Wireless audio transmission dynamic carrier density and power adjusting method based on QoS (Quality of Service)

Abstract

The invention relates to the technical field of wireless communication, and discloses a wireless audio transmission dynamic carrier density and power adjustment method based on QoS (Quality of Service), which comprises the following steps of: acquiring channel information in a network in real time through wireless equipment, including signal strength, channel quality, user distribution, interference strength, mobility information and the like; and the channel state of the current network is evaluated through the channel evaluation module, the network load is predicted through the deep learning model based on historical data (including network traffic, QoS indexes, user activities, equipment performance and the like), and the future network state and demand change are calculated. The network load change is predicted through machine learning, the complex real-time calculation requirement in a traditional method is reduced, the calculation burden of a system is reduced, an intelligent power control mechanism can dynamically adjust the transmission power according to the actual network requirement, too high power consumption is avoided, the service life of a battery of wireless equipment is prolonged, and the method is particularly outstanding in low-power-consumption equipment.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and specifically to a method for dynamically adjusting carrier density and power for wireless audio transmission based on QoS. Background Art

[0002] The method for dynamically adjusting carrier density and power for wireless audio transmission based on QoS is a technology for optimizing the quality of wireless audio transmission. This method dynamically adjusts the carrier density and transmission power in the network by real-time monitoring of the quality of service (QoS) metrics of the network, such as latency, packet loss rate, and throughput. By reasonably configuring carrier resources and adjusting the transmission power, signal interference can be effectively reduced, and the data transmission rate can be increased, thereby ensuring the stability and high-quality transmission of audio signals. This method is particularly suitable for audio applications with high real-time requirements, such as VoIP and wireless audio broadcasting, and can provide a high-quality user experience in complex wireless network environments.

[0003] In the prior art, although the method for dynamically adjusting carrier density and power for wireless audio transmission based on QoS can effectively improve the quality of audio transmission, there are still some drawbacks: complex dynamic adjustment mechanism: dynamically adjusting the carrier density and power requires real-time monitoring of the network status and making precise decisions, which involves complex algorithms and a large amount of calculations. Especially in large-scale wireless networks, real-time calculation and adjustment may increase the computational burden of the system, thereby affecting the overall performance of the network; energy consumption problem: dynamically adjusting the transmission power may cause the power consumption of some nodes to be too high, especially in low-power devices or mobile devices. Although the signal quality can be improved by adjusting the power, excessive power consumption may have an adverse impact on the battery life of the device; interference management difficulty: although adjusting the carrier density and power can reduce interference, in complex wireless environments, factors such as signal interference and multipath effects still exist and are difficult to completely eliminate. Especially in high-density user environments, spectrum resource conflicts or interference phenomena may occur, affecting the quality of audio transmission; poor adaptability: some solutions in the prior art may lack sufficient adaptability in different network environments. For example, in scenarios with large changes in user distribution, the network parameters may not be adjusted in a timely manner, resulting in unstable audio transmission quality; high implementation cost: real-time monitoring and adjustment of network parameters require high hardware and software support, especially in large-scale deployments, which may increase the implementation and maintenance costs of the system; incomplete QoS evaluation: existing QoS evaluation methods may not comprehensively consider all factors that may affect the quality of audio transmission, such as user mobility, environmental changes, etc., and therefore may not provide an optimal adjustment scheme in some cases.

[0004] Therefore, we propose a method for dynamically adjusting carrier density and power for wireless audio transmission based on QoS. Summary of the Invention

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for dynamically adjusting carrier density and power in wireless audio transmission based on QoS, comprising the following steps:

[0006] S1: Dynamic channel assessment: Real-time collection of channel information in the network through wireless devices, including signal strength, channel quality, user distribution, interference intensity, mobility information, etc., and evaluation of the current network channel condition through a channel assessment module.

[0007] S2: Machine learning prediction: Based on historical data (including network traffic, QoS metrics, user activities, device performance, etc.), prediction of network load through a deep learning model, and calculation of future network status and demand changes.

[0008] S3: Intelligent power control: According to the results of dynamic channel assessment and machine learning prediction, real-time adjustment of the transmission power of audio data through an intelligent power control algorithm to ensure improved transmission quality under high load conditions and reduced power consumption under low load conditions.

[0009] S4: Dynamic adjustment of carrier density: According to the distribution of users in the network, channel quality, and real-time load demand, dynamically adjust the carrier density through a carrier management algorithm to avoid spectrum resource conflicts and ensure the efficient use of spectrum resources.

