SYSTEMS AND METHODS FOR IMPROVING DEMODULATION REFERENCE SIGNAL CHANNEL ESTIMATION

A disclosed computer-implemented method may include receiving, as part of a demodulation reference signal (DMRS) channel estimation operation, a frequency domain channel estimation signal comprising a plurality of DMRS samples, and generating an extended channel estimation signal by: (A) determining an extended DMRS sample that extends at least one edge of the channel estimation signal based on: (i) an edge DMRS sample included in the plurality of DMRS samples at the edge of the channel estimation signal, and (ii) at least one additional DMRS sample included in the plurality of DMRS samples, and (B) extending the edge of the channel estimation signal by including the DMRS samples and the extended DMRS sample in the extended channel estimation signal. The method may also include generating an augmented channel estimation signal by extrapolating a frequency edge for the augmented channel estimation signal. Various other systems and methods are also disclosed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate a number of example embodiments and are a part of the specification. Together with the following description, these drawings demonstrate and explain various principles of the instant disclosure.

FIG.1shows a block diagram of a Multiple-Input, Multiple-Output (MIMO) system that includes an antenna panel that may receive radiations from one or more user equipment devices (UEs).

FIG.2shows a flow diagram for an example MIMO processing chain.

FIG.3is a block diagram of an example system for improving demodulation reference signal (DMRS) channel estimation.

FIG.4is a block diagram of an example implementation of a system for improving DMRS channel estimation.

FIG.5is a flow diagram of an example method for improving DMRS channel estimation.

FIG.6includes a diagram that illustrates a possible algorithm for right edge extension for large packets.

FIG.7includes a diagram that illustrates a possible algorithm for left edge extension for large packets.

FIG.8illustrates an additional or alternative edge extension algorithm for large packets that further includes a windowing function.

FIG.9-11illustrate possible edge extension algorithms for small packets.

FIG.12illustrates an edge extension algorithm for medium-sized packets.

FIG.13includes a diagram that illustrates a possible edge extrapolation algorithm in accordance with the DMRS channel estimation and extrapolation architecture described herein.

FIGS.14through 18 illustrate additional or alternative example algorithms or methods for further improving DMRS channel estimation in accordance with the DMRS channel estimation and extrapolation architecture disclosed herein.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

New Radio (NR) is a radio access technology (RAT) developed by the 3rd Generation Partnership Project (3GPP) for the fifth generation (5G) mobile network. In 5G NR, a physical uplink shared channel (PUSCH) is a physical uplink channel that carries user data from a UE device to a base station (BS). A DMRS is a reference signal associated with PUSCH. DMRS is used for channel estimation as part of coherent demodulation of PUSCH. The DMRS, known to both the BS and the UE, is sent by the UE, and is used by the BS receiver to acquire a propagation channel to recover data from each UE.

In some examples, a DMRS channel estimation architecture may include edge extrapolation for least-squares (LS) channel estimation to reduce edge effects. The “edge effect” may be a phenomenon that occurs when a signal is transformed using the fast Fourier transform (FFT) algorithm. An edge effect may occur due to the fact that the FFT assumes that the signal is periodic; any discontinuities or abrupt changes at the boundaries of the signal can cause artifacts in the frequency domain.

In general, for large packets, conventional edge extrapolation techniques may be effective to remove some edge effects. However, the resulting DMRS channel estimation may still be impacted on the edges. The impact of edge effects relative to the overall DMRS channel in a large band may be small because scrambling in the decoding process may spread edge impacts to the entire band. Thus, the net impact of edge effects may be insignificant for large packets.

However, the net impact of the edge effects may grow as the bandwidth (also “BW” herein) becomes smaller. Furthermore, in some examples (e.g., a multiuser environment with different user grouping of different user packet sizes, operating within a multi-core operational environment, etc.) large packets may also generally be broken into multiple resource block (RB) segments. In such examples, algorithm design may call for a smaller segment size without a significant compromise in performance.

For medium packets, such as bandwidths below 20 physical resource blocks (PRB), the net impact of edge effects on link performance becomes significant, especially for high order modulation such as 256 Quadrature Amplitude Modulation (QAM). This may limit the system throughput, especially for highly loaded systems with low latency requirement applications, where the system may be unable to allocate large bandwidth for individual or single users.

For smaller packets, such as only 1 or 2 PRBs in applications of short messaging with low latency, conventional edge extrapolation techniques may simply be ineffective because there might not be enough samples to effectively extrapolate. For example, in DMRS configuration type 1, one PRB may have only three samples of LS channel estimation; whereas, in DMRS configuration type 2, one PRB may have only two samples of LS channel estimation. Hence, the present application identifies and addresses a need for an improved systems and methods for DMRS channel estimation in 5G NR PUSCH that may reduce and/or mitigate edge effects for all packet sizes.

The present application is directed to systems and methods for improving DMRS channel estimation in 5G-NR PUSCH communications. As described in greater detail below, the systems and methods described herein may improve DMRS channel estimation by extending one or more edges of a received frequency-domain channel estimation signal that may include multiple DMRS samples. Embodiments may further extrapolate the one or more extended edges as part of an overall DMRS channel estimation architecture that may include additional FFT and/or inverse FFT (IFFT) operations, DMRS measurements, windowing operations, frequency interpolation operations, and so forth. Moreover, the systems and methods for edge extension described herein may, in some embodiments, be unique and distinct from conventional repetition of edge DMRS. Additional techniques are disclosed that may address all packet sizes (e.g., small packets of 1-2 RB, medium sized packets of up to twenty-five RB, large sized packets of greater than 25 RB, and so forth). In some examples of large packet sizes, edge extension may be skipped, with only a windowing operation after the edge extension being sufficient. In some examples, the windowing function may be a raised-cosine filter or any other suitable windowing function.

