Estimation device, estimation method, and program

Abstract

To provide an estimation device, an estimation method, and a program capable of estimating a channel quality with a small amount of processing even in an environment with many radio resources.SOLUTION: An estimation device according to the present disclosure includes: generation means of generating one or more first channel information vectors using channel information related to each of a plurality of radio resources; and specification means of specifying one or more second channel information vectors from among a plurality of predetermined channel information vectors according to a distance from the one or more first channel information vectors, and output channel quality information corresponding to the one or more second channel information vectors.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an estimation device, an estimation method, and a program. [Background technology]

[0002] Multi-user Multi-Input Multi-Output (MIMO) is a well-known technique for achieving high-capacity communications. In this technique, a base station with multiple antennas simultaneously communicates with multiple wireless terminals (user terminals). Multi-user MIMO can be used in multi-access cellular systems, such as the 5th generation mobile communications (5G) system.

[0003] In multi-user MIMO, multiple wireless terminals transmit and receive wireless signals (layers) by performing spatial multiplexing using the same wireless resources. Therefore, depending on the combination of spatially multiplexed wireless terminals and layers, interference may increase, degrading reception quality (e.g., received signal to interference and noise power ratio (SINR)). This may result in a decrease in throughput.

[0004] Therefore, in order to improve reception quality, the wireless terminals and layers to be spatially multiplexed, as well as the Modulation and Coding Scheme (MCS), are selected by scheduling.To improve reception quality, scheduling needs to estimate the reception quality of each layer for each combination of wireless terminals and layers to be spatially multiplexed.

[0005] For example, Non-Patent Document 1 discloses a related technology system that outputs an estimated value of channel quality. This system uses channel information between a base station and a wireless terminal as a key and channel quality as a value, and outputs a value corresponding to a key that is close to the channel information at the time of transmission as an estimated value of channel quality. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] J. Li, T. Yang, H. Chen, and W. Wang, “Link adaptation in MIMO systems by using machine learning,” IEEE International Conference on Information and Automation, 2018. Summary of the Invention [Problem to be solved by the invention]

[0007] In the system described in Non-Patent Document 1, when communication is performed using the Orthogonal Frequency Division Multiplexing (OFDM) method, it is necessary to estimate the channel quality for each subcarrier and then calculate the average channel quality for each radio resource allocation unit. Therefore, when the bandwidth is wide and the number of subcarriers is large, as in 5G, there is a problem that the amount of processing required for estimation becomes large. Generally, since reception quality estimation is performed repeatedly, it is desirable to speed up the processing by reducing the amount of processing.

[0008] One of the objectives to be achieved by the embodiments of the present disclosure is to provide an estimation device, an estimation method, and a program that can estimate channel quality with a small amount of processing even in an environment with many wireless resources. It should be noted that this objective is only one of multiple objectives to be achieved by multiple embodiments disclosed herein. Other objectives or problems and novel features will become apparent from the description of this specification or the accompanying drawings. [Means for solving the problem]

[0009] An estimation device according to one aspect includes: generating means for generating one or more first channel information vectors using channel information related to each of a plurality of radio resources; a specifying means for specifying one or more second channel information vectors from a plurality of predetermined channel information vectors according to distances from the one or more first channel information vectors, and outputting channel quality information corresponding to the one or more second channel information vectors; Equipped with.

[0010] An estimation method according to one aspect includes the steps of: generating one or more first channel information vectors using channel information associated with each of a plurality of radio resources; identifying one or more second channel information vectors from a plurality of predetermined channel information vectors according to distances from the one or more first channel information vectors, and outputting channel quality information corresponding to the one or more second channel information vectors; This is what a computer does.

[0011] A program according to one aspect includes: generating one or more first channel information vectors using channel information associated with each of a plurality of radio resources; identifying one or more second channel information vectors from a plurality of predetermined channel information vectors according to distances from the one or more first channel information vectors, and outputting channel quality information corresponding to the one or more second channel information vectors; This is what causes a computer to execute the above. [Effects of the Invention]

[0012] According to the present disclosure, it is possible to provide an estimation device, an estimation method, and a program that can estimate channel quality with a small amount of processing even in an environment with many wireless resources. [Brief explanation of the drawings]

