Qos-aware refined resource mapping and adaptive configuration method and system based on dynamic security root

Abstract

This invention discloses a QoS-aware fine-grained resource mapping and adaptive configuration method and system based on dynamic security foundation, belonging to the field of 6G wireless communication technology. Based on the DSF framework, this invention extends RULE_ID from a rule identifier to a multi-dimensional QoS configuration file, including parameters such as logical period T_dsf, number of resource blocks N_PRB, number of symbols N_symbol, priority, frame mode Frame_Mode, and pilot ratio Pilot_Ratio. The core innovations include: revealing that under the secure pilot frame design, the channel estimation gain G_CE = 10 × log10(N_PRB × N_symbol × 12) dB, establishing a quantitative relationship between resource allocation and demodulation reliability; providing two modes, full pilot and hybrid frame, flexibly balancing reliability and rate through the pilot proportion coefficient; elucidating mechanisms such as 0.1ms high-frequency update of measurement reports, 0.125ms closed-loop response of power control, and 0.01μs accuracy of TA adjustment, reducing the HARQ retransmission probability by two orders of magnitude; designing four adaptive optimization algorithms for latency, rate, reliability, and energy efficiency; and supporting maintaining the N_PRB × N_symbol product unchanged during inter-system handover to preserve the channel estimation gain. This invention establishes a direct mathematical link between resource allocation and QoS assurance, and can be widely applied in scenarios such as industrial control, autonomous driving, and satellite communication.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a refined resource allocation and Quality of Service (QoS) assurance method based on Dynamic Security Foundation (DSF) in next-generation wireless communication systems (such as 5G-Advanced and 6G). This invention represents a key extension and systematic enhancement of the applicant's prior series of patent applications in the dimensions of resource mapping and QoS configuration. Background Technology

[0002] This application cites the prior patent applications of the following applicants as its technical basis: Application No. 2026100015014, Invention Title: Method, System and Device for Generating Wireless Communication Parameters Based on Dynamic Security Foundation (hereinafter referred to as "Parameter Generation Patent"); Application No. 2026101229059, Invention Title: Hard Deterministic Wireless Communication Method and System Based on Unified Anchor Point Time and Dual Fixed Offset (hereinafter referred to as "Fixed Offset Patent"); Application No. 2026102049886, Invention Title: Autonomous Access Method and System Based on Protocol Security State Machine Mirroring and Programmable Deterministic Arbitration (hereinafter referred to as "Autonomous Access Patent"); Application No. 2026102173652, Invention Title: Physical Layer Control Signaling Transmission Method and System Based on Dynamic Security Foundation (hereinafter referred to as "Control Signaling Patent").

[0003] This parameter generation patent is the first to propose a protocol-secure state machine paradigm centered on a Dynamic Security Foundation (DSF). This paradigm is defined by the triple DSF = (K_sec, Init_Anchor, RULE_ID). Both communicating parties independently generate a consistent time-varying state S(t) based on a shared DSF, and then generate all communication parameters using Param_x = F(K_sec, S(t), Context_x). RULE_ID serves as the identifier for state transition rules and includes at least the state transition rule type and update tick parameters.

[0004] The fixed offset patent introduces a unified anchor time T_anchor and dual fixed offsets (Δ_dl, Δ_ul) to construct a hard deterministic timing chain from logical decision-making to physical execution.

[0005] The autonomous access patent proposes mechanisms such as PSSM mirroring, two-step deterministic arbitration, and downlink implicit authorization, which realize autonomous deterministic handover across base stations.

[0006] This control signaling patent extends the DSF framework to the physical layer control signaling domain, fully defining the closed-loop process of control signaling such as measurement reporting, power control, and TA adjustment, as well as mechanisms such as modulation mode adaptation and multiplexing of multiple signaling. In particular, this patent proposes a secure pilot frame design, which deeply integrates cross-propagation pilot sequences with user data at the physical layer to achieve parallel processing of channel estimation and data demodulation.

[0007] Although the previous patents constructed a complete zero-signaling deterministic communication framework, the following key undisclosed aspects remain regarding the refinement of resource mapping and QoS configuration: First, the granularity of the mapping from resource intention to physical resources is unclear. The previous patent only defined the concept of "resource intention" R_u, generating a resource index by R_u = H(K_sec || S(t) || Context) mod M. However, the previous patent did not disclose how many physical resource blocks (PRBs) and OFDM symbols a resource index corresponds to, or the intrinsic relationship between these parameters and service QoS.

[0008] Second, the definition of RULE_ID needs to be further refined. The parameter generation patent defines RULE_ID as "a set of parameters that identify state transition rules," but it does not deeply bind RULE_ID to the business's QoS requirements, nor does it provide a mechanism for dynamically adjusting the Rule_ID parameter according to QoS requirements.

[0009] Third, the QoS gain of the secure pilot frame design is not quantified. Although the control signaling patent proposes a secure pilot frame design, it does not explain how this design interacts with the resource allocation parameters N_PRB and N_symbol to jointly determine the channel estimation accuracy and demodulation reliability.

