A beacon-free high-precision inter-station synchronization method for indoor 5G base stations

Abstract

The present invention discloses a beacon-free indoor 5G base station high-precision inter-station synchronization method, which belongs to the field of communication perception. The method firstly constructs a TOA model considering TAE based on the long-term unchanged characteristic of TAE and the TOA measurement data accumulated by the base station in the historical moment; based on the reference pRRU and the reference UE, the TOA model is differentiated twice to obtain the TDDOA model, and the variable to be solved is the UE coordinate; the UE coordinate solution problem is converted into a weighted least squares estimation problem, and the optimal solution is approximated by the Gauss-Newton iteration method; the solved UE coordinates are substituted into the TDOA model to solve TDAE, which can be used to compensate the TDOA measurement data for a long time to achieve high-precision inter-station synchronization. The method does not require the modification of communication hardware equipment, does not increase the additional system maintenance burden, and has the characteristics of simple implementation, fast iteration speed, and small system requirements.

Description

Technical Field

[0001] The present invention belongs to the field of communication perception, and particularly relates to a beacon-free indoor 5G base station high-precision inter-station synchronization method. Background Art

[0002] With the rise of the intelligent wave, the vertical industry has an increasingly urgent demand for high-precision indoor positioning, and the large bandwidth and low-latency 5G technology has expanded new application scenarios for the indoor positioning industry. On the basis of the original communication business, 5G technology can provide high-precision positioning services, realize the integration of communication and positioning, and help all walks of life to transform into intelligent ones. Therefore, the high-precision indoor positioning technology based on distributed 5G base stations has attracted widespread attention.

[0003] The currently popular indoor distributed 5G base station system mainly consists of three parts: picoRemote Radio Unit (pRRU), Remote Radio Unit Hub (RHUB) and Base Band Unit (BBU). Among them, pRRU performs RF processing on 5G signals, RHUB connects to external distributed pRRU to realize signal diversity merging, and BBU provides baseband processing, system monitoring and other functions. In the Time Difference Of Arrival (TDOA) positioning system constructed by this type of 5G base station, the synchronization accuracy between pRRUs needs to reach nanoseconds, otherwise it cannot achieve sub-meter positioning accuracy. The traditional distributed base station system inter-station synchronization algorithm mainly studies the clock offset and clock drift problems. However, in actual scenarios, due to the differences in pRRU hardware and the different fiber lengths from pRRU to BBU, the analog device processing delay and link transmission delay of the RF front end will also bring fixed time errors to each pRRU. 3GPP requires that wireless signals be strictly aligned at the air interface, so this error can be called time alignment error (TAE). Based on the TDOA positioning architecture, after the arrival time (TOA) is differentiated, TDOA will have a time difference alignment error (TDAE) of tens of nanoseconds, which does not meet the synchronization requirements of high-precision indoor positioning. To solve the inter-station synchronization problem caused by TAE, there are currently three feasible solutions: 1) Reference beacon method: set a reference beacon in the positioning scenario, and realize the estimation and compensation of TDAE through methods such as two-way communication. However, reference beacons will bring additional costs, and the deployment of beacons is limited by the scene; 2) Inter-station communication methods: base stations communicate over the air interface and calculate the time difference based on the timestamp to achieve TDAE estimation and compensation. However, this method requires no obstruction between stations and is limited by the scene; 3) Delay measurement methods: optical fiber loopback is set at the remote end of the RF to measure the transmission delay and compensate, but the measurement accuracy is limited. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a high-precision inter-station synchronization method for indoor 5G base stations, which has the characteristics of simple implementation, fast solution speed, small scene restrictions, and no need for additional equipment such as beacons.

[0005] In order to achieve the above object, the technical solution provided by the present invention is: a beacon-free indoor 5G base station high-precision inter-station synchronization method, comprising the following steps:

[0006] Step 1: Based on the long-term invariance of TAE, a TOA model considering TAE is constructed using TOA measurement data accumulated by base stations at historical moments.

