Patent ID: 12237899

DESCRIPTION OF THE EMBODIMENTS

Channel state information, CSI, is needed for precoding in massive multiple-input multiple-output, MIMO communications with frequency division duplex, FDD, schemes. Accurate CSI can be used by a base station, BS, to obtain higher signal-to-noise-ratio, SNR, and channel capacity. In FDD networks, the UE estimates the downlink CSI. The CSI is shared with the BS which introduces overhead to the network. The CSI is compressed by an encoder at the UE to an encoding of the CSI, which results in reducing the overhead. The quantization methods quantize the encoding of the CSI with an quantizer to a quantized encoding of the CSI at the UE and de-quantize the quantized encoding with a de-quantizer at the gNB to an encoding of the CSI. The gNB reconstructs the CSI from the information about the CSI with a decoder. To reduce the overhead, scalar or vector quantization methods are used. Vector quantization methods use codebooks that achieve a high compression rate, CR, i.e. ratio of compressed size to uncompressed size.

FIG.1schematically depicts a simplified block diagram of a scheme for UE first separate training.

A first UE1-1comprises a first encoder102-1that is configured to encode a first CSI G to a first encoding E1in a latent space of the first encoder102-1. A M-th UE M comprises a M-th encoder102-M that is configured to encode a M-th CSI CMto a M-th encoding EMin a latent space of the M-th encoder102-M.

The first UE1-1comprises a first quantizer106-1that is configured to quantize the first encoding E to a first quantized encoding Q1. The first UE1-1is configured to send the first quantized encoding Q1to the gNB2.

The M-th UE1-M comprises a M-th quantizer106-M that is configured to quantize the M-th encoding EMto a M-th quantized encoding QM. The M-th UE1-M is configured to send the M-th quantized encoding QMto a gNB2.

The gNB2is configured to receive a first quantized encoding Y1from the first UE1-1. The gNB2is configured to receive a M-th quantized encoding YMfrom the M-th UE1-M.

The gNB2comprises a first de-quantizer106-1that is configured to de-quantize the first quantized encoding Y1to a first encoding W1in a latent space of a first decoder.

The gNB2comprises a M-th de-quantizer106-M that is configured to de-quantize the M-th quantized encoding YMto a M-th encoding WMin a latent space of a M-th decoder.

The gNB2comprises a first model108-1that is configured to translate the first encoding W1in the latent space of the first decoder to a first encoding T1in a latent space of a common decoder110of the gNB2.

The gNB2comprises a M-th model108-M that is configured to translate the M-th encoding WMin the latent space of the M-th decoder to a M-th encoding TMin the latent space of the common decoder110.

The common decoder110is configured to reconstruct the CSI C from a m-th encoding Tmin the latent space of the common decoder110.

The gNB2comprises a selector that is configured to select the m-th encoding Tmfrom the first encoding T1to M-th encoding TMin the latent space of the common decoder110. The selector comprises switches112-1, . . . ,112-M for selectively providing one of the first encoding T1to M-th encoding TMas the m-th encoding Tmto the common decoder110.

The models108-1, . . . ,108-M in the gNB2represent translation blocks that map the encoding in a respective vendor specific latent space to a latent space of the common decoder110of the gNB.

FIG.2schematically depicts a simplified block diagram of a scheme for gNB first separate training.

A UE1comprises a common encoder202that is configured to encode a CSI C to an encoding E in a latent space of the common encoder202.

The UE1comprises a first model204-1that is configured to translate the encoding E in the latent space of the encoder202to a first encoding T1in a latent space of a first decoder.

The UE1comprises a selector that is configured to select the n-th model204-nfrom the first model204-1to N-th model204-N for translating. The selector comprises switches206-1, . . . ,206-M for selectively providing the encoding E to the N-th model for translating.

The UE1comprises a first quantizer210-1that is configured to quantize the first encoding T1to a first quantized encoding Q1. The UE1is configured to send the first quantized encoding Q1to a first gNB2-1. The UE1comprises a N-th quantizer210-N that is configured to quantize the N-th encoding TNto a n-th quantized encoding QN. The UE1is configured to send the N-th quantized encoding QNto a N-th gNB2-N.

The first gNB2-1is configured to receive a first quantized encoding Y1from the UE1. The N-th gNB2-N is configured to receive a N-th quantized encoding YNfrom the UE1.

The first gNB2-1comprises a first de-quantizer212-1that is configured to de-quantize the first quantized encoding Y1to a first encoding W1in a latent space of a first decoder214-1. The N-th gNB2-N comprises a N-th de-quantizer212-N that is configured to de-quantize the N-th quantized encoding YNto a N-th encoding WMin a latent space of a N-th decoder214-N.

The first decoder214-1is configured to reconstruct a first CSI C1from the first encoding W1in the latent space of the first decoder214-1. The N-th decoder214-N is configured to reconstruct a N-th CSI CNfrom the N-th encoding WNin the latent space of the N-th decoder214-N.