The following will provide, with reference toFIGS.1-4and6-18, detailed descriptions of systems for improving DMRS channel estimation. Detailed descriptions of corresponding computer-implemented methods will also be provided in connection withFIG.5.

The time-frequency structure of DMRS depends on the type of waveform configured for PUSCH, as defined in 3rd Generation Partnership Project; Technical Specification Group Radio Access Network (TS) 38.211 “NR; Physical channels and modulation,” §§ 6.4.1.1 and 6.4.1.2. The basic transmission scheme in LTE is orthogonal frequency-division multiplexing (OFDM). NR supports a flexible OFDM numerology with subcarrier spacings ranging from 15 kHz up to 240 kHz with a proportional change in cyclic prefix (CP) duration.

In general, an uplink (UL) RB is the smallest resource allocation unit, which is 12 resource elements (RE) in the frequency domain and up to 14 symbols per slot. The frequency separation between REs may be referred to as sub-carrier spacing (SCS). As mentioned above, SCS may be 15×2μKHz, such that μ=0, 1, 2, 3, 4, resulting in SCS values of 15 KHz, 30 KHz, 60 KHz, 120 KHz, and 240 KHz, respectively. A symbol duration Tsmay be related to SCS by

Each symbol has a cyclic prefix (CP) with a duration related to SCS or μ.

DMRS signals are partitioned into code division multiplexing (CDM) groups. Within CDM groups, ports are coded with an orthogonal cover code (OCC). DMRS has different configurations: configuration type 1 includes 2 CDM groups for OCC, with a frequency density of 3 DMRS anchors per RB per port, whereas configuration type 2 includes 3 CDM groups for OCC, with a frequency density of 2 DMRS anchors per RB per port. NR UL supports symbol sharing data and DMRS; configuration type 2 has lower DMRS cost if fewer ports are actually used. REs on unused CDM groups may be used for data, while unused ports within a used CDM may not be used for data. For example, in type 1 single symbol, a maximum of 4 ports are supported. If only port 2/3 is used, the DMRS position for port 0/1 can be used for data. Furthermore, discrete Fourier transform (DFT) spread coded OFDM (DFT-s-OFDM) (e.g., for data) is only defined for DMRS configuration type 1.

In general, massive MIMO systems use one or more antenna panels to receive radiations from multiple UEs, each sending a signal over the same radio resources. Data from a UE can be sent with one or more antenna ports. Each UE is allocated one or more unique antenna ports by a BS.FIG.1shows a block diagram of a MIMO system100that includes an antenna panel102that may receive radiations from one or more UE104(e.g., UE104(1), UE104(2), UE104(N)). Note that althoughFIG.1shows three UEs, this is provided by way of example only and a MIMO system100may include any suitable number of UE devices.

FIG.2shows a flow diagram for an example MIMO processing chain200. As shown, the example massive MIMO processing chain200may include operations of receiving of an antenna signal202, a fast Fourier transform (FFT) operation204, a beamforming operation206, an LS OCC channel estimation operation208, a DMRS channel estimation operation210, a data channel estimation operation212, a MIMO equalizer operation214, and a low-density parity check code (LDPC) decoding operation216. In general, the systems and methods disclosed herein relate to the DMRS channel estimation operation210.

In some examples, a DMRS channel estimation architecture may include edge extrapolation for LS channel estimation to reduce edge effects that may be introduced by an IFFT. The IFFT may be implemented to convert the channel estimation signal from the frequency domain to the time domain and to further apply noise reduction. In some examples, an additional windowing operation may be applied after the IFFT to reduce noise on the channel estimation signal. Likewise, a zero-insertion operation may be performed to interpolate the noise-reduced channel estimation signal in the frequency domain from a density of ¼ or ⅙ to a density of 1. An FFT may then be performed to bring the noise-reduced channel estimation signal back to the frequency domain.

FIG.3is a block diagram of an example system300for improving DMRS channel estimation. As illustrated in this figure, example system300may include one or more modules302for performing one or more tasks. As will be explained in greater detail below, modules302may include a receiving module304that receives, as part of a DMRS channel estimation operation, a DMRS comprising a plurality of DMRS samples.

Example system300may also include an extending module306that generates an extended channel estimation signal. In some examples, extending module306may generate the extended channel estimation signal by (1) determining at least one extended DMRS sample that extends at least one edge of the channel estimation signal based on (A) an edge DMRS sample included in the plurality of DMRS samples at the edge of the channel estimation signal, and (B) at least one additional DMRS sample included in the plurality of DMRS samples. Extending module306may further generate the extended channel estimation signal by extending the edge of the channel estimation signal by including the plurality of DMRS samples and the at least one extended DMRS sample as part of the extended channel estimation signal. As further illustrated inFIG.3, example system300may also include an extrapolating module308that generates an augmented channel estimation signal by extrapolating, based on the extended channel estimation signal, a frequency edge for the augmented channel estimation signal.

As also illustrated inFIG.3, example system300may also include one or more memory devices, such as memory320. Memory320generally represents any type or form of volatile or non-volatile storage device or medium capable of storing data and/or computer-readable instructions. In one example, memory320may store, load, and/or maintain one or more of modules302. Examples of memory320include, without limitation, Random Access Memory (RAM), Read Only Memory (ROM), flash memory, Hard Disk Drives (HDDs), Solid-State Drives (SSDs), optical disk drives, caches, variations or combinations of one or more of the same, or any other suitable storage memory.

As further illustrated inFIG.3, example system300may also include one or more physical processors, such as physical processor330. Physical processor330generally represents any type or form of hardware-implemented processing unit capable of interpreting and/or executing computer-readable instructions. In one example, physical processor330may access and/or modify one or more of modules302stored in memory320. Additionally or alternatively, physical processor330may execute one or more of modules302to facilitate improving of DMRS channel estimation. Examples of physical processor330include, without limitation, microprocessors, microcontrollers, central processing units (CPUs), Field-Programmable Gate Arrays (FPGAs) that implement softcore processors, Application-Specific Integrated Circuits (ASICs), digital signal processors (DSPs), portions of one or more of the same, variations or combinations of one or more of the same, or any other suitable physical processor.

