Low complexity time domain based channel estimation techniques

Abstract

The invention relates to a low-complexity time domain-based channel estimation technique. Techniques for channel state estimation are provided. An example method may include processing a set of signals including a first noisy pilot signal, a second noisy pilot signal, and a noisy message signal. The method may also include determining a first noisy channel estimate based on the first noisy pilot signal and a second noisy channel estimate based on the second noisy pilot signal. The method may also include determining a first de-noised channel estimate based on the noisy pilot signal channel estimate and the second noisy pilot signal channel estimate. The method may also include determining a de-noised message signal based on the first de-noised channel estimate.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit and priority of U.S. Patent Application No. 18 / 825,499, filed September 5, 2024, entitled “LOW COMPLEXITYTIME DOMAIN-BASED CHANNEL ESTIMATION TECHNIQUES”, pursuant to 35U.SC119(e), the entire contents of which are incorporated herein by reference for all applicable purposes. Background Technology

[0003] Cellular communication can be defined in various standards to enable communication between user equipment and cellular networks. For example, Long Term Evolution (LTE) networks and fifth-generation mobile networks (5G) are wireless standards designed to improve data transmission speed, reliability, availability, and other aspects. Attached Figure Description

[0004] Figure 1 This is an example of a channel estimation system based on one or more implementation schemes.

[0005] Figure 2 This is an example of a channel estimation system based on one or more implementation schemes.

[0006] Figure 3 This is an example of a channel estimation unit based on one or more implementation schemes.

[0007] Figure 4 Examples of noisy signals and denoised signals are provided according to one or more implementation schemes.

[0008] Figure 5 Examples of noisy signals and denoised signals according to one or more implementation schemes are provided.

[0009] Figure 6 This is an example of interpolation of channel estimation based on one or more implementation schemes.

[0010] Figure 7 This is an example of a procedure for channel estimation in the time domain according to one or more implementation schemes.

[0011] Figure 8 This is an example of a channel estimation process in the frequency domain according to one or more implementation schemes.

[0012] Figure 9 This is an example of a receiving component based on some implementation schemes.

[0013] Figure 10This is an example of a user equipment (UE) based on some implementation schemes.

[0014] Figure 11 This is an example of a network node based on some implementation schemes. Detailed Implementation

[0015] The following detailed description refers to the accompanying drawings. The same reference numerals may be used to identify the same or similar elements in different drawings. In the following description, specific details, such as particular structures, architectures, interfaces, technologies, etc., are set forth for illustrative and non-limiting purposes to provide a thorough understanding of various aspects of the embodiments. However, it will be apparent to those skilled in the art that various aspects of the embodiments may be practiced in other examples departing from these specific details. In some cases, descriptions of well-known devices, circuits, and methods have been omitted so as not to obscure the description of the various embodiments with unnecessary detail. For the purposes of this document, the phrase “A or B” means (A), (B), or (A and B); and the phrase “based on A” means “at least partially based on A,” for example, it can be “based solely on A” or it can be “partially based on A.”

[0016] The following is a glossary of terms that may be used in this disclosure.

[0017] As used herein, the term "processor circuit" means, is part of, or includes a circuit capable of sequentially and automatically performing a series of arithmetic or logical operations or recording, storing, or transmitting digital data. The term "processor circuit" may also refer to an application processor, baseband processor, central processing unit (CPU), graphics processing unit, single-core processor, dual-core processor, triple-core processor, quad-core processor, or any other device capable of executing or otherwise operating computer-executable instructions such as program code, software modules, and / or functional processes.

[0018] As used herein, the term "user equipment" or "UE" refers to a device with radio communication capabilities and can describe network resources in a communication network. Furthermore, the term "user equipment" or "UE" can be considered synonymous and can refer to a client, mobile phone, mobile device, mobile terminal, user terminal, mobile unit, mobile station, mobile user, subscriber, user, remote station, access agent, user agent, receiver, radio equipment, reconfigurable radio equipment, reconfigurable mobile device, etc. Additionally, the term "user equipment" or "UE" can include any type of wireless / wired equipment or any computing device that includes a wireless communication interface.

[0019] As used herein, the term "base station" refers to a device with radio communication capabilities that is a network component of a communication network (or more simply, a network) and can be configured as an access node within the communication network. The UE's access to the communication network can be managed at least partially by the base station, thereby connecting the UE to the base station to access the communication network. Depending on the Radio Access Technology (RAT), the base station may be referred to as a gNodeB (gNB), eNodeB (eNB), access point, etc.

[0020] As used herein, the term "channel" refers to any tangible or intangible transmission medium used to transmit data or data streams. The term "channel" may be synonymous or equivalent with "communication channel," "data communication channel," "transmission channel," "data transmission channel," "access channel," "data access channel," "link," "data link," "carrier," "radio frequency carrier," or any other similar term indicating a means or medium through which data is transmitted. Additionally, as used herein, the term "link" refers to a connection between two devices used for transmitting and receiving information.

[0021] Channel estimation can include techniques for estimating the characteristics of a wireless channel between a transmitter and a receiver. Channel estimation techniques can be used to estimate and compensate for the effects of the transmission medium (e.g., wires, air, and other media) on the communication signal. These effects can include path loss, Doppler shift, noise, multipath propagation, and other influences. Channel estimation techniques can be used in broadcast communications, where transmission may be directed to multiple receivers. Channel estimation techniques can also be used in unicast communications, where transmission is directed to a single receiver. One channel estimation technique uses a pilot signal, which involves embedding a known sequence (e.g., a pilot signal) into the communication signal transmitted to the receiver. The receiver can compare the received sequence with the known sequence to determine the channel's effect on the communication signal.

