A high-performance collaborative sensing and resource allocation method for hybrid spectrum sharing

Abstract

The present invention relates to a high-performance collaborative perception and resource allocation method suitable for hybrid spectrum sharing, which belongs to the field of signal processing. The method includes: performing system modeling and defining key parameters; obtaining an energy efficiency optimization model under multi-dimensional constraints by constructing an objective function and multi-dimensional constraints; designing a collaborative perception mechanism that is resistant to control channel errors through an improved hard decision fusion rule; converting the objective function into a parameterized reduction form, and solving the optimal perception duration and number of collaborative nodes through a two-stage optimization algorithm; and performing dynamic parameter configuration and real-time optimization through environmental perception and parameter prediction. The present invention jointly optimizes the perception duration, collaboration scale, and power parameters, breaking through the limitations of traditional fixed configurations and achieving a dynamic balance between spectrum detection accuracy, transmission efficiency, and energy economy; a dual-power level adaptive switching mechanism maximizes spectrum access opportunities while minimizing interference to the main device.

Description

Technical Field

[0001] The present invention relates to the field of signal processing, and in particular to a high-performance collaborative sensing and resource allocation method suitable for hybrid spectrum sharing. Background Art

[0002] Hybrid spectrum sharing technology, by combining the advantages of interleaved and underlay modes, has become a key means of improving spectrum resource utilization. In interleaved mode, secondary units (SUs) must strictly limit their interference power to primary units (PUs), allowing spectrum sharing without spectrum sensing. In underlay mode, SUs must confirm spectrum availability through spectrum sensing before access. Hybrid spectrum sharing combines the characteristics of both modes, allowing SUs to dynamically switch power levels based on sensing results: transmitting at normal power when spectrum availability is detected and at reduced power when occupied, thereby maximizing spectrum access opportunities while ensuring PU communication quality.

[0003] However, existing methods have the following shortcomings: 1. Single-device perception is susceptible to environmental influences such as shadow fading and multipath effects, resulting in unreliable perception results, which may cause interference to the main device or waste spectrum access opportunities; 2. Existing work mostly adopts fixed perception duration or static collaborative node configuration, ignoring the impact of dynamic resource allocation on energy consumption economy, making it difficult to achieve balanced optimization of spectrum detection accuracy and energy efficiency; 3. Although collaborative spectrum sensing can improve perception robustness, existing research has not fully considered the interference of control channel non-idealities (such as bit errors and delays) on the reporting of perception results, which may cause the fusion center to misjudge the spectrum status and further reduce system reliability. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a high-performance collaborative sensing and resource allocation method suitable for hybrid spectrum sharing, which solves the shortcomings of the existing technology.

[0005] The object of the present invention is achieved by the following technical solution: a high-performance collaborative sensing and resource allocation method suitable for hybrid spectrum sharing, the method comprising:

[0006] Step 1: Model the system and define key parameters;

[0007] Step 2: Obtain an energy efficiency optimization model under multi-dimensional constraints by constructing an objective function and multi-dimensional constraints;

[0008] Step 3: Design a cooperative sensing mechanism that is resistant to control channel errors through an improved hard decision fusion rule;

[0009] Step 4: Convert the objective function into a parameterized reduction form and solve the optimal perception duration and number of collaborative nodes through a two-stage optimization algorithm;

[0010] Step 5: Dynamic parameter configuration and real-time optimization through environmental perception and parameter prediction.

[0011] The system modeling includes:

[0012] Build a heterogeneous network consisting of primary devices (PUs) and secondary devices (SUs) that perform different communication tasks;

[0013] In hybrid spectrum sharing mode, the SU detects the PU spectrum status through collaborative spectrum sensing and adaptively selects two power levels: normal power mode and low power mode based on the sensing results. In normal power mode, when the sensing result is that the spectrum is idle, the SU accesses the authorized frequency band at normal power. In low power mode, when the sensing result is that the spectrum is occupied, the SU reduces the transmit power to ensure that the interference to the PU is below the preset threshold.

[0014] The key parameters defined include: setting the sensing time τ, the data transmission time T – τ, T is the total frame length, the number of collaborative nodes participating in CSS N, and the control channel error probability p e , global detection probability Q d and false alarm probability Q f , SU average transmit power constraint P avg , peak power constraint P peak , SU network minimum rate threshold R min , the average interference power threshold I that PU can tolerate th .

