CN110958102A - Pilot pollution suppression method based on pilot distribution and power control joint optimization - Google Patents
Pilot pollution suppression method based on pilot distribution and power control joint optimization Download PDFInfo
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Abstract
本发明公开了一种基于导频分配和功率控制联合优化的导频污染抑制方法，包括以下步骤：(1)接收导频信号，进行信道估计；(2)接收上行数据信号，进行信号检测；(3)根据推导渐进的信干噪比SINR表达式，建立最大化最小频效优化目标；(4)根据优化目标，利用WGC‑PD‑UPC算法进行导频分配和上行功率控制优化，该算法主要采用交替迭代的方法，在迭代过程中，首先固定大尺度衰落因子进行上行功率控制优化，然后固定上行发射功率进行导频分配优化。本发明提供了一种导频分配和上行功率控制联合优化的方法，在计算复杂度较低的情况下，有效提高了系统中的最小频效，从而大幅提高了小区边缘用户的通信质量。
The invention discloses a pilot frequency pollution suppression method based on pilot frequency allocation and power control joint optimization, comprising the following steps: (1) receiving a pilot frequency signal, and performing channel estimation; (2) receiving an uplink data signal, and performing signal detection; (3) According to the derivation of the progressive signal-to-interference-noise ratio SINR expression, the optimization objective of maximizing the minimum frequency efficiency is established; (4) According to the optimization objective, the WGC-PD-UPC algorithm is used to optimize the pilot frequency allocation and uplink power control. The method of alternating iteration is mainly used. In the iterative process, the large-scale fading factor is first fixed for uplink power control optimization, and then the uplink transmit power is fixed for pilot frequency allocation optimization. The present invention provides a method for joint optimization of pilot frequency allocation and uplink power control, which effectively improves the minimum frequency efficiency in the system under the condition of low computational complexity, thereby greatly improving the communication quality of cell edge users.
Description
Technical Field
The invention belongs to the technical field of signal processing in wireless communication, and particularly relates to a pilot frequency pollution suppression method based on pilot frequency distribution and power control joint optimization.
Background
The massive MIMO technology has become one of the 5G key technologies because it can greatly improve the performance such as frequency efficiency, energy efficiency, and link reliability. In a massive MIMO system, a time division duplex TDD mode is generally adopted for data transmission. This is because in the TDD mode, it is convenient to perform channel estimation according to the uplink pilot signal sent by the terminal by using the reciprocity of the uplink and downlink channels within the same coherence time. However, since the number of orthogonal pilots is limited by the coherence duration of the channel, and in order to ensure that more effective data is transmitted within a certain coherence duration, a large-scale MIMO system generally employs a full-multiplexing pilot scheduling strategy, that is, the same set of orthogonal pilots are completely multiplexed by terminals of all cells. This strategy leads to a problem of mutual interference between signals generated by terminals multiplexing the same pilot among different cells, i.e., pilot pollution. The problem of pilot pollution seriously affects the accuracy of the base station on channel estimation and the communication rate of users, and becomes a bottleneck for further improving the performance of a large-scale MIMO system.
At present, the problem of pilot pollution has been widely studied, and the specific scheme is as follows: starting from the pilot frequency distribution angle, namely intelligently scheduling the pilot frequency according to the different channel gains corresponding to the terminal and the signal interference degrees of different pilot frequencies, and searching the optimal pairing relation between the terminal and the pilot frequency by taking the maximum system total capacity as an optimization target, thereby reducing the pilot frequency pollution and improving the system performance, but the method usually takes the cost of sacrificing the communication quality of the terminal with poor performance and has higher algorithm complexity; starting from the angle of time-shifting pilot frequency, classifying the cells, and then enabling the terminals of the cells of different types to asynchronously send the pilot frequency in a time slot manner, thereby effectively improving the performance of the edge terminal with serious pilot frequency pollution; however, the asynchronous pilot transmission can compress the pilot transmission time, so that the number of terminals which can be served by the system is reduced, and the number of the terminals served by the system is influenced to a certain extent; however, in both schemes, only the design of the pilot frequency is considered, and the pilot frequency and the power allocation of uplink data are not considered to reduce the pilot frequency pollution; and thirdly, pilot power allocation is carried out by utilizing the user groups to inhibit pilot pollution. Although this method can obtain better performance, it does not perform power and pilot allocation of uplink data, so it has a larger lifting space.