[0022] For satellite communication systems, channel estimation can be critical to system performance. In typical use cases, satellite communication systems can be used where communication equipment (e.g., user equipment) is in remote, off-grid locations. Typically, in these off-grid locations, there are no nearby charging stations, and therefore, conserving battery power may be a relevant issue. Pilot signaling techniques can be used for channel estimation in satellite communication systems. However, the use of pilot signals may require additional power, which could further deplete the battery.

[0023] For satellite systems that use a single carrier frequency to transmit signals to user equipment, many typical problems, such as delay spread, frequency offset, and sampling clock skew, can be predicted and pre-compensated. However, one issue is that additive white Gaussian noise (AWGN) can contaminate the pilot signal, potentially leading to inaccurate channel estimation by the receiver after comparing the contaminated received sequence with a known sequence. If the receiver makes an inaccurate channel estimation, it may incorrectly compensate for its signal processing. This imperfect channel estimation can affect the performance of both single-antenna (e.g., single-input single-output (SISO)) and multi-antenna (e.g., multiple-input multiple-output (MIMO)) systems. Conventional channel estimation techniques using pilot signals do not require removing the AWGN from the pilot signal. Conventional techniques can result in high signal-to-noise ratio (SNR) requirements to achieve an acceptable block error rate (BLER) or acceptable bit error rate (BER). Therefore, improved channel estimation techniques capable of addressing AWGN may be needed. Furthermore, due to the sparse pilot positions in waveform design and the need to conserve battery power, it may be desirable to design a low-complexity channel estimation technique that improves channel estimation while achieving this goal with minimal computational overhead.

[0024] The embodiments described herein address the aforementioned problems by providing a low-complexity channel estimation technique that reconstructs the channel after minimizing noise in the time-domain pilot estimation. For example, a computing system can be configured to identify a noisy pilot signal based on received communication. The computing system can determine a noisy pilot estimate based on the noisy pilot signal. The computing system can also determine a denoised channel estimate based on the noisy channel estimate using a convolution-based moving average. The computing system can then use linear interpolation to determine the channel estimate of the message included in the communication. The computing system can also use the channel estimate to denoise and reconstruct the original message. The embodiments described herein provide a low-complexity channel estimation technique that can be used in conjunction with satellite systems and saves battery life.

[0025] Figure 1This is an example 100 of an example channel estimation system according to one or more embodiments. The satellite communication system may include a combination of ground stations (e.g., User Equipment (UE) 102, satellite antenna 104) and non-terrestrial satellites (e.g., satellite 106) through which radio frequency (RF) signals can be transmitted from one ground station to another. For example, satellite 106 may be used to relay RF communication between UE 102 and satellite antenna 104 (e.g., a base station), which are located at a large distance from each other. As illustrated, satellite antenna 104 has already transmitted a message to satellite 106 via uplink (UL) transmission, which may relay the message to UE 102 via downlink (DL) transmission. Satellite 106 may be a transparent satellite or a regenerating satellite. In the case of a transparent satellite, satellite antenna 104 may embed pilot signals (e.g., reference signals such as demodulation reference signal DM-RS, channel state information reference signal CSI-RS, etc.) into the transmitted messages for channel estimation. In the case of a regenerating satellite, satellite 106 may embed the aforementioned pilot signals into DL transmission. The pilot signal can be, for example, a reference signal with known characteristics. For time-domain waveforms, satellite 106 can embed the pilot signals in an equidistant pattern. For example, satellite 106 can embed the pilot signals at equidistant time points. In the frequency domain, satellite 106 can embed the pilot signals within subcarriers. In some embodiments, each pilot signal can be represented as a complex number to indicate the phase and magnitude of the pilot signal. Message data can be located between two pilot signals.

[0026] In some instances, DL transmission may be affected by the channel. In this example, the channel is an over-the-air (OTA) channel from satellite 106 to UE 102, causing signal degradation between satellite 106 and UE 102. Furthermore, in some instances, UE 102 may be located in a remote location, making it difficult to charge the UE's battery. Therefore, it may be desirable to utilize channel estimation techniques that conserve UE battery life.

[0027] As illustrated, UE 102 may include a channel estimation unit 108, which includes a noisy pilot estimation unit 110, a denoising unit 112, and an interpolation unit 114 for channel estimation. Channel estimation unit 108 may be configured to receive signals including pilot signals and message signals (e.g., messages transmitted from satellite antenna 104 to satellite 106 and relayed to UE 102). Since the pilot signals can be uniformly spaced, noisy pilot estimation unit 110 may identify the pilot signals based on a timing pattern according to the message signals. Noisy pilot estimation unit 110 may also perform channel estimation for each of the identified noisy pilot signals. The noisy pilot channel estimates may be sent to denoising unit 112, which may then use an averaging technique to denoise two or more noisy pilot channel estimates. For example, denoising unit 112 may use a moving window to identify a set of noisy pilot channel estimates and use a convolution operation to determine the average of the noisy pilot channel estimates to generate a denoised channel estimate. The denoised channel estimates can be sent to interpolation unit 114. Interpolation unit 114 performs interpolation to determine the channel estimate of the noisy message signal located between the two denoised channel estimates in the time domain. The channel estimates can be used to denoise the noisy message signal. Relative to... Figures 3 to 6 The channel estimation unit 108 is described in more detail.

[0028] Figures 2 to 6 A low-complexity channel estimation system is described in more detail. Figure 2 An overview of the transmitter and receiver components for a low-complexity channel estimation system is provided. Figures 3 to 6 The channel estimation unit is described in more detail.