[0007] Step 2: Based on the TOA model, a TDOA model considering TDAE is constructed by setting a reference pRRU;

[0008] Step 3: Based on the TDOA model, a TDDOA model is constructed by setting a reference UE;

[0009] Step 4: Calculate the residual vector according to the TDDOA model;

[0010] Step 5: Solve the noise covariance matrix according to the TDDOA model, and use its inverse matrix as the weight;

[0011] Step 6: According to the residual vector and the weight matrix, the weighted least square method is used to find the best match of the UE coordinates;

[0012] Step 7: According to the residual vector and the weighted least squares expression, the Gauss-Newton iteration method is used to solve the UE coordinates;

[0013] Step 8: Substitute the UE coordinates into the TDOA model to solve TDAE;

[0014] Step 9: Compensate the TDAE into the TDOA measurement data, thereby achieving high-precision inter-station synchronization.

[0015] 9. Optionally, the TOA model is as follows:

[0016]

[0017]

[0018] in, ξ represents the TOA measurement value received by the sth pRRU when the nth UE transmits the signal in the kth frame; (n,k) Indicates the coordinates of the nth UE in the kth frame, whose height is known, that is, ξ (n,k) =(x (n,k) ,y (n,k) ,z);τ toa,s (ξ (n,k) ) represents the air interface delay between the nth UE and the sth pRRU in the kth frame, which is the propagation delay of the signal from the UE to the pRRU antenna along a straight line; δ (n,k) represents the synchronization error between the nth UE and the 5G base station system in the kth frame; τ tae,s represents the temporal registration error of the sth pRRU; represents the measurement noise, which follows a zero-mean Gaussian distribution with a variance of σ 2 ; S represents the number of pRRUs, S≥4; N (k) Indicates the number of UEs at different positions in the kth frame, K represents the number of frames; k0 is the starting time.

[0019] Optionally, the reference pRRU indicates that the pRRU is numbered 1.

[0020] Optionally, the TDOA model is:

[0021]

[0022]

[0023] in

[0024]

[0025]

[0026]

[0027] Optionally, the reference UE represents that the UE number n is 1, and the signal frame number k is k0.

[0028] Optionally, the TDDOA model is:

[0029]

[0030]

[0031] in

[0032]

[0033]

[0034] Optionally, the residual vector is calculated by the following formula:

[0035] e=τ TDDOA -τ tddoa (x)

[0036] in

[0037]

[0038]

[0039]

[0040] Optionally, step 7 includes:

[0041] 7.1: Construct the Jacobian matrix based on the residual vector:

[0042]

[0043] in

[0044]

[0045] 7.2: Construct the iterative expression of Gauss-Newton iteration method:

[0046]

[0047] Among them, x j represents the UE coordinate variable solved in the jth iteration; J xj represents the Jacobian matrix solved at the jth iteration; e j represents the residual vector of the jth iteration;

[0048] 7.3: Establish a Cartesian coordinate system based on the indoor positioning scenario, set the value range of the UE coordinate variable, randomly select the initial value and then enter it into step 7.2 for solution, and set the iterative convergence condition of the Gauss-Newton iteration method:

[0049] x j+1 -x j <ε

[0050] Among them, ε represents the convergence threshold;

[0051] 7.4: If the iteration diverges and cannot meet the convergence condition of step 7.3, reselect the initial value of the UE coordinate and substitute it into step 7.2 for solution; if the convergence condition still cannot be met after repeating the calculation M times, select other TOA data and recalculate from step 1.

[0052] The beneficial effects of the present invention compared with the prior art are: the method proposes a TOA model taking TAE into account, and on this basis proposes a TDDOA model, which can be used to solve TDAE; the Gauss-Newton iteration method is used to solve the weighted least squares estimation problem, and the method is simple and fast; it can be solved through historically accumulated TOA data, and the scene requirements are small; TDAE estimation between 5G base stations can be realized under beacon-free conditions, and no changes are required to the original functions and equipment of the 5G system, and it has wide versatility. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A flowchart of a beacon-free indoor 5G base station high-precision inter-station synchronization method provided by an embodiment of the present invention;

[0054] Figure 2A solution flow chart based on the Gauss-Newton iteration method provided in one embodiment of the present invention; DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] This embodiment provides a beacon-free indoor 5G base station high-precision inter-station synchronization method. The method first constructs a TOA model that takes TAE into consideration based on the long-term unchanged characteristic of TAE and the TOA measurement data accumulated by the base station in historical moments; based on the reference pRRU and the reference UE, the TOA model is differentiated twice to obtain the TDDOA model, and the variables to be solved are the UE coordinates; the UE coordinate solution problem is converted into a weighted least squares estimation problem, and the optimal solution is approximated by the Gauss-Newton iteration method; the solved UE coordinates are substituted into the TDOA model to solve TDAE, which can be used to compensate for TDOA measurement data over a long period of time to achieve high-precision inter-station synchronization. This method does not require the assistance of equipment such as reference beacons, and has the characteristics of simple implementation, fast iteration speed, and low system requirements.