Example system300inFIG.3may be implemented in a variety of ways. For example, all or a portion of example system300may represent portions of an example system400(“system400”) inFIG.4. As shown inFIG.4, system400may include computing device402in communication with a base station404. Base station404may further be in communication with a user equipment406. In at least one example, computing device402may be programmed with one or more of modules302.

In at least one embodiment, one or more of modules302fromFIG.3may, when executed by computing device402, enable computing device402to perform one or more operations to improve DMRS channel estimation. For example, as will be described in greater detail below, receiving module304may cause computing device402to receive, as part of a DMRS channel estimation operation, a frequency domain channel estimation signal (e.g., channel estimation signal408) that includes a plurality of DMRS samples (e.g., DMRS samples410).

Additionally, extending module306may cause computing device402to generate an extended channel estimation signal (e.g., extended channel estimation signal412). For example, in some embodiments, extending module306may cause computing device402to generate the extended channel estimation signal by determining at least one extended DMRS sample (e.g., extended channel estimation signal412) that extends at least one edge of the channel estimation signal based on an edge DMRS sample (e.g., edge DMRS sample414) included in the plurality of DMRS samples at the edge of the channel estimation signal and at least one additional DMRS sample included in the plurality of DMRS samples (e.g., at least one of DMRS samples410excluding edge DMRS sample414). Furthermore, extending module306may cause computing device402to further generate the extended channel estimation signal by extending the edge of the channel estimation signal by including the plurality of DMRS samples and the at least one extended DMRS sample as part of the extended channel estimation signal.

Moreover, as will be described in greater detail below, extrapolating module308may cause computing device402to generate an augmented channel estimation signal (e.g., augmented channel estimation signal418) by extrapolating, based on the extended channel estimation signal, a frequency edge (e.g., frequency edge420) for the augmented channel estimation signal.

Computing device402generally represents any type or form of computing device capable of reading and/or executing computer-executable instructions and/or hosting executables. Examples of computing device402include, without limitation, application servers, storage servers, database servers, web servers, signal processing devices, and/or any other suitable computing device configured to run certain software applications and/or provide various application, storage, and/or signal processing services.

In at least one example, computing device402may be a computing device programmed with one or more of modules302. All or a portion of the functionality of modules302may be performed by computing device402and/or any other suitable computing system. As will be described in greater detail below, one or more of modules302fromFIG.3may, when executed by at least one processor of computing device402, enable computing device402to improve DMRS channel estimation by reducing edge effects for one or more signals used for a DMRS channel estimation process.

Base station404may generally represent an element within a wireless communication system (e.g., system400) that provides radio coverage and connectivity to user equipment (e.g., user equipment406) within a specific area or cell. A 5G base station may also be referred to as a gNodeB (gNB). Base station404may include a variety of components including, without limitation, an antenna array, a transceiver unit, and one or more baseband processing units. The antenna array may be used to transmit and receive radio signals, while the transceiver unit may be responsible for processing the signals and converting them to digital data that can be sent to the baseband processing units. The baseband processing units may be responsible for performing signal processing, error correction, and modulation and demodulation of the signals. Although not so illustrated inFIG.4, in some examples, computing device402may be included as part of base station404and/or may be in communication with one or more components of base station404.

User equipment406may include any mobile device or endpoint that connects to a 5G network to access various services, such as voice, video, and data communication. user equipment406can be a smartphone, tablet, laptop, or any other wireless device that is designed to operate with 5G networks. In some examples, user equipment406may include a 5G modem, one or more antennas, and/or any other suitable hardware that may facilitate communication with base station404.

Many other devices or subsystems may be connected to system300inFIG.3and/or system400inFIG.4. Conversely, all of the components and devices illustrated inFIGS.3and4need not be present to practice the embodiments described and/or illustrated herein. The devices and subsystems referenced above may also be interconnected in different ways from those shown inFIG.4. Systems300and400may also employ any number of software, firmware, and/or hardware configurations. For example, one or more of the example embodiments disclosed herein may be encoded as a computer program (also referred to as computer software, software applications, computer-readable instructions, and/or computer control logic) on a computer-readable medium.

FIG.5is a flow diagram of an example computer-implemented method500for allocating shared resources in multi-tenant environments. The steps shown inFIG.5may be performed by any suitable computer-executable code and/or computing system, including system300inFIG.3, system400inFIG.4, and/or variations or combinations of one or more of the same. In one example, each of the steps shown inFIG.5may represent an algorithm whose structure includes and/or is represented by multiple sub-steps, examples of which will be provided in greater detail below.

As illustrated inFIG.5, at step510, one or more of the systems described herein may receive, as part of a DMRS channel estimation operation, a frequency domain channel estimation signal that includes a plurality of DMRS samples. For example, receiving module304may, as part of computing device402, cause computing device402to receive channel estimation signal408that includes DMRS samples410.

Receiving module304may cause computing device402to receive channel estimation signal408in a variety of contexts. For example, user equipment406may seek to establish an uplink with base station404. User equipment406may send a DMRS to base station404as part of the uplink transmission. As mentioned above, the DMRS contains a specific pattern of bits that may allow base station404to identify and extract the signal from the received waveform. The DMRS may help to mitigate the effects of interference and noise in the wireless channel and improve the reliability and performance of the communication system.

