A 5G direct repeater background noise optimization and energy saving method

Abstract

This invention discloses a method for optimizing background noise and saving energy in 5G repeaters, relating to the field of 5G technology. It constructs a dual-constraint balance mechanism of coverage demand gain and noise constraint gain by calculating the shielding attenuation value through multi-point measurement: the coverage demand gain defines the coverage baseline, and the noise constraint gain delineates the noise red line. The constraint range is dynamically adjusted through a service priority factor. When constraints conflict, the feasible region is re-converged by adjusting the service priority factor. Finally, the legal gain is determined through a dynamic weight factor. The scheme introduces a dynamic load adjustment mechanism: it calculates the load factor of user connections and traffic in real time, maintaining the legal gain under light load, releasing gain elasticity proportionally under medium load, and releasing 90% gain to ensure coverage under heavy load. By using different gain strategies through load grading, the energy consumption of 5G repeaters can be dynamically reduced, significantly improving the network operation quality and maintenance efficiency of 5G repeaters.

Description

Technical Field

[0001] This invention relates to the field of 5G technology, specifically to a method for optimizing background noise and saving energy in 5G repeaters. Background Technology

[0002] Underground parking lots, due to their enclosed nature and reinforced concrete structure, possess a natural signal shielding structure. When external signal sources enter their interior, the signal strength decreases accordingly. 5G repeaters, as core equipment for extending coverage, amplify base station signals to cover signal blind spots within underground parking lots. However, traditional solutions have significant drawbacks. In fixed-gain mode, when link attenuation is high deep within the underground parking garage, the fixed gain cannot cover the weak signal areas deep within. If the gain is simply increased, the noise fed back from the 5G repeater to the macro base station will continuously increase. Furthermore, in scenarios with high service priority, resource allocation cannot be dynamically adjusted, resulting in insufficient coverage stability.

[0003] The core problem with existing technologies is the inability to dynamically balance background noise suppression and signal coverage requirements. Specifically, the nonlinear coupling between link attenuation and service load leads to gain adjustment mismatch. For example, when the service load in a parking garage is below 50%, traditional repeaters still operate with a fixed gain, which wastes energy and causes a decrease in base station sensitivity due to noise accumulation. The dynamic adjustment mechanism relies on preset thresholds, which are difficult to adapt to signal fluctuations in complex environments. This technical limitation directly makes it difficult for traditional repeaters to achieve a balance between signal coverage and energy efficiency optimization. Especially in the context of high energy consumption in 5G networks, this further exacerbates the operational cost pressure on operators. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes a method for optimizing background noise and saving energy in 5G repeaters, solving the problem that traditional repeaters struggle to balance signal coverage and energy efficiency.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The received power and output power are obtained, and the difference between the two is marked as the attenuation value;

[0007] The downlink transmit power and preset target signal strength of the macro base station are obtained, and the coverage requirement gain is calculated based on the downlink transmit power, target signal strength and attenuation value.

[0008] Acquire noise data and calculate the noise constraint gain based on the noise data;

[0009] Set the upper and lower limits of the coverage demand gain and the noise constraint gain, and determine the feasible upper and lower limits of the legal gain range based on the upper and lower limits of the coverage demand gain and the noise constraint gain.

[0010] The legal gain is calculated based on the feasible upper and lower limits of the legal gain interval, combined with the dynamic weighting factor.

[0011] Furthermore, the transmitted signal strength is obtained and used as the output power. The transmitted signal strength is collected at preset intervals after being attenuated by the shielding structure, and the arithmetic mean of the n signal strengths is calculated. The arithmetic mean of the n signal strengths is used as the received power, and the attenuation value is obtained by subtracting the received power from the output power.

[0012] Furthermore, the downlink transmit power of the macro base station is obtained, and the target signal strength is preset;

[0013] The input power of the 5G repeater is obtained by subtracting the attenuation value from the downlink transmit power of the macro base station, and the coverage requirement gain is obtained by subtracting the input power from the target signal strength.

[0014] Furthermore, noise data is acquired, including the upper limit of macro base station noise floor, 12.5kHz bandwidth thermal noise, 5G repeater noise figure, and donor link loss;

[0015] The uplink noise of the 5G repeater is obtained by adding the 12.5kHz bandwidth thermal noise and the noise figure of the 5G repeater.

