Electronic device, communication method, and computer program product

Abstract

The present disclosure relates to electronic devices, communication methods, and computer program products. An electronic device for a user equipment (UE) comprising a processor; and memory including computer program code, wherein the computer program code, when executed by the processor, causes the electronic device to perform operations comprising: receiving, at a plurality of time instances, a plurality of signals superimposed by beam training signals simultaneously transmitted by at least two base stations, wherein for each of the at least two base stations, the base station uses, at the plurality of time instances, a plurality of different combined beams formed by combining a set of spatially-sparse beams through different sets of combining coefficients; and determining, based on the plurality of signals, a beam response of a respective set of beams of each of the at least two base stations.

Description

Technical Field

[0001] This disclosure generally relates to the field of wireless communications, and more specifically, to electronic devices, communication methods, and computer program products for beam management based on compressed sensing (CS). Background Technology

[0002] Current wireless communication systems widely employ multi-antenna technologies such as Multiple-Input Multiple-Output (MIMO), where both base stations and terminal devices have multiple antennas. Beamforming can be used to form narrow, directional spatial beams to provide strong power coverage in specific directions, thus mitigating the significant path loss present in high-frequency channels. Beam sets with different transmission directions are used to achieve cell coverage. To improve the reception quality of the beam signal, base stations and terminal devices need to select beams that best match the direction of the wireless channel. Traditionally, base stations and terminal devices can select and manage beams through beam training.

[0003] There remains a need to provide improved beam management mechanisms to enhance their applicability and efficiency. Summary of the Invention

[0004] This disclosure provides several aspects. The above-mentioned needs can be met by applying one or more aspects of this disclosure.

[0005] A brief overview of this disclosure is given below to provide a basic understanding of some aspects of it. However, it should be understood that this overview is not an exhaustive summary of this disclosure. It is not intended to identify key or essential parts of this disclosure, nor is it intended to limit the scope of this disclosure. Its purpose is merely to present certain concepts of this disclosure in a simplified form as a prelude to the more detailed description that follows.

[0006] According to one aspect of this disclosure, an electronic device for a user equipment (UE) is provided, comprising: a processor; and a memory including computer program code, wherein the computer program code, when executed by the processor, causes the electronic device to perform operations including: receiving at multiple times a plurality of signals superimposed from beam training signals simultaneously transmitted by at least two base stations, wherein for each of the at least two base stations, the base station uses a plurality of different combined beams formed by combining spatially sparse beam sets with different combination coefficients at the multiple times; and determining a beam response of a corresponding beam set of each of the at least two base stations based on the plurality of signals.

[0007] According to another aspect of this disclosure, an electronic device for a base station is provided, comprising: a processor; and a memory including computer program code, wherein the computer program code, when executed by the processor, causes the electronic device to perform operations including: forming a plurality of different combined beams by combining spatially sparse beam sets with different combination coefficients; and transmitting beam training signals to a user equipment (UE) at multiple times through the plurality of combined beams.

[0008] According to another aspect of this disclosure, it is provided to receive multiple signals superimposed from beam training signals simultaneously transmitted by at least two base stations at multiple times, wherein for each of the at least two base stations, the base station uses multiple different combined beams formed by combining spatially sparse beam sets through different combination coefficients at the multiple times; and to determine the beam response of the corresponding beam set of each of the at least two base stations based on the multiple signals.

[0009] According to another aspect of this disclosure, a method is provided, comprising: forming a plurality of different combined beams by combining spatially sparse beam sets with different combination coefficients; and transmitting beam training signals to a user equipment (UE) at multiple times through the plurality of combined beams.

[0010] According to another aspect of this disclosure, a computer program product comprising executable instructions is provided, which, when executed, implement any of the methods described above. Attached Figure Description

[0011] This disclosure can be better understood by referring to the detailed description given below in conjunction with the accompanying drawings, in which the same or similar reference numerals are used throughout the drawings to denote the same or similar elements. All the drawings, together with the following detailed description, are incorporated in and form a part of this specification, and are used to further illustrate embodiments of this disclosure and explain the principles and advantages of this disclosure. Wherein:

[0012] Figure 1 The traditional beam training process is illustrated schematically.

[0013] Figure 2 This is a schematic diagram illustrating beam training for multiple base stations;

[0014] Figure 3 A flowchart of beam training based on compressed sensing according to this disclosure is shown;

[0015] Figure 4 According to the schematic diagram of multi-base station cooperative beam training disclosed herein;

[0016] Figure 5Simulation results are shown for multi-base station cooperative beam training based on compressed sensing and multi-base station beam training based on exhaustive search.

[0017] Figure 6 The beam alignment of a single base station and multiple base stations is shown in the case of unobstructed LoS paths.

[0018] Figure 7 A flowchart of the beam fine-tuning process according to this disclosure is shown;

[0019] Figure 8 Simulation diagrams comparing beam tuning with and without this disclosure are shown;

[0020] Figure 9 This is a block diagram of an electronic device on the UE side according to this disclosure;

[0021] Figure 10 This is a flowchart of the method on the UE side according to this disclosure;

[0022] Figure 11 This is a block diagram of an electronic device on the base station side according to this disclosure;

[0023] Figure 12 This is a flowchart of a method for the base station side according to this disclosure;

[0024] Figure 13 An example block diagram of a computer that can be implemented as a user equipment or a base station device according to the present disclosure is shown;

[0025] Figure 14 A first example of an illustrative configuration of a base station according to this disclosure is shown;

[0026] Figure 15 A second example of an illustrative configuration of a base station according to this disclosure is shown;

[0027] Figure 16 An illustrative configuration example of a smartphone according to this disclosure is shown;

[0028] Figure 17 An illustrative configuration example of a car navigation device according to this disclosure is shown.

[0029] The features and aspects of this disclosure will become clear from the following detailed description taken in conjunction with the accompanying drawings. Detailed Implementation

[0030] Various exemplary embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The descriptions of the exemplary embodiments below are merely illustrative and are not intended to limit the scope of this disclosure or its application. For clarity and brevity, not all features of the embodiments are described in this specification. However, it should be noted that many implementation-specific settings can be made in implementing embodiments of this disclosure to meet, for example, those constraints related to the device and services, and these constraints may vary depending on the implementation.

[0031] Furthermore, it should be noted that, in order to avoid obscuring this disclosure with unnecessary details, some figures only show processing steps and / or device structures that are closely related to at least the technical content of this disclosure, while in other figures, existing processing steps and / or device structures are additionally shown for better understanding of this disclosure.

[0032] For ease of explanation, one or more aspects of this disclosure may be described below in the context of 5G New Radio (NR). However, it should be noted that this is not a limitation on the scope of this disclosure, and one or more aspects of this disclosure may also be applied to commonly used wireless communication systems such as 4G LTE / LTE-A, or various future wireless communication systems. The architectures, entities, functions, processes, etc., mentioned in the following description are not limited to those in NR communication systems, but can be found in other communication standards.

[0033] In wireless communication systems such as 4G LTE or 5G NR, base stations and terminal equipment (also known as "user equipment," hereinafter referred to as "UE") can employ technologies such as massive MIMO (Multiple-Input Multiple-Output). To support applications like MIMO, both base stations and UEs have numerous antennas, such as dozens, hundreds, or even thousands. These antennas are arranged in a specific configuration into one or more antenna arrays. An antenna array can consist of rows, columns, multiple rows, or multiple columns of antenna elements, forming an independently configurable transceiver unit (TXRU). By configuring the amplitude and / or phase parameters of the antenna elements that make up the TXRU, the antenna pattern of the TXRU can be adjusted. The electromagnetic waves emitted by all the antenna elements within the antenna array form a narrow beam pointing in a specific spatial direction, i.e., beamforming is achieved.

[0034] It should be noted that the term "base station" as used in this disclosure is an example of a network-side control device and has the full breadth of its usual meaning. For example, in addition to gNB and ng-eNB as specified in the 5G communication standard, depending on the scenario in which the technical solutions of this disclosure are applied, a "base station" can also be an eNB, a Transmit / Receive Point (TRP), a Remote Radio Header (RRH), a Radio Access Point (AP), a Roadside Unit (RSU), a drone control tower, or a communication device performing similar functions in an LTE communication system. Examples of base station applications will be described in detail below.