[0013] [Figure 1]FIG. 1 is a block diagram illustrating an example of an estimation device according to the present disclosure. [Figure 2] FIG. 2 is a flowchart showing an example of a typical process performed by the estimation device. [Figure 3] FIG. 3 illustrates an example configuration of a wireless communication system according to the present disclosure. [Figure 4] FIG. 4 is a block diagram illustrating an example of a base station according to the present disclosure. [Figure 5] FIG. 5 is a block diagram illustrating an example of a processor according to the present disclosure. [Figure 6] FIG. 6 is a flowchart showing an example of a typical process performed by the base station. [Figure 7] FIG. 7 shows the relationship between the channel quality estimated by the database searcher and the actual channel quality. [Figure 8] FIG. 8 is a block diagram illustrating an example of the hardware configuration of an information processing device. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. Note that the following descriptions and drawings in the embodiments have been omitted or simplified as appropriate for clarity of explanation. Furthermore, in this disclosure, unless otherwise specified, when multiple items are defined as "at least one of multiple items," the definition may mean any one item, or any multiple items including all items.

[0015] Each drawing referenced in the embodiments is merely an example for describing one or more embodiments. Each drawing may not relate to only one specific embodiment, but may also relate to one or more other embodiments. As will be understood by those skilled in the art, various features or steps described with reference to any one drawing can be combined with features or steps shown in one or more other drawings to create, for example, an embodiment not explicitly shown or described. Not all features or steps shown in any one drawing are necessarily required to describe an exemplary embodiment, and some features or steps may be omitted. The order of steps described in any drawing may be changed as appropriate. Furthermore, components or processes described with the same reference numerals throughout multiple drawings indicate the same or corresponding components or processes. For clarity of description, redundant description will be omitted as necessary.

[0016] Embodiment 1 [Configuration Description] FIG. 1 is a block diagram showing an example of an estimation device according to the present disclosure. The estimation device 10 includes a generation unit 102 and an identification unit 104. The estimation device 10 may be configured as part of a wireless device, for example. This wireless device may be configured as part of a base station. However, the device on which the wireless device is mounted is not limited to this. Each unit (each means) of the estimation device 10 is controlled by a control unit (controller) not shown. Each unit of the estimation device 10 will be described below.

[0017] The generating unit 102 generates one or more first channel information vectors using channel information related to each of the plurality of radio resources. The generating unit 102 may be implemented as a combiner that generates one or more first channel information vectors by, for example, combining channel information. As another example, the generating unit 102 may generate one or more first channel information vectors by adding channel information related to each of adjacent radio resources.

[0018] Furthermore, the number of first channel information vectors generated by the generating unit 102 may be equal to or less than the number of radio resources, or may be greater than the number of radio resources. For example, when the generating unit 102 generates multiple types of channel information, the number of first channel information vectors generated by the generating unit 102 may be greater than the number of radio resources. Examples of types of channel information and examples of combining channel information will be described in the second embodiment. The radio resources include at least any of resources used for radio, such as resources related to carrier waves (e.g., subcarriers), resources related to time, and resources related to codes. The channel information includes, for example, any of information related to the phase of the channel, information related to the amplitude, etc. The channel information may be information on a received signal received by a radio device equipped with the estimation device 10.

[0019] For example, the channel information may be calculated based on an N×M (i.e., N rows and M columns) channel matrix defined between M antennas of the wireless terminal and N antennas of a wireless device having the estimation apparatus 10. M is one or more numbers, and N is more than one number. The channel matrix is ​​estimated using a reference signal transmitted from the wireless terminal (user) to the wireless device (base station) or transmitted from the wireless device to the wireless terminal. Here, the wireless terminal is a device that transmits and receives signals to and from the wireless device. Furthermore, the N antennas may be included in the wireless device or may be configured as devices separate from the wireless device.

[0020] The identifying unit 104 identifies one or more second channel information vectors from a plurality of predetermined channel information vectors according to the distance from one or more first channel information vectors. The identifying unit 104 may identify one second channel information vector that is considered to be closest to one first channel information vector by performing a data search using, for example, an approximate nearest neighbor (ANN). However, the identifying unit 104 may also identify the second channel information vector that is considered to be closest to the first channel information vector by performing a search using any nearest neighbor search means other than an ANN. When there are multiple first channel information vectors, the identifying unit 104 may identify one second channel information vector for each first channel information vector by performing a similar data search for each first channel information vector.

