KR-102960680-B1 - METHOD AND APPARATUS FOR ESTIMATING CHANNEL OF MULTI ANTENNA

Abstract

The following disclosure relates to a method for estimating communication channels of multiple antennas, and may include the operation of receiving received signals including input signals and non-orthogonal pilot signals received from one or more devices; the operation of estimating the channel of each device using a learning-based channel estimation model based on the non-orthogonal pilot signals; the operation of decoding the input signal and outputting a detection signal based on the output value of the learning-based channel estimation model; and the operation of updating the learning-based channel estimation model using a predetermined loss function, output value, detection signal, and input signal.

Inventors

• 양현종
• 박소정
• 김영준
• 장종규

Assignees

• 포항공과대학교 산학협력단

Dates

Publication Date

20260507

Application Date

20240130

Priority Date

20231226

Claims (19)

1. In a method for estimating communication channels of multiple antennas, An operation of receiving received signals including input signals and non-orthogonal pilot signals received from one or more devices; An operation of estimating the channel of each of the devices using a learning-based channel estimation model based on the above non-orthogonal pilot signals; An operation of decoding the input signal and outputting a detection signal based on the output value of the above-mentioned learning-based channel estimation model; and An operation to update the learning-based channel estimation model using a predetermined loss function, the output value, the detection signal, and the input signal. Includes, The above learning-based channel estimation model Learning the correlation between the channels above to improve the accuracy of the channel estimation, and The above predetermined loss function is It is a function that applies Mean Square Error (MSE) between the symbol value of the detected signal and the symbol value of the input signal using the information vector of the estimated channel included in the output value, and The operation of updating the above-mentioned learning-based channel estimation model is After running the learning-based channel estimation model as many times as the number of the aforementioned multiple antennas, the operation of updating the learning-based channel estimation model and The operation of training the above-mentioned learning-based channel estimation model in real time while communication is ongoing Includes, The above receiving operation is A method including the operation of separating the above-mentioned receive signal into a real part and an imaginary part, Method for estimating communication channels of multiple antennas.
2. In paragraph 1, The above learning-based channel estimation model A method for estimating communication channels of multiple antennas, which is a model trained by a semi-supervised learning method and estimates channel information of the said devices based on pilot information.
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7. In paragraph 1, The above input signal is A method for estimating communication channels of multiple antennas, including symbol modulation by OFDM (Orthogonal Frequency-Division Multiplexing).
8. In paragraph 1, The above non-orthogonal pilot signal is A method for estimating communication channels of multiple antennas generated using random QPSK (Quadrature Phase Shift Keying) symbols.
9. In paragraph 1, The above decoding operation is The operation of inputting the above input signal and the output value of the above learning-based channel estimation model into a matching filter A method for estimating communication channels of multiple antennas, including
10. A computer program stored on a computer-readable recording medium in combination with hardware to execute the method of any one of claims 1 to 2 and 7 to 9.
11. In an electronic device for driving a Massive MIMO system, An antenna that receives input signals from one or more devices; Memory for storing instructions; and processor Includes, When the above instructions are executed by the processor, the electronic device, Received signals including input signals and non-orthogonal pilot signals received from one or more devices, and Based on the above non-orthogonal pilot signals, the channel of each of the above devices is estimated using a learning-based channel estimation model, and Based on the output value of the above-mentioned learning-based channel estimation model, the input signal is decoded to output a detection signal, and Using a predetermined loss function, the estimated channel, the output value, and the input signal, the learning-based channel estimation model is updated, and The above learning-based channel estimation model Learning the correlation between the channels above to improve the accuracy of the channel estimation, and The above predetermined loss function is It is a function that applies Mean Square Error (MSE) between the symbol value of the detected signal and the symbol value of the input signal using the information vector of the estimated channel included in the output value, and The above processor After running the above learning-based channel estimation model for as many times as the number of multiple antennas, the above learning-based channel estimation model is updated, and the above learning-based channel estimation model is trained in real time while communication continues, The above processor Separating the above-mentioned receive signal into a real part and an imaginary part, Electronic device.
12. In Paragraph 11, The above learning-based channel estimation model An electronic device, which is a model trained by a semi-supervised learning method and estimates channel information of the said devices based on pilot information.
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17. In Paragraph 11, The above input signal is An electronic device comprising symbol modulation by OFDM (Orthogonal Frequency-Division Multiplexing).
18. In Paragraph 11, The above non-orthogonal pilot signal is An electronic device generated using random QPSK (Quadrature Phase Shift Keying) symbols.
19. In Paragraph 11, The above processor An electronic device that inputs the above input signal and the output value of the above learning-based channel estimation model to a matching filter.