Disclosure of Invention
Aiming at the problem of pilot pollution in the prior art, the invention aims to provide a pilot pollution suppression method based on pilot allocation and power control joint optimization, which improves the minimum communication rate of cell users by performing joint optimization on pilot design and uplink power control.
In order to achieve the purpose, the invention provides the following technical scheme: a pilot pollution suppression method based on pilot allocation and power control joint optimization comprises the following steps:
step 1: receiving a pilot signal and carrying out channel estimation;
step 2: receiving an uplink data signal and carrying out signal detection;
and step 3: establishing a maximized minimum frequency effect optimization target according to a deduced progressive signal to interference plus noise ratio (SINR) expression;
and 4, step 4: according to an optimization target, utilizing a WGC-PD-UPC algorithm to perform pilot frequency allocation and uplink power control optimization, wherein the WGC-PD-UPC algorithm specifically comprises the following steps:
step 4.1: performing equal-power pilot frequency distribution optimization;
step 4.2: judging whether an iteration termination condition is met, terminating iteration after the iteration reaches a preset iteration number or tends to a stable value, and otherwise, continuously performing alternate iteration execution on the following two substeps;
step 4.3: fixing a large-scale fading factor according to a previous pilot frequency allocation scheme, and then performing uplink power control optimization;
step 4.4: in the step, the uplink transmitting power is firstly fixed, and then pilot frequency power allocation optimization is carried out. The step is the same as the step 4.1, the only difference is that the pilot frequency distribution with equal power is not, but the influence of fixed uplink power is considered when the pilot frequency distribution is optimized;
step 4.5: and comparing the stored iteration results of each time, and selecting a pilot frequency distribution scheme and uplink power corresponding to the optimal result.
In one embodiment, step 4.3, the large-scale fading factor is fixed according to the result of the previous pilot allocation, and then the optimization problem is transformed as follows:
wherein ξ is expressed as the minimum approximate SINR in L cells,
represents the power at which the kth user in the ith cell transmits a pilot,
expressed as the power of the transmission of uplink data for the kth user in the ith cell, βiikExpressed as the large-scale fading factor from the kth user in the ith cell to the central base station of the ith cell,
expressed as the power at which the kth user in the jth cell transmits the pilot,
denoted as the power of the transmission of uplink data for the kth user in the jth cell, βijkExpressed as the large-scale fading factor, V, from the kth user in the jth cell to the ith cell center base stationLExpressed as the cut-off power, V, of the power amplifierHExpressed as the saturation work of the power amplifierAnd (4) rate.
Obviously, this power control optimization sub-problem is a Geometric Planning (GP) problem, and this problem can be solved by using a MOSEK toolkit and using an interior point method.
In one embodiment, step 4.4, the uplink transmission power is fixed, and the sub-problem of the problem decomposition can be expressed as:
thus, the problem is only a pilot frequency distribution optimization problem, but in a multi-cell Massive MIMO system, if pilot frequency distribution is carried out in an exhaustive search mode, a large amount of calculation time is consumed, and real-time processing is difficult to realize in actual engineering;
to solve this sub-problem well, a weight graph coloring algorithm is adopted for processing, and the main idea of the algorithm is to first measure a metric of mutual pilot pollution level of two users potentially having the same pilot in different cells, which can be expressed as:
and then based on the metric weight
Constructing an Edge Weight Interference Graph (EWIG);
and finally, carrying out greedy pilot frequency distribution according to the EWIG.
In a specific embodiment, in step 4.5, it is determined whether an iteration termination condition is satisfied, and the iteration is terminated after the iteration reaches a preset iteration number or tends to a stable value, otherwise, the following two sub-steps are continuously executed by alternating iteration.
The invention provides a pilot frequency pollution suppression method based on pilot frequency distribution and power control joint optimization, which aims to improve the communication rate of cell users, and because the problem is an NP problem, the calculation complexity is hard to bear, in order to reduce the calculation complexity, the invention provides a joint optimization algorithm, which decomposes the problem into two sub-problems: pilot allocation and power control.