The models204-1, . . . ,204-N in the UE1represent translation blocks that map the encoding in the latent space of the common encoder202of the UE1to the latent spaces that are understandable by respective vendor specific decoders.

The quantizers and de-quantizers are optional. A UE may be configured to map the encoding of the CSI with the selected model and share the resulting encoding with a predetermined gNB without quantizing. A gNB may be configured to receive the encoding of the CSI without quantizing from a predetermined UE und map the received encoding of the CSI with the selected model. A UE may be configured to share the resulting encoding of the CIS with some gNB without quantizing and to quantize the resulting encoding and share the quantized encoding with some gNB. A gNB may be configured to receive some encoding of the CIS from some UE without quantizing and to receive some quantized encoding of the CIS from some UE.

Some model may be based on machine learning, ML. Some models comprise a ML model. Some model may comprise a one layer neural network. Some model may comprise a deep neural network.

Some model may be obtained, e.g. trained, updated or fine tuned, vendor specific. In case a new encoder or decoder is provided e.g. by a new vendor, the UE or gNB may be provided a vendor specific model while the common encoder or decoder may remain unchanged.

The training comprises separate training at a network side, i.e. gNB side, and UE side. The UE-side comprises a CSI generation part and the network-side comprises a CSI reconstruction part. The sides are trained by UE side and network side, respectively.

In the UE first separate training by a vendor, a vendor specific encoder model is trained. UE vendors may consider different ML architectures for the encoder and potential nominal decoder, different quantization methods, and different quantization configurations including number of quantization bits for scalar or vector quantization.

FIG.3schematically depicts the training of an m-th UE1-m.

The m-th UE comprises an encoder102-mfor encoding the CSI C to an encoding Em, a quantizer104-mto map the encoding Emto a quantized encoding Qm, a de-quantizer302-mto map the quantized encoding Qmto a encoding Wmin a latent space of a nominal decoder304-m. The m-th UE quantizes and de-quantizes according to a quantization method306-m.

The nominal decoder304-mis an entity that the m-th UE uses for the training. The nominal decoder304-mneeds neither to be shared nor used in an inference phase.

Based on the m-th UE a dataset Ξmthat comprises Nmsamples is prepared. The dataset Ξmmay be shared with the gNB.

Ξm={(C1,Qm1),(C2,Qm2),…,(CNm,QmNm)}

The dataset Ξmcomprises per sample i, a CSI Cithat the encoder102-mencodes to an encoding Emiand a quantized encoding Qmiof the encoding Emi.

The other UEs may be trained as described for the m-th UE.

FIG.4schematically depicts the training of the gNB2based on a training data set for the gNB that comprises samples, wherein sample comprises a given CSI C′ and a given reconstructed CSI C.

The gNB2comprises a nominal encoder402that is configured to map the given CSI Cito an encoding E. The gNB2comprises the common decoder110. The common decoder110is configured to map the encoding E to the reconstructed CSI C.

The dataset Ξmand the quantization method306-mare shared for training the gNB. In the example, multiple datasets Ξm, m∈[1, . . . , M] are shared for training the gNB.

The training of the gNB2comprises training at least some of the models108-1, . . . ,108-M that represent translation blocks of the gNB2.

FIG.5schematically depicts a supervised learning setup for an m-th model108-mand a sample i of the de-quantized encoding Wmof a CSI C.

A training dataset Ψmcomprises per sample i and dataset Ξm, m∈[1, . . . , M] a de-quantized encoding Wmiof a CSI Ci, and a label Lmifor the supervised training:

Ψm={(Wm1,Lm1),(Wm2,Lm2),(WmNm,LmNm)}

The label Lmiis in the example determined with the nominal encoder402from the CSI Ci. The CSI Ciis for example the CSI Cifrom the dataset Ξm. The de-quantized encoding Wmiis for example determined with a m-th de-quantizer106-mof the gNB2from the quantized encoding Qmi.

Some of the dataset Ξmmay comprise the encoding Emiin the samples instead of the quantized encoding Qmi. The training of the m-th model108-min the gNB2may be based on the encoding Emias input for the m-th model108-minstead of using the de-quantized encoding Wmi.

The m-th model108-mis trained with a loss function502that is configured to determine a loss Lom. The training may comprise gradient descent based on the loss Lom.

The other models that represent translation blocks of the gNB2may be trained as described for the m-th model108-m.

The gNB2may comprise an input for an encoding of the CSI or a quantized encoding of the CSI that requires no translation because it is an encoding in the latent space of the common decoder110.

