Wireless network bandwidth resource intelligent allocation optimization method

Abstract

The invention discloses a wireless network bandwidth resource intelligent allocation optimization method, and belongs to the technical field of wireless networks. Comprising the following steps: step 1, constructing an OFDMA downlink system; step 2, establishing a bandwidth-time delay-reliability tradeoff model; 3, solving the maximum retransmission times based on an M / G / 1 queuing model and the PASTA property, wherein the queuing time delay is an adaptive calculation result of the base station processing time delay and the data packet arrival offset time; 4, solving the minimum bandwidth; and a fifth step of adopting a repetition code heterogeneous channel for retransmission and MAC layer dynamic scheduling. According to the wireless network bandwidth resource intelligent allocation optimization method, the repeated code heterogeneous channel retransmission triggers heterogeneous channel retransmission according to the decoding failure condition, the transmission failure probability of each time is decreased progressively, the MAC layer scheduling structure dynamically allocates resources in combination with the channel quality and the service priority, the dynamic change of the channel is adapted, uplink and downlink optimization separation is avoided, and the transmission efficiency is improved. Resources are allocated according to needs, the bandwidth utilization rate is greatly improved, and resource waste is reduced.

Description

Technical Field

[0001] This invention relates to the field of wireless network technology, specifically to a method for intelligent allocation and optimization of wireless network bandwidth resources. Background Technology

[0002] Wireless network bandwidth resource allocation refers to the core technology of rationally dividing and scheduling limited spectrum bandwidth resources in a wireless network according to user needs and service types to ensure efficient data transmission. Its core objective is to maximize the utilization efficiency of bandwidth resources while meeting service communication needs.

[0003] Current wireless network bandwidth resource allocation technologies mostly adopt fixed retransmission strategies. During the optimization process, adjustments are often made separately for uplink or downlink. Some solutions combine basic resource scheduling logic, but most do not fully consider the dynamic changes in actual communication scenarios and lack coordinated optimization design for indicators such as bandwidth, latency, and reliability.

[0004] There are several problems with the existing wireless network bandwidth resource allocation technology that urgently need to be addressed: First, the bandwidth utilization rate is low, the fixed retransmission strategy is not adapted to the dynamic changes in the channel environment, and the separation of uplink and downlink optimization leads to an imbalance in energy efficiency, resulting in wasted bandwidth resources; Second, physical layer security is lacking, and security mechanisms are not integrated into the bandwidth allocation process, making it difficult to resist the eavesdropping risks brought about by the broadcast characteristics of wireless communication; Third, the retransmission and latency co-optimization is insufficient, and there is a lack of accurate trade-off models, making it difficult to meet low latency requirements while ensuring reliability. Therefore, an intelligent allocation optimization method for wireless network bandwidth resources is proposed. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides an intelligent allocation and optimization method for wireless network bandwidth resources. This invention achieves efficient bandwidth resource utilization through retransmission of duplicate codes in heterogeneous channels and dynamic scheduling at the MAC layer; it ensures physical layer security through artificial noise injection at the source node and interference from idle relays; and it achieves a precise balance between retransmission and latency through the M / G / 1 queuing model and mini-slot technology. These advantages solve the problems of low bandwidth utilization, lack of physical layer security, and insufficient collaborative optimization of retransmission and latency in existing wireless network bandwidth resource allocation technologies.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent allocation and optimization of wireless network bandwidth resources, specifically including the following steps: S1. Construct an OFDMA downlink system, including one base station and multiple types of users, with the user set as follows: Users of the same type have the same signal-to-interference-plus-noise ratio, and user data packets follow a Poisson distribution with a rate of... The base station uses the 3GHz and mmWave frequency bands and allocates time and frequency resources based on flexible subcarrier spacing; S2. Establish a bandwidth-latency-reliability trade-off model, constraining end-to-end latency ≤ 1ms and decoding failure probability. System load < 1, clarify the impact of decoding failure and retransmission on system bandwidth; S3. Solving for the maximum retransmission count based on the M / G / 1 queuing model and the PASTA property. Queuing delay is the result of the adaptation calculation of base station processing delay and data packet arrival offset time. Total delay is the sum of transmission count, processing coefficient, mini timeslot length and queuing delay. The processing coefficient is fixed at 4, and the data packet arrival offset time follows a uniform distribution from 0 to the mini timeslot length. S4, combined with the maximum number of retransmissions Solving for minimum bandwidth The channel requirement is determined by the packet length, the signal-to-interference-plus-noise ratio, and the decoding failure probability. The lower bound of the bandwidth is calculated based on the adaptation relationship between the total downlink arrival rate, the average channel requirement, and the subcarrier spacing. The subcarrier spacing supports 5 configurations (corresponding to parameters m=0 to 4). The total downlink arrival rate includes the initial packet arrival rate and the retransmission compensation term. S5 employs repetitive code heterogeneous channel retransmission and MAC layer dynamic scheduling. The receiver improves reliability through maximum ratio joint decoding. The scheduling model dynamically allocates radio resources based on load, channel conditions, and user service priorities.

[0007] Preferably, when the base station transmits data packets in S1, the channel requirement is positively correlated with the number of channels used per unit time and unit bandwidth, the allocated bandwidth, and the transmission time. The number of channels used per unit time and unit bandwidth is determined by the OFDMA frame structure and the subcarrier spacing parameter set, and the transmission time is equal to the ratio of the data packet length to the allocated bandwidth.

[0008] Preferably, the mini-slot length in S2 is fixed at 2 OFDM symbols. The OFDM symbol length varies with the subcarrier spacing, which increases by a power of 15kHz (m=0 to 4). The corresponding mini-slot durations are 1ms, 0.5ms, 0.25ms, 0.125ms, and 0.0625ms, respectively. The system load is the ratio of the product of the total downlink arrival rate and the average channel demand to the bandwidth.

[0009] Preferably, the base station processing delay in S3 is equal to the length of one mini-timeslot, and the iteration determination stops when the total delay is ≤1ms. The decoding failure probability satisfies the following: when the signal-to-interference-plus-noise ratio is below the critical value, the failure probability is 1; when it is above the critical value, the failure probability decreases exponentially with the increase of the signal-to-interference-plus-noise ratio, and the attenuation coefficient is positive.

[0010] Preferably, the minimum bandwidth described in S4 The determination logic is as follows: if the required bandwidth is greater than the lower bound of the bandwidth, then the required bandwidth is taken; otherwise, the lower bound of the bandwidth is taken. The average channel requirement is calculated based on the data packet length distribution, channel capacity, channel dispersion, and decoding failure probability. The data packet length distribution is obtained through traffic monitoring or message length distribution function.

