CN116232525A - Method for eliminating uplink interference with low complexity in multi-cell cellular network - Google Patents

Method for eliminating uplink interference with low complexity in multi-cell cellular network Download PDF

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CN116232525A
CN116232525A CN202310218350.4A CN202310218350A CN116232525A CN 116232525 A CN116232525 A CN 116232525A CN 202310218350 A CN202310218350 A CN 202310218350A CN 116232525 A CN116232525 A CN 116232525A
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孙彦赞
康佳琦
张舜卿
陈小静
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University of Shanghai for Science and Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04J—MULTIPLEX COMMUNICATION
    • H04J11/00—Orthogonal multiplex systems, e.g. using WALSH codes
    • H04J11/0023—Interference mitigation or co-ordination
    • H04J11/0026—Interference mitigation or co-ordination of multi-user interference
    • H04J11/0036—Interference mitigation or co-ordination of multi-user interference at the receiver
    • H04J11/0046—Interference mitigation or co-ordination of multi-user interference at the receiver using joint detection algorithms
    • H—ELECTRICITY
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    • H04B17/30—Monitoring; Testing of propagation channels
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04L25/0228—Channel estimation using sounding signals with direct estimation from sounding signals
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
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Abstract

A method for eliminating interference in uplink with low complexity in multi-cell cellular network includes calculating difference degree of active state information of all interfered users at moment and last moment when interference time window is located, namely proportion of newly added interfered users to original interfered users, carrying out semi-blind interference detection on newly added interfered users when difference degree is too high to obtain interference data signals; and obtaining channel parameters between the target data signal and all interference data signals and the base station through channel estimation, and improving the accuracy of MIMO equalization by using a minimum mean square error interference rejection combining algorithm (MMSE-IRC) to recover the target user data signal, thereby realizing the interference elimination of the target user data signal. The invention fully utilizes the cell information and has low system calculation complexity.

Description

Method for eliminating uplink interference with low complexity in multi-cell cellular network
Technical Field
The invention relates to a technology in the field of wireless communication, in particular to a low-complexity uplink interference elimination method in a multi-cell cellular network.
Background
In the existing LTE system, the phenomenon that a base station is severely interfered by users at the edge of an adjacent cell is common, the interference signal is subjected to demodulation and even decoding processing similar to the reception of useful signals at a receiving end through an interference elimination technology, then the interference signal after demodulation is subtracted from the received signal, and then the self expected signal is decoded from the rest received signal with smaller interference. However, in a practical communication system, interference tends to change with time, and there may be some similarity between interfering users at adjacent times, and duplicate detection of interference at each time wastes a lot of resources.
Disclosure of Invention
The invention aims at the prior interference coordination technology to carry out the interaction of the information among cells, thereby increasing the signaling overhead and the complexity of the system and having higher requirement on time delay. While the blind interference elimination receiver does not need the help of other adjacent base stations or cells, when all possible RSs are generated for cross correlation, the defect that the searching complexity is increased due to huge alternative sets is provided, the uplink interference elimination method with low complexity in the multi-cell cellular network is provided, the cell information is fully utilized, and the system calculation complexity is low.
The invention is realized by the following technical scheme:
the invention relates to an uplink interference elimination method based on low complexity, when the uplink interference elimination method is positioned in an interfered time window, calculating the difference degree of active state information of all interfered users at the moment and the last moment, namely the proportion of newly added interfered users to original interfered users, and when the difference degree is too high, carrying out semi-blind interference detection on the newly added interfered users to obtain an interference data signal; and obtaining channel parameters between the target data signal and all interference data signals and the base station through channel estimation, and improving the accuracy of MIMO equalization by using a minimum mean square error interference rejection combining algorithm (MMSE-IRC) to recover the target user data signal, thereby realizing the interference elimination of the target user data signal.
Preferably, by predicting future interference user status information, compared with the current time, the newly added interference is ignored to a certain extent, and only the same interference users are considered. Therefore, the related interference information at the current moment can be directly used at the next moment without semi-blind interference detection, so that the calculation complexity of the system is greatly reduced.
