A method and system for distributing communication signals with deep coverage

Abstract

The present invention relates to the field of transmission technology, and in particular to a method and system for allocating communication signals with deep coverage, the method comprising the steps of: obtaining historical channel estimation parameters of each pilot signal transmitted at the current moment; determining interference difference values ​​of different pilot signals during transmission based on the historical channel estimation parameters; determining predicted channel estimation parameters based on the interference difference values; and determining the target transmission frequency of each pilot signal based on the predicted channel estimation parameters and the historical channel estimation parameters. Through the method for allocating communication signals with deep coverage of the present invention, the coverage range of communication signals can be significantly improved, the signal transmission strategy is optimized, the energy consumption of the communication system is reduced, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of transmission technology, and in particular to a method and system for allocating communication signals with deep coverage. Background Art

[0002] With the rapid development of wireless communication technology, mobile users have an increasing demand for high-speed, wide-coverage communications. In particular, in 5G and future communication networks, deep coverage and efficient energy management have become the focus of the industry. The coverage range and signal strength of mobile communication networks directly affect the user experience, especially in areas where signals are weak, such as indoors and underground. Traditional signal coverage solutions often cannot meet the needs of deep coverage. At the same time, as the density of communication equipment increases, problems such as signal interference and energy consumption are becoming more prominent. How to reasonably adjust the distribution and transmission frequency of communication signals based on the dynamic changes in user channel status has become a technical challenge in deep coverage scenarios.

[0003] Existing communication signal allocation strategies usually fail to fully consider the differences in channel stability of different users. In actual communication environments, due to factors such as terrain, user mobility, and environmental interference, the user's channel state will fluctuate significantly, especially for users with fast moving speeds or in areas with weak signal coverage, the attenuation and phase changes of their channels will be more drastic. In current wireless communication systems, energy-saving optimization is mostly focused on the transmission power of pilot signals, and the power is adjusted to save energy. However, the existing technology does not fully consider the differences in the stability of user channel states, and rarely dynamically adjusts the transmission frequency of pilot signals. In most cases, the transmission frequency of pilot signals is fixed, regardless of how the user's channel state fluctuates. This will cause some users to frequently send pilot signals when their channel states are stable, resulting in unnecessary energy waste; when the channel states of other users are unstable, the fixed transmission frequency cannot meet the actual communication needs. Summary of the invention

[0004] In order to solve the technical problem of how to reduce the energy consumption of communication while meeting the communication needs of users, the purpose of the present invention is to provide a communication signal distribution method and system with deep coverage, and the technical solution adopted is as follows:

[0005] The present invention provides a communication signal distribution method with deep coverage, the method comprising:

[0006] Obtaining historical channel estimation parameters of each pilot signal transmitted at the current moment;

[0007] Determining interference difference values ​​of different pilot signals during transmission based on the historical channel estimation parameters;

[0008] Determining a predicted channel estimation parameter based on the interference difference value;

[0009] A target transmission frequency of each pilot signal is determined according to the predicted channel estimation parameter and the historical channel estimation parameter.

[0010] Further, the historical channel estimation parameters include: path information of the pilot signal;

[0011] The step of determining interference difference values ​​of different pilot signals during transmission based on the historical channel estimation parameters includes:

[0012] Based on the path information, determining the delay difference, signal gain difference and phase change difference between any two pilot signals;

[0013] The interference difference value between the two pilot signals is calculated by using the delay difference, the signal gain difference and the phase change difference.

[0014] Furthermore, the path information includes: the number of paths, the signal power of the pilot signal of each path, and the signal delay, signal gain and phase change of each path;

[0015] The step of determining the delay difference, signal gain difference and phase change difference between any two pilot signals based on the path information includes:

[0016] The delay difference between any two pilot signals is calculated using the number of paths, the signal power of the pilot signal of each path, and the signal delay of each path;

[0017] The signal gain difference between any two pilot signals is calculated using the number of paths, the signal power of the pilot signal of each path, and the signal gain of each path;

[0018] The phase change difference between any two pilot signals is calculated using the number of paths, the signal power of the pilot signal of each path, and the phase change of each path.

[0019] Further, based on the interference difference value, the step of determining the predicted channel estimation parameter includes:

[0020] Obtain the pilot signal in the most recent historical period at the current moment and perform cluster analysis to obtain a cluster analysis result;

[0021] Determining the degree of outlier of each pilot signal according to the cluster analysis result and the interference difference value;

[0022] Determining a prediction index of each pilot signal according to the outlier degree;

[0023] The prediction index and the EWMA prediction model are used to determine the prediction channel estimation parameters corresponding to the next moment at the current moment.

