Method and device for allocating resources of multiple service slices in cognitive wireless network

Abstract

This invention provides a method and apparatus for multi-service slice resource allocation in a cognitive wireless network. The method includes: establishing a revenue function for an operator in licensed frequency bands and a revenue function in unlicensed frequency bands, and determining the operator's total revenue function; establishing a revenue function for a user in licensed frequency bands and a revenue function in unlicensed frequency bands, and determining the user's total revenue function based on the user's revenue function in licensed frequency bands and unlicensed frequency bands; calculating, based on the operator's total revenue function and the user's total revenue function, the price per unit spectrum slice of a certain type of service allocated by the operator in licensed frequency bands, the interference price paid by the user to the operator in unlicensed frequency bands, the proportion of spectrum demand for the certain type of service purchased by the user from the operator, and the Nash equilibrium solution corresponding to the transmit power allocated by the user in unlicensed frequency bands; and using each Nash equilibrium solution as the optimal resource allocation strategy.

Description

Technical Field

[0001] This invention relates to the field of multi-service slice resource allocation technology, and in particular to a method and apparatus for multi-service slice resource allocation in a cognitive wireless network. Background Technology

[0002] In this era of rapid information development, numerous communication devices transmit data via radio frequency bands. As a scarce communication resource, traditional spectrum allocation strategies employ fixed frequency band allocation, dividing the required spectrum resources into fixed bands and allocating them to licensed users on demand. Due to the explosive growth of information and emerging applications, existing wireless communication applications have already occupied most of the available radio frequency bands, leaving fewer and fewer bands available for these emerging applications. Meanwhile, licensed spectrum is largely underutilized, leading to a waste of spectrum resources. How to achieve efficient resource allocation under limited spectrum conditions is one of the most pressing problems in wireless communication technology today. Cognitive radio is considered a key technology for solving the problems of spectrum resource shortage and low spectrum utilization. Carrier aggregation technology based on cognitive radio can combine multiple fragmented spectrum resources for communication transmission, using unlicensed spectrum to overcome the spectrum resource shortage problem. Combining cognitive radio technology with resource allocation in different systems is an important means to achieve effective and rational utilization of resources in 5G systems.

[0003] One of the key issues for cognitive radio technology is how to achieve coexistence of multiple users on a shared channel. Since the transmission of secondary users on unlicensed spectrum can affect the transmission of primary users, failure to effectively control interference from secondary users will severely impact the transmission quality of primary users. Another key issue is how secondary users can rationally allocate their limited communication resources in licensed and unlicensed frequency bands to obtain better service quality. Different resource allocation strategies by users in licensed and unlicensed frequency bands will greatly affect channel communication quality and user service experience. Game theory, as a theory that studies the impact of each participant's behavior on the entire system, plays an important role in studying resource allocation in complex network environments. Coalition games are used to study the sharing rights of unlicensed spectrum, allowing two or more mobile network operators to cooperate and share their licensed frequency bands to support a set of public service types; cooperative Nash negotiation games are used to share time resources in an effective coexistence mechanism between LTE-U and Wi-Fi systems. Operators utilize bankruptcy game theory to allocate unlicensed resources among LTE-U users, improving system resource utilization. Existing technologies employ non-cooperative bargaining game theory to analyze cognitive cellular networks composed of primary and secondary network operators, achieving better revenue through a near-closed Nash solution. Others combine federated and bankruptcy game theory to achieve fair channel allocation in heterogeneous networks. However, these studies do not consider the impact of dynamic resource allocation in licensed and unlicensed frequency bands on overall transmission quality, nor do they extend the research to multi-operator, multi-service slicing scenarios. Therefore, resource allocation strategies obtained through these methods cannot guarantee that both operators and users can maximize their revenue. Summary of the Invention

[0004] In view of this, the present invention provides a method and apparatus for allocating multi-service slice resources in a cognitive wireless network to solve one or more problems existing in the prior art.

[0005] According to one aspect of the present invention, a method for allocating multi-service slice resources in a cognitive wireless network is disclosed, the method comprising:

[0006] Establish the operator's revenue function in licensed frequency bands and revenue function in unlicensed frequency bands, and determine the operator's total revenue function based on the operator's revenue function in licensed frequency bands and revenue function in unlicensed frequency bands;

[0007] Establish the user's revenue function in licensed frequency bands and revenue function in unlicensed frequency bands, and determine the user's total revenue function based on the user's revenue function in licensed frequency bands and revenue function in unlicensed frequency bands;

[0008] The total revenue function of the operator and the total revenue function of the user are used to calculate the price per unit spectrum slice of a certain type of service allocated by the operator in the licensed frequency band, the interference price paid by the user to the operator in the unlicensed frequency band, the proportion of spectrum demand for the certain type of service supported by the operator purchased by the user, and the Nash equilibrium solution corresponding to the transmit power allocated by the user in the unlicensed frequency band.