The invention relates to a system for realizing the method, which comprises the following steps: the system comprises a prediction module, a semi-blind interference parameter detection module and an interference elimination module, wherein: the prediction module establishes an LSTM model for each user by utilizing historical data of all user states, predicts whether the state of the user is active or not after a period of time, namely, whether the user interacts with a base station or other equipment or not, selects a proper time window T according to a predicted result, and eliminates interference suffered by a target user in the time window; the semi-blind interference parameter detection module carries out cross-correlation on the received signal and a demodulation reference signal (DMRS) alternative set generated by a base station side, adopts a method combining the current detection (Hypothesis Testing) and continuous interference elimination to judge the existence of strong interference by using a cross-correlation value, and obtains corresponding strong interference information; the interference elimination module uses the detected interference information as prior information to perform channel estimation and MIMO equalization, so that more accurate target signals are recovered, and the effect of interference elimination is realized.
The strong interference information includes: the location and length of Resource blocks (Resource blocks), symbol length, type, transmit ports, etc.
The prediction module comprises: training unit and prediction unit, wherein: the training unit divides the historical state data of the single user into a training set and a testing set to train the LSTM model. Since the states of the users are divided into active users and inactive users, the states are represented by 1 and 0, and therefore, the data can be directly used without normalization processing during model training. And the prediction unit inputs historical data according to the LSTM model obtained by the training unit to obtain a predicted result.
The semi-blind interference parameter detection module comprises: the device comprises an alternative set generating unit, an interference existence detecting unit and a strong interference detecting unit, wherein: the alternative set generating unit generates a pilot frequency in each cell by using the ID number information of the target cell and the adjacent cell according to the pilot frequency generating mode in the 3GPP protocol. Since there is a difference in the resource blocks used by each user, all possible pilot sequences are generated according to the size and position of the resource blocks that may be used, thereby obtaining an alternative set. The interference existence detection unit calculates power according to the received pilot signal, compares the power with the power when only Gaussian white noise (AWGN) exists, and judges whether interference exists in the received signal. And the strong interference detection unit performs cross-correlation operation on the received pilot signal and the alternative set, and compares the pilot signal with a set threshold value to obtain a pilot sequence used by strong interference.
The interference cancellation module comprises: channel estimation unit and MIMO equalization, wherein: the channel estimation unit carries out Least Square (LS) channel estimation according to the strong interference pilot frequency information obtained by the semi-blind interference parameter detection module to obtain channel parameters between an interference user and a target base station, and the MIMO equalization unit recovers the data signals sent by the target user by utilizing a minimum mean square error interference rejection combining (MMSE-IRC) algorithm according to the channel parameters and the received data signals.
Technical effects
Aiming at uplink interference, the invention combines the LSTM prediction model based on the user state with the detection and elimination based on semi-blind interference, and utilizes the similarity of the interference users at adjacent moments to realize low-complexity interference elimination. Compared with the prior art, the method reduces the calculation complexity of the system, improves the compromise level of the performance and the complexity and improves the interference elimination speed under the condition of ensuring the ideal Mean Square Error (MSE) level.
Drawings
FIG. 1 is a schematic diagram of a system according to the present invention;
FIG. 2 is a flow chart of an embodiment;
FIG. 3 is a schematic diagram of the probability of missing detection at a dry-to-noise ratio of-5-15 dB;
FIG. 4 is a schematic diagram of false alarm probability at a dry-to-noise ratio of-5-15 dB;
fig. 5 is a graph showing the performance contrast at a signal-to-interference-and-noise ratio of-5 to 15 dB.
Detailed Description
As shown in fig. 2, this embodiment relates to a method for eliminating uplink interference based on low complexity, when the uplink interference is located in an interfered time window, calculating the difference degree of active state information of all interfered users at the moment and the last moment, that is, the proportion of newly added interfered users to original interfered users, and when the difference degree is too high, performing semi-blind interference detection on the newly added interfered users to obtain an interference data signal; and obtaining channel parameters between the target data signal and all interference data signals and the base station through channel estimation, and improving the accuracy of MIMO equalization by using a minimum mean square error interference rejection combining algorithm (MMSE-IRC) to recover the target user data signal, thereby realizing the interference elimination of the target user data signal.