As mentioned above in reference toFIG.2, a base station (e.g., base station404) may perform one or more processes on a received antenna signal (e.g., antenna signal202) prior to a DMRS channel estimation operation (e.g., DMRS channel estimation operation210). For example, as shown inFIG.2, a base station may perform an FFT operation (e.g., FFT operation204), a beamforming operation (e.g., beamforming operation206), and an LS OCC channel estimation operation (e.g., LS OCC channel estimation operation208) prior to passing a frequency domain channel estimation signal that includes a plurality of DMRS samples (e.g., channel estimation signal408that includes DMRS samples410) as input to a DMRS channel estimation operation (e.g., DMRS channel estimation operation210). LS OCC channel estimation operation208is to obtain a raw channel estimation signal for each pair of port(s) and antenna(s). Once LS OCC channel estimation operation208obtains the raw channel estimation signal, the raw channel estimation signal is passed to the DMRS channel estimation operation210to further process the raw channel estimation signal to improve the accuracy and reliability of data demodulation in the uplink transmission.

Hence, receiving module304may cause computing device402to receive channel estimation signal408from one or more components of base station404.

Returning toFIG.5, at step520, one or more of the systems described herein may generate an extended channel estimation signal. For example, extending module306may, as part of computing device402, cause computing device402to generate extended channel estimation signal412.

As further shown inFIG.5, one or more of the systems described herein may generate the extended channel estimation signal by determining at least one extended DMRS sample that extends at least one edge of the channel estimation signal based on (1) an edge DMRS sample included in the plurality of DMRS samples at the edge of the channel estimation signal, and (2) at least one additional DMRS sample included in the plurality of DMRS samples. For example, extending module306may cause computing device402to generate extended channel estimation signal412by determining, based on edge DMRS sample414and at least one other DMRS sample included in DMRS samples410, extended DMRS sample416.

FIG.5also shows that one or more of the systems described herein may generate the extended channel estimation signal by also extending the edge of the channel estimation signal by including the plurality of DMRS samples and the at least one extended DMRS sample as part of the extended channel estimation signal. For example, extending module306may cause computing device402to generate extended channel estimation signal412by extending the edge of channel estimation signal408by including DMRS samples410and extended DMRS sample416in extended channel estimation signal412.

In some examples, extending module306may apply different edge extension techniques and/or algorithms depending on a size of a received packet. For example,FIG.6includes a diagram600that illustrates a possible algorithm for right edge extension for large packets (e.g., packets greater than 25 RB). In this example, extending module306may select the additional DMRS sample from the plurality of DMRS samples (e.g., DMRS samples410) based on a target frequency interval between the extended DMRS sample (e.g., extended DMRS sample416) and the edge DMRS sample (e.g., edge DMRS sample414). Extending module306may further determine the extended DMRS sample (e.g., a magnitude or phase of the extended DMRS sample) based on a relationship between a magnitude or phase of the extended DMRS sample and a magnitude or phase of the selected additional DMRS sample, such as a normalized spectral density of the edge DMRS sample and a complex conjugate of the selected additional DMRS sample.

InFIG.6, the illustrated algorithm may extend a right edge by NRsamples. As illustrated, R represents a right edge sample (e.g., edge DMRS sample414) and C represents an extended DMRS sample to be generated (e.g., extended DMRS sample416) at a target frequency interval n from R. B represents a “mirror image” of C: a sample from the plurality of DMRS samples that is a target frequency interval of n from R. Extending module306may determine a value of C(n) based on a normalized power spectral density of R(n) and a complex conjugate of B(n) (e.g.,

in accordance with function602.

Likewise,FIG.7includes a diagram700that illustrates a possible algorithm for left edge extension for large packets (e.g., greater than 25 RB) that may extend a left edge by NLsamples. As shown, L represents a left edge sample (e.g., edge DMRS sample414) and C represents an extended DMRS sample to be generated (e.g., extended DMRS sample416) at a target frequency interval n from L. B represents a “mirror image” of C: a sample from the plurality of DMRS samples that is a target frequency interval of n from L. Extending module306may determine a value of C(n) based on a normalized power spectral density of L(n) and a complex conjugate of B(n) (e.g.,

in accordance with function702.

In many cases, NL≤16 and NR≤16, but NL=NRis not required. In cases where NL=NR≥16, a windowing function may be applied to extended NRand NLsamples on the right and left edges prior to or as part of an edge extrapolation (e.g., by extrapolating module308, as will be described in greater detail below in reference toFIG.13). The windowing function may be a raised-cosine filter or other suitable windowing function (e.g., Hamming window, Blackman window, Kaiser window, etc.) and may be applied to any suitable number of samples (e.g., one sample, two samples, twelve samples, etc.) on each edge. In some examples, this technique may be referred to as “edge taping”.

FIG.8includes a plot800that illustrates an additional or alternative edge extension algorithm for large packets that further includes a windowing function. As shown inFIG.8, channel estimation signal802has been extended by extending module306, generating left edge extension804and right edge extension806. In this example, one or more of the systems described herein (e.g., receiving module304, extending module306, and/or extrapolating module308) may have applied a windowing function to left edge extension804and/or right edge extension806, “taping down” left edge extrapolation808and right edge extrapolation810.

In some embodiments, the systems and methods described herein may provide edge extension for smaller packets (e.g., packets having a BW of 1 or 2 RB). In such examples, embodiments of the systems and methods described herein (e.g., one or more of modules302) may perform multiple extensions in multiple iterations. For example, a channel estimation signal may include both a left edge DMRS sample and a right edge DMRS sample, and extending module306may generate the extended channel estimation signal (e.g., extended channel estimation signal412) by determining at least one left edge extended DMRS sample that extends the left edge of the channel estimation signal and by determining at least one right edge extended DMRS sample that extends the right edge of the channel estimation signal (e.g., extended DMRS sample416). Extending module306may further extend the edge of the channel estimation signal by including left edge extended DMRS sample, the plurality of DMRS samples (e.g., DMRS samples410), and the right edge extended DMRS sample as part of the extended channel estimation signal.

