A novel channel estimation method with enhanced beam domain resolution

Abstract

This invention discloses a novel channel estimation method with enhanced beam domain resolution, belonging to the field of channel estimation technology in communications. The implementation steps of this invention are as follows: Step 1, beam domain oversampling; Step 2, beam domain channel estimation based on enhanced spatial resolution; Step 3, beam domain oversampling reconstruction. This application can effectively improve the accuracy and performance of channel estimation; it can also enhance the effect of channel clustering, thereby improving the performance of various channel clustering processing technologies.

Description

Technical Field

[0001] This invention relates to the field of channel estimation technology in communications, and in particular to a novel channel estimation method with enhanced beam domain resolution. Background Technology

[0002] The effective operation of wireless communication systems, including signal detection, precoding, and radio resource allocation, all depend on the acquisition of channel information. Therefore, acquiring Channel State Information (CSI) is a crucial aspect of 3GPP standards. From the 4G LTE standard (R8) released in 2009 to the 5G NR standard (R15) first released in 2017, 3GPP has continuously improved and enhanced its ability to acquire channel information. The quality of channel estimation and channel information acquisition directly determines the overall performance of a wireless communication system and is critical to its success or failure.

[0003] Massive MIMO (Massively Multi-Action Array) technology, by equipping more antennas, can significantly improve data throughput and spectral efficiency, and is therefore widely used in various wireless communication systems.

[0004] For a multiple-input multiple-output (MIMO) wireless communication system equipped with multiple antenna elements, assuming that the number of antenna elements at the base station is M, the number of antenna elements at the user equipment is N, and the number of transmit streams is S (S≤N), then the uplink channel matrix can be expressed as H∈C. MXS Where C represents the complex field. During the training phase, a pre-designed reference signal x... t ∈C SX1 The observed signal y is sent by the user terminal at time t and received at the base station. t Expressed as

[0005] y t =H t x t +n (1)

[0006] Where n∈C MX1 This represents noise. Since mobile communication devices at the user end are battery-powered, their power consumption and computing power are relatively limited; therefore, in practice, N = S = 1. Because the reference signal x... t Given that, the least squares (LS) channel estimation can be expressed as:

[0007]

[0008] Where H LS The original channel signal Ht Least Squares Estimation. The reference signal x t Generally satisfies orthogonality, and its conjugate transpose is also often used to replace it. Since the Least Squares channel estimation is simple to implement, it has been widely used in the industry. However, its disadvantages are also obvious. Since the noise term is not considered, when the channel condition is poor, the noise will be greatly amplified, resulting in a significant decline in the channel estimation performance. Therefore, the Linear Minimum Mean Square Error (LMMSE) channel estimation is often used to improve the Least Squares (LS) channel estimation;

[0009]

[0010] where R HH = E{HH H} is the autocorrelation matrix of the channel signal, β is a constant determined by the transmitted signal constellation points (for example, for 16-QAM, β = 17 / 9), and SNR represents the signal-to-noise ratio. The LMMSE-based method can improve the channel estimation performance, but its implementation complexity is very high. Especially, it is assumed that the channel autocorrelation matrix R HH is known, which does not hold in practice. Generally, it is obtained by collecting and statistically analyzing the channel after LS estimation for a long time to approximately obtain the value of R HH . This makes it very difficult and inefficient to obtain the channel statistical information R HH required by LMMSE in practical applications. To reduce the computational and implementation complexity of LMMSE channel estimation, researchers often use the time-domain sparsity of wireless signals to approximately implement LMMSE channel estimation. For example, in the time domain, only the first L (1 < L < N) elements of the channel time-domain impulse response vector g (the channel response is a vector when the user has a single antenna) are considered. In its cross-correlation matrix R gg , only the first L elements are retained, and the other elements are set to zero, thereby reducing the computational complexity. The time-domain sparsity of the signal is also reflected in the eigenvalue space. In [4], the autocorrelation function R hh of the channel frequency-domain impulse response vector of a single-antenna user is subjected to SVD (Singular Value Decomposition) to obtain R hh = UΛU H . Only the eigenvalue vector with rank p is retained, and the low-rank channel estimation is obtained as

[0011] h LowRank = UΔ p U H h LS (4)

[0012] where Δ p is a diagonal matrix, and the first p diagonal elements δ k = λk / (λ k +β / SNR)(λ k For R hh The eigenvalues ​​of the channel are zero, while the elements at other positions are zero. Low-rank channel estimation can approximate the theoretical optimal performance, but it still requires computationally complex singular value decomposition operations. On the other hand, both LMMSE and low-rank channel estimation are based on channel statistics R. hh Based on what is known. In practice, channel statistics R hh Obtaining R is very difficult and inefficient, requiring long-term channel statistics to obtain sufficiently accurate R. hh The approximation means that the implementation complexity and cost of LMMSE and its low-rank channel estimation algorithms remain very high.