[0035] Furthermore, in this disclosure, the term "UE" has the full breadth of its usual meaning, encompassing various terminal devices or vehicle-mounted devices that communicate with a base station. As examples, a UE can be a terminal device or component thereof, such as a mobile phone, laptop, tablet, vehicle-mounted communication device, drone, etc. Application examples of UEs will be described in detail later.

[0036] Higher operating frequencies, such as millimeter wave bands, can lead to significant path loss. This path impairment can be compensated for by using massive MIMO systems with large-scale antenna arrays to generate highly directional beams. Beam training involves probing massive MIMO channels, such as millimeter wave communications, using transmit and receive beams to find the transmit-receive beam combination that maximizes the received signal energy. Specifically, the base station and UE need to select a transmit or receive beam from their available beams that best matches the channel direction; that is, at the transmitting end, the transmit beam is aligned with the channel departure angle (AOD), and at the receiving end, the receive beam is aligned with the channel arrival angle (AOA).

[0037] Specifically, in network-based collaborative sensing systems, achieving accurate target estimation requires matching transmit and receive beams among collaborative nodes. High-precision target perception typically involves two phases: target detection and target tracking. In the target detection phase, since the target's location is unknown, a full-domain beam scan is usually required; in the target tracking phase, beam tracking and beam switching are necessary.

[0038] Traditionally, base stations and UEs can select or switch beams through beam training. (See reference...) Figure 1 To briefly describe the beam training process in a wireless communication system. For example... Figure 1As shown in (a), the base station can send a set of beams with different directions to the UE using an exhaustive search (ES) method, also known as beam scanning. Each beam can transmit a corresponding downlink reference signal, such as a non-zero power CSI-RS (NZP-CSI-RS) resource or an SSB resource. The UE receives each base station beam through its receive beam and measures the beam signal, such as the reference signal received power (RSRP), reference signal received quality (RSRQ), and signal-to-interference-plus-noise ratio (SINR). The UE then reports the beam measurement results to the base station. Based on the reported beam measurement results, the base station can select the optimal beam from its beams for downlink transmission with the UE. Furthermore, the base station indicates the reference signal corresponding to the optimal beam to the UE, allowing the UE to determine the optimal receive beam corresponding to that reference signal during beam scanning. This method achieves alignment between the base station beams and the UE beams.

[0039] To save costs, a phased beam training scheme can be considered to reduce training overhead. For example... Figure 1 As shown in (b), the base station first performs a small number of wide beam searches, narrows the beam search range based on user feedback, and then performs a narrow beam search within the coverage area of ​​the selected wide beam to finally obtain the optimal narrow beam.

[0040] However, reducing the overhead of multi-user access and beam alignment remains a significant challenge. On one hand, traditional exhaustive search-based beam training schemes sequentially transmit candidate beams, allowing multiple UEs within the coverage area to share measurement pilots. The training overhead is directly proportional to the number of beams to be trained. As antenna size increases and the number of beams rises, the beam alignment speed of exhaustive search-based beam training schemes slows down. On the other hand, in staged beam training schemes, multiple UEs typically cannot share measurement pilots. Different users need to utilize Time Division Multiple Access (TDMA) for beam training. Therefore, this scheme only saves overhead when the number of UEs is small and requires multiple UE feedbacks, increasing latency.

[0041] Furthermore, in cooperative scenarios such as Co-multipoint cooperative transmission (CoMP), beam alignment between the UE and multiple base stations is typically performed independently. Figure 2 A schematic diagram of beam training for multiple base stations is shown. As illustrated, multiple base stations (e.g., RSU1, RSU2, RSU3, RSU4) are connected to the core network, such as the 5G core network (5GC), via wired connections such as fiber optic connections. It should be understood that, although Figure 2The base station is shown as a roadside unit (RSU), but the type and number of base stations are not limited to this. One or more base stations can cooperate to provide communication services for a UE (such as a vehicle-mounted device). Each base station independently determines the transceiver beam with the UE through beam training.

[0042] In the case of cooperation between, for example, two base stations, it is actually impossible to determine the beam of another base station (such as RSU2) based on the beam sequence number selected by one base station (such as RSU1). This is because only the angle of the target UE with respect to RSU1 is known, and the angle between the target UE and RSU2 cannot be directly calculated. Separately training the two base stations will result in suboptimal results in the multi-base station cooperation scenario because the coupling effect between the base stations is ignored. Existing exhaustive search and staged search need to consider combinations of multiple base station beams, which will undoubtedly increase the beam training overhead.

[0043] Therefore, it is necessary to improve the beam training mechanism to reduce the scanning and measurement required for beam training, thereby reducing the beam training overhead.

[0044] According to an embodiment of the present disclosure, a beam training method based on compressive sensing is provided. Compressive sensing is also known as compressive sampling, sparse sampling, or compressive sensing. As a new sampling theory, it exploits the sparse characteristics of a signal to obtain discrete samples of the signal with random sampling under conditions far less than the Nyquist sampling rate, and then perfectly reconstructs the signal through a non-linear reconstruction algorithm.

[0045] As described above, the compressive sensing theory mainly includes three core points. The applicability of compressive sensing in beam training is discussed in detail below.

[0046] The first core point is the sparsity of the signal. A sparse signal can be represented by a linear combination of a few eigenvectors. Specifically, it means that an n-dimensional real signal can be expanded into a k-sparse vector under a set of bases, that is, the number of non-zero elements of this vector is k, and k << n. The sparsity of the signal is a prerequisite for sparse reconstruction. In some actual scenarios, the signal is not directly sparse, and in this case, it is necessary to perform sparse representation of the signal. The so-called sparse representation is to transform the signal from the time domain to a domain that can exhibit its sparsity, such as the frequency domain, the spatial domain, etc. In the transform domain, the energy of the signal is concentrated on a small number of transform coefficients.

[0047] The wireless signals used for beam training often exhibit sparsity in the following aspects:

[0048] Time-domain sparsity: During the beam training process, the signal is only transmitted in specific time slots and no signal is transmitted in other time periods, thus presenting a sparse distribution on the time axis;

[0049] Frequency domain sparsity: The signal occupies only a portion of the frequency band in the frequency domain, rather than the entire bandwidth, which makes the signal exhibit sparsity characteristics on the frequency axis as well.

[0050] Spatial domain sparsity: Beam training signals are usually transmitted in a specific direction or region. Therefore, in the spatial domain, the signal has energy only in a specific direction or region, while it is almost zero in other directions or regions.

[0051] The second key point is the design of the observation matrix. Also known as the measurement matrix, the observation matrix plays a crucial role in compressed sensing, responsible for extracting key information from the raw signal for subsequent reconstruction. The core of observation matrix design lies in its ability to effectively extract information from the raw signal without introducing excessive redundancy. To design a good observation matrix, it's necessary to ensure that each row (or column, depending on the dimensions of the signal and measurement) covers the entire space as uniformly as possible. An ideal measurement matrix should satisfy two important conditions: first, it should be orthogonal (or approximately orthogonal) to the sparse basis, known as "incoherence"; second, it should possess a good "Restricted Isometry Property" (RIP) to ensure that the structure of all sparse signals is preserved.

[0052] The beam training problem can be abstracted as obtaining the beam response of a specific beam set of a base station from the received signal. A pre-designed observation matrix can be applied to the base station's beam set to achieve compressed sampling. Any observation matrix that satisfies the properties of incoherence and restricted isometry can be used, such as a random matrix following a Bernoulli distribution. For the base station, this compressed sampling process is equivalent to combining the beam signals in its beam set using the elements of the corresponding rows or columns of the observation matrix (hereinafter referred to as "combination coefficients"). To obtain multiple observation signals, the base station can perform multiple combinations, each time using different combination coefficients.

[0053] The final key point is the reconstruction algorithm. The reconstruction algorithm determines the accuracy and efficiency of recovering the original signal from the observed signal. The goal of the reconstruction algorithm is to solve for a sparse representation of the original signal based on the observed signal and the observation matrix using some optimization strategy. Common reconstruction algorithms include, for example, Orthogonal Matching Pursuit (OMP), which approximates the original signal by iteratively selecting the optimal atoms (or basis functions). However, other algorithms can also be used, such as Matching Pursuit, Basis Pursuit, and Particle Swarm Optimization (PSO).