In additional embodiments, extending module306may further extend the channel estimation signal by performing an additional extension, using the first extended channel estimation signal as input to an additional extension operation. Extending module306may then extend the first extended channel estimation signal by determining at least one extended intermediate left edge DMRS sample that extends the first extended left edge of the first extended channel estimation signal, determining at least one extended intermediate right edge DMRS sample that extends the first extended right edge of the first extended channel estimation signal. Extending module306may then extend the edge of the channel estimation signal by including the at least one extended intermediate left edge DMRS sample, the first extended channel estimation signal, and the at least one extended intermediate right edge DMRS sample as part of the extended channel estimation signal.

FIG.9andFIG.10illustrate example edge extension algorithms for packets having a bandwidth of 1 RB. InFIG.9, diagram900illustrates that, for DMRS configuration type 1, there may be three DMRS anchors per port per RB. Diagram902shows that a first extension may extend up to two samples on each side. A second extension, as shown in diagram904, may result in 19 samples in total. Moving toFIG.10, diagram1000illustrates that, in DMRS configuration type 2, there may be two DMRS anchors per port per RB. Diagram1002shows that a first extension may extend one sample on each side, and diagram1004shows that a second extension may result in 10 samples total.

FIG.11illustrates an edge extension algorithm for small packets having a BW of two RBs. As shown in diagram1100, for DMRS configuration type 1, there may be three DMRS anchors per port per RB. Thus, as shown in diagram1102, five samples may be extended on each side, resulting in 16 samples total. As shown in diagram1104, there may be two DMRS anchors per port per RB. Thus, as shown in diagram1106, three samples may be extended on each side, resulting in seven samples total.

FIG.12shows a diagram1200that illustrates an edge extension algorithm for medium-sized packets in the range of two RBs up to 25 RBs. In this example, the edges may be extended by at least four samples on each side. As shown, extending module306may perform a right extension operation using DMRS samples1202, generating extended right edge1204. Extending module306may further perform a left extension operation using DMRS samples1202, generating extended left edge1206. Extending module306may then generate an extended channel estimation signal1208by including extended left edge1206, DMRS samples1202, and extended right edge1204in the extended channel estimation signal.

In summary, extending module306may extend the original channel estimation signal (i.e., output from a LS OCC channel estimation process) by determining additional DMRS samples at one or both of the left and right edges. This edge extension may improve channel estimation in 5G NR PUSCH, ultimately enhancing the quality of a received uplink signal.

Returning toFIG.5, at step530, one or more of the systems described herein may generate an augmented channel estimation signal by extrapolating, based on the extended channel estimation signal, a frequency edge for the augmented channel estimation signal. For example, extrapolating module308may, as part of computing device402, cause computing device402to generate augmented channel estimation signal418by extrapolating, based on extended channel estimation signal412, frequency edge420for augmented channel estimation signal418.

Extrapolating module308may extrapolate frequency edge420in a variety of contexts. For example, extrapolating module308may, as described above in reference to FIG.8, when a packet size meets a predetermined threshold, apply a windowing function (e.g., a raised cosine window, a Hamming window, a Hann window, etc.) to at least a portion of a plurality of extended DMRS samples prior to or as part of extrapolating a frequency edge for the extended channel estimation signal.

Additionally or alternatively, for smaller or medium sized packets (e.g., packets where BW is less than 25 RBs), extrapolating module308may extrapolate frequency edge420by applying a precalculated interpolation matrix to the edge of the extended channel estimation signal. By way of illustration,FIG.13includes a diagram that illustrates a possible edge extrapolation algorithm. Frequency domain plot1300shows an extended channel estimation signal with extrapolated edges. As shown, channel estimation signal1302has an extended left edge1304and an extended right edge1306. Extrapolating module308may therefore execute a left edge extrapolation operation using channel estimation signal1302, extended left edge1304, and/or extended right edge1306to generate extrapolated left edge1308. Similarly, extrapolating module308may execute a right edge extrapolation operation using channel estimation signal1302, extended left edge1304, and/or extended right edge1306to generate extrapolated right edge1310.

Frequency domain plot1320illustrates a frequency domain plot1320that describes different portions, bins, or segments of an extended channel estimation signal that may be used to extrapolate a left and a right edge using left matrix operation1330and matrix operation1340, respectively. In some embodiments, interpolation matrices WLand WRmay be applied to different portions of a channel to further extend the right and left edges, respectively.

In some examples, the interpolation matrices WLand WRmay be pre-determined (e.g., pre-calculated) and may be determined based on Weiner filter theory. A Wiener filter may be used to produce an estimate of a desired or target random process by linear time-invariant (LTI) filtering of an observed noisy process and additive noise, assuming a known stationary signal and noise spectra. A Wiener filter may minimize a mean square error (MSE) between an estimated random process and a desired process.

One or more of modules302(e.g., one or more of receiving module304, extending module306, and/or extrapolating module308) may perform one or more additional operations to further improve DMRS channel estimation in accordance with the architecture disclosed herein.FIG.14includes a block diagram1400of possible additional operations that one or more of modules302may perform to further improve DMRS channel estimation.

As shown in block diagram1400, one or more of modules302may cause a channel estimation signal1402(such as channel estimation signal408) to undergo a channel extension and extrapolation process1404. In some examples, the channel extension and extrapolation process1404may include or represent any of the operations described above in relation to modules302, which may result in an augmented channel estimation signal (e.g., augmented channel estimation signal418). One or more of modules302(e.g., extrapolating module308) may execute an IFFT process1406, resulting in a time domain representation of the augmented channel estimation signal (e.g., a time-domain representation of augmented channel estimation signal418), represented inFIG.14as h(n).

In some examples, one or more of modules302(e.g., extrapolating module308) may cause the time domain representation of the augmented channel estimation signal (e.g., signal h(n)) to undergo a DMRS measurements process1408, which may provide parameters N0, N1, and N2. In the example illustrated inFIG.14, parameter N0may define a span of windowing function w(n) having a raised cosine edge with a smooth transition from a zero value to a value represented by the N2region on the left of plot1420and a smooth transition from a value represented by the N1region on the right of plot1420. Parameter N2may define a span of a main lobe of windowing function w(n) and parameter N1may define a span of a flat top or plateau region of the windowing function w(n). As further shown inFIG.14, at windowing process1410, signal h(n) may be windowed by windowing function w(n) in accordance with h(n)w(n), which may result in noise-reduced windowed signal ĥ(n).