The disturbed time window is obtained by the following steps: the base station side respectively establishes an LSTM model for each user according to the historical data of all user states to predict the active state information of the user, and judges the interfered time window T= { T of the target user to be interference eliminated according to the active state information i (s i ,l i )|i∈N * -wherein: s is(s) 1 ＝n 1 ,
Figure BDA0004115810280000031
l 1 ＝m 1 -n 1 ,/>
Figure BDA0004115810280000032
Figure BDA0004115810280000033
s i Is the T i Time windowStart index of l i Is the T i The length of the time window ρ 0 The state information of the target user is expanded into ρ 0 ＝[ρ 0 (1),ρ 0 (2),…,ρ 0 (j),…,ρ 0 (N)]，0<j, k is less than or equal to N, j is less than or equal to Z, k is less than or equal to Z, the calculation formula of the first time window is shown as above, and j=n when the second time window is calculated 1 +m 1 Obtaining s 2 ＝n 2 Calculate l 2 Time k=n 2 The remaining calculations and so on.
The alternative set of pilot DMRS is obtained by the following method: the base station side generates all pseudo random sequences by using the inherent information of the cell and the adjacent cell, namely the physical cell number PCI, the pseudo random sequences regenerate reference signals and are mapped to resource blocks, and finally, the pilot frequency DMRS alternative set is generated on all possible RBs according to the reference signals, specifically: first generating a pseudo random sequence according to 3GPP protocol 38.211: c (n) = (x) 1 (n+N c )+x 2 (n+N C ))mod2,x 1 (n+31)＝(x 1 (n+3)+x 1 (n))mod2,x 2 (n+31)＝(x 2 (n+3)+x 2 (n+2)+x 2 (n+1)+x 2 (n)) mod2, wherein: n (N) c ＝1600，x 1 (0)＝1，x 1 (n)＝0，n＝1,2,...,30，x 2 (n) is represented by c init The decision is made that,
Figure BDA0004115810280000034
wherein:
Figure BDA0004115810280000035
representing the number of symbols in each slot, +.>
Figure BDA0004115810280000036
The number of slots per frame when the time subcarrier spacing is mu
Figure BDA0004115810280000037
n SCID Given by DCI or 0. The reference signal is generated by the following steps: />
Figure BDA0004115810280000038
Figure BDA0004115810280000039
J at this time represents an imaginary number. Mapping it onto RB according to mapping rules, i.e. +.>
Figure BDA00041158102800000310
Figure BDA00041158102800000311
j=0, 1, …, -1, wherein: />
Figure BDA0004115810280000041
These parameters are used to determine the mapping location and specific values can be obtained by querying tables in the protocol. Finally, a DMRS alternative set Z is generated in consideration of the size and location of resource blocks that the base station may allocate, for example, allocating only the first RB for use by the user, or allocating the last two RBs to the user.
The semi-blind interference detection comprises the following steps:
step 1, judging whether interference exists or not: the base station side calculates the average RB power of the received signal when the received signal only contains AWGN (Additive white Gaussian noise, additive Gaussian white noise), and calculates the average RB power of the received signal after the received signal is received, when the average RB power of the received signal is larger than the average RB power of the received signal only containing AWGN, an interference signal exists in the received signal, and step 2 is executed, otherwise, no interference exists.
Step 2, judging whether the interference is strong or not: after cross-correlating the received signal with the candidate set, the method comprises the steps of: comparing the maximum value with a threshold value, namely, carrying out cross-correlation calculation on the received signal with the AWGN only and the alternative set, and obtaining a cross-correlation value when the cumulative distribution function is 99.9%, and carrying out step 3 when the cumulative distribution function is greater than the threshold value, wherein strong interference exists in the received signal, specifically: cross-correlating the alternative set with the received signal with AWGN only to obtain a cross-correlation value of
Figure BDA0004115810280000042
Figure BDA0004115810280000043
Wherein: n (N) r Is the number of receiving antennas, N RB Is the number of RBs occupied, < >>
Figure BDA0004115810280000044
Is the number of subcarriers in one RB, < +.>
Figure BDA0004115810280000045
Is the signal which is received by the a-th antenna and only contains AWGN, Z i The ith pilot sequence in the finger set is calculated to obtain C 0 The cross-correlation value at 99.9% of the cumulative distribution function is used as the threshold value. After determining the threshold value, when a signal is received, the cross-correlation value of the received signal and the candidate set is calculated +.>
Figure BDA0004115810280000046
Wherein: />
Figure BDA0004115810280000047
For the received pilot signal, the maximum value is compared with a threshold value to determine whether it is strong interference.
