A method for improving noise estimation through imbalance detection in self-clustered resource blocks.

Abstract

This paper introduces procedures for estimating noise covariance across resource blocks with similar noise distributions. These procedures lead to more accurate estimates of noise occurring in a given channel because accuracy can be improved by increasing the number of resource blocks examined, while also identifying and filtering out those contaminated by interference. In summary, these procedures represent an automated method for detecting imbalances between noisy resource blocks and resource blocks that also contain interference, then forming clusters of resource blocks with similar characteristics to provide more samples that can be used to estimate the noise covariance.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. Provisional Application No. 63 / 039,268, filed June 15, 2020, entitled “Self-Clustering Noise Estimation with Disturbance Imbalance Detection,” the entire contents of which are incorporated herein by reference. Technical Field

[0003] Various embodiments relate to schemes for estimating the noise experienced by a wireless communication system. Background Technology

[0004] In wireless communication, estimating noise covariance is a crucial step in receiver design to ensure proper demodulation and decoding of the input signal. Noise covariance is estimated by comparing the input signal (called the "reference signal") on a reference tone with the corresponding estimated channel on that reference tone. From a demapping perspective, the reference signal can be a demodulated reference signal (DMRS) or a cell-specific reference signal (CRS). For example, suppose the received signal on the reference tone can be represented as follows:

[0005] y = Hx + n Equation 1

[0006] Where y is a dimensionless n r A vector of dimension n × 1, representing the received signal, where n is an n-dimensional vector. r A vector of dimension ×1, representing noise. Meanwhile, x is the known transmitted reference signal to the receiver, and H is a vector of dimension n. r ×n t The channel matrix, where n t This refers to the number of ports for the reference signal. In the case of a multi-user (MU) multiple-input multiple-output (MIMO) device, n... t It can be greater than the rank of the transmitted data. (Assuming...) The channel is estimated, and the noise estimation can be expressed as follows:

[0007]

[0008] Furthermore, estimating the noise covariance becomes

[0009]

[0010] Multiple reference frequency moduli typically exist within a resource block (RB). For example, in 5G New Radio (NR), each RB included in the Physical Broadcast Channel (PBCH) and Physical Downlink Control Channel (PDCCH) contains three reference frequency moduli. Meanwhile, in the Physical Downlink Shared Channel (PDSCH), each RB may contain four or six reference frequency moduli, depending on the type of the corresponding reference signal (e.g., DMRS, CRS). Attached Figure Description

[0011] Figure 1 This includes a high-order schematic diagram showing two PDSCH-DMRS configurations within the RB.

[0012] Figure 2 This includes a high-level illustration of the workflow implemented by the receiver of a wireless communication system.

[0013] Figure 3 This illustrates how interference can occur in the middle of the channel's bandwidth.

[0014] Figure 4 It shows how imbalances in interference can cause the RBs of a given channel to be split into three clusters.

[0015] Figure 5 By using Figures 2-3 The example channel shown is simulated to illustrate the advantages of the method presented here.

[0016] Figure 6 This demonstrates how, after the first iteration of the method, the RBs contained in the channel are divided into four clusters.

[0017] Figure 7 A flowchart is depicted illustrating the process for distinguishing RBs with dissimilar noise distributions.

[0018] Figure 8 A flowchart is depicted for another process for dividing RBs into clusters based on the amount of interference contained in the RBs.

[0019] Figure 9 A flowchart is depicted for the process of combining non-adjacent RB clusters with relatively high noise distribution.

[0020] Figure 10 Includes a high-order block diagram illustrating an example computing system that can implement at least some of the operations described herein.

[0021] The various features of the technology described herein will become more apparent to those skilled in the art upon study of the detailed specification in conjunction with the accompanying drawings. Embodiments are shown in the drawings by way of example rather than limitation, wherein similar reference numerals may indicate similar elements. While the drawings depict various embodiments for illustrative purposes, those skilled in the art will recognize that alternative embodiments may be used without departing from the principles of the technology. Therefore, although specific embodiments are shown in the drawings, various modifications can be made to the technology. Detailed Implementation

[0022] For wireless communication systems (also known as "digital communication systems"), the maximum probability that a receiver will detect a signal originating from a source is based on the conditional probability given by signal measurements at the demodulator. This is illustrated below:

[0023]

[0024] Here, p(x|d=i) follows the probability distribution function of additive noise introduced by the wireless communication system, p(d=i) is the intrinsic probability of the source transmitting signal i, and p(x) is the probability of obtaining measurement x at the receiver. Typically, p(x) is ignored when using the likelihood ratio for decision-making.