[0029] Figure 2 Example 110 is an example channel estimation system according to one or more embodiments. A transmitter 112 (e.g., satellite 106) may be configured to transmit a signal to a receiver 114 (e.g., user equipment 102). The transmitter 112 may include a waveform generation and pilot embedding unit 116 that generates a waveform including a message signal and pilot signals (e.g., a reference signal). The pilot signals may be uniformly spaced in the time domain. The pilot signals may also include characteristics known and defined to the receiver 114. For example, the pilot signals may be embedded between message signals every x time units, every y symbols, etc. The waveform with the embedded pilot signals may be passed to a first pulse shaping unit 210. Pulse shaping may include signal processing techniques for modifying the waveform to optimize various characteristics. For example, the pulse shaping unit 111 may be used to shape the waveform to band-limit the signal and improve the signal-to-noise ratio (SNR) through matched filtering.

[0030] Transmitter 112 can transmit a signal to receiver 114. Along the signal path, the signal may be contaminated by noise (e.g., AWGN 116), which may degrade signal quality. The noisy signal can be received by receiver 114. The noisy signal can pass through a second pulse shaping unit 212, which can shape the waveform of the signal to improve the channel estimation process. The second pulse shaping unit 212 can transmit the noisy signal to a frequency offset (FO), time offset (TO), and sampling offset (SO) recovery unit 214. The FO / TO / SO recovery unit 214 can estimate FO based on pilot signals, cyclic prefix correlation, statistical characteristics, or other techniques. The FO / TO / SO recovery unit 214 can then use a local oscillator (LO) to adjust the frequency based on the estimate, or it can apply a phase shift to the received signal to remove the frequency offset. The FO / TO / SO recovery unit 214 can estimate the TO between transmitter 112 and receiver 114. The FO / TO / SO recovery unit 214 can then adjust the sampling points to adjust the timing offset. The FO / TO / SO recovery unit 214 can estimate SO based on pilot signals, statistical characteristics, or other techniques. Then, the FO / TO / SO recovery unit 214 can adjust user interpolation or resampling to adjust the sampling offset. Finally, the FO / TO / SO recovery unit 214 can send a noisy signal to the channel estimation unit.

[0031] Usable Figures 3 to 6 This describes the channel estimation unit 108. Figure 3 This is an example 300 of an example channel estimation unit according to one or more embodiments. Channel estimation unit 108 can receive input data. For example, channel estimation unit 108 can receive a noisy signal from FO / TO / SO recovery unit 214. The noisy signal can be received by noisy pilot estimation unit 110.

[0032] Figure 4 Example 400, based on one or more implementation schemes, shows a noisy signal and a denoised signal. See also... Figure 4 The input data may include the received sample 402, which includes noisy pilot signals and noisy message signals. For illustrative purposes, the noisy pilot signals have been illustrated with dashed lines, and the noisy message signals have been illustrated with solid lines. As illustrated, the noisy pilot signals are equally spaced, as each surrounds seven message signals. Each noisy pilot signal and noisy message signal may represent the signal at a point in time in the time domain. For example, a first noisy pilot signal 404 is followed by seven noisy message signals 408, which are followed by a second noisy pilot signal 406. It should be understood that seven message signals are used for illustration, and in other instances, there may be fewer or more than seven noisy message signals.

[0033] The noisy pilot estimation unit 110 can identify the noisy pilot signal based on a known timing pattern (e.g., every x time units, every y symbols, etc.). The noisy pilot estimation unit 110 can then generate a noisy pilot channel estimate 410. For example, the noisy pilot estimation unit 110 can compare the received noisy pilot signal with known characteristics of the pilot signal. By analyzing the difference, the noisy pilot estimation unit 110 can estimate the impulse response of the channel. The channel impulse response characterizes the channel's influence on the pilot signal (e.g., path loss, Doppler shift, noise, multipath propagation, and other effects). As illustrated, each noisy pilot signal corresponds to a noisy pilot estimate. For example, a first noisy pilot signal 404 can correspond to a first noisy pilot estimate 412. A second noisy pilot signal 406 can correspond to a second noisy pilot estimate 414.

[0034] Denoising unit 112 has access to and denoises the noisy pilot channel estimate 410. Denoising unit 112 can determine the window length for capturing two or more noisy pilot channel estimates 410 and perform a convolution-based moving average. As illustrated, the window length is four. However, it should be understood that in other instances, the window length may be less than or greater than four. Denoising unit 112 can use a moving average operation, which includes statistical techniques for creating an average value based on a subset of the noisy pilot channel estimates 410. The moving average can be determined based on convolving the input (e.g., the noisy pilot channel estimate 410) with a window function, where the window function defines the weights to be applied to the noisy pilot channel estimates 410 to generate a denoised channel estimate 416. The output of the convolution-based moving average can be obtained by starting with the sample number and using the following formula:

[0035] start_sample = (window length) / 2, (1)

[0036] The scaling factor of the moving average can be adjusted based on the window length and the number of overlapping samples.

[0037] As illustrated, the denoising unit 11 may determine a first channel estimate based on noisy pilot estimates captured by a moving average window at T0 (e.g., a first noisy pilot estimate 412, a second noisy pilot estimate 414, a third noisy pilot estimate 418, and a fourth noisy pilot estimate 420). Similarly, a second channel estimate 424 may be determined based on noisy pilot estimates captured by a moving window at T1, and so on. Figure 5 Example 500 is an example of a noisy signal and a denoised signal according to one or more embodiments. A dashed line is used to represent the noisy signal, while a bold solid line is used to represent the denoised signal 504. Example 500 is provided to indicate the effect of denoising on the waveform of the pilot signal.

[0038] One problem with convolution-based moving averages is the limitation on the fixed-point word length used to perform the calculations, such as int16. The UE processor circuitry should not become saturated during the convolution process, as this could disrupt the channel estimation process. One option is to store the current moving sum as a 32-bit signed number (int32). The current moving sum is then multiplied by the reciprocal of the window length to determine the moving average, and the result is saturated to 32 bits. This operation generates a Q19.12 number, which can be rounded using a six-bit bias and saturated to 16 bits. This operation produces a final number in Q9.6 format. This allows the remainder of the demodulation operation to be designed in the format of its operation.