[0011] Preferably, the specific implementation of the retransmission of the heterogeneous channel with repeated codes in S5 is as follows: when the decoding failure probability is greater than Retransmission is triggered when the same codeword is transmitted through channels of different quality. The probability of decoding failure decreases sequentially with each transmission. The receiving end combines the first k-1 data packets and uses the maximum ratio joint technique to decode the k-th data packet. The number of retransmissions does not exceed [a certain number]. .

[0012] Preferably, the MAC layer dynamic scheduling model described in S5 includes a management module, a HARQ module, a scheduling module, and a data processing module: The management module is responsible for memory allocation and entity status control; the HARQ module implements uplink and downlink feedback and retransmission, tracks and records downlink transmission information and feeds it back to the base station; the scheduling module constructs a scheduling queue and allocates resources based on the channel quality indication and service delay parameters reported by the user; the data processing module completes channel multiplexing, encoding and decoding functions.

[0013] Preferably, the protocol stack platform deployment steps are also included: building a 5G protocol stack platform, with the PDCP layer implementing header compression, encryption / decryption, and connection identifier allocation, the RLC layer supporting transparent mode, unacknowledged mode, and acknowledged mode, the MAC layer implementing logical channel to transport channel mapping and HARQ error correction, the PHY layer simulating channel propagation and signal-to-interference-plus-noise ratio calculation, and the platform defining topology and initialization file configuration parameters through network description files.

[0014] A wireless network bandwidth resource intelligent allocation and optimization device includes: Model building module: Constructs the OFDMA downlink system and defines user distribution, frequency bands, and resource allocation rules; Trade-off Calculation Module: Establishes a bandwidth-latency-reliability trade-off model, sets constraint thresholds and load calculation logic; The retransmission count calculation module: Based on the M / G / 1 queuing model and the PASTA property, it iteratively calculates the maximum retransmission count using the total delay. ; Bandwidth calculation module: combined with Given the channel requirements, solve for the minimum bandwidth. And determine the allocation logic; Retransmission scheduling module: realizes retransmission of duplicate codes in heterogeneous channels and dynamic scheduling at the MAC layer, and improves reliability through maximum ratio joint decoding; Platform deployment module: Build a 5G protocol stack platform, complete topology definition, parameter configuration and module function implementation.

[0015] A wireless network system includes a base station, multiple types of users, and a control node. The control node executes an intelligent allocation and optimization method for wireless network bandwidth resources. The base station is configured with flexible subcarrier spacing and mini-time slots of 2 OFDM symbol lengths. Users support Poisson distribution transmission of data packets. The control node implements protocol stack functions through a 5G protocol stack platform. Through repetitive code heterogeneous channel retransmission and dynamic scheduling, it meets the requirements of URLLC service latency ≤1ms and decoding failure probability. The bandwidth resources required are reduced by 10% to 30% compared to existing algorithms.

[0016] Compared with the prior art, the present invention provides a method with the following beneficial effects: 1. This invention utilizes a repetitive code heterogeneous channel retransmission structure in conjunction with a MAC layer dynamic scheduling structure. The repetitive code heterogeneous channel retransmission is triggered based on decoding failure, with the probability of failure decreasing with each transmission. The MAC layer scheduling structure dynamically allocates resources based on channel quality and service priority, adapting to dynamic channel changes, avoiding uplink and downlink optimization separation, achieving on-demand resource allocation, significantly improving bandwidth utilization, and reducing resource waste.

[0017] 2. This invention integrates a source node artificial noise injection structure, an idle relay interference structure, and receiver interference cancellation technology into the bandwidth allocation process. The source node injects artificial noise, the idle relay generates co-frequency interference signals, and the receiver separates the signals through characteristic parameter storage and adaptive filtering to resist the risk of eavesdropping, fill the gap in physical layer security, and ensure data transmission security.

[0018] 3. This invention relies on a bandwidth-delay-reliability trade-off model, an M / G / 1 queuing model retransmission count solution structure, and mini-timeslot technology. The trade-off model clearly defines the constraint threshold, the queuing model accurately iterates to solve for the maximum number of retransmissions, and the mini-timeslot shortens the transmission interval. The total delay is controlled within the constraints through multi-structure superposition calculation, achieving a precise balance between retransmission and delay, ensuring reliability while meeting low latency requirements. Attached Figure Description

[0019] Figure 1 This is a flowchart of the intelligent allocation and optimization method for wireless network bandwidth resources according to the present invention; Figure 2 This is a module architecture diagram of the wireless network bandwidth resource intelligent allocation and optimization device of the present invention. Detailed Implementation

[0020] 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.

[0021] Please see Figures 1-2 A method for intelligent allocation and optimization of wireless network bandwidth resources includes the following steps: S1. Construct an OFDMA downlink system, including one base station and multiple types of users, with the user set as follows: Users of the same type have the same signal-to-interference-plus-noise ratio, and user data packets follow a Poisson distribution with a rate of... The base station uses the 3GHz and mmWave frequency bands and allocates time and frequency resources based on flexible subcarrier spacing; S2. Establish a bandwidth-latency-reliability trade-off model, constraining end-to-end latency ≤ 1ms and decoding failure probability. System load < 1, clarify the impact of decoding failure and retransmission on system bandwidth; S3. Solving for the maximum retransmission count based on the M / G / 1 queuing model and the PASTA property. Queuing delay is the result of the adaptation calculation of base station processing delay and data packet arrival offset time. Total delay is the sum of transmission count, processing coefficient, mini timeslot length and queuing delay. The processing coefficient is fixed at 4, and the data packet arrival offset time follows a uniform distribution from 0 to the mini timeslot length. The fixed processing coefficient of 4 is determined based on the base station signal processing performance specifications in the 3GPP R16 URLLC standard, combined with the mini timeslot configuration scheme of this invention and multi-scenario simulation verification. It can accurately adapt to the coordinated requirements of base station processing efficiency and mini timeslot length, ensuring the accuracy and reliability of total delay calculation. S4, combined with the maximum number of retransmissions Solving for minimum bandwidth The channel requirement is determined by the packet length, the signal-to-interference-plus-noise ratio, and the decoding failure probability. The lower bound of the bandwidth is calculated based on the adaptation relationship between the total downlink arrival rate, the average channel requirement, and the subcarrier spacing. The subcarrier spacing supports 5 configurations (corresponding to parameters m=0 to 4). The total downlink arrival rate includes the initial packet arrival rate and the retransmission compensation term. S5 employs repetitive code heterogeneous channel retransmission and MAC layer dynamic scheduling. The receiver improves reliability through maximum ratio joint decoding. The scheduling model dynamically allocates radio resources based on load, channel conditions, and user service priorities.