[0010] Fourth, the principle for dividing the pilot band and data segment in the composite frame structure is not clearly defined. For services requiring high speeds, how to determine the pilot band length to ensure channel estimation accuracy while simultaneously enabling the data segment to use higher-order modulation to increase the speed is not addressed in previous patents.

[0011] Fifth, the synergistic contribution of the control signaling closed loop to HARQ retransmission is not systematically described. Previous patents have not quantitatively analyzed how mechanisms such as high-frequency update of measurement reports, adaptive modulation, power control closed loop, and precise TA adjustment work together to increase the first-transmission success rate to over 99.9999%.

[0012] This invention is proposed to address the aforementioned undisclosed and insufficiently disclosed technical content. It aims to extend the DSF framework of the previous patent towards refined resource mapping and deep binding with QoS, and to construct a complete QoS-aware resource configuration system based on DSF. Summary of the Invention

[0013] I. Purpose of the Invention The purpose of this invention is to provide a refined resource mapping and adaptive configuration method and system that shares the same philosophical origin as the aforementioned DSF framework and can seamlessly integrate with it. This method should achieve: Define the mapping granularity from resource intention to physical resource, define the number of physical resource blocks N_PRB and the number of symbols N_symbol corresponding to resource intention R_u, and deeply bind them with RULE_ID; The design of the security pilot frame reveals that the characteristic of the mutual propagation pilot sequence and data transmission makes the number of pilot symbols used for channel estimation equal to the total number of resource units occupied by transmission, thereby establishing a quantitative relationship between N_PRB, N_symbol and channel estimation gain. A method for dividing pilot and data segments in a composite frame structure is provided, which dynamically determines the pilot segment length and data segment modulation scheme according to different QoS requirements; This paper elucidates the synergistic contribution of control signaling mechanisms such as high-frequency update of measurement reports, adaptive modulation scheme, closed-loop power control, and precise TA adjustment to HARQ retransmission. The definition of RULE_ID is extended from a single rule identifier to a multi-dimensional QoS configuration file, which includes parameters such as logical cycle, number of resources, number of symbols, priority, frame mode, and pilot ratio. Provides an adaptive optimization mechanism for RULE_ID, dynamically adjusting the RULE_ID parameter based on business QoS monitoring results; Supports adaptive resource configuration during system switching.

[0014] II. Core Invention Concept: The Essential Difference Between Deterministic QOS and Traditional Statistical QOS To clearly illustrate the innovativeness of this invention, it is first necessary to clarify the essential differences between the two QoS guarantee modes.

[0015] In traditional 5G / 6G systems, QoS assurance is based on statistical multiplexing. The network tries its best to meet service demands through scheduling algorithms, but due to factors such as channel variations, load fluctuations, and contention, QoS metrics can only be presented in statistical form. For example, "95% of the latency is less than 10ms" means that 5% of the data packets may have a latency much greater than 10ms.

[0016] The fundamental flaw of this statistical QoS is that it cannot provide a provable deterministic upper bound. For critical applications such as industrial control and remote surgery, it is precisely that 5% long-tail latency that can lead to catastrophic consequences.

[0017] The deterministic QoS architecture constructed in this invention is based on the "zero signaling, deterministic" philosophy of the DSF framework. Its core idea is that QoS is no longer the result of statistical measurements, but rather a deterministic parameter directly configured through RULE_ID. Delay has a provable upper bound, rate has a minimum guarantee per logical cycle, and reliability is jointly guaranteed by resource allocation and channel estimation gain.

[0018] III. Core Inventive Concept: Refined Definition of RULE_ID This invention deepens and expands upon the Rule_ID of the previous patent, extending it from a single rule identifier to a multi-dimensional QoS configuration file.

[0019] RULE_ID is defined as: { / / Part 1: State Transition Rules (inherited from previous patents) Type: State machine type, with values ​​of "Hash_Chain" or "Logical_Epoch"; / / Part Two: Time Dimension Parameters (Inheritance and Refinement) T_dsf: Logical period, in milliseconds, configurable to values ​​such as 0.0625, 0.125, 0.25, 0.5, 1, 10, 100, etc. / / Part Three: Spatial Dimension Parameters (New in this invention) N_PRB: The number of physical resource blocks occupied by each resource intention, which can be configured as 1, 2, or 4; N_symbol: The number of OFDM symbols used, which can be configured to 1, 2, 4, 7, or 14; / / Part Four: QOS Dimension Parameters (New in this invention) Priority: Priority level, with a value ranging from 1 to 7; / / Part 5: Frame Structure Parameters (New in this invention) Frame_Mode: Frame mode, with values ​​of "Full_Pilot" or "Hybrid"; Pilot_Ratio: Pilot band ratio, with values ​​of 0.25, 0.33, 0.5, etc. }

[0020] IV. Quantitative Contribution of Safety Pilot Frame Design to Channel Estimation Gain 4.1 Basic Principles of Security Pilot Frames The secure pilot frame design upon which this invention is based is a core contribution of the preceding control signaling patent. This design deeply integrates the cross-propagation pilot sequence with user data at the physical layer, and the transmitted signal is: S[k] = P_base[k] × (1 - 2 × Data_bit[i]) Where P_base is the cross-guide frequency sequence generated based on DSF. The receiver processes it as follows: Z[k] = Y[k] × conj(P_base[k]) = H[k] × (1 - 2 × Data_bit[i]) + N' The channel response |H[k]| can be extracted from the amplitude of Z[k], and the data bits can be determined from the real part of Z[k]. This process realizes channel estimation and data demodulation in one step.