[0054] Next reference Figure 3 This disclosure describes beam training based on compressed sensing. According to embodiments of this disclosure, multiple base stations can collaboratively perform beam training. It should be understood that, although... Figure 3 Only two base stations are shown (i.e., base station 1 and base station 2), but this is not an limitation; more base stations can perform similar operations.

[0055] Multi-base station cooperative beam training can begin with a combined beam scan (S1). Unlike conventional beam scanning, according to embodiments of this disclosure, each base station scans a combined beam obtained by combining its beam set using combination coefficients. The beams in the base station's beam set are spatially sparse and can be based on, for example, a Discrete Fourier Transform (DFT) codebook. The combination coefficients used by the base station can be derived from a designed observation matrix. Each combined beam includes a beam training signal, such as a synchronization signal block (SSB) or a CSI reference signal (CSI-RS).

[0056] like Figure 3 As shown, each base station can transmit multiple times, each time using different combination coefficients to form different combined beams. For example, each base station can use elements from different rows or columns of the observation matrix as combination coefficients. Base station 1 and base station 2 can transmit combined beams simultaneously, thereby the UE receives the superimposed signal of the combined beam from base station 1 and the combined beam from base station 2.

[0057] The UE can then determine the beam response (S2) of each base station's beam set based on the received superimposed signals. As an example, the UE can use an orthogonal matched pursuit (OMP) algorithm to reconstruct the beam response of all beams for each base station from the received superimposed signals. As used in this disclosure, beam response refers to the UE's receiver's response to beam signals passing through the wireless channel, reflecting the spatial response characteristics of the UE's antenna to the base station beams. Relying on compressed sensing technology, the UE can recover the responses of all beams from a small amount of superimposed signals, avoiding the large amount of signal reception and measurement required for beam training based on exhaustive search.

[0058] The UE can perform beam selection based on a determined beam response result. Optionally, such as Figure 3As shown by the dashed line, the UE can report a determined beam response (S3). As an example, a conventional beam reporting method can be used, whereby the UE can report a configured number of beam indices (e.g., reference signal indices) and beam response results (e.g., RSRPs) to each base station in the form of Channel State Information (CSI) reports. Based on the UE's CSI reports, the base station can determine the optimal beam for downlink transmission and indicate this optimal beam to the UE via, for example, a Transmission Configuration Indication (TCI) status.

[0059] As another example, the UE can determine the optimal beam itself and feed it back to the corresponding base station. For instance, for each base station, the UE can identify the beam with the best response as the optimal beam and report the index of that optimal beam, along with the possible beam response results, to the base station. As described below, the UE can use, for example, a greedy algorithm to determine the optimal beam for each base station.

[0060] Figure 4 A schematic diagram of multi-base station cooperative beam training according to this disclosure is shown. Figure 4 As shown, for a vehicle acting as a UE, multiple base stations simultaneously transmit beam training signals, such as synchronization signal blocks (SSBs) or CSI reference signals (CSI-RS), through their respective combined beams. The UE receives superimposed signals from multiple base stations. Due to the use of compressed sensing, the number of superimposed signals received by the UE can be far less than the number of beam signals scanned by the base stations in an exhaustive search beam training scheme. The UE can determine the beam responses of multiple base stations within a single beam training cycle, thereby reducing latency.

[0061] Furthermore, since each base station transmits a combined beam, and the combination coefficients use a random matrix that satisfies the restricted equidistant property, and are not specifically designed for any particular UE, the beam training scheme disclosed herein can be directly extended to preliminary beam training in multi-UE scenarios. Specifically, the combined beam transmitted by each base station can be received by UEs in any direction. Each UE performs the aforementioned compressed sensing-based beam response recovery on the received signal and determines the optimal beam for signal transmission between each UE and the base station.

[0062] The compressed sensing-based beam training process according to this disclosure will be described using a mathematical model. A cooperative scenario is considered, in which J base stations provide services to a single-antenna UE through coherent joint transmission. To simplify the model, it is assumed that each base station is equipped with a uniform linear array (ULA) consisting of N antennas with an antenna spacing of half a wavelength. However, it should be understood that the antenna configuration of the base stations and UE is not limited to this. The multi-base station beam training model is constructed as follows:

[0063]

[0064] Where h j This represents the sparse wireless channel between the j-th base station and the UE. Let represent the beam of the j-th base station, s represent the training symbols, and the matrix formed by the DFT codebooks of the base stations is denoted as . This represents the beam set of the base station, where This is the steering vector. Furthermore, a wireless sparse channel can be specifically represented as:

[0065]

[0066] Where, α j,l and θ j,l These represent the gain and angle of the l-th path between the UE and the j-th base station, respectively. The index l = 0 indicates a line-of-sight (LoS) path, while l ≥ 1 indicates a non-line-of-sight (NLoS) path. Traditional beam training schemes, such as exhaustive search and phased search, require combining beams from different base stations for joint training.

[0067] According to embodiments of this disclosure, due to the spatial domain sparsity of the channel, the codebook Only a few beams have non-zero responses (corresponding to sparse paths in the channel), meaning that this sparsity characteristic can effectively reduce the training overhead required to recover the beam responses. To apply compressed sensing theory, it is necessary to construct observation signal models for all beam responses. Therefore, all beams from each base station are combined, and then multiple base stations simultaneously transmit the combined beams. To obtain multiple observation signals, multiple transmissions are required, each using a different combination coefficient b. j,m Without loss of generality, the training symbol s is set to 1. After M transmissions, a simplified multi-base station multi-beam training model can be obtained:

[0068]

[0069] Where B represents the beam combination coefficient b from M transmissions. j,m The matrix formed Let q be the vector composed of the combined beam responses of all base stations, where q j =h jLet \(F\) denote the beam responses of all the beams in the \(j\)-th beam concentration passing through the wireless channel. Recover \(q\) from the signal \(y\) obtained through \(M\) transmissions, that is, recover an \(N_J\)-dimensional signal from an \(M\)-dimensional signal. In theory, solving for \(N_J\) unknowns requires the same number of observation signals. However, by leveraging the sparsity of the beam signals and using the compressed sensing theory for signal reconstruction, \(q\) can be recovered when \(M\ll N_J\). At this time, \(B\) acts as the observation matrix and should satisfy the restricted isometry property. For example, it can be a random matrix obeying the Bernoulli distribution.

[0070] Thus, reconstructing a high-dimensional sparse signal from a small number of received signals (sampling), the problem can be expressed as

[0071] min q \(\|q\|_0\) subject to \(y = B T q,

[0072] Subsequently, common algorithms such as orthogonal matching pursuit (OMP) can be used to solve this compressed sensing problem.

[0073] After recovering the beam responses with low training overhead, based on the beam responses of each base station, the UE can determine the optimal multi-base station beam combination, that is, This process no longer requires training overhead and only requires calculations. For example, an exhaustive combination or a greedy algorithm can be used to determine the optimal beams of multiple base stations.

[0074] As an example, the process of using the greedy algorithm to select the optimal beam combination of multiple base stations is given below:

[0075] The greedy algorithm is used for beam selection

[0076] Input: Beam response \(q\), number of candidate beams \(N\), number of base stations \(J\).

[0077] Output: Selected beam index and the corresponding base station index

[0078] 1. Initialization:

[0079] 1. Traverse all beam responses \(q\) to find the beam response index with the maximum global gain

[0080]

[0081] Corresponding to the codebook The beam index in is and the corresponding base station index:

[0082]

[0083] 2. Initialize the cumulative beam response:

[0084]

[0085] 3. Set the remaining beam index set to

[0086] 2. Iteration steps (for k = 2 to J):

[0087] 1. Calculate the cumulative signal response and iterate through the remaining beam response index. To select the beam response index that maximizes the total gain

[0088]

[0089] 2. Determine the codebook Beam index in:

[0090]

[0091] And the corresponding base station index:

[0092]

[0093] 3. Update cumulative responses:

[0094]

[0095] 4. Update the remaining beam index set:

[0096]

[0097] 3. Return: Selected beam index and the corresponding base station index

[0098] Figure 5 Simulation results are shown for Cooperative Multi-Base Station Beam Training (CSMBT) based on Compressed Sensing and Cooperative Multi-Base Station Beam Training (ESMBT) based on Exhaustive Search. For traditional ESMBT, joint beam training between two base stations requires 4096 transmissions. In contrast, the CSMBT scheme disclosed herein achieves excellent results (90% success rate) even with more than 30 pilots, significantly reducing overhead. Assuming a specified beam success rate of 95%, it can be found that at a signal-to-noise ratio of 20 dB, the disclosed CSMBT scheme only requires 50 transmissions to approach optimal performance, which is only 1.2% of the exhaustive search, reducing the required overhead by more than 98%.