[0025] When designing receivers for wireless communication systems, additive white Gaussian noise (AWGN) is the most fundamental model used to estimate natural noise. Each of these modifiers represents a different characteristic of the model. The term "additive" indicates that it is added to any noise inherent in the wireless communication system, the term "white" indicates that the noise has a uniform power within the frequency band used in the wireless communication system, and the term "Gaussian" indicates that the noise follows a normal distribution with an average time-domain value of zero.

[0026] To improve the accuracy of AWGNs, it is important to estimate the characteristics of this natural noise as accurately as possible. However, several factors hinder this estimation. First, the number of observations is often insufficient due to the limited number of reference signals. Second, imbalances often exist at different frequencies caused by interference.

[0027] In 4G and 5G broadband cellular networks, reference signals occupy a portion of the total bandwidth to reduce overhead. For example, in 5G broadband cellular networks, coded data carried along the Physical Downlink Shared Channel (PDSCH) is transmitted in combination with a demodulation reference signal (DMRS) using the same precoding and antenna ports. This ensures that user equipment (UE) can compare the received DMRS with the transmitted DMRS to infer the propagation channel and noise distribution. The 5G technology standard describes two PDSCH-DMRS configurations.

[0028] Figure 1 This includes a high-level schematic showing two PDSCH-DMRS configurations within a resource block (RB). The first configuration (referred to as "Class I configuration") uses 50% of the resource elements of the symbols allocated to the DMRS (e.g., 6 resource elements per antenna port per RB), while the second configuration (referred to as "Class II configuration") uses 33% of the resource elements of the symbols allocated to the DMRS (e.g., 4 resource elements per antenna port per RB).

[0029] To obtain the raw noise covariance, the estimated noise covariance can be averaged within the RB. The resulting metric can be called "per-RB noise covariance." However, in both configurations, the number of samples within the RB is insufficient to provide an accurate estimate of the noise covariance. Therefore, it is preferable to combine samples from multiple RBs to improve the accuracy of the estimation. However, in real-time broadband cellular networks, there may be interference from other sources at certain frequencies in the channel of interest. This interference will contaminate the DMRS that can be used to eliminate or suppress Gaussian noise characteristics (thus contaminating at least some of the samples).

[0030] Ideally, the noise covariance per RB should be averaged across the allocated bandwidth to improve the accuracy of noise estimation. However, due to dynamic interference from the environment, different RBs within the allocated bandwidth may experience varying degrees of interference. For example, some RBs may experience moderate interference, while others may not experience any interference. Furthermore, the inference across different RBs may also differ. Because interference is quite dynamic, using a large window size to estimate the noise covariance per RB is impractical.

[0031] Therefore, several methods are introduced here where the noise covariance is estimated over multiple RBs with similar noise distributions (or simply "distributions"). In general, these methods use a sliding window to compute the covariance (e.g., statistically determining whether noise distributions are similar between consecutive RBs), and then define clusters of RBs accordingly. The average covariance of each cluster can then be calculated, and each RB included in the cluster can be represented by the average covariance computed for that cluster. This approach of confirming the covariance based on the analysis of multiple RBs rather than a single RB can aid in subsequent detection and / or modulation. Therefore, the average covariance can be inserted into Equation 4 to improve the accuracy of the probabilistic determination of the likelihood that the receiver will detect a signal.

[0032] These methods lead to more accurate noise estimations because accuracy is improved by increasing the sample size and by identifying and eliminating contamination caused by interference. In other words, this disclosure introduces an automated method for detecting imbalances between RBs with pure noise and RBs with interference, and then forming clusters of RBs with similar properties to provide more samples that can be used to estimate the noise covariance.

[0033] As discussed further below, the algorithm can be applied to a series of redundancies (RBs) to detect those contaminated by interference. This series of RBs can represent a "window" of RBs being examined. The series of RBs can then be filtered to remove contaminated RBs. The noise covariance can then be estimated based on the filtered series of RBs to ensure the impact of the interference is minimized. For those RBs identified as containing interference, there are several ways to estimate their covariance in practice. For example, a minimum average window size can be applied, or a dynamic window size can be further determined based on the characteristics of the interference.