[0021] 4.2 Determination of Pilot Sequence Length In a secure pilot frame design, each data symbol is also a pilot symbol. Therefore, the total number of pilot symbols used for channel estimation is equal to the total number of resource units occupied by the transmission. N_pilot = N_PRB × N_symbol × 12 (each PRB contains 12 subcarriers) This relationship is an important discovery of the present invention: N_PRB and N_symbol in RULE_ID not only determine the data rate, but also directly determine the number of pilot symbols used for channel estimation.

[0022] 4.3 Quantization formula for channel estimation gain The signal-to-noise ratio gain of channel estimation is proportional to the number of pilot symbols: G_CE = 10 × log10(N_pilot) dB For different configurations, the channel estimation gain is as follows: When N_PRB=2 and N_symbol=14, N_pilot=336 and G_CE≈25.3dB; When N_PRB=1 and N_symbol=14, N_pilot=168 and G_CE≈22.3dB; When N_PRB=1 and N_symbol=7, N_pilot=84 and G_CE≈19.2dB; When N_PRB=1, N_symbol=1, N_pilot=12, G_CE≈10.8dB.

[0023] 4.4 Impact of Channel Estimation Gain on Demodulation Reliability For every 3dB improvement in channel estimation accuracy, the block error rate can be reduced by approximately one order of magnitude. Therefore, the configuration of N_PRB and N_symbol directly determines the reliability of transmission.

[0024] For example, configuring N_PRB=1 and N_symbol=14 for industrial control services yields a channel estimation gain of 22.3dB and a block error rate of up to 10⁻. 6 The following sensor service configuration, N_PRB=1, N_symbol=1, achieves a gain of 10.8dB and a block error rate of approximately 10⁻³, which still meets the requirements of the Internet of Things.

[0025] V. Methods for Determining Composite Frame Structure and Pilot Band Length This invention supports two frame structure modes to adapt to different QoS requirements.

[0026] 5.1 Full Pilot Mode The entire transmission resource is used to carry security pilot frames, meaning each resource unit serves as both a pilot and data unit. In this case, the pilot sequence length is equal to the total number of resource units occupied by the transmission, N_total = N_PRB × N_symbol × 12.

[0027] In this mode, the channel estimation gain is maximized and demodulation reliability is highest, but higher-order modulation cannot be used. It is suitable for applications with extremely high reliability requirements, such as industrial control and remote surgery.

[0028] 5.2 Hybrid Frame Mode The transmission frame is divided into a pilot segment and a data segment. The pilot segment uses low-order modulation (BPSK / QPSK) to carry interleaved pilot sequences and control information; the data segment can use high-order modulation (16QAM, 64QAM) to carry user data.

[0029] 5.2.1 Principles for determining the pilot band length The pilot band length needs to ensure sufficient channel estimation accuracy, and is typically configured to be 1 / 4 to 1 / 2 of the total resources. The specific method for determining this length is as follows: L_pilot = α × N_PRB × N_symbol × 12 Where α is the pilot ratio coefficient, specified by the Pilot_Ratio parameter in RULE_ID, and can be configured to values ​​such as 0.25, 0.33, and 0.5. The value of α must satisfy the following conditions: 10 × log10(α × N_PRB × N_symbol × 12) ≥ G_CE_min Where G_CE_min is the minimum channel estimation gain that meets the service reliability requirements.

[0030] 5.2.2 Determining the length of the data segment The data segment length is: L_data = (1-α) × N_PRB × N_symbol × 12 The data segment can use high-order modulation, and the modulation method is determined by the modulation method adaptive mechanism defined in the preceding control signaling patent.

[0031] 5.2.3 Timing for Determining the Data Segment Modulation Mode At the logical decision point, the UE selects the modulation scheme for the data segment based on the current channel quality. The modulation scheme information is implicitly transmitted through control signaling carried in the pilot segment (e.g., through cyclic shifting of the pilot sequence). The receiver first performs channel estimation from the pilot segment, demodulates the control information (including the data segment modulation scheme), and then demodulates the data segment using a high-precision channel response.

[0032] 5.3 QOS Trade-offs between the Two Modes In full-pilot mode, the channel estimation gain is the highest and the block error rate is the lowest, making it suitable for high-reliability services. In hybrid frame mode, while maintaining basic channel estimation accuracy, the data rate can be increased through higher-order modulation, making it suitable for high-speed services. The system can dynamically select the optimal frame structure based on service requirements using the Frame_Mode and Pilot_Ratio parameters in RULE_ID.

[0033] VI. The Collaborative Contribution of Control Signaling Closed Loop to HARQ Retransmission The preceding control signaling patent defines closed-loop processes such as measurement reporting, power control, and TA adjustment. These mechanisms make key synergistic contributions to QoS assurance.

