {1, … , ^^} and ^ ∈ {1, … , ^^}.In some embodiments, the unique AoAs defined for all the paths may beuniformly distributed in a predetermined interval. In some embodiments, ^^^ may bedetermined as: where ^^^^^ and ^^^^^ may be the predetermined minimum and maximum valuesof the AoAs, respectively. In some embodiments, the unique AoAs defined for all the paths may have their sine values uniformly distributed in a predetermined interval. In some embodiments, ^^^may be determined such that: where sin ^^^^^ and sin ^^^^^ may be the predetermined minimum and maximumsine values of the AoAs, respectively. In some embodiments, the ^^^ element of the complex vector may bedefined as: where ^^ may b ^^ e the ^ element of the complex vector ^ maybe the imaginary unit, ^ may be the wavelength, may be the distance between the ^^^antenna element in the antenna array and a sampled point for which acorresponding path has the AoA of ^^^ and the distance of ^^̃. In some embodiments, ^(^)^̃,^may be defined as: where ^(^)may be the coordinate of the ^-th antenna element and sin(∙)may be the sine function. In some embodiments, the antenna array may be a uniform linear array (ULA). In some embodiments, ^(^)may be defined as: where ^ may be the antenna spacing of the antenna array. In some embodiments, before the step of determining the first number of array response candidates, the method 800 may further comprise: determining the first number as one of: a number of total paths associated with the one or more transmitting nodes when the number of total paths associated with the one or more transmitting nodes is known; and an estimated number of total paths associated with the one or more transmitting nodes when the number of total paths associated with the one or more transmitting nodes is unknown. In some embodiments, the step of determining the first number of array response candidates may comprise: solving the following problem: minimize^ ^ subject to ^^^^^,^= ^^where ‖∙‖^may be the Frobenius norm operator, ^^^^^,^may be the number of non-zero rows of ^^, ^ may be an ^-by-^ complex matrix corresponding to pilot signals received by the receiving node from the one or more transmitting nodes, ^ may be the number of symbols in each of corresponding pilot signals, ^^may be an ^^^^-by-^complex matrix, and ^^may be the first number.In some embodiments, the first number of array response candidates may be defined as: where ^^^^^, ^^ may be an ^-by-^^ complex matrix for the first number of arrayresponse candidates,where ^ may be an ^-by-1 complex vector in the ^^ ^ which maycorrespond to a path that has an AoA of ^^^ and a distance of ^^̌, for all ^ ∈ ^1, … , andthat has a corresponding non-zero row in ^^.In some embodiments, before the step of determining the second number of array responses, the method 800 may further comprise: vectorizing one or more pilot signals received by the receiving node from the one or more transmitting nodes. In some embodiments, the one or more received pilot signals that are vectorized may be defined as: ^= vec(^)where ^ may be a ^^-by-1 complex vector corresponding to the vectorized received pilot signals, ^ may be an ^-by-^ complex matrix corresponding to one or more received pilot signals, and vec(∙) may be the vectorizing operator. In some embodiments, before the step of determining the second number of array responses, the method 800 may further comprise: calculating a set of transmitting node specific codewords for each of the one or more transmitting nodes based on at least the one or more pilot signals received by the receiving node from the one or more transmitting nodes and the first number of array response candidates. In some embodiments, a set of transmitting node specific codewords for a transmitting node may be calculated as: may be an ^^-by-^^complex matrix corresponding to the set oftransmitting node specific codewords for the ^^^transmitting node, ^ may be the number of the one or more transmitting nodes, ^^may be a ^-by-1 complex vectorcorresponding to the pilot signal transmitted by the ^^^ transmitting node, maybe an ^-by-^^complex matrix for the first number of array response candidates, and ⊗ may be the Kronecker product operator. In some embodiments, before the step of determining the second number of array responses, the method 800 may further comprise: determining the second number for each of the one or more transmitting nodes as one of: a number of total paths associated with the corresponding transmitting node when the number of total paths associated with the corresponding transmitting node is known; and an estimated number of total paths associated with the corresponding transmitting node when the number of total paths associated with the corresponding transmitting node is unknown. In some embodiments, the step of determining the second number of array responses may comprise: solving the following problem: subject to ‖^^‖^ = ^^^ ∀^ ∈ {1, 2, … , ^}where‖∙‖^ may be the Frobenius norm operator,‖^^‖^ may be the L0 norm of^^, ^ may be a ^^-by-1 complex vector corresponding to the vectorized received pilotsignals, ^ = may be an ^^-by-^^^ complex matrix for all the codewordsassociated with the one or more transmitting nodes, ^ may be the number of the one or more transmitting nodes, ^^^may be the number of paths that are associated withthe ^^^ transmitting node, ^ = [^^ ^ ^^, ^^, … , ^^ ]^ may be an ^^^-by-1 complex vectorcorresponding to path gains, and ^^may be an ^^-by-1 complex vector corresponding tothe path gain for the ^^^ path that has an AoA of ^^^ and a distance of ^^̌ for all ^ ∈^1, … , ^^^.In some embodiments, the non-zero rows of ^^ may form an ^^^-by-1 complexvector ^^defined as: where ^^^,^may be a complex path gain for the ^^^path from the ^^^transmittingnode that has an AoA of ^^^ and a distance of ^^̂ for all ^ ∈ ^1, … , ^^^^,where[∙]^may be a matrix transpose operator.In some embodiments, the second number of array response candidates may be defined as: may be an ^-by-^^^ complex matrix for the second number ofarray response candidates, where may be an ^-by-1 complex vector in the ^^^^^, ^^, which maycorrespond to the ^^^path from the ^^^transmitting node that has an AoA of ^^^and adistance of ^^̂, for all In some embodiments, the channel estimate for the ^^^transmitting node may be determined as:^ ^ … , ^}where ^^^ may be an ^-by-1 complex vector for channel estimate for the ^^^transmitting node. Fig. 9 schematically shows an embodiment of an arrangement 900 which may be used in a receiving node according to an embodiment of the present disclosure. Comprised in the arrangement 900 are a processing unit 906, e.g., with a Digital Signal Processor (DSP) or a Central Processing Unit (CPU). The processing unit 906 may be a single unit or a plurality of units to perform different actions of procedures described herein. The arrangement 900 may also comprise an input unit 902 for receiving signals from other entities, and an output unit 904 for providing signal(s) to other entities. The input unit 902 and the output unit 904 may be arranged as an integrated entity or as separate entities. Furthermore, the arrangement 900 may comprise at least one computer program product 908 in the form of a non-volatile