At 0-insertion process1412, a zero-insertion or zero-padding process inserts zeros between the original samples of the discrete ĥ(n) signal. This zero-insertion process is used to interpolate the noise-reduced DMRS channel to all REs in the frequency-time grid. As will be explained in greater detail below in reference toFIGS.15-18, channel estimation signal1402may have a frequency density of ¼ or ⅙ relative to a frequency density of a desired output signal HDMRS. The zero-insertion process may bring a frequency density of signal ĥ(n) to the desired frequency density (e.g., a frequency density of 1). As shown inFIG.14, the zero-insertion process may vary depending on a DMRS configuration type, with 4× or 6× zero-insertion for DMRS configuration type 1 or 2, respectively.

At FFT process1414, the time-domain signal may be converted back to the frequency domain via an additional FFT operation, resulting in frequency-domain signal HDMRS.

After conversion back to the frequency domain, one or more of modules302may perform one or more additional operations on a frequency-domain signal (e.g., HDMRS) to further improve DMRS channel estimation.FIGS.15through18illustrate example algorithms or methods for further improving DMRS channel estimation in accordance with the DMRS channel estimation and extrapolation architecture disclosed herein.

In some examples, the foregoing processes and/or operations may cause samples of a resulting output frequency-domain signal (e.g., HDMRS) to become misaligned with ports of a target CDM group. Hence, one or more of modules302(e.g., receiving module304, extending module306, and/or extrapolating module308) may perform one or more operations to ensure proper alignment of the output frequency-domain signal to ports of CDM groups.

FIG.15illustrates an algorithm for adjusting an FFT output for ports of CDM groups for DMRS configuration type 1. Diagram1500illustrates a misalignment of the last four RBs with CDM group 1 and CDM group 2. Hence, as shown in diagram1510, when the channel estimation signal corresponds to CDM group 1, one or more of modules302may adjust the output frequency-domain signal FFT1512(e.g., HDMRS) by moving a far-right end sample1514in the output frequency-domain signal to a first position1516in a series of samples included in the output frequency domain signal. Alternatively, as shown in diagram1520, for DMRS configuration type 1, when the channel estimation signal corresponds to CDM group 2, one or more of modules302may adjust the output frequency-domain signal FFT1522by moving the two samples1524from the far-right end of the output frequency-domain signal to the first position1526in the series.

For DMRS configuration type 2, ports are separated into three CDM groups. In this scenario, none of the FFT output samples may correspond to a port start, with an offset of ½ of the SCS. To resolve this, one or more of modules302may execute a phase rotation of the output frequency-domain signal by

k=0, 1, 2, . . . , Nifft−1, where Nifft2=6×Nifft1. In this phase rotation process, Nfft2 may represent an expected number of FFT points following an anticipated zero-insertion process. Hence, prior to applying the fast Fourier transform to the noise-reduced time domain representation of the augmented channel estimation signal, one or more of modules302may apply a phase rotation to the noise-reduced time-domain signal. By applying a phase rotation, the systems and methods described herein may shift a center of the FFT output to align the frequency-domain DMRS channel estimation signal with the ports of different CDM groups.

FIG.16includes a block diagram1600of additional operations that one or more of modules302may perform to further improve DMRS channel estimation for DMRS configuration type 2 that includes a phase rotation. As shown, block diagram1600includes channel extension and extrapolation process1404, IFFT process1406, windowing process1410, 0-insertion process1412, and FFT process1414fromFIG.14, with phase rotation process1602interposed between windowing process1410and 0-insertion process1412. Diagram1620further illustrates the foregoing phase rotation process whereby the output frequency-domain signal is adjusted by

FIGS.17and18illustrate adjusting an FFT output for ports of CDM groups for DMRS configuration type 2. Diagram1700illustrates a misalignment of the last six RBs with CDM group 1, CDM group 2, and CDM group 3. Hence, as shown in diagram1800inFIG.18, when the channel estimation signal corresponds to CDM group 1, one or more of modules302may adjust the output frequency-domain signal by moving the two samples1802from the far-right end of the output frequency-domain signal to the first position1804in the series. Additionally, as shown in diagram1810, when the channel estimation signal corresponds to CDM group 2, one or more of modules302may adjust the output frequency-domain signal by moving the two samples1812from the far-right end of the output frequency-domain signal to the first position1814in the series. Finally, as shown in diagram1820, when the channel estimation signal corresponds to CDM group 3, one or more of modules302may adjust the output frequency-domain signal by moving the four samples1822from the far-right end of the output frequency-domain signal to the first position1824in the series.

The systems and methods disclosed herein may have many benefits over conventional options for DMRS channel estimation. For example, by extending one or more edges of a received frequency-domain signal as described above, embodiments of the systems and methods described herein may reduce impacts at edges, and therefore improve DMRS channel estimation for all packet sizes. The systems and methods disclosed herein may have particular benefit for small- and medium-sized packets. Furthermore, the systems and methods described herein may be effective and simple to implement and may improve the functioning of wireless telecommunication systems.

The following example embodiments are also included in this disclosure:

Example 1: A computer-implemented method comprising (1) receiving, as part of a demodulation reference signal (DMRS) channel estimation operation, a frequency domain channel estimation signal comprising a plurality of DMRS samples, (2) generating an extended channel estimation signal by (A) determining at least one extended DMRS sample that extends at least one edge of the channel estimation signal based on (i) an edge DMRS sample included in the plurality of DMRS samples at the edge of the channel estimation signal, (ii) at least one additional DMRS sample included in the plurality of DMRS samples, (B) extending the edge of the channel estimation signal by including the plurality of DMRS samples and the at least one extended DMRS sample as part of the extended channel estimation signal, and (3) generating an augmented channel estimation signal by extrapolating, based on the extended channel estimation signal, a frequency edge for the augmented channel estimation signal.