[0015] The second step specifically includes:

[0016] A1. Objective function construction: The objective function is to maximize the energy efficiency of the SU network, and the energy efficiency is set as the total data transmission efficiency R total and total energy consumption E total The ratio of the total data transmission rate R total , depends on the perception result and power selection, expressed as Total energy consumption E total Including perception energy consumption and transmission energy consumption, let the single node perception power consumption be P s , then the total energy consumption E total Expressed as in, , are the SU receiving signal-to-noise ratios under two power modes, and represent the probability of the PU spectrum being in idle and occupied states respectively;

[0017] A2. Multi-dimensional constraint: Set the average interference power received by the PU to ≤ I th , R total ≥R min, instantaneous power P0≤P peak , P1≤P peak , long-term average power Lower limit of detection probability Upper limit of false alarm probability in, Represents the mathematical expectation operation.

[0018] The step three specifically includes:

[0019] B1. Improved hard decision fusion rule: Considering the influence of control channel error on the perception result reporting, the modified perception result u of the i-th SU received by the fusion center is set i ∈{0,1}, where 0 means idle and 1 means occupied, then

[0020] B2. The fusion center adopts a channel quality weighted strategy To reduce the impact of high bit error rate nodes, where w i is the node weight, the condition for judging the existence of PU is Λ≥K, and K is the dynamically adjusted judgment threshold.

[0021] The fourth step includes:

[0022] C1. Convert the optimization problem of the objective function into a reduction form of optimization problem: Update the parameter η iteratively until convergence;

[0023] C2. Decompose the reduction form optimization problem into power optimization subproblems and perception parameter optimization subproblems. Fix N and τ and derive the closed-form solution of power as follows: Fixed power parameter, solving the optimal perception time τ * and the number of collaborative nodes N * They are where c, α, is a constant related to the channel environment, Indicates rounding down;

[0024] C3, alternately optimize the power parameters and perception parameters until the change in the objective function is less than the preset tolerance .

[0025] The step five includes:

[0026] D1. Predict the optimal number of collaborative nodes N based on historical data and real-time channel estimation * and the perceived duration τ * , and then dynamically adjust the fusion weight w i and the decision threshold K to adapt to the time-varying nature of the control channel quality;

[0027] D2. Generate a joint optimization parameter set, configure it to the SU network, and then periodically update the parameters to ensure the system's continuous optimal performance in a dynamic environment.

[0028] The present invention has the following advantages:

[0029] 1. Highly robust spectrum sensing: The collaborative sensing mechanism combined with dynamic weighted fusion rules significantly reduces the impact of single-node sensing errors and control channel errors, improving detection reliability in complex environments. The error-resistant HPO rule ensures that the fusion center can still accurately determine the spectrum status under non-ideal control channel conditions.

[0030] 2. Energy efficiency and resource utilization optimization: Jointly optimizes sensing duration, collaboration scale, and power parameters to break through the limitations of traditional fixed configurations and achieve a dynamic balance between spectrum detection accuracy, transmission efficiency, and energy economy. A dual-power level adaptive switching mechanism maximizes spectrum access opportunities while minimizing interference with the main device.

[0031] 3. Multi-dimensional constraint compatibility: It simultaneously meets multiple constraints such as interference suppression of primary equipment, rate assurance of secondary equipment, and power limitation, adapting to the differentiated needs of heterogeneous scenarios such as drone swarms; the dynamic parameter configuration framework supports real-time environment adaptation to ensure system stability under time-varying channel conditions.

[0032] 4. Algorithm efficiency and scalability: The two-stage decomposition and convex relaxation techniques for non-convex problems significantly reduce computational complexity and are suitable for resource-constrained embedded devices. The solution can be expanded to scenarios such as 5G / 6G dynamic spectrum sharing, dense IoT deployment, and intelligent transportation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application provided below in conjunction with the drawings is not intended to limit the scope of protection of the present application for which protection is claimed, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application. The present invention is further described below in conjunction with the drawings.

[0035] like Figure 1As shown in the figure, the present invention specifically relates to a high-performance cooperative sensing and resource allocation method applicable to hybrid spectrum sharing. Aiming at the multi-objective optimization problem of heterogeneous hybrid spectrum sharing systems under non-ideal control channel conditions, it specifically includes the following contents:

[0036] Step 1: System modeling and parameter definition.