[0099] Figure 6The diagram illustrates the beam alignment of a single base station and multiple base stations (e.g., dual base stations) with and without obstruction of the LosS path. When the LosS path is obstructed, the beamforming gain of both single and dual base stations is affected. However, dual base stations can obtain additional beamforming gain by utilizing the LosS path of another base station, thereby mitigating the disadvantages of obstructed LosS paths in the single base station scenario.

[0100] The above describes an embodiment of determining the optimal beam of a base station based on compressed sensing beam training according to the present disclosure. Further, according to embodiments of the present disclosure, the determined optimal beam can be fine-tuned.

[0101] Figure 7 A flowchart of the beam fine-tuning process according to this disclosure is shown. As an optional preparatory step in the beam fine-tuning process, the UE may report its sensing capabilities to the base station (not shown in the figure). Depending on the UE's sensing capabilities or other factors, step S11 or step S11' may be performed selectively.

[0102] In step S11, the base station actively transmits downlink sensing signals to the UE using the beam to be fine-tuned. In one example, the base station can use the optimal beam determined by the compressed sensing-based beam training scheme described above to transmit the sensing signals. As an example implemented independently of the beam training scheme according to this disclosure, the base station can use the beam determined by a conventional beam training scheme (e.g., an exhaustive search-based beam training scheme or a phased beam training scheme) or other beam selection scheme to transmit the sensing signals.

[0103] The sensing signals transmitted by the base station may include downlink reference signals, such as Channel State Information Reference Signal (CSI-RS) or Demodulation Reference Signal (DMRS). The sensing signals can even be data transmission signals. The base station then receives the echo of the sensing signals reflected from the UE (referred to as the "reflected sensing signal").

[0104] Alternatively, in step S11', the UE transmits an uplink sensing signal to the base station. The UE may use a beam paired with the base station beam to be fine-tuned. The sensing signal transmitted by the UE may include an uplink reference signal, such as a sounding reference signal (SRS).

[0105] In step S12, the base station receives the echo of the uplink sensing signal sent by the UE or the downlink sensing signal sent by the base station itself, and performs sensing measurements.

[0106] Suppose the signal received by the j-th base station can be modeled as:

[0107]

[0108] in, Let x be the optimal beam matrix of the j-th base station, where x can be the sensing signal transmitted by the UE or the sensing signal transmitted by the base station.

[0109] The base station processes y to obtain fine-grained angle information. As an example, the base station can utilize the beamspace MUSIC algorithm. MUSIC (Multiple Signal Classification) is a spatial spectrum estimation algorithm. Its idea is to use the covariance matrix of the received signal for eigenvalue decomposition, separating the signal subspace and noise subspace. It then uses the orthogonality between the signal direction vector and the noise subspace to construct a spatial scanning spectrum, performing a global search for spectral peaks to achieve signal parameter estimation. Beamspace MUSIC is an improvement on the MUSIC algorithm, transforming the signal from element space to beamspace using a beamforming matrix. This effectively reduces the SNR threshold required for accurate estimation and improves resolution and estimation accuracy. Based on the obtained angle information, the base station can adjust the beam angle accordingly.

[0110] Figure 8 Simulation diagrams comparing beam fine-tuning with and without this disclosure are shown. The diagrams use beamforming gain as an indicator, introducing fine-tuning in the case of multi-base station cooperative beam training based on compressed sensing (CS), while not introducing fine-tuning in the case of beam training based on exhaustive search (ES). By applying beam fine-tuning, the problem of insufficient angular resolution in the original codebook of beam training can be compensated for. For example, the original codebook corresponds to angles 0, 2, 4, and 6, but the actual angle between the UE and the base station is 1.2. In the original codebook, only a relatively optimal beam can be selected. However, through fine-tuning, the ability of integrated sensing is used to assist in confirming fine angles, thereby improving beam alignment and producing higher beamforming gain.

[0111] The following describes electronic devices and methods that can be applied to embodiments of this disclosure.

[0112] Figure 9 This is a block diagram illustrating an electronic device 100 according to the present disclosure, and Figure 10 This is a flowchart illustrating a method that can be executed by the electronic device 100. The electronic device 100 can be implemented as a UE or a component thereof.

[0113] like Figure 9 As shown, the electronic device 100 includes a processing circuit 101. The processing circuit 101 includes at least a receiving unit 102 and a determining unit 103. The processing circuit 101 can be configured to perform... Figure 10 The method shown. Processing circuit 101 may refer to various implementations of digital circuitry, analog circuitry, or mixed-signal (a combination of analog and digital signals) circuitry that perform functions in the UE.

[0114] The receiving unit 102 is configured to receive multiple signals superimposed from beam training signals simultaneously transmitted by at least two base stations at multiple times, i.e., to perform... Figure 10 Step S101. For each of the at least two base stations, the base station uses multiple different combined beams formed by combining spatially sparse beam sets through different combination coefficients at the multiple times. For example, the beam set of each base station may be based on a DFT codebook, and the base station uses elements in the observation matrix as combination coefficients to combine the beams in its beam set.

[0115] The determining unit 103 is configured to determine the beam response of the corresponding beam set of each of the at least two base stations based on multiple signals received by the receiving unit 102, i.e., to perform... Figure 10 Step S102 in the process. As an example, the determining unit 103 may use, for example, an OMP algorithm to reconstruct the beam response of each base station from multiple received signals.

[0116] Optionally, the processing circuit 101 may determine the optimal beam of each base station based on the beam response of each base station and feed it back to the base station.

[0117] Electronic device 100 may also include a communication unit 105. Communication unit 105 may be configured to communicate with a base station (e.g., electronic device 200 described below) under the control of processing circuitry 101. In one example, communication unit 105 may be implemented as a transceiver, including communication components such as an antenna array and / or a radio frequency link. Communication unit 105 is drawn with dashed lines because it may also be located outside electronic device 100.

[0118] The electronic device 100 may also include a memory 106. The memory 106 can store various data and instructions, such as programs and data for the operation of the electronic device 100, various data generated by the processing circuit 101, and various control signals or service data sent or received by the communication unit 105. The memory 106 is drawn with dashed lines because it may be located inside the processing circuit 101 or outside the electronic device 100.

[0119] Figure 11 This is a block diagram illustrating an electronic device 200 according to the present disclosure, and Figure 12 This is a flowchart illustrating a method that can be executed by the electronic device 200. The electronic device 200 can be implemented as a base station device or a component thereof.

[0120] like Figure 11 As shown, the electronic device 200 includes a processing circuit 201. The processing circuit 201 includes at least a combination unit 202 and a transmission unit 203. The processing circuit 201 can be configured to perform... Figure 12The method shown. Processing circuit 201 can refer to various implementations of digital circuit systems, analog circuit systems, or mixed-signal (combination of analog and digital signals) circuit systems that perform functions in base station equipment.

[0121] Combining unit 202 is configured to form multiple different combined beams by combining sparse beam sets in the spatial domain with different combination coefficients, i.e., to perform... Figure 12 Step S201. The base station's beam set can be, for example, based on a DFT codebook, and the base station uses elements in the observation matrix as combination coefficients to combine the beams in its beam set. The observation matrix can, for example, be a random matrix following a Bernoulli distribution.

[0122] The transmitting unit 203 is configured to transmit beam training signals to the UE at multiple times through the multiple combined beams, i.e., to perform... Figure 12 Step 202. In multi-base station cooperative beam training, the base station's transmitting unit 203 can transmit beam training signals simultaneously with other base stations, so that the UE can receive superimposed signals from multiple base stations.

[0123] As an example, the processing circuit 201 can also receive information from the UE indicating the optimal beam in the beamset used for data transmission with the UE. Additionally, the processing circuit 201 can use the optimal beam to transmit a sensing signal and receive a reflected sensing signal, or use the optimal beam to receive a sensing signal from the UE, determine angle information relative to the UE based on measurements of the sensing signal, and fine-tune its beam based on the angle information.