[0039] Figure 6 This is an example of interpolation for channel estimation according to one or more embodiments. Interpolation unit 114 can use linear interpolation to determine the channel estimate of a noisy message signal (e.g., noisy message signal 408). Since the performance of the interpolation unit may be affected by the noise level of the pilot signal, a denoising process can be performed before linear interpolation. Because the pilot signal is transmitted at specific points in time, rather than continuously, linear interpolation can be used for channel estimation between points in the pilot signal. Interpolation unit 114 may rely on the following formula:

[0040]

[0041] Where y is the channel estimate of the message signal, y0 is the first channel estimate 422, y1 is the second channel estimate 424, x is the time point of the message signal channel estimate 602, x0 is the time point of the first channel estimate 422, and x1 is the time point of the second channel estimate 424. The interpolation unit 114 can determine the channel estimate of each noisy message signal between the first channel estimate 422 and the second channel estimate 424. (See also...) Figure 4 Since there are seven noisy message signals between the first channel estimate 422 and the second channel estimate 424, the interpolation unit 114 can determine the channel estimates of the seven message signals.

[0042] See back Figure 2 The waveform restoration unit 216 can use the channel estimation of the noisy message signal to denoise the message signal. For example, the waveform restoration unit 216 can use the channel estimation of each noisy message signal and apply a deconvolution process to reconstruct the original signal transmitted by the transmitter 202.

[0043] Figure 7This is an example 700 of an example procedure for channel estimation in the time domain according to one or more embodiments. At 702, procedure 700 may include a computing system (e.g., user equipment 102) processing a set of signals including a first noisy pilot signal (e.g., first noisy pilot signal 404), a second noisy pilot signal (e.g., second noisy pilot signal 406), and a noisy message signal (e.g., noisy message signal 408). For example, the computing system may identify a timing pattern of the signal set. The first noisy pilot signal and the second noisy pilot signal may be identified based on the timing pattern. In some instances, the computing system may sample the signal set received from a satellite to generate the signal set. The sampling frequency of the sampling may be based on the timing pattern of the first noisy pilot signal and the second noisy pilot signal.

[0044] The signal set can be transmitted by the satellite using a single-carrier communication system. Furthermore, the noise of the first and second noisy pilot signals can be based on AWGN.

[0045] At 704, process 700 may include a computing system determining a first noisy channel estimate (e.g., first noisy pilot estimate 412) based on a first noisy pilot signal and determining a second noisy channel estimate based on a second noisy pilot signal.

[0046] At 706, process 700 may include a computing system determining a first denoised channel estimate (e.g., first channel estimate 422) based on a noisy pilot signal channel estimate and a second noisy pilot signal channel estimate.

[0047] At 708, process 700 may include a computing system determining a denoised message signal based on a first denoised channel estimate. For example, the computing system may determine a channel estimate of the noisy message signal based on a linear interpolation of the first and second denoised channel estimates. The denoised message may also be based on a channel estimate of the noisy message signal. It should be understood that determining the denoised message signal may include reconstructing a message signal, such as one transmitted by a transmitter.

[0048] As mentioned above, there can be multiple message signals. Therefore, the computing system can determine the corresponding channel estimate for each message signal.

[0049] In some implementations, the computing system may determine a first BLER or a first BLE before processing the signal set. Then, after determining the channel estimate, the computing system may determine a second BLER or a second BLE. The computing system may then send a message to the satellite to update the density of the pilot signals used for downlink transmission.

[0050] Figure 8This is an example of a channel estimation process 800 in the frequency domain according to one or more embodiments. At 802, the process may include a computing system (e.g., UE 102) processing a set of signals including a first noisy pilot signal associated with a first subcarrier, a second noisy pilot signal associated with a second subcarrier, and a noisy message signal. The first noisy channel estimate may correspond to a first frequency response. The second noisy channel estimate corresponds to a second frequency response. The set of signals may be transmitted by a satellite using a multi-carrier communication system.

[0051] For example, the pilot signal may include a cell-specific reference signal (CRS), a demodulation reference signal (DM-RS), or a channel state reference signal (CS-RS). The computing system can determine the known pilot signal. The computing system can then compare the known pilot signal with the first noisy pilot signal. The computing system can then identify the first noisy pilot signal based on the first subcarrier by comparing the known pilot signal with the first noisy pilot signal.

[0052] In some instances, the set of signals is in the time domain. In these instances, the computing system can process the set of Orthogonal Frequency Division Multiplexing (OFDM) signals to convert OFDM symbols from the time domain to the frequency domain.

[0053] At 804, process 800 may include a computing system determining a first noisy channel estimate in the frequency domain based on a first noisy pilot signal and determining a second noisy channel estimate in the frequency domain based on a second noisy pilot signal.

[0054] At 806, process 800 may include a computing system determining a first denoised channel estimate based on a noisy pilot signal channel estimate and a second noisy pilot signal channel estimate.

[0055] At 808, process 800 may include determining the denoised message signal based on a first denoised channel estimate. In some embodiments, the computing system may determine a first BLER or a first BLE before processing the signal set. Then, the computing system may determine a second BLER or a second BLE after determining a third channel estimate. The computing system may then send a message to the satellite to update the density of the pilot signal used for downlink transmission.

[0056] Figure 9 A receiver assembly 900 of a UE 906 according to some embodiments is illustrated. The receiver assembly 900 may include an antenna panel 904 that includes a plurality of antenna elements. The panel 904 is shown as having four antenna elements, but other embodiments may include other numbers of antenna elements.