In an inference phase, the UEs1-1, . . . ,1-M use their respective encoder104-1, . . . ,104-M to compress the respective CSI C1, . . . , CMand the gNB2uses the UE-specific model108-1, . . . ,108-M that corresponds to the respective UE1-1, . . . ,1-M to determine the UE-specific encodings W1, . . . , WMand to determine a respective translation to the encoding T1, . . . , TMin the latent space that the common decoder110has learned.

In the gNB first separate training, specific decoder models are trained. eNB vendors may consider different ML architectures for the dencoder and potential nominal encoder, different quantization methods, and different quantization configurations including number of quantization bits for scalar or vector quantization.

FIG.6schematically depicts the training of a n-th eNB2-n.

The n-th eNB2-ncomprises a n-th nominal encoder602-nfor encoding a CSI C to an encoding En.

The n-th eNB2-ncomprises a quantizer604-mto map the encoding Ento a quantized encoding Qn, the de-quantizer212-nto map the quantized encoding Qnto the encoding Wnin the latent space of the m-th decoder224-nfor decoding an encoding Wnof the CSI C to a reconstructed CSI Cn.

The n-th eNB2-nquantizes and de-quantizes according to a quantization method606-m.

The n-th nominal encoder602-nis an entity that the n-th gNB uses for the training. The nominal encoder602-nneeds neither to be shared nor used in an inference phase.

Based on the n-th gNB a dataset Xnthat comprises Nnsamples is prepared. The dataset Xnmay be shared with the UE or the UEs.

𝒳n={(C1,Qn1),(C2,Qn2),…,(CNn,QnNn)}

The dataset Xncomprises per sample i, a CSI Cithat the nominal encoder602-mencodes to an encoding Eniand a quantized encoding Qniof the encoding Eni.

The other eNBs may be trained as described for the n-th eNB.

FIG.7schematically depicts the training of the UE1based on a training data set for the UE1that comprises samples, wherein sample comprises a given CSI Ciand a given reconstructed CSI C.

The UE1comprises the encoder202that is configured to map the given CSI Cito an encoding E. The UE1comprises a nominal decoder702. The nominal decoder702is configured to map the encoding E to the reconstructed CSI C.

The dataset χnand the quantization method606-nare shared for training the UE1. In the example, multiple datasets χn, n∈[1, . . . , N] are shared for training the UE. The training of the UE1comprises training at least some of the models210-1, . . . ,210-N that represent translation blocks of the UE1.

FIG.8schematically depicts a supervised learning setup for a n-th model210-mand a sample i of the encoding Enof a CSI C.

A training dataset θncomprises per sample i and dataset χn, n∈[1, . . . , N] an encoding Eniof a CSI Ci, and a label Lnifor the supervised training:

θn={(En1,Ln1),(En2,Ln2),…⁢(EnNn,LnNn)}

The label Lniis in the example determined from the dataset χn. The label Lniis for example the encoding QniThe CSI Ciis for example the CSI Cifrom the dataset χn.

The n-th model108-nis trained with a loss function802that is configured to determine a loss Lon. The training may comprise gradient descent based on the loss Lon.

The other models that represent translation blocks of the gNB2may be trained as described for the n-th model108-n.

The gNB2may comprise an input for an encoding of the CSI or a quantized encoding of the CSI that requires no translation because it is an encoding in the latent space of the common decoder110.

In an inference phase, the UE1uses the common encoder202to compress the CSI C to the encoding E and then uses the gNB-specific model204-1, . . . ,204-N to map the encoding E to a respective translation T1, . . . , TNin the latent space of the decoder214-1, . . . ,214-N of the respective gNB2-1, . . . ,2-N.

FIG.9schematically depicts a method of reconstructing a CSI.

The method of reconstructing is explained for m=1. The method may be applied accordingly to m=2, . . . , M.

The method of reconstructing comprises a step900

The step900comprises training the model108-1for determining the encoding T1with the loss Lo1that depends on the encoding T1and a reference L1for the encoding (T1.

The step900may comprise a step900-1.

The step900-1comprises receiving a reference for the channel state information C.

The step900may comprise a step900-2.

The step900-2comprises determining the reference L for the encoding T1depending on the reference for the channel state information C.

Determining the reference L1for the encoding T1may comprise encoding the reference for the channel state information C in the latent space for the first decoder110.

The method of reconstructing comprises a step902

The step902comprises receiving the encoding W1or receiving the quantized encoding Y and determining the encoding W1depending on the quantized encoding Y1.

The method of reconstructing comprises a step904.

The step904comprises reconstructing the channel state information C depending on the encoding T1of the channel state information C in the latent space for the first decoder110.

The method of reconstructing comprises a step906.

The step906comprises determining the encoding T1depending on the encoding W1of the channel state information C in the latent space for the second decoder304-1.

FIG.10schematically depicts a method of encoding a CSI C.

The method of encoding is explained for n=1. The method may be applied accordingly to n=2, . . . , N.

The method of encoding comprises a step1002.

