Wireless communication transmission control method and device based on internet of things and electronic equipment

Abstract

This invention relates to a wireless communication transmission control method, apparatus, and electronic device based on the Internet of Things (IoT). The method includes: determining a link quality degradation metric based on block bit error rate and uplink noise floor rise, and determining a channel quality fluctuation metric based on signal-to-noise ratio (SNR); calculating voltage standing wave ratio (VSWR) based on forward transmit power and reverse reflective power, and then determining a radio frequency (RF) link mismatch index; determining a standby depth adjustment factor based on service load data and channel quality fluctuation metric; determining the target transmit power for the next analysis cycle based on the link quality degradation metric and RF link mismatch index; and determining the DRX cycle duration for the next analysis cycle based on the standby depth adjustment factor, the number of gain adjustment events, and service load data. This solution significantly improves energy efficiency while ensuring the stability of backhaul links in densely populated urban areas by collaboratively sensing the multi-dimensional states of the wireless environment, RF hardware, and service load.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a wireless communication transmission control method, apparatus, and electronic device based on the Internet of Things. Background Technology

[0002] With the large-scale deployment of 5G mobile communication technology in densely populated urban areas, the challenges faced by base station wireless backhaul links are becoming increasingly prominent. In these scenarios, signal reflection and scattering due to tall buildings are common, and coupled with time-varying user activities and external interference sources, the quality of the wireless channel may fluctuate drastically in a short period of time. At the same time, outdoor radio frequency front-end components, such as antennas and feeders, are exposed to long-term temperature and humidity changes and air pollution, which gradually degrades their impedance matching characteristics, resulting in additional power loss and reflection.

[0003] Existing transmission control methods often focus on solving single problems, such as using fixed power back-off strategies or preset discontinuous reception periods, failing to effectively correlate and coordinate the dynamics of the wireless environment, the state of RF hardware, and the real-time service load. This fragmented control approach easily leads to increased bit error rate and decreased transmission efficiency when link quality deteriorates, while failing to adequately reduce equipment power consumption when link conditions are good and service load is low, resulting in a double loss of network performance and energy efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a wireless communication transmission control method, device, and electronic device based on the Internet of Things, so as to solve at least one of the problems existing in the prior art.

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

[0006] A wireless communication transmission control method based on the Internet of Things, comprising:

[0007] The link quality degradation metric is determined based on the block bit error rate and the uplink noise floor rise, and the channel quality fluctuation metric is determined based on the signal-to-noise ratio.

[0008] The voltage standing wave ratio (VSWR) is calculated based on the forward transmit power and the reverse reflect power, and then the RF link mismatch index is determined.

[0009] The standby depth adjustment factor is determined based on service load data and channel quality fluctuation metrics.

[0010] The target transmit power for the next analysis period is determined based on the link quality degradation metric and the RF link mismatch index.

[0011] The DRX cycle duration for the next analysis period is determined based on the standby depth adjustment factor, the number of gain adjustment events, and the service load data.

[0012] Furthermore, the average block error rate Ba within the analysis period is calculated, and the average block error rate Ba is compared with the target error rate threshold Bd to determine the bit error degradation factor Db;

[0013] The average upward noise floor rise Ra within the analysis period is calculated, and then the interference rise factor Dr is determined.

[0014] The link quality degradation metric Lq is determined by combining the bit error rate degradation factor Db and the interference boosting factor Dr.

[0015] Furthermore, the signal-to-noise ratio (SNR) sequence within the current analysis period is obtained, and the standard deviation σ(SINR) and average value μ(SINR) of the SNR sequence are calculated respectively, thereby determining the channel quality fluctuation metric WD, WD=σ(SINR) / μ(SINR).

[0016] Furthermore, the voltage standing wave ratio (VSWR) V of each acquisition point is calculated based on the forward transmission power Pf and the reverse reflection power Pr of each acquisition point within the analysis period. The average VSWR of each acquisition point within the analysis period is calculated and denoted as Vp. Then, the RF link mismatch index RF is determined, RF=tanh{max(0,(Vp-Vr))}.

[0017] Where Vr is the preset VSWR threshold.