[0024] Further, the step of obtaining the pilot signal in the most recent historical period at the current moment and performing cluster analysis to obtain the cluster analysis result includes:

[0025] Obtain the pilot signal in the most recent historical period at the current moment and cluster it using the DBSCAN clustering algorithm;

[0026] Among them, in the DBSCAN clustering parameters, the preset value of the clustering radius is that the minimum number of samples in the lower decile of each interference difference value is 5.

[0027] Further, the step of determining the degree of outlier of each pilot signal according to the cluster analysis result and the interference difference value includes:

[0028] Determine the directional interference difference value between each pilot signal and the pilot signal at the center of the cluster where it is located from the cluster analysis result;

[0029] The degree of outlier of each pilot signal is calculated using the interference difference value, the number of pilot signals and the directional interference difference value.

[0030] Further, the step of determining the prediction index of each pilot signal according to the outlier degree includes:

[0031] The prediction index of each pilot signal is calculated using the outlier degree, the preset minimum prediction index and the preset maximum prediction index.

[0032] Furthermore, after the step of determining the predicted channel estimation parameter corresponding to the next moment at the current moment by using the prediction index and the EWMA prediction model, the method further includes:

[0033] The corrected predicted channel estimation parameter at the next moment is calculated by using the number of pilot signals, the outlier degree and the predicted channel estimation parameter at the next moment.

[0034] Further, the step of determining the target transmission frequency of each pilot signal according to the predicted channel estimation parameter and the historical channel estimation parameter includes:

[0035] Determining an average value of the historical channel estimation parameters;

[0036] Calculating and obtaining a difference value between the predicted channel estimation parameter and the average value of the historical channel estimation parameter;

[0037] The target transmission frequency of each pilot signal is calculated using the difference value, the initial transmission frequency of the pilot signal and the preset adjustment coefficient.

[0038] The present invention also provides a communication signal distribution system with deep coverage, the system is used to implement the communication signal distribution method with deep coverage as described in any one of the above items; the system comprises:

[0039] A signal feedback module is used to obtain the historical channel estimation parameters of each pilot signal transmitted at the current moment;

[0040] A comparison and analysis module, used to determine the interference difference value of different pilot signals during the transmission process based on the historical channel estimation parameters;

[0041] A channel prediction module, used to determine a predicted channel estimation parameter based on the interference difference value;

[0042] The frequency adjustment module is used to determine the target transmission frequency of each pilot signal according to the predicted channel estimation parameter and the historical channel estimation parameter.

[0043] The present invention has the following beneficial effects:

[0044] The present invention predicts and dynamically adjusts the transmission frequency of pilot signals based on the interference differences of different pilot signals by analyzing the historical channel estimation parameters of each pilot signal in real time, which can significantly improve the coverage of communication signals, especially in areas with weak signal coverage, and ensure the quality of user communication. Secondly, optimizing the signal transmission strategy reduces the energy consumption of the communication system and improves the efficiency of energy use. At the same time, real-time monitoring of the channel status enhances the stability of the signal and improves the overall communication quality. In addition, the communication system can intelligently allocate channel resources, adapt to changes in different users and environments, enhance flexibility and scalability, and ultimately improve network capacity under limited spectrum resources, support more users to access at the same time, and meet the high performance requirements of modern communication systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A flowchart of a method for allocating communication signals with deep coverage provided by an embodiment of the present invention;

[0047] Figure 2 A detailed flow chart of step S3 in a method for allocating communication signals with deep coverage provided by an embodiment of the present invention;

[0048] Figure 3A detailed flow chart of step S4 in a method for allocating communication signals with deep coverage provided by an embodiment of the present invention;

[0049] Figure 4 A schematic diagram of the structure of the hardware operating environment of the deep coverage communication signal distribution device involved in the embodiment of the present invention;

[0050] Figure 5 It is a schematic diagram of the framework structure of the deep coverage communication signal distribution system involved in the embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a deep coverage communication signal distribution method proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0052] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0053] A specific scheme of a communication signal allocation method with deep coverage provided by the present invention is described in detail below with reference to the accompanying drawings.

[0054] Embodiment 1:

[0055] For a communication signal distribution method with deep coverage provided by the present invention, please refer to Figure 1 , which shows a flowchart of the steps of a method for allocating communication signals with deep coverage provided by an embodiment of the present invention.