Description

Method and apparatus for estimating communication channels of multiple antennas The following embodiments relate to a method and apparatus for estimating communication channels of multiple antennas. Massive multiple-input and multiple-output (Massive MIMO) systems can meet the increasing user capacity and diverse requirements resulting from Massive Machine Type Communications (mmTC) and Ultra-Reliable Low Latency Communications (URLC). Recently, Massive MIMO systems utilizing Grant-Free (GF) multiple access are being studied to effectively satisfy the uplink transmission requirements of URLLC. In MIMO systems, the approach to channel estimation requires orthogonal pilot signals, which may necessitate synchronization between devices. Due to these limitations, integrating GF multiple access with Massive MIMO may be difficult, potentially necessitating channel estimation using non-orthogonal pilots. FIG. 1 is a flowchart illustrating a method for estimating communication channels of multiple antennas according to one embodiment. FIG. 2 is a schematic diagram illustrating the access process of a Massive MIMO system and a device according to one embodiment. FIG. 3 is a schematic diagram illustrating a Massive MIMO system according to one embodiment. FIGS. 4 and FIGS. 5 are schematic diagrams illustrating a learning-based estimation model according to one embodiment. FIG. 6 is a block diagram of an electronic device according to one embodiment. Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be modified and implemented in various forms. Accordingly, actual implementations are not limited to the specific embodiments disclosed, and the scope of this specification includes modifications, equivalents, or substitutions included in the technical concept described by the embodiments. Terms such as "first" or "second" may be used to describe various components, but these terms should be interpreted solely for the purpose of distinguishing one component from another. For example, the first component may be named the second component, and similarly, the second component may be named the first component. When it is stated that a component is "connected" to another component, it should be understood that it may be directly connected to or coupled with that other component, or that there may be other components in between. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this specification, terms such as "comprising" or "having" are intended to specify the existence of the described features, numbers, steps, actions, components, parts, or combinations thereof, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof. In this document, each of the phrases such as "A or B", "at least one of A and B", "at least one of A or B", "A, B or C", "at least one of A, B and C", and "at least one of A, B, or C" may include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this specification. Hereinafter, embodiments will be described in detail with reference to the attached drawings. In the description with reference to the attached drawings, identical components are given the same reference numeral regardless of the drawing number, and redundant descriptions thereof will be omitted. Massive MIMO systems can be important at the 5G physical layer because they can efficiently multiplex many devices using the same time-frequency resources. Massive MIMO systems can meet the various requirements arising from mMTC and URLLC, as well as the requirements for larger user capacity. To enable GF multiple access in Massive MIMO systems, pilot design for channel estimation of GF devices may be required. In conventional communications, orthogonal pilots may have been used for channel estimation. On the other hand, in Massive MIMO systems for mMTC, pilot orthogonality issues may arise due to non-orthogonal pilots caused by pilot overhead. In particular, the probability of non-orthogonal pilots appearing may increase in GF environments. In wireless communication, when a channel is maintained stably within a specific range, longer packet lengths can reduce resource allocation for channel estimation, thereby enabling more efficient communication. However, longer packet lengths can increase transmission latency when packets are generated for devi