or volatile memory, e.g., an Electrically Erasable Programmable Read-Only Memory (EEPROM), a flash memory and / or a hard drive. The computer program product 908 comprises a computer program 910, which comprises code / computer readable instructions, which when executed by the processing unit 906 in the arrangement 900 causes the arrangement 900 and / or the communication device in which it is comprised to perform the actions, e.g., of the procedure described earlier in conjunction with Fig. 4, Fig. 5, and Fig. 8 or any other variant. The computer program 910 may be configured as a computer program code structured in computer program modules 910A, 910B, and 910C. Hence, in an exemplifying embodiment when the arrangement 900 is used in a receiving node for channel estimation for one or more transmitting nodes, the code in the computer program of the arrangement 900 includes: a module 910A configured to determine a first number of array response candidates for the one or more transmitting nodes; a module 910B configured to determine a second number of array responses for each of the one or more transmitting nodes based on at least the first number of array response candidates; and a module 910C configured to determine a channel estimate for each of the one or more transmitting nodes based on at least the second number of array responses associated with the corresponding transmitting node. The computer program modules could essentially perform the actions of the flow illustrated in Fig. 4, Fig. 5, and Fig. 8, to emulate the receiving node. In other words, when the different computer program modules are executed in the processing unit 906, they may correspond to different modules in the receiving node. Although the code means in the embodiments disclosed above in conjunction with Fig. 9 are implemented as computer program modules which when executed in the processing unit causes the arrangement to perform the actions described above in conjunction with the figures mentioned above, at least one of the code means may in alternative embodiments be implemented at least partly as hardware circuits. The processor may be a single CPU (Central processing unit), but could also comprise two or more processing units. For example, the processor may include general purpose microprocessors; instruction set processors and / or related chips sets and / or special purpose microprocessors such as Application Specific Integrated Circuit (ASICs). The processor may also comprise board memory for caching purposes. The computer program may be carried by a computer program product connected to the processor. The computer program product may comprise a computer readable medium on which the computer program is stored. For example, the computer program product may be a flash memory, a Random-access memory (RAM), a Read-Only Memory (ROM), or an EEPROM, and the computer program modules described above could in alternative embodiments be distributed on different computer program products in the form of memories within the receiving node. Correspondingly to the method 800 as described above, an exemplary receiving node for channel estimation for one or more transmitting nodes is provided. Fig. 10 is a block diagram of a receiving node 1000 according to an embodiment of the present disclosure. The receiving node 1000 may be, e.g., the gNB 105 and / or the UE 100 in some embodiments. The receiving node 1000 may be configured to perform the method 800 as described above in connection with Fig. 8. As shown in Fig. 10, the receiving node 1000 may comprise: a first determining module 1010 configured to determine a first number of array response candidates for the one or more transmitting nodes; a second determining module 1020 configured to determine a second number of array responses for each of the one or more transmitting nodes based on at least the first number of array response candidates; and a third determining module 1030 configured to determine a channel estimate for each of the one or more transmitting nodes based on at least the second number of array responses associated with the corresponding transmitting node. The above modules 1010, 1020, and 1030 may be implemented as a pure hardware solution or as a combination of software and hardware, e.g., by one or more of: a processor or a micro-processor and adequate software and memory for storing of the software, a Programmable Logic Device (PLD) or other electronic component(s) or processing circuitry configured to perform the actions described above, and illustrated, e.g., in Fig. 8. Further, the receiving node 1000 may comprise one or more further modules, each of which may perform any of the steps of the method 800 described with reference to Fig. 8. Fig. 11 shows an example of a communication system QQ100 in accordance with some embodiments. In the example, the communication system QQ100 includes a telecommunication network QQ102 that includes an access network QQ104, such as a radio access network (RAN), and a core network QQ106, which includes one or more core network nodes QQ108. The access network QQ104 includes one or more access network nodes, such as network nodes QQ110a and QQ110b (one or more of which may be generally referred to as network nodes QQ110), or any other similar 3rdGeneration Partnership Project (3GPP) access node or non-3GPP access point. The network nodes QQ110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs QQ112a, QQ112b, QQ112c, and QQ112d (one or more of which may be generally referred to as UEs QQ112) to the core network QQ106 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 QQ100 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 QQ100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system. The UEs QQ112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes QQ110 and other communication devices. Similarly, the network nodes QQ110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs QQ112 and / or with other network nodes or equipment in the telecommunication network QQ102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network QQ102. In the depicted example, the core network QQ106 connects the network nodes QQ110 to one or more hosts, such as host QQ116. 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 QQ106 includes one more core network nodes (e.g., core network node QQ108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node QQ108. Example core network nodes include 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), and / or a User Plane Function (UPF). The host QQ116 may be under the ownership or control of a service provider other than an operator or provider of the access network QQ104 and / or the telecommunication network QQ102, and may be operated by the service provider or on behalf of the service provider. The host QQ116 may host a variety of applications to provide one or more service. 