[0034] 6.1 Measurement reports are updated frequently. The measurement reporting cycle has been reduced from 5-10ms in traditional 5G to less than 0.1ms, ensuring that the Channel Quality Indicator (CQI) always matches the current channel, guaranteeing correct modulation selection, and avoiding demodulation failures due to outdated CQI. Event triggering reporting latency has been reduced from tens of milliseconds to less than 0.1ms, enabling more timely mobility decisions.

[0035] 6.2 Adaptive Modulation The modulation scheme adaptive mechanism dynamically selects the modulation scheme based on real-time channel quality. When the channel is good, higher-order modulation is used to improve spectral efficiency, and when the channel deteriorates, it switches to lower-order modulation in a timely manner to ensure transmission reliability.

[0036] 6.3 Power Control Closed Loop The power control closed-loop response time is reduced from the traditional 1ms to less than 0.125ms, quickly compensating for channel fading and keeping the received signal-to-noise ratio consistently near the target value. This stable signal-to-noise ratio provides a reliable basis for modulation scheme selection.

[0037] 6.4 Precise TA Adjustment The TA adjustment accuracy has been improved from the traditional 0.1μs to within 0.01μs, ensuring symbol alignment, eliminating inter-symbol interference, and providing an accurate benchmark for channel estimation.

[0038] 6.5 Quantitative Analysis of Collaborative Contributions The combined effect of the above mechanisms can be quantified as follows: In traditional 5G, the single-transmission block error rate P_e ≈ 10⁻³, and the HARQ retransmission probability P_retrans ≈ 1 - (1-10⁻³). 4 ≈ 0.4%.

[0039] In the present invention: - Frequent updates to measurement reports reduce the probability of CQI errors by more than 90%; - Adaptive modulation reduces the block error rate from 10⁻³ to 10⁻ 6 the following; - Power control closed loop reduces signal-to-noise ratio fluctuation by more than 80%; - Precise TA adjustment effectively eliminates inter-symbol interference.

[0040] Overall, the single-transmission block error rate is reduced to 10⁻ 5 Below this, the HARQ retransmission probability drops to below 0.001%, a reduction of at least two orders of magnitude compared to traditional 5G. This not only improves spectrum efficiency but, more importantly, eliminates the latency uncertainty caused by retransmissions.

[0041] VII. Abstract Method for Determining Symbol Position The symbol position is determined by the logical channel identifier (LCID), a DSF-based deterministic pseudo-random function, and N_symbol.

[0042] Suppose a time slot contains 14 OFDM symbols. Based on the value of N_symbol, the time slot is divided into several symbol blocks: When N_symbol=14, the entire time slot is a single symbol block, the traffic flow occupies all 14 symbols, there is no time division multiplexing, and the channel estimation gain is maximized; When N_symbol=7, a time slot is divided into 2 symbol blocks, each block contains 7 consecutive symbols, and 2 service streams can be time-division multiplexed on the same PRB; When N_symbol=4, a time slot is divided into 3 symbol blocks, with a block size of 4 symbols, and the remaining 2 symbols can be reserved. When N_symbol=2, one time slot is divided into 7 symbol blocks, each block containing 2 symbols; When N_symbol=1, the 14 symbols are independent and can support time-division multiplexing of 14 service streams, with a channel estimation gain of approximately 10.8dB.

[0043] The symbol block index is calculated as follows: Block_Index = DeterministicFunction(K_sec, S(t), LCID) mod (14 / N_symbol) The symbol starting position is Symbol_Start = Block_Index × N_symbol, and the symbol set occupied is N_symbol consecutive symbols starting from Symbol_Start.

[0044] 8. Refined mapping of resource intentions to physical resources First, calculate the resource index: Resource_Index = DeterministicFunction(K_sec, S(t), LCID, "RESOURCE")mod M Where M is the total number of system resource units, which is determined by system bandwidth and resource granularity.

[0045] Then determine the location of the physical resource block: Start_PRB = Resource_Index × N_PRB PRB_Set = {Start_PRB, Start_PRB+1, ..., Start_PRB + N_PRB - 1} Here, Resource_Index multiplied by N_PRB means that each resource index represents a starting position, and the business flow continuously occupies N_PRB PRBs. This continuous occupancy design ensures the integrity of the resources required by multi-PRB businesses and avoids fragmentation.

[0046] The symbol position is determined according to the method described in Part VII.

[0047] The total number of resource units used for transmission is: N_total = N_PRB × N_symbol × 12 In the two-step arbitration, the N_PRB PRBs compete as a whole, and the arbitration can only succeed if all N_PRB PRBs are available at their corresponding sign positions.

[0048] IX. Adaptive Optimization Mechanism of RULE_ID The network side can monitor real-time QoS metrics for services and trigger RULE_ID optimization when the following conditions occur.

[0049] This invention designs four adaptive optimization algorithms: Delay-sensitive optimization: When the measured delay value is close to the target delay, prioritize shortening the logic period T_dsf, and then increase the priority.