The method for solving the pilot frequency allocation sub-problem is to fix the power of the pilot frequency and uplink data transmission at first and then carry out pilot frequency allocation design. Because the sub-problem has higher computational complexity if an exhaustive search mode is adopted, the invention adopts a graph coloring method to carry out pilot frequency distribution.
The method for solving the power control sub-problem is to fix the large-scale fading factor based on the previous pilot frequency allocation scheme and then perform power allocation. This sub-problem then becomes a geometric planning problem. This problem can be solved well with the MOSEK kit. By performing the alternative iteration processing on the two sub-problems, the invention can improve the minimum rate of the cell users under lower computation complexity.
The WGC-PD-UPC algorithm mainly adopts an alternate iteration method, and in the iteration process, firstly, a large-scale fading factor is fixed to perform uplink power control optimization, and then, uplink transmitting power is fixed to perform pilot frequency distribution optimization.
The invention provides a method for joint optimization of pilot frequency allocation and uplink power control, which effectively improves the minimum frequency efficiency in a system under the condition of low computational complexity and is close to the boundary under an ideal state, thereby greatly improving the communication quality of cell edge users.
Drawings
Fig. 1 is a schematic block diagram of a working flow of a pilot pollution suppression method based on pilot allocation and power control joint optimization according to the present invention.
Fig. 2 is a schematic block diagram of a WGC-PD-UPC algorithm flow based on pilot allocation and power control joint optimization provided by the present invention.
Fig. 3 is a schematic diagram comparing the minimum frequency efficiency performance of the WGC-PD-UPC algorithm based on pilot allocation and power control joint optimization and other algorithms provided in the present invention.
Fig. 4 is a schematic diagram showing a comparison of a minimum frequency-efficiency performance curve of the WGC-PD-UPC algorithm based on pilot allocation and power control joint optimization provided by the present invention with other algorithms as the number of users in a cell increases.
Fig. 5 is a schematic diagram of the convergence situation of the WGC-PD-UPC algorithm based on pilot allocation and power control joint optimization provided by the present invention.
Detailed Description
The invention will be further elucidated with reference to the embodiments and the accompanying drawings.
In this example we consider a multi-cell multi-user TDD Massive MIMO system consisting of L hexagonal cells. In each cell, a base station located in the center of the cell is configured with M antennas and simultaneously serves single antenna users (K) randomly distributed in K cells<<M). Without loss of generality, the channel gain h from the kth user to the base station of the i cell in the j cellijkCan be expressed as:
wherein g isijkIs a small scale fading factor which follows a circularly symmetric complex Gaussian distribution, i.e. CN (0, I)M)；
βijkIs a large scale fading factor, which can be expressed in the general case as:
where z isijkRepresenting shadow fading, which is logarithmically distributed (i.e.,10 log)10(zijk) Obey Gaussian distribution CN (0, sigma)shadow)，rijkRepresenting the distance from the kth user in the jth cell to the base station at the center of the ith cell, and R representing the radius of the cell βijkIs constant within a coherence time, varies slowly and is easily tracked over several thousands of channel coherence times.
In this embodiment, a specific working flow diagram of a pilot pollution suppression method based on pilot allocation and power control joint optimization is shown in fig. 1, and includes the following steps:
in order to overcome the interference in the cell, the system adopts orthogonal pilot frequency for the users in the cell. Meanwhile, in order to increase frequency efficiency, a full multiplexing strategy is adopted for pilot frequency between cells. The pilot sequence employed by the present system can therefore be represented as
And satisfies ΨH＝IK. Each user in each cell uses one column in Ψ as a pilot with a sequence length τ, and different users in the same cell use different column vectors in Ψ.