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. As a whole, the communication system QQ100 of Fig. 11 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 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 (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox. In some examples, the telecommunication network QQ102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network QQ102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network QQ102. For example, the telecommunications network QQ102 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 IoT services to yet further UEs. In some examples, the UEs QQ112 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 QQ104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network QQ104. 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). In the example, the hub QQ114 communicates with the access network QQ104 to facilitate indirect communication between one or more UEs (e.g., UE QQ112c and / or QQ112d) and network nodes (e.g., network node QQ110b). In some examples, the hub QQ114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub QQ114 may be a broadband router enabling access to the core network QQ106 for the UEs. As another example, the hub QQ114 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 QQ110, or by executable code, script, process, or other instructions in the hub QQ114. As another example, the hub QQ114 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 QQ114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub QQ114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub QQ114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub QQ114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy IoT devices. The hub QQ114 may have a constant / persistent or intermittent connection to the network node QQ110b. The hub QQ114 may also allow for a different communication scheme and / or schedule between the hub QQ114 and UEs (e.g., UE QQ112c and / or QQ112d), and between the hub QQ114 and the core network QQ106. In other examples, the hub QQ114 is connected to the core network QQ106 and / or one or more UEs via a wired connection. Moreover, the hub QQ114 may be configured to connect to an M2M service provider over the access network QQ104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes QQ110 while still connected via the hub QQ114 via a wired or wireless connection. In some embodiments, the hub QQ114 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 QQ110b. In other embodiments, the hub QQ114 may be a non-dedicated hub – that is, a device which is capable of operating to route communications between the UEs and network node QQ110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels. Fig. 12 shows a UE QQ200 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop- embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE. A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter). The UE QQ200 includes processing circuitry QQ202 that is operatively coupled via a bus QQ204 to an input / output interface QQ206, a power source QQ208, a memory QQ210, a communication interface QQ212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Fig. 12. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc. The processing circuitry QQ202 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 in the memory QQ210. The processing circuitry QQ202 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 QQ202 may include multiple central processing units (CPUs). In the example, the input / output interface QQ206 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 the UE QQ200. 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. In some embodiments, the power source QQ208 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. The power source QQ208 may further include power circuitry for delivering power from the power source QQ208 itself, and / or an external power source, to the various parts of the UE QQ200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source QQ208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source QQ208 to make the power suitable for the respective components of the UE QQ200 to which power is supplied. The memory QQ210 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 memory QQ210 includes one or more application programs QQ214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data QQ216. The memory QQ210 may store, for use by the UE QQ200, any of a variety of various operating systems or combinations of operating systems. The memory QQ210 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 memory QQ210 may allow the UE QQ200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory QQ210, which may be or comprise a device-readable storage medium. The processing circuitry QQ202 may be configured to communicate with an access network or other network using the communication interface QQ212. The communication interface QQ212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna QQ222. The communication interface QQ212 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 (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter QQ218 and / or a receiver QQ220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter QQ218 and receiver QQ220 may be coupled to one or more antennas (e.g., antenna QQ222) and may share circuit components, software or firmware, or alternatively be implemented separately. In the illustrated embodiment, communication functions of the communication interface QQ212 may include cellular communication, Wi-Fi communication, 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 in 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 / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth. Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface QQ212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The 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). As another example, a UE 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, the UE 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. A UE, when in the form of an Internet of Things (IoT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non- limiting examples of such an IoT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an IoT device comprises circuitry and / or software in dependence of the intended application of the IoT device in addition to other components as described in relation to the UE QQ200 shown in Fig. 12. As yet another specific example, in an IoT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation. In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators. Fig. 13 shows a network node QQ300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)). Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS). Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi- cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs). The network node QQ300 includes a processing circuitry QQ302, a memory QQ304, a communication interface QQ306, and a power source QQ308. The network node QQ300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node QQ300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node QQ300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory QQ304 for different RATs) and some components may be reused (e.g., a same antenna QQ310 may be shared by different RATs). The network node QQ300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node QQ300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node QQ300. The processing circuitry QQ302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node QQ300 components, such as the memory QQ304, to provide network node QQ300 functionality. In some embodiments, the processing circuitry QQ302 includes a system on a chip (SOC). In some embodiments, the processing circuitry QQ302 includes one or more of radio frequency (RF) transceiver circuitry QQ312 and baseband processing circuitry QQ314. In some embodiments, the radio frequency (RF) transceiver circuitry QQ312 and the baseband processing circuitry QQ314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry QQ312 and baseband processing circuitry QQ314 may be on the same chip or set of chips, boards, or units. The memory QQ304 may comprise any form of volatile or non-volatile computer- readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device- readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry QQ302. The memory QQ304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry QQ302 and utilized by the network node QQ300. The memory QQ304 may be used to store any calculations made by the processing circuitry QQ302 and / or any data received via the communication interface QQ306. In some embodiments, the processing circuitry QQ302 and memory QQ304 is integrated. The communication interface QQ306 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface QQ306 comprises port(s) / terminal(s) QQ316 to send and receive data, for example to and from a network over a wired connection. The communication interface QQ306 also includes radio front-end circuitry QQ318 that may be coupled to, or in certain embodiments a part of, the antenna QQ310. Radio front-end circuitry QQ318 comprises filters QQ320 and amplifiers QQ322. The radio front-end circuitry QQ318 may be connected to an antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry may be configured to condition signals communicated between antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry QQ318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry QQ318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters QQ320 and / or amplifiers QQ322. The radio signal may then be transmitted via the antenna QQ310. Similarly, when receiving data, the antenna QQ310 may collect radio signals which are then converted into digital data by the radio front-end circuitry QQ318. The digital data may be passed to the processing circuitry QQ302. In other embodiments, the communication interface may comprise different components and / or different combinations of components. In certain alternative embodiments, the network node QQ300 does not include separate radio front-end circuitry QQ318, instead, the processing circuitry QQ302 includes radio front-end circuitry and is connected to the antenna QQ310. Similarly, in some embodiments, all or some of the RF transceiver circuitry QQ312 is part of the communication interface QQ306. In still other embodiments, the communication interface QQ306 includes one or more ports or terminals QQ316, the radio front-end circuitry QQ318, and the RF transceiver circuitry QQ312, as part of a radio unit (not shown), and the communication interface QQ306 communicates with the baseband processing circuitry QQ314, which is part of a digital unit (not shown). The antenna QQ310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna QQ310 may be coupled to the radio front-end circuitry QQ318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna QQ310 is separate from the network node QQ300 and connectable to the network node QQ300 through an interface or port. The antenna QQ310, communication interface QQ306, and / or the processing circuitry QQ302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna QQ310, the communication interface QQ306, and / or the processing circuitry QQ302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment. The power source QQ308 provides power to the various components of network node QQ300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source QQ308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node QQ300 with power for performing the functionality described herein. For example, the network node QQ300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source QQ308. As a further example, the power source QQ308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail. Embodiments of the network node QQ300 may include additional components beyond those shown in Fig. 13 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node QQ300 may include user interface equipment to allow input of information into the network node QQ300 and to allow output of information from the network node QQ300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node QQ300. Fig. 14 is a block diagram of a host QQ400, which may be an embodiment of the host QQ116 of Fig. 11, in accordance with various aspects described herein. As used herein, the host QQ400 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host QQ400 may provide one or more services to one or more UEs. The host QQ400 includes processing circuitry QQ402 that is operatively coupled via a bus QQ404 to an input / output interface QQ406, a network interface QQ408, a power source QQ410, and a memory QQ412. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Fig. 12 and Fig. 13, such that the descriptions thereof are generally applicable to the corresponding components of host QQ400. The memory QQ412 may include one or more computer programs including one or more host application programs QQ414 and data QQ416, which may include user data, e.g., data generated by a UE for the host QQ400 or data generated by the host QQ400 for a UE. Embodiments of the host QQ400 may utilize only a subset or all of the components shown. The host application programs QQ414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs QQ414 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host QQ400 may select and / or indicate a different host for over-the-top services for a UE. The host application programs QQ414 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc. Fig. 15 is a block diagram illustrating a virtualization environment QQ500 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments QQ500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. Applications QQ502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment QQ500 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein. Hardware QQ504 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers QQ506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs QQ508a and QQ508b (one or more of which may be generally referred to as VMs QQ508), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer QQ506 may present a virtual operating platform that appears like networking hardware to the VMs QQ508. The VMs QQ508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer QQ506. Different embodiments of the instance of a virtual appliance QQ502 may be implemented on one or more of VMs QQ508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment. In the context of NFV, a VM QQ508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non- virtualized machine. Each of the VMs QQ508, and that part of hardware QQ504 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs QQ508 on top of the hardware QQ504 and corresponds to the application QQ502. Hardware QQ504 may be implemented in a standalone network node with generic or