[0124] Electronic device 200 may also include communication unit 205. Communication unit 205 may be configured to communicate with UE (e.g., electronic device 100 described above) under the control of processing circuitry 201. In one example, communication unit 205 may be implemented as a transmitter or transceiver, including communication components such as antenna arrays and / or radio frequency links. Communication unit 205 is drawn with dashed lines because it may also be located outside electronic device 200.

[0125] The electronic device 200 may also include a memory 206. The memory 206 may store various data and instructions, programs and data for the operation of the electronic device 200, various data generated by the processing circuit 201, data to be transmitted by the communication unit 205, etc. The memory 206 is drawn with dashed lines because it may be located inside the processing circuit 201 or outside the electronic device 200.

[0126] Various aspects of the embodiments of this disclosure have been described in detail above. However, it should be noted that the above description of the structure, arrangement, type, number, etc. of the illustrated antenna arrays, ports, reference signals, communication devices, communication methods, etc., is not intended to limit the aspects of this disclosure to these specific examples.

[0127] It should be understood that the various units of the electronic devices 100 or 200 described in the above embodiments are merely logical modules divided according to their specific functions, and are not intended to limit the specific implementation method. In actual implementation, the above units can be implemented as independent physical entities, or they can be implemented by a single entity (e.g., a processor (CPU or DSP, etc.), integrated circuit, etc.).

[0128] It should be understood that the processing circuitry 101 or 201 described in the above embodiments may include, for example, circuitry such as integrated circuits (ICs), application-specific integrated circuits (ASICs), portions or circuitry of a single processor core, an entire processor core, a single processor, programmable hardware devices such as field-programmable gate arrays (FPGAs), and / or systems including multiple processors. Memory 106 or 206 may be volatile memory and / or non-volatile memory. For example, memory may include, but is not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), read-only memory (ROM), and flash memory.

[0129] [Exemplary Implementation of this Disclosure]

[0130] Based on embodiments of this disclosure, various implementations of the concepts of this disclosure are conceivable, including but not limited to the following exemplary embodiments (EE):

[0131] EE1. An electronic device for a user equipment (UE), comprising:

[0132] processor; and

[0133] The memory includes computer program code, wherein the computer program code, when executed by the processor, causes the electronic device to perform operations, the operations including:

[0134] Receives multiple signals superimposed from beam training signals simultaneously transmitted by at least two base stations at multiple times, wherein for each of the at least two base stations, the base station uses multiple different combined beams formed by combining spatially sparse beam sets through different combination coefficients at the multiple times; and

[0135] Based on the plurality of signals, determine the beam response of the corresponding beam set of each of the at least two base stations.

[0136] EE2, the electronic device according to EE1, wherein the operation further includes:

[0137] Based on the determined beam response, determine the optimal beam for each of the at least two base stations; and

[0138] The corresponding optimal beam is reported to each of the at least two base stations.

[0139] EE3, the electronic device according to EE1, wherein the beam set of each of the at least two base stations is based on a Discrete Fourier Transform (DFT) codebook.

[0140] EE4, the electronic device according to EE1, wherein the combination coefficients are based on an observation matrix that satisfies the restricted isometry property.

[0141] EE5, the electronic device according to EE4, wherein the observation matrix is ​​a random matrix following a Bernoulli distribution.

[0142] EE6. The electronic device according to EE1, wherein the beam training signal includes a synchronization signal block (SSB) signal or a channel state information reference signal (CSI-RS).

[0143] EE7. The electronic device according to EE1, wherein the beam response of the corresponding beam set of each of the at least two base stations is determined using an orthogonal matching pursuit algorithm.

[0144] EE8, the electronic device according to EE2, wherein the optimal beam of each of the at least two base stations is determined using a greedy algorithm.

[0145] EE9. The electronic device according to EE1, wherein the operation further includes:

[0146] A sensing signal is sent to each of the at least two base stations.

[0147] EE10. An electronic device according to EE9, wherein the sensing signal is a detection reference signal (SRS).

[0148] EE11. An electronic device for a base station, comprising:

[0149] processor; and

[0150] The memory includes computer program code, wherein the computer program code, when executed by the processor, causes the electronic device to perform operations, the operations including:

[0151] Multiple different combined beams are formed by combining sparse beam sets in the spatial domain with different combination coefficients; and

[0152] Beam training signals are transmitted to the user equipment (UE) at multiple times via the multiple combined beams.

[0153] EE12. The electronic device according to EE11, wherein the operation further includes:

[0154] The UE receives information indicating the optimal beam in the beam set for downlink transmission with the UE.

[0155] EE13, the electronic device according to EE11, wherein the beam set is based on a Discrete Fourier Transform (DFT) codebook.

[0156] EE14, the electronic device according to EE11, wherein the combination coefficients are based on an observation matrix that satisfies the restricted isometry property.

[0157] EE15, the electronic device according to EE14, wherein the observation matrix is ​​a random matrix following a Bernoulli distribution.

[0158] EE16, the electronic device according to EE11, wherein the beam training signal includes a synchronization signal block (SSB) signal or a channel state information reference signal (CSI-RS).

[0159] EE17. The electronic device according to EE12, wherein the operation further includes:

[0160] The sensing signal from the UE is received via the optimal beam; and

[0161] Based on the sensed signal, the angle information of the UE relative to the base station is determined.

[0162] EE18. The electronic device according to EE17, wherein the sensing signal is one of the following:

[0163] The detection reference signal (SRS) sent by the UE;

[0164] Channel State Information Reference Signal (CSI-RS) or Demodulation Reference Signal (DMRS) transmitted by the base station and reflected from the UE.

[0165] EE19, the electronic device according to EE17, wherein the angle information of the UE relative to the base station is determined using a multiple signal classification (MUSIC) algorithm.

[0166] EE20, the electronic device according to EE17, wherein the operation further includes:

[0167] Based on the angle information, the optimal beam is adjusted.

[0168] EE21, a method comprising:

[0169] Receives multiple signals superimposed from beam training signals simultaneously transmitted by at least two base stations at multiple times, wherein for each of the at least two base stations, the base station uses multiple different combined beams formed by combining spatially sparse beam sets through different combination coefficients at the multiple times; and

[0170] Based on the plurality of signals, determine the beam response of the corresponding beam set of each of the at least two base stations.

[0171] EE22, A method comprising:

[0172] Multiple different combined beams are formed by combining sparse beam sets in the spatial domain with different combination coefficients; and

[0173] Beam training signals are transmitted to the user equipment (UE) at multiple times via the multiple combined beams.

[0174] EE23. A computer program product comprising executable instructions, which, when executed, cause an electronic device to perform any one of EE21 or EE22.

[0175] [Application Examples of this Disclosure]

[0176] Figure 13 An example block diagram of a computer 1300, which can be implemented as a user equipment or a base station device according to an embodiment of the present disclosure, is shown.

[0177] exist Figure 13 In this system, the central processing unit (CPU) 1301 performs various processes based on the program stored in the read-only memory (ROM) 1302 or the program loaded into the random access memory (RAM) 1303 from the storage section 1308. The RAM 1303 also stores, as needed, the data required when the CPU 1301 performs various processes.

[0178] CPU 1301, ROM 1302 and RAM 1303 are connected to each other via bus 1304. Input / output interface 1305 is also connected to bus 1304.

[0179] The following components are connected to the input / output interface 1305: input section 1306, including a keyboard, mouse, etc.; output section 1307, including a display, such as a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; storage section 1308, including a hard disk, etc.; and communication section 1309, including a network interface card, such as a LAN card, modem, etc. The communication section 1309 performs communication processing via a network, such as the Internet.

[0180] As needed, drive 1310 is also connected to input / output interface 1305. Removable media 1311, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on drive 1310 as needed, so that computer programs read from them can be installed into storage section 1308 as needed.

[0181] When the above series of processes are implemented by software, the program constituting the software is installed from a network such as the Internet or a storage medium such as removable media 1311.

[0182] Those skilled in the art will understand that such storage media are not limited to Figure 13 The illustration shows a removable medium 1311 containing a program, distributed separately from the device to provide the program to the user. Examples of removable media 1311 include magnetic disks (including floppy disks (registered trademark)), optical disks (including optical disc read-only memory (CD-ROM) and digital versatile disks (DVD)), magneto-optical disks (including mini-discs (MD) (registered trademark)), and semiconductor memory. Alternatively, the storage medium may be ROM 1302, a hard disk included in storage section 1308, etc., containing programs and distributed to the user along with the device containing them.