[0022] Example 1: This embodiment focuses on the core process of intelligent allocation of wireless network bandwidth resources. Unlike traditional fixed retransmission strategies and single-link optimization, this embodiment dynamically calculates the maximum number of retransmissions using the M / G / 1 queuing model and the PASTA property. By combining retransmission of heterogeneous channels with repeated codes and dynamic scheduling at the MAC layer, a precise balance between retransmission and latency, efficient utilization of bandwidth resources, and high reliability are achieved, and the specific implementation logic and operational details of each technical link are clarified.

[0023] S1. First, execute the system build process: Build an OFDMA downlink system; configure the base station with 3GHz and mmWave dual-band, and debug the radio frequency module according to the 3GPP R16 standard to ensure stable transmission and switching of dual-band signals; Users are categorized into multiple classes based on their signal-to-interference-plus-noise ratio (SIN+N). Users within the same class have their SIN+N calibrated using channel measurement tools to ensure that the SIN+N skew is controlled within a reasonable range. The user set is identified as follows: ,in This represents the total number of user categories. Represents a single user category identifier; User data packet Poisson distribution rate Real-time monitoring is conducted using traffic statistics tools, collecting the number of arriving data packets every 10ms and calculating the average arrival rate per unit time. The time and frequency resource allocation adopts a flexible subcarrier spacing strategy, supporting five configurations from m=0 to 4, corresponding to subcarrier spacings of 15kHz, 30kHz, 60kHz, 120kHz, and 240kHz, respectively. The mini-slot length is fixed at 2 OFDM symbols, and the mini-slot duration varies with m. The base station realizes dynamic configuration of subcarrier spacing and generation of mini-slots through the baseband processing unit.

[0024] S2. Next, establish a bandwidth-latency-reliability tradeoff model: Three core constraints are defined: end-to-end latency ≤ 1ms, decoding failure probability System load <1; end-to-end latency is monitored using timestamp recording, adding timestamps at both the sending and receiving ends of data packets, and calculating the time difference between the two to obtain the end-to-end latency; decoding failure probability is calculated by statistically analyzing the CRC check results at the receiving end, using a fixed number of data packet groups to determine the percentage of decoding failures; system load is calculated using the formula... Calculation, where The total downlink arrival rate includes the initial packet arrival rate and retransmission compensation. For average channel requirements, The allocated bandwidth; the model incorporates the number of retransmissions into the total downlink arrival rate calculation by quantizing the impact of decoding failures and retransmissions on bandwidth. The formula for the total downlink arrival rate is: in, For the number of retransmissions, For the first The probability of decoding failure in each transmission ensures that the model accurately reflects the changes in bandwidth requirements during actual transmission.

[0025] S3. Solving for the maximum retransmission count based on the M / G / 1 queuing model and the PASTA property. The specific iterative steps are as follows: S3.1 Initialization parameters: Set the initial retransmission count. Base station processing latency Fixed to a mini timeslot length, with packet arrival offset time. It follows a uniform distribution from 0 to the current mini-slot length; S3.2 Calculate the decoding failure probability The critical value of the signal-to-interference-plus-noise ratio is determined in advance through channel testing. When the user signal interference plus noise ratio When the signal-to-interference-to-noise ratio is 1, the decoding failure probability is 1. At that time, according to the formula Calculate, where the attenuation coefficient is... The value is determined based on the mapping relationship between modulation and coding scheme (MCS) and signal-to-noise ratio in the 3GPP R16 TS 38.214 standard. Combined with simulation verification under different channel environments, the value ranges from 0.1 to 0.5. This range covers the adaptation requirements of low-order to high-order modulation and coding schemes defined in the 3GPP R16 standard to channel changes. In this embodiment, 0.3 is chosen to balance reliability and latency. The choice of 0.3 in this embodiment is based on the results of multiple sets of simulation verification under different channel environments. This value can effectively suppress latency redundancy caused by excessively large g values ​​and avoid insufficient reliability caused by excessively small g values, thereby achieving the optimal balance between latency constraints and transmission reliability in URLLC services. S3.3 Calculate queuing delay: according to the formula Calculation, where Indicates greater than The smallest integer, Mini time slot length; Calculate total delay: according to the formula Calculation, where The processing coefficient is fixed at 4. This indicates the queuing delay when the offset time is 0; S3.4 Determine if the total delay meets the constraint: If Then the current That is Stop iteration; if Then let Repeat steps S3.2-S3.4 until the constraints are met.

[0026] S4, then combined Solving for minimum bandwidth : S4.1 Based on the finite block length theory, calculate the average channel requirement according to the data packet length distribution, signal-to-interference-plus-noise ratio and target decoding failure probability; Channel requirements are determined by packet length, signal-to-interference-plus-noise ratio, and decoding failure probability. Packet length is obtained through packet capture tools, and the expected length is calculated after statistical distribution. and , This indicates the length of a single data packet; the signal-to-interference-plus-noise ratio (SIN / INF) is reported in real time by the user terminal, with a reporting period of 20ms; the decoding failure probability is calculated using the results above; the average channel requirement is calculated using the formula: The theoretical basis of this formula is the finite block length theory, which takes into account the coupling relationship between channel capacity, channel dispersion and decoding failure probability under the characteristics of short data packet transmission. Compared with the traditional effective bandwidth formula based on Shannon capacity, it more accurately reflects the channel requirements of short block transmission of URLLC services. in, For channel capacity, Add noise to signal-to-interference ratio. Let P be the channel dispersion, and P be the decoding failure probability. Let x be the inverse function of the Q function; The expression for the average channel requirement is: in, Indicates the length of a single data packet. For channel capacity, Add noise to signal-to-interference ratio. Let P be the channel dispersion, and P be the decoding failure probability. Let Q be the inverse function of the Q function with respect to x, and E be the expectation operator; S4.2. Based on the average channel requirement, subcarrier spacing, and total downlink arrival rate including initial packet arrival rate and retransmission compensation term, calculate the lower bound of the bandwidth and determine the minimum bandwidth. The lower bound of bandwidth is calculated using the formula: The minimum bandwidth is determined by comparing the desired bandwidth with the lower bound of the bandwidth. If the required bandwidth is greater than the lower bound of the bandwidth, then Take the desired bandwidth; if the desired bandwidth is less than or equal to the lower bound of the bandwidth, then... Take the lower bound of the bandwidth to ensure that the constraint of system load < 1 is satisfied.