[0021] As another example, the identifying unit 104 may identify one second channel information vector from among a plurality of predetermined channel information vectors according to the distances from a plurality of first channel information vectors. For example, the identifying unit 104 may calculate the distance between one predetermined channel information vector and one first channel information vector for each first channel information vector. Then, the identifying unit 104 may identify one second channel information vector from among the plurality of predetermined channel information vectors, the second channel information vector having the smallest sum of the calculated distances.

[0022] As yet another example, the identifying unit 104 may identify a plurality of second channel information vectors from among a plurality of predetermined channel information vectors according to their distance from one first channel information vector. For example, the identifying unit 104 may identify a predetermined number of second channel information vectors in order of decreasing distance from one first channel information vector.

[0023] The identifying unit 104 outputs channel quality information corresponding to the identified one or more second channel information vectors. When the identifying unit 104 identifies one second channel information vector, it outputs channel quality information corresponding to the identified second channel information vector. On the other hand, when the identifying unit 104 identifies multiple second channel information vectors, it may output corresponding channel quality information for each identified second channel information vector. As another example, the identifying unit 104 may average the channel quality values ​​corresponding to each identified second channel information vector and output the average value as the channel quality information. The channel quality information may be information indicating, for example, an average channel quality, a degree of fluctuation in channel quality, etc.

[0024] A plurality of predetermined channel information vectors and channel quality information corresponding to each channel information vector may be provided, for example, as a database. In the database, the channel information vectors are treated as keys and the channel quality information as values. The identifying unit 104 then refers to the database to identify channel quality information corresponding to the identified one or more second channel information vectors. The identifying unit 104 outputs the identified channel quality information as an estimated value. In this case, the identifying unit 104 is provided as a database searcher. The database is created in advance by simulation, actual measurement, or other methods.

[0025] [Flow description] 2 is a flowchart showing an example of a typical process of the estimation device 10. This flowchart explains the process of the estimation device 10. Note that the details of each process are as described above, and therefore will not be explained again.

[0026] First, the generating unit 102 generates one or more first channel information vectors using channel information related to each of a plurality of radio resources (step S12). Then, the identifying unit 104 identifies one or more second channel information vectors from a plurality of predetermined channel information vectors according to the distance from the one or more first channel information vectors. The identifying unit 104 outputs channel quality information corresponding to the one or more second channel information vectors (step S14).

[0027] [Effect description] As described above, the identification process executed by the identification unit 104 is performed on one or more first channel information vectors generated by the generation unit 102. That is, the identification process, such as search, can be performed fewer times compared to when the identification process is performed using the channel information related to each of the multiple radio resources as is. Therefore, the estimation device 10 can reduce the amount of processing required for channel quality estimation. This effect becomes more pronounced as the number of radio resources increases. For example, even in an environment with a large number of radio resources, such as OFDM, the estimation device 10 can achieve channel quality estimation with a small amount of processing. The estimation device 10 can reduce the amount of processing required for estimation, even in communications such as 5G.

[0028] The estimation device 10 may be configured as a single computer device or as a distributed system having multiple computer devices. In a distributed system, the processing performed by the estimation device 10 can be shared and executed by multiple computer devices. In other words, the generation unit 102 and the identification unit 104 may be distributed and installed on two or more computer devices.

[0029] In the following embodiments, specific examples of the estimation device 10 described in embodiment 1 are disclosed. However, the specific examples of the estimation device 10 described in embodiment 1 are not limited to those shown below. Furthermore, the configurations and processes described below are examples and are not limited to these. All or any part of the configurations and processes described in embodiment 2 can be applied as appropriate to the estimation device described in embodiment 1, and it goes without saying that the drawings shown in embodiment 2 can be applied as appropriate to embodiment 1.

[0030] Embodiment 2 [Configuration Description] FIG. 3 illustrates an exemplary configuration of a wireless communication system according to the present disclosure. Referring to FIG. 3, a base station 20 provides wireless access to one or more wireless terminals 30 (users). The base station 20 may be referred to as an access point, a transmission / reception point (TRP), or other name. The base station 20 may be, for example, a system including a gNodeB (gNB) or a distributed unit (DU) in a 5G system. In some implementations, the wireless communication system S may utilize multi-user multi-input multi-output (MIMO) technology for uplinks from multiple wireless terminals 30 to the base station 20 or for downlinks from the base station 20 to multiple wireless terminals 30. The base station 20 may estimate channels between the base station 20 and one or more wireless terminals 30 based on reference signals transmitted from the one or more wireless terminals 30. By estimating the channels, the base station 20 selects a combination of wireless terminals 30 to be spatially multiplexed for transmission and a modulation and coding scheme (MCS) to be used for transmission. Furthermore, the base station 20 may receive a channel estimation result based on a reference signal transmitted to one or more wireless terminals 30, and select a combination of wireless terminals 30 to spatially multiplex and transmit, and an MCS to use for transmission.