[0056] The method comprises:

[0057] Step S1, obtaining historical channel estimation parameters of each pilot signal transmitted at the current moment;

[0058] In a communication system, when each user equipment (UE) communicates with a base station (BS), the base station will periodically send pilot signals, and the user equipment measures the current channel status by receiving these pilot signals. The specific operations include: the base station sends pilot signals regularly, and the user equipment feeds back the channel estimation results (including multiple channel estimation parameters) to the base station after receiving the pilot signals; the channel estimation parameters include channel gain, signal delay, signal scattering caused by multipath effects, and phase change, etc.

[0059] Based on the channel estimation parameters acquired in real time, the historical channel estimation parameters of each pilot signal within the most recent historical period from the current moment can be recorded and saved. The historical period can be set as needed, such as 5 minutes.

[0060] Step S2, determining interference difference values ​​of different pilot signals during transmission based on the historical channel estimation parameters;

[0061] For the following scenarios:

[0062] In subway systems or underground cable tunnels, tunnels, underground stations, and cable tunnels are mostly made of reinforced concrete, which will greatly attenuate or even completely shield wireless signals from the surface. Wireless signals have difficulty penetrating these obstacles, resulting in the inability to effectively transmit signals from ground base stations underground. Therefore, a dedicated signal distribution system is required to provide deep coverage to ensure normal communication. However, electromagnetic interference generated by large substations or power supply systems can affect multi-band signal transmission in nearby communication systems.

[0063] The electromagnetic noise interference generated by the above-mentioned devices usually affects a wider frequency range, which in turn causes the channel gains of signals on multiple frequencies to exhibit similar characteristics, making the fading characteristics of multiple signals in the frequency band tend to be consistent within the frequency band.

[0064] Based on this or similar scenarios, the interference difference values ​​(interference variability) of different pilot signals during transmission can be determined by analyzing historical channel estimation parameters of pilot signals of different frequencies.

[0065] In a preferred embodiment, the historical channel estimation parameters include: path information of the pilot signal; the step S2 includes:

[0066] Based on the path information, determining the delay difference, signal gain difference and phase change difference between any two pilot signals;

[0067] The interference difference value between the two pilot signals is calculated by using the delay difference, the signal gain difference and the phase change difference.

[0068] Specifically, the path information includes: the number of paths, the signal power of the pilot signal of each path, and the signal delay, signal gain and phase change of each path;

[0069] The step of determining the delay difference, signal gain difference and phase change difference between any two pilot signals based on the path information includes:

[0070] The delay difference between any two pilot signals is calculated using the number of paths, the signal power of the pilot signal of each path, and the signal delay of each path;

[0071] The signal gain difference between any two pilot signals is calculated using the number of paths, the signal power of the pilot signal of each path, and the signal gain of each path;

[0072] The phase change difference between any two pilot signals is calculated using the number of paths, the signal power of the pilot signal of each path, and the phase change of each path.

[0073] Record any time The frequency of the signal sent by the base station is After the signal reaches the receiving end, in the channel estimation result, the signal delay, signal gain and phase change can be expressed as , , Based on the channel estimation results under different channels, the following analyzes the interference differences of different channels during transmission.

[0074] (1) Consider the pilot signals with different frequencies in a historical period of 5 minutes (take 5 minutes as an example, the historical period can be set as needed), and record the same signal base station at time The frequencies sent in the previous 5 minutes are For the signal data sent at the same time, its aliased signal will be subject to different interferences, and the signal will be transmitted to the receiving end through multiple different paths. When multiple signal paths reach the receiving end, due to the different path lengths, the signal arrival time is also different, resulting in time delay differences. Considering the power differences of different signals, the following time recording The frequency of the signal sent by the base station is The signal reaches The weighted average delay of each receiving device is :

[0075] ;

[0076] in, Indicates time The lower frequency is The transmitted signal reaches The number of paths of the receiving device (obtained according to the channel estimation result, which can be obtained through the autocorrelation delay estimation parameter); Indicates The signal power of each path; Indicates The delay of the signal under each path (corresponding signal delay).

[0077] (2) Considering that the aliased signal will be received at multiple different receiving ends, if the delays of two signals of different frequencies when arriving at multiple receiving ends are similar, the degree of interference between the two signals is also highly similar. Therefore, at time Next, let's calculate two different frequencies and Delay differences between signals for:

[0078] ;

[0079] in, Indicates the number of receiving devices (same as receiving end, user equipment); Indicates at time The sending frequency is The signal reaches The weighted average delay of each receiving device; Indicates at time The sending frequency is The signal reaches The weighted average delay of each receiving device.