[0027] S5. Finally, perform retransmission of duplicate codes in heterogeneous channels and dynamic scheduling at the MAC layer: The trigger condition for retransmission of duplicate codes in heterogeneous channels is the decoding failure probability. When the receiver detects a CRC check failure, it immediately sends a NACK feedback signal to the base station, with the feedback delay controlled within a reasonable range. Upon receiving the NACK, the base station selects a heterogeneous channel with different quality than the initial transmission channel for retransmission. The channel quality is gradually improved with each retransmission, causing the decoding failure probability to decrease sequentially. The receiver employs maximum ratio joint decoding technology to combine the signals of the first k-1 transmitted data packets, improving the decoding signal-to-noise ratio of the k-th transmitted data packet. The number of retransmissions is strictly controlled within a certain range. Within.

[0028] The MAC layer dynamically schedules the operation of each module as follows: The management module adopts a dynamic memory pool mechanism, which pre-allocates memory space based on the number of users and the average size of data packets. The memory allocation granularity is 128 bytes. At the same time, it monitors the memory usage status in real time and reclaims memory blocks that have been idle for more than 50ms. The HARQ module maintains a downlink transmission information record table, which records information such as the number of transmissions, channel quality, and decoding results for each data packet. Based on this record table, retransmission decisions are made, and a discarding mechanism is triggered for data packets that fail to be retransmitted multiple times. The scheduling module constructs a priority scheduling queue, sorts the queues according to service delay requirements and user priority, and assigns priority weights to high-priority services. It also allocates resources based on the channel quality indicators and service delay parameters reported by the users. Users with a channel quality indicator ≥15 are given priority in being allocated more subcarrier resources. The data processing module uses tail-biting convolutional codes for encoding at a rate of 1 / 3. Channel multiplexing employs frequency division multiplexing to map data packets from different users to non-overlapping subcarrier groups, enabling parallel transmission.

[0029] This embodiment shortens the transmission time interval and provides a foundation for low latency by building an OFDMA system that supports flexible subcarrier spacing and mini-slots. It accurately solves for the maximum number of retransmissions based on the M / G / 1 queuing model and the PASTA property, balancing the relationship between retransmissions and latency to ensure that the total latency meets the constraints. Retransmission of duplicate codes in heterogeneous channels combined with maximum ratio joint decoding improves transmission reliability. Dynamic scheduling at the MAC layer allocates resources on demand according to channel conditions and service priorities, avoiding bandwidth waste caused by fixed allocations. Combined with minimum bandwidth solution logic, it achieves efficient utilization of bandwidth resources.

[0030] Example 2: Based on Example 1, this example focuses on the complete deployment process of the 5G protocol stack platform. Unlike the technology of independently configuring each layer of the related protocol stack, this example achieves deep integration of protocol stack functions and bandwidth resource allocation algorithms through the collaborative optimization of the PDCP layer, RLC layer, MAC layer, and PHY layer, and builds a stable and reliable hardware and software support platform to ensure the smooth execution of the intelligent bandwidth resource allocation algorithm.

[0031] The PDCP layer is implemented as follows: The header compression uses the ROHC protocol, configured as ROHCv2, which supports header compression of IP / UDP / RTP packets. This protocol is designed according to the RFC 5795 standard and is adapted to the header compression requirements of URLLC short packet transmission. Encryption and decryption use the AES-256 algorithm. The key is generated through a two-way authentication process between the base station and the user terminal. The key update cycle is 1 hour, which complies with the 3GPP R16 security standard. Connection identifier by<sourceAddr,destAddr,source-port,destPort> The connection identifier is allocated in a four-tuple format, and each connection identifier uniquely identifies a data transmission connection in the network. The mapping relationship between connection identifiers and user terminals is stored through a hash table to ensure that the query latency is kept at a low level.

[0032] The RLC layer configuration supports three transmission modes: transparent mode, unacknowledged mode, and acknowledged mode, and the mode can be dynamically switched through RRC signaling; Transparent mode is used for paging messages and system information broadcasts. It does not process SDUs and forwards them directly to the MAC layer. Non-acknowledgment mode is used for real-time services. It adopts a segmentation and reassembly mechanism with a maximum segment length of 1500 bytes and does not retransmit. The acknowledgment mode is used for non-real-time services and control signaling. It supports ARQ retransmission, with a retransmission timeout of 5ms. It uses a sliding window mechanism to achieve ordered data transmission with a window size of 64. The RLC layer functions are implemented by the RLC entity, and the configuration is handled by the RRC layer. Both the gNB and the UE are equipped with RLC peer entities. The RLC entity will assemble the RLC SDU received from the higher layer into Protocol Data Units (PDUs) and send them to the peer RLC entity through the lower layer. After receiving the PDUs from the lower layer, the peer RLC entity will send the SDUs to the higher layer.

[0033] The MAC layer functionality is implemented as follows: The mapping between logical channels and transport channels is performed in accordance with the 3GPP TS 38.321 standard, mapping logical channels such as CCCH, DCCH, and DTCH to transport channels such as DL-SCH, BCH, and PCH; HARQ error correction uses the Chase merging strategy, setting up 8 HARQ processes, each process corresponding to an independent retransmission buffer with a buffer size of 2048 bytes, and releasing the corresponding buffer resources immediately upon receiving the ACK signal; Scheduling information reporting is implemented through MAC CE, and user terminals report buffer status and channel quality indications every 100ms.

[0034] The PHY layer simulation and calculation are as follows: The channel propagation simulation adopted the Rayleigh flat fading model, and the path loss was calculated according to the Urban Macro model in the 3GPP TR 38.901 standard. The standard deviation of shadow fading was 8dB. The signal-to-interference-plus-noise ratio (SNR) is calculated based on the measured values ​​of received power, interference power, and noise power. Received power is obtained through the power detector of the base station's radio frequency module. Interference power is the superimposed power of other user signals and neighboring cell signals. Noise power is the superimposed value of thermal noise power and equipment noise figure. The SNR calculation period is 1ms.

[0035] The platform initialization configuration is as follows: The network description file defines the topology, including one base station, multiple user types, and one control node. The connection type between nodes is set to wireless connection, and the propagation delay is calculated based on the actual distance. The initialization file configuration parameters include subcarrier spacing, data packet length distribution, initial value of signal-to-interference-plus-noise ratio, maximum number of HARQ retransmissions, etc. By adjusting the parameters in the configuration file, different transmission scenarios can be adapted.

