A fractional delay and Doppler estimation method for massive MIMO-OTFS communication system

Abstract

The present invention discloses a method for estimating fractional delay and Doppler in a massive MIMO-OTFS communication system, belonging to the field of channel estimation. First, considering the favorable channel conditions in massive MIMO scenarios, its beamforming and progressive orthogonality are utilized to distinguish the echo signals of different targets at the receiving end. Second, after estimating the target angle, a two-stage estimation method is used to correlate the prepared known signal with the received echo signal in the time domain to obtain accurate fractional delay and Doppler estimates. The present invention directly performs correlation operations in the time domain at the receiving end, eliminating the step of converting the time domain signal to the DD domain. The proposed two-stage estimation method roughly estimates the integer part of the delay and Doppler in the first stage, and then finely estimates the fractional part of the delay and Doppler in the second stage. This method can obtain an accurate estimate of the fractional delay Doppler, greatly improving the accuracy of the fractional delay Doppler estimation.

Description

Technical Field

[0001] The present invention belongs to the field of channel estimation, and in particular to a fractional delay and Doppler estimation method in a large-scale MIMO-OTFS communication system. Background Art

[0002] In traditional fractional delay-Doppler estimation methods, threshold-based embedded pilot channel estimation only achieves good channel estimation performance for integer delays and integer Dopplers. For fractional delays and fractional Dopplers, while increasing the guard interval can improve channel estimation accuracy, the improvement is limited and results in significant waste of grid resources, reducing spectral efficiency. Existing fractional delay-Doppler estimation methods include cross-correlation estimation and collaborative estimation, but these methods suffer from high complexity and require additional communication overhead. Therefore, designing a high-performance, low-complexity fractional delay-Doppler estimation method is a major research challenge. Summary of the Invention

[0003] In response to the fractional delay and fractional Doppler situations that exist in actual scenarios due to insufficient delay and Doppler accuracy, the present invention provides a fractional delay and Doppler estimation method in a massive MIMO-OTFS communication system. The present invention uses a two-stage maximum likelihood estimation method to perform delay-Doppler processing on a prepared known signal in the time domain and then perform correlation operation with the echo signal to obtain an accurate fractional time-Doppler estimation.

[0004] Technical solution: A fractional delay and Doppler estimation method for a massive MIMO-OTFS communication system, comprising the following steps:

[0005] Step 1: Prepare the transmit signal in the DD domain at the transmitter. After OTFS modulation, generate a time domain transmit signal and add a cyclic prefix. The transmit signals of multiple targets are integrated into a multi-dimensional multi-beam signal, and beamforming is used to send the signal in a preset direction.

[0006] Step 2: After receiving the radar echo, the receiver distinguishes different targets and obtains their angle estimates:

[0007] Step 3: Use the two-stage maximum likelihood estimation method to obtain the target's fractional delay and Doppler estimation.

[0008] Furthermore, in step 1, the ISFFT process in OTFS modulation is as follows:

[0009]

[0010] Where M and N are the number of grids along the delay domain and the number of grids along the Doppler domain in the delay-Doppler domain grid, respectively; xi [k, l] represents the signal modulated in the delay-Doppler domain, k and l represent the indexes along the delay axis and Doppler axis in the delay-Doppler grid respectively; X i [m,n] represents the signal modulated in the time-frequency domain, and m and n represent the indexes of the frequency axis and time axis in the time-frequency domain grid, respectively.

[0011] Furthermore, in step 1:

[0012] The transmitted signal after adding the cyclic prefix is ​​s(t), which can be expressed as follows using the vector form of discrete-time signal:

[0013]

[0014] Among them, s i Indicates that X i [m,n] is the time domain signal with L cyclic prefixes added after Heisenberg transform;

[0015] The multi-dimensional multi-beam signal is expressed as:

[0016] s=[s1,…,s p ] T ,

[0017] Among them, p means there are p targets in total;

[0018] The signal transmitted after beamforming is expressed as:

[0019]

[0020] Where F represents the beamforming matrix Indicates sending steering vector, N t and N r Represent the number of transmitting antennas and receiving antennas respectively, p i represents the transmission power allocated to the i-th target; θ i Represents the estimated angle of the i-th target.

[0021] Furthermore, in step 2, the received radar echo signal is:

[0022]

[0023] in, is the reflection coefficient, ξ is the radar cross section, d i represents the distance from the i-th target to the base station; b(θ i ) is the received steering vector; z i represents an additive white Gaussian noise vector with zero mean and variance; π is the permutation matrix of the forward cyclic shift, Δ is a diagonal matrix of (MN+L)×(MN+L), k i and I i Respectively represent the index of the i-th target along the delay axis and Doppler axis in the delay-Doppler grid.