[0016] The noise constraint gain is obtained by subtracting the uplink noise from the upper limit of the macro base station noise floor and then adding the donor link loss.

[0017] Furthermore, the lower limit of the coverage demand gain is set to the calculated value of the coverage demand gain, and the upper limit is the lower limit of the coverage demand gain plus... dB, where This is the preset signal measurement error margin. The elastic coefficient, As a business priority factor, the coverage constraint range is defined based on the upper and lower limits of the coverage demand gain;

[0018] The upper limit of the noise constraint gain is set to the calculated value of the noise constraint gain, and the lower limit is the upper limit of the noise constraint gain minus... dB, where The noise tolerance margin is defined based on the upper and lower limits of the noise constraint gain, which define the noise constraint range.

[0019] Furthermore, the maximum value of the lower bound of the coverage constraint range and the lower bound of the noise constraint range is defined as the feasible lower bound;

[0020] The minimum value between the upper limit of the coverage constraint range and the upper limit of the noise constraint range is defined as the feasible upper limit;

[0021] If the lower feasible limit is less than or equal to the upper feasible limit, a valid gain interval is defined. The upper and lower limits of the valid gain interval are the upper and lower feasible limits, respectively.

[0022] If the lower feasible limit is greater than the upper feasible limit, adjust the business priority factor.

[0023] Furthermore, if the lower limit of the coverage constraint range is greater than the upper limit of the noise constraint range, the service priority factor will be increased by 0.1;

[0024] If the lower limit of the noise constraint range is greater than the upper limit of the coverage constraint range, the service priority factor will be reduced by 0.1;

[0025] Recalculate the upper limit of coverage demand gain and the lower limit of noise constraint gain based on the adjusted business priority factor, until the feasible lower limit is less than or equal to the feasible upper limit.

[0026] Furthermore, the dynamic weighting factor is calculated based on the attenuation value and the business priority factor. The specific calculation formula is as follows:

[0027]

[0028] In the formula, As a dynamic weighting factor, This is the attenuation value. Business priority factor;

[0029] The legal gain is obtained by multiplying the value obtained by subtracting the lower feasible limit from the upper feasible limit within the legal gain interval, multiplying it by the dynamic weighting factor, and adding it to the lower feasible limit within the legal gain interval.

[0030] The dynamic gain is obtained based on the legal gain calculation.

[0031] Furthermore, real-time data and equipment parameters of the 5G repeater are obtained. The real-time data includes the number of real-time user connections and the real-time traffic. The equipment parameters are the number of full-load user connections, the full-load traffic, and the maximum gain limit that the 5G repeater can withstand.

[0032] The load factor is the minimum of real-time user connections ÷ full-load user connections and real-time traffic ÷ full-load traffic.

[0033] The load is graded and the gain is adjusted according to the magnitude of the load factor, as follows:

[0034] When the load factor is less than or equal to the load threshold N1, it is determined to be a light load, and the gain is kept at the legal gain.

[0035] When the load factor > N1, subtract N1 from the load factor and multiply by . The gain scaling factor is then obtained, where This is the preset maximum gain scaling factor. <1;

[0036] The dynamic gain is obtained by subtracting the legal gain from the maximum gain limit, multiplying the value by the gain scaling factor, and then adding the legal gain back.

[0037] Furthermore, when the load factor is greater than N1 and less than 1, it is determined to be a medium load, and the gain is adjusted to dynamic gain.

[0038] When the load factor is ≥1, it is determined to be overloaded. In this case, the gain is adjusted to the gain scaling factor = The dynamic gain.