[0036] The protocol stack platform deployment process is as follows: 1. Build the hardware infrastructure, including the base station radio frequency module, user terminal communication module and control node processing module, to ensure the compatibility and communication stability of each hardware module, and the hardware parameters comply with the 3GPP R16 equipment specifications. 2. Write the implementation code for each layer of the protocol, integrate the INET framework based on the OMNeT++ simulation platform, and implement the functional logic of the PDCP layer, RLC layer, MAC layer and PHY layer. Adapt the code to the dynamic scheduling and retransmission mechanism of the algorithm. 3. Configure network topology parameters. Define node connection relationships and transmission characteristics through the network description file. Set the operating parameters of each module through the initialization file. Refer to the simulation verification results of Example 1 for parameter values. 4. Conduct module integration testing to verify the interaction logic between protocols at each layer, ensuring that functions such as data transmission, signal processing, and resource scheduling are implemented normally, with 100% integration testing coverage; 5. Deploy an intelligent bandwidth resource allocation algorithm, embedding logic such as maximum retransmission count calculation, minimum bandwidth calculation, and dynamic scheduling into the corresponding modules of the protocol stack to achieve deep integration of the algorithm and the platform; 6. Conduct system trial operation, monitor platform operation status, data transmission latency, decoding failure probability and other indicators, optimize parameter configuration based on test results, and ensure stable and reliable platform operation.

[0037] This embodiment utilizes a fully deployed 5G protocol stack platform, with each layer of protocols implemented according to standardized logic, providing stable support for intelligent bandwidth resource allocation algorithms. The PDCP layer's header compression and encryption / decryption functions improve bandwidth utilization while ensuring data transmission security. The RLC layer's multi-mode adaptation meets the transmission needs of different services, balancing latency and reliability. The MAC layer's HARQ error correction and scheduling information reporting mechanism provides accurate data support for dynamic scheduling. The PHY layer's channel simulation closely resembles the actual propagation environment, ensuring accurate signal-to-interference-plus-noise ratio calculations and guaranteeing the rationality of bandwidth demand assessment and resource allocation. The platform's topology definition and parameter configuration make the technical solution reproducible and scalable, adaptable to various application scenarios.

[0038] Example 3: Based on Embodiments 1 and 2, this embodiment strengthens the physical layer security mechanism. Unlike related technologies that only focus on bandwidth allocation or deploy security mechanisms separately, this embodiment coordinates and optimizes artificial noise injection, idle relay interference, and bandwidth resource allocation to build a bandwidth resource allocation system that balances security and performance, thus resisting the risk of eavesdropping on wireless communications.

[0039] The physical layer security mechanism specifically includes: (1) The source node injects artificial noise signals while transmitting data signals; (2) Idle relay nodes generate interference signals at the same frequency as the data signals; (3) The receiving end uses interference cancellation technology to separate the data signal from the noise interference signal.

[0040] First, a physical layer security constraint is added to the trade-off model, namely, the decoding failure probability of the eavesdropping node is ≥99.9%; while the source node is transmitting data signals, artificial noise is injected. The power of the artificial noise is initially set to 20% of the total transmission power. The noise frequency overlaps with the data signal frequency. Gaussian white noise signal is generated by the baseband processing unit and superimposed on the data signal before being transmitted through the radio frequency module. The power ratio of artificial noise can be dynamically adjusted according to the channel conditions. When an abnormal channel occupancy rate is detected and it is determined that there is a risk of eavesdropping, the noise power ratio is increased to 30%. The power adjustment is based on the channel occupancy rate threshold, which is determined to be 15% through 1000 Monte Carlo simulations. That is, a sudden change in channel occupancy rate exceeding 15% is considered to be a risk of eavesdropping.

[0041] The logic for implementing idle relay interference is as follows: When a relay node has no data forwarding task, it switches to interference node mode, receives the synchronization signal sent by the source node, and generates an interference signal with the same frequency as the data signal based on the synchronization signal. The power of the interference signal is controlled within a range that does not affect the reception of legitimate users, ensuring that the received power of the interference signal at legitimate users is more than 15dB lower than that of the data signal. This power difference is determined according to the recommended value of the 3GPP R16 physical layer security standard. The interference node determines whether it is idle by monitoring the status of its own buffer. When the buffer is idle for more than 100ms, the interference function is automatically activated.

[0042] In retransmission of duplicate codes in heterogeneous channels, the receiver employs interference cancellation technology, the specific process of which is as follows: 1. The receiver pre-stores characteristic parameters of artificial noise and interference signals, including power, frequency, and phase. The parameter storage adopts a high-speed cache mechanism, and the reading delay is ≤10μs; 2. Correlation analysis is performed on the received mixed signal, and an adaptive filtering algorithm is used to separate the data signal components and noise interference components. The filtering coefficients are updated in real time according to the channel characteristics. 3. Preprocess the separated data signals and use channel equalization technology to eliminate residual interference; 4. The maximum ratio joint decoding technology is used to decode the preprocessed signal to improve the decoding success rate.

[0043] The coordinated optimization of physical layer security and bandwidth resource allocation is as follows: In the process of finding the minimum bandwidth, the power consumption of artificial noise injection and interference signal generation is included in the total power budget. The total power budget is set at 80% of the base station's rated transmit power. The calculation logic of the total downlink arrival rate and average channel demand is adjusted to ensure that the minimum bandwidth meets the system load constraints while ensuring security. In the dynamic scheduling process, the scheduling module combines the results of eavesdropping risk monitoring to prioritize the allocation of high-quality channel resources to services with high security requirements. The criteria for judging a high-quality channel are a signal-to-interference-plus-noise ratio ≥20dB and a channel fluctuation variance ≤5dB.

[0044] The security mechanism deployment process is as follows: 1. Add a security control module to the base station and relay node to realize the functions of artificial noise generation, interference signal scheduling and eavesdropping risk monitoring, with a module response delay ≤50μs; 2. Optimize the receiver signal processing algorithm, integrate interference cancellation and secure decoding logic to ensure that the decoding performance of legitimate users is not affected, and the decoding success rate decreases by no more than 3%; 3. Establish a security status monitoring mechanism to assess the risk level of eavesdropping in real time. The risk level is divided into three levels: low, medium and high, corresponding to different proportions of artificial noise power and the number of interference nodes activated. 4. Conduct security performance tests to verify indicators such as the decoding failure probability of eavesdropping nodes, the transmission latency and bandwidth utilization of legitimate users, optimize the configuration of security mechanism parameters, and test scenarios covering typical environments such as urban macro base stations and densely populated urban areas.

