Patent Description:
The subject matter herein generally relates to radio communications.

<CIT> discloses an adaptive antenna radio communication device comprises a divided band direction estimating unit for estimating the direction by calculating the cross correlations between a pilot signal and sub-carrier signals of the respective divided bands received by an array antenna and calculating a spatial profile from correlation matrices determined by combining the correlation values between antenna elements of the different sub-carriers according to the output of the cross correlation calculation; a divided band array weight creating unit for creating a weight of a receive array having a directional beam in the direction of estimation for each divided band; and a sub-carrier directivity creating unit for creating a directivity by multiplication-combining the created receive array weight with the corresponding sub-carrier signal.

<CIT> discloses a method for estimating angle information for a wireless communication system includes receiving, by a wireless transmit/receive unit (WTRU), a plurality of first training symbols during a first period of time, wherein the plurality of first training symbols is transmitted using a first transmit beamforming vector fixed during the first period of time and received using a first receive beamforming, vector varied during the first period of time; estimating, by the WTRU, a plurality of first channel delay values based on the plurality of first training symbols; estimating, by the WTRU, a plurality of first channel values based on the plurality of first channel delay values; and estimating, by the WTRU, a first angle value of a path of a wireless channel based on the plurality of first channel values.

<NPL> discloses Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) with hybrid precoding is a promising technique for the future <NUM> wireless communications. Due to a large number of antennas but a much smaller number of radio frequency chains, estimating the high-dimensional mmWave massive MIMO channel will bring the large pilot overhead. To overcome this challenge, this paper proposes a super-resolution channel estimation scheme based on <NUM>-D unitary ESPRIT algorithm. By exploiting the angular sparsity of mmWave channels, the continuously distributed angle of arrivals/departures (AoAs/AoDs) can be jointly estimated with high accuracy. Specifically, by designing the uplink training signals at both base station and mobile station, we first use low pilot overhead to estimate a low-dimensional effective channel, which has the same shift-invariance of array response as the high-dimensional mmWave MIMO channel to be estimated. From the low-dimensional effective channel, the super-resolution estimates of AoAs and AoDs can be jointly obtained by exploiting the <NUM>-D unitary ESPRIT channel estimation algorithm. Furthermore, the associated path gains can be acquired based on the least squares criterion. Finally, we can reconstruct the high-dimensional mmWave MIMO channel according to the obtained AoAs, AoDs, and path gains. Simulation results have confirmed that the proposed scheme is superior to conventional schemes with a much lower pilot overhead.

Millimeter-wave (mmWave) communication is a key element in the fifth generation (<NUM>) New Radio (NR) wireless communication system. Severe propagation losses in the mmWave channel call for massive antenna array to conduct beamforming, thus a receiver has to know angle of arrival (AoA) information.

In indoor environments, transmitted signals may propagate through multiple paths resulting in close time delays, which are not resolvable, this is the problem of one channel tap with multiple AoAs (OCMA).

Thus, there is room for improvement within the art.

Implementations of the present technology will now be described, by way of embodiment, with reference to the attached figures, wherein:.

It will be appreciated that for simplicity and clarity of illustration, where appropriate, reference numerals have been repeated among the different figures to indicate corresponding or analogous elements. In addition, numerous specific details are set forth in order to provide a thorough understanding of the embodiments described herein. However, it will be understood by those of ordinary skill in the art that the embodiments described herein can be practiced without these specific details. In other instances, methods, procedures, and components have not been described in detail so as not to obscure the related relevant feature being described. Also, the description is not to be considered as limiting the scope of the embodiments described herein. The drawings are not necessarily to scale and the proportions of certain parts may be exaggerated to better illustrate details and features of the present disclosure.

References to "an" or "one" embodiment in this disclosure are not necessarily to the same embodiment, and such references mean "at least one".

In general, the word "module" as used hereinafter, refers to logic embodied in computing or firmware, or to a collection of software instructions, written in a programming language, such as Java, C, or assembly. One or more software instructions in the modules may be embedded in firmware, such as in an erasable programmable read-only memory (EPROM). The modules described herein may be implemented as either software and/or computing modules and may be stored in any type of non-transitory computer-readable medium or another storage device. Some non-limiting examples of non-transitory computer-readable media include CDs, DVDs, BLU-RAY, flash memory, and hard disk drives. The term "comprising", when utilized, means "including, but not necessarily limited to"; it specifically indicates open-ended inclusion or membership in a so-described combination, group, series, and the like.

<FIG> illustrates a block diagram of an apparatus <NUM> for estimating an angle of arrival (AoA) of signals according to one embodiment. The apparatus <NUM> acts with a User Equipment (UE), a base station, and a wireless transmitting/receiving unit (WTRU). The apparatus <NUM> comprises a processor <NUM>, a storage unit <NUM>, and a communication unit <NUM>.

The processor <NUM> controlling the apparatus <NUM> comprises a microcontroller, a microprocessor, or another circuit with processing capabilities, and executes or processes instructions, data, and computer programs stored in the storage unit <NUM>.