[0010] S5: Interference suppression and adaptive scheduling: Adopt interference suppression technology and adaptive scheduling algorithm to perform interference management and resource scheduling for audio transmission according to real-time interference conditions and network topology changes to ensure low-latency transmission of audio data.

[0011] Preferably, the machine learning prediction algorithm is a deep neural network (DNN), and the neural network includes multiple hidden layers for extracting non-linear relationships in network historical data to improve the prediction accuracy of future network load changes.

[0012] Preferably, the intelligent power control algorithm is based on the "minimum power consumption first" strategy, and dynamically adjusts the transmission power of audio data on the premise of ensuring audio transmission quality to reduce the energy consumption of network devices.

[0013] Preferably, the carrier density adjustment module dynamically allocates wireless spectrum resources through channel quality and user distribution information, improves spectrum utilization rate by increasing carrier density, and reduces the occurrence of signal attenuation areas in the network.

[0014] Preferably, the interference suppression technology adopts a spectrum sensing algorithm to identify interference sources and adjust the transmission strategy through real-time monitoring of the current spectrum resources to reduce the impact of interference on audio transmission.

[0015] Preferably, the adaptive scheduling algorithm prioritizes the transmitted audio data according to the real-time load and QoS requirements to ensure that high-priority audio data is transmitted first when the network load is high.

[0016] Preferably, the method further includes intelligent route selection based on the network topology to ensure that the audio data is transmitted through the optimal path, reducing network latency and packet loss.

[0017] Preferably, the machine learning model used in the method is regularly trained and updated through the network management system to optimize the prediction algorithm in real time.

[0018] Compared with the prior art, the present invention provides a method for dynamically adjusting the carrier density and power of wireless audio transmission based on QoS, which has the following beneficial effects:

[0019] 1. The method for dynamically adjusting the carrier density and power of wireless audio transmission based on QoS predicts the network load change through machine learning, reducing the complex real-time calculation requirements in the traditional method, reducing the computational burden of the system. The intelligent power control mechanism can dynamically adjust the transmission power according to the actual network demand, avoiding excessive power consumption and prolonging the battery life of wireless devices, especially prominent in low-power devices.

[0020] 2. The method for dynamically adjusting the carrier density and power of wireless audio transmission based on QoS ensures low latency and high stability of audio transmission by dynamically adjusting the carrier density, transmission power and interference suppression, providing a good user experience. The machine learning model can adaptively adjust according to different network environments, improving the applicability of the method in various network scenarios, especially in the case of large changes in user distribution and network load.

[0021] 3. The method for dynamically adjusting the carrier density and power of wireless audio transmission based on QoS can improve the utilization efficiency of spectrum resources in high-load situations by flexibly adjusting the carrier density, reducing spectrum resource conflicts and optimizing the allocation of network resources. Detailed implementation

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] Embodiment

[0024] Embodiment of the method for dynamically adjusting the carrier density and power of wireless audio transmission based on QoS

[0025] QoS-based Dynamic Carrier Density and Power Adjustment Method for Wireless Audio Transmission, including the following steps:

[0026] S1: Dynamic Channel Assessment: Real-time collect channel information in the network through wireless devices, including signal strength, channel quality, user distribution, interference intensity, mobility information, etc., and evaluate the channel condition of the current network through the channel assessment module.

[0027] S2: Machine Learning Prediction: Based on historical data (including network traffic, QoS metrics, user activities, device performance, etc.), predict the network load through a deep learning model, and calculate the future network state and demand changes.

[0028] S3: Intelligent Power Control: According to the results of dynamic channel assessment and machine learning prediction, adjust the transmission power of audio data in real time through an intelligent power control algorithm to ensure improved transmission quality under high load conditions and reduced power consumption under low load conditions.

[0029] S4: Dynamic Carrier Density Adjustment: According to the user distribution, channel quality, and real-time load demand in the network, dynamically adjust the carrier density through a carrier management algorithm to avoid spectrum resource conflicts and ensure the efficient use of spectrum resources.

[0030] S5: Interference Suppression and Adaptive Scheduling: Adopt interference suppression technology and adaptive scheduling algorithms to perform interference management and resource scheduling for audio transmission according to real-time interference conditions and network topology changes to ensure low-latency transmission of audio data.

[0031] Specifically, the machine learning prediction algorithm is a deep neural network (DNN), and this neural network includes multiple hidden layers, which are used to extract the non-linear relationships in the network historical data to improve the prediction accuracy of future network load changes.