[0037] 1.1 System scenario definition: Construct a heterogeneous network composed of primary devices (PUs) and secondary devices (SUs), where PUs and SUs perform different communication tasks. In the hybrid spectrum sharing (HSS) mode, SUs detect the spectrum status of PUs through cooperative spectrum sensing (CSS) and adaptively select two power levels according to the sensing results. Conventional power mode (P0): When the sensing result is "spectrum idle", SUs access the authorized frequency band with conventional power; Low power mode (P1): When the sensing result is "spectrum occupied", SUs reduce the transmit power (P1 < P0) to ensure that the interference to PUs is lower than the preset threshold.

[0038] 1.2 Key parameter definition: Sensing duration τ, data transmission duration T - τ (total frame length T); Number of cooperative nodes N participating in CSS, control channel error probability p e ; Global detection probability Q d and false alarm probability Q f ; SU average transmit power constraint P avg , peak power constraint P peak ; SU network minimum rate threshold R min , PU tolerable average interference power threshold I th .

[0039] Step 2: Construct an energy efficiency optimization model under multi-dimensional constraints

[0040] 2.1 Objective function construction: The objective function is to maximize the energy efficiency of the SU network. Define energy efficiency (EnergyEfficiency, EE) as the ratio of the total data transmission rate R total to the total energy consumption E total . The total data transmission rate R total depends on the sensing result and power selection, and the expression is:

[0041]

[0042] where , are the SU received signal-to-noise ratios in the two power modes respectively, and represent the probabilities that the PU spectrum is idle and occupied respectively. The total energy consumption E total includes sensing energy consumption and transmission energy consumption. Let the sensing power consumption of a single node be Ps , total energy consumption E total The expression is:

[0043]

[0044] 2.2 Multi-dimensional Constraints: Main Equipment Protection: The average interference power received by the PU is ≤I th ;SU communication quality: R total ≥R min ; Power limit: instantaneous power P0≤P peak , P1≤P peak , long-term average power It is a mathematical expectation operation; Perception reliability: lower limit of detection probability Upper limit of false alarm probability

[0045] Step 3: Design of a cooperative sensing mechanism that is resistant to control channel errors.

[0046] 3.1. Improved hard decision fusion rule (HPO rule): Considering the impact of control channel error on the perception result reporting, the modified perception result u of the i-th SU received by the fusion center is defined as i ∈{0,1}, where 0 indicates idle and 1 indicates occupied, then:

[0047]

[0048] 3.2 To reduce the impact of high bit error rate nodes, the fusion center adopts a channel quality weighting strategy:

[0049]

[0050] where w i The condition for determining the existence of a PU is Λ≥K, where K is the dynamically adjusted decision threshold.

[0051] Step 4: Non-convex problem transformation and two-stage optimization algorithm.

[0052] 4.1. Fractional Programming and Dinkelbach Transformation. Rewrite the objective function into a subtraction form and iteratively update the parameter η until convergence. The objective function is to maximize EE. EE is defined as a ratio, expressed as EE(τ,N,P0,P1)=R total (τ,N,P0,P1) / E total (τ, N, P0, P1), but for the convenience of calculation, the optimization problem converted into a reduction form is:

[0053]

[0054] 4.2. Decompose the optimization problem into power optimization sub-problem and perception parameter optimization sub-problem.

[0055] Power optimization subproblem: Fix N and τ and derive the closed-form solution of power:

[0056]

[0057] Perception parameter optimization sub-problem: Fixed power parameter, solving the optimal perception duration τ * and the number of collaborative nodes N * :

[0058]

[0059] where c, α, is a constant related to the channel environment, Indicates rounding down.

[0060] 4.3. Alternating iteration and global convergence. Alternately optimize the power parameters and perception parameters until the change in the objective function is less than the preset tolerance. .

[0061] Step 5: Dynamic parameter configuration and real-time optimization.

[0062] 5.1. Environmental perception and parameter prediction: Based on historical data and real-time channel estimation (such as signal-to-noise ratio γ, bit error rate p e ), predict the optimal number of collaborative nodes N * and the perceived duration τ * , and then dynamically adjust the fusion weight w i and the decision threshold K to adapt to the time-varying nature of the control channel quality.

[0063] 5.2. Output and Implementation: Generating Joint Optimization Parameter Sets Configured to the SU network, and then periodically updated parameters to ensure continuous optimal performance of the system in a dynamic environment.

[0064] The foregoing description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein and should not be construed as excluding other embodiments. Rather, the present invention is capable of various other combinations, modifications, and improvements, and is capable of modification within the scope of the concepts described herein, through the above teachings, or through techniques or knowledge in the relevant field. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention are intended to be within the scope of the appended claims.