[0018] Furthermore, calculate the average length of the downlink data buffer queue Qavg within the current analysis period, and use the ratio of this length to the maximum queue capacity Qmax as the load intensity LI;

[0019] The standby depth adjustment factor DP is determined by combining load intensity (LI) and channel quality fluctuation metric (WD).

[0020] If LI is greater than or equal to the preset load threshold and WD is greater than or equal to the preset fluctuation threshold, the standby depth adjustment factor DP is determined to be 0; otherwise, the standby depth adjustment factor DP = max(tz, 1-LI / L0) × (1-WD).

[0021] Where tz is the preset adjustment value and L0 is the preset load threshold.

[0022] Furthermore, the process for determining the target transmit power Pta is as follows:

[0023] The basic transmit power adjustment ΔP is determined based on the link quality degradation metric Lq: when Lq is less than k1, ΔP = r1 is determined; when Lq is greater than or equal to k1 and less than or equal to k2, ΔP = r2 is determined; and when Lq is greater than k2, ΔP = r3 is determined.

[0024] The standing wave compensation adjustment Prf is determined based on the radio frequency link mismatch index RF, Prf = 10 × lg(1 + ρ × RF);

[0025] Pta is determined by combining the basic transmit power adjustment ΔP and the standing wave compensation adjustment Prf, where Pta = min(Pmax, Pnom + ΔP + Prf).

[0026] Wherein, k1 is the first preset link degradation threshold, k2 is the second preset link degradation threshold, r1 is the first preset adjustment amount, r2 is the second preset adjustment amount, r3 is the third preset adjustment amount, ρ is the preset compensation gain coefficient, Pmax is the rated maximum transmit power of the device, and Pnom is the nominal transmit power of the device.

[0027] Furthermore, the standby depth adjustment factor DP is compared with each preset depth adjustment threshold to determine the basic DRX cycle duration Tbase;

[0028] Determine the Business Burst Index (BI) based on business load data:

[0029] Calculate the mean μ(AT) and standard deviation σ(AT) of the time series of business arrival intervals within the current analysis period, and then determine the business burst index BI, BI=min(1,σ(AT) / (μ(AT)×BO)), where BO is the preset burst threshold;

[0030] The adjustment activity AG is determined based on the number of gain adjustment events Na in the current analysis period, where AG = min(1,Na / Nm), and Nm is the preset event count threshold.

[0031] The periodic adjustment coefficient ZT is determined based on the business suddenness index BI and the adjustment activity level AG, where ZT = 1 - φ1 × BI - φ2 × AG; φ1 is the preset suddenness penalty coefficient and φ2 is the preset activity level penalty coefficient.

[0032] Furthermore, the DRX cycle length for the next analysis cycle is determined based on the cycle adjustment factor ZT and the base DRX cycle length Tbase:

[0033] Calculate the candidate value of DRX cycle duration Tcand, Tcand = ZT × Tbase;

[0034] The standard DRX cycle length TDRX is determined by comparing Tcand with each standard value in the standard DRX cycle value set {T1, T2, T3, T4, T5} one by one and selecting the standard value with the smallest absolute difference from Tcand. If two standard values ​​have the same absolute difference from Tcand, the smaller standard value is selected.

[0035] Wherein, T1 is the first preset duration, T2 is the second preset duration, T3 is the third preset duration, T4 is the fourth preset duration, and T5 is the fifth preset duration.

[0036] According to another aspect of this application, a wireless communication transmission control device based on the Internet of Things is also provided, comprising:

[0037] The metric determination unit is used to determine the link quality degradation metric based on the block bit error rate and the uplink noise floor rise, and to determine the channel quality fluctuation metric based on the signal-to-noise ratio.

[0038] The link mismatch analysis unit is used to calculate the voltage standing wave ratio based on the forward transmit power and the reverse reflect power, and then determine the RF link mismatch index;

[0039] The adjustment factor determination unit is used to determine the standby depth adjustment factor based on service load data and channel quality fluctuation measurement.

[0040] The power determination unit is used to determine the target transmit power for the next analysis cycle based on the link quality degradation metric and the RF link mismatch index.

[0041] The duration determination unit is used to determine the DRX cycle duration for the next analysis cycle based on the standby depth adjustment factor, the number of gain adjustment events, and service load data.