[0034] A careful study of this disclosure will make it clear that the technology is designed with performance, efficiency, and usability in mind. For example, the technology can be readily implemented in a modem chip designed to facilitate communication over 4G and 5G broadband cellular networks. The term "modem chip" refers to an integrated circuit configured to modulate signals in a manner that encodes data to be transmitted to another modem and / or decodes data received from another modem. However, it should be noted that while embodiments may be described in the context of a particular wireless communication system, those skilled in the art will recognize that these features can be similarly applied to other wireless communication systems.

[0035] Specific computing devices, channels, etc., may be referenced to the described embodiments. However, those skilled in the art will recognize that these features are equally applicable to other computing devices, channels, etc. For example, while embodiments may be described in the context of estimating the noise experienced by the modem, the features of these embodiments can be extended to other types of computing devices. For instance, the methods described herein can be implemented by any computing device that includes a receiver capable of processing services received over 4G and 5G broadband cellular networks.

[0036] Various aspects of the technology can be embodied using hardware, firmware, software, or any combination thereof. Therefore, embodiments may include a machine-readable medium having instructions that, when executed by a processor, cause the processor to perform a process in which noise experienced by the wireless communication system is calculated by identifying the type of each resource block under study, clustering resource blocks of the same type, and then determining the average noise covariance of each cluster of resource blocks.

[0037] Terminology

[0038] In this specification, references to "an embodiment," "one embodiment," and "some specific embodiments" mean that the described features, functions, structures, or characteristics are included in at least one embodiment. The appearance of these phrases does not necessarily refer to the same embodiment, nor does it necessarily refer to mutually exclusive alternative embodiments.

[0039] Unless the context explicitly requires otherwise, the words “including,” “containing,” and “comprise” should be understood in an inclusive sense (i.e., “including but not limited to”), rather than an exclusive or exhaustive sense. The term “based on” should also be understood in an inclusive sense, rather than an exclusive or exhaustive sense. Therefore, unless otherwise indicated, the term “based on” is intended to mean “at least partially based on.”

[0040] The terms “connection,” “linkage,” or any variation thereof are intended to include any direct or indirect connection or link between two or more elements. A connection / linkage can be physical, logical, or a combination thereof. For example, objects can be electrically or communicatively linked to each other even without sharing a physical connection.

[0041] When used to list multiple items, the word "or" is intended to cover all of the following interpretations: any item in the list, all items in the list, and any combination of items in the list.

[0042] The order of steps performed in the process described herein is exemplary. However, unless contrary to physical possibilities, these steps may be performed in different orders and combinations. For example, steps may be added or removed from the process described herein. Similarly, steps may be replaced or reclassified. Therefore, any description of a process is intended to be open-ended.

[0043] Overview of noise estimation scheme

[0044] In wireless communication systems, estimating the noise covariance is a necessary step when performing noise whitening transformation on the data signal and the estimated channel matrix before equalization and decoding. Figure 2 This includes a high-level illustration of the workflow implemented by the receiver in a wireless communication system. In the physical traffic channels of 4G and 5G broadband cellular networks, the cross-channel bandwidth of the reference signal is embedded in the RB so that the receiver can estimate both the channel and the noise from the reference signal. The whitening matrix can then be obtained from the noise covariance, for example, by applying the inverse Cholesky decomposition of the noise covariance.

[0045] To obtain the raw noise covariance for a wireless communication system, the estimated noise covariance can be averaged across the examined Restricted Blocks (RBs). However, since the number of samples within an RB is insufficient to provide an accurate estimate of the noise covariance, it is necessary to combine samples from multiple RBs to improve the accuracy of the estimate.

[0046] Historically, entities have attempted to address insufficient sample size by simply combining a fixed number of Resource Units (REs) from a predetermined number of Resource Blocks (RBs) to increase the number of observations available for noise estimation. However, the number of RBs cannot be too large due to interference. Otherwise, interference will be included in the observations (and thus introduce errors when estimating noise). In practice, typically only 2 to 4 RBs are combined to account for the trade-off between the accuracy of noise estimation and the likelihood of errors due to interference.

[0047] The method described here addresses this issue by allowing clusters of RBs of the same type to be grouped together without limiting the size of these clusters. Instead of using a fixed number of RBs for noise estimation, this method detects RBs that are affected by disturbances (causing abrupt changes in the distribution characteristics of the RB's covariance matrix) and then removes these RBs from consideration.

[0048] It is important to note that this method can be implemented as hardware, firmware, or software within the demodulator module. For example, the demodulator module can implement the method by executing computer-readable instructions. As another example, the demodulator module can provide information (as input) to an integrated circuit designed to implement the method. As discussed further below, this information may include the covariance matrix of each examined RB generated by the channel estimation module (also referred to as the "channel estimator"). The demodulator module can generate another covariance matrix as output for each RB after the clustering and averaging processes discussed below. For example, the integrated circuit may be an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA). Furthermore, the demodulation module and the channel estimator can be included in a computing device, such as a modem, which includes a wireless communication system.