[0031] The following description focuses on the uplink as a specific example, but the processing described below can also be applied to the downlink. For simplicity, it is assumed that an Orthogonal Frequency Division Multiplexing (OFDM) signal transmitted by each wireless terminal 30 has a cyclic prefix of an appropriate length inserted into the transmitted signal, and that each subcarrier channel is flat-fading. However, the configuration of the OFDM signal is not limited to this.

[0032] Hereinafter, it is assumed that transmission signals are transmitted from a total of M transmission antennas of one or more wireless terminals 30 and received at a base station 20 equipped with N reception antennas. In this case, the complex signal model expressed using the equivalent low-pass expression in the k-th subcarrier is expressed as (1):

number

[0033] 4 is a block diagram showing an example of a base station according to the present disclosure. Referring to FIG. 4, the base station 20 includes an antenna array 202, a radio frequency (RF) transceiver 204, a processor 206, a network interface 208, and a memory 210. Each component of the base station 20 will now be described.

[0034] The RF transceiver 204 performs analog RF signal processing for communicating with one or more wireless terminals 30. The RF transceiver 204 may include one or more transceivers. The RF transceiver 204 is coupled to the antenna array 202 and the processor 206. The RF transceiver 204 generates a baseband received signal based on the received RF signal received by the antenna array 202 and provides the baseband received signal to the processor 206.

[0035] The processor 206 performs digital baseband communication processing (data plane processing) and control plane processing for wireless communication. The processor 206 may include one processor or multiple processors. For example, the processor 206 may include at least one of a modem processor (e.g., a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), or a Digital Signal Processor (DSP)) that performs digital baseband processing and a protocol stack processor (e.g., a CPU or a Micro Processing Unit (MPU)) that performs control plane processing.

[0036] For example, digital baseband signal processing by processor 206 may include signal processing of at least one of a Service Data Adaptation Protocol (SDAP) layer, a Packet Data Convergence Protocol (PDCP) layer, a Radio Link Control (RLC) layer, a Medium Access Control (MAC) layer, and a Physical (PHY) layer. Also, control plane processing by processor 206 may include processing of at least one of Non-Access Stratum (NAS) messages, Radio Resource Control (RRC) messages, Medium Access Control (MAC) Control Elements (CEs), and Downlink Control Information (DCI).

[0037] The network interface 208 is used to communicate with network nodes (e.g., other base stations and core network nodes) and may include, for example, a Network Interface Card (NIC) compliant with the IEEE (Institute of Electrical and Electronics Engineers) 802.3 series.

[0038] The memory 210 may be configured, for example, by a combination of volatile and non-volatile memory. The volatile memory may be, for example, Static Random Access Memory (SRAM) or Dynamic RAM (DRAM), or a combination thereof. The non-volatile memory may be, for example, Mask Read Only Memory (MROM), Electrically Erasable Programmable ROM (EEPROM), flash memory, or a hard disk drive, or any combination thereof. The memory 210 may also include storage located remotely from the processor 206. In this case, the processor 206 may access the memory 210 via the network interface 208 or other I / O interface.

[0039] The memory 210 may include a computer-readable medium storing one or more software modules (computer programs) including instructions and data for performing at least a portion of the processing by the base station 20. In some implementations, the processor 206 may be configured to read and execute the software modules from the memory 210 to perform at least a portion of the processing by the base station 20 described in the above embodiments.

[0040] 5 is a block diagram illustrating an example of a processor according to the present disclosure. According to this embodiment, the processor 206 can cause the base station 20 to perform signal processing for channel quality estimation. To perform the signal processing, the processor 206 can include a plurality of channel information estimators 222, a channel information combiner 224, and a database searcher 226, and the memory 210 can include a database 232, as shown in FIG.

