# US 12,223,443 B1 · Learning approximate estimation networks for communication channel state information > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,223,443 B1 - Document ID: US12223443B1 - Status: Granted - Kind: B1 - Application number: 18/218,855 - Filed: 2023-07-06 - Granted: 2025-02-11 - Earliest claimed: 2017-06-23 - Assignees: Virginia Tech Intellectual Properties, Inc. - Applicants: Virginia Tech Intellectual Properties, Inc. - Inventors: Timothy James O'Shea; Kiran Karra; T. Charles Clancy - Web page: https://patentgrep.com/patent/US12223443B1 ## CPC - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 5/046: Forward inferencing; Production systems - G06N 3/02: Neural networks - G06N 3/045: Combinations of networks - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/09: Supervised learning - G06N 20/00: Machine learning - H04B 17/309: Measuring or estimating channel quality parameters - H04B 17/3912: {Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region} - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04B: TRANSMISSION ## Abstract Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learning estimation networks in a communications system. One of the methods includes: processing first information with ground truth information to generate a first RF signal by altering the first information by channel impairment having at least one channel effect, using a receiver to process the first RF signal to generate second information, training a machine-learning estimation network based on a network architecture, the second information, and the ground truth information, receiving by the receiver a second RF signal transmitted through a communication channel including the at least one channel effect, inferring by the trained estimation network the receiver to estimate an offset of the second RF signal caused by the at least one channel effect, and correcting the offset of the RF signal with the estimated offset to obtain a recovered RF signal. ## Claims ### Claim 1 A method of deploying one or more machine-learning estimation network to a communications system, the method comprising: receiving, by at least one receiver, a radio frequency (RF) signal transmitted through a communication channel; generating, by a machine-learning estimation network, estimated channel state information of the communication channel through which the RF signal was transmitted, wherein the machine-learning estimation network is trained based on at least one channel effect of the communication channel; and generating, using the estimated channel state information of the communication channel and the RF signal transmitted through the communication channel, a recovered RF signal by correcting an offset of the RF signal using the estimated channel state information. ### Claim 2 (depends on 1) The method of claim 1, comprising: training a machine-learning estimation network as the machine-learning estimation network based on the at least one channel effect of the communication channel. ### Claim 3 (depends on 2) The method of claim 2, wherein training the machine-learning estimation network comprises: receiving, by the at least one receiver, a second RF signal, wherein the second RF signal is transmitted through the communication channel; obtaining ground truth information corresponding to one or more transmissions through the communication channel; and training, using the second RF signal and the ground truth information, the machine-learning estimation network. ### Claim 4 (depends on 3) The method of claim 3, wherein the ground truth information comprises a modulation parameter. ### Claim 5 (depends on 4) The method of claim 4, comprising: generating, using (1) the modulation parameter and (2) at least one modulation method, the second RF signal. ### Claim 6 (depends on 5) The method of claim 5, wherein the modulation method includes at least one of a digital quadrature amplitude modulation (QAM), phase-shift keying (PSK), or orthogonal frequency-division multiplexing (OFDM). ### Claim 7 (depends on 6) The method of claim 6, wherein the OFDM includes cyclic prefix orthogonal frequency-division multiplexing (CP-OFDM). ### Claim 8 (depends on 3) The method of claim 3, wherein the ground truth information corresponds to one or more measurements of the communication channel. ### Claim 9 (depends on 8) The method of claim 8, wherein the one or more measurements include measurements from an interpolated pilot response. ### Claim 10 (depends on 9) The method of claim 9, wherein the interpolated pilot response includes multiplexing known pilot symbols with data symbols within a time duration of a frame. ### Claim 11 (depends on 1) The method of claim 1, wherein the RF signal is sent using one of the following: wireless local area network (WLAN), Wideband Code Division Multiple Access (WCDMA), Wi-Fi, or cellular. ### Claim 12 (depends on 11) The method of claim 11, wherein the cellular protocol includes long-term evolution (LTE), 5G, or 6G protocols. ### Claim 13 (depends on 11) The method of claim 11, wherein the cellular protocol includes one or more of Global System for Mobile communications (GSM), Short Message Service (SMS), Enhanced Messaging Service (EMS), Multimedia Messaging Service (MMS), code division multiple access (CDMA), CDMA1350, time division multiple access (TDMA), Personal Digital Cellular (PDC), or General Packet Radio Service (GPRS). ### Claim 14 (depends on 1) The method of claim 1, wherein generating the recovered RF signal comprises: removing, using the estimated channel state information, one or more channel effects from the RF signal. ### Claim 15 (depends on 14) The method of claim 14, wherein removing the one or more channel effects from the RF signal comprises: performing one or more of: sample timing adjustment, frequency adjustment, or impulse response adjustment. ### Claim 16 (depends on 1) The method of claim 1, wherein the estimated channel state information comprises at least one or more estimates of frequency offset, timing offset, phase offset, noise values, rate offset information, impulse response, multiple-input and multiple-output (MIMO) equalization, interpolated pilot response estimation, fading effects, symbol estimation, spatial characterization, interference characterization, or hardware distortion. ### Claim 17 (depends on 1) The method of claim 1, wherein generating the recovered RF signal by correcting the offset of the RF signal using the estimated channel state information comprises: correcting one or more of a frequency offset, a timing offset, a phase offset, a rate offset, a multiple-input and multiple-output (MIMO) equalization offset, or an interpolated pilot response offset. ### Claim 18 One or more non-transitory computer-readable storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: receiving, by at least one receiver, a radio frequency (RF) signal transmitted through a communication channel; generating, by a machine-learning estimation network, estimated channel state information of the communication channel through which the RF signal was transmitted, wherein the machine-learning estimation network is trained based on at least one channel effect of the communication channel; and generating, using the estimated channel state information of the communication channel and the RF signal transmitted through the communication channel, a recovered RF signal by correcting an offset of the RF signal using the estimated channel state information. ### Claim 19 (depends on 18) The media of claim 18, wherein the RF signal is sent using one of the following: wireless local area network (WLAN), Wideband Code Division Multiple Access (WCDMA), Wi-Fi, or cellular. ### Claim 20 A system comprising: one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving, by at least one receiver, a radio frequency (RF) signal transmitted through a communication channel; generating, by a machine-learning estimation network, estimated channel state information of the communication channel through which the RF signal was transmitted, wherein the machine-learning estimation network is trained based on at least one channel effect of the communication channel; and generating, using the estimated channel state information of the communication channel and the RF signal transmitted through the communication channel, a recovered RF signal by correcting an offset of the RF signal using the estimated channel state information.