Signals received by base stations in the ith cell during a pilot transmission phase
Can be expressed as:
here, the
Indicating the power of the pilot transmission of the k-th user in the jth cell, #jkThe pilot sequence transmitted at the kth user in the jth cell is shown. The user uses the kth column in Ψ as a pilot sequence,
represented by additive white Gaussian noise, which is independently identically distributed and obeys CN (0, sigma)p)。
When the base station in the cell receives the pilot signal transmitted by the user, the base station can be based on
Least Squares (LS) channel estimation is performed. So the channel estimation value between k users in the ith cell and the base station of the ith cell
Can be expressed as:
step 2: receiving an uplink data signal and carrying out signal detection;
in the uplink data transmission phase, the data received by the base station can be represented as:
here, the
It shows the uplink data received by the ith cell center base station,
indicating the transmission power when the kth user transmits uplink data in the jth cell,
the uplink data transmitted by the kth user in the jth cell is shown,
expressed is additive white Gaussian noise generated in the uplink data transmission phase and obeys CN (0, sigma)u)。
According to signals received by the base station in the uplink transmission stage
And channel estimation
Performing matched filtering detection, and recovering data transmitted by the kth user in the ith cell after detection
Can be expressed as:
where the second term represents inter-cell interference,
represents the sum of the interference and other uncorrelated noise in the cell, and has the expression:
and step 3: deducing a progressive signal to interference plus noise ratio (SINR) expression, and establishing a maximized minimum frequency effect optimization target;
as can be seen from expression (7)
As the number M of antennas of the base station increases, it is greatly reduced, and when M → ∞,
and then the uplink signal-to-interference-and-noise ratio of the kth user in the ith cell can be deduced according to the expression (6)
Can be expressed as:
when the rate of M → ∞ is,
when in use
The formula (9) can be further simplified to：
Frequency efficiency of kth user in ith cell
Can be expressed as:
where μ τ/t represents the uplink frequency loss, and t is the uplink coherence interval.
The invention aims to improve the frequency efficiency of the minimum frequency efficiency user in the system, so as to improve the communication quality of the user with serious pilot pollution at the edge of a cell. The problem can therefore be expressed as:
according to equation (12) and the nature of the logarithmic function, the optimization problem can be converted into:
again, this problem can be translated into:
in addition, considering the linear range of the transmission power amplifier, the linear range should be limited to a certain range regardless of the pilot transmission power or the uplink data transmission power. That is, the uplink transmission power should not exceed V at maximumHMinimum should be not less than VL. Wherein VHAmplified saturation power, V, of associated power amplifierLThe cutoff power of the associated power amplifier. Then, with this constraint, the optimization problem of the present invention can be re-tabulatedShown as follows:
and 4, step 4: according to an optimization target, pilot frequency distribution and uplink power control optimization are carried out by utilizing the WGC-PD-UPC algorithm provided by the invention;
it is obvious that equation (15) is not a convex optimization problem, but is a joint problem of scattered pilot allocation optimization and continuous uplink transmission power optimization. This problem is an NP problem that is difficult to solve. To solve this problem well, the present invention decomposes the problem into two sub-problems and completes them in two steps, and performs alternate iterations on the two sub-problems. And when the iteration reaches a certain number of times and is stable, the optimal value in the iteration process is selected for pilot frequency distribution and uplink power control.
The specific algorithm flow is shown in fig. 2:
step 4.1:
in order to optimize without falling into local optimization, first, assuming that the uplink transmission powers are all equal in the first step, the problem can be expressed as:
thus the problem is only one pilot allocation optimization problem. However, in a multi-cell Massive MIMO system, if an exhaustive search mode is performed to perform pilot allocation, a large amount of calculation time will be consumed, which is difficult to implement in real-time processing in actual engineering. In order to solve the sub-problem well, the invention adopts a weight map coloring algorithm to process. The main idea of the algorithm is to first measure a metric of the mutual pilot pollution level of two users potentially having the same pilot in different cells, which can be expressed as:
and then based on the metric weight
Constructing an Edge Weight Interference Graph (EWIG); and finally, carrying out greedy pilot frequency distribution according to the EWIG.
Step 4.2:
and judging whether an iteration termination condition is met, terminating iteration after the iteration reaches a preset iteration number or a region stable value, and otherwise, continuously performing alternate iteration execution on the following two substeps.
Step 4.3:
this step is first applied to all β in equation (15)iikAnd βijkAfter the result of the previous pilot allocation is fixed, the following conversion is performed on equation (17):
obviously, this power control optimization sub-problem is a Geometric Planning (GP) problem, and this problem can be solved by using a MOSEK toolkit and using an interior point method.
Step 4.4:
this step first fixes the uplink transmit power, then the sub-problem of the problem decomposition can be expressed as:
the step is the same as the step 4.1, the only difference is that the measurement of the mutual coherence considers the uplink power, and the measurement expression is as follows:
step 4.5:
and comparing the stored iteration results of each time, and selecting a pilot frequency distribution scheme and uplink power corresponding to the optimal result.