[0041] The channel information estimator 222-k calculates the channel matrix H of the k-th subcarrier based on the reference signal received from the RF transceiver 204. k and estimate the estimated channel matrix H kBased on the channel information I of the k-th subcarrier k The channel information estimator 222-k is coupled to the channel information combiner 224 and supplies the calculated channel information Ik to the channel information combiner 224. Here, the reference signal is, for example, a sounding reference signal (SRS).

[0042] Hereinafter, the channel information estimator 222 calculates the channel information I k Here, an example of calculating Ik will be described. However, the channel information estimator 222 may calculate the channel information of a block in which a k-th plurality of subcarriers are grouped as the channel information Ik. In this case, the channel information may be the channel information of one subcarrier that represents the k-th block, or may be the average value of the channel information of the subcarriers included in the k-th block. For example, the block may be a resource block (RB), with 12 subcarriers in one block.

[0043] Channel Information I k A first implementation of is a vector consisting of matrix elements of the estimated channel matrix, as shown in (2).

number

[0044] Channel Information I k The second implementation example is the indicator shown in (3).

number

[0045] Channel Information I k The third implementation example is the indicator shown in (4).

number

number

[0046] The channel information combiner 224 combines the channel information I of each subcarrier supplied from the channel information estimator 222. k The channel information combiner 224 combines the channel information I of each subcarrier to generate a combined channel information vector C. The channel information combiner 224 combines with the database searcher 226 and provides the combined channel information vector C to the database searcher 226. An example of the implementation of the combined channel information vector C is shown in (6) below. k is a vector with vector elements.

number

[0047] Another implementation of the combined channel information vector C may be as shown in (7). In (7), K skip The channel information of one subcarrier per subcarrier is a vector element.

number

[0048] The database 232 is a database that uses the combined channel information vector C as a key and the average channel quality Q as a value. The base station 20 acquires a pair of the combined channel information vector C and the average channel quality Q in advance through simulation, actual measurement, or other methods, and then configures the database 232. The database 232 is connected to the database searcher 226 and used for data search. The average channel quality Q is calculated based on a receiving algorithm (e.g., MMSE (Minimum Mean Square Error), ZF (Zero Forcing), or BP (Belief Propagation)) used when estimating actual channel quality. In the following, an example will be described in which the base station 20 calculates the average channel quality Q. However, the average channel quality Q may be calculated by another device, and the database 232 may be configured using the average channel quality Q calculated by the base station 20.

[0049] The first implementation example of the average channel quality Q is the average SINR based on the MMSE. First, the base station 20 calculates the SINR of each subcarrier, γ k Calculate.

number

number

[0050] The base station 20 calculates the average channel quality Q as shown in (10) by averaging the SINR of each subcarrier shown in (8) for each unit of radio resource allocation.

number

number

[0051] A second implementation example of the average channel quality Q is the average mutual information calculated based on the average signal-to-noise power ratio (SNR) based on ZF. First, the base station 20 calculates the channel matrix H k Based on this, the SNR of each subcarrier is calculated as the average SNR based on ZF. k Calculate.

number

number

[0052] A third implementation example of the average channel quality Q is the average mutual information calculated by actually performing reception processing. The base station 20 actually performs signal transmission and reception processing based on (1), and calculates the mutual information in units of radio resource allocation using the log-likelihood ratio (LLR) of the output received signal. In this way, the base station 20 calculates the average mutual information. Here, the signal transmission and reception processing may be performed in an actual system or may be performed using a simulation. Furthermore, the base station 20 can perform the reception processing using any reception algorithm. Regardless of which algorithm is used, the base station 20 can calculate the detector output LLR after the reception processing is completed.

[0053] Now, let the bit sequence transmitted by the mth wireless terminal on the allocated wireless resource be b 1,m ,…,b L,m and the corresponding bit LLR sequence λ 1,m ,…,λ L,m is obtained as the detector output, where L is the bit length of the bit sequence. In this case, as shown in (14), the base station 20 calculates the mutual information for each radio resource allocation unit to derive the average channel quality Q.

number

[0054] A fourth implementation example of the average channel quality Q is an index obtained by discretizing the average SINR. The index may be discretized using a threshold. For example, this index may be the index of the largest MCS whose average SINR is below the target block error rate. In this case, the threshold is the SINR that achieves the target block error rate for each MCS.