[0032] Specifically, the intelligent power control algorithm is based on the "minimum power consumption first" strategy, and dynamically adjusts the transmission power of audio data on the premise of ensuring the audio transmission quality, reducing the energy consumption of network devices.

[0033] Specifically, the carrier density adjustment module dynamically allocates wireless spectrum resources through channel quality and user distribution information, improves the spectrum utilization rate by increasing the carrier density, and reduces the occurrence of signal attenuation areas in the network.

[0034] Specifically, the interference suppression technology adopts a spectrum sensing algorithm, and through real-time monitoring of the current spectrum resources, identifies interference sources and adjusts the transmission strategy to reduce the impact of interference on audio transmission.

[0035] Specifically, the adaptive scheduling algorithm prioritizes the transmitted audio data according to the real-time load and QoS requirements, ensuring that high-priority audio data is transmitted first when the network load is high.

[0036] Specifically, the method further includes intelligent route selection based on the network topology to ensure that the audio data is transmitted through the optimal path, reducing network latency and packet loss.

[0037] Specifically, the machine learning model used in the method is regularly trained and updated through the network management system to optimize the prediction algorithm in real time.

[0038] Through the above technical solutions, in the present invention, by predicting the network load change through machine learning, the complex real-time calculation requirements in the traditional method are reduced, the computing burden of the system is lowered, and the intelligent power control mechanism can dynamically adjust the transmission power according to the actual network requirements, avoiding excessive power consumption and prolonging the battery life of wireless devices, especially being prominent in low-power devices. By dynamically adjusting the carrier density, transmission power, and interference suppression, low latency and high stability of audio transmission are ensured, providing a good user experience. The machine learning model can adaptively adjust according to different network environments, improving the applicability of the method in various network scenarios. Especially in the case of large changes in user distribution and network load, by flexibly adjusting the carrier density, the utilization efficiency of spectrum resources can be improved under high-load conditions, reducing spectrum resource conflicts and optimizing the allocation of network resources.

[0039] Embodiment 1: Hardware Architecture

[0040] Wireless communication device configuration: This method can be implemented in existing wireless communication devices, such as base stations, routers, wireless access points, or terminal devices, etc. Each device needs to integrate the following modules:

[0041] Channel evaluation module: Responsible for real-time monitoring and collecting channel state information in the network, such as signal strength, interference, packet loss rate, etc.

[0042] Machine learning prediction module: Responsible for predicting the network load based on historical data, and using the deep neural network (DNN) algorithm to model and predict the network state.

[0043] Power control module: Adjusts the transmission power of audio data according to the real-time channel evaluation and prediction data to ensure low-power and high-quality transmission.

[0044] Carrier management module: Adjusts the carrier density according to the real-time load of the network to avoid conflicts and waste of spectrum resources and optimize the spectrum utilization rate.

[0045] Interference suppression module: Adopts spectrum sensing technology to adjust the use of spectrum resources by real-time monitoring of interference conditions to ensure the transmission quality.

[0046] Adaptive Scheduling Module: Based on QoS requirements and real-time network load, intelligently schedule the transmission priority of audio data to ensure the stable transmission of high-priority audio data.

[0047] Communication Protocol:

[0048] This system communicates based on existing wireless communication protocols (such as LTE, 5G, etc.) to enhance its performance in audio transmission.

[0049] Use the QoS management function in the protocol stack and combine it with the dynamic adjustment algorithm of the present invention to achieve priority control and resource scheduling of audio data.

[0050] Embodiment 2: Machine Learning and Intelligent Adjustment

[0051] Training and Updating of Machine Learning Model:

[0052] Train the machine learning model through historical data (including signal quality, latency, packet loss rate, user distribution, etc.).

[0053] The model adopts structures such as deep neural network (DNN) or convolutional neural network (CNN) to extract deep features in the data and predict future network load and audio transmission requirements.

[0054] The machine learning model is continuously updated according to the real-time network state, and the prediction parameters are adjusted to improve the accuracy of the algorithm.

[0055] Intelligent Power Control and Carrier Density Adjustment:

[0056] After each channel assessment, the intelligent power control module adjusts the transmission power of audio data according to the current network load and interference situation, adopting the "minimum power consumption first" strategy to avoid unnecessary energy waste.

[0057] At the same time, the carrier density adjustment module dynamically adjusts the carrier density of the wireless spectrum according to user distribution and channel quality changes to reduce spectrum conflicts and resource waste, and improve the stability and quality of audio transmission.