[0042] According to another aspect of this application, an electronic device is provided, the electronic device comprising:

[0043] One or more processors;

[0044] Storage device for storing one or more programs;

[0045] When the one or more programs are executed by the one or more processors, the one or more processors implement the IoT-based wireless communication transmission control method.

[0046] The beneficial effects of this invention are as follows: This solution provides an adaptive transmission control strategy for complex wireless environments in densely populated urban areas. By jointly evaluating the degree of link degradation and radio frequency mismatch, it dynamically adjusts the transmit power, effectively combating performance degradation caused by interference, bit errors, and feeder losses, ensuring stable backhaul links under adverse conditions. By integrating service load intensity and channel fluctuation characteristics, it flexibly adjusts the duration of discontinuous reception cycles, enabling base stations to enter deep sleep during off-peak hours and quickly wake up during service bursts or channel changes. This achieves a good balance between precise energy saving and rapid data response. By using multi-dimensional sensing data from the Internet of Things to drive control decisions, it reduces the need for manual intervention, enabling base stations to autonomously adapt to changes in the environment and status, improving the intelligence level of network operation and the convenience of long-term maintenance. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating the wireless communication transmission control method based on the Internet of Things in this embodiment.

[0049] Figure 2 This is a flowchart illustrating the measurement determination method in this embodiment.

[0050] Figure 3 This is a flowchart illustrating the method for determining the DRX cycle duration in this embodiment.

[0051] Figure 4 This is a schematic diagram of the wireless communication transmission control device based on the Internet of Things in this embodiment.

[0052] Figure 5 This is a schematic diagram of the electronic device in this embodiment. Detailed Implementation

[0053] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments and accompanying drawings, further explains the invention. Similar components in the drawings are indicated by the same reference numerals. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.

[0054] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0055] Specifically, this embodiment is applied to the wireless backhaul link of 5G communication base stations in densely populated urban areas, aiming to solve the technical problems of decreased transmission performance and increased energy consumption caused by fluctuations in wireless link quality, deterioration of radio frequency front-end matching status, and changes in service load.

[0056] Please see Figure 1 As shown, this is a flowchart illustrating the wireless communication transmission control method based on the Internet of Things in this embodiment. Before the method is executed, the system synchronously collects data through various sensing and statistical units deployed on the base station communication equipment and auxiliary monitoring terminals, including:

[0057] Signal quality data: Real-time signal-to-noise ratio is collected once per second via the base station's wireless backhaul unit;

[0058] Transmission performance data: The cyclic redundancy check results of downlink transmission blocks are read from the medium access control layer of the base station at a frequency of once per second, and the block error rate in the current analysis period is calculated, which is the ratio of the number of erroneous transmission blocks to the total number of transmission blocks. In this embodiment, all bit error rate parameters are expressed in decimal form, for example, 1% is written as 0.01.

[0059] The uplink noise floor rise is read from the physical layer uplink receiving link of the base station at a frequency of once per second. This value is defined as the ratio of the currently measured total uplink received power to the preset receiver noise floor reference power, in dB. The receiver noise floor reference power is a reference value stored after calibration by measuring the receiver output power in a silent state with no user access during system deployment or maintenance.

[0060] Radio frequency link status data: The forward transmit power and reverse reflect power are collected once per second through a bidirectional directional coupler and power detection unit deployed at the connection between the base station antenna port and the feeder.

[0061] Gain adjustment event count: The base station's automatic gain control module reads the current gain value once per second and records gain adjustment events where the gain value change exceeds a preset threshold, such as ±1dB, between two adjacent samples. Within one analysis period, the number of gain adjustment events is counted.

[0062] Service load data: The real-time downlink data buffer queue length is obtained from the base station packet data aggregation protocol layer. This queue length represents the amount of service data to be transmitted. At the same time, the time interval between the arrival of two adjacent downlink data packets is recorded to form a service arrival interval time series.

[0063] This embodiment does not specifically limit the communication protocol and transmission method of the above data; those skilled in the art can freely set them according to the on-site situation. The method uses a 1-minute analysis cycle.