[0055] The database searcher 226 searches for the combined channel information vector in the pre-created database 232 that has the closest distance ε to the combined channel information vector C supplied from the channel information combiner 224. The database searcher 226 uses, for example, an ANN to search for the combined channel information vector that is considered to have the closest distance ε to the combined channel information vector C.

[0056] When the database searcher 226 uses an ANN, the database searcher 226 may learn the database 232 in advance, structure the data into multiple groups, and perform distance comparison only for groups close to the search target. This enables the database searcher 226 to perform high-speed searches even when the database 232 contains a large amount of data or the number of dimensions of the joint channel information vector is large. Methods for structuring the database 232 may include Randomized kd-tree (RKD), Hierarchical k-means (HKM), Locality Sensitive Hashing (LSH), Spectral Hashing (SH), Inverted File with Asymmetric Distance Calculation (IVFADC), and Inverted Multi-Index (IMI).

[0057] The first implementation example of the distance ε that is the norm for database search is the Euclidean distance shown in (15).

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[0058] A second implementation of the distance ε is the cosine similarity shown in (16).

number

[0059] The database searcher 226 outputs, as an estimate, the average channel quality Q associated with the combined channel information vector found by the search in the database 232. Based on the estimated average channel quality Q, the base station 20 can determine at least one of the wireless terminals or layers to be spatially multiplexed for transmission and the MCS to be used by each wireless terminal for transmission.

[0060] [Flow description] 6 is a flowchart showing an example of a typical process of the base station 20. This flowchart explains the process of the base station 20. Note that the details of each process are as described above, and therefore will not be explained again.

[0061] First, the processor 206 (particularly the channel information estimator 222-k) estimates a channel matrix for each subcarrier based on a reference signal received via the RF transceiver 204. The processor 206 calculates the channel information I for each subcarrier based on the estimated channel matrix. k is calculated (step S22).

[0062] The processor 206 (particularly the channel information combiner 224) combines the channel information I of each subcarrier calculated in step S22. k are combined to generate one combined channel information vector C (step S24).

[0063] The processor 206 (particularly the database searcher 226) calculates the distance ε between each of the combined channel information vectors in the pre-created database 232 and the combined channel information vector C generated in step S24. Then, the processor 206 uses the ANN to search for the combined channel information vector that is considered to have the closest distance ε. The processor 206 determines the average channel quality Q corresponding to the searched combined channel information vector as an estimate of the average channel quality Q (step S26).

[0064] [Effect description] As described above, the base station 20 uses an ANN for the combined channel information vector C, which is obtained by combining channel information estimated for multiple subcarriers. As a result, the base station 20 searches and identifies, from the database 232, a combined channel information vector that is considered to have the shortest distance ε to the combined channel information vector C. The base station 20 uses the channel quality corresponding to the identified combined channel information vector as an estimate. For the same reason as the effect of the first embodiment, the base station 20 can estimate the channel quality with a small number of searches even in an environment with a large number of subcarriers, such as OFDM.

[0065] Furthermore, the base station 20 generates a combined channel information vector C with a large number of dimensions by combining channel information of multiple subcarriers. At this time, the base station 20 can identify data that is close to the combined channel information vector C by using an ANN, which is an approximation method. This enables the base station 20 to estimate channel quality with a small amount of processing even for a combined channel information vector C with a large number of dimensions.

[0066] Furthermore, the use of an ANN by the base station 20 provides the following advantages: The base station 20 can search a large-scale database. Furthermore, the base station 20 can output discrete values, and can estimate channel quality as a pseudo-continuous value even in channel quality estimation using a database search that cannot output continuous values. In this way, the ability to estimate channel quality as a continuous value allows the base station 20 to flexibly control resources.

[0067] Furthermore, the base station 20 can use either the Euclidean distance or the cosine similarity as the distance ε that is the criterion for database search, which allows the base station 20 to more accurately identify the data that is closest to the combined channel information vector C, thereby improving the accuracy of the estimation process.

[0068] In addition, the base station 20 receives channel information I kBy using one of the first to third implementation examples as k Furthermore, the base station 20 can estimate the average SINR after reception processing or the average mutual information after reception processing.

[0069] 7 shows the relationship between the channel quality (horizontal axis) estimated by the search of database searcher 226 and the actual channel quality (vertical axis). The channel quality in FIG. 7 indicates the communication throughput characteristics.