specific components. Hardware QQ504 may implement some functions via virtualization. Alternatively, hardware QQ504 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration QQ510, which, among others, oversees lifecycle management of applications QQ502. In some embodiments, hardware QQ504 is coupled to one or more radio units that each includes one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system QQ512 which may alternatively be used for communication between hardware nodes and radio units. Fig. 16 shows a communication diagram of a host QQ602 communicating via a network node QQ604 with a UE QQ606 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE QQ112a of Fig. 11 and / or UE QQ200 of Fig. 12), network node (such as network node QQ110a of Fig. 11 and / or network node QQ300 of Fig. 13), and host (such as host QQ116 of Fig. 11 and / or host QQ400 of Fig. 14) discussed in the preceding paragraphs will now be described with reference to Fig. 16. Like host QQ400, embodiments of host QQ602 include hardware, such as a communication interface, processing circuitry, and memory. The host QQ602 also includes software, which is stored in or accessible by the host QQ602 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE QQ606 connecting via an over- the-top (OTT) connection QQ650 extending between the UE QQ606 and host QQ602. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection QQ650. The network node QQ604 includes hardware enabling it to communicate with the host QQ602 and UE QQ606. The connection QQ660 may be direct or pass through a core network (like core network QQ106 of Fig. 11) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet. The UE QQ606 includes hardware and software, which is stored in or accessible by UE QQ606 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE QQ606 with the support of the host QQ602. In the host QQ602, an executing host application may communicate with the executing client application via the OTT connection QQ650 terminating at the UE QQ606 and host QQ602. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection QQ650 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection QQ650. The OTT connection QQ650 may extend via a connection QQ660 between the host QQ602 and the network node QQ604 and via a wireless connection QQ670 between the network node QQ604 and the UE QQ606 to provide the connection between the host QQ602 and the UE QQ606. The connection QQ660 and wireless connection QQ670, over which the OTT connection QQ650 may be provided, have been drawn abstractly to illustrate the communication between the host QQ602 and the UE QQ606 via the network node QQ604, without explicit reference to any intermediary devices and the precise routing of messages via these devices. As an example of transmitting data via the OTT connection QQ650, in step QQ608, the host QQ602 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE QQ606. In other embodiments, the user data is associated with a UE QQ606 that shares data with the host QQ602 without explicit human interaction. In step QQ610, the host QQ602 initiates a transmission carrying the user data towards the UE QQ606. The host QQ602 may initiate the transmission responsive to a request transmitted by the UE QQ606. The request may be caused by human interaction with the UE QQ606 or by operation of the client application executing on the UE QQ606. The transmission may pass via the network node QQ604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step QQ612, the network node QQ604 transmits to the UE QQ606 the user data that was carried in the transmission that the host QQ602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step QQ614, the UE QQ606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE QQ606 associated with the host application executed by the host QQ602. In some examples, the UE QQ606 executes a client application which provides user data to the host QQ602. The user data may be provided in reaction or response to the data received from the host QQ602. Accordingly, in step QQ616, the UE QQ606 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE QQ606. Regardless of the specific manner in which the user data was provided, the UE QQ606 initiates, in step QQ618, transmission of the user data towards the host QQ602 via the network node QQ604. In step QQ620, in accordance with the teachings of the embodiments described throughout this disclosure, the network node QQ604 receives user data from the UE QQ606 and initiates transmission of the received user data towards the host QQ602. In step QQ622, the host QQ602 receives the user data carried in the transmission initiated by the UE QQ606. One or more of the various embodiments improve the performance of OTT services provided to the UE QQ606 using the OTT connection QQ650, in which the wireless connection QQ670 forms the last segment. More precisely, the teachings of these embodiments may improve the data rate, latency, power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, improved content resolution, better responsiveness, extended battery lifetime. In an example scenario, factory status information may be collected and analyzed by the host QQ602. As another example, the host QQ602 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host QQ602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host QQ602 may store surveillance video uploaded by a UE. As another example, the host QQ602 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host QQ602 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and / or transmitting data. In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection QQ650 between the host QQ602 and UE QQ606, in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host QQ602 and / or UE QQ606. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection QQ650 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection QQ650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node QQ604. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host QQ602. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection QQ650 while monitoring propagation times, errors, etc. 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. 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. The present disclosure is described above with reference to the embodiments thereof. However, those embodiments are provided just for illustrative purpose, rather than limiting the present disclosure. The scope of the disclosure is defined by the attached claims as well as equivalents thereof. Those skilled in the art can make various alternations and modifications without departing from the scope of the disclosure, which all fall into the scope of the disclosure. Abbreviation Explanation AoA Angle of Arrival BS Base Station LoS Line-of-Sight LS Least Square MIMO Multiple-Input-Multiple-Output NLoS Non-Line-of-Sight NMSE Normalized Mean-Squared Error OMP Orthogonal Matching Pursuit P-SOMP Polar-domain Simultaneous Orthogonal Matching Pursuit QAM Quadrature Amplitude Modulation QPSK Quadrature Phase Shift Keying SNR Signal-to-Noise Ratio SOMP Simultaneous Orthogonal Matching Pursuit UE User Equipment ULA Uniform Linear Array XL-MIMO eXtremely Large-scale Multiple-Input Multiple-Output