Claims

1. A high-performance collaborative sensing and resource allocation method for hybrid spectrum sharing, characterized by: The method comprises: Step 1: Model the system and define key parameters; Step 2: Obtain an energy efficiency optimization model under multi-dimensional constraints by constructing an objective function and multi-dimensional constraints; Step 3: Design a cooperative sensing mechanism that is resistant to control channel errors through an improved hard decision fusion rule; Step 4: Convert the objective function into a parameterized reduction form and solve the optimal perception duration and number of collaborative nodes through a two-stage optimization algorithm; Step 5: Dynamic parameter configuration and real-time optimization through environmental perception and parameter prediction; The key parameters defined include: setting the sensing time τ, the data transmission time T – τ, T is the total frame length, the number of collaborative nodes participating in CSS N, and the control channel error probability p e , global detection probability Q d and false alarm probability Q f , SU average transmit power constraint P avg , peak power constraint P peak , SU network minimum rate threshold R min , the average interference power threshold I that PU can tolerate th ; The second step specifically includes: A1. Objective function construction: The objective function is to maximize the energy efficiency of the SU network, and the energy efficiency is set as the total data transmission efficiency R total and total energy consumption E total The ratio of the total data transmission rate R total , depends on the perception result and power selection, expressed as Total energy consumption E total Including perception energy consumption and transmission energy consumption, let the single node perception power consumption be P s , then the total energy consumption E total Expressed as in, , are the SU receiving signal-to-noise ratios under two power modes, and They represent the probability of the PU spectrum being in idle and occupied states, respectively, P0 is the normal power mode, and P1 is the low power mode; A2. Multi-dimensional constraint: Set the average interference power received by the PU to ≤ I th , R total ≥R min , instantaneous power P0≤P peak , P1≤P peak , long-term average power Lower limit of detection probability Upper limit of false alarm probability in, Represents the mathematical expectation operation.

2. The high-performance collaborative sensing and resource allocation method for hybrid spectrum sharing according to claim 1, characterized in that: The system modeling includes: Build a heterogeneous network consisting of primary devices (PUs) and secondary devices (SUs) that perform different communication tasks; In hybrid spectrum sharing mode, the SU detects the PU spectrum status through collaborative spectrum sensing and adaptively selects two power levels: normal power mode and low power mode based on the sensing results. In normal power mode, when the sensing result is that the spectrum is idle, the SU accesses the authorized frequency band at normal power. In low power mode, when the sensing result is that the spectrum is occupied, the SU reduces the transmit power to ensure that the interference to the PU is below the preset threshold.

3. The high-performance collaborative sensing and resource allocation method for hybrid spectrum sharing according to claim 1, characterized in that: The step three specifically includes: B1. Improved hard decision fusion rule: Considering the influence of control channel error on the perception result reporting, the modified perception result u of the i-th SU received by the fusion center is set i ∈{0,1}, where 0 means idle and 1 means occupied, then B2. The fusion center adopts a channel quality weighted strategy To reduce the impact of high bit error rate nodes, where w i is the node weight, the condition for judging the existence of PU is Λ≥K, and K is the dynamically adjusted judgment threshold.

4. The high-performance collaborative sensing and resource allocation method for hybrid spectrum sharing according to claim 1, characterized in that: The fourth step includes: C1. Convert the optimization problem of the objective function into a reduction form of optimization problem: Update the parameter η iteratively until convergence; C2. Decompose the reduction form optimization problem into power optimization subproblems and perception parameter optimization subproblems. Fix N and τ and derive the closed-form solution of power as follows: Fixed power parameter, solving the optimal perception time τ * and the number of collaborative nodes N * They are where c, α, is a constant related to the channel environment, Indicates rounding down; C3, alternately optimize the power parameters and perception parameters until the change in the objective function is less than the preset tolerance .

5. The high-performance collaborative sensing and resource allocation method for hybrid spectrum sharing according to claim 4, characterized in that: The step five includes: D1. Predict the optimal number of collaborative nodes N based on historical data and real-time channel estimation * and the perceived duration τ * , and then dynamically adjust the fusion weight w i and the decision threshold K to adapt to the time-varying nature of the control channel quality; D2. Generate a joint optimization parameter set, configure it to the SU network, and then periodically update the parameters to ensure the system's continuous optimal performance in a dynamic environment.

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