[0039] Compared with existing technologies, it has the following advantages:

[0040] This solution proposes a method for optimizing background noise and saving energy in 5G repeaters. Through multi-dimensional technical design, it achieves synergy between precise background noise control and dynamic energy efficiency optimization, accurately quantifying shielding attenuation characteristics and providing a reliable basis for gain design. The solution avoids the random errors of single-point measurements by setting multiple measurement points deep within an underground parking lot to collect signal strength and calculate the attenuation value using the arithmetic mean. This attenuation value is directly related to the actual loss of the macro base station signal after passing through the shielding structure, ensuring that subsequent coverage requirement gain calculations are more aligned with the actual scenario, solving the problem of insufficient coverage or excessive amplification caused by attenuation estimation errors in traditional solutions. A dual-constraint balance mechanism of signal coverage and noise constraints is constructed to achieve precise control of noise backfeedback. The solution defines the coverage baseline by the coverage requirement gain, delineates the noise red line by the noise constraint gain, and then dynamically adjusts the constraint range in conjunction with the service priority factor. When constraints conflict, the feasible region is re-converged by adjusting the service priority factor, and finally, the legal gain is determined within the feasible region by a dynamic weight factor. This mechanism ensures signal coverage meets standards at the weakest point in the underground parking lot while keeping the rise in macro base station noise within operator specifications, avoiding the contradiction in traditional fixed-gain solutions where "coverage meets standards but noise exceeds limits, and noise meets standards but coverage is insufficient." Based on dynamic load adjustment of gain, energy consumption is significantly reduced. In summary, this solution, through precise attenuation quantification, dual-constraint dynamic balancing, load-aware energy saving, and service priority adaptation, achieves the triple goals of "coverage compliance, controllable noise, and optimal energy consumption" for 5G repeaters in complex scenarios, significantly improving the network operation quality and maintenance efficiency of 5G repeaters. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the method flow of the present invention; Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Please see Figure 1 This application provides a method for optimizing background noise and saving energy in 5G repeaters;

[0044] The method specifically includes the following steps:

[0045] Step 1: Obtain the output power and the received power. Subtract the received power from the output power to obtain the attenuation value. The received power is the average signal strength after attenuation by the shielding structure, and the output power is the transmitted signal strength of the signal source.

[0046] Specifically, in this example, a low-power signal source is deployed at the entrance or exit of an underground parking lot, ensuring that the signal propagation path only passes through the reinforced concrete shielding structure of the parking lot. The sweep frequency signal of the low-power signal source is set to a 5G signal in the n78 band, and the transmitted signal strength is set to -80dBm. A handheld spectrum analyzer is used deep within the underground parking lot (assuming a distance of approximately 50 meters from the entrance / exit, pre-tested as the weakest signal point), with a measurement point set every 5 meters, for a total of 3 measurement points. At each measurement point, the receiver stays for 10 seconds, recording the signal strength of the low-power signal source received by the handheld spectrum analyzer. For example, if the signal strengths received at the three measurement points are -112dBm, -110dBm, and -113dBm respectively, the arithmetic mean of these three data points yields a received power of -111.7dBm. The attenuation value is then calculated. The attenuation value is 31.7dB, which is the amount of shielding attenuation of the 5G signal in the n78 band by the parking lot.

[0047] Step 2: Obtain the downlink transmit power of the macro base station and preset the target signal strength;

[0048] The input power of the 5G repeater is obtained by subtracting the attenuation value from the downlink transmit power of the macro base station, and the coverage requirement gain is obtained by subtracting the input power from the target signal strength.

[0049] Specifically, the target signal strength is set based on the actual coverage requirements of the 5G repeater in the underground parking lot (to meet the signal requirements of the weakest point in the underground parking lot). When the downlink transmission power of the macro base station reaches the 5G repeater after being attenuated by the shielding structure, the power that arrives at this time is the input power of the 5G repeater. The required gain obtained by subtracting the input power from the target signal strength quantifies the gain that needs to be supplemented after the signal penetrates, ensuring that the coverage of the weakest point of the signal meets the standard. For example, if the downlink transmission power is -80dBm, the target signal strength is -95dBm, and the attenuation value is 30dB, then the calculated coverage required gain is 15dB.

[0050] Acquire noise data, which includes the upper limit of macro base station noise floor, 12.5kHz bandwidth thermal noise, 5G repeater noise figure, and donor link loss;

[0051] The uplink noise of the 5G repeater is obtained by adding the 12.5kHz bandwidth thermal noise and the noise figure of the 5G repeater.