Step 3, eliminating interference pilot signals: channel estimation is carried out according to the DMRS sequence corresponding to the obtained strong interference information, and the channel estimation is eliminated from the pilot signal of the received signal, specifically: when the pilot frequency sequence of the strong interference obtained in the step 2 is Z s Obtaining channel parameters between the interference and the base station by channel estimation
Figure BDA0004115810280000048
Then eliminating from the received signal to obtain the residual interference pilot signal +.>
Figure BDA0004115810280000049
Preferably, by repeating steps 1 to 3, a plurality of interferences in the received signal can be sequentially eliminated from strong to weak.
Preferably, when there is no interference in the received signal and the result obtained in step 1 is determined to be interference, the false alarm probability is updated
Figure BDA00041158102800000410
Preferably, when there is interference in the received signal and the result obtained in step 1 is determined to be no interference, the missing detection probability is updated
Figure BDA00041158102800000411
The interference elimination of the data signal is realized by the following modes: pilot sequence Z obtained by detection according to semi-blind interference parameters s Channel estimation is performed
Figure BDA00041158102800000412
Then the MMSE-IRC algorithm is carried out to obtain the recovered target data signal of +.>
Figure BDA00041158102800000413
Wherein: y is Y da Is the received data signal, N I E Is the number of interfering users>
Figure BDA0004115810280000051
Is the estimated channel parameter, σ 2 Is the variance of the noise.
Through specific practical experiments, in a cellular network coexisting in 7 cells, wherein the center is a target cell, the physical cell number is set to 0, the surrounding adjacent 6 cells are interference cells, the cell numbers are respectively set to 1-6, and the cells use the same frequency, so that the edge users of the adjacent cells may generate uplink interference to the base station of the target cell. The base stations in the cell are all arranged in the center of the cell, the number of antennas is 8, and the number of antennas of all users is set to be 1. The system bandwidth is set to 20M and the number of resource blocks is 100. According to the mapping rule of 3GPP, when the configuration type is type 1, the number of antenna ports is 4; when configured as type2, there are 6 antenna ports.
Because the signal received by the target base station can obtain the related information of strong interference and the false alarm missing detection probability through the semi-blind interference detection module, the false alarm and missing detection probability of the experimental test module are designed first, and as shown in fig. 3, the missing detection probability of three interference signals under different power levels is detected when the dry-to-noise ratio is-5-15 dB. The power of the interference signal 1, the power of the interference signal 2 and the power of the interference signal 3 in the figure are set from large to small, and obviously accords with the rule that the larger the power of the interference is, the smaller the detection omission probability is. As shown in fig. 4, the false alarm probability is-5-15 dB, that is, the probability that interference can be detected when there is no interference.
The present invention was compared with other schemes. The abscissa is the signal-to-interference-and-noise ratio, and the ordinate is the performance complexity ratio, so that the lower the MSE meets the requirement, the better the calculation complexity FLOP of the system, namely the larger the ordinate, the better. As shown in fig. 5, in order to compare the performance of the signal-to-interference-and-noise ratio of-5-15 dB, the performance of the invention is superior as proved by experiments by comparing the invention with an interference elimination scheme and a poor search scheme based on semi-blind detection respectively.
Compared with the prior art, the invention fully utilizes the user state information in the cell, predicts through the LSTM model, designs an experimental scheme when the Mean Square Error (MSE) of the recovered target signal is least satisfied, and reduces the calculation complexity of the system as much as possible. Better performance can be obtained at the same SINR.
The foregoing embodiments may be partially modified in numerous ways by those skilled in the art without departing from the principles and spirit of the invention, the scope of which is defined in the claims and not by the foregoing embodiments, and all such implementations are within the scope of the invention.

Claims (10)

1. The uplink interference elimination method based on low complexity is characterized in that when the interference elimination method is located in an interfered time window, the difference degree of active state information of all interfered users at the moment and the last moment is calculated, namely the proportion of newly added interfered users to original interfered users is calculated, and when the difference degree is too high, semi-blind interference detection is carried out on the newly added interfered users to obtain interference data signals; and obtaining channel parameters between the target data signal and all interference data signals and the base station through channel estimation, improving the accuracy of MIMO equalization by using a minimum mean square error interference suppression merging algorithm, and recovering the target user data signal, thereby realizing interference elimination of the target user data signal.