[0057] Reference Figure 1 The beacon-free indoor 5G base station high-precision inter-station synchronization method includes the following steps: 10. Step 1, using the long-term unchanged characteristics of TAE, a new TOA model is defined as follows:

[0058]

[0059]

[0060] in, ξ represents the TOA measurement value received by the sth pRRU when the nth UE transmits the signal in the kth frame; (n,k) Indicates the coordinates of the nth UE in the kth frame, whose height is known, i.e., ξ (n,k) =(x (n,k) ,y (n,k) ,z);τ toa,s (ξ (n,k) ) represents the air interface delay between the nth UE and the sth pRRU in the kth frame, which is the propagation delay of the signal from the UE to the pRRU antenna along a straight line; δ (n,k) represents the synchronization error between the nth UE and the 5G base station system in the kth frame; τ tae,s represents the temporal registration error of the sth pRRU; represents the measurement noise, which follows a zero-mean Gaussian distribution with a variance of σ 2 ; S represents the number of pRRUs, S≥4; N (k) Indicates the number of UEs at different positions in the kth frame, K represents the number of frames; k0 is the starting time.

[0061] Step 2: Based on the TOA model in step 1, set the reference pRRU (s=1) and construct the TDOA model:

[0062]

[0063]

[0064] in

[0065]

[0066]

[0067]

[0068] Step 3: Based on the TDOA model in step 2, set the reference UE (n=1, k=k0) and define the TDDOA model:

[0069]

[0070]

[0071] in

[0072]

[0073]

[0074] Step 4: Based on the TDDOA model constructed in step 3, the residual vector is calculated as:

[0075] e=τ TDDOA -τ tddoa (x)

[0076] in

[0077]

[0078]

[0079]

[0080] Step 5: Based on the TDDOA model in step 3, the covariance matrix of the noise is solved as follows:

[0081]

[0082] in, It represents the measured noise of TOA between the n′th UE and the s′th pRRU in the k′th frame.

[0083] Step 6: Based on the residual vector of step 4 and the covariance matrix of step 5, use the weighted least squares method to find the best match of the UE coordinates:

[0084]

[0085] in

[0086]

[0087] Step 7: Based on the residual vector in step 4 and the weighted least squares expression in step 6, the Gauss-Newton iteration method is used to solve the UE coordinates.

[0088] Reference Figure 2 , step 7 specifically includes:

[0089] 7.1. Construct the Jacobian matrix based on the residual vector in step 6:

[0090]

[0091] in

[0092]

[0093] 7.2. Construct the iterative expression of Gauss-Newton iteration method:

[0094]

[0095] Among them, x j represents the UE coordinate variable solved in the jth iteration; represents the Jacobian matrix solved at the jth iteration; e j represents the residual vector of the jth iteration.

[0096] 7.3. Establish a Cartesian coordinate system based on the indoor positioning scenario, set the value range of the UE coordinate variable based on the coordinate system, randomly select the initial value and then enter it into step 7.2 for solution. Set the iterative convergence condition of the Gauss-Newton iteration method:

[0097] x j+1 -x j <ε

[0098] Among them, ε represents the convergence threshold.

[0099] 7.4. If the iteration diverges and fails to meet the convergence condition of step 7.3, reselect the initial value of the UE coordinates and substitute it into step 7.2 for solution. If the convergence condition is still not met after repeating the calculation M times, select other TOA data and recalculate from step 1.

[0100] Step 8: Substitute the UE coordinates obtained in step 7 into the TDOA equation in step 2 to solve TDAE:

[0101]

[0102] Step 9: Based on the TDAE solved in step 8, compensate it into TDOA:

[0103]

[0104] Among them, the distance between UE and base station is R s , The coordinates of the sth base station are (X s ,Y s ,Z s ); c represents the speed of light.