[0013] By further exploring signal sparsity, Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP) have freed themselves from dependence on channel statistics. Thus, the traditional channel estimation problem has transformed into a sparse recovery and reconstruction problem. Traditional time-domain channel estimation only collects the channel's power delay profile (PDP) and estimates the delays corresponding to multipath peaks from the obtained PDP, then reconstructs the channel based on the locations of the main power delays. With the increase in the number of base station antennas, array signal processing becomes increasingly useful. Taking a linear antenna array (ULA) equipped with M antenna elements as an example, its channel model with a single-antenna user can be expressed as...

[0014]

[0015] Where a0u(θ0) is the line-of-sight (LOS) component, a0 is the path gain, the components corresponding to 1≤p≤P are the non-line-of-sight (NLOS) path components, P is the total multipath number, and θ p It is the angle of arrival AoA (Angle of Arrive) of path p, u(θ) p This is usually referred to as the steering vector of the antenna array, and its elements can be represented as: m∈[q-(M-1) / 2,q=0,1,…,M-1], λ is the wavelength, and d is the antenna spacing, usually satisfying d=λ / 2. In a typical 3GPP environment, angular spread is relatively limited, generally between 2°-5°. Similar to time-domain sparsity, wireless signals also exhibit spatial sparsity. Therefore, the channel estimation problem is transformed into spatial orientation estimation of the main path. Angle of arrival estimation is a classic problem in array signal processing, such as the MUSIC and ESPRIT algorithms. These classic array signal processing algorithms were originally designed for radar sensing, based on the assumption of blind estimation, unlike commercial wireless communication systems which have pre-designed training sequences. Furthermore, these algorithms are highly complex and difficult to apply to commercial wireless communication systems. Existing beam-domain low-rank channel estimation samples the channel signal in the spatial domain using the Fast Fourier Transform (FFT) algorithm. After transforming the channel signal to the beam domain using FFT, beam selection is performed. Generally, beams are arranged in descending order of energy, and beams whose sum of energy is greater than a certain threshold, such as 85%, are selected. Next, the other beams are set to zero, and the channel is reconstructed in reverse using the selected beam points. These beam-domain low-rank channel estimations utilize the spatial sparsity of wireless signals to reduce the dimensionality of the originally massive channel, thereby reducing the demand for radio frequency resources in large-scale antenna array systems and effectively alleviating the data pressure on fronthaul between the radio frequency and digital ends of edge antennas.

[0016] like Figure 1 As shown, a typical antenna array A consists of a single dual-polarized antenna B. The antenna array can be divided into multiple subarrays C, where each subarray D connects two radio frequency links, one for each polarization direction. For 3GPP base stations, commonly used antenna arrays are configured in 2×8×2, 4×8×2, and 8×8×2 in the vertical, horizontal, and polarization dimensions. It can be seen that these commonly used antenna arrays do not have many antennas in a single horizontal or vertical dimension (2, 4, or 8), which results in low spatial resolution in a single dimension. This leads to the problem of missing incoming waves in the main direction, causing errors in the selection of the main energy beam point location in beam domain channel estimation, and significantly reducing the accuracy of channel estimation. Increasing the number of antennas can solve this problem, but the cost is too high and often not practically applicable. Summary of the Invention

[0017] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0018] A novel channel estimation method with enhanced beam domain resolution consists of the following steps;

[0019] Step 1, Beam Domain Oversampling: For the antenna dimensions requiring enhanced spatial resolution, perform a Fourier transform based on oversampling to obtain the spatially enhanced beam domain channel H.EhdBeam ; the specific oversampling coefficient ρ os It depends on the antenna configuration and system design requirements;

[0020] Step 2, Beam domain channel estimation based on spatial resolution enhancement: Estimating the spatial resolution enhanced beam domain channel H obtained in Step 1... EhdBeam Then, perform the corresponding beam domain channel estimation processing;

[0021] Step 3, beam domain oversampling reconstruction: retain the beam positions selected in step 2, set the beams at other positions to zero, and perform inverse Fourier transform on the filtered beam domain channels to obtain the time-frequency domain channel signal estimation results with enhanced spatial resolution.

[0022] Furthermore, the beam domain transform based on Fourier oversampling in step 1 can be applied to any or a combination of the horizontal and vertical dimensions of the antenna array corresponding to the received channel signal, thereby enhancing the spatial resolution of the antenna array in the corresponding dimension.