2. The low complexity based uplink interference cancellation method according to claim 1, wherein by predicting future interfering user state information, comparing with the current time to ignore newly added interference, only the same interfering users are considered; the related interference information at the current moment is directly used subsequently, and semi-blind interference detection is not needed, so that the calculation complexity of the system is greatly reduced.
3. The low complexity uplink interference cancellation method according to claim 1, wherein the interfered time window is obtained by: the base station side respectively establishes an LSTM model for each user according to the historical data of all user states to predict the active state information of the user, and judges the interfered time window T= { T of the target user to be interference eliminated according to the active state information i (s i ,l i )|i∈N * -wherein s i Is the T i Start index of each time window, l i Is the T i The length of the individual time windows; first time window T 1 (s 1 ,l 1 ) Calculation time s 1 ＝n 1 ,
Figure FDA0004115810270000011
Figure FDA0004115810270000012
Figure FDA0004115810270000013
ρ 0 The state information of the target user is expanded into ρ 0 ＝[ρ 0 (1),ρ 0 (2),...,ρ 0 (j),...,ρ 0 (N)]，0<j,k≤N,j∈Z,k∈Z；
J=n when calculating the second time window 1 +m 1 Obtaining s 2 ＝n 2 Calculate l 2 Time k=n 2 The remaining calculations and so on.
4. The low complexity uplink interference cancellation method of claim 1 wherein the alternative set of pilot DMRS is obtained by: the base station side generates all pseudo random sequences by using the inherent information of the cell and the adjacent cell, namely the physical cell number PCI, the pseudo random sequences regenerate reference signals and are mapped to resource blocks, and finally, the pilot frequency DMRS alternative set is generated on all possible RBs according to the reference signals, specifically:
(1) generating a pseudo-random sequence according to 3GPP protocol 38.211: c (n) = (x) 1 (n+N c )+x 2 (n+N C ))mod2,x 1 (n+31)＝(x 1 (n+3)+x 1 (n))mod2,x 2 (n+31)＝(x 2 (n+3)+x 2 (n+2)+x 2 (n+1)+x 2 (n)) mod2, wherein: n (N) c ＝1600，x 1 (0)＝1，x 1 (n)＝0，n＝1,2,...,30，c init ＝
Figure FDA0004115810270000021
Wherein: />
Figure FDA0004115810270000022
Representing the number of symbols in each slot, +.>
Figure FDA0004115810270000023
The number of slots per frame when the time subcarrier spacing is mu
Figure FDA0004115810270000024
n SCID Given by DCI or 0; reference signal->
Figure FDA0004115810270000025
Figure FDA0004115810270000026
j represents an imaginary number;
(2) mapping it onto RBs according to mapping rules, i.e
Figure FDA0004115810270000027
Figure FDA0004115810270000028
Wherein: for determining the mapping position w f (k′),w t (l′),k′,l′,/>
Figure FDA0004115810270000029
All are obtained through inquiry protocol;
(3) the DMRS alternative set Z is generated in consideration of the resource block size and location that the base station may allocate.