[0024] Furthermore, in step 2, the method for distinguishing different targets is:

[0025] Using the received steering vector b(θ i ) to distinguish the echo signals of different targets:

[0026]

[0027] Among them, b(θ j ) represents and b(θ i ) at different angles are the accepted steering vectors.

[0028] Furthermore, in step 2, the angle estimation method is:

[0029]

[0030] Among them, r i represents the echo signal received by the i-th target, G m Indicates the energy of the echo signal; represents the true angle of the i-th target; θ i represents the estimated angle of the i-th target,

[0031]

[0032] Furthermore, the step 3 includes the following steps:

[0033] Step 301: Delay and Doppler processing are performed on the known transmission signal, and then correlation operation is performed with the echo signal:

[0034]

[0035] in, is the objective function, The objective function after correlating the received echo signal with the prepared transmit signal; ΔT represents the duration of the signal; when it is close to the integer part of the actual delay and Doppler, A maximum value appears;

[0036] is the likelihood function described, τ i and v idenote the time delay and Doppler shift of the i-th target respectively; the known DD domain parameter space range is discretized and divided into integer grids:

[0037]

[0038] Where Λ represents the discrete grid in the delay-Doppler plane; Δf represents the subcarrier spacing; T represents the symbol duration;

[0039] Get the estimated maximum value Then we can get the search range of the second stage:

[0040]

[0041] Where τ and v represent the time delay and Doppler shift of the i-th target respectively; Λ i represents the discrete grid in the Doppler plane of the i-th target;

[0042] Step 302: In the search range The two-dimensional golden section method is used to reduce the uncertainty interval according to the golden section ratio at each step to obtain accurate score and delay-Doppler estimation.

[0043] Compared with the prior art, the present invention has the following advantages:

[0044] (1) The traditional radar matched filter method directly searches within the entire search range and often cannot shrink towards the correct delay and Doppler, making it difficult to obtain an accurate estimate of the fractional delay Doppler. The present invention combines a two-stage maximum likelihood estimation method, which only estimates the integer part of the delay Doppler in the first stage and then uses the two-dimensional golden section method in the second stage to obtain an accurate estimate of the fractional delay Doppler, greatly improving the accuracy of the fractional delay Doppler estimation.

[0045] (2) The traditional two-step method for estimating parameters of the OTFS system requires modulating the received signal into the DD domain through FFT and SFFT transformations and then performing correlation operations. However, the present invention uses the progressive orthogonality of the received steering vector to distinguish the echo signals of different targets in the time domain. Combined with the known transmitted signal of the base station, fractional delay and Doppler estimation can be performed directly in the time domain, reducing the complexity of the traditional estimation algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A block diagram of a massive MIMO-OTFS communication system provided in an embodiment of the present invention;

[0047] Figure 2 This is a flow chart of a fractional delay and Doppler estimation method in a massive MIMO-OTFS communication system of the present invention;

[0048] Figure 3 for Figure 2 Flowchart of the specific implementation steps of step 3. DETAILED DESCRIPTION

[0049] The technical solutions of the present invention are described in detail below through examples, but the protection scope of the present invention is not limited to the examples.

[0050] This embodiment provides a method for fractional delay and Doppler estimation in a massive MIMO-OTFS communication system. First, considering the favorable channel conditions in a massive MIMO scenario, its beamforming and progressive orthogonality can be used to distinguish the echo signals of different targets at the receiving end. Second, after estimating the angle of the target, a two-stage estimation method is used to correlate the prepared known signal with the received echo signal in the time domain to obtain accurate fractional delay and Doppler estimation.

[0051] The block diagram of the massive MIMO-OTFS communication system is as follows Figure 1 As shown, the flowchart of the fractional delay and Doppler estimation method in the massive MIMO-OTFS communication system is as follows Figure 2 As shown, the specific steps include:

[0052] Step 1: The transmitting end prepares the transmission signal in the Delay-Doppler (DD) domain. After OTFS modulation, the time domain transmission signal is generated and a cyclic prefix is ​​added. The transmission signals of multiple targets are integrated into a multi-dimensional multi-beam signal, and beamforming is used to send the signal in a predetermined direction.

[0053] In this embodiment, the multiple targets correspond to i vehicles.