[0049] Figure 3 This illustrates how interference can occur in the middle of a channel bandwidth. Here, the channel is a PDSCH channel. Figure 3 As shown, there are 30 RBs in a PDSCH channel. In this example, the central 10 RBs (RB11-RB20) are affected by interference. In summary, the method presented here allows for the detection of abrupt changes in the distribution of noise and interference, enabling the 30 RBs to be segmented into clusters, i.e., interpolating "interference" clusters between pairs of "noise" clusters. The covariance matrix can then be averaged within each cluster to improve the estimation using more samples (also called "observations"). It is important to note that the term "noise cluster" refers to a cluster of one or more RBs whose distribution roughly indicates pure noise. Conversely, the term "interference cluster" refers to a cluster of one or more RBs whose distribution indicates interference in addition to noise.

[0050] First, the demodulator module can initialize a sliding window, starting from the first noise block (RB) occupying the given channel bandwidth. Then, the demodulator module can define the size of the sliding window as m RBs, where m is a predefined parameter with default values. Next, the demodulator module can calculate the average covariance of the noise of the RBs contained within the sliding window. This average covariance can then be compared with the average covariance of the noise of the next n RBs occupying the given channel bandwidth, where n is a predefined parameter with default values, to generate a distance metric. In summary, the distance metric represents the similarity between these average covariance values.

[0051] If the distance metric exceeds a threshold, this means there is a significant probability that the next n RBs have different distribution characteristics than the m RBs included in the sliding window. In this case, the demodulator module can mark these RBs included in the sliding window as belonging to a cluster. As discussed further below, each RB in the cluster can be represented as a whole by the average covariance of the cluster. The demodulator module can then reinitialize the sliding window to start at the next RB (i.e., the first RB in the next n RBs). However, if the distance does not exceed the threshold, the demodulator module can expand the sliding window to include the next n RBs. The above steps can be performed until all RBs occupying a given channel bandwidth are allocated to clusters. Note that each cluster can have a minimum size of m RBs, as this is the minimum size that defines the sliding window.

[0052] After this process is complete, the demodulator module can output a list of Restricted Blocks (RBs) occupying the given channel bandwidth. For each RB, the list can specify the corresponding cluster and / or average covariance. The demodulator module can store this information in memory for later use.

[0053] For example, suppose the default value of m is 2, and the default value of n is 1. In this case, the demodulator module will initialize the sliding window so that it contains RB1 and RB2, as follows. Figure 3 As shown. The demodulator module then calculates the average covariance of RB1 and RB2, and compares this average covariance with the covariance of RB3. If these covariance values ​​are similar to each other, the sliding window can be expanded to include RB1, RB2, and RB3. However, if these covariance values ​​are dissimilar to each other, the sliding window can be reinitialized to include RB3 and RB4. In this case, RB1 and RB2 can be defined as RB clusters of the same type. Note that these default values ​​m and n are provided for illustrative purposes. Those skilled in the art will recognize that the default values ​​m and n can be arbitrary values.

[0054] Another example of this process is... Figure 4 As shown in the diagram, the imbalance in the interference causes the RB to be divided into three clusters.

[0055] For example, distance metrics and thresholds in this context can be predetermined based on, for example, noise and interference statistics, to optimize the accuracy of interference detection in terms of the computational complexity of the target-to-interference-to-noise ratio. Candidate distance metrics may include the following:

[0056] • If the noise per RB with a covariance matrix of Σ has zero mean, then KL divergence can be used to express the similarity between two distributions.

[0057] If the noise on different antennas and different RBs can be modeled as independent and identically distributed random variables with a normal distribution, then the chi-square test can be used; and

[0058] • If the noise can be modeled as independent and identically distributed random variables, and has a normal distribution on each antenna, then an exponential distribution can be used on a per-antenna basis.

[0059] This approach of clustering RBs with considerable interference leads to several significant benefits. For example, clustering RBs allows for the consideration of more samples when estimating noise in a given RB. Unlike relying on noise in a single RB, estimation can be improved by observing noise across multiple RBs with similar distributions, while also avoiding errors that might be introduced by imbalanced distributions. More accurate noise estimation ultimately leads to improved receiver performance. This is particularly beneficial for broadband data channels, such as those in 4G and 5G broadband cellular networks, where spur interference typically occurs at certain frequencies within the bandwidth of some channels.