For convenience of numerical analysis, we now briefly describe some algorithms to which the present invention algorithm (WGC-PD-UPC) is compared:
(a) RPA: the algorithm carries out equal-power random pilot frequency distribution;
(b) WGC-PD: the algorithm performs equal-power pilot frequency distribution according to a weighted graph coloring method;
(c) ESPA: the algorithm adopts an exhaustive pilot frequency distribution scheme, and then carries out searching on the optimal pilot frequency distribution scheme according to the optimization target of the formula (16);
(d) RPA-PPC: the algorithm only considers pilot frequency power control optimization, pilot frequency allocation adopts a random mode, the pilot frequency power optimization is executed according to the step 4.3 of the algorithm provided by the invention, and all uplink data transmitting power is considered to be equal;
(e) WGC-PD-PPC: the algorithm is similar to the WGC-PD-UPC algorithm provided by the invention, and the only difference is that when the step 4.3 is executed, the WGC-PD-PPC algorithm considers that all uplink data transmitting powers are equal;
(f) ESPA-PPC: the algorithm is completed in two steps, wherein in the first step, an ESPA algorithm is adopted for pilot frequency distribution, and in the second step, an RPA-PPC algorithm is adopted for pilot frequency power control optimization;
(g) RPA-UPC: the algorithm considers pilot frequency and uplink data power control optimization, pilot frequency allocation adopts a random mode, and power optimization is executed according to the algorithm step 4.3 provided by the invention;
(h) ESPA-UPC: the algorithm is completed in two steps, wherein the first step adopts an ESPA algorithm to perform pilot frequency distribution, and the second step adopts an RPA-UPC algorithm to perform pilot frequency power control optimization;
(i) the ideal optimal solution: the method adopts a method of combining exhaustive search and geometric planning to solve the optimization problem of the formula (15), the method is an exhaustive pilot frequency allocation scheme, power control optimization is carried out on each pilot frequency scheme according to the step 4.3 provided by the invention, and then the pilot frequency allocation scheme and the corresponding power are selected according to an objective function of the formula (15).
Fig. 3 shows the cumulative distribution function of the minimum frequency efficiency in the system under different algorithm processes, where the number of system cells is set to L-3, and the number of users in each cell is set to K-4. As can be seen from the figure: the random pilot allocation mode (RPA) without pilot allocation optimization and power control has the worst performance; after the WGC-PD algorithm is adopted for pilot frequency distribution optimization, the performance is greatly improved; although the performance of the pilot allocation optimization by adopting the ESPA algorithm is slightly improved compared with that of the WGC-PD algorithm, the ESPA algorithm is difficult to adopt in practical application due to high computational complexity. The three algorithms of RPA-PPC, WGC-PD-PPC and ESPA-PPC are respectively optimized for pilot power control on the basis of RPA, WGC-PD and ESPA. It can be found that the performance of the method is greatly improved compared with the corresponding algorithm without pilot control optimization. Wherein the performance of the RPA-PPC algorithm is close to that of the ESPA algorithm. This illustrates the need to optimize not only pilot allocation but also pilot power control in order to improve minimum frequency efficiency.
In addition, we can see from FIG. 3 that WGC-PD-UPC, ESPA-UPC and idea Optimal Solution algorithms are superior to other algorithms in terms of minimum frequency-efficiency performance. This shows that the performance can be further improved by performing joint optimization on pilot allocation, pilot and uplink data power control. Wherein the WGC-PD-UPC algorithm provided by the invention is higher than the ESPA-UPC algorithm by 0.219b/s/Hz in average minimum frequency efficiency performance, and the ESPA-UPC algorithm spends a great deal of time due to the exhaustive search pilot frequency scheme. The performance of the WGC-PD-UPC algorithm provided by the invention is close to that of the idea optimal solution algorithm, and the difference between the performance of the WGC-PD-UPC algorithm and the performance of the idea optimal solution algorithm is only 0.243 b/s/Hz. The idea optimal solution algorithm is an ideal optimization scheme in performance, but the computation complexity is very high. This demonstrates that the WGC-PD-UPC algorithm is efficient and feasible.