[0057] Antenna panel 904 may be coupled to an analog beamforming (BF) assembly comprising several phase shifters 908(1)-908(4). Phase shifters 908(1)-908(4) may be coupled to a radio frequency (RF) chain 913. RF chain 913 amplifies the received analog RF signal, down-converts the RF signal to baseband, and converts the analog baseband signal into a digital baseband signal that can be provided to a baseband processor for further processing.

[0058] In various implementations, control circuitry residing in the baseband processor may provide BF weights (e.g., W1-W4) (which may represent phase shift values) to phase shifters 908(1)-908(4) to provide a receive beam at antenna panel 904. These BF weights may be determined based on channel-based beamforming.

[0059] Figure 10 An example of a UE 1000 according to some implementation schemes is shown. UE 1000 may be similar to... Figure 1 The UE 102 is essentially interchangeable with it.

[0060] Processor 1004 may include processor circuitry, such as, for example, baseband processor circuitry (BB) 1004A, central processing unit circuitry (CPU) 1004B, and graphics processing unit circuitry (GPU) 1004C. Processor 1004 may include any type of circuitry or processor circuitry that executes or otherwise operates computer-executable instructions (such as program code, software modules, or functional processes from memory / storage device 1012) to cause UE 1000 to perform the operations described herein. Processor 1004 may also include interface circuitry 1004D for communicatively coupling processor circuitry to one or more other components of UE 1000.

[0061] In some implementations, the baseband processor circuit 1004A can access the communication protocol stack 1036 in the memory / storage device 1012 to communicate over a 3GPP-compliant network. Generally, the baseband processor circuit 1004A can access the communication protocol stack 1036 to perform user plane functions at the PHY, MAC, RLC, PDCP, SDAP, and PDU layers; and control plane functions at the PHY, MAC, RLC, PDCP, RRC, and NAS layers. In some implementations, PHY layer operations may additionally / optionally be performed by components of the RF interface circuit 1008.

[0062] The baseband processor circuit 1004A can generate or process baseband signals or waveforms carrying information in a 3GPP-compliant network. In some implementations, the waveforms used for NR can be based on cyclic prefix OFDM (CP-OFDM) in the uplink or downlink, and Discrete Fourier Transform Extended OFDM (DFT-S-OFDM) in the uplink.

[0063] The memory / storage device 1012 may include one or more non-transitory computer-readable media, which include instructions (e.g., a communication protocol stack 1036) that can be executed by one or more processors in the processor 1004 to cause the UE 1000 to perform various delayed PRACH operations described herein.

[0064] The memory / storage device 1012 includes any type of volatile or non-volatile memory that can be distributed throughout the UE 1000. In some embodiments, some of the memory / storage devices 1012 may be located on the processor 1004 itself (e.g., the memory / storage device 1012 may be part of a chipset corresponding to the baseband processor circuitry 1004A), while other memory / storage devices 1012 may be located external to the processor 1004 but accessible via a memory interface. The memory / storage device 1012 may include any suitable volatile or non-volatile memory, such as, but not limited to, dynamic random access memory (DRAM), static random access memory (SRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, solid-state memory, or any other type of memory device technology.

[0065] The RF interface circuit 1008 may include transceiver circuitry and a radio frequency front-end module (RFEM) that allows the UE 1000 to communicate with other devices via a radio access network. The RF interface circuit 1008 may include various components arranged in the transmit or receive path. These components may include, for example, switches, mixers, amplifiers, filters, synthesizer circuitry, and control circuitry.

[0066] In the receiving path, the RFEM can receive the radiated signal from the air interface via antenna 1026 and continue to filter and amplify the signal (using a low-noise amplifier). This signal can be provided to the receiver of the transceiver, which downconverts the RF signal into a baseband signal that is provided to the baseband processor of processor 1004.

[0067] In the transmission path, the transceiver's transmitter up-converts the baseband signal received from the baseband processor and provides the RF signal to the RFEM. The RFEM can then amplify the RF signal using a power amplifier before it is radiated across the air interface via antenna 1026.

[0068] In various implementations, the RF interface circuit 1008 can be configured to transmit / receive signals in a manner compatible with NR access technologies.

[0069] Antenna 1026 may include antenna elements to convert electrical signals into radio waves for propagation through the air, and to convert received radio waves back into electrical signals. These antenna elements may be arranged in one or more antenna panels. Antenna 1026 may have omnidirectional, directional, or combinations thereof antenna panels to enable beamforming and multiple-input multiple-output (MIMO) communication. Antenna 1026 may include a microstrip antenna, patch antenna, phased array antenna, or a printed antenna fabricated on the surface of one or more printed circuit boards. Antenna 1026 may have one or more panels designed for a specific frequency band (including bands in FR1 or FR2).

[0070] User interface 1016 includes various input / output (I / O) devices designed to enable users to interact with UE 1000. User interface 1016 includes input device circuitry and output device circuitry. Input device circuitry includes any physical or virtual components for accepting input, particularly including one or more physical or virtual buttons (e.g., a reset button), a physical keyboard, a keypad, a mouse, a touchpad, a touchscreen, a microphone, a scanner, or a headset. Output device circuitry includes any physical or virtual components for displaying information or otherwise conveying information (such as sensor readings, actuator positions, or other similar information). Output device circuitry may include any number or combination of audio or visual displays, particularly including one or more simple visual outputs / indicators (e.g., binary status indicators such as light-emitting diodes (LEDs) and multi-character visual outputs), or more complex outputs (such as display devices or touchscreens such as liquid crystal displays (LCDs), LED displays, quantum dot displays, and projectors)), wherein the output of characters, graphics, and multimedia objects, etc., is generated or produced through the operation of UE 1000.