Claims

Claims What is claimed is:

1. A method (800) at a receiving node (105, 100) for channel estimation for one or more transmitting nodes (100, 105), the method (800) comprising: determining (S530, S810) a first number of array response candidates for the one or more transmitting nodes (100, 105); determining (S570, S820) a second number of array responses for each of the one or more transmitting nodes (100, 105) based on at least the first number of array response candidates; and determining (S580, S830) a channel estimate for each of the one or more transmitting nodes (100, 105) based on at least the second number of array responses associated with the corresponding transmitting node (100, 105).

2. The method (800) of claim 1, wherein the receiving node (105, 100) is a network node (105), and the one or more transmitting nodes (100, 105) are one or more User Equipments (UEs) (100).

3. The method (800) of claim 1 or 2, wherein one or more radio signals transmitted from the one or more transmitting nodes (100, 105) are received by the receiving node (105, 100) via an antenna array (200), wherein the one or more transmitting nodes (100, 105) are located in the near field region associated with the antenna array (200).

4. The method (800) of any of claims 1 to 3, wherein before the step of determining (S530, S810) the first number of array response candidates, the method (800) further comprises: identifying the one or more transmitting nodes (100, 105) for channel estimation; and performing one or more measurements on one or more pilot signals transmitted from the one or more transmitting nodes (100, 105).