[0054] In step 101, the ISFFT process in OTFS modulation is as follows:

[0055]

[0056] Where, M and N are the number of grids along the delay domain and the number of grids along the Doppler domain in the delay-Doppler domain grid, respectively;

[0057] x i [k, l] represents the signal modulated in the delay-Doppler domain, k and l represent the indexes along the delay axis and Doppler axis in the delay-Doppler grid, respectively, 0≤k≤M-1, 0≤l≤N-1;

[0058] X i [m,n] represents the signal modulated in the time-frequency domain. m and n represent the indexes of the frequency axis and time axis in the time-frequency domain grid, respectively. The range of the grid numbers m and n is the same as k and l, with m corresponding to k and n corresponding to l.

[0059] is the expression in Fourier transform, e is the constant e, and j is a complex number.

[0060] In step 1, the transmitted signal after adding the cyclic prefix is ​​s(t), and s(t) is expressed as follows using the vector form of discrete-time signal:

[0061]

[0062] Among them, s i Indicates that X i [m,n] is the time domain signal with L cyclic prefixes added after Heisenberg transform;

[0063] In step 1, the multi-dimensional multi-beam signal is expressed as:

[0064] s=[s1,...,s p ] T , p means there are p vehicles in total.

[0065] In step 1, the signal sent after beamforming is expressed as: F represents the beamforming matrix

[0066] Indicates sending steering vector, N t and N r Represent the number of transmitting antennas and receiving antennas respectively, p i Represents the transmission power allocated to the i-th vehicle. i Represents the estimated angle of the i-th vehicle.

[0067] Step 2: After receiving the radar echo at the receiver, different targets are distinguished and their angle estimates are obtained:

[0068] In step 2, the received radar echo signal is:

[0069]

[0070] in, represents the reflection coefficient, ξ represents the radar cross section, d i represents the distance from the i-th vehicle to the base station; b(θ i ) is the received steering vector; z i represents an additive white Gaussian noise vector with zero mean and variance; π is the permutation matrix of the forward cyclic shift, Δ is a diagonal matrix of (MN+L)×(MN+L), a H (θ i), the superscript H indicates its conjugate transpose; k i and I i Respectively represent the index of the i-th vehicle along the delay axis and Doppler axis in the delay-Doppler grid; they have the same range as k and l mentioned in step 1, but the actual values ​​corresponding to specific vehicles are different.

[0071] In step 2, the method for distinguishing different targets is:

[0072] Using the received steering vector b(θ i ) to distinguish the echo signals of different targets:

[0073]

[0074] Among them, b(θ j ) represents and b(θ i ) at different angles are the accepted steering vectors.

[0075] In step 2, the angle estimation method is expressed as:

[0076]

[0077] Among them, r i represents the received echo signal of the i-th vehicle, G m Indicates the energy of the echo signal; represents the true angle of the i-th vehicle; θ i represents the estimated angle of the i-th vehicle,

[0078]

[0079] When the estimated angle is close to the true angle, the above formula will reach a maximum value. After obtaining accurate estimates of the delay (distance) and Doppler (speed) of the moving target (vehicle), the target of the moving vehicle at the next moment can be estimated.

[0080] Step 3: Use the two-stage maximum likelihood estimation method to obtain the target's fractional delay and Doppler estimation.

[0081] like Figure 3 As shown, step 3 specifically includes the following steps:

[0082] Step 301: Delay and Doppler processing are performed on the known transmission signal, and then correlation operation is performed with the echo signal:

[0083]

[0084] in, is the objective function, The objective function after correlating the received echo signal with the prepared transmit signal; ΔT represents the duration of the signal; when it is close to the integer part of the actual delay and Doppler, A maximum value appears;

[0085] is the likelihood function described, τ i and v i denote the time delay and Doppler shift of the i-th vehicle respectively; the known DD domain parameter space range is discretized and divided into integer grids:

[0086]

[0087] Where Λ represents the discrete grid in the delay-Doppler plane; Δf represents the subcarrier spacing; T represents the symbol duration;

[0088] Get the estimated maximum value Then we can get the search range of the second stage:

[0089]

[0090] Where τ and v represent the time delay and Doppler shift of the i-th vehicle respectively; Λ i represents the discrete grid in the longitudinal Doppler plane of the i-th vehicle;

[0091] Step 302: In the search range The two-dimensional golden section method is used to reduce the uncertainty interval according to the golden section ratio at each step to obtain accurate score and delay-Doppler estimation.

[0092] The traditional radar matched filtering method directly uses the maximum likelihood estimation method, and the estimated time delay and Doppler often cannot shrink in the correct direction. The two-stage estimation method proposed in the present invention roughly estimates the integer part of the time delay and Doppler in the first stage, and then finely estimates the fractional part of the time delay and Doppler in the second stage. Compared with the traditional radar matched filtering method, the present invention can obtain an accurate estimation of the fractional delay Doppler, greatly improving the accuracy of the fractional delay Doppler estimation.