[0052] The noise constraint gain is obtained by subtracting the uplink noise from the upper limit of the macro base station noise floor and then adding the donor link loss.

[0053] Specifically, the upper limit of the macro base station noise floor is the operator's specified value, such as -125dBm / 12.5kHz. The thermal noise of the 12.5kHz bandwidth is -133dBm. The noise figure of the 5G repeater is the equipment's own noise, such as 6dB. Since noise backfeedback can be offset by link attenuation, it is necessary to consider the reverse compensation of donor link loss. For example, if the donor link loss is 10dB, the noise constraint gain can be calculated to be 12dB (the maximum gain allowed for noise; exceeding this gain will increase the noise floor). By quantifying the coupling relationship between noise backfeedback and gain, the increase in macro base station noise floor caused by excessive gain is avoided.

[0054] Step 3: Set the lower limit of the coverage demand gain to the calculated value of the coverage demand gain, and the upper limit to the lower limit of the coverage demand gain plus... dB, where This is a preset signal measurement error margin, which is set according to actual needs in this example. 3dB The elasticity coefficient quantitatively controls the elastic contribution of business priorities to the upper limit of coverage demand gain. In this example, it will be... Set to 0.5. As a business priority factor, it compensates for measurement errors by setting an upper limit for coverage demand gain, and defines the coverage constraint range based on the upper and lower limits of coverage demand gain;

[0055] The upper limit of the noise constraint gain is set to the calculated value of the noise constraint gain, and the lower limit is the upper limit of the noise constraint gain minus... dB, where As a noise tolerance margin, this example sets it according to actual needs. The noise constraint range is defined based on the upper and lower limits of the noise constraint gain, with a limit of 2dB, allowing for a moderate sacrifice of noise constraint gain in exchange for coverage redundancy.

[0056] The maximum value between the lower bound of the coverage constraint range and the lower bound of the noise constraint range is defined as the feasible lower bound.

[0057] The minimum value between the upper limit of the coverage constraint range and the upper limit of the noise constraint range is defined as the feasible upper limit;

[0058] If the lower feasible limit is less than or equal to the upper feasible limit, a valid gain interval is defined. The upper and lower limits of the valid gain interval are the upper and lower feasible limits, respectively.

[0059] If the lower feasible limit is greater than the upper feasible limit, it indicates a constraint conflict, and the business priority factor needs to be adjusted. The specific adjustments are as follows:

[0060] If the lower limit of the coverage constraint range is greater than the upper limit of the noise constraint range, it indicates that the coverage requirement is dominant, and the business priority factor is increased by 0.1.

[0061] If the lower limit of the noise constraint range is greater than the upper limit of the coverage constraint range, it indicates that the noise constraint is dominant, and the business priority factor will be reduced by 0.1.

[0062] Recalculate the upper limit of coverage demand gain and the lower limit of noise constraint gain based on the adjusted business priority factor until the feasible lower limit is less than or equal to the feasible upper limit.

[0063] Specifically, the service priority factor is the minimum proportion of the weakest area where the signal strength meets the standard. It is set by the operator according to the scenario and reflects the importance of coverage requirements. In this example, the service priority factor ranges from [0,1], for example, 0.9 for hospital underground parking lots and 0.5 for ordinary shopping mall underground parking lots.

[0064] Based on attenuation value and business priority factors Calculate the dynamic weighting factor The specific calculation formula is as follows:

[0065]

[0066] Specifically, the larger the attenuation value in the formula (e.g., severe signal attenuation deep in underground parking lots), the closer the dynamic weighting factor is to the service priority factor, tending to prioritize meeting signal coverage requirements. It is a quantitative tool that can dynamically allocate priorities between signal coverage requirements and noise constraints. It can intelligently select a gain value that is more inclined to prioritize signal coverage or noise constraints based on business strategies and physical environment, breaking through the rigid decision-making of traditional fixed midpoint or hard threshold.

[0067] The legal gain is obtained by multiplying the value obtained by subtracting the lower feasible limit from the upper feasible limit within the legal gain interval, multiplying it by the dynamic weighting factor, and adding it to the lower feasible limit within the legal gain interval.