[0070] In the estimation in the situation of FIG. 7, when the number of subcarriers is 192, one average channel quality value is estimated. For example, if estimation is performed for each subcarrier, the number of channel quality estimations required is 192. However, as described above, the number of channel quality estimations required in the database searcher 226 is only one. Note that in the example of FIG. 7, the channel information Ik described in the third implementation example and the average channel quality Q described in the second implementation example are used as the information for each subcarrier. Furthermore, the distance ε described in the first implementation example is used as the distance ε.

[0071] In Fig. 7, when the estimated value and the actual value match, the plots are distributed along the diagonal line. Referring to Fig. 7, it can be seen that the plots are distributed around the diagonal line, and that the channel quality can be estimated accurately with a small number of estimations. Therefore, the base station 20 can estimate the channel quality value with high accuracy.

[0072] The base station 20, like the estimation device 10, may be configured as a single computer device or as a distributed system having multiple computer devices.

[0073] In the above-described embodiments, this disclosure has been described as a hardware configuration, but this disclosure is not limited to this. This disclosure can also be realized by having a processor in a computer execute a computer program to perform the processing (steps) of the base station described in the above-described embodiments.

[0074] 8 is a block diagram showing an example of the hardware configuration of an information processing device that executes the above-described processing. Referring to FIG. 8, this information processing device 90 is a device that constitutes the estimation device 10 or the base station 20, and includes a signal processing circuit 91, a processor 92, and a memory 93.

[0075] The signal processing circuit 91 is a circuit for processing signals in accordance with the control of the processor 92. Note that the signal processing circuit 91 may include a communication circuit for receiving signals from a transmitting device.

[0076] The processor 92 is connected (coupled) to the memory 93, and performs the processing of the device described in the above-mentioned embodiments by reading and executing software (computer programs) from the memory 93. For example, the processor 206 in the second embodiment is configured by the processor 92. Examples of the processor 92 include a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an FPGA (Field-Programmable Gate Array), a DSP (Demand-Side Platform), and an ASIC (Application Specific Integrated Circuit). A single processor may be used as the processor 92, or multiple processors may be used in cooperation with each other.

[0077] The memory 93 may be a volatile memory, a nonvolatile memory, or a combination thereof. The volatile memory may be, for example, a random access memory (RAM) such as a dynamic random access memory (DRAM) or a static random access memory (SRAM). The nonvolatile memory may be, for example, a read only memory (ROM) such as a programmable random only memory (PROM) or an erasable programmable read only memory (EPROM), a flash memory, or a solid state drive (SSD). The memory 93 may be a single memory or a plurality of memories operating in cooperation with each other.

[0078] The memory 93 is used to store one or more instructions. Here, the one or more instructions are stored as a group of software modules in the memory 93. The processor 92 can perform the processes described in the above embodiments by reading and executing the group of software modules from the memory 93.

[0079] The memory 93 may include memory built into the processor 92 in addition to memory provided outside the processor 92. The memory 93 may also include storage located away from the processors constituting the processor 92. In this case, the processor 92 can access the memory 93 via an I / O (Input / Output) interface.

[0080] As described above, one or more processors included in each device in the above-described embodiments execute one or more programs including instructions for causing a computer to execute the algorithms described using the drawings. This processing enables the information processing described in each embodiment to be realized.

[0081] The program includes instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more functions described in the embodiments. The program may be stored in a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, computer-readable media or tangible storage media include random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technologies, CD-ROM, digital versatile disk (DVD), Blu-ray® disk or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may also be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, transitory computer-readable media or communication media include electrical, optical, acoustic, or other forms of propagated signals.

[0082] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure.