[0093] Compared with the existing two-stage estimation method in the DD (Delay-Doppler) domain, this method performs correlation operations directly in the time domain at the receiving end, eliminating the step of converting the time domain signal to the DD domain, and can also obtain accurate fractional and delay-Doppler estimation.

[0094] As described above, although the present invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the present invention itself. Various changes may be made to the form and details without departing from the spirit and scope of the present invention.

Claims

1. A fractional delay and Doppler estimation method in a massive MIMO-OTFS communication system, characterized in that: The steps include: Step 1: Prepare the transmit signal in the DD domain at the transmitter. After OTFS modulation, generate a time domain transmit signal and add a cyclic prefix. The transmit signals of multiple targets are integrated into a multi-dimensional multi-beam signal, and beamforming is used to send the signal in a preset direction. Step 2: After receiving the radar echo at the receiving end, different targets are distinguished and their angle estimates are obtained; Step 3: Use the two-stage maximum likelihood estimation method to obtain the target's fractional delay and Doppler estimation; The step 3 comprises the following steps: Step 301: Delay and Doppler processing are performed on the known transmission signal, and then correlation operation is performed with the echo signal: ； in, is the objective function, The objective function after correlating the received echo signal with the prepared transmit signal; represents the duration of the signal; when approaching the integer part of the actual delay and Doppler, A maximum value appears; To receive the steering vector; represents the estimated angle of the i-th target; Indicates the received radar echo signal; Indicates that The time domain signal with L cyclic prefixes added after Heisenberg transform, Represents the signal modulated in the time-frequency domain; is the permutation matrix of the forward cyclic shift, ; yes The diagonal matrix of ; and are the number of grids along the delay domain and the number of grids along the Doppler domain in the delay-Doppler domain grid, respectively; and Represent the indexes along the delay axis and Doppler axis in the delay-Doppler grid respectively; is the likelihood function describing the and Respectively represent The time delay and Doppler shift of each target are calculated; the known DD domain parameter space range is discretized and divided into integer grids: ， in, represents the discrete grid in the delay-Doppler plane; represents the subcarrier spacing; T represents the symbol duration; Get the estimated maximum value , and then obtain the search range of the second stage: ； in, and Respectively represent Time delay and Doppler shift of each target; Indicates the The discrete grid in the extended Doppler plane of the target; Step 302: In the search range The two-dimensional golden section method is used to reduce the uncertainty interval according to the golden section ratio at each step to obtain accurate score and delay-Doppler estimation.

2. The estimation method according to claim 1, characterized in that In step 1, the ISFFT process in OTFS modulation is as follows: ； in, and are the number of grids along the delay domain and the number of grids along the Doppler domain in the delay-Doppler domain grid, respectively; represents the signal modulated in the delay-Doppler domain, and Represent the indexes along the delay axis and Doppler axis in the delay-Doppler grid respectively; Represents the signal modulated in the time-frequency domain, m and n represent the indexes of the frequency axis and time axis in the time-frequency domain grid, respectively.

3. The estimation method according to claim 2, characterized in that In step 1: The transmitted signal after adding the cyclic prefix is , using the vector form of discrete-time signals Expressed as: ； in, Indicates that The time domain signal with L cyclic prefixes added after Heisenberg transform; The multi-dimensional multi-beam signal is expressed as: ； in, Indicates shared goals; The signal transmitted after beamforming is expressed as: ； in, represents the beamforming matrix , , Indicates sending the steering vector, and Represent the number of transmitting antennas and the number of receiving antennas, respectively. Representatives assigned to The transmit power of each target; Represents the estimated angle of the i-th target.

4. The estimation method according to claim 3, characterized in that In step 2, the received radar echo signal is: ； in, is the reflection coefficient, represents the radar cross section, Indicates the The distance from the target to the base station; To receive the steering vector; represents an additive white Gaussian noise vector with zero mean and variance; is the permutation matrix of the forward cyclic shift, ; yes The diagonal matrix of , ; and represent the index of the i-th target along the delay axis and Doppler axis in the delay-Doppler grid respectively.

5. The estimation method according to claim 4, characterized in that In step 2, the method for distinguishing different targets is: Using the received steering vector The asymptotic orthogonality of can distinguish the echo signals of different targets: ； in, Represents and Accepted steering vector at different angles.

6. The estimation method according to claim 5, characterized in that In step 2, the angle estimation method is: ， in, represents the echo signal received by the i-th target, Indicates the energy of the echo signal; represents the true angle of the i-th target; represents the estimated angle of the i-th target, .

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