[0064] The method includes:

[0065] Step S1: Determine the link quality degradation metric based on the block bit error rate and the uplink noise floor rise, and determine the channel quality fluctuation metric based on the signal-to-noise ratio.

[0066] Please see Figure 2 As shown, the metric determination method includes:

[0067] Step S11: Determine the link quality degradation metric based on the block bit error rate and the uplink noise floor rise.

[0068] Specifically, the average block error rate Ba is calculated within the analysis period, and then compared with the target bit error rate threshold Bd to determine the bit error degradation factor Db.

[0069] When Ba is less than or equal to Bd, the bit error rate degradation factor Db is determined to be 0; otherwise, the bit error rate degradation factor Db is determined to be min(1,(Ba-Bd) / △B).

[0070] The average upward noise floor rise Ra within the analysis period is calculated, and then the interference rise factor Dr is determined. ;

[0071] The link quality degradation metric Lq is determined by fusing the bit error rate degradation factor Db and the interference rise factor Dr, where Lq = α1 × Db + α2 × Dr;

[0072] Where △B is the preset bit error rate deviation threshold, Rn is the preset nominal rise amount, Rm is the preset rise amount deviation threshold, α1 is the bit error rate degradation weight, α2 is the interference rise weight, and α1+α2=1.

[0073] Preferably, in this embodiment, the target bit error rate threshold is 0.01, the preset bit error rate deviation threshold is 0.1, the preset nominal rise is 3dB, the preset rise deviation threshold is 9dB, the bit error rate degradation weight is 0.6, and the interference rise weight is 0.4.

[0074] Please continue reading. Figure 2 As shown, the metric determination method further includes:

[0075] Step S12: Determine the channel quality fluctuation metric based on the signal-to-noise ratio.

[0076] Specifically, the signal-to-noise ratio (SNR) sequence within the current analysis period is obtained, and the standard deviation σ (SINR) and average value μ (SINR) of the SNR sequence are calculated respectively. Then, the channel quality fluctuation metric WD is determined, where WD = σ (SINR) / μ (SINR).

[0077] Specifically, step S1 assesses the degree of link quality degradation by fusing block bit error rate and uplink noise floor rise, and quantifies channel fluctuation status by using the dispersion of signal-to-noise ratio. It integrates two different dimensions of degradation phenomena, user plane data transmission errors and physical layer interference background, into a unified metric, avoiding one-sided dependence on a single metric. At the same time, it extracts the fluctuation characteristics of channel quality separately, providing an independent and intuitive basis for subsequent judgment of channel stability.

[0078] Please continue reading. Figure 1 As shown, the IoT-based wireless communication transmission control method further includes:

[0079] Step S2: Calculate the voltage standing wave ratio (VSWR) based on the forward transmit power and the reverse reflect power, and then determine the RF link mismatch index.

[0080] Specifically, the voltage standing wave ratio (VSWR) V of each acquisition point is calculated based on the forward transmitted power Pf and the reverse reflected power Pr within the analysis period, where V = (1 + (Pr / Pf)). 0.5 ) / (1-(Pr / Pf) 0.5 );

[0081] The average voltage standing wave ratio (VSWR) of each acquisition point within the analysis period is calculated and denoted as Vp. Then, the RF link mismatch index RF is determined, RF=tanh{max(0,(Vp-Vr)}.

[0082] Where Vr is the preset VSWR threshold.

[0083] Preferably, in this embodiment, the preset VSWR threshold is 1.

[0084] Specifically, step S2 can assess the impedance mismatch of the antenna feeder system in real time and quantitatively due to physical factors such as aging, water ingress, and loose connections. It maps the change in VSWR to a continuous mismatch index, rather than a simple threshold alarm, which helps to perceive the working health of the RF front end more precisely. This allows the system to take into account the additional power loss or reflection risk caused by link mismatch when adjusting the transmit power, thereby achieving proactive adaptation to the RF link status and ensuring the effectiveness of power utilization and the safety of equipment operation.

[0085] Please continue reading. Figure 1 As shown, the IoT-based wireless communication transmission control method further includes:

[0086] Step S3: Determine the standby depth adjustment factor based on service load data and channel quality fluctuation measurement.