Example 2: The computer-implemented method of example 1, wherein determining the extended DMRS sample comprises (1) selecting the additional DMRS sample from the plurality of DMRS samples included in the channel estimation signal based on a target frequency interval between the extended DMRS sample and the edge DMRS sample, and (2) determining the extended DMRS sample based on a normalized power spectral density of the edge DMRS sample and a complex conjugate of the additional DMRS sample.

Example 3: The computer-implemented method of any of examples 1-2, wherein (1) the edge of the channel estimation signal comprises a right edge of the channel estimation signal, (2) the edge DMRS sample comprises a right edge DMRS sample, the right edge DMRS sample having a higher frequency than other DMRS samples included in the plurality of DMRS samples, and (3) the additional DMRS sample comprises a DMRS sample included in the plurality of DMRS samples having a lower frequency than the right edge DMRS sample.

Example 4: The computer-implemented method of any of examples 1-3, wherein (1) the edge of the channel estimation signal comprises a left edge of the channel estimation signal, (2) the edge DMRS sample comprises a left edge DMRS sample, the left edge DMRS sample having a lower frequency than other DMRS samples included in the plurality of DMRS samples, and (3) the additional DMRS sample comprises a DMRS sample included in the plurality of DMRS samples having a higher frequency than the left edge DMRS sample.

Example 5: The computer-implemented method of any of examples 1-4, wherein (1) the channel estimation signal corresponds to a data packet including a number of resource blocks that exceed a predetermined threshold number of resource blocks, (2) the at least one extended DMRS sample comprises a plurality of extended DMRS samples, a quantity of extended DMRS samples included in the plurality of extended DMRS samples exceeding a predetermined threshold quantity of DMRS samples, and (3) the computer-implemented method further comprises applying a windowing function to at least a portion of the plurality of extended DMRS samples prior to extrapolating the frequency edge for the augmented channel estimation signal.

Example 6: The computer-implemented method of example 5, wherein at least one of (1) the predetermined threshold number of resource blocks is greater than twenty-five resource blocks, (2) the plurality of extended DMRS samples includes at least sixteen extended DMRS samples, or (3) the windowing function comprises a raised cosine windowing function.

Example 7: The computer-implemented method of any of examples 1-6, wherein (1) the channel estimation signal comprises a left edge DMRS sample and a right edge DMRS sample, (2) generating the extended channel estimation signal further comprises (A) determining the at least one extended DMRS sample that extends the edge of the channel estimation signal by determining at least one left edge extended DMRS sample that extends the left edge of the channel estimation signal, (B) determining at least one right edge extended DMRS sample that extends the right edge of the channel estimation signal, and (3) extending the edge of the channel estimation signal by including the at least one left edge extended DMRS sample, the plurality of DMRS samples, and the at least one right edge extended DMRS sample as part of the extended channel estimation signal.

Example 8: The computer-implemented method of example 7, wherein generating the extended channel estimation signal further comprises (1) designating the at least one left edge extended DMRS sample, the plurality of DMRS samples, and the at least one right edge extended DMRS sample as a first extended channel estimation signal comprising a first extended right edge and a first extended left edge, (2) determining at least one extended intermediate left edge DMRS sample that extends the first extended left edge of the first extended channel estimation signal, (3) determining at least one extended intermediate right edge DMRS sample that extends the first extended right edge of the first extended channel estimation signal, and (4) extending the edge of the channel estimation signal by including the at least one extended intermediate left edge DMRS sample, the first extended channel estimation signal, and the at least one extended intermediate right edge DMRS sample as part of the extended channel estimation signal.

Example 9: The computer-implemented method of any of examples 1-8, wherein extrapolating the frequency edge for the augmented channel estimation signal comprises applying a precalculated interpolation matrix to the edge of the extended channel estimation signal.

Example 10: The computer-implemented method of any of examples 1-9, further comprising generating (1) a time-domain representation of the augmented channel estimation signal by applying an inverse fast Fourier transform to the augmented channel estimation signal, (2) a noise-reduced time domain representation of the augmented channel estimation signal by applying a noise reduction filter to the time-domain representation of the augmented channel estimation signal, and (3) a noise-reduced frequency-domain signal by applying a fast Fourier transform to the noise-reduced time domain representation of the augmented channel estimation signal, the noise-reduced frequency-domain signal comprising a plurality of frequency domain samples in a series of signals having at least one left end sample and at least one right end sample.

Example 11: The computer-implemented method of example 10, wherein (1) the channel estimation operation corresponds to a first DMRS configuration type, (2) the computer-implemented method further comprises at least one of (A) when the channel estimation signal corresponds to a first CDM group, moving the at least one right end sample to a first position in the series that precedes the left end sample, or (B) when the channel estimation signal corresponds to a second CDM group and the at least one right end sample comprises two samples, moving the two samples from the right end of the noise-reduced frequency-domain signal to the first position in the series.

Example 12: The computer-implemented method of example 10, wherein (1) the channel estimation operation corresponds to a second DMRS configuration type, (2) the computer-implemented method further comprises (A) prior to applying the fast Fourier transform to the noise-reduced time domain representation of the augmented channel estimation signal, applying a phase rotation to the noise-reduced time-domain signal, (B) at least one of (i) when the channel estimation signal corresponds to a first CDM group and the at least one right end sample comprises two samples, moving the two samples from a right end of the noise-reduced frequency-domain signal to a first position in the series that precedes the left end sample, (ii) when the channel estimation signal corresponds to a second CDM group and the at least one right end sample comprises two samples, moving the two samples from the right end of the noise-reduced frequency-domain signal to the first position, or (iii) when the channel estimation signal corresponds to a third CDM group and the at least one right end sample comprises four samples, moving the four samples from the right end of the noise-reduced frequency-domain signal to the first position.