[0060] Figure 5 By using Figures 3-4 The example channel shown is simulated to illustrate the advantages of the method presented here. Figure 5 In the diagram, the crosshairs indicate the KL divergence between the per-RB covariance and the genie noise covariance, while the circles indicate the KL divergence between the 2-RB mean covariance and the genie noise covariance. The line segments represent the cluster and the corresponding KL divergence between the cluster mean covariance and the genie noise covariance. According to simulations, the divergence between the cluster mean covariance and the genie noise covariance is much smaller than that of the 2-RB mean covariance and the per-RB covariance (e.g., close to zero for a 10RB cluster).

[0061] When implemented, the method described here will result in neighboring RBs being classified as clusters with a computable average covariance. According to statistical theory, the sample size (N) required for an estimation error less than δ = 0.5σ (with a probability of 90%) is 35. This implies a configuration of 6 RBs for class I and 9 RBs for class II.

[0062] The situation may be that after implementing this method, at least some clusters will be smaller than the minimum size required to meet this performance goal. Therefore, it may be necessary to perform multiple iterations of this method to further combine those clusters that share similar distributions. Figure 6This demonstrates how, after the first iteration of the method, the RBs included in the channel are classified into four clusters. These four clusters can include two noise clusters and two interference clusters, each noise cluster having four RBs and each interference cluster having two RBs. After the second iteration of the method, the average noise covariance of the first and third clusters can be further combined because these clusters share similar distributions. Because the second and fourth clusters experience interference from different sources, these clusters may not be combined into a superset cluster.

[0063] Methodology for distinguishing RBs with dissimilar noise distributions

[0064] Figure 7 A flowchart depicts a process 700 for distinguishing RBs with dissimilar noise distributions. Initially, the demodulation module initializes a sliding window of size m RBs, which occupy the bandwidth of a given channel (step 701). For example, the demodulation module may initialize the sliding window starting from the first RB occupying the bandwidth of the given channel. While the value of m is typically at least 2, in some embodiments the value of m may be 1. Note that the bandwidth occupied by each RB can be configured based on the spacing of subcarriers occupied by the RBs in a given channel. The number of RBs associated with a given channel can be based on the network technology (such as 4G or 5G) for which the given channel is designed and configured.

[0065] The demodulation module can then calculate the average covariance of the noise across the m blocks (RBs) contained in the sliding window (step 702). Covariance is a measure of the joint variability of two random variables. For example, the random variables could be noise in a first RB and noise in a second RB. A positive covariance occurs if a larger value of one random variable largely corresponds to a larger value of the other random variable, and vice versa for smaller values. Conversely, a negative covariance occurs when a larger value of one random variable primarily corresponds to a smaller value of the other random variable. As discussed further below, the demodulator module can determine whether to expand or reinitialize the sliding window based on a threshold representing or based on the covariance.

[0066] Subsequently, the demodulator module can generate a distance metric by comparing the average covariance of the m RBs with the covariance of the next RB following the m RBs (step 703). It should be noted that in some embodiments, the demodulator module is configured to compare the average covariance of the m RBs with the average covariance of the next n RBs (where n is at least 2). Therefore, the demodulator module can compare the m RBs with one or more RBs in terms of noise distribution.

[0067] The demodulator module can then compare the distance metric to a threshold. This threshold can be programmed in the memory of the computing device to which the demodulator module belongs. Furthermore, this threshold can be based on a given channel. If the distance metric does not exceed the threshold, the demodulator module can infer that the next RB has a similar noise distribution to the m RBs. In this case, the demodulator module can expand the sliding window to include the next RB beyond the m RBs.

[0068] However, if the demodulator module determines that the distance metric does indeed exceed the threshold (step 704), then the demodulator module can define the m RBs as representing a cluster of RBs of the same type (step 705). In other words, the demodulator module can define the m RBs as representing a cluster of RBs with a comparable noise distribution. As mentioned above, there are two types of RBs: RBs with pure noise and RBs with both interference and noise. Therefore, the RB cluster may be affected by interference while the next RB is not, or the RB cluster may not be affected by interference while the next RB is.

[0069] The demodulator module can then associate the average covariance with each of the m RBs (step 706). For example, the demodulator module can indicate in a data structure that the average covariance represents each of the m RBs. The data structure can include separate entries for each RB of a given channel, each entry associated with one of the m RBs being populated with the average covariance.