[0009] Each calculated Nash equilibrium solution is taken as the optimal allocation strategy.

[0010] In some embodiments of the present invention

[0011] The revenue function for the operator in the licensed frequency band is:

[0012]

[0013] The revenue function for the operator in the unlicensed frequency band is:

[0014]

[0015] Where L is the set of business types, N i,l The set of users for spectrum slicing services of type l provided by operator i, where M is the total number of operators, and W is the number of users. i,a For operator i's revenue in the licensed frequency band, W i,b For operator i's revenue in unlicensed frequency bands, The unit spectrum slice price for operator i serving type l services. The proportion of spectrum required for service type l by operator i purchased for user j. r represents the total length of spectrum slice bandwidth owned by operator i that supports Class l services. i (l) For interference management fees paid by users to operator i, g j p represents the transmission gain of user j in the unlicensed frequency band. j The transmit power allocated to user j in the unlicensed frequency band. This is a summation operation for all L-type service users of all operators.

[0016] In some embodiments of the present invention

[0017] The user's revenue function in the licensed frequency band is:

[0018]

[0019] in, For user j's revenue in the licensed frequency band, γ j Let J be the revenue coefficient for user j. The unit spectrum slice price for operator i serving type l services. The proportion of spectrum required for service type l by operator i purchased for user j. The total length of spectrum slice bandwidth allocated to operator i to support Class l services, R i,j For the spectrum efficiency of user j managed by operator i in the licensed frequency band, N i,l The set of users providing spectrum slicing services for the l-th type of service to operator i;

[0020] The user's revenue function in the unlicensed frequency band is:

[0021]

[0022] in, For user j's revenue in the unlicensed frequency band, γ j Let J be the revenue coefficient for user j. Let j be the spectral efficiency of user j in the unlicensed frequency band. z j p represents the magnitude of primary user interference experienced by user j in the unlicensed frequency band. -j g -j Let M be the sum of interference experienced by user j in the unlicensed frequency band from users other than the primary user, and r be the total number of operators. i (l) For interference management fees paid by users to operator i, g i p represents the transmission gain of user j in the unlicensed frequency band. j The transmit power allocated to user j in the unlicensed frequency band. This refers to the sub-band bandwidth of the unlicensed frequency band.

[0023] In some embodiments of the present invention, the formula for calculating the Nash equilibrium solution corresponding to the price of a unit spectrum slice of a certain type of service allocated by the operator within the licensed frequency band is as follows:

[0024]

[0025] in, The optimal unit spectrum slice price allocated by operator i within the licensed frequency band to support Class l services, N i,l The set of users for the spectrum slice service of type l provided by operator i, where N is the total number of users, γ j Let R be the revenue coefficient for user j. i,j For the spectrum efficiency of user j managed by operator i in the licensed frequency band, a -j Let a be the sum of the predicted number of spectrum slices purchased by users other than user j within the licensed frequency band. j The number of spectrum slices within the licensed frequency band purchased for user j;

[0026] The formula for calculating the Nash equilibrium solution corresponding to the spectrum demand ratio of a certain type of service purchased by the user from the operator is as follows:

[0027]

[0028] in, The optimal spectrum requirement ratio for operator i's support service type l business purchased for user j, γ j Let R be the revenue coefficient for user j. i,j For the spectrum efficiency of user j managed by operator i in the licensed frequency band, a -j This represents the sum of the predicted number of spectrum slices purchased by users other than user j within the licensed frequency band. The unit spectrum slice price for operator i serving type l services, θ j Assign throughput coefficients to user j, η j For user j's throughput requirements, N i,l N represents the set of users for the spectrum slice service of type l provided by operator i, where N is the total number of users. The total spectrum length of the type l service slice owned by operator i.

[0029] In some embodiments of the present invention, the formula for calculating the Nash equilibrium solution corresponding to the interference price paid by the user to the operator in the unlicensed frequency band is as follows:

[0030]

[0031] Where, r i (l)* The optimal interference price to be paid by a user of Category l services in an unlicensed frequency band to operator i. M represents the interference management fees paid by the user to operators other than operator i, where M is the total number of operators. For the summation operation of all operators' Class L service users, γ j Let J be the revenue coefficient for user j. For the sub-band bandwidth of the unlicensed frequency band, z j p represents the magnitude of primary user interference experienced by user j in the unlicensed frequency band. -j g -j The sum of interference experienced by user j in the unlicensed frequency band from users other than the primary user;

[0032] The formula for calculating the Nash equalization solution corresponding to the user's transmit power in the unlicensed frequency band is as follows:

[0033]

[0034] in, γ is the optimal transmit power allocated to user j in the unlicensed frequency band. j Let J be the revenue coefficient for user j. For the sub-band bandwidth of the unlicensed frequency band, g j Let M be the transmission gain of user j in the unlicensed frequency band, and M be the total number of operators. z is the sum of interference management fees paid by the user to all operators. j p represents the magnitude of primary user interference experienced by user j in the unlicensed frequency band. -j g -j Let g be the sum of the interference experienced by user j in the unlicensed frequency band from users other than the primary user. j Let θ be the transmission gain of user j in the unlicensed frequency band. j Assign throughput coefficients to user j, η j For user j's throughput requirements, p max This is the upper limit of the transmission power.