Example 13: A system comprising (1) a receiving module, stored in memory, that receives, as part of a DMRS channel estimation operation, a frequency domain channel estimation signal comprising a plurality of DMRS samples, (2) an extending module, stored in memory, that generates an extended channel estimation signal by (A) determining at least one extended DMRS sample that extends at least one edge of the channel estimation signal based on (i) an edge DMRS sample included in the plurality of DMRS samples at the edge of the channel estimation signal, (ii) at least one additional DMRS sample included in the plurality of DMRS samples, (B) extending the edge of the channel estimation signal by including the plurality of DMRS samples and the at least one extended DMRS sample as part of the extended channel estimation signal, (3) an extrapolating module, stored in memory, that generates an augmented channel estimation signal by extrapolating, based on the extended channel estimation signal, a frequency edge for the augmented channel estimation signal, and (4) at least one physical processor that executes the receiving module, the extending module, and the extrapolating module.

Example 14: The system of example 13, wherein the extending module determines the extended DMRS sample by (1) selecting the additional DMRS sample from the plurality of DMRS samples included in the channel estimation signal based on a target frequency interval between the extended DMRS sample and the edge DMRS sample, and (2) determining the extended DMRS sample based on a normalized power spectral density of the edge DMRS sample and a complex conjugate of the additional DMRS sample.

Example 15: The system of any of examples 13-14, wherein (1) the channel estimation signal corresponds to a data packet including a number of resource blocks that exceed a predetermined threshold number of resource blocks, (2) the at least one extended DMRS sample comprises a plurality of extended DMRS samples, a quantity of extended DMRS samples included in the plurality of extended DMRS samples exceeding a predetermined threshold quantity of DMRS samples, and (3) the extrapolating module further applies a windowing function to at least a portion of the plurality of extended DMRS samples prior to extrapolating the frequency edge for the augmented channel estimation signal.

Example 16: The system of any of examples 13-15, wherein (1) the channel estimation signal comprises a left edge DMRS sample and a right edge DMRS sample, (2) the extending module generates the extended channel estimation signal by (A) determining the at least one extended DMRS sample that extends the edge of the channel estimation signal by determining at least one left edge extended DMRS sample that extends the left edge of the channel estimation signal, (B) determining at least one right edge extended DMRS sample that extends the right edge of the channel estimation signal, and (C) extending the edge of the channel estimation signal by including the at least one left edge extended DMRS sample, the plurality of DMRS samples, and the at least one right edge extended DMRS sample as part of the extended channel estimation signal.

Example 17: The system of example 16, wherein the extending module generates the extended channel estimation signal by further (1) designating the at least one left edge extended DMRS sample, the plurality of DMRS samples, and the at least one right edge extended DMRS sample as a first extended channel estimation signal comprising a first extended right edge and a first extended left edge, (2) determining at least one extended intermediate left edge DMRS sample that extends the first extended left edge of the first extended channel estimation signal, (3) determining at least one extended intermediate right edge DMRS sample that extends the first extended right edge of the first extended channel estimation signal, and (4) including the at least one extended intermediate left edge DMRS sample, the first extended channel estimation signal, and the at least one extended intermediate right edge DMRS sample as part of the extended channel estimation signal.

Example 18: A system comprising (1) a fifth-generation new radio base station that receives an uplink signal from a user equipment device, the uplink signal comprising a frequency domain channel estimation signal comprising a plurality of DMRS samples, (2) a channel estimation device comprising (A) a receiving module that receives, as part of a DMRS channel estimation operation, the channel estimation signal comprising the plurality of DMRS samples, (B) an extending module that generates an extended channel estimation signal by (i) determining at least one extended DMRS sample that extends at least one edge of the channel estimation signal based on (a) an edge DMRS sample included in the plurality of DMRS samples at the edge of the channel estimation signal, (b) at least one additional DMRS sample included in the plurality of DMRS samples, (ii) extending the edge of the channel estimation signal by including the plurality of DMRS samples and the at least one extended DMRS sample as part of the extended channel estimation signal, and (C) an extrapolating module that generates an augmented channel estimation signal by extrapolating, based on the extended channel estimation signal, a frequency edge for the augmented channel estimation signal.

Example 19: The system of example 18, wherein the extending module determines the extended DMRS sample by (1) selecting the additional DMRS sample from the plurality of DMRS samples included in the channel estimation signal based on a target frequency interval between the extended DMRS sample and the edge DMRS sample, and (2) determining the extended DMRS sample based on a normalized power spectral density of the edge DMRS sample and a complex conjugate of the additional DMRS sample.

Example 20: The system of any of examples 18-19, wherein (1) the channel estimation signal corresponds to a data packet including a number of resource blocks that exceed a predetermined threshold number of resource blocks, (2) the at least one extended DMRS sample comprises a plurality of extended DMRS samples, a quantity of extended DMRS samples included in the plurality of extended DMRS samples exceeding a predetermined threshold quantity of DMRS samples, and (3) the extrapolating module further applies a windowing function to at least a portion of the plurality of extended DMRS samples prior to extrapolating the frequency edge for the augmented channel estimation signal.

In addition, one or more of the modules described herein may transform data, physical devices, and/or representations of physical devices from one form to another. For example, one or more of the modules recited herein may receive a frequency domain signal to be transformed, transform the frequency domain signal, output a result of the transformation to perform a channel estimation function, use the result of the transformation to estimate an uplink channel, and store the result of the transformation to maintain or reestablish a connection with a user equipment device via the uplink channel. Additionally or alternatively, one or more of the modules recited herein may transform a processor, volatile memory, non-volatile memory, and/or any other portion of a physical computing device from one form to another by executing on the computing device, storing data on the computing device, and/or otherwise interacting with the computing device.