[0070] Furthermore, the demodulator module can reinitialize the sliding window so that it contains n RBs following m RBs (step 707). That is, the demodulator module can reinitialize the sliding window starting from the next resource block so that process 700 can be executed again. As described above, process 700 can be repeated until all RBs occupying bandwidth in a given channel are allocated to the cluster.

[0071] Figure 8 A flowchart of another process 800 is depicted, which is used to group RBs into clusters based on the amount of interference they contain. Initially, the demodulator module may initialize a sliding window such that the sliding window contains a series of RBs occupying a given channel bandwidth (step 801). Figure 8 Step 801 can be with Figure 7 Step 701 is largely similar. For example, the given channel could be a physical channel defined according to the 5G New Radio (NR) standard. The demodulator module can then calculate the average covariance of a series of RBs contained within the sliding window (step 802). Figure 8 Step 802 can be with Figure 7 Step 702 is roughly the same.

[0072] The average covariance of the series of RBs can then be compared with the covariance of a first RB following the series of RBs (step 803). As described above, in some embodiments, the first RB is part of a second series of RBs to which the series of RBs is compared. Therefore, the demodulator module can be configured to compare the average covariance of the series of RBs with the average covariance of the second series of RBs to which the first RB belongs. The demodulator module can determine, based on the comparison result, whether the first RB has an interference amount comparable to that of the series of RBs (step 804).

[0073] In some embodiments, performing step 803 results in the generation of a distance metric that indicates the similarity of interference in a series of RBs to interference in a first RB. If the demodulator module determines that the distance metric exceeds a threshold, the demodulator module may define the series of RBs as representative of an RB cluster with a considerable amount of interference. However, if the demodulator module determines that the distance metric does not exceed the threshold, the demodulator module may expand the sliding window such that the sliding window includes both the series of RBs and the first RB.

[0074] Figure 9 A flowchart depicts a process 900 of combining non-adjacent clusters of RBs with comparable noise distributions. Initially, the demodulation module can determine that the RBs occupying a given channel bandwidth have been classified into a series of clusters (step 901). For example, this can be achieved by repeating the process... Figure 7 or Figure 8 The process involves establishing a series of clusters until all RBs for a given channel are assigned to clusters. Each cluster consists of one or more RBs with considerable interference. Therefore, all RBs in each cluster have similar noise distributions.

[0075] The demodulation module can then identify a given cluster from a set of clusters where the number of RBs is below a threshold (step 902). The threshold can represent a static value programmed into the memory of the computing device to which the demodulation module belongs. In general, the threshold indicates the minimum number of RBs that should be included in each cluster. The demodulation module can then confirm that a first cluster preceding a given cluster and a second cluster following a given cluster have comparable levels of interference (step 903). This can be achieved by comparing the average covariance of the first cluster with the average covariance of the second cluster. In this case, the demodulation module can combine the first and second clusters into a superset cluster, provided that the first and second clusters originate from the same source. More specifically, the demodulation module can calculate a covariance metric based on the average covariance of the first and second clusters and then associate the covariance metric with each RB included in the first and second clusters. If the interference in the first cluster originates from a different source than the interference in the second cluster, the demodulation module can avoid combining the first and second clusters.

[0076] In implementation, this approach of combining non-adjacent clusters may result in a reduction in the number of clusters created for a given channel without filtering any RBs. As described above, this can be achieved... Figure 9 The demodulation module performs process 900 to increase the number of samples available for noise estimation purposes. Thus, if a minimum number of samples has been defined, the demodulation module can repeat process 900 until the superset cluster includes at least a predetermined number of RBs.

[0077] It should be noted that, although Figures 7-9 The process is discussed in the context of the demodulation module, but these processes can be performed by another processing component of the computing device. For example, the channel state feedback module can also use the noise covariance generated by the same process. More generally, the process can be applied to any module that needs to perform noise whitening. The process can be implemented in specially designed hardware or in software running on a general-purpose processor. Whether the process is implemented in hardware or software can depend on design constraints in terms of latency and power.

[0078] The steps of these processes can be performed in various combinations and sequences. For example, they can be repeated. Figures 7-8 The process continues until all RBs occupying the channel bandwidth being examined are allocated to clusters. In some embodiments, additional steps may be included. For example, the demodulator module may be configured to output a list of RBs occupying the channel bandwidth being examined. This list may specify a covariance value for each RB, representing the average covariance calculated for the corresponding cluster. Additionally or optionally, the list may also specify the cluster to which each RB has been allocated.