[0087] Specifically, calculate the average length of the downlink data buffer queue Qavg within the current analysis period, and use the ratio of this length to the maximum queue capacity Qmax as the load intensity LI;

[0088] The standby depth adjustment factor DP is determined by combining load intensity (LI) and channel quality fluctuation metric (WD).

[0089] If LI is greater than or equal to the preset load threshold and WD is greater than or equal to the preset fluctuation threshold, the standby depth adjustment factor DP is determined to be 0; otherwise, the standby depth adjustment factor DP = max(tz, 1-LI / L0) × (1-WD).

[0090] Where tz is the preset adjustment value and L0 is the preset load threshold.

[0091] Preferably, in this embodiment, the preset load threshold is 0.3, the preset fluctuation threshold is 0.15, and the preset adjustment value is 0.2.

[0092] Specifically, step S3 combines the amount of service data to be transmitted with the degree of channel quality fluctuation to determine a key factor for adjusting the energy-saving depth. This factor can comprehensively reflect the system's busy level and the stability of the wireless environment. When the service is light and the channel is stable, this factor will indicate that the system can enter a deeper energy-saving state; while when the service is busy or the channel fluctuates drastically, this factor will limit the energy-saving depth to prioritize transmission efficiency and response speed.

[0093] Please continue reading. Figure 2 As shown, the IoT-based wireless communication transmission control method further includes:

[0094] Step S4: Determine the target transmit power for the next analysis cycle based on the link quality degradation metric and the RF link mismatch index.

[0095] Specifically, the process for determining the target transmit power Pta is as follows:

[0096] The basic transmit power adjustment ΔP is determined based on the link quality degradation metric Lq: when Lq is less than k1, ΔP = r1 is determined; when Lq is greater than or equal to k1 and less than or equal to k2, ΔP = r2 is determined; and when Lq is greater than k2, ΔP = r3 is determined.

[0097] The standing wave compensation adjustment Prf is determined based on the radio frequency link mismatch index RF, Prf = 10 × lg(1 + ρ × RF);

[0098] Pta is determined by combining the basic transmit power adjustment ΔP and the standing wave compensation adjustment Prf, where Pta = min(Pmax, Pnom + ΔP + Prf).

[0099] Wherein, k1 is the first preset link degradation threshold, k2 is the second preset link degradation threshold, r1 is the first preset adjustment amount, r2 is the second preset adjustment amount, r3 is the third preset adjustment amount, ρ is the preset compensation gain coefficient, Pmax is the rated maximum transmit power of the device, and Pnom is the nominal transmit power of the device.

[0100] Preferably, in this embodiment, the first preset link degradation threshold is 0.2, the second preset link degradation threshold is 0.5, the first preset adjustment amount is 0dB, the second preset adjustment amount is 2dB, the third preset adjustment amount is 4dB, the preset compensation gain coefficient is 0.3, the rated maximum transmit power of the device is 37dBm, and the nominal transmit power of the device is 33dBm.

[0101] Specifically, step S4 collaboratively decides the target transmit power value for the next cycle based on the degree of link quality degradation and the RF link mismatch status. The performance degradation during signal transmission is attributed to two aspects: wireless environment degradation and RF front-end mismatch. The corresponding power compensation amount is calculated for each. When the link quality deteriorates due to interference or bit errors, the system will increase the basic transmit power to counteract the degradation. When impedance mismatch is detected in the RF link, the system will apply additional compensation to overcome reflection loss. Finally, the two are superimposed and limited within the safe range of the device, realizing on-demand and precise adjustment of transmit power.

[0102] Please continue reading. Figure 1 As shown, the IoT-based wireless communication transmission control method further includes:

[0103] Step S5: Based on the standby depth adjustment factor, the number of gain adjustment events, and the service load data, determine the DRX cycle duration for the next analysis period. The DRX cycle duration is a sleep-wake cycle set by the base station to reduce equipment power consumption.

[0104] Please see Figure 3 As shown, the method for determining the DRX cycle duration includes:

[0105] Step S51: Determine the basic DRX cycle duration based on the standby depth adjustment factor.

[0106] Specifically, the standby depth adjustment factor DP is compared with each preset depth adjustment threshold to determine the base DRX cycle duration Tbase.