[0050] Rate-sensitive optimization: When the measured rate is lower than the target rate, the current frame mode is checked first. If it is in full pilot mode, it can be switched to hybrid frame mode and an appropriate pilot ratio can be configured; if it is already in hybrid frame mode, N_PRB is increased first, then T_dsf is shortened, and then N_symbol is increased.

[0051] Reliability optimization: When reliability metrics (such as block error rate) are higher than the target value, prioritize increasing N_symbol to improve channel estimation gain, then increase N_PRB, and then increase the priority further. If the current frame mode is a hybrid frame mode, the pilot proportion coefficient α can be increased.

[0052] Energy efficiency optimization: When energy efficiency is lower than the target, switch to rule C first, then extend T_dsf, and then reduce N_symbol or N_PRB.

[0053] The RULE_ID optimization results can be updated via empty frames, RRC signaling, or handover commands.

[0054] 10. Adaptive adjustment during inter-system switching When a UE switches from a source cell to a target cell, two different handover modes can be used depending on the degree of difference between the resource pool configuration of the target cell and that of the source cell.

[0055] Mode 1: PSSM Mirroring Mode (when resource pools are identical) If the resource pool parameters of the target cell (such as M, number of subcarriers per PRB) are basically the same as those of the source cell, the PSSM mirroring mechanism described in the previous autonomous access patent is adopted. The source base station transmits the complete DSF context of the UE to the target base station through the Xn interface, including (K_sec, Init_Anchor, RULE_ID). The target base station establishes a PSSM mirror, and the logical state of the UE and the target base station remains synchronized, with RULE_ID remaining unchanged. After handover, the UE directly resumes transmission under the original configuration, achieving seamless handover with zero signaling.

[0056] Mode 2: Adaptive Adjustment Mode (when resource pool differences are significant) If the resource pool parameters of the target cell differ significantly from those of the source cell, directly using the original RULE_ID may lead to a sharp increase in the probability of collisions or insufficient channel estimation accuracy. In this case, adaptive adjustment of the RULE_ID is required. The adjustment process is as follows: First, the source base station transmits the UE's current RULE_ID configuration to the target base station through the Xn interface.

[0057] Then, the target base station calculates the appropriate RULE_ID based on its own resource pool size M_tgt and current load. The calculation principle is to keep the QoS level of the service as unchanged as possible, and at least keep the product of N_PRB and N_symbol unchanged to maintain the channel estimation gain. If necessary, the priority can be adjusted to compensate for the impact of resource changes.

[0058] Next, the target base station sends the new RULE_ID configuration to the UE in the handover command.

[0059] Finally, after the handover is completed, the UE enables the new configuration at the new logical decision moment, and the service is seamlessly restored in the target cell.

[0060] A unified framework for the two models This invention treats the PSSM mirroring mode as a special case of the adaptive adjustment mode—when the resource pool is consistent, the adjustment result is "unchanged". This unified framework ensures full compatibility with previous patents while expanding the applicability of inter-system switching.

[0061] XI. Beneficial Effects Compared with existing technologies and prior patents, the present invention brings the following significant and synergistic beneficial effects: First, a quantitative relationship between resource allocation parameters and channel estimation gain is established. This invention reveals for the first time that, under a secure pilot frame design, the number of pilot symbols used for channel estimation is equal to N_PRB × N_symbol × 12, and the channel estimation gain G_CE = 10 × log10(N_pilot) dB. This relationship establishes a direct mathematical link between resource allocation and demodulation reliability.

[0062] Second, a method for dividing pilot and data segments in a composite frame structure is provided. This invention defines a pilot proportion coefficient α for the first time, allowing the system to flexibly balance channel estimation accuracy and data rate according to service requirements. High-reliability services can select the full pilot mode to obtain maximum gain, while high-speed services can select the hybrid frame mode and use higher-order modulation to increase the rate.

[0063] Third, the system quantifies the collaborative contribution of the control signaling closed loop to HARQ retransmission. This invention clarifies for the first time that the measurement report is updated frequently with a period of no more than 0.1ms, the power control closed loop response time is no more than 0.125ms, and the TA adjustment accuracy is better than 0.01μs. These mechanisms work together to reduce the single-transmission block error rate to 10⁻⁻⁶. 5 The HARQ retransmission probability is reduced by at least two orders of magnitude compared to traditional 5G.

[0064] Fourth, the definition of RULE_ID is expanded. This invention extends RULE_ID from a single rule identifier to a multi-dimensional QoS configuration file, adding parameters such as N_PRB, N_symbol, Priority, Frame_Mode, and Pilot_Ratio, making QoS a configurable deterministic parameter.

[0065] Fifth, an adaptive optimization mechanism is provided. This invention designs optimization algorithms for latency, speed, reliability, and energy efficiency, enabling the system to dynamically adjust resource allocation according to changes in business needs and achieve self-optimization. XII. Detailed Implementation Example 1: Industrial robotic arm control (full pilot mode) Industrial robotic arms require latency <10ms, speed >16kbps, and reliability >99.999%.

[0067] Network allocation RULE_ID: T_dsf=0.0625ms, N_PRB=1, N_symbol=14, Frame_Mode="Full_Pilot", Priority=1.