[0080] Considering that in different signal transmission processes, a single frequency signal may contain information required by multiple receiving devices. For example, in non-orthogonal multiple access technology, the signals of different receiving ends can be distinguished by power. Therefore, the same analysis process as above can be used to obtain two frequencies. and The difference in signal gain and phase change and That is, for the form of the above formula, it is only necessary to replace the signal delay parameter with the corresponding signal gain parameter and phase change parameter.

[0081] (3) Then, the channel estimation results of different pilot signals are combined to measure the difference between the signals at two different frequencies. Then, the two frequencies at time t are recorded as and The difference between the following signals for:

[0082] ;

[0083] in, Indicates the signal gain difference (value) of two frequency signals; Indicates the difference (value) of the phase change of two frequency signals. Indicates the delay difference (value) between two frequency signals.

[0084] (4) Considering that there will be multiple transmissions of pilot signals in the historical period, the two frequencies are recorded according to the difference between the signals at different times. and The interference difference value of the pilot transmission signal in the historical period under for:

[0085] ;

[0086] in, Indicates the time set of sending pilot signals in the historical period, here, for example, 5 minutes is taken as above; express The number of elements in ; Indicates the time point corresponding to the current moment; Represents the difference between the signals at two frequencies at any historical time t.

[0087] Step S3, determining a predicted channel estimation parameter based on the interference difference value;

[0088] The difference in interference received by the pilot signals at the two different frequencies during the historical period can characterize the difference in the signal propagation environment. When the interference difference between the two frequencies is small, when using the pilot signals at various historical moments for prediction, it is also possible to comprehensively consider signals with smaller interference differences at adjacent or similar frequencies. That is, two pilot signals with adjacent frequencies and interference difference values ​​less than a preset threshold (set as needed) can be given priority for prediction, so as to improve the accuracy of subsequent interference predictions and obtain more accurate predicted channel estimation parameters.

[0089] Please refer to Figure 2 , step S3 specifically includes:

[0090] Step S30, obtaining the pilot signal in the most recent historical period at the current moment and performing cluster analysis to obtain a cluster analysis result;

[0091] Step S30 specifically includes:

[0092] Obtain the pilot signal in the most recent historical period at the current moment and cluster it using the DBSCAN clustering algorithm;

[0093] Among them, in the DBSCAN clustering parameters, the preset value of the clustering radius is the lower decile of each interference difference value, and the minimum number of samples is 5.

[0094] Considering the interference difference between the signals at the above two different frequencies, the pilot signals in the time period of the historical cycle are clustered. The pilot signals at multiple frequencies in the historical cycle are clustered by the traditional DBSCAN clustering algorithm. Among the DBSCAN clustering parameters, the recommended value of the clustering radius is multiple The lower decile of , the minimum number of samples is 5; thus, the clustering results of pilot signals at multiple different frequencies can be obtained.

[0095] Step S31, determining the outlier degree of each pilot signal according to the cluster analysis result and the interference difference value;

[0096] Taking into account that in environments such as subways, multiple receiving ends may move at high speed within the signal coverage area. At this time, the transmission signal at the same frequency will change the signal of its receiving end within the historical period. When multiple receiving ends change, the signal at some frequencies will be interrupted or regenerated, but due to the difference in receiving ends, the signal transmission path will also change accordingly. At this time, the pilot signal at the corresponding frequency will increase the interference difference value between the signals at other frequencies, resulting in isolated points in the clustering results. The prediction of the channel parameters of the signals at these frequencies cannot make good use of the correlation between the signals at similar frequencies. Therefore, it is necessary to combine the clustering analysis results and the interference difference value to determine the degree of outlier of each pilot signal, so as to further obtain more accurate prediction of the channel parameters.

[0097] Step S31 specifically includes:

[0098] Determine the directional interference difference value between each pilot signal and the pilot signal at the center of the cluster where it is located from the cluster analysis result;

[0099] The degree of outlier of each pilot signal is calculated using the interference difference value, the number of pilot signals and the directional interference difference value.