[0183] exist Figure 13 In the computer 1300 shown, by reference Figure 9 The described processing circuit 101 or reference Figure 11 The described processing circuit 201 can be implemented by CPU 1301.

[0184] The technology described in this disclosure can be applied to a variety of products.

[0185] For example, the electronic device 200 according to embodiments of the present disclosure can be implemented as various base stations or installed in base stations, and the electronic device 100 can be implemented as various user equipment or installed in various user equipment.

[0186] The communication methods according to embodiments of this disclosure can be implemented by various base stations or user equipment; the methods and operations according to embodiments of this disclosure can be embodied as computer-executable instructions, stored in a non-transitory computer-readable storage medium, and can be executed by various base stations or user equipment to achieve one or more of the functions described above.

[0187] The techniques according to embodiments of this disclosure can be used to create various computer program products that can be used in various base stations or user equipment to achieve one or more of the functions described above.

[0188] The base station described in this disclosure can be implemented as any type of base station, preferably such as macro gNB and ng-eNB as defined in the 3GPP 5G NR standard. A gNB can be a gNB covering a cell smaller than a macro cell, such as a pico gNB, micro gNB, and femtocell gNB. Alternatively, the base station can be implemented as any other type of base station, such as a NodeB, eNodeB, and Base Transceiver Station (BTS). The base station may also include: a main body configured to control wireless communication and one or more remote radio heads (RRHs), wireless relay stations, drone towers, control nodes in automated factories, etc., located at locations different from the main body.

[0189] User equipment can be implemented as a mobile terminal (such as a smartphone, tablet PC, laptop PC, portable gaming terminal, portable / dongle-type mobile router, and digital camera device) or an in-vehicle terminal (such as a car navigation device). User equipment can also be implemented as a terminal performing machine-to-machine (M2M) communication (also known as a machine-type communication (MTC) terminal), a drone, a sensor and actuator in an automated factory, etc. Furthermore, user equipment can be a wireless communication module (such as an integrated circuit module comprising a single chip) installed on each of the aforementioned terminals.

[0190] First application example of base stations

[0191] Figure 14 This is a block diagram illustrating a first example of a schematic configuration of a base station to which the technologies of this disclosure can be applied. Figure 14 In this implementation, the base station can be a gNB 1400. The gNB 1400 includes multiple antennas 1410 and a base station device 1420. The base station device 1420 and each antenna 1410 can be connected to each other via RF cables. In one implementation, the gNB 1400 (or base station device 1420) here can correspond to the aforementioned electronic device 200.

[0192] Antenna 1410 includes multiple antenna elements. Antenna 1410 can be arranged, for example, as an antenna array matrix and used by base station equipment 1420 to transmit and receive wireless signals. For example, multiple antennas 1410 can be compatible with multiple frequency bands used by gNB 1400.

[0193] The base station equipment 1420 includes a controller 1421, a memory 1422, a network interface 1423, and a wireless communication interface 1425.

[0194] The controller 1421 may be, for example, a CPU or a DSP, and operates various higher-level functions of the base station equipment 1420. For example, the controller 1421 may include the processing circuitry 201 described above, or various components of the control electronics 200. For instance, the controller 1421 generates data packets based on data in signals processed by the wireless communication interface 1425, and transmits the generated packets via the network interface 1423. The controller 1421 may bundle data from multiple baseband processors to generate bundled packets and transmit the generated bundled packets. The controller 1421 may have logical functions that perform controls such as radio resource control, radio bearer control, mobility management, admission control, and scheduling. This control may be performed in conjunction with nearby gNBs or core network nodes. The memory 1422 includes RAM and ROM, and stores programs executed by the controller 1421 and various types of control data (such as terminal lists, transmission power data, and scheduling data).

[0195] Network interface 1423 is a communication interface for connecting base station equipment 1420 to core network 1424 (e.g., a 5G core network). Controller 1421 can communicate with core network nodes or other gNBs via network interface 1423. In this case, gNB 1400 and core network nodes or other gNBs can be connected to each other via logical interfaces (such as NG and Xn interfaces). Network interface 1423 can also be a wired communication interface or a wireless communication interface for wireless backhaul. If network interface 1423 is a wireless communication interface, it can use a higher frequency band for wireless communication compared to the frequency band used by wireless communication interface 1425.

[0196] Wireless communication interface 1425 supports any cellular communication scheme (such as 5G NR) and provides wireless connectivity to terminals located in the cell of gNB 1400 via antenna 1410. Wireless communication interface 1425 typically includes, for example, a baseband (BB) processor 1426 and RF circuitry 1427. BB processor 1426 can perform, for example, encoding / decoding, modulation / demodulation, and multiplexing / demultiplexing, and performs various types of signal processing at each layer (e.g., physical layer, MAC layer, RLC layer, PDCP layer, SDAP layer). Instead of controller 1421, BB processor 1426 may have some or all of the above-described logical functions. BB processor 1426 may be a memory storing communication control programs, or a module including a processor and associated circuitry configured to execute programs. Update programs can change the functionality of BB processor 1426. The module may be a card or blade inserted into a slot in base station equipment 1420. Alternatively, the module may be a chip mounted on a card or blade. Meanwhile, the RF circuit 1427 may include, for example, a mixer, a filter, and an amplifier, and transmits and receives wireless signals via the antenna 1410. Although Figure 14 An example of an RF circuit 1427 connected to an antenna 1410 is shown, but this disclosure is not limited to the illustration, and an RF circuit 1427 can be connected to multiple antennas 1410 simultaneously.

[0197] like Figure 14 As shown, the wireless communication interface 1425 may include multiple BB processors 1426. For example, the multiple BB processors 1426 may be compatible with multiple frequency bands used by the gNB 1400. Figure 14 As shown, the wireless communication interface 1425 may include multiple RF circuits 1427. For example, the multiple RF circuits 1427 may be compatible with multiple antenna elements. Although Figure 14 An example is shown in which the wireless communication interface 1425 includes multiple BB processors 1426 and multiple RF circuits 1427, but the wireless communication interface 1425 may also include a single BB processor 1426 or a single RF circuit 1427.

[0198] exist Figure 14 In the gNB 1400 shown, refer to Figure 11One or more units included in the described processing circuitry 201 may be implemented in the wireless communication interface 825. Alternatively, at least a portion of these components may be implemented in the controller 821. For example, the gNB 1400 may include a portion (e.g., BB processor 1426) or the entirety of the wireless communication interface 1425, and / or a module including the controller 1421, and one or more components may be implemented in the module. In this case, the module may store a program for allowing the processor to function as one or more components (in other words, a program for allowing the processor to perform the operation of one or more components), and may execute the program. As another example, a program for allowing the processor to function as one or more components may be installed in the gNB 1400, and the wireless communication interface 1425 (e.g., BB processor 1426) and / or the controller 1421 may execute the program. As described above, the gNB 1400, the base station device 1420, or the module may be provided as an apparatus including one or more components, and a program for allowing the processor to function as one or more components may be provided. Additionally, a readable medium in which the program is recorded may be provided.

[0199] Second application example of base stations

[0200] Figure 15 This is a block diagram illustrating a second example of a schematic configuration of a base station to which the techniques of this disclosure can be applied. Figure 15 In the diagram, the base station is shown as gNB 1530. gNB 1530 includes multiple antennas 1540, base station equipment 1550, and RRH 1560. RRH 1560 and each antenna 1540 can be connected to each other via RF cables. Base station equipment 1550 and RRH 1560 can be connected to each other via high-speed lines such as fiber optic cables. In one implementation, gNB 1530 (or base station equipment 1550) here may correspond to the aforementioned electronic equipment 200.

[0201] Antenna 1540 includes multiple antenna elements. Antenna 1540 can be arranged, for example, as an antenna array matrix and used by base station equipment 1550 to transmit and receive wireless signals. For example, multiple antennas 1540 can be compatible with multiple frequency bands used by gNB 1530.

[0202] Base station equipment 1550 includes a controller 1551, a memory 1552, a network interface 1553, a wireless communication interface 1555, and a connection interface 1557. The controller 1551, memory 1552, and network interface 1553 are related to a reference... Figure 14 The controller 1421, memory 1422 and network interface 1423 described are the same.