[0068] Every 0.0625ms, the robotic arm's PSSM reaches the logical decision time, calculates the resource index based on the current state S(t), occupies 1 PRB and 14 symbols, for a total of 168 resource units, all of which are used for security pilot frame transmission. The channel estimation gain is 22.3dB, and the block error rate is <10⁻ 6 In a two-step arbitration process, the highest priority ensures its victory.

[0069] Based on system parameters, the end-to-end latency is calculated to be 8.5ms, which meets the requirement of less than 10ms. Theoretical analysis shows that, given resource configuration, the probability of transmission failure during 24-hour continuous operation is less than 10⁻. 9 .

[0070] Example 2: Video Backhaul Service (Hybrid Frame Mode) Video services require a speed of >32kbps and a latency of <50ms.

[0071] Network allocation RULE_ID: T_dsf=0.125ms, N_PRB=2, N_symbol=14, Frame_Mode="Hybrid", Pilot_Ratio=0.25, Priority=3.

[0072] The pilot band length is 0.25 × 2 × 14 × 12 = 84 resource elements, using QPSK modulation, with a channel estimation gain of 19.2dB. The data segment length is 252 resource elements, using 16QAM modulation. Based on resource configuration calculations, the data rate is 36kbps, meeting the requirement of greater than 32kbps; the theoretically calculated end-to-end delay is 45ms, meeting the requirement of less than 50ms.

[0073] Example 3: Environmental Sensor Network The factory is deploying 10,000 sensors, requiring a battery life of >5 years.

[0074] Network allocation of RULE_ID follows rule C: T_dsf=10ms, N_PRB=1, N_symbol=1, Priority=7.

[0075] The sensor wakes up at a dedicated micro-moment, occupying one symbol in one PRB, with a total of 12 resource units. The channel estimation gain is 10.8dB, which is sufficient to correctly demodulate 4 bytes of data. The duty cycle is 0.007%, and the theoretical battery life is over 5 years. System-level simulations show that the resource conflict probability is less than 0.01% when 10,000 sensors are running concurrently.

[0076] Example 4: Adaptive Optimization of RULE_ID A certain service was initially configured in hybrid frame mode, with T_dsf=0.5ms, N_PRB=1, N_symbol=14, and α=0.25. Monitoring showed that the block error rate was higher than the target value.

[0077] Trigger reliability optimization: Increase α to 0.5, double the pilot band length, improve channel estimation gain by 3dB, and reduce the theoretically calculated block error rate to below the target value.

[0078] Example 5: Adaptive Handover Between Different Systems The UE is switching from cell A (M_A=100) to cell B (M_B=50). The current configuration is T_dsf=0.25ms, N_PRB=1, N_symbol=7.

[0079] After calculating the target base station, the Priority is increased from 3 to 2 to compensate for the increased probability of collisions caused by the halved compensation resources, while keeping the N_PRB×N_symbol product unchanged to maintain the same channel estimation gain. Theoretical calculations show that the latency and rate after handover are basically the same as before handover, and the service is seamlessly restored.

[0080] XIII. Systems and Devices 13.1 Network Equipment A network device implementing the method of the present invention includes a processor, a memory, a transceiver, and a network interface, wherein the processor is configured to execute a program to implement the following functional modules: The RULE_ID management module is used to store and manage the RULE_ID configuration of each UE. When a service is established, an initial RULE_ID is allocated according to QoS requirements. During the service, the RULE_ID is dynamically optimized based on monitoring results. During handover, the RULE_ID is adapted according to the resource configuration of the target cell. The resource calendar module is used to maintain the global resource calendar, record the status of each resource unit at every moment, and provide a data foundation for two-step arbitration; The two-step arbitration module is used to perform deterministic arbitration and resolve resource conflicts according to priority and other rules. The QoS monitoring module is used to monitor metrics such as end-to-end latency, data rate, and error rate of various services. When the metrics deviate from the target value, the RULE_ID optimization algorithm is triggered. The adaptive optimization engine executes algorithms such as latency-sensitive, rate-sensitive, reliability, and energy efficiency optimization based on monitoring results to generate optimized RULE_ID configurations. The RULE_ID update module sends RULE_ID update information via empty frames, RRC signaling, or handover commands; The inter-system adaptation module adjusts the RULE_ID parameter according to the resource pool configuration of the target cell during handover to ensure the continuity of service QoS.

[0081] 13.2 User Equipment A user equipment implementing the method of the present invention includes a processor, a memory, a transceiver, and a security element, wherein the processor is configured to execute a program to implement the following functional modules: The refined resource calculation module calculates the resource index, PRB location, and symbol location based on the current RULE_ID configuration, and generates a complete set of physical resources. The RULE_ID storage module securely stores the currently used RULE_ID configuration and supports parallel management of RULE_IDs for multiple service flows. The RULE_ID update receiving module parses the RULE_ID update information from downlink empty frames, RRC signaling, or handover commands, verifies its legality, and enables the new configuration at a specified time. The QoS measurement module measures the actual QoS metrics for this service and can optionally report them to the network side for optimization decisions. The adaptive execution module updates the RULE_ID configuration based on the optimization instructions issued by the network and applies it to subsequent transmissions.