[0100] Record The degree of outlier of the pilot signal at the frequency for:

[0101] ;

[0102] in, Indicates The directional interference difference (value) between the pilot signal at the frequency and the pilot signal at the cluster center where it is located; Indicates The number of samples in the cluster where the pilot signal belongs to at the frequency (the number of pilot signals of different frequencies); Indicates two frequencies within the same cluster and The interference difference value between the pilot signals under

[0103] Step S32, determining a prediction index of each pilot signal according to the outlier degree;

[0104] Step S32 specifically includes:

[0105] The prediction index of each pilot signal is calculated using the outlier degree, the preset minimum prediction index and the preset maximum prediction index.

[0106] Considering that pilot signals at different frequencies perform differently in clustering results, when the outlier degree of a pilot signal at a certain frequency is high, there is no similar interference signal in the historical period to assist it in estimating the channel parameters at future moments. The change of its channel parameters is greatly affected by its own historical data, and it should be given a relatively small value, otherwise a relatively large value.

[0107] Based on this, the frequency is The prediction index of the pilot signal The value of is:

[0108] ;

[0109] in, and They represent the upper and lower limits of the prediction index (i.e., the maximum prediction index and the minimum prediction index), respectively. The recommended values ​​are 0.99 and 0.75 to prevent the values ​​from being too large or too small. Indicates frequency The outlier degree of the lower pilot signal.

[0110] Step S33, using the prediction index and the EWMA prediction model, determine the predicted channel estimation parameter corresponding to the next moment at the current moment.

[0111] According to the degree of outliers of the pilot signals at each frequency at the historical moment, the channel estimation parameters at the subsequent moment are predicted. Based on the historical channel estimation parameters of the pilot signals at a single frequency, the traditional EMMA (Exponentially Weighted Moving-Average) exponential moving weighted average algorithm is considered to predict the channel estimation parameter results at the subsequent moment. However, the traditional EMMA selects different exponents, which has a greater impact on the prediction results. Taking the signal gain data at multiple historical moments as an example, the signal delay (here it can refer to the average delay) and phase change are the same; the traditional EWMA prediction model is as follows:

[0112] ;

[0113] in, Represents the pending prediction index, as As increases, the prediction results are more affected by the historical data at adjacent moments; Indicates at time The frequency of the signal sent is The signal gain, Indicates at time The frequency of the signal sent is The signal gain, Indicates at time The frequency of the signal sent is The signal gain of , and so on, here is the signal gain of the signal frequency in the historical period The signal is divided into N moments; Indicates time The signal gain prediction value corresponding to the next moment; It also indicates the number of pilot signals in the selected historical period, that is, each time a pilot signal is transmitted, its time is recorded at the same time.

[0114] By bringing the above different prediction indices into the traditional EWMA prediction model, we can obtain the prediction results of each channel estimation parameter. The predicted values ​​of the signal gain and phase change and average delay of the pilot signal are , , .

[0115] After step S33, the method further includes:

[0116] The corrected predicted channel estimation parameter at the next moment is calculated by using the number of pilot signals, the outlier degree and the predicted channel estimation parameter at the next moment.

[0117] Considering that the traditional prediction still only considers the data change of itself, when a single pilot signal generates some abnormal data values ​​near the current time, it will lead to a large error in the prediction result. Therefore, it is considered to use the preliminary prediction results of the pilot signal in the same cluster in the above clustering results to further correct the above prediction value. Taking the signal gain data as an example, the other channel estimation parameters are similar; the frequency is recorded as The corrected prediction value of the pilot signal at the next moment is:

[0118] ;

[0119] in, Indicates frequency The number of samples in the cluster where the lower pilot signal is located; represents the normalization function; Indicates frequency The outlier degree of the lower pilot signal; Indicates frequency The signal gain prediction value of the next pilot signal at the next moment.

[0120] Step S4: determining a target transmission frequency of each pilot signal according to the predicted channel estimation parameter and the historical channel estimation parameter.

[0121] Please refer to Figure 3 , step S4 specifically includes:

[0122] Step S40, determining an average value of the historical channel estimation parameter;

[0123] Step S41, calculating and obtaining a difference value between the average value of the predicted channel estimation parameter and the average value of the historical channel estimation parameter;

[0124] Step S42: Calculate the target transmission frequency of each pilot signal using the difference value, the initial transmission frequency of the pilot signal and the preset adjustment coefficient.

[0125] (1) Based on the above prediction results, first calculate the average value from the historical channel estimation parameters, taking the signal gain parameter as an example:

[0126] ;

[0127] in, Indicates the number of times the pilot signal is sent in the historical moment; Representing historical moments Next, frequency The signal gain in the channel estimation result of the pilot signal.