[0071] Sensor 1020 may include devices, modules, or subsystems designed to detect events or changes in its environment and transmit information about the detected events (sensor data) to other devices, modules, or subsystems. Examples of such sensors include: inertial measurement units including accelerometers, gyroscopes, or magnetometers; microelectromechanical systems (MEMS) or nanoelectromechanical systems (NEMS) including 3-axis accelerometers, 3-axis gyroscopes, or magnetometers; level sensors; flow sensors; temperature sensors (e.g., thermistors); pressure sensors; barometric pressure sensors; gravimeters; altimeters; image capture devices (e.g., cameras or lensless aperture sensors); light detection and ranging sensors; proximity sensors (e.g., infrared radiation detectors); depth sensors; ambient light sensors; ultrasonic transceivers; and microphones or other similar audio capture devices.

[0072] The driving circuit 1022 may include software and hardware elements that operate to control a specific device embedded in, attached to, or otherwise communicatively coupled to the UE 1000. The driving circuit 1022 may include various drivers that allow other components to interact with or control various input / output (I / O) devices that may exist within or be connected to the UE 1000. For example, the driving circuit 1022 may include: a display driver for controlling and allowing access to a display device; a touchscreen driver for controlling and allowing access to a touchscreen interface; a sensor driver for acquiring sensor readings of sensor 1020 and controlling and allowing access to sensor 1020; a driver for acquiring actuator positioning of an electromechanical component or controlling and allowing access to an electromechanical component; a camera driver for controlling and allowing access to an embedded image capture device; and an audio driver for controlling and allowing access to one or more audio devices.

[0073] The PMIC 1024 manages the power supplied to various components of the UE 1000. Specifically, relative to the processor 1004, the PMIC 1024 controls power source selection, voltage scaling, battery charging, or DC-DC conversion.

[0074] Battery 1028 can power UE 1000, but in some examples, UE 1000 may be installed and deployed in a fixed location and may have a power source coupled to the power grid. Battery 1028 may be a lithium-ion battery, a metal-air battery such as a zinc-air battery, an aluminum-air battery, a lithium-air battery, etc. In some specific implementations, such as in vehicle-based applications, battery 1028 may be a typical lead-acid automotive battery.

[0075] Figure 11Network device 1100 is illustrated according to some implementation schemes. Network device 1100 may be similar to or interchangeable with equipment in a base station, core network, or external data network.

[0076] Network device 1100 may include processor 1104, RF interface circuitry 1108 (if implemented as a base station), core network (CN) interface circuitry 1114, memory / storage device circuitry 1112, and antenna structure 1126.

[0077] The components of network device 1100 can be coupled to various other components via one or more interconnects 1128.

[0078] The processor 1104, RF interface circuit 1108, memory / storage device circuit 1112 (including communication protocol stack 1110), antenna structure 1126, and interconnect 1128 can be similar to those relative to... Figure 10 Similar named elements are shown and described.

[0079] Processor 1104 may include processor circuitry, such as, for example, baseband processor circuitry (BB) 1104A, central processing unit circuitry (CPU) 1104B, and graphics processing unit circuitry (GPU) 1104C. Processor 1104 may include any type of circuitry or processor circuitry that executes or otherwise operates computer-executable instructions (such as program code, software modules, or functional processes from memory / storage device circuitry 1112) to cause the network device to perform latency-adaptive operation as described herein. Processor 1104 may also include interface circuitry 1104D for communicatively coupling the processor circuitry to one or more other components of network device 1100.

[0080] CN interface circuitry 1114 can provide connectivity to a core network (e.g., a 5GC using a 5G core network (5GC) compatible network interface protocol, such as Carrier Ethernet or some other suitable protocol). Network connectivity can be provided to / from network device 1100 via fiber optic or wireless backhaul. CN interface circuitry 1114 may include one or more dedicated processors or FPGAs for communicating using one or more of the aforementioned protocols. In some implementations, CN interface circuitry 1114 may include multiple controllers for providing connectivity to other networks using the same or different protocols.

[0081] As is widely recognized, the use of personally identifiable information should comply with privacy policies and practices that are generally accepted to meet or exceed industry or governmental requirements for protecting user privacy. Specifically, personally identifiable information data should be managed and processed to minimize the risk of unintentional or unauthorized access or use, and the nature of authorized use should be clearly explained to users.

[0082] For one or more embodiments, at least one of the components shown in one or more of the foregoing figures may be configured to perform one or more operations, techniques, processes, or methods described in the Embodiments section below. For example, the baseband circuitry described above in conjunction with one or more of the foregoing figures may be configured to operate according to one or more embodiments described below. As another example, circuitry associated with one or more of the UE, base station, or network elements described above in conjunction with the foregoing figures may be configured to operate according to one or more examples shown in the Examples section below.

[0083] Example

[0084] Additional example implementations are provided in the following sections.

[0085] Example 1 may include a method comprising: processing a set of signals including a first noisy pilot signal, a second noisy pilot signal, and a noisy message signal; determining a first noisy channel estimate based on the first noisy pilot signal and determining a second noisy channel estimate based on the second noisy pilot signal; determining a first denoised channel estimate based on the first noisy channel estimate and the second noisy channel estimate; and determining a denoised message signal based on the first denoised channel estimate.

[0086] Example 2 may include the method according to Example 1, wherein the method further includes:

[0087] The channel estimate of the noisy message signal is determined based on the linear interpolation of the first denoised channel estimate and the second denoised channel estimate, wherein the denoised message signal is further based on the channel estimate of the noisy message signal.

[0088] Example 3 may include the method according to any one of Examples 1 or 2, wherein the method further includes: identifying a second denoised channel estimate based on a convolution-based moving average, wherein the denoised message signal is further determined based on the second denoised channel estimate.

[0089] Example 4 may include the method according to Example 3, wherein the method further includes: determining a window length for a convolution-based moving average; and determining a scaling factor for the convolution-based moving average based on the window length, wherein the first denoised channel estimation is based on the scaling factor.