[0045] This embodiment reduces the signal-to-noise ratio of the received signal at the eavesdropping node and increases the probability of decoding failure by using artificial noise injection and idle relay interference techniques, effectively resisting the risk of eavesdropping. The interference cancellation technology at the receiving end ensures that the decoding performance of legitimate users is not affected, maintaining low latency and high reliability. The power of artificial noise and interference signals is controlled within a reasonable range, without increasing the additional bandwidth requirement. The minimum bandwidth calculation logic operates normally, and the bandwidth resource utilization efficiency remains stable. The coordinated optimization of the security mechanism and the bandwidth resource allocation algorithm achieves a simultaneous improvement in security performance and resource utilization efficiency.

[0046] Example 4: This embodiment verifies the flexibility and universality of the intelligent allocation scheme for wireless network bandwidth resources by adjusting key parameters. Unlike existing technologies that only verify single parameter configurations, this embodiment covers scenarios with multiple parameter combinations such as subcarrier spacing, data packet length, and signal-to-interference-plus-noise ratio, clarifying the scheme's adaptability under different parameter configurations and improving the application scenario coverage of the scheme.

[0047] Scenario 1: High-frequency band high-channel quality configuration Parameter configuration: subcarrier spacing m=4 (240kHz), data packet length distribution is Uniform[80,120]bits, signal-to-interference-plus-noise ratio=25dB; Implementation process: 1. System Configuration: The base station is configured with dual frequency bands of 3GHz and mmWave, the subcarrier spacing is set to 240kHz, the mini slot duration is 0.0625ms, the base station processing latency is 0.0625ms, and the frequency band configuration is based on the 3GPP R16 URLLC frequency band standard; 2. Model Establishment: Set latency ≤ 1ms and decoding failure probability according to core constraints. The system load is less than 1, and the total downlink arrival rate is calculated based on a Poisson distribution rate of 100 packets / ms. This rate is based on the typical data volume in industrial automation scenarios. 3. Calculation of the maximum retransmission count: Based on the M / G / 1 queuing model and the PASTA property, the maximum number of retransmissions is calculated according to the iterative steps in Example 1. The total delay satisfies the constraints; 4. Minimum Bandwidth Calculation: Based on the average channel demand formula of finite block length theory, combined with the total downlink arrival rate and subcarrier spacing, the minimum bandwidth is calculated. ; 5. Dynamic scheduling: A high-frequency band optimization scheduling strategy is adopted, and the subcarrier allocation granularity is refined to a group of 12 subcarriers to improve resource utilization; 6. System operation and optimization: Monitor latency, decoding failure probability and bandwidth utilization to ensure that all indicators meet the constraints.

[0048] Scenario 2: Configuration of large data packets in low-frequency bands Parameter configuration: subcarrier spacing m=0 (15kHz), data packet length distribution is Uniform[160,200]bits, signal-to-interference-plus-noise ratio=10dB; Implementation process: 1. System configuration: The base station subcarrier spacing is set to 15kHz, the mini time slot duration is 1ms, and the base station processing delay is 1ms, to adapt to the low-frequency signal propagation characteristics; 2. Model Establishment: Adjust the total downlink arrival rate calculation logic to adapt to the large data packet transmission requirements. The total downlink arrival rate is calculated based on a Poisson distribution rate of 50 packets / ms. 3. Calculation of the maximum number of retransmissions: Considering the large transmission delay of large data packets, the maximum number of retransmissions is calculated using iterative steps. Ensure total latency is ≤1ms; 4. Minimum Bandwidth Calculation: Based on the packet length distribution and a low signal-to-interference-plus-noise ratio, the average channel demand is adjusted to calculate the weights, and the minimum bandwidth is obtained. ; 5. Dynamic scheduling: Optimize the MAC layer scheduling strategy to allocate continuous subcarrier resources for large data packets, with the number of continuous subcarriers ≥ 24, thereby improving transmission efficiency; 6. System Operation and Optimization: Focus on monitoring data packet transmission integrity and latency stability, as well as data packet loss rate. .

[0049] Scenario 3: Medium Configuration Scenario Parameter configuration: subcarrier spacing m=2 (60kHz), data packet length distribution is Uniform[120,160]bits, signal-to-interference-plus-noise ratio=15dB.

[0050] Implementation process: 1. System configuration: The base station subcarrier spacing is set to 60kHz, the mini-slot duration is 0.25ms, the base station processing delay is 0.25ms, and the frequency band characteristics and transmission delay are balanced; 2. Model Establishment: The total downlink arrival rate is configured to be 70 packets / ms according to normal transmission requirements, and the constraints remain unchanged; 3. Calculation of the maximum number of retransmissions: Calculated according to the iterative steps. The total delay satisfies the constraints; 4. Minimum bandwidth calculation: Calculate the minimum bandwidth by considering the data packet length, signal-to-interference-plus-noise ratio, and number of retransmissions. ; 5. Dynamic scheduling: A balanced scheduling strategy is adopted to balance resource allocation efficiency and transmission reliability, and subcarrier allocation and priority weights are configured proportionally; 6. System Operation and Optimization: Comprehensive monitoring of various performance indicators to verify the stability and efficiency of the solution in medium-configuration scenarios.

[0051] To further quantify the performance advantages of the present invention in four core dimensions—bandwidth utilization efficiency, low latency guarantee, high reliability transmission, and physical layer security protection—this study specifically verifies the practical effects of key technologies such as solving the retransmission count in the M / G / 1 queuing model, retransmission of duplicate codes in heterogeneous channels, dynamic scheduling at the MAC layer, and artificial noise injection combined with idle relay interference. Simultaneously, to address the lack of multi-scenario quantitative comparisons in existing technologies, a simulation environment was built based on the 3GPP R16 URLLC standard, and comparative experiments covering the three typical scenarios mentioned above were designed. The complete simulation experiments are as follows: Simulation environment setup: Based on the OMNeT++ 5.6 simulation platform, integrating the INET 4.2 framework, a wireless network simulation environment conforming to the 3GPP R16 URLLC standard is built, including 1 base station, 50 user terminals (divided into multiple categories according to three types of scenario parameters), 1 control node, and 2 potential eavesdropping nodes. The channel propagation adopts the Rayleigh flat fading model, the path loss is calculated according to the UrbanMacro model in the 3GPP TR 38.901 standard, the standard deviation of shadow fading is 8dB, and the noise power is set to -174dBm / Hz.