The storage unit <NUM> comprises a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, electrical, optical, or other physical/tangible (e.g., non-transitory) memory device, etc. The storage unit <NUM> is used to store one or more computer programs that control the operation of the apparatus <NUM> and which are executed by the processor <NUM>. In the embodiment, the storage unit <NUM> stores or encodes one or more computer programs, and stores models, configurations, and computing parameters data, for the processor <NUM>, to execute a method for estimating AOA according to various embodiments.

The communication unit <NUM> performs functions for transmitting and receiving signals through a wireless channel. The communication unit <NUM> comprises a transmission filter, a reception filter, an amplifier, a mixer, an oscillator, a digital-to-analog converter (DAC), and an analog-to-digital converter (ADC). The communication unit <NUM> may comprise multiple transmission/reception paths. Further, the communication unit <NUM> may comprise an antenna array comprising a plurality of antenna elements.

<FIG> illustrates a block diagram of an antenna array <NUM> of the communication unit <NUM> according to one embodiment. The antenna array <NUM> comprises Mγ antennas <NUM> and one ADC <NUM>. The signals received at each antenna <NUM> are first phase-shifted, i.e., multiplied by the phase-shifter coefficient, and then summed up as the input of the ADC <NUM>.

<FIG> illustrates an example of a channel delay profile of the antenna array <NUM> with Mγ = <NUM> antennas in a MIMO-OFDM system. The channel delay profile exists for every channel and link, respectively, formed by each beam arrangement between the receiving side and the transmitting side and indicates the intensity of a signal received through a multipath channel as a function of time delay. As <FIG> shows, at Tap Delay <NUM>, there are two taps with two different AoAs. This is the OCMA problem for the tap at Tap Delay <NUM>. Various embodiments on AoA estimation for the tap with the OCMA problem in a wireless communication system are disclosed.

In one embodiment, Q consecutive OFDM symbols are transmitted at the transmitting side in a MIMO-OFDM system as training symbols for channel estimation. The transmission of Q consecutive OFDM symbols is referred to as a training block. In order to resolve the OCMA problem, <FIG> illustrates an example of a transmitting scheme of transmitting training blocks with different transmitting beamforming vectors such that different paths experience different transmit beamforming gains. Let t be the index of each training block, and T be the number of training blocks, then the total number of OFDM symbols for the estimation is T × Q. As shown in <FIG>, the transmitting beamforming vectors b<NUM>, b<NUM> are different for different training blocks, but bt remains the same within each training block. The wq, denoting the receiving beamforming vectors, varies within each block.

<FIG> illustrates a method for estimating AoA performed by the apparatus <NUM> at the receiving side according to one embodiment.

In the embodiment, the method for estimating AoA comprises three stages. The first stage is to estimate the time-domain channel impulse response for each channel tap. The second stage uses different transmitting beamforming vectors with different receiving beamforming vectors to decouple the channel responses for each antenna element of the antenna array <NUM>. The third stage is to calculate the correlation matrix and use a subspace-based algorithm such as Multiple Signal Classification (MUSIC), Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) to estimate multiple AoAs.

The detailed steps of the method are shown in <FIG>.

In order to remove distortion from the received input signals, the effects of the channels need to be estimated. In one embodiment, the channel estimation is implemented using pilot symbols. The pilot symbols may be transmitted by the transmitting side in the OFDM symbols at certain subcarriers. The pilot symbols have known values for both the transmitting side and the receiving side, thus the channel can be estimated using the pilot symbols.

At step S502, the apparatus <NUM> extracts pilot symbols from the received OFDM symbols. The extracted pilot symbols x̃<NUM>,. , x̃P are expressed as a diagonal matrix X̃ = diag{x̃<NUM>,. The partial Discrete Fourier Transform (DFT) matrix of size P × L, where L is the length of the cyclic prefix (CP), is denoted as F. Then the noiseless frequency-domain received signal at P pilot-subcarriers, beamformed by the hybrid antenna array of the apparatus <NUM>, at the q-th received OFDM symbol can be expressed as: <MAT>
where F̃ = X̃F, and hc(q) is the spare beamformed time-domain channel impulse response (CIR) vector with I (I << L) non-zero entries at the q-th received OFDM symbol.

At step S504, the apparatus <NUM> estimates the time-domain CIR vector using a compressive sensing algorithm based on the extracted pilot symbols. In one embodiment, the apparatus <NUM> uses an extended subspace pursuit algorithm, which exploits the property that hc(q)'s share the same tap delay.

At step S506, the apparatus <NUM> recovers spatial channel responses based on the time-domain CIR vector.

In one embodiment, hc(q) can be expressed as a linear combination of CIRs for all antennas: <MAT>.

In one embodiment, with multiple transmissions at the transmitting side, the measurements lost in the spatial domain can be compensated for by those obtained in the time domain. In this embodiment, each channel tap is flat fading, and W is designed as a unitary or semi-unitary matrix to avoid amplification of noise.