[0090] Example 5 may include the method according to any one of Examples 1 to 4, wherein processing the signal set to identify the first noisy pilot signal and the second noisy pilot signal includes: identifying a timing pattern of the signal set, wherein the first noisy pilot signal and the second noisy pilot signal are identified based on the timing pattern.

[0091] Example 6 may be a method according to any one of Examples 1 to 5, wherein the signal set is a first set of signals, and wherein the method further includes: sampling a second set of signals to generate the first set of signals; wherein the sampling frequency of the sampling is based on a timing pattern of the first noisy pilot signal and the second noisy pilot signal.

[0092] Example 7 may include the method according to any one of Examples 1 to 6, wherein the method further includes: determining a first BLER before processing the signal set; determining a second BLER after determining the denoised message signal; and sending a message to the satellite to update the density of the pilot signal for downlink transmission.

[0093] Example 8 may include the method according to any one of Examples 1 to 7, wherein the noise of the first noisy pilot signal is based on AWGN.

[0094] Example 9 may include the method according to any one of Examples 1 to 8, wherein the signal set includes a plurality of noisy pilot signals, the plurality of noisy pilot signals including the first noisy pilot signal and the second noisy pilot signal, and wherein the plurality of noisy pilot signals are equally spaced in the time domain.

[0095] Example 10 may include the method according to any one of Examples 1 to 9, wherein determining the denoised message signal includes reconstructing the message signal as transmitted by the transmitter.

[0096] Example 11 may include the method according to any one of Examples 1 to 10, wherein a plurality of noisy message signals are located between the first denoised channel estimate and the second denoised channel estimate, and wherein the method further includes: determining a plurality of channel estimates based on the plurality of noisy message signals.

[0097] Example 12 may include the method according to any one of Examples 1 to 11, wherein the signal set is transmitted by a satellite using a single-carrier communication system.

[0098] Example 13 may include the method according to any one of Examples 1 to 12, wherein the denoised message signal is determined based on the first denoised channel estimate using the following interpolation operation:

[0099]

[0100] Where y is the channel estimate of the noisy message signal, y0 is the first channel estimate, y1 is the second channel estimate, and x is the time point of the noisy message signal, x0 is the time point of the first channel estimate, and x1 is the time point of the second channel estimate.

[0101] Example 14 may include an apparatus comprising: a processing circuit configured to perform any of the steps described in Examples 1 to 13; and a memory coupled to the processing circuit, the memory being configured to store signal information.

[0102] Example 15 may include one or more non-transitory computer-readable media storing a sequence of instructions that, when executed by one or more processors, cause processing circuitry to perform any of the steps described in Examples 1 to 13.

[0103] Example 16 may include an apparatus comprising: a processing circuit configured to: identify a first noisy pilot signal and a second noisy pilot signal from a set of signals; determine a first noisy pilot signal channel estimate based on the first noisy pilot signal and determine a second noisy pilot signal channel estimate based on the second noisy pilot signal; determine a first denoised channel estimate based on the first noisy channel estimate and the second noisy channel estimate; and determine a denoised message signal based on the first denoised channel estimate; and a memory coupled to the processing circuit, the memory being configured to store signal information.

[0104] Example 17 may include the apparatus according to Example 16, wherein the first noisy pilot signal is represented as a complex signal, and wherein the processing circuitry is further configured to: determine a channel estimate of the first noisy pilot signal for the real and imaginary domains.

[0105] Example 18 may include the apparatus according to any one of Examples 16 or 17, wherein the processing circuitry is further configured to: determine a window length for a moving average operation; and determine a scaling factor for the moving average operation based on the window length, wherein the first denoised channel estimation is based on the scaling factor.

[0106] Example 19 may include the apparatus according to any one of Examples 16 to 18, wherein the signal set is a first set of signals, and wherein the processing circuit is further configured to sample a second set of signals to generate the first set of signals, wherein the sampling frequency of the sampling is based on a timing pattern of the first noisy pilot signal and the second noisy pilot signal.

[0107] Example 20 may include a method for performing any of the steps described in Examples 16 to 19.

[0108] Example 21 may include one or more non-transitory computer-readable media storing a sequence of instructions that, when executed by one or more processors, cause processing circuitry to perform any of the steps described in Examples 16 to 19.

[0109] Example 22 may include one or more non-transitory computer-readable media storing a sequence of instructions that, when executed by one or more processors, cause processing circuitry to: process a first noisy pilot signal and a second noisy pilot signal from a set of signals; determine a first noisy estimate based on the first noisy pilot signal and determine a second noisy channel estimate based on the second noisy pilot signal; determine a first denoised channel estimate based on the first noisy channel estimate and the second noisy channel estimate; and determine a denoised message signal based on the first denoised channel estimate.

[0110] Example 23 may include one or more non-transitory computer-readable media according to Example 22, wherein the instruction sequence, when executed by one or more processors, causes processing circuitry to: determine a channel estimate of the noisy message signal based on a linear interpolation of the first denoised channel estimate and the second denoised channel estimate, wherein the denoised message signal is further based on the channel estimate of the noisy message signal.

[0111] Example 24 may include one or more non-transitory computer-readable media according to any one of Examples 22 or 23, wherein the instruction sequence, when executed by one or more processors, causes processing circuitry to: determine a first BER before processing the set of signals; determine a second BER after determining the denoised message signal; and send a message to a satellite to update the density of pilot signals for downlink transmission.

[0112] Example 25 may include a method for performing any of the steps described in Examples 22 to 24.

[0113] Example 26 may include an apparatus comprising: a processing circuit configured to perform any of the steps described in Examples 22 to 24; and a memory coupled to the processing circuit, the memory being configured to store signal information.