[0079] Computing system

[0080] Figure 10 This includes a high-level block diagram illustrating an example of a computing system 1000 that can implement the processes described herein. Therefore, components of the computing system 1000 can reside on a computing device that includes processing components (e.g., a demodulation module) operable to perform the processes described herein.

[0081] The computing system 1000 may include a processor 1002, main memory 1006, non-volatile memory 1010, network adapter 1012, video display 1018, input / output device 1020, control device 1022 (e.g., keyboard, pointing device, or mechanical input such as buttons), drive unit 1024 including storage medium 1026, and signal generation device 1030 communicatively connected to bus 1016. Bus 1016 is shown as an abstract concept representing one or more physical buses and / or point-to-point connections via appropriate bridges, adapters, or controllers. Therefore, bus 1016 may include a system bus, Peripheral Component Interconnect (PCI) bus, PCI Fast bus, HyperTransport bus, Industry Standard Architecture (ISA) bus, Small Computer System Interface (SCSI) bus, Universal Serial Bus (USB), Interconnect Integrated Circuits (ICI) bus, etc. 2 C) Bus or bus conforming to IEEE Standard 1394.

[0082] The computing system 1000 may share a similar computer processor architecture with another electronic device that is a server, router, desktop computer, tablet computer, mobile phone, video game console, wearable electronic device (such as a watch or fitness tracker), network-connected (“smart”) device (such as a television or home assist device), augmented or virtual reality system (such as a head-mounted display), or another electronic device capable of executing a set of (sequential or other) instructions that specify the actions to be taken by the computing system 1000.

[0083] Although the main memory 1006, non-volatile memory 1010, and storage medium 1026 are shown as a single medium, the terms "storage medium" and "machine-readable medium" should be considered as including a single medium or multiple media storing one or more sets of instructions 1028. The terms "storage medium" and "machine-readable medium" should also be considered as including any medium capable of storing, encoding, or carrying a set of instructions for execution by the computing system 1000.

[0084] Typically, routines executed to implement embodiments of the present disclosure may be implemented as part of an operating system or a particular application, component, program, object, module, or sequence of instructions (collectively, a "computer program"). A computer program typically includes one or more instructions (e.g., instructions 1004, 1008, 1028) that are disposed at different times in various memories and storage devices in a computing device. When read and executed by processor 1002, the instructions cause computing system 1000 to perform operations to execute various aspects of the present disclosure.

[0085] While embodiments have been described in the context of a full-featured computing device, those skilled in the art will understand that various embodiments can be distributed as program products in various forms. This disclosure applies to specific types of machine-readable or computer-readable media that are actually performed on the distribution. Further examples of machine-readable and computer-readable media include recordable media such as volatile and non-volatile memory 1010, removable disks, hard disks, optical discs (e.g., optical disc read-only memory (CD-ROM) and digital versatile disks (DVD)), cloud-based storage, and transmission-type media such as digital and analog communication links.

[0086] Network adapter 1012 enables computing system 1000 to exchange data with entities outside computing system 1000 on network 1014 via any communication protocol supported by computing system 1000 and external entities. Network adapter 1012 may include a network adapter card, wireless network card, switch, protocol converter, gateway, bridge, hub, receiver, repeater, or transceiver containing integrated circuits (e.g., via...). or (To achieve communication).

[0087] The techniques described herein can be implemented using software, firmware, hardware, or a combination of these forms. For example, various aspects of the invention can be implemented using dedicated hardwired (i.e., non-programmable) circuitry in the form of application-specific integrated circuits (ASICs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), etc.

[0088] Notes

[0089] For ease of illustration, descriptions of the various embodiments described above have been provided. It is not intended to be exhaustive, nor is it intended to limit the claimed subject matter to the precise forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to best illustrate the principles of the invention and its practical application, thereby enabling those skilled in the art to understand the claimed subject matter, the various embodiments, and the various modifications suited to the particular intended use.

[0090] Although various embodiments are described in detail, the technology can be practiced in many ways, no matter how detailed the description is presented. Embodiments may vary considerably in implementation details, but are still included in the specification. Specific terms used in describing certain features or aspects of the various embodiments should not be construed as implying that the terms are redefined herein as limited to any particular characteristic, feature, or aspect of the technology associated with that term. In general, unless these terms are expressly defined herein, the terms used in the following claims should not be construed as limiting the technology to the specific embodiments disclosed in the specification. Therefore, the actual scope of the technology includes not only the disclosed embodiments but also all equivalent ways of practicing or implementing the embodiments.