At step S508, for each channel tap delay, the apparatus <NUM> calculates a correlation matrix based on the recovered spatial channel responses.

In one embodiment, the tap delay of the i-th path is referred to as Ti, and the Ti-th row of H is referred to as <MAT>. To estimate the AoA of the i-th path, denoted as θi, the apparatus <NUM> uses yi, which is the corresponding channel estimation vector: <MAT>.

In some situations, some channel taps may contain responses of two paths or more. To address this problem, (<NUM>) can be re-written as <MAT>.

To resolve the OCMA problem, the apparatus <NUM> notifies the transmitting side to transmit training blocks with different transmitting beamforming vectors such that different channel paths will experience different channel gains. As illustrated in <FIG>, the transmitting beamforming vector b(t) of the transmitting side remains the same for each training block, and the receiving beamforming vector w(q) varies in each training block. Letting h(i)(t) be the Ki-by-<NUM> channel gain vector for the i-th channel tap at the t-th transmission training block, and h(i)(t) be variant for the T blocks. The apparatus <NUM> can calculate the correlation matrix as; <MAT>
where <MAT>, and Rêi, is the matrix formed by error vectors. Rank(Rh(i)) = Ki, meaning that T ≥ Ki.

At step S510, the apparatus <NUM> performs singular value decomposition on the correlation matrix.

At step S512 the apparatus <NUM> determines whether a channel tap having multiple channel responses is caused by multiple signal paths. When the apparatus <NUM> determines that the number of path responses on the channel tap is equal to one, the apparatus <NUM> executes step S514, and when the apparatus determines that the number of path responses on the channel tap is more than one, the apparatus executes step S516.

At step S514, the apparatus <NUM> estimates AoA of the one path response using a line-fitting or correlation algorithm.

At step S516, the apparatus <NUM> estimates multiple AoAs of the multiple path responses using a subspace-based algorithm, such as MUSIC or ESPRIT.

In one embodiment, the apparatus <NUM> performs singular value decomposition on Rŷi = ARh(i)AH, and obtains <MAT>
where <MAT> is a diagonal matrix with non-zero diagonal entries in descending order, and <MAT> is the matrix spanning the same column space as A, while UO is its orthogonal complement, i.e., span(A) = span(US) ⊥ span(UO).

Then, the MUSIC algorithm uses the orthogonal subspace UO to find multiple AoAs by searching for the peaks of the function defined as <MAT>
where ÛO is the estimation of orthogonal subspace due to the presence of additive error in (<NUM>). Since the signal-to-noise ratio (SNR) of the channel estimation is much higher than that of the received signal for the antennas (due to the fact P >> I), the non-white property of the error is not apparent. In this embodiment, Mr should be larger than K to render the orthogonal subspace non-empty.

In one embodiment, span(A) = span(US), hence there exists a unique and invertible matrix P such that AP = US. Although P is unknown, US can be used to find θi,<NUM>,. , θi,K as follows: <MAT>.

The AoA estimation method and apparatus of the present disclosure resolve the OCMA problem with a hybrid antenna array. Conventional subspace based algorithms such as MUSIC and ESPRIT can be applied, and the number of AoAs that can be estimated by the method and the apparatus is not limited by the number of antennas.

Claim 1:
A method for estimating angle of arrival of signals in a wireless communication system include a transmitting side and a receiving side, the method comprising:
receiving, by the receiving side, a plurality of signals transmitted by the transmitting side, characterised in that the plurality of signals transmitted by the transmitting side further comprises a plurality of training blocks and each one of the plurality of training block comprises a plurality of consecutive Orthogonal Frequency Division Multiplexing (OFDM) symbols, wherein the OFDM symbols comprise pilot symbols at certain subcarriers, the plurality of training blocks are transmitted by the transmitting side using different transmitting beamforming vectors, and the plurality of consecutive OFDM symbols of each one of the plurality of training blocks are received by the receiving side using different receiving beamforming vectors;
extracting, by the receiving side, a plurality of pilot symbols from the received signals (S502);
estimating, by the receiving side, time-domain channel responses using a compressive sensing algorithm based on the extracted pilot symbols (S504);
recovering, by the receiving side, spatial channel responses based on the estimated time-domain channel responses (S506);
obtaining, by the receiving side, a plurality of channel taps based on the recovered spatial channel responses;
calculating, by the receiving side, a correlation matrix, for each one of the plurality of channel taps, based on the recovered spatial channel responses (S508);
performing, by the receiving side, singular value decomposition on the correlation matrix for each one of the plurality of channel taps to obtain a singular value representation of the correlation matrix (S510);
determining, by the receiving side, a number of responses caused by different paths for each one of the plurality of channel taps (S512); and
estimating, by the receiving side, angle of arrival, for each one of the plurality of channel taps, based on the determined number of responses and the singular value representation of the correlation matrix (S516).