[0114] Unless otherwise expressly stated, any of the above embodiments may be combined with any other embodiment (or combination of embodiments). The foregoing description of one or more specific embodiments provides illustration and description, but is not intended to be exhaustive or to limit the scope of the embodiments to the precise forms disclosed. In view of the teachings above, modifications and variations are possible, or modifications and variations may be obtained from the practice of various embodiments.

[0115] Although the above embodiments have been described in considerable detail, many variations and modifications will become apparent to those skilled in the art once the above disclosure is fully understood. It is intended that the following claims be construed as encompassing all such variations and modifications.

Claims

1. A method comprising: processing a set of signals including a first noisy pilot signal, a second noisy pilot signal, and a noisy message signal; determining a first noisy channel estimate based on the first noisy pilot signal and a second noisy channel estimate based on the second noisy pilot signal; determining a first de-noised channel estimate based on the first noisy channel estimate and the second noisy channel estimate; and determining a de-noised message signal based on the first de-noised channel estimate.

2. The method of claim 1, wherein method further comprises: determining a channel estimate for the noisy message signal based on a linear interpolation of the first de-noised channel estimate and a second de-noised channel estimate, wherein the de-noised message signal is further determined based on the channel estimate for the noisy message signal.

3. The method of claim 1, wherein the method further comprises: identifying a second de-noised channel estimate based on a convolution-based moving average, wherein the de-noised message signal is further determined based on the second de-noised channel estimate.

4. The method of claim 3, wherein the method further comprises: determining a window length for the convolution-based moving average; and determining a scaling factor for the convolution-based moving average based on the window length, wherein the first de-noised channel estimate is based on the scaling factor.

5. The method of claim 1, wherein processing the set of signals to identify the first noisy pilot signal and the second noisy pilot signal comprises: identifying a timing pattern of the set of signals, wherein the first noisy pilot signal and the second noisy pilot signal are identified based on the timing pattern.

6. The method of claim 1, wherein the set of signals is a first set of signals, and wherein the method further comprises: sampling a second set of signals to generate the first set of signals; wherein a sampling frequency of the sampling is based on a timing pattern of the first noisy pilot signal and a second noisy pilot signal.

7. The method of claim 1, wherein the method further comprises: determining a first block error rate (BLER) prior to processing the set of signals; determining a second BLER after determining the de-noised message signal; and sending a message to a satellite to update a density of pilot signals used for downlink transmissions.

8. The method of claim 1, wherein noise of the first noisy pilot signal is based on additive white Gaussian noise (AWGN).

9. The method of claim 1, wherein the set of signals includes a plurality of noisy pilot signals, the plurality of noisy pilot signals including the first noisy pilot signal and the second noisy pilot signal, and wherein the plurality of noisy pilot signals are equally spaced in a time domain.

10. The method of claim 1, wherein determining the de-noised message signal comprises reconstructing a message signal transmitted by a transmitter.

11. The method of claim 1, wherein a plurality of noisy message signals are located between the first de-noised channel estimate and a second de-noised channel estimate, and wherein the method further comprises: determining a plurality of channel estimates based on the plurality of noisy message signals.

12. The method of claim 1, wherein the set of signals are transmitted by a satellite using a single carrier communication system.

13. The method of claim 1, wherein determining a de-noised message signal based on the first de-noised channel estimate is based on an interpolation operation using: where y is a channel estimate of a noisy message signal, yo is a first channel estimate, yi is a second channel estimate, x is a time point of the noisy message signal, xo is a time point of the first channel estimate, and xi is a time point of the second channel estimate.

14. An apparatus comprising: processing circuitry configured to: identify a first noisy pilot signal and a second noisy pilot signal from a set of signals, determine a first noisy pilot signal channel estimate based on the first noisy pilot signal and a second noisy pilot signal channel estimate based on the second noisy pilot signal, determine a first de-noised channel estimate based on the first noisy channel estimate and the second noisy pilot signal channel estimate, and determine a de-noised message signal based on the first de-noised channel estimate; and a memory coupled to the processing circuitry, the memory configured to store signal information.

15. The apparatus of claim 14, wherein the first noisy pilot signal is represented as a complex signal, and wherein the processing circuitry is further configured to: determine a first noisy pilot signal channel estimate for a real domain and an imaginary domain.

16. The apparatus of claim 14, wherein the processing circuitry is further configured to: determine a window length for a moving average operation; and determine a scaling factor for the moving average operation based on the window length, wherein the first de-noised channel estimate is based on the scaling factor.

17. The apparatus of claim 14, wherein the set of signals is a first set of signals, and wherein the processing circuitry is further configured to: sample a second set of signals to generate the first set of signals, wherein a sampling frequency of the sampling is based on a timing pattern of the first noisy pilot signal and a second noisy pilot signal.

18. One or more non-transitory computer-readable media having stored thereon sequences of instructions that, when executed by one or more processors, cause processing circuitry to: process a first noisy pilot signal and a second noisy pilot signal from a set of signals; determine a first noisy estimate based on the first noisy pilot signal and a second noisy channel estimate based on the second noisy pilot signal; determine a first de-noised channel estimate based on the first noisy channel estimate and the second noisy channel estimate; and and determining a denoised message signal based on the first denoised channel estimate.

19. The one or more non-transitory computer-readable media of claim 18, wherein the sequences of instructions, when executed by one or more processors, cause processing circuitry to: determine a channel estimate for the noisy message signal based on a linear interpolation of the first denoised channel estimate and a second denoised channel estimate, wherein the denoised message signal is further based on the channel estimate for the noisy message signal.

20. The one or more non-transitory computer-readable media of claim 18, wherein the sequences of instructions, when executed by one or more processors, cause processing circuitry to: determine a first bit error rate (BER) prior to processing the set of signals; determine a second BER after determining the denoised message signal; and transmit a message to a satellite to update a density of pilot signals used for downlink transmissions.

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