[0023] Furthermore, in step 2, when performing subsequent beam domain channel estimation on the obtained spatial resolution enhanced channel signal, the following methods may be used, including but not limited to: channel estimation based on threshold filtering, channel estimation based on noise energy filtering, and channel estimation based on beam domain peak iteration elimination.

[0024] Furthermore, the oversampling Fourier transform and inverse transform performed in steps 1 and 3 are scaled and restored based on the oversampling coefficients and the number of days, respectively.

[0025] Furthermore, the beam domain signal is clustered; the parameters referenced for clustering include, but are not limited to: beam delay, beam angle of arrival, and beam energy; the specific parameters used for clustering can be one or more combinations of the listed reference parameters.

[0026] The beneficial effects of the present invention are as follows:

[0027] This invention proposes a novel channel estimation method with enhanced beam domain resolution, which can effectively improve the accuracy and performance of channel estimation, as well as enhance the effect of channel clustering, thereby improving the performance of various channel clustering-based processing technologies.

[0028] Simulations were conducted using a channel model established according to 3GPP TR 38.901, with the antenna array being a uniform linear array (ULA). The novel beam-domain resolution-enhanced channel estimation method proposed in this application achieves significant performance gains. Here, BmSelThrd represents threshold-based beam-domain channel estimation, selecting beams with energy greater than 85% of the total beam-domain channel energy, requiring the minimum number of beams; BmAbvNs represents noise energy-based beam-domain channel estimation, selecting all beams with energy greater than the noise energy; Os1 represents a spatial resolution enhancement factor of 2; Os2 represents a spatial resolution enhancement factor of 4; SNR is the signal-to-noise ratio; and MSE is the mean square error between the obtained channel estimate and the actual channel. Even when the number of antennas increases to 128, this technique and method still achieve excellent performance gains. Attached Figure Description

[0029] Figure 1 Example of an antenna array model;

[0030] Figure 2 This is a 3GPP UMi channel model with 4 ULA antennas, a carrier frequency of 2GHz, and a user speed of 30km / h.

[0031] Figure 3 This is a 3GPP UMi channel model with 64 ULA antennas, a carrier frequency of 5GHz, and a user speed of 30km / h.

[0032] Figure 4 It is a 3GPP UMi channel model with 128 ULA antennas, a carrier frequency of 10GHz, and a user speed of 30KM / H. Detailed Implementation

[0033] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby providing a clearer and more explicit definition of the scope of protection of the present invention.

[0034] To address the issue of insufficient spatial resolution in a single dimension for low-rank channel estimation in the beam domain, this application introduces an oversampling method, amplifying the FFT sampling rate by 2 or 4 times, effectively increasing the spatial resolution in a single dimension by 2 or 4 times. It is worth noting that oversampling can disrupt the orthogonality of the original FFT sampling, leading to spectral leakage. To overcome this problem, this application reconstructs the multipath represented by the peak points obtained from each oversampling and then removes them from the remaining channel matrix or tensor. This allows for the accurate identification of the next beam peak point. Once all major beam peak points have been found, the channel can be accurately reconstructed. Compared to increasing the number of antennas, this application achieves higher accuracy channel estimation and reconstruction at a lower cost.

[0035] The implementation steps of this invention are as follows:

[0036] Step 1, beam domain oversampling;

[0037] For antenna dimensions requiring enhanced spatial resolution, both vertical and / or horizontal, an oversampling-based Fourier transform is performed to obtain the spatially enhanced beam domain channel H. EhdBeam ; the specific oversampling coefficient ρ os The specific value depends on the antenna configuration and system design requirements; typically, a beam domain oversampling factor of 2 or 4 is sufficient.

[0038] Step 2, beam domain channel estimation based on spatial resolution enhancement;

[0039] Traditional beam domain channel estimation is based on the preliminary estimation of the channel signal H obtained by the least mean square (LS) channel estimation method. LS By using Fourier transform, it is transformed to the beam domain to obtain the beam domain channel signal H. Beam Then for H Beam Further beam domain channel estimation processing is performed; unlike traditional beam domain channel estimation, this invention performs beam domain channel H with enhanced spatial resolution obtained in step 1. EhdBeam Then, perform the corresponding beam domain channel estimation processing.

[0040] As one implementation, beam domain channel estimation can be based on a threshold-based screening method. That is, according to a threshold set in the system design, such as 85% of the total energy, the beam domain channel H after spatial resolution enhancement is selected. EhdBeam Beam filtering is performed. The minimum number of beams that meet the defined threshold is selected, which are the main directions of arrival of the beams. These directions of arrival are then retained, and beams in other directions are set to zero. The designed threshold can be fixed, or it can be set in segments or adaptively adjusted according to the signal-to-noise ratio of the channel signal.