[0128] (2) Calculate the difference between the predicted value (predicted channel estimation parameter) and the historical average value , taking the signal gain parameter as an example:

[0129] ;

[0130] in, Represents the average signal gain of multiple historical pilot signals; Represents the predicted value of the signal gain at the next moment.

[0131] (3) If the difference between the predicted value and the historical average value is small, it means that the channel state is stable and the transmission interval can be appropriately increased (the frequency can be reduced). Otherwise, it means that the channel state has changed significantly and the transmission interval needs to be shortened (the frequency needs to be increased). Then the adjusted pilot signal transmission interval is recorded as for:

[0132] ;

[0133] in, Indicates the initial sending time interval (initial transmission frequency); Indicates the preset adjustment coefficient (set according to actual system needs); , , They respectively represent the differences between the signal gain, phase change and average delay and the corresponding historical average values.

[0134] At this point, the adjusted transmission time interval (target transmission frequency) of each pilot signal is obtained.

[0135] In addition, according to the adjusted sending time interval, the base station can reduce the sending frequency of the pilot signal when the channel state is stable, thereby increasing the transmission power within a specific time, enhancing the signal strength, and further improving the coverage range. At the same time, the base station can use adaptive beamforming technology to transmit the signal in a direction to areas with weaker signals, thereby improving the signal reception quality of the target user, thereby improving the deep coverage range of the communication signal, and ensuring that users can get a good communication experience in various environments.

[0136] The present invention predicts and dynamically adjusts the transmission frequency of pilot signals based on the interference differences of different pilot signals by analyzing the historical channel estimation parameters of each pilot signal in real time, which can significantly improve the coverage of communication signals, especially in areas with weak signal coverage, and ensure the quality of user communication. Secondly, optimizing the signal transmission strategy reduces the energy consumption of the communication system and improves the efficiency of energy use. At the same time, real-time monitoring of the channel status enhances the stability of the signal and improves the overall communication quality. In addition, the communication system can intelligently allocate channel resources, adapt to changes in different users and environments, enhance flexibility and scalability, and ultimately improve network capacity under limited spectrum resources, support more users to access at the same time, and meet the high performance requirements of modern communication systems.

[0137] Embodiment 2:

[0138] The embodiment of the present invention further provides a communication signal distribution device with deep coverage. The communication signal distribution device with deep coverage may be a device with communication and data processing functions, such as a base station, a server, a computer, a workstation, and the like.

[0139] like Figure 4 As shown, Figure 4 It is a structural diagram of the hardware operating environment of the deep coverage communication signal distribution device involved in the embodiment of the present invention.

[0140] like Figure 4 As shown, the deeply covered communication signal distribution device may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display (Display), an input unit such as a control panel, and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WIFI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may optionally be a storage device independent of the aforementioned processor 1001. The memory 1005 as a computer storage medium may include a deeply covered communication signal distribution program.

[0141] Those skilled in the art will understand that Figure 4 The hardware structure shown in the figure does not constitute a limitation of the device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0142] Continue to refer to Figure 4 , Figure 4 The memory 1005 as a computer-readable storage medium may include an operating system, a user interface module, a network communication module, and a communication signal distribution program with deep coverage.

[0143] exist Figure 4 In the embodiment, the network communication module is mainly used to connect to the server and can communicate data with the server; and the processor 1001 can call the deep coverage communication signal allocation program stored in the memory 1005 and execute the steps in the above embodiments.

[0144] The hardware structure of the communication signal distribution device with deep coverage described above is used to implement various embodiments of the communication signal distribution method with deep coverage of the present invention.

[0145] Embodiment three:

[0146] In addition, the present invention also provides a communication signal distribution system with deep coverage, please refer to Figure 5 , the deep coverage communication signal distribution system comprises:

[0147] The signal feedback module A10 is used to obtain the historical channel estimation parameters of each pilot signal transmitted at the current moment;

[0148] A comparison and analysis module A20, configured to determine interference difference values ​​of different pilot signals during transmission based on the historical channel estimation parameters;

[0149] A channel prediction module A30, configured to determine a predicted channel estimation parameter based on the interference difference value;

[0150] The frequency adjustment module A40 is used to determine the target transmission frequency of each pilot signal according to the predicted channel estimation parameter and the historical channel estimation parameter.