[0107] If DP is less than or equal to s1, then set Tbase to T1;

[0108] If DP is greater than s1 and less than s2, then set Tbase to T2;

[0109] If DP is greater than or equal to s2 and less than s3, then set Tbase to T3;

[0110] If DP is greater than or equal to s3 and less than s4, then set Tbase to T4;

[0111] If DP is greater than or equal to s4, then set Tbase to T5;

[0112] Wherein, s1 is the first depth adjustment threshold, s2 is the second depth adjustment threshold, s3 is the third depth adjustment threshold, s4 is the fourth depth adjustment threshold, T1 is the first preset duration, T2 is the second preset duration, T3 is the third preset duration, T4 is the fourth preset duration, and T5 is the fifth preset duration.

[0113] Preferably, in this embodiment, the first depth adjustment threshold is 0.3, the second depth adjustment threshold is 0.5, the third depth adjustment threshold is 0.7, the fourth depth adjustment threshold is 0.9, the first preset duration is 80ms, the second preset duration is 160ms, the third preset duration is 320ms, the fourth preset duration is 640ms, and the fifth preset duration is 1280ms.

[0114] Please continue reading. Figure 3 As shown, the method for determining the DRX cycle duration further includes:

[0115] Step S52: Determine the periodic adjustment coefficient based on the service load data and the number of gain adjustment events, and then determine the DRX period duration based on the periodic adjustment coefficient and the basic DRX period duration.

[0116] Specifically, the Business Burst Index (BI) is determined based on business load data:

[0117] Calculate the mean μ(AT) and standard deviation σ(AT) of the time series of business arrival intervals within the current analysis period, and then determine the business burst index BI, BI=min(1,σ(AT) / (μ(AT)×BO)), where BO is the preset burst threshold;

[0118] The adjustment activity AG is determined based on the number of gain adjustment events Na in the current analysis period, where AG = min(1,Na / Nm), and Nm is the preset event count threshold.

[0119] The periodic adjustment coefficient ZT is determined based on the business suddenness index BI and the adjustment activity level AG, ZT = 1 - φ1 × BI - φ2 × AG; φ1 is the preset suddenness penalty coefficient, and φ2 is the preset activity level penalty coefficient;

[0120] The DRX cycle length for the next analysis cycle is determined based on the cycle adjustment factor ZT and the base DRX cycle length Tbase:

[0121] Calculate the candidate value of DRX cycle duration Tcand, Tcand = ZT × Tbase;

[0122] The Tcand is compared one by one with each value in the standard DRX cycle value set {T1,T2,T3,T4,T5}, and the standard value with the smallest absolute difference from Tcand is selected as the final DRX cycle duration TDRX; if two standard values ​​have the same absolute difference from Tcand, the smaller standard value is selected.

[0123] The base station sends the determined target transmit power Pta and the determined DRX cycle duration TDRX to the radio backhaul unit for execution via control plane signaling.

[0124] Preferably, in this embodiment, the preset suddenness threshold is 2, the preset event frequency threshold is 60, the preset suddenness penalty coefficient is 0.2, and the preset activity penalty coefficient is 0.15.

[0125] Specifically, step S5 ensures that the final cycle length not only considers the macroscopic load level but also more precisely adapts to the microscopic characteristics of service bursts and link change frequencies. For scenarios with high burstiness and rapid channel changes, the system shortens the listening interval to ensure low-latency response; conversely, it lengthens the cycle to maximize energy-saving benefits. Alignment with a preset set of standard values ​​also ensures the standardization and compatibility of configuration instructions.

[0126] Please see Figure 4 As shown, the IoT-based wireless communication transmission control device includes:

[0127] The metric determination unit is used to determine the link quality degradation metric based on the block bit error rate and the uplink noise floor rise, and to determine the channel quality fluctuation metric based on the signal-to-noise ratio.

[0128] The link mismatch analysis unit is used to calculate the voltage standing wave ratio based on the forward transmit power and the reverse reflect power, and then determine the RF link mismatch index;

[0129] The adjustment factor determination unit is used to determine the standby depth adjustment factor based on service load data and channel quality fluctuation measurement.

[0130] The power determination unit is used to determine the target transmit power for the next analysis cycle based on the link quality degradation metric and the RF link mismatch index.