[0041] As one embodiment, beam domain channel estimation can be based on a noise-based filtering method. That is, the system-defined beam filtering threshold is fixed at the noise energy, filtering out beams with noise energy greater than the noise energy, and setting other beams to zero.

[0042] As one embodiment, beam domain channel estimation can be an iterative elimination filtering method. That is, the system first selects the spatial resolution enhanced beam domain channel H. EhdBeamThe first energy peak point is identified, and then the spatial impulse response in the direction of arrival at this peak point is reconstructed. This response is subtracted from the beam domain channel signals from multiple aliased directions of arrival. The second peak point is then selected from the beam domain channel signals after subtracting the influence of the first peak point. This process continues until the required number of peak points for the system is reached, for example, if the total energy of the selected peak points is greater than the initial spatial resolution enhancement beam domain channel H. EhdBeam 85% of the total energy.

[0043] Step 3, beam domain oversampling reconstruction;

[0044] Retain the beam positions selected in step 2, set the beams at other positions to zero, and perform an inverse Fourier transform on the filtered beam domain channel to obtain the time-frequency domain channel signal estimation result with enhanced spatial resolution. It is worth noting that, in order to maintain the normalization of system energy, after performing a spatial domain oversampling Fourier transform on the input channel signal in step 1, the resulting transformed signal should be divided by... Eliminate oversampling coefficients. Oversampling coefficient ρ os And the number of antennas N ant The resulting impact. In step 3, the obtained time-frequency domain channel signal estimation result needs to be multiplied by... Compensation will be provided.

[0045] As an important supplement, the novel channel estimation method with enhanced beam domain resolution proposed in this invention can also be extended to enhance channel clustering.

[0046] In wireless channels, signal propagation generates multipath components caused by different propagation paths. When the angles of arrival or departure of multipath components are similar, they can be grouped into the same cluster. This is particularly pronounced in millimeter-wave communication. Due to the high path loss of high-frequency signals, millimeter-wave channel signals typically exhibit strong sparsity; not all angles have significant signal components, only a few strong directions of arrival do. Accordingly, these signal components from the same direction of arrival are considered a cluster. The spatial resolution enhancement technique of this invention performs an oversampling-based Fourier transform on the original channel signal in the spatial beam domain, effectively encrypting the sampling points in the spatial beam domain. This significantly enhances the clustering effect of the channel signal. The clustered-enhanced channel signal can be applied to various subsequent processing methods, including but not limited to: cluster-based channel estimation, cluster-based user resource scheduling and allocation, cluster-based user grouping, cluster-based beamforming, and cluster-based precoding. This enhances the performance of these cluster-based processing methods.

[0047] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A novel channel estimation method with enhanced beam domain resolution, characterized in that: It consists of the following steps; Step 1, Beam Domain Oversampling: For the antenna dimensions requiring enhanced spatial resolution, perform a Fourier transform based on oversampling to obtain the spatially enhanced beam domain channel H. EhdBeam ; the specific oversampling coefficient ρ os It depends on the antenna configuration and system design requirements; Step 2, Beam domain channel estimation based on spatial resolution enhancement: Estimating the spatial resolution enhanced beam domain channel H obtained in Step 1... EhdBeam Then, perform the corresponding beam domain channel estimation processing; Step 3, beam domain oversampling reconstruction: retain the beam positions selected in step 2, set the beams at other positions to zero, and perform inverse Fourier transform on the filtered beam domain channels to obtain the time-frequency domain channel signal estimation results with enhanced spatial resolution.

2. The novel channel estimation method with enhanced beam domain resolution according to claim 1, characterized in that: The beam domain transform based on Fourier oversampling in step 1 can be applied to any or a combination of the horizontal and vertical dimensions of the antenna array corresponding to the received channel signal, thereby enhancing the spatial resolution of the antenna array in the corresponding dimension.

3. The novel channel estimation method with enhanced beam domain resolution according to claim 1, characterized in that: In step 2, when performing subsequent beam domain channel estimation on the obtained spatial resolution enhanced channel signal, the following methods may be used, including but not limited to: channel estimation based on threshold filtering, channel estimation based on noise energy filtering, and channel estimation based on beam domain peak iteration elimination.

4. The novel channel estimation method with enhanced beam domain resolution according to claim 1, characterized in that: The oversampling Fourier transform and inverse transform performed in steps 1 and 3 are scaled and restored according to the oversampling coefficients and the number of days, respectively.

5. The novel channel estimation method with enhanced beam domain resolution according to claim 1, characterized in that: Clustering is performed on the beam domain signal; the parameters referenced for clustering include, but are not limited to, beam delay, beam angle of arrival and beam energy; the specific parameters used for clustering are one or more combinations of the listed reference parameters.

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