[0105] Through the above steps, based only on the TOA obtained by the base station, the solution expression of TDAE can be obtained and TDAE can be compensated, thereby achieving high-precision inter-station synchronization without beacons. Other distributed indoor positioning systems based on TDOA positioning architecture can also use this method to achieve TDAE estimation and compensate TDOA positioning.

[0106] This method has the following advantages: 1) The newly constructed TOA model takes into account the influence of TAE and is suitable for the current distributed positioning system based on the TDOA positioning architecture, and can be extended to fields such as wireless sensor networks; 2) The Gauss-Newton iteration method is used to solve the weighted least squares estimation problem, with fast iteration speed and small number of iterations; 3) TOA data accumulated in multiple frames can be used for solution, reducing the requirement for the number of user devices; 4) The scene requirements are small, and no additional equipment such as reference beacons is required to assist in synchronization.

[0107] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A beacon-free indoor 5G base station high-precision inter-station synchronization method, characterized in that: The following steps are involved: Step 1: Based on the long-term unchanged characteristics of TAE, a TOA model considering TAE is constructed by using TOA measurement data accumulated by base stations in historical moments; the TOA model is as follows: in, ξ represents the TOA measurement value received by the sth pRRU when the nth UE transmits the signal in the kth frame; (n,k) Indicates the coordinates of the nth UE in the kth frame, whose height is known, that is, ξ (n,k) =(x (n,k) ,y (n,k) ,z);τ toa,s (ξ (n,k) ) represents the air interface delay between the nth UE and the sth pRRU in the kth frame, which is the propagation delay of the signal from the UE to the pRRU antenna along a straight line; δ (n,k) represents the synchronization error between the nth UE and the 5G base station system in the kth frame; τ tae,s represents the temporal registration error of the sth pRRU; represents the measurement noise, which follows a zero-mean Gaussian distribution with a variance of σ 2 ; S represents the number of pRRUs, S≥4; N (k) Indicates the number of UEs at different positions in the kth frame, K represents the number of frames; k0 is the starting time; Step 2: Based on the TOA model, a TDOA model considering TDAE is constructed by setting a reference pRRU; the TDOA model is: in, Step 3: On the basis of the TDOA model, a TDDOA model is constructed by setting a reference UE; the TDDOA model is: in Step 4: Calculate the residual vector according to the TDDOA model; Step 5: Solve the noise covariance matrix according to the TDDOA model, and use its inverse matrix as the weight; Step 6: According to the residual vector and the weight matrix, the weighted least square method is used to find the best match of the UE coordinates; Step 7: According to the residual vector and the weighted least squares expression, the Gauss-Newton iteration method is used to solve the UE coordinates; Step 8: Substitute the UE coordinates into the TDOA model to solve TDAE; Step 9: Compensate the TDAE into the TDOA measurement data, thereby achieving high-precision inter-station synchronization.

2. The indoor 5G base station high-precision inter-station synchronization method according to claim 1 is characterized in that: The reference pRRU indicates that the number s of the pRRU is 1.

3. The indoor 5G base station high-precision inter-station synchronization method according to claim 1 is characterized in that: The reference UE indicates that the UE number n is 1, and the signal frame number k is k0.

4. The indoor 5G base station high-precision inter-station synchronization method according to claim 1 is characterized in that: The residual vector is calculated by the following formula: e=τ TDDOA -t tddoa (x) in 5. The indoor 5G base station high-precision inter-station synchronization method according to claim 1 is characterized in that: The step 7 comprises: 7.1: Construct the Jacobian matrix based on the residual vector: in 7.2: Construct the iterative expression of Gauss-Newton iteration method: in, x j represents the UE coordinate variable solved in the jth iteration; represents the Jacobian matrix solved at the jth iteration; e j represents the residual vector of the jth iteration; 7.3: Establish a Cartesian coordinate system based on the indoor positioning scenario, set the value range of the UE coordinate variable, randomly select the initial value and then enter it into step 7.2 for solution, and set the iterative convergence condition of the Gauss-Newton iteration method: x j+1 -x j <e Among them, ε represents the convergence threshold; 7.4: If the iteration diverges and cannot meet the convergence condition of step 7.3, reselect the initial value of the UE coordinate and substitute it into step 7.2 for solution; if the convergence condition still cannot be met after repeating the calculation M times, select other TOA data and recalculate from step 1.

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