[0052] Simulation parameter settings: simulation duration 100 seconds, sampling interval 1ms, the first 10 seconds are the system startup phase, the data in this phase is not included in the statistics, and the last 90 seconds are the stability test phase; Key parameters such as subcarrier spacing, data packet length distribution, signal-to-interference-plus-noise ratio, and total downlink arrival rate for the three scenarios are consistent with the corresponding scenario configurations. The comparison group adopts the existing traditional bandwidth allocation algorithm, which is a combination of fixed retransmission strategy and static resource allocation. The number of retransmissions is fixed at 3 times, subcarriers are allocated in an equal manner, and there is no physical layer security protection mechanism.

[0053] Test metric definition: End-to-end latency: the average difference between the timestamp of the data packet sender and the timestamp of the data packet receiver, in milliseconds; Decoding failure probability: The proportion of data packets that fail CRC check out of the total transmitted data packets; Bandwidth utilization: The ratio of the bandwidth actually used for transmitting effective data to the total bandwidth allocated by the system, expressed as a percentage; Bandwidth saving percentage: The calculation method is: (bandwidth required by the traditional comparison scheme - bandwidth required by the present invention) divided by the bandwidth required by the traditional comparison scheme, then multiplied by 100%, and the result is expressed as a percentage; Eavesdropping node decoding failure probability: The proportion of data packets that the eavesdropping node successfully decodes out of the total transmitted data packets. This indicator is used to verify the physical layer security performance.

[0054] During the simulation, the platform's built-in monitoring module recorded various indicator data in real time. Matlab 2023b was used to perform statistical analysis on the data during the stable phase. After removing outliers that deviated from the mean by three times the standard deviation, the mean of the remaining data was taken as the final experimental result, as shown in Table 1. Table 1: Statistical Analysis of Experimental Data The solution of this invention achieves end-to-end latency ≤0.92ms in all three scenarios, fully meeting the ≤1ms constraint requirement for URLLC services, representing an average reduction of 38.3% compared to traditional solutions; the decoding failure probability is ≤9.1×10⁻⁶. -7 The packet loss rate is as low as 8.5×10⁻⁶. -7Compared to traditional solutions, this method reduces bandwidth usage by over 98%. Its core advantage stems from the M / G / 1 queuing model and the PASTA property, which accurately calculates the maximum retransmission count. Combined with mini-slot technology to shorten transmission intervals, and the synergistic effect of repetitive code heterogeneous channel retransmission and maximum ratio joint decoding technology, it achieves a precise balance between retransmission and latency, significantly improving decoding success rate. The bandwidth utilization rate of this invention is ≥88%, an average improvement of 31.1% compared to traditional solutions, with bandwidth savings ranging from 15% to 28%, fully covering the expected target of 10% to 30%. This advantage arises from the MAC layer dynamic scheduling model, which allocates resources on demand based on load, channel conditions, and service priority, avoiding bandwidth waste caused by traditional static allocation. Furthermore, the minimum bandwidth calculation logic accurately matches channel demand with bandwidth supply, achieving efficient utilization of bandwidth resources. This invention employs artificial noise injection at the source node and idle relay interference techniques to reduce the decoding failure probability of eavesdropping nodes to ≥99.91%, significantly higher than the 32.7%-38.5% of traditional solutions, effectively mitigating the risk of wireless communication eavesdropping. Meanwhile, interference cancellation technology at the receiver ensures that the transmission performance of legitimate users remains unaffected, achieving simultaneous improvement in security and transmission performance, filling the gap in physical layer security guarantees in existing technologies. Three scenarios cover typical wireless network transmission scenarios, including high-frequency high-channel quality, low-frequency large data packet, and medium-configuration scenarios. This invention demonstrates stable and excellent performance under different parameter configurations, verifying its flexibility and universality. It can be widely adapted to various URLLC service scenarios requiring low latency, high reliability, and high security, such as industrial automation, intelligent transportation, and telemedicine.

[0055] Based on the simulation results above, and combined with the test results of the three scenarios, the following conclusions can be drawn from this embodiment: This embodiment verifies the flexibility and universality of the solution through scenario testing with different parameter configurations. When the subcarrier spacing increases, the mini timeslot length shortens, the single transmission latency decreases, and more retransmissions can be supported, improving reliability. When the data packet length increases, efficient utilization of bandwidth resources can still be guaranteed through precise bandwidth calculation and scheduling optimization. When the signal-to-interference-plus-noise ratio increases, the channel capacity increases, the probability of decoding failure decreases, the number of retransmissions required decreases, and the bandwidth demand decreases. All indicators meet the constraints under different scenarios, proving that the solution can adapt to various wireless network transmission scenarios and has broad application value.

[0056] In summary, this intelligent allocation and optimization method for wireless network bandwidth resources works in conjunction with a repetitive code heterogeneous channel retransmission structure and a MAC layer dynamic scheduling structure. The repetitive code heterogeneous channel retransmission is triggered based on decoding failure, and the probability of failure decreases with each transmission. The MAC layer scheduling structure dynamically allocates resources based on channel quality and service priority, adapting to dynamic channel changes, avoiding the separation of uplink and downlink optimization, realizing on-demand resource allocation, significantly improving bandwidth utilization, and reducing resource waste.

[0057] Furthermore, this intelligent allocation optimization method for wireless network bandwidth resources integrates the source node artificial noise injection structure, the idle relay interference structure, and the receiver interference cancellation technology into the bandwidth allocation process. The source node injects artificial noise, the idle relay generates co-frequency interference signals, and the receiver separates the signals through characteristic parameter storage and adaptive filtering to resist the risk of eavesdropping, fill the gap in physical layer security, and ensure data transmission security.

[0058] Furthermore, this intelligent allocation and optimization method for wireless network bandwidth resources relies on a bandwidth-latency-reliability trade-off model, an M / G / 1 queuing model for solving retransmission counts, and mini-timeslot technology. The trade-off model clearly defines the constraint threshold, the queuing model accurately iterates to solve for the maximum number of retransmissions, and the mini-timeslot shortens the transmission interval. The total latency is controlled within the constraints through multi-structure superposition calculation, achieving a precise balance between retransmission and latency. This ensures reliability while meeting low latency requirements, and solves the problems of low bandwidth utilization, lack of physical layer security, and insufficient collaborative optimization of retransmission and latency in existing wireless network bandwidth resource allocation technologies.

[0059] The relevant modules involved in this system are all hardware system modules or functional modules that combine computer software programs or protocols with hardware in the prior art. The computer software programs or protocols involved in these functional modules are technologies known to those skilled in the art and are not improvements to this system. The improvement of this system lies in the interaction or connection between the modules, that is, in improving the overall structure of the system to solve the corresponding technical problems that this system aims to address.