[0035] In some embodiments of the present invention, the method further includes:

[0036] When operators adjust the unit slice pricing in licensed frequency bands and the interference price in unlicensed frequency bands, the user's spectrum demand ratio in licensed frequency bands and transmit power in unlicensed frequency bands are iteratively updated.

[0037] When users adjust the proportion of spectrum demand in licensed frequency bands and the transmit power in unlicensed frequency bands, the operator's unit slice pricing in licensed frequency bands and interference pricing in unlicensed frequency bands are iteratively updated.

[0038] In some embodiments of the present invention, when an operator adjusts its unit slice pricing in licensed frequency bands and interference pricing in unlicensed frequency bands, iteratively updating the user's spectrum demand ratio in licensed frequency bands and transmit power in unlicensed frequency bands includes:

[0039] by The spectrum requirement ratio of a certain type of service purchased by the user from the operator's support services is updated iteratively with a step size.

[0040] by The transmit power allocated to the user in the unlicensed frequency band is updated iteratively with a step size.

[0041] Where, α j β is the iteration step size coefficient for the proportion of spectrum demand in the licensed frequency band. j Configure iteration step size coefficients for power transmission in unlicensed frequency bands. Let be the first-order partial derivative of user j's revenue in the licensed frequency band with respect to the number of spectrum slices purchased by user j within the licensed frequency band. The first-order partial derivative of the benefit of user j in the unlicensed frequency band with respect to the transmit power allocated to user j in the unlicensed frequency band.

[0042] In some embodiments of the present invention, when the user adjusts the proportion of spectrum demand in licensed frequency bands and the transmit power in unlicensed frequency bands, the operator's unit slice pricing in licensed frequency bands and interference pricing in unlicensed frequency bands are iteratively updated, including:

[0043] by The price per unit spectrum slice for Class L services allocated by the operator within the licensed frequency band is updated iteratively with a step size.

[0044] by The step size is used to iteratively update the interference price paid by the user to the operator in the unlicensed frequency band;

[0045] Where, λ i σ is the iteration step size coefficient for the price of licensed frequency band spectrum slices. i This represents the iteration step size coefficient for unlicensed frequency band interference prices. Let be the first-order partial derivative of operator i's revenue in the licensed frequency band with respect to the unit spectrum slice price for operator i's service of class l services. Let be the first partial derivative of the revenue of operator i in the unlicensed frequency band with respect to the interference management fees obtained by operator i for serving Class l services.

[0046] According to another aspect of the present invention, a multi-service slice resource allocation system in a cognitive wireless network is also disclosed. The system includes a processor and a memory, characterized in that the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps of the method as described in any of the above embodiments.

[0047] According to another aspect of the present invention, a computer-readable storage medium is also disclosed, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the steps of the method as described in any of the above embodiments.

[0048] This invention discloses a multi-service slice resource allocation method for cognitive wireless networks, considering the dynamic multi-service slice allocation problem involving multiple operators and multiple users in licensed and unlicensed frequency bands in the uplink. By studying the spectrum allocation mechanism for multiple operators in licensed frequency bands, the power control mechanism in unlicensed frequency bands, and the dynamic throughput allocation mechanism for multiple service users, it improves the economic benefits and communication quality for both operators and users without affecting the primary users in unlicensed frequency bands. The problem is decomposed into two sub-problems: licensed and unlicensed frequency bands. A simplified Vickrey-Clarke-Groves algorithm and an interference price are used to ensure fairness in user resource allocation, respectively. A Stackelberg game with multiple leaders and followers is used to solve the problem. The impact of the throughput allocation ratio of users in licensed and unlicensed frequency bands on Nash equilibrium and revenue is studied, and a distributed dynamic update algorithm is established to ensure that both operators and users maximize their revenue.

[0049] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the text, or may be learned by practice of the invention. The objects and other advantages of the invention can be realized and obtained by means of the structures specifically pointed out in the written description, claims, and drawings.

[0050] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0051] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, are not intended to limit the scope of the invention. The components in the drawings are not drawn to scale but are merely illustrative of the principles of the invention. For ease of illustration and description of certain parts of the invention, corresponding portions in the drawings may be enlarged, i.e., may appear larger relative to other components in an exemplary device actually manufactured according to the invention. In the drawings:

[0052