Fig. 4 shows the minimum frequency effect variation with the increase of users in the cell under different algorithms. As can be seen from fig. 4: the performance of the system is ranked from good to bad after the number of users is increased to 4, namely WGC-PD-UPC>WGC-PD-PPC>WGC-PD>RPA-UPC>RPA-PPC>And (4) RPA. When the number of each user is 8, compared with the RPA algorithm, the WGC-PD-UPC, the WGC-PD-PPC, the WGC-PD, the RPA-UPC and the RPAPPC are respectively increased by 4.551b/s/Hz, 3.757b/s/Hz, 2.153b/s/Hz and 1.154b/s/Hz, 0.494 b/s/Hz. The result shows that the change trend is consistent as the number of users increases, and the WGC-PD-UPC is still the best solution. With the increase of the number of users, the performance of the WGC-PD-UPA algorithm, the WGC-PD-PPC algorithm and the WGC-PD algorithm is better and better, and the performance of the RPA-UPC algorithm, the RP A-PPC algorithm and the RPA algorithm is worse and worse. This also leads to increasingly more distant performance. When the number of users is increased from 2 to 8, the performance gap of the WGC-PD-UPC algorithm is improved from 3.126b/s/Hz to 4.551b/s/Hz compared with the RPA case without any optimization. This is because the denominator of the objective function in equation (15) increases as the number of users increases
The number of the middle summation sub-items is correspondingly increased, and when random pilot frequency distribution is adopted, the large-scale fading factor β in each sub-item is causedijkAre relatively large, the denominator value is significantly increased resulting in a gradual decrease of the minimum frequency efficiency. When pilot allocation is optimized, although
The number of the middle summation sub-items still increases with the number of the users, but the value of each sub-item is greatly reduced, so that the minimum frequency efficiency is gradually increased.
Fig. 5 shows the convergence of the WGC-PD-UPC algorithm provided by the present invention, and it is apparent from fig. 5 that after a certain iteration, the algorithm provided by the present invention converges to a stable state, and the maximum value thereof is close to the result of the ideal Optimal Solution algorithm. Since the algorithm provided by the present invention performs alternate iterations of the two sub-problems of the decomposition, the algorithm may converge on two fixed values, the result of which is shown in fig. 5 in channel 2.
The above-mentioned embodiments only express the centralized implementation mode of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
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|---|---|---|---|---|
| CN111726879A (en) * | 2020-06-10 | 2020-09-29 | 中南大学 | A time-shifted pilot-based fixed power allocation method and system |
| CN113193945A (en) * | 2021-05-07 | 2021-07-30 | 中山大学 | Pilot frequency and power distribution joint optimization method, system, medium and communication equipment |
| CN113498157A (en) * | 2021-06-01 | 2021-10-12 | 北京邮电大学 | Transmission power control method and device |
| CN114302487A (en) * | 2021-12-28 | 2022-04-08 | 中南大学 | Energy efficiency optimization method, device and equipment based on adaptive particle swarm power distribution |
| CN114826532A (en) * | 2022-04-11 | 2022-07-29 | 北京邮电大学 | Pilot frequency distribution method, device, electronic equipment and storage medium |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111726879A (en) * | 2020-06-10 | 2020-09-29 | 中南大学 | A time-shifted pilot-based fixed power allocation method and system |
| CN113193945A (en) * | 2021-05-07 | 2021-07-30 | 中山大学 | Pilot frequency and power distribution joint optimization method, system, medium and communication equipment |
| CN113498157A (en) * | 2021-06-01 | 2021-10-12 | 北京邮电大学 | Transmission power control method and device |
| CN113498157B (en) * | 2021-06-01 | 2022-06-28 | 北京邮电大学 | Method and device for controlling transmitting power |
| CN114302487A (en) * | 2021-12-28 | 2022-04-08 | 中南大学 | Energy efficiency optimization method, device and equipment based on adaptive particle swarm power distribution |
| CN114302487B (en) * | 2021-12-28 | 2024-03-05 | 中南大学 | Energy efficiency optimization method, device and equipment based on self-adaptive particle swarm power distribution |
| CN114826532A (en) * | 2022-04-11 | 2022-07-29 | 北京邮电大学 | Pilot frequency distribution method, device, electronic equipment and storage medium |
| CN114826532B (en) * | 2022-04-11 | 2023-11-14 | 北京邮电大学 | Pilot frequency distribution method, device, electronic equipment and storage medium |
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