[0203] The wireless communication interface 1555 supports any cellular communication scheme (such as 5G NR) and provides wireless communication to terminals located in the sector corresponding to RRH 1560 via RRH 1560 and antenna 1540. The wireless communication interface 1555 may typically include, for example, a BB processor 1556. In addition to the BB processor 1556 being connected to the RF circuitry 1564 of RRH 1560 via connection interface 1557, the BB processor 1556 is connected to the reference... Figure 14 The BB processor 1426 is described as identical. Figure 15 As shown, the wireless communication interface 1555 may include multiple BB processors 1556. For example, the multiple BB processors 1556 may be compatible with multiple frequency bands used by the gNB 1530. Although Figure 15 An example is shown in which the wireless communication interface 1555 includes multiple BB processors 1556, but the wireless communication interface 1555 may also include a single BB processor 1556.

[0204] Connection interface 1557 is an interface for connecting base station device 1550 (wireless communication interface 1555) to RRH 1560. Connection interface 1557 may also be a communication module for communication in the aforementioned high-speed line connecting base station device 1550 (wireless communication interface 1555) to RRH 1560.

[0205] The RRH 1560 includes a connectivity interface 1561 and a wireless communication interface 1563.

[0206] Connection interface 1561 is an interface for connecting RRH 1560 (wireless communication interface 1563) to base station equipment 1550. Connection interface 1561 can also be a communication module for communication in the aforementioned high-speed line.

[0207] Wireless communication interface 1563 transmits and receives wireless signals via antenna 1540. Wireless communication interface 1563 typically includes, for example, RF circuitry 1564. RF circuitry 1564 may include, for example, a mixer, filter, and amplifier, and transmits and receives wireless signals via antenna 1540. Although Figure 15 An example of an RF circuit 1564 connected to an antenna 1540 is shown, but this disclosure is not limited to the illustration, and an RF circuit 1564 can be connected to multiple antennas 1540 simultaneously.

[0208] like Figure 15 As shown, the wireless communication interface 1563 may include multiple RF circuits 1564. For example, the multiple RF circuits 1564 may support multiple antenna elements. Although Figure 15An example is shown in which the wireless communication interface 1563 includes multiple RF circuits 1564, but the wireless communication interface 1563 may also include a single RF circuit 1564.

[0209] exist Figure 15 In the gNB 1500 shown, refer to Figure 11 One or more units included in the described processing circuitry 201 may be implemented in the wireless communication interface 1525. Alternatively, at least a portion of these components may be implemented in the controller 1521. For example, the gNB 1500 may include a portion (e.g., BB processor 1526) or the entirety of the wireless communication interface 1525, and / or a module including the controller 1521, and one or more components may be implemented in the module. In this case, the module may store a program for allowing the processor to function as one or more components (in other words, a program for allowing the processor to perform the operation of one or more components), and may execute the program. As another example, a program for allowing the processor to function as one or more components may be installed in the gNB 1500, and the wireless communication interface 1525 (e.g., BB processor 1526) and / or the controller 1521 may execute the program. As described above, the gNB 1500, the base station device 1520, or the module may be provided as an apparatus including one or more components, and a program for allowing the processor to function as one or more components may be provided. Additionally, a readable medium in which the program is recorded may be provided.

[0210] First application example of user equipment

[0211] Figure 16 This is a block diagram illustrating an example of a schematic configuration of a smartphone 1600 to which the technologies of this disclosure can be applied. In one example, the smartphone 1600 may be implemented as the electronic device 100 described in this disclosure.

[0212] The smartphone 1600 includes a processor 1601, a memory 1602, a storage device 1603, an external connection interface 1604, a camera device 1606, a sensor 1607, a microphone 1608, an input device 1609, a display device 1610, a speaker 1611, a wireless communication interface 1612, one or more antenna switches 1615, one or more antennas 1616, a bus 1617, a battery 1618, and an auxiliary controller 1619.

[0213] Processor 1601 may be, for example, a CPU or a system-on-a-chip (SoC), and controls the application layer and other functions of smartphone 1600. Processor 1601 may include or act as a reference. Figure 9The processing circuitry 101 is described. The memory 1602 includes RAM and ROM, and stores data and programs executed by the processor 1601 to implement the communication method described above. The storage device 1603 may include storage media such as semiconductor memory and hard disk. The external connection interface 1604 is an interface for connecting external devices (such as memory cards and Universal Serial Bus (USB) devices) to the smartphone 1600.

[0214] The camera device 1606 includes an image sensor (such as a charge-coupled device (CCD) and complementary metal-oxide-semiconductor (CMOS)) and generates captured images. The sensor 1607 may include a set of sensors, such as a measurement sensor, a gyroscope sensor, a magnetometer sensor, and an accelerometer sensor. The microphone 1608 converts sound input to the smartphone 1600 into an audio signal. The input device 1609 includes, for example, a touch sensor, keypad, keyboard, buttons, or switches configured to detect touches on the screen of the display device 1610 and receive operations or information input from the user. The display device 1610 includes a screen (such as a liquid crystal display (LCD) and an organic light-emitting diode (OLED) display) and displays the output image of the smartphone 1600. The speaker 1611 converts the audio signal output from the smartphone 1600 into sound.

[0215] The wireless communication interface 1612 supports any cellular communication scheme (such as 4G LTE or 5G NR, etc.) and performs wireless communication. The wireless communication interface 1612 typically includes, for example, a BB processor 1613 and RF circuitry 1614. The BB processor 1613 can perform, for example, encoding / decoding, modulation / demodulation, and multiplexing / demultiplexing, and performs various types of signal processing for wireless communication. Meanwhile, the RF circuitry 1614 can include, for example, a mixer, filters, and amplifiers, and transmits and receives wireless signals via antenna 1616. The wireless communication interface 1612 can be a single chip module on which the BB processor 1613 and RF circuitry 1614 are integrated. Figure 16 As shown, the wireless communication interface 1612 may include multiple BB processors 1613 and multiple RF circuits 1614. Although Figure 16 An example is shown in which the wireless communication interface 1612 includes multiple BB processors 1613 and multiple RF circuits 1614, but the wireless communication interface 1612 may also include a single BB processor 1613 or a single RF circuit 1614.

[0216] In addition to cellular communication schemes, wireless communication interface 1612 can support other types of wireless communication schemes, such as short-range wireless communication schemes, near-field communication schemes, and wireless local area network (LAN) schemes. In this case, wireless communication interface 1612 may include a BB processor 1613 and RF circuitry 1614 for each wireless communication scheme.

[0217] Each of the antenna switches 1615 switches the connection destination of the antenna 1616 among multiple circuits (e.g., circuits for different wireless communication schemes) included in the wireless communication interface 1612.

[0218] Antenna 1616 includes multiple antenna elements. Antenna 1616 may be arranged, for example, as an antenna array matrix, and used for transmitting and receiving wireless signals through wireless communication interface 1612. Smartphone 1600 may include one or more antenna panels (not shown).

[0219] Furthermore, the smartphone 1600 may include an antenna 1616 for each wireless communication scheme. In this case, the antenna switch 1615 can be omitted from the configuration of the smartphone 1600.

[0220] Bus 1617 connects processor 1601, memory 1602, storage device 1603, external connection interface 1604, camera device 1606, sensor 1607, microphone 1608, input device 1609, display device 1610, speaker 1611, wireless communication interface 1612, and auxiliary controller 1619 to each other. Battery 1618 supplies power to... Figure 16 The various blocks of the smartphone 1600 shown are powered, and the feeders are partially shown as dashed lines in the figure. The auxiliary controller 1619 operates the minimum necessary functions of the smartphone 1600, for example, in sleep mode.

[0221] exist Figure 16 Among the smartphones 1600 shown, refer to Figure 9One or more components included in the described processing circuitry 101 may be implemented in the wireless communication interface 1612. Alternatively, at least a portion of these components may be implemented in the processor 1601 or the auxiliary controller 1619. As an example, the smartphone 1600 includes a portion (e.g., a BB processor 1613) or the entirety of the wireless communication interface 1612, and / or a module including the processor 1601 and / or the auxiliary controller 1619, and one or more components may be implemented in the module. In this case, the module may store and execute a program that allows the processor to function as one or more components (in other words, a program that allows the processor to perform the operation of one or more components). As another example, a program that allows the processor to function as one or more components may be installed in the smartphone 1600, and the wireless communication interface 1612 (e.g., the BB processor 1613), the processor 1601, and / or the auxiliary controller 1619 may execute the program. As described above, the smartphone 1600 or the module may be provided as an apparatus including one or more components, and a program that allows the processor to function as one or more components may be provided. Additionally, a readable medium in which the program is recorded can be provided.