5. The method (800) of claim 4, wherein the one or more pilot signals are associated with one or more pilot sequences, respectively, wherein the one or more pilot sequences are non-orthogonal to each other.

6. The method (800) of any of claims 1 to 5, wherein before the step of determining (S530, S810) the first number of array response candidates, the method (800) further comprises: generating (S510) a set of a third number of array response candidates comprising the first number of array response candidates.

7. The method (800) of claim 6, wherein each of the array response candidates in the set corresponds to a path that is defined in the Angle of Arrival (AoA) and distance domain.

8. The method (800) of claim 6 or 7, wherein the set of the array response candidates is defined as:where ^^^^^, ^^ is an ^-by-^^^^ complex matrix for the set of the array responsecandidates, ^ is the number of antennas in the antenna array (200), ^^is the total number of unique distances defined for all the paths, and ^^is the total number of unique AoAs defined for all the paths, whereis an ^-by-1 complex vector corresponding to a path having anAoA of ^^^ and a distance of ^^̃ with respect to the antenna array (200) for all ^ ∈{1, … , ^^} and ^ ∈ {1, … , ^^}.

9. The method (800) of claim 8, wherein the unique AoAs defined for all the paths are uniformly distributed in a predetermined interval.

10. The method (800) of claim 8 or 9, wherein ^^^is determined as:where ^^^^^ and ^^^^^ are the predetermined minimum and maximum values ofthe AoAs, respectively.

11. The method (800) of claim 8, wherein the unique AoAs defined for all the paths have their sine values uniformly distributed in a predetermined interval.

12. The method (800) of claim 8 or 9, wherein ^^^ is determined such that:where sin ^^^^^ and sin ^^^^^ are the predetermined minimum and maximum sinevalues of the AoAs, respectively.

13. The method (800) of any of claims 8 to 12, wherein the ^^^element of thecomplex vectoris defined as:where ^^is the ^^^ element of the complex vector^ is theimaginary unit, ^ is the wavelength, is the distance bet^^ween the ^ antenna element in the antenna array (200) and a sampled point for which a corresponding pathhas the AoA of ^^^ and the distance of ^^̃,whereindefined as:where ^(^)is the coordinate of the ^-th antenna element and sin(∙) is the sine function.

14. The method (800) of claim 13, wherein the antenna array (200) is a uniform linear array (ULA), wherein ^(^)is defined as:where ^ is the antenna spacing of the antenna array (200).

15. The method (800) of any of claims 1 to 14, wherein before the step of determining (S530, S810) the first number of array response candidates, the method (800) further comprises: determining (S520) the first number as one of: - a number of total paths associated with the one or more transmitting nodes (100, 105) when the number of total paths associated with the one or more transmitting nodes (100, 105) is known; and - an estimated number of total paths associated with the one or more transmitting nodes (100, 105) when the number of total paths associated with the one or more transmitting nodes (100, 105) is unknown.

16. The method (800) of any of claims 1 to 15, wherein the step of determining (S530, S810) the first number of array response candidates comprises: solving the following problem: minimize ^^ − ^^^^ ^^^^ ^, ^^^^^subject to ^^^^^,^= ^^where‖∙‖^ is the Frobenius norm operator, ^^^^^,^ is the number of non-zerorows of ^^, ^ is an ^-by-^ complex matrix corresponding to pilot signals received by the receiving node (105, 100) from the one or more transmitting nodes (100, 105), ^ is thenumber of symbols in each of corresponding pilot signals, ^^is an ^^^^-by-^ complexmatrix, and ^^is the first number.

17. The method (800) of claim 16, wherein the first number of array response candidates are defined as:where ^^^^^, ^^ is an ^-by-^^ complex matrix for the first number of array responsecandidates,is an ^-by-1 complex vector in the ^^^^^, ^^, which corresponds toa path that has an AoA of ^^^ and a distance of ^^̌, for all ^and that has acorresponding non-zero row in ^^.

18. The method (800) of any of claims 1 to 17, wherein before the step of determining (S570, S820) the second number of array responses, the method (800) further comprises: vectorizing (S540) one or more pilot signals received by the receiving node (105, 100) from the one or more transmitting nodes (100, 105).

19. The method (800) of claim 18, wherein the one or more received pilot signals that are vectorized is defined as: ^= vec(^)where ^ is a ^^-by-1 complex vector corresponding to the vectorized received pilot signals, ^ is an ^-by-^ complex matrix corresponding to one or more received pilotsignals, and vec(∙)is the vectorizing operator.