[0091] The language used in this specification is primarily for readability and pedagogical purposes. It may not have been chosen to describe or limit the subject matter. Therefore, its scope is not limited by this detailed description, but rather by any claims made in the application based on this detailed description. Thus, the disclosure of various embodiments is intended to illustrate, but not limit, the scope of the technology set forth in the following claims.

Claims

1. A method for distinguishing resource blocks having disparate noise profiles, the method comprising: initializing a sliding window having a size of m resource blocks, the m resource blocks occupying a given channel bandwidth, m being a value of at least 2; computing an average covariance of noise in the m resource blocks contained in the sliding window; producing a distance metric by comparing the average covariance of noise in the m resource blocks with a covariance of noise in a next resource block located after the m resource blocks; determining that the distance metric exceeds a threshold value; in response to the determining, defining the m resource blocks as representative of a cluster of resource blocks having comparable noise profiles; and associating the average covariance with each of the m resource blocks; in response to the determining, reinitializing the sliding window so that the sliding window contains n resource blocks located after the m resource blocks, n being a value of at least 2, wherein the next resource block is a first resource block of the n resource blocks. The threshold value is determined based on a channel bandwidth of the given channel.

2. The method of claim 1, wherein, A bandwidth occupied by each resource block is configured based on a spacing of subcarriers occupied by resource blocks of the given channel.

3. The method of claim 1, wherein, The determining indicates that the next resource block is of a different type than the series of resource blocks.

4. The method of claim 1, wherein, Each resource block of the series of resource blocks is affected by interference, while the next resource block is not affected by interference.

5. The method of claim 4, wherein, Each resource block of the series of resource blocks is not affected by interference, while the next resource block is affected by interference.

6. The method of claim 4, wherein, The associating includes:

7. The method of claim 1, wherein, in a data structure, indicating that the average covariance is representative of each of the m resource blocks, wherein the data structure includes an entry for each resource block in the given channel, and wherein each entry associated with one of the m resource blocks is populated with the average covariance.

8. A non-transitory computer-readable medium having instructions that, when executed by a processor of a computing device, cause the computing device to perform operations comprising: initializing a sliding window so that the sliding window contains a series of resource blocks occupying a given channel bandwidth; computing an average covariance of noise in the series of resource blocks contained in the sliding window; comparing the average covariance of noise in the series of resource blocks with a covariance of noise in a first resource block located after the series of resource blocks; and based on a result of the comparing, determining whether the first resource block has a comparable amount of interference to the series of resource blocks; the comparing results in producing a distance metric that indicates a similarity in interference in the series of resource blocks and the first resource block, and wherein the determining includes: confirming that the distance metric does not exceed a threshold value, and expanding the sliding window so that the sliding window contains the series of resource blocks and the first resource block; the series of resource blocks and the first resource block are representative of a second series of resource blocks, and wherein the operations further comprise: ​ ​ computing an average covariance of noise in the second series of resource blocks contained in the extended sliding window; comparing the average covariance of noise in the second series of resource blocks to a covariance of noise in a second resource block located after the second series of resource blocks; and based on a result of the comparison, determining whether the second resource block has a comparable amount of interference to the second series of resource blocks; repeating the operations until all resource blocks occupying the given channel bandwidth are assigned to clusters of resource blocks having comparable amounts of interference, and wherein each cluster of resource blocks includes at least one resource block.

9. The non-transitory computer-readable medium of claim 8, wherein, The given channel is a physical channel defined according to the 5G New Radio (NR) standard.

10. The non-transitory computer-readable medium of claim 8, wherein the comparison results in a distance metric that indicates a similarity in interference in the series of resource blocks and the first resource block, and wherein the determining includes: confirming that the distance metric exceeds a threshold, and defining the series of resource blocks as a representative of a cluster of resource blocks having comparable amounts of interference.

11. The non-transitory computer-readable medium of claim 10, further comprising: associating the average covariance with each resource block in the series of resource blocks.

12. The non-transitory computer-readable medium of claim 8, wherein, The operations further include: outputting a list of all resource blocks occupying the given channel bandwidth, wherein the list specifies a covariance value for each resource block, the covariance value being a representative of the average covariance computed for a corresponding cluster of resource blocks.

12. The non-transitory computer-readable medium of claim 11, wherein the operations further include: repeating the operations until all resource blocks occupying the given channel bandwidth are assigned to clusters of resource blocks having comparable amounts of interference.

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