[0151] Furthermore, the comparison and analysis module A20 is also used for:

[0152] The step of determining interference difference values ​​of different pilot signals during transmission based on the historical channel estimation parameters includes:

[0153] Based on the path information, determining the delay difference, signal gain difference and phase change difference between any two pilot signals;

[0154] The interference difference value between the two pilot signals is calculated by using the delay difference, the signal gain difference and the phase change difference.

[0155] Furthermore, the comparison and analysis module A20 is also used for:

[0156] The delay difference between any two pilot signals is calculated using the number of paths, the signal power of the pilot signal of each path, and the signal delay of each path;

[0157] The signal gain difference between any two pilot signals is calculated using the number of paths, the signal power of the pilot signal of each path, and the signal gain of each path;

[0158] The phase change difference between any two pilot signals is calculated using the number of paths, the signal power of the pilot signal of each path, and the phase change of each path.

[0159] Furthermore, the channel prediction module A30 is also used for:

[0160] Obtain the pilot signal in the most recent historical period at the current moment and perform cluster analysis to obtain a cluster analysis result;

[0161] Determining the degree of outlier of each pilot signal according to the cluster analysis result and the interference difference value;

[0162] Determining a prediction index of each pilot signal according to the outlier degree;

[0163] The prediction index and the EWMA prediction model are used to determine the prediction channel estimation parameters corresponding to the next moment at the current moment.

[0164] Furthermore, the channel prediction module A30 is also used for:

[0165] Obtain the pilot signal in the most recent historical period at the current moment and cluster it using the DBSCAN clustering algorithm;

[0166] Among them, in the DBSCAN clustering parameters, the preset value of the clustering radius is the lower decile of each interference difference value, and the minimum number of samples is 5.

[0167] Furthermore, the channel prediction module A30 is also used for:

[0168] Determine the directional interference difference value between each pilot signal and the pilot signal at the center of the cluster where it is located from the cluster analysis result;

[0169] The degree of outlier of each pilot signal is calculated using the interference difference value, the number of pilot signals and the directional interference difference value.

[0170] Furthermore, the channel prediction module A30 is also used for:

[0171] The prediction index of each pilot signal is calculated using the outlier degree, the preset minimum prediction index and the preset maximum prediction index.

[0172] Furthermore, the channel prediction module A30 is also used for:

[0173] The corrected predicted channel estimation parameter at the next moment is calculated by using the number of pilot signals, the outlier degree and the predicted channel estimation parameter at the next moment.

[0174] Furthermore, the frequency adjustment module A40 is also used for:

[0175] Determining an average value of the historical channel estimation parameters;

[0176] Calculating and obtaining a difference value between the predicted channel estimation parameter and the average value of the historical channel estimation parameter;

[0177] The target transmission frequency of each pilot signal is calculated using the difference value, the initial transmission frequency of the pilot signal and the preset adjustment coefficient.

[0178] The specific implementation of the deep coverage communication signal distribution system of the present invention is basically the same as the various embodiments of the deep coverage communication signal distribution method described above, and will not be repeated here.

[0179] In addition, the present invention also provides a computer-readable storage medium. The computer-readable storage medium of the present invention stores a communication signal allocation program with deep coverage, wherein when the communication signal allocation program with deep coverage is executed by a processor, the steps of the communication signal allocation method with deep coverage as described above are implemented.

[0180] Among them, the method implemented when the deep coverage communication signal allocation program is executed can refer to the various embodiments of the deep coverage communication signal allocation method of the present invention, and will not be repeated here.

[0181] It should be noted that the sequence of the above embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0182] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0183] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0184] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0185] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0186] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

Claims

1. A communication signal distribution method with deep coverage, characterized in that: The method comprises: Obtaining historical channel estimation parameters of each pilot signal transmitted at the current moment; Determining interference difference values ​​of different pilot signals during transmission based on the historical channel estimation parameters; Determining a predicted channel estimation parameter based on the interference difference value; Determining a target transmission frequency of each pilot signal according to the predicted channel estimation parameter and the historical channel estimation parameter; The historical channel estimation parameters include: path information of the pilot signal; The step of determining interference difference values ​​of different pilot signals during transmission based on the historical channel estimation parameters includes: Based on the path information, determining the delay difference, signal gain difference and phase change difference between any two pilot signals; Calculate the interference difference value between the two pilot signals by using the delay difference, the signal gain difference and the phase change difference; The step of determining a predicted channel estimation parameter based on the interference difference value comprises: Obtain the pilot signal in the most recent historical period at the current moment and perform cluster analysis to obtain a cluster analysis result; Determining the degree of outlier of each pilot signal according to the cluster analysis result and the interference difference value; Determining a prediction index of each pilot signal according to the outlier degree; The prediction index and the exponentially weighted moving average (EWMA) prediction model are used to determine the prediction channel estimation parameter corresponding to the next moment at the current moment.