[0222] Second application example of user equipment

[0223] Figure 17 This is a block diagram illustrating an example of a schematic configuration of a car navigation device 1720 to which the techniques of this disclosure can be applied. The car navigation device 1720 can be implemented as described above. Figure 9 The described electronic device 100. The car navigation device 1720 includes a processor 1721, a memory 1722, a Global Positioning System (GPS) module 1724, a sensor 1725, a data interface 1726, a content player 1727, a storage medium interface 1728, an input device 1729, a display device 1730, a speaker 1731, a wireless communication interface 1733, one or more antenna switches 1736, one or more antennas 1737, and a battery 1738.

[0224] The processor 1721 can be, for example, a CPU or a SoC, and controls the navigation functions and other functions of the car navigation device 1720. The memory 1722 includes RAM and ROM, and stores data and programs executed by the processor 1721.

[0225] GPS module 1724 uses GPS signals received from GPS satellites to measure the location (such as latitude, longitude, and altitude) of car navigation device 1720. Sensor 1725 may include a set of sensors, such as a gyroscope sensor, a geomagnetic sensor, and an air pressure sensor. Data interface 1726 is connected to, for example, an in-vehicle network 1741 via a terminal not shown, and acquires data generated by the vehicle (such as vehicle speed data).

[0226] Content player 1727 reproduces content stored on storage media (such as CDs and DVDs), which is inserted into storage media interface 1728. Input device 1729 includes, for example, a touch sensor, button, or switch configured to detect touch on the screen of display device 1730, and receives operations or information input from the user. Display device 1730 includes a screen such as an LCD or OLED display and displays images or reproduced content for navigation functions. Speaker 1731 outputs sound for navigation functions or reproduced content.

[0227] The wireless communication interface 1733 supports any cellular communication scheme (such as 4G LTE or 5G NR) and performs wireless communication. The wireless communication interface 1733 typically includes, for example, a BB processor 1734 and RF circuitry 1735. The BB processor 1734 can perform, for example, encoding / decoding, modulation / demodulation, and multiplexing / demultiplexing, and performs various types of signal processing for wireless communication. Meanwhile, the RF circuitry 1735 can include, for example, a mixer, filter, and amplifier, and transmits and receives wireless signals via antenna 1737. The wireless communication interface 1733 can also be a chip module on which the BB processor 1734 and RF circuitry 1735 are integrated. Figure 17 As shown, the wireless communication interface 1733 may include multiple BB processors 1734 and multiple RF circuits 1735. Although Figure 17 An example is shown in which the wireless communication interface 1733 includes multiple BB processors 1734 and multiple RF circuits 1735, but the wireless communication interface 1733 may also include a single BB processor 1734 or a single RF circuit 1735.

[0228] In addition to cellular communication schemes, the wireless communication interface 1733 can support other types of wireless communication schemes, such as short-range wireless communication schemes, near-field communication schemes, and wireless LAN schemes. In this case, for each wireless communication scheme, the wireless communication interface 1733 may include a BB processor 1734 and an RF circuit 1735.

[0229] Each of the antenna switches 1736 switches the connection destination of the antenna 1737 among multiple circuits (such as circuits for different wireless communication schemes) included in the wireless communication interface 1733.

[0230] Antenna 1737 includes multiple antenna elements. Antenna 1737 may be arranged, for example, as an antenna array matrix, and used by wireless communication interface 1733 to transmit and receive wireless signals.

[0231] Furthermore, the car navigation device 1720 may include an antenna 1737 for each wireless communication scheme. In this case, the antenna switch 1736 can be omitted from the configuration of the car navigation device 1720.

[0232] Battery 1738 via feeder to Figure 17 The various blocks of the car navigation device 1720 shown are powered, and the feeders are partially shown as dashed lines in the figure. Battery 1738 accumulates the power supplied from the vehicle.

[0233] exist Figure 17 In the car navigation device 1720 shown in the figure, refer to Figure 9 One or more components included in the described processing circuitry 101 may be implemented in the wireless communication interface 1733. Alternatively, at least a portion of these components may be implemented in the processor 1721. As an example, the car navigation device 1720 includes a portion (e.g., BB processor 1734) or the entirety of the wireless communication interface 1733, and / or a module including the processor 1721, and one or more components may be implemented in the module. In this case, the module may store a program that allows the processor to function as one or more components (in other words, a program that allows the processor to perform the operation of one or more components), and may execute the program. As another example, a program that allows the processor to function as one or more components may be installed in the car navigation device 1720, and the wireless communication interface 1733 (e.g., BB processor 1734) and / or the processor 1721 may execute the program. As described above, the car navigation device 1720 or the module may be provided as a device including one or more components, and a program that allows the processor to function as one or more components may be provided. Additionally, a readable medium in which the program is recorded may be provided.

[0234] The technology disclosed herein can also be implemented as an in-vehicle system (or vehicle) 1740 including one or more blocks of an automotive navigation device 1720, an in-vehicle network 1741, and a vehicle module 1742. The vehicle module 1742 generates vehicle data (such as vehicle speed, engine speed, and fault information) and outputs the generated data to the in-vehicle network 1741.

[0235] Exemplary embodiments of the present disclosure have been described above with reference to the accompanying drawings; however, the present disclosure is by no means limited to the examples described above. Various changes and modifications can be made by those skilled in the art within the scope of the appended claims, and it should be understood that such changes and modifications naturally fall within the technical scope of the present disclosure.

[0236] For example, the multiple functions included in one unit in the above embodiments can be implemented by separate devices. Alternatively, the multiple functions implemented by multiple units in the above embodiments can be implemented by separate devices respectively. In addition, one of the above functions can be implemented by multiple units. Needless to say, such a configuration is included within the scope of the present disclosure.

[0237] In this specification, the steps described in the flowchart include not only processes executed sequentially in the stated order, but also processes executed in parallel or individually, rather than necessarily sequentially. Furthermore, even within the steps of sequential processing, needless to say, the order can be appropriately altered.

[0238] While this disclosure and its advantages have been described in detail, it should be understood that various changes, substitutions, and modifications can be made without departing from the spirit and scope of this disclosure as defined by the appended claims. Furthermore, the terms "comprising," "including," or any other variations thereof used in embodiments of this disclosure are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. An electronic device for a user equipment (UE), comprising: processor; and The memory includes computer program code, wherein the computer program code, when executed by the processor, causes the electronic device to perform operations, the operations including: Multiple signals superimposed by beam training signals simultaneously transmitted by at least two base stations are received at multiple times, wherein for each of the at least two base stations, the base station uses multiple different combined beams formed by combining spatially sparse beam sets through different combination coefficients at the multiple times. as well as Based on the plurality of signals, determine the beam response of the corresponding beam set of each of the at least two base stations.

2. The electronic device according to claim 1, wherein the operation further comprises: Based on the determined beam response, determine the optimal beam for each of the at least two base stations; as well as The corresponding optimal beam is reported to each of the at least two base stations.

3. The electronic device of claim 1, wherein the beam set of each of the at least two base stations is based on a Discrete Fourier Transform (DFT) codebook.

4. The electronic device according to claim 1, wherein the combination coefficients are based on an observation matrix that satisfies the restricted isometry property.

5. The electronic device according to claim 4, wherein the observation matrix is ​​a random matrix following a Bernoulli distribution.

6. The electronic device according to claim 1, wherein the beam training signal includes a synchronization signal block (SSB) signal or a channel state information reference signal (CSI-RS).

7. The electronic device of claim 1, wherein the beam response of the corresponding beam set of each of the at least two base stations is determined using an orthogonal matching pursuit algorithm.

8. The electronic device of claim 2, wherein the optimal beam for each of the at least two base stations is determined using a greedy algorithm.

9. The electronic device of claim 1, wherein the operation further comprises: A sensing signal is sent to each of the at least two base stations.

10. The electronic device of claim 9, wherein the sensing signal is a detection reference signal (SRS).

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