[0083] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes. (Appendix 1) generating means for generating one or more first channel information vectors using channel information related to each of a plurality of radio resources; and a specifying means for specifying one or more second channel information vectors from a plurality of predetermined channel information vectors according to a distance from the one or more first channel information vectors, and outputting channel quality information corresponding to the one or more second channel information vectors. Estimation device. (Appendix 2) the specifying means specifies the one or more second channel information vectors by calculating a Euclidean distance between the one or more first channel information vectors and the predetermined channel information vector for each of the predetermined channel information vectors. 10. The estimation apparatus of claim 1. (Appendix 3) the identifying means identifies the one or more second channel information vectors by calculating a cosine similarity between the one or more first channel information vectors and the predetermined channel information vector for each of the predetermined channel information vectors. 10. The estimation apparatus of claim 1. (Appendix 4) The channel information is a vector whose vector elements are matrix elements of a channel matrix between the wireless terminal and the wireless device. 4. The estimation device according to any one of appendixes 1 to 3. (Appendix 5) The channel information is a ratio of a desired signal power to an interference signal power. 4. The estimation device according to any one of appendixes 1 to 3. (Appendix 6) The channel information is a ratio of a desired signal power to an interference signal power and a sum of the noise power. 4. The estimation device according to any one of appendixes 1 to 3. (Appendix 7) The channel quality information is an average signal-to-interference-plus-noise power ratio after reception processing in the plurality of radio resources. 7. The estimation device according to any one of appendixes 1 to 6. (Appendix 8) The channel quality information is an average mutual information after reception processing in the plurality of radio resources. 7. The estimation device according to any one of appendixes 1 to 6. (Appendix 9) generating one or more first channel information vectors using channel information associated with each of a plurality of radio resources; identifying one or more second channel information vectors from a plurality of predetermined channel information vectors according to distances from the one or more first channel information vectors, and outputting channel quality information corresponding to the one or more second channel information vectors; This is an estimation method performed by a computer. (Appendix 10) generating one or more first channel information vectors using channel information associated with each of a plurality of radio resources; identifying one or more second channel information vectors from a plurality of predetermined channel information vectors according to distances from the one or more first channel information vectors, and outputting channel quality information corresponding to the one or more second channel information vectors; A program that makes a computer do something.

[0084] Some or all of the elements (e.g., configurations and functions) described in Supplementary Notes 2 to 8 that are dependent on Supplementary Note 1 may also be dependent on Supplementary Notes 9 and 10 in the same dependency relationship as Supplementary Notes 2 to 8. Some or all of the elements described in any Supplementary Note may be applied to various hardware, software, recording means for recording software, systems, and methods. [Explanation of symbols]

[0085] 10 Estimation device 102 Generation section 104 Specification section 20 base station 202 Antenna array 204 RF transceiver 206 Processor 208 Network Interface 210 memory 222 Channel Information Estimator 224 Channel Information Combiner 226 Database Searcher

Claims

1. generating means for generating one or more first channel information vectors using channel information related to each of a plurality of radio resources; and a specifying means for specifying one or more second channel information vectors from a plurality of predetermined channel information vectors according to a distance from the one or more first channel information vectors, and outputting channel quality information corresponding to the one or more second channel information vectors. Estimation device.

2. the specifying means specifies the one or more second channel information vectors by calculating a Euclidean distance between the one or more first channel information vectors and the predetermined channel information vector for each of the predetermined channel information vectors. The estimation device according to claim 1 .

3. the specifying means specifies the one or more second channel information vectors by calculating a cosine similarity between the one or more first channel information vectors and the predetermined channel information vector for each of the predetermined channel information vectors. The estimation device according to claim 1 .

4. The channel information is a vector whose vector elements are matrix elements of a channel matrix between the wireless terminal and the wireless device. The estimation device according to any one of claims 1 to 3.

5. The channel information is a ratio of a desired signal power to an interference signal power. The estimation device according to any one of claims 1 to 3.

6. The channel information is a ratio of a desired signal power to an interference signal power and a sum of the noise power. The estimation device according to any one of claims 1 to 3.

7. The channel quality information is an average signal-to-interference-plus-noise power ratio after reception processing in the plurality of radio resources. The estimation device according to any one of claims 1 to 3.

8. The channel quality information is an average mutual information after reception processing in the plurality of radio resources. The estimation device according to any one of claims 1 to 3.

9. generating one or more first channel information vectors using channel information associated with each of a plurality of radio resources; identifying one or more second channel information vectors from a plurality of predetermined channel information vectors according to distances from the one or more first channel information vectors, and outputting channel quality information corresponding to the one or more second channel information vectors; This is an estimation method performed by a computer.

10. generating one or more first channel information vectors using channel information associated with each of a plurality of radio resources; identifying one or more second channel information vectors from a plurality of predetermined channel information vectors according to distances from the one or more first channel information vectors, and outputting channel quality information corresponding to the one or more second channel information vectors; A program that makes a computer do something.

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