[0058] Embodiment 3: Interference Management and Adaptive Scheduling

[0059] Interference Management:

[0060] This method uses spectrum sensing technology to detect interference sources in the wireless environment in real time. The interference suppression module adjusts the spectrum resource allocation according to the detected interference information to avoid the degradation of audio quality caused by interference.

[0061] In addition, the interference suppression algorithm combines intelligent power control and carrier density adjustment, enabling stable audio transmission quality even in complex environments.

[0062] Adaptive scheduling mechanism:

[0063] Based on real-time network load and QoS requirements, the adaptive scheduling module prioritizes audio data and preferentially transmits high-priority audio data to ensure timely transmission of audio data under high load conditions.

[0064] When the network load is low, the scheduling algorithm reduces unnecessary power consumption according to the energy efficiency optimization strategy.

[0065] Embodiment 4: Network topology optimization

[0066] Intelligent route selection:

[0067] This method can combine network topology optimization technology to select the optimal path to transmit audio data according to the load conditions and topology structure of network nodes.

[0068] In a complex network environment, through intelligent route selection, delays and packet losses in audio transmission are reduced, and the overall transmission performance is optimized. Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for dynamically adjusting carrier density and power in wireless audio transmission based on QoS, characterized in that: It includes the following steps: S1: Dynamic channel assessment: The wireless device collects channel information in the network in real time, including signal strength, channel quality, user distribution, interference intensity, mobility information, etc., and the channel assessment module evaluates the channel condition of the current network. S2: Machine learning prediction: Based on historical data (including network traffic, QoS metrics, user activities, device performance, etc.), the deep learning model is used to predict the network load, and calculate the future network state and demand changes. S3: Intelligent power control: According to the results of dynamic channel assessment and machine learning prediction, the transmission power of audio data is adjusted in real time through the intelligent power control algorithm to ensure the improvement of transmission quality under high load conditions and the reduction of power consumption under low load conditions. S4: Dynamic adjustment of carrier density: According to the user distribution, channel quality and real-time load demand in the network, the carrier density is dynamically adjusted through the carrier management algorithm to avoid spectrum resource conflicts and ensure the efficient utilization of spectrum resources. S5: Interference suppression and adaptive scheduling: The interference suppression technology and adaptive scheduling algorithm are adopted. According to the real-time interference situation and network topology changes, interference management and resource scheduling are carried out for audio transmission to ensure the low-latency transmission of audio data.

2. The method for dynamically adjusting carrier density and power in wireless audio transmission based on QoS according to claim 1, wherein: The machine learning prediction algorithm is a deep neural network (DNN). The neural network includes multiple hidden layers, which are used to extract the non-linear relationships in the network historical data to improve the prediction accuracy of future network load changes.

3. The method for dynamically adjusting carrier density and power of wireless audio transmission based on QoS according to claim 1, wherein: The intelligent power control algorithm is based on the "minimum power consumption first" strategy. On the premise of ensuring the audio transmission quality, the transmission power of audio data is dynamically adjusted to reduce the energy consumption of network devices.

4. The method for dynamically adjusting carrier density and power of wireless audio transmission based on QoS according to claim 1, wherein: The carrier density adjustment module dynamically allocates wireless spectrum resources through channel quality and user distribution information, improves the spectrum utilization rate by increasing the carrier density, and reduces the occurrence of signal attenuation areas in the network.

5. The method for dynamically adjusting carrier density and power based on QoS for wireless audio transmission according to claim 1, wherein: The interference suppression technology adopts a spectrum sensing algorithm. By real-time monitoring of the current spectrum resources, the interference sources are identified and the transmission strategy is adjusted to reduce the impact of interference on audio transmission.

6. The method for dynamically adjusting carrier density and power based on QoS for wireless audio transmission according to claim 1, characterized in that: The adaptive scheduling algorithm prioritizes the transmitted audio data according to the real-time load and QoS requirements to ensure that high-priority audio data is transmitted first when the network load is high.

7. The method for dynamically adjusting carrier density and power in QoS-based wireless audio transmission according to claim 1, wherein: The method further includes intelligent route selection based on the network topology to ensure the transmission of audio data through the optimal path and reduce network latency and packet loss.

8. The method for dynamically adjusting carrier density and power based on QoS for wireless audio transmission according to claim 1, characterized in that: The machine learning model used in the method is regularly trained and updated through the network management system to optimize the prediction algorithm in real time.