5. The low complexity uplink interference cancellation method according to claim 1, wherein the semi-blind interference detection includes:
step 1, judging whether interference exists or not: the base station side calculates the average RB power of the received signal when the received signal only contains AWGN, and calculates the average RB power of the received signal after the received signal is received, when the average RB power of the received signal is larger than the average RB power of the received signal only contains AWGN, an interference signal exists in the received signal and the step 2 is executed, otherwise, no interference exists;
step 2, judging whether the interference is strong or not: after cross-correlating the received signal with the candidate set, the method comprises the steps of: comparing the maximum value with a threshold value, namely, carrying out cross-correlation calculation on the received signal with the AWGN only and the alternative set, and obtaining a cross-correlation value when the cumulative distribution function is 99.9%, and carrying out step 3 when the cumulative distribution function is greater than the threshold value, wherein strong interference exists in the received signal, specifically: will be an alternativeThe set is cross-correlated with the received signal with AWGN only, and the obtained cross-correlation value is
Figure FDA00041158102700000210
Figure FDA00041158102700000211
Wherein: n (N) r Is the number of receiving antennas, N RB Is the number of RBs occupied, < >>
Figure FDA00041158102700000212
Is the number of subcarriers in one RB, < +.>
Figure FDA00041158102700000213
Is the signal which is received by the a-th antenna and only contains AWGN, Z i The ith pilot sequence in the finger set is calculated to obtain C 0 The cross-correlation value when the cumulative distribution function is 99.9% is taken as a threshold value; when a signal is received, calculating the cross-correlation value of the received signal and the alternative set>
Figure FDA00041158102700000214
Figure FDA00041158102700000215
Wherein: />
Figure FDA00041158102700000216
Comparing the maximum value with a threshold value for the received pilot signal, and judging whether the pilot signal is strong interference or not;
step 3, eliminating interference pilot signals: channel estimation is carried out according to the DMRS sequence corresponding to the obtained strong interference information, and the channel estimation is eliminated from the pilot signal of the received signal, specifically: when the pilot frequency sequence of the strong interference obtained in the step 2 is Z s Obtaining channel parameters between the interference and the base station by channel estimation
Figure FDA00041158102700000217
Then eliminating from the received signal to obtain the residual interference pilot signal +.>
Figure FDA00041158102700000218
6. The low complexity uplink interference cancellation method according to claim 5, wherein multiple interference in the received signal can be sequentially cancelled from strong to weak by repeating steps 1 through 3.
7. The low complexity uplink interference cancellation method according to claim 1, wherein the false alarm probability is updated when no interference exists in the received signal and the result obtained in step 1 is determined to be interference
Figure FDA0004115810270000031
When interference exists in the received signal and the result obtained in the step 1 is judged to be no interference, updating the missing detection probability
Figure FDA0004115810270000032
Figure FDA0004115810270000033
8. The low complexity uplink interference cancellation method according to claim 5, wherein the interference cancellation of the data signal is achieved by: pilot sequence Z obtained by detection according to semi-blind interference parameters s Channel estimation is performed
Figure FDA0004115810270000034
Then the MMSE-IRC algorithm is carried out to obtain the recovered target data signal of +.>
Figure FDA0004115810270000035
Figure FDA0004115810270000036
Wherein: y is Y da Is the received data signal, N I E Is the number of interfering users and,
Figure FDA0004115810270000037
is the estimated channel parameter, σ 2 Is the variance of the noise.
9. A system for implementing the low complexity uplink interference cancellation method of any one of claims 1-8, comprising: the system comprises a prediction module, a semi-blind interference parameter detection module and an interference elimination module, wherein: the prediction module establishes an LSTM model for each user by utilizing historical data of all user states, predicts whether the state of the user is active or not after a period of time, namely, whether the user interacts with a base station or other equipment or not, selects a proper time window T according to a predicted result, and eliminates interference suffered by a target user in the time window; the semi-blind interference parameter detection module carries out cross-correlation on the received signal and a demodulation reference signal alternative set generated by a base station side, judges the existence of strong interference by using a cross-correlation value by adopting a method combining detection and continuous interference elimination, and obtains corresponding strong interference information; the interference elimination module uses the detected interference information as prior information to perform channel estimation and MIMO equalization, so that a more accurate target signal is recovered, and the interference elimination effect is realized;
the strong interference information includes: the location and length of the resource block, the symbol length, the type, and the transmit port.
10. The system of claim 9, wherein the semi-blind interference parameter detection module comprises: the device comprises an alternative set generating unit, an interference existence detecting unit and a strong interference detecting unit, wherein: the method comprises the steps that an alternative set generating unit generates pilot frequency in each cell by utilizing ID (identification) number information of a target cell and adjacent cells according to a pilot frequency generating mode in a 3GPP (third generation protocol), and generates all possible pilot frequency sequences according to the size and the position of a possibly used resource block, so that an alternative set is obtained; the interference existence detection unit calculates power according to the received pilot signal, compares the power with the power when only Gaussian white noise exists, and judges whether interference exists in the received signal; and the strong interference detection unit performs cross-correlation operation on the received pilot signal and the alternative set, and compares the pilot signal with a set threshold value to obtain a pilot sequence used by strong interference.
CN202310218350.4A 2023-03-09 2023-03-09 Method for eliminating uplink interference with low complexity in multi-cell cellular network Pending CN116232525A (en)

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