20. The method (800) of any of claims 1 to 19, wherein before the step of determining (S570, S820) the second number of array responses, the method (800) further comprises: calculating (S550) a set of transmitting node (100, 105) specific codewords for each of the one or more transmitting nodes (100, 105) based on at least the one or more pilot signals received by the receiving node (105, 100) from the one or more transmitting nodes (100, 105) and the first number of array response candidates.

21. The method (800) of claim 20, wherein a set of transmitting node (100, 105) specific codewords for a transmitting node (100, 105) is calculated as:is an ^^-by-^^complex matrix corresponding to the set of transmitting node (100, 105) specific codewords for the ^^^transmitting node (100, 105), ^ is the number of the one or more transmitting nodes (100, 105), ^^is a ^-by-1 complex vector corresponding to the pilot signal transmitted by the ^^^transmitting node (100,105), ^^^^^, ^^ is an ^-by-^^ complex matrix for the first number of array responsecandidates, and ⊗ is the Kronecker product operator.

22. The method (800) of any of claims 1 to 21, wherein before the step of determining (S570, S820) the second number of array responses, the method (800) further comprises: determining (S560) the second number for each of the one or more transmitting nodes (100, 105) as one of: - a number of total paths associated with the corresponding transmitting node (100, 105) when the number of total paths associated with the corresponding transmitting node (100, 105) is known; and - an estimated number of total paths associated with the corresponding transmitting node (100, 105) when the number of total paths associated with the corresponding transmitting node (100, 105) is unknown.

23. The method (800) of any of claims 1 to 22, wherein the step of determining (S570, S820) the second number of array responses comprises: solving the following problem: subjectwhere ‖∙‖^is the Frobenius norm operator, ‖^^‖^is the L0 norm of ^^, ^ is a^^-by-1 complex vector corresponding to the vectorized received pilot signals, ^ =is an ^^-by-^^^ complex matrix for all the codewords associated withthe one or more transmitting nodes (100, 105), ^ is the number of the one or more transmitting nodes (100, 105), ^^^is the number of paths that are associated with the^^^ transmitting node (100, 105), ^ = [^^^, ^^ ^^, … , ^^ ]^ is an ^^^-by-1 complex vectorcorresponding to path gains, and ^^is an ^^-by-1 complex vector corresponding to thepath gain for the ^^^ path that has an AoA of ^^ and a distance of ^^̌ for all ^ ∈ ^1, … ,24. The method (800) of claim 23, wherein the non-zero rows of ^^form an ^^^-by-1 complex vector ^^defined as:where ^^^,^is a complex path gain for the ^^^path from the ^^^transmitting node(100, 105) that has an AoA of ^^^ and a distance of ^^̂ for all ^ ∈ ^1, … , ^^^^,where [∙]^is a matrix transpose operator.

25. The method (800) of claim 24, wherein the second number of array response candidates are defined as:where ^^^^^^, ^^^ is an ^-by-^^^ complex matrix for the second number of arrayresponse candidates, where ^^^^^,^, ^^̂,^^ is an ^-by-1 complex vector in the ^^^^^, ^^, which correspondsto the ^^^path from the ^^^transmitting node (100, 105) that has an AoA of ^^^ and adistance of ^^̂, for all ^ ∈ ^1, … , ^^^^.

26. The method (800) of claim 25, wherein the channel estimate for the ^^^transmitting node (100, 105) is determined as:where ^^^ is an ^-by-1 complex vector for channel estimate for the ^^^transmitting node (100, 105).

27. A receiving node (105, 100, 900) for channel estimation for one or more transmitting nodes (100, 105), the receiving node (105, 100, 900) comprising: a processor (906); a memory (908) storing instructions which, when executed by the processor (906), cause the receiving node (105, 100, 900) to: determine a first number of array response candidates for the one or more transmitting nodes (100, 105); determine a second number of array responses for each of the one or more transmitting nodes (100, 105) based on at least the first number of array response candidates; and determine a channel estimate for each of the one or more transmitting nodes (100, 105) based on at least the second number of array responses associated with the corresponding transmitting node (100, 105).

28. The receiving node (105, 100, 900) of claim 27, wherein the instructions, when executed by the processor (906), further cause the receiving node (105, 100, 900) to perform the method (800) of any of claims 2 to 26.

29. A computer program (910) comprising instructions which, when executed by at least one processor (906), cause the at least one processor (906) to carry out the method (800) of any of claims 1 to 26.

30. A carrier (908) containing the computer program (910) of claim 29, wherein the carrier (908) is one of an electronic signal, optical signal, radio signal, or computer readable storage medium.

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