[0131] The duration determination unit is used to determine the DRX cycle duration for the next analysis cycle based on the standby depth adjustment factor, the number of gain adjustment events, and service load data.

[0132] The IoT-based wireless communication transmission control device provided in this application can execute the IoT-based wireless communication transmission control method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the execution method.

[0133] Please see Figure 5 As shown, it is a structural schematic diagram of an electronic device in this embodiment. The electronic device in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), wearable electronic devices, etc., as well as fixed terminals such as digital TVs, desktop computers, smart home devices, etc. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0134] like Figure 5As shown, the electronic device includes: a processor 501, a memory 502, a communication interface 503, and a system bus 504. The processor includes at least one of a central processing unit (CPU), a graphics processing unit (GPU), or a field-programmable gate array (FPGA), configured to call computer programs and data stored in the memory and generate control instructions; the memory includes random access memory (RAM) and / or non-volatile memory (NVM), the NVM including flash memory, solid-state drive (SSD), or a combination thereof, used to store computer programs, process intermediate data, and historical data sets; the communication interface includes a wired communication module and a wireless communication module, the wired communication module supporting Ethernet or RS-485 protocols for connecting to sensor networks; the wireless communication module supporting LoRa, 5G, or satellite communication protocols for transmitting processing results to a remote server; the system bus adopts a PCI Express or AXI bus architecture to achieve high-speed data interaction and clock synchronization between the processor, memory, and communication interface.

[0135] This embodiment also provides a computer-readable storage medium, which physically stores computer-executable instructions. When the instructions are transmitted to the processing unit via the integrated circuit substrate, they are encapsulated and processed through the data channel of the bus system and then solidified into the non-volatile storage area of ​​the storage module. The executable instructions are configured to implement the complete technical solution of the IoT-based wireless communication transmission control method when executed by the processor.

[0136] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is impossible to exhaustively list all the implementation methods here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A wireless communication transmission control method based on the Internet of Things, characterized in that, include: The link quality degradation metric is determined based on the block bit error rate and the uplink noise floor rise, and the channel quality fluctuation metric is determined based on the signal-to-noise ratio. The voltage standing wave ratio (VSWR) is calculated based on the forward transmit power and the reverse reflect power, and then the RF link mismatch index is determined. The standby depth adjustment factor is determined based on service load data and channel quality fluctuation metrics. The target transmit power for the next analysis period is determined based on the link quality degradation metric and the RF link mismatch index. The DRX cycle duration for the next analysis period is determined based on the standby depth adjustment factor, the number of gain adjustment events, and the service load data.

2. The wireless communication transmission control method based on the Internet of Things according to claim 1, characterized in that, Calculate the average block error rate Ba within the analysis period, and compare the average block error rate Ba with the target error rate threshold Bd to determine the bit error degradation factor Db; The average upward noise floor rise Ra within the analysis period is calculated, and then the interference rise factor Dr is determined. The link quality degradation metric Lq is determined by combining the bit error rate degradation factor Db and the interference boosting factor Dr.

3. The wireless communication transmission control method based on the Internet of Things according to claim 2, characterized in that, Obtain the signal-to-noise ratio (SNR) sequence within the current analysis period, calculate the standard deviation σ(SINR) and average value μ(SINR) of the SNR sequence, and then determine the channel quality fluctuation metric WD, WD=σ(SINR) / μ(SINR).

4. The wireless communication transmission control method based on the Internet of Things according to claim 3, characterized in that, The voltage standing wave ratio (VSWR) V of each acquisition point is calculated based on the forward transmit power Pf and the reverse reflect power Pr of each acquisition point within the analysis period. The average VSWR of each acquisition point within the analysis period is calculated and denoted as Vp. Then, the RF link mismatch index RF is determined, RF=tanh{max(0,(Vp-Vr))}. Where Vr is the preset VSWR threshold.