[0068] Specifically, a rigid-flexible decision-making system is formed by achieving hard boundary control through the overlap of feasible domains of coverage demand gain and noise constraint gain, and achieving soft balance between service priority and shielding attenuation through dynamic weights. The legal gain calculated based on this system is the gain value that the 5G repeater needs to be configured after shielding attenuation. The legal gain directly determines the signal amplification factor of the 5G repeater power amplifier. This not only ensures the full-range signal coverage requirements of the underground parking lot (especially the signal requirements in areas with weak signals), but also ensures that the noise fed back to the macro base station by the 5G repeater will not exceed the standard, thus achieving a balance between signal coverage and noise control.

[0069] Step 4: Obtain real-time data and equipment parameters of the 5G repeater. The real-time data includes the real-time number of user connections and real-time traffic. The equipment parameters are the full-load number of user connections, full-load traffic, and maximum gain limit that the 5G repeater can handle.

[0070] The load factor is the minimum of real-time user connections ÷ full-load user connections and real-time traffic ÷ full-load traffic. The specific load factor reflects the real-time load of the 5G repeater. Taking the minimum of user connections ÷ full-load user connections and real-time traffic ÷ full-load traffic can avoid misjudgment based on a single dimension, such as the case of many users but low traffic.

[0071] The load is graded and the gain is adjusted according to the magnitude of the load factor, as follows:

[0072] When the load factor is less than or equal to the load threshold N1, it is determined to be a light load, and the gain is kept at the legal gain.

[0073] When the load factor > N1, subtract N1 from the load factor and multiply by . The gain scaling factor is then obtained, where This is the preset maximum gain scaling factor. <1;

[0074] The dynamic gain is obtained by subtracting the legal gain from the maximum gain limit, multiplying the value by the gain scaling factor, and then adding the legal gain back.

[0075] When the load factor is greater than N1 and less than 1, it is determined to be a medium load, and the gain is adjusted to dynamic gain.

[0076] When the load factor is ≥1, it is determined to be overloaded. In this case, the gain is adjusted to the gain scaling factor = Dynamic gain;

[0077] Specifically, the load threshold N1 used to determine load grading is set based on the actual signal load requirements in different environments. In this example, N1 can be set to 0.3, which is the maximum value of the gain scaling factor. This can prevent the dynamic gain from exceeding the maximum gain limit, thus avoiding signal distortion. In this example, it is set to... This indicates that when the service load reaches full capacity, 90% of the gain elasticity needs to be released (10% is reserved to avoid power amplifier saturation and signal distortion) to ensure that the signal strength in the coverage area meets the standard. Under light load, the 5G repeater only needs to maintain the legal gain to meet the demand. Under medium and heavy load, it needs to be adjusted to dynamic gain to meet the load demand brought about by the increase in the number of user connections or traffic. By using different gain strategies through load grading, the energy consumption of the 5G repeater can be dynamically reduced.

[0078] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for optimizing background noise and saving energy in a 5G repeater, characterized in that, include: The received power and output power are obtained, and the difference between the two is marked as the attenuation value; The downlink transmit power and preset target signal strength of the macro base station are obtained, and the coverage requirement gain is calculated based on the downlink transmit power, target signal strength and attenuation value. Acquire noise data and calculate the noise constraint gain based on the noise data; Set the upper and lower limits of the coverage demand gain and the noise constraint gain, and determine the feasible upper and lower limits of the legal gain range based on the upper and lower limits of the coverage demand gain and the noise constraint gain. The legal gain is calculated based on the feasible upper and lower bounds of the legal gain interval, combined with the dynamic weighting factor. The method for calculating the coverage demand gain is as follows: Obtain the downlink transmit power of the macro base station and preset the target signal strength, which is the actual coverage requirement of the 5G repeater; The input power of the 5G repeater is obtained by subtracting the attenuation value from the downlink transmit power of the macro base station, and the coverage requirement gain is obtained by subtracting the input power from the target signal strength. The method for calculating the noise constraint gain is as follows: Acquire noise data, including the upper limit of macro base station noise floor, 12.5kHz bandwidth thermal noise, 5G repeater noise figure, and construction link loss; The uplink noise of the 5G repeater is obtained by adding the 12.5kHz bandwidth thermal noise and the noise figure of the 5G repeater. The noise constraint gain is obtained by subtracting the uplink noise from the upper limit of the macro base station noise floor and then adding the construction link loss. Set the lower bound of the coverage demand gain to the calculated value of the coverage demand gain, and the upper bound to the lower bound of the coverage demand gain plus... Where x is the preset signal measurement error margin, and k is the elasticity coefficient. As a business priority factor, the coverage constraint range is defined based on the upper and lower limits of the coverage demand gain; The upper limit of the noise constraint gain is set to the calculated value of the noise constraint gain, and the lower limit is the upper limit of the noise constraint gain minus... , where y is the noise tolerance margin, and the noise constraint range is defined based on the upper and lower limits of the noise constraint gain; The dynamic weighting factor is calculated based on the attenuation value and the business priority factor. The specific calculation formula is as follows: ; In the formula, Here, L is the dynamic weighting factor, and L is the decay value. Business priority factor; the legal gain is obtained by multiplying the value obtained by subtracting the lower limit of the feasible upper limit within the legal gain interval by the dynamic weight factor and adding it to the lower limit of the legal gain interval. The dynamic gain is obtained based on the legal gain calculation.