[0082] XIV. Industrial Applicability This invention can be widely applied to all wireless communication scenarios that require refined QoS assurance, including but not limited to: In industrial IoT scenarios, this invention is used for AGV collaborative scheduling, remote control of robotic arms, and closed-loop process control. It allows for configuring different RULE_IDs for industrial control flows of varying priorities, achieving maximum reliability through full pilot mode and ensuring latency through a priority mechanism.

[0083] In vehicle-to-everything (V2X) scenarios, this invention is used for autonomous driving, platooning, and intersection collision avoidance. It provides adaptive resource allocation for high-speed moving vehicles, ensures timely CQI (Content Quality Index) updates with a 0.1ms high-frequency update in measurement reports, and rapidly compensates for channel fading with a 0.125ms closed-loop power control response.

[0084] In satellite communication scenarios, this invention is used for low-Earth orbit satellite internet and direct mobile phone connection to satellites. The multi-dimensional adjustment mechanism of this invention can provide differentiated services for different businesses on resource-constrained satellite links, and achieve high-concurrency access for massive numbers of IoT devices by setting N_symbol=1.

[0085] In smart grid scenarios, this invention is used for grid differential protection and distributed energy coordination. It provides configurable QoS guarantees for wide-area protection and control of power systems, meeting the latency requirements of different protection services through combinations of logical cycles and priorities.

[0086] In telemedicine scenarios, this invention is used for remote surgery and haptic feedback. It can control end-to-end latency jitter in mobile scenarios to the sub-millisecond level and ensure service quality when network conditions change through RULE_ID adaptive optimization.

[0087] XV. Scope of Protection of this Invention The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0088] In particular, the quantitative relationship between resource allocation parameters and channel estimation gain under the secure pilot frame design, the method for determining the pilot segment length of the composite frame structure, the cooperative contribution of the control signaling closed loop to HARQ retransmission, the multidimensional extended definition of RULE_ID, the adaptive optimization algorithm, the adaptive resource configuration method during inter-system handover, and various variations, combinations and evolutions derived from the above mechanisms, all fall within the protection scope of this invention.

Claims

1. A QoS-aware resource configuration method based on dynamic security foundation, characterized in that, include: The two communicating parties synchronize a dynamic security foundation DSF, which is uniquely determined by a triple (K_sec, Init_Anchor, RULE_ID), where K_sec is the security key, Init_Anchor is the initial anchor information of the state machine, and RULE_ID is a set of multi-dimensional parameters that identify the state transition rules. Both communicating parties independently run their local protocol security state machines based on the RULE_ID to obtain a consistent time-varying state S(t); At the logical decision moment, both communicating parties map the logical moment to a unified anchor moment T_anchor on the physical time axis, and determine the time for sending or receiving physical layer signals based on a pre-configured fixed offset. The RULE_ID includes at least a time dimension parameter and a space dimension parameter, wherein the time dimension parameter defines the logical period T_dsf, and the space dimension parameter defines the number of physical resource blocks N_PRB and the number of OFDM symbols N_symbol occupied by each resource intention; Based on K_sec, S(t) and the RULE_ID, the two communicating parties independently determine the set of physical resource blocks and the set of symbols corresponding to the resource intention R_u, and transmit them at the physical layer time.

2. The method according to claim 1, characterized in that, The transmission employs a secure pilot frame structure, in which the cross-propagation pilot sequence and user data are deeply integrated at the physical layer, and each data symbol simultaneously serves as a pilot symbol for channel estimation.

3. The method according to claim 2, characterized in that, The total number of transmission resource units determined by N_PRB and N_symbol is the total number of pilot symbols used for channel estimation, and this total number directly determines the channel estimation gain.

4. The method according to claim 1, characterized in that, The RULE_ID also includes the frame mode parameter Frame_Mode, which indicates whether the transmission adopts full pilot mode or hybrid frame mode; in full pilot mode, all resource units are used for safe pilot frame transmission, and in hybrid frame mode, the transmission frame is divided into pilot segment and data segment.

5. The method according to claim 4, characterized in that, The RULE_ID also includes the pilot ratio coefficient Pilot_Ratio, which is used to determine the proportion of resources occupied by the pilot segment in the hybrid frame mode. The pilot segment length L_pilot = Pilot_Ratio × N_PRB × N_symbol × number of subcarriers per PRB, and the data segment length L_data = (1 - Pilot_Ratio) × N_PRB × N_symbol × number of subcarriers per PRB.

6. The method according to claim 5, characterized in that, The pilot band uses low-order modulation to carry the cross-propagation pilot sequence and control information, while the data band uses variable modulation to carry user data. The modulation method of the data band is dynamically selected according to the channel quality and implicitly indicated by the control information carried in the pilot band.

7. The method according to claim 1, characterized in that, It also includes a control signaling closed-loop mechanism, including at least one of the following: The measurement report is updated frequently with a period of no more than 0.1ms, so that the time error between the channel quality indication and the current channel match is less than 0.1ms; The power control closed-loop response time is no greater than 0.125ms, ensuring that the tracking delay between transmit power adjustment and channel fading is less than 0.125ms; The timing advance adjustment accuracy is better than 0.01μs, so that the symbol timing error is less than 0.01μs.