2. The method for distributing communication signals with deep coverage according to claim 1, characterized in that: The path information includes: the number of paths, the signal power of the pilot signal of each path, and the signal delay, signal gain and phase change of each path; The step of determining the delay difference, signal gain difference and phase change difference between any two pilot signals based on the path information includes: The delay difference between any two pilot signals is calculated using the number of paths, the signal power of the pilot signal of each path, and the signal delay of each path; The signal gain difference between any two pilot signals is calculated using the number of paths, the signal power of the pilot signal of each path, and the signal gain of each path; The phase change difference between any two pilot signals is calculated using the number of paths, the signal power of the pilot signal of each path, and the phase change of each path.

3. The method for distributing communication signals with deep coverage according to claim 1, characterized in that: The step of obtaining the pilot signal in the most recent historical period at the current moment and performing cluster analysis to obtain the cluster analysis result includes: Obtain the pilot signal in the most recent historical period at the current moment and cluster it using the DBSCAN clustering algorithm; Among them, in the DBSCAN clustering parameters, the preset value of the clustering radius is the lower decile of each interference difference value, and the minimum number of samples is 5.

4. The method for distributing communication signals with deep coverage according to claim 1, characterized in that: The step of determining the degree of outlier of each pilot signal according to the cluster analysis result and the interference difference value comprises: Determine the directional interference difference value between each pilot signal and the pilot signal at the center of the cluster where it is located from the cluster analysis result; The degree of outlier of each pilot signal is calculated using the interference difference value, the number of pilot signals and the directional interference difference value.

5. The method for distributing communication signals with deep coverage according to claim 1, characterized in that: The step of determining the prediction index of each pilot signal according to the outlier degree comprises: The prediction index of each pilot signal is calculated using the outlier degree, the preset minimum prediction index and the preset maximum prediction index.

6. The method for distributing communication signals with deep coverage according to claim 1, characterized in that: After the step of determining the predicted channel estimation parameter corresponding to the next moment at the current moment by using the prediction index and the EWMA prediction model, the method further includes: The corrected predicted channel estimation parameter at the next moment is calculated by using the number of pilot signals, the outlier degree and the predicted channel estimation parameter at the next moment.

7. The method for distributing communication signals with deep coverage according to claim 1, characterized in that: The step of determining a target transmission frequency of each pilot signal according to the predicted channel estimation parameter and the historical channel estimation parameter comprises: Determining an average value of the historical channel estimation parameters; Calculating and obtaining a difference value between the predicted channel estimation parameter and the average value of the historical channel estimation parameter; The target transmission frequency of each pilot signal is calculated using the difference value, the initial transmission frequency of the pilot signal and the preset adjustment coefficient.

8. A deep coverage communication signal distribution system, characterized in that: The system is used to implement the deep coverage communication signal allocation method according to any one of claims 1 to 7; the system comprises: A signal feedback module is used to obtain the historical channel estimation parameters of each pilot signal transmitted at the current moment; A comparison and analysis module, used to determine the interference difference value of different pilot signals during the transmission process based on the historical channel estimation parameters; A channel prediction module, used to determine a predicted channel estimation parameter based on the interference difference value; A frequency adjustment module, used to determine the target transmission frequency of each pilot signal according to the predicted channel estimation parameter and the historical channel estimation parameter; The historical channel estimation parameters include: path information of the pilot signal; The step of determining interference difference values ​​of different pilot signals during transmission based on the historical channel estimation parameters includes: Based on the path information, determining the delay difference, signal gain difference and phase change difference between any two pilot signals; Calculate the interference difference value between the two pilot signals by using the delay difference, the signal gain difference and the phase change difference; The step of determining a predicted channel estimation parameter based on the interference difference value comprises: Obtain the pilot signal in the most recent historical period at the current moment and perform cluster analysis to obtain a cluster analysis result; Determining the degree of outlier of each pilot signal according to the cluster analysis result and the interference difference value; Determining a prediction index of each pilot signal according to the outlier degree; The prediction index and the exponentially weighted moving average (EWMA) prediction model are used to determine the prediction channel estimation parameter corresponding to the next moment at the current moment.

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