2. The method for optimizing background noise and saving energy in a 5G repeater according to claim 1, characterized in that, include: The transmitted signal strength is obtained and used as the output power. The transmitted signal strength is collected at preset intervals after being attenuated by the shielding structure, and the arithmetic mean of the n signal strengths is calculated. The arithmetic mean of the n signal strengths is used as the received power, and the attenuation value is obtained by subtracting the received power from the output power.

3. The method for optimizing background noise and saving energy in a 5G repeater according to claim 1, characterized in that, include: The maximum value between the lower bound of the coverage constraint range and the lower bound of the noise constraint range is defined as the feasible lower bound. The minimum value between the upper limit of the coverage constraint range and the upper limit of the noise constraint range is defined as the feasible upper limit; If the lower feasible limit is less than or equal to the upper feasible limit, a valid gain interval is defined. The upper and lower limits of the valid gain interval are the upper and lower feasible limits, respectively. If the lower feasible limit is greater than the upper feasible limit, adjust the business priority factor.

4. The method for optimizing background noise and saving energy in a 5G repeater according to claim 3, characterized in that... Adjusting business priority factors, including: If the lower limit of the coverage constraint range is greater than the upper limit of the noise constraint range, the service priority factor will be increased by 0.1; If the lower limit of the noise constraint range is greater than the upper limit of the coverage constraint range, the service priority factor will be reduced by 0.1; Recalculate the upper limit of coverage demand gain and the lower limit of noise constraint gain based on the adjusted business priority factor, until the feasible lower limit is less than or equal to the feasible upper limit.

5. A method for optimizing background noise and saving energy in a 5G repeater according to claim 1, characterized in that... The dynamic gain is calculated, including: Obtain real-time data and equipment parameters of the 5G repeater. The real-time data includes the number of real-time user connections and real-time traffic. The equipment parameters are the number of full-load user connections, full-load traffic and maximum gain limit that the 5G repeater can handle. The load factor is the minimum of real-time user connections ÷ full-load user connections and real-time traffic ÷ full-load traffic. The load is graded and the gain is adjusted according to the magnitude of the load factor, as follows: The preset load threshold N1 is the actual signal load requirement of the current environment. When the load factor is less than or equal to the load threshold N1, it is determined to be a light load, and the gain is kept at the legal gain. When the load factor > N1, subtract N1 from the load factor and multiply by . The gain scaling factor is then obtained, where This is the preset maximum gain scaling factor. ; The dynamic gain is obtained by subtracting the legal gain from the maximum gain limit, multiplying the value by the gain scaling factor, and then adding the legal gain.

6. The method for optimizing background noise and saving energy in a 5G repeater according to claim 5, characterized in that, include: When the load factor is greater than N1 and less than 1, it is determined to be a medium load, and the gain is adjusted to dynamic gain. When the load factor is ≥1, it is determined to be overloaded. In this case, the gain is adjusted to the gain scaling factor = The dynamic gain.

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