Step1002comprises determining t model204-1for determining the encoding T1with the loss Lo1that depends on the encoding T1and the reference L1for the encoding T1.

The step1002may comprise receiving the reference L2for the encoding T1.

The step1002may comprise a step1002-1.

The step1002-1comprises receiving1002-1a quantized reference for the encoding T1.

The step1002may comprise a step1002-2.

The step1002-2comprises determining the reference L1for the encoding T1depending on the quantized reference.

The method of encoding comprises a step1004.

Step1004comprises receiving1004the channel state information C. The method may comprise determining the channel state information C.

The method of encoding comprises a step1006.

Step1006comprises determining the encoding E of the CSI C in the latent space of the encoder202depending on the channel state information C.

The method of encoding comprises a step1008.

Step1008comprises determining the encoding T1of the CSI C in the latent space for the decoder214-1depending on the encoding E.

The method of encoding comprises a step1010.

Step1010may comprise sending the encoding T1.

The step101may comprise quantizing the encoding T1and sending the quantized encoding Q1.

The methods may be configured to interact. For example, the method for encoding the channel state information C is executed in a UE to provide the encoding T1or the quantized encoding Q1to a gNB that executes the method for reconstructing the channel state information C.

The device1for reconstructing the channel state information C and the device2for encoding the channel state information C may be interoperable.

For example the device2for encoding the channel state information C is configured as described for one of the UEs1-1, . . . ,1-M.

For example the device1for reconstructing the channel state information C is configured as described for one of the gNBs2-1, . . . ,2-N.

In some examples, the nominal encoder and the common decoder or the common encoder and the nominal decoder are configured as autoencoder (AE) structure from deep learning (DL). In some examples, the encoder of the AE is configured for compression of CSI and the decoder of the AE is configured for reconstruction of CSI.

A program may comprise instructions to perform the method of reconstructing or the method of encoding.

A non-transitory computer readable medium may comprise the instructions.

The term “non-transitory,” as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM).

FIG.11schematically depicts a first apparatus1100. The first apparatus1100is described by way of example for m=1 using the decoder110, the encoding T1, the decoder304-1, the encoding W1and the model108-1.

The first apparatus1100comprises at least one first processor1102and at least one first memory1104. The at least one first memory1104comprises a non-transitory memory.

The at least one first memory1104stores instructions that, when executed by the at least one first processor1102, cause the first apparatus1100at least to reconstruct the CSI C on the encoding T1of the CSI C in the latent space for the common decoder110, and determine the encoding T1depending on the encoding W1of the CSI C in the latent space for the nominal decoder304-1.

The instructions, when executed by the at least one first processor1102, may cause the second apparatus1100to at least train the model108-mfor determining the encoding T1with the loss Lo1that depends on the encoding T1and the reference L1for the encoding Tm.

The instructions, when executed by the at least one first processor1102, may cause the second apparatus1100to at least receive a reference for the CSI C and determe the reference L1for the encoding T1depending on the reference for the CSI C.

The instructions, when executed by the at least one first processor1102, may cause the second apparatus1100to at least receive the encoding W1or to receive the quantized encoding Y1and determine the encoding W1depending on the quantized encoding Y1.

FIG.12schematically depicts a second apparatus1200. The second apparatus1200is described by way of example for n=1 using the encoder202, the encoding E, the decoder214-1, the encoding T1and the model204-1.

The second apparatus1200comprises at least one second processor1202and at least one second memory1204.

The at least one second memory1204comprises a non-transitory memory.

The at least one second memory1204stores instructions that, when executed by the at least one second processor1202, cause the second apparatus1200at least to determine the first encoding E of the CSI C in the latent space of the encoder202, and determine the encoding T1of the CSI C in the latent space for the decoder214-1depending on the encoding E.

The instructions, when executed by the at least one second processor1202, may cause the second apparatus1200to train the model204-1for determining the encoding T1with the loss Lo1that depends on the encoding T1and a reference L1for the encoding T1.

The instructions, when executed by the at least one second processor1202, may cause the second apparatus1200to determine or receive the CSI C and determine the encoding E depending on the CSI C.

The instructions, when executed by the at least one second processor1202, may cause the second apparatus1200to receive the reference L1for the encoding T1or to receive a quantized reference for the encoding T1and determine the reference L1for the encoding T1depending on the quantized reference.

The instructions, when executed by the at least one second processor1202, may cause the second apparatus1200send the encoding T1or quantize the encoding T1and send the quantized encoding Q1.

According to the description, an encoding in a latent space of an encoder is an output of the encoder that is understandable by a corresponding decoder. A vendor specific output of a vendor specific encoder may be understandable by a vendor specific decoder.

According to the description, an encoding in a latent space of a decoder is an input of the decoder that is understandable by the decoder. A vendor specific encoder may produce a vendor specific input of a vendor specific decoder.