5. The wireless communication transmission control method based on the Internet of Things according to claim 4, characterized in that, Calculate the average length of the downlink data buffer queue Qavg within the current analysis period, and use the ratio of this length to the maximum queue capacity Qmax as the load intensity LI; The standby depth adjustment factor DP is determined by combining load intensity (LI) and channel quality fluctuation metric (WD). If LI is greater than or equal to the preset load threshold and WD is greater than or equal to the preset fluctuation threshold, the standby depth adjustment factor DP is determined to be 0; otherwise, the standby depth adjustment factor DP = max(tz, 1-LI / L0) × (1-WD). Where tz is the preset adjustment value and L0 is the preset load threshold.

6. The wireless communication transmission control method based on the Internet of Things according to claim 5, characterized in that, The process for determining the target transmit power Pta is as follows: The basic transmit power adjustment ΔP is determined based on the link quality degradation metric Lq: when Lq is less than k1, ΔP = r1 is determined; when Lq is greater than or equal to k1 and less than or equal to k2, ΔP = r2 is determined; and when Lq is greater than k2, ΔP = r3 is determined. The standing wave compensation adjustment Prf is determined based on the radio frequency link mismatch index RF, Prf = 10 × lg(1 + ρ × RF); Pta is determined by combining the basic transmit power adjustment ΔP and the standing wave compensation adjustment Prf, where Pta = min(Pmax, Pnom + ΔP + Prf). Wherein, k1 is the first preset link degradation threshold, k2 is the second preset link degradation threshold, r1 is the first preset adjustment amount, r2 is the second preset adjustment amount, r3 is the third preset adjustment amount, ρ is the preset compensation gain coefficient, Pmax is the rated maximum transmit power of the device, and Pnom is the nominal transmit power of the device.

7. The wireless communication transmission control method based on the Internet of Things according to claim 6, characterized in that, The standby depth adjustment factor DP is compared with each preset depth adjustment threshold to determine the base DRX cycle duration Tbase. Determine the Business Burst Index (BI) based on business load data: Calculate the mean μ(AT) and standard deviation σ(AT) of the time series of business arrival intervals within the current analysis period, and then determine the business burst index BI, BI=min(1,σ(AT) / (μ(AT)×BO)), where BO is the preset burst threshold; The adjustment activity AG is determined based on the number of gain adjustment events Na in the current analysis period, where AG = min(1,Na / Nm), and Nm is the preset event count threshold. The periodic adjustment coefficient ZT is determined based on the business suddenness index BI and the adjustment activity AG, ZT=1-φ1×BI-φ2×AG; φ1 is the preset suddenness penalty coefficient, and φ2 is the preset activity penalty coefficient.

8. The wireless communication transmission control method based on the Internet of Things according to claim 7, characterized in that, The DRX cycle length for the next analysis cycle is determined based on the cycle adjustment factor ZT and the base DRX cycle length Tbase: Calculate the candidate value of DRX cycle duration Tcand, Tcand = ZT × Tbase; The standard DRX cycle length TDRX is determined by comparing Tcand with each standard value in the standard DRX cycle value set {T1, T2, T3, T4, T5} one by one and selecting the standard value with the smallest absolute difference from Tcand. If two standard values ​​have the same absolute difference from Tcand, the smaller standard value is selected. Wherein, T1 is the first preset duration, T2 is the second preset duration, T3 is the third preset duration, T4 is the fourth preset duration, and T5 is the fifth preset duration.

9. A wireless communication transmission control device based on the Internet of Things (IoT), applied to the wireless communication transmission control method based on the IoT as described in any one of claims 1-8, characterized in that, include: The metric determination unit is used to determine the link quality degradation metric based on the block bit error rate and the uplink noise floor rise, and to determine the channel quality fluctuation metric based on the signal-to-noise ratio. The link mismatch analysis unit is used to calculate the voltage standing wave ratio based on the forward transmit power and the reverse reflect power, and then determine the RF link mismatch index; The adjustment factor determination unit is used to determine the standby depth adjustment factor based on service load data and channel quality fluctuation measurement. The power determination unit is used to determine the target transmit power for the next analysis cycle based on the link quality degradation metric and the RF link mismatch index. The duration determination unit is used to determine the DRX cycle duration for the next analysis cycle based on the standby depth adjustment factor, the number of gain adjustment events, and service load data.

10. An electronic device, characterized in that, The electronic device includes: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the Internet of Things-based wireless communication transmission control method as described in any one of claims 1-8.

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