8. The method according to claim 7, characterized in that, The control signaling closed-loop mechanism works together to reduce the single-transmission block error rate to 10⁻ 5 The HARQ retransmission probability is reduced by at least two orders of magnitude compared to traditional 5G.

9. The method according to claim 1, characterized in that, It also includes an adaptive optimization step for RULE_ID: Monitor real-time QoS metrics for operations; When the metric deviates from the target value, the optimization algorithm is triggered to adjust the corresponding parameters in RULE_ID; The updated RULE_ID configuration is issued via empty frames, RRC signaling, or handover commands; The UE enables the new configuration at a specified time.

10. The method according to claim 9, characterized in that, The optimization algorithm includes at least one of the following: Latency-sensitive optimization: When the measured latency is close to the target latency, prioritize shortening the logic period T_dsf, and then increase the priority. Rate-sensitive optimization: When the rate measurement value is lower than the target rate, if the current mode is full pilot mode, switch to hybrid frame mode and configure the pilot proportion coefficient. If the current mode is hybrid frame mode, increase N_PRB first, then shorten T_dsf, and then increase N_symbol. Reliability optimization: When the reliability index is lower than the target value, prioritize increasing N_symbol to improve the channel estimation gain, then increase N_PRB, and then increase the priority. If the current frame mode is a hybrid frame mode, increase the pilot proportion coefficient. Energy efficiency optimization: When energy efficiency is lower than the target, switch to rule C first, then extend T_dsf, and then reduce N_symbol or N_PRB.

11. The method according to claim 1, characterized in that, The method for determining the symbol set includes: The symbol block index is calculated using a deterministic function based on K_sec, S(t) and the logical channel identifier LCID. The range of values ​​for the symbol block index is determined by N_symbol and the total number of symbols in the time slot; The starting position of the symbol is the symbol block index multiplied by N_symbol; The set of symbols occupied consists of N_symbol consecutive symbols starting from the first symbol position.

12. The method according to claim 1, characterized in that, The method for determining the set of physical resource blocks includes: Resource indexes are calculated using deterministic functions based on K_sec, S(t), and LCID; The range of values ​​for the resource index is determined by the total number M of system resource units; The starting position of the physical resource block is the resource index multiplied by N_PRB; The set of physical resource blocks occupied consists of N_PRB consecutive physical resource blocks starting from the starting position.

13. The method according to claim 1, characterized in that, The RULE_ID also includes a priority parameter, which is used to resolve resource conflicts in a two-step deterministic arbitration, with the higher-priority service winning the conflict.

14. The method according to claim 1, characterized in that, It also includes adaptive adjustment steps during inter-system switching: The source base station transmits the UE's current RULE_ID configuration to the target base station; The target base station determines whether the RULE_ID parameter needs to be adjusted based on its own resource pool configuration. If adjustments are needed, calculate the appropriate RULE_ID, keeping at least the product of N_PRB and N_symbol unchanged to maintain the channel estimation gain; If no adjustment is needed, keep the original RULE_ID unchanged; The target base station issues the final RULE_ID configuration in the handover command; During the handover process, the UE communicates with the target base station based on the final determined RULE_ID.

15. A QoS-aware resource allocation system based on dynamic security foundation, characterized in that, include: Network devices are used to manage the UE's RULE_ID configuration, perform two-step deterministic arbitration, monitor QoS metrics and trigger optimizations, and issue RULE_ID updates. User equipment is used to store the current RULE_ID configuration, calculate the physical resource set based on RULE_ID, perform data transmission, and receive and apply RULE_ID updates.

16. The system according to claim 15, characterized in that, The network device includes: The RULE_ID management module is used to store and manage the RULE_ID configuration of each UE; The resource calendar module is used to maintain the global resource calendar; The two-step arbitration module is used to enforce deterministic arbitration. The QOS monitoring module is used to monitor business QOS metrics; An adaptive optimization engine is used to generate optimized RULE_ID configurations; The RULE_ID update module is used to send RULE_ID update information. The inter-system adaptation module is used for resource configuration adaptation during switching.

17. The system according to claim 15, characterized in that, The user equipment includes: The refined resource calculation module is used to calculate the physical resource set based on RULE_ID; The RULE_ID storage module is used to securely store the current RULE_ID configuration; The RULE_ID update receiving module is used to receive and parse RULE_ID update information; The QOS measurement module is used to measure business QOS metrics; The adaptive execution module is used to apply the optimized RULE_ID configuration.

18. A network device, characterized in that, The device includes a processor, a memory, a transceiver, and a network interface. When the processor executes a program stored in the memory, it implements the steps performed by the network side in the method of any one of claims 1 to 14.

19. A user equipment, characterized in that, It includes a processor, a memory, a transceiver, and a security element. When the processor executes a program stored in the memory, it implements the steps performed by the terminal side in the method of any one of claims 1 to 14.

20. A wireless communication system, characterized in that, This includes the network equipment as described in claim 18 and the user equipment as described in claim 19.

21. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 14.