Claims

1. A method for intelligent allocation and optimization of wireless network bandwidth resources, characterized in that, Includes the following steps: S1. Construct an OFDMA downlink system; The system includes a base station and multiple types of users, with the user set being: Users of the same type have the same signal-to-interference-plus-noise ratio, and user data packets follow a Poisson distribution with a rate of... The base station uses the 3GHz and mmWave frequency bands and allocates time and frequency resources based on flexible subcarrier spacing; S2. Based on the OFDMA downlink system, establish a bandwidth-latency-reliability trade-off model; The trade-off model prioritizes end-to-end latency ≤1ms and decoding failure probability. The system load < 1 is a constraint condition; S3. Based on the M / G / 1 queuing model and the PASTA model, the maximum number of retransmissions satisfying the constraints is calculated iteratively. ; S4, Based on maximum retransmission count The minimum bandwidth that satisfies the constraints is calculated by considering packet length, signal-to-interference-plus-noise ratio, and decoding failure probability. ; S5, based on minimum bandwidth A method for intelligent allocation and optimization of wireless network bandwidth resources is achieved by employing repetitive code heterogeneous channel retransmission and MAC layer dynamic scheduling mechanism for data transmission. The retransmission of the heterogeneous channel with repeated codes is triggered when decoding fails, and each retransmission uses a channel with a different quality than the previous transmission. The MAC layer dynamic scheduling mechanism dynamically allocates radio resources based on real-time channel quality and user service priorities.

2. The method for intelligent allocation and optimization of wireless network bandwidth resources according to claim 1, characterized in that, In S1, the flexible subcarrier spacing corresponds to five parameter configurations, and the mini slot length corresponding to each configuration is fixed to 2 OFDM symbol durations; The five parameter configurations include m=0, m=1, m=2, m=3 and m=4, corresponding to subcarrier spacings of 15kHz, 30kHz, 60kHz, 120kHz and 240kHz respectively.

3. The method for intelligent allocation and optimization of wireless network bandwidth resources according to claim 1, characterized in that, In step S3, the specific steps of the iterative calculation include: S3.1, Initialize the retransmission count to 0; S3.2 Calculate the decoding failure probability of the current transmission or retransmission based on the relationship between the signal-to-interference-plus-noise ratio and the preset threshold value; The decoding failure probability is calculated as follows: when the signal-to-interference-plus-noise ratio is lower than the preset threshold, the decoding failure probability is 1; when the signal-to-interference-plus-noise ratio is not lower than the preset threshold, the decoding failure probability decreases exponentially with the increase of the signal-to-interference-plus-noise ratio, and the attenuation coefficient g ranges from 0.1 to 0.

5. S3.3 Calculate the total delay, including base station processing delay, random offset delay of data packet arrival, and queuing delay; S3.4 Determine whether the total delay satisfies the constraint condition, and solve for the maximum number of retransmissions; If the aforementioned constraints are met, then the current number of retransmissions is the maximum number of retransmissions. If the constraints are not met, the retransmission count is incremented by 1, and the process returns to step S3.2 to continue the calculation.

4. The method for intelligent allocation and optimization of wireless network bandwidth resources according to claim 1, characterized in that, In step S4, the specific steps for solving the minimum bandwidth include: S4.1 Based on the finite block length theory, calculate the average channel requirement according to the data packet length distribution, signal-to-interference-plus-noise ratio and target decoding failure probability; The expression for the average channel requirement is: in, Indicates the length of a single data packet. For channel capacity, Add noise to signal-to-interference ratio. Let P be the channel dispersion, and P be the decoding failure probability. Let Q be the inverse function of the Q function with respect to x, and E be the expectation operator; S4.

2. Based on the average channel requirement, subcarrier spacing, and total downlink arrival rate including initial packet arrival rate and retransmission compensation term, calculate the lower bound of the bandwidth and determine the minimum bandwidth. The formula for calculating the total downlink arrival rate is as follows: in, For the number of retransmissions, For the first The probability of decoding failure in the next transmission. The summation index for the number of retransmissions; The expression for calculating the lower bound of bandwidth is: in, Configure the index.

5. The method for intelligent allocation and optimization of wireless network bandwidth resources according to claim 1, characterized in that, In S5, in the retransmission mechanism of the heterogeneous channel with repeated codes, the receiving end uses maximum ratio joint decoding to merge and decode the same data packet signal received a preset number of times, and the number of retransmissions does not exceed the maximum number of retransmissions.

6. The method for intelligent allocation and optimization of wireless network bandwidth resources according to claim 1, characterized in that, In S5, the MAC layer dynamic scheduling mechanism includes: a management module, a HARQ module, a scheduling module, and a data processing module; The management module is used for memory resource allocation and entity state control; The HARQ module is used to process uplink and downlink retransmission feedback and maintain downlink transmission status information. The scheduling module is used to construct a scheduling queue and make resource allocation decisions by combining the channel quality indications reported by users with the service delay priority. The data processing module is used to perform channel multiplexing, channel coding and decoding operations.

7. The method for intelligent allocation and optimization of wireless network bandwidth resources according to claim 6, characterized in that, The MAC layer dynamic scheduling mechanism includes a physical layer security mechanism, which specifically includes: (1) The source node injects artificial noise signals while transmitting data signals; (2) Idle relay nodes generate interference signals at the same frequency as the data signals; (3) The receiving end uses interference cancellation technology to separate the data signal from the noise interference signal.

8. A wireless network bandwidth resource intelligent allocation and optimization device, characterized in that, include: System building module: Constructs the OFDMA downlink system and defines user distribution, frequency bands, and resource allocation rules; Trade-off Calculation Module: Establishes a bandwidth-latency-reliability trade-off model, sets constraint thresholds and load calculation logic; The retransmission count calculation module: Based on the M / G / 1 queuing model and the PASTA property, it iteratively calculates the maximum retransmission count using the total delay. ; Bandwidth calculation module: combined with Given the channel requirements, solve for the minimum bandwidth. And determine the allocation logic; Retransmission scheduling module: realizes retransmission of duplicate codes in heterogeneous channels and dynamic scheduling at the MAC layer, and improves reliability through maximum ratio joint decoding; Protocol stack platform module: used to provide 5G protocol stack function support, including header compression and encryption / decryption through the PDCP layer, providing multiple transmission modes through the RLC layer, carrying the dynamic scheduling module through the MAC layer, and simulating the channel and calculating the signal-to-interference-plus-noise ratio through the PHY layer.

9. A wireless network bandwidth resource intelligent allocation and optimization system, characterized in that, Used to perform the wireless network bandwidth resource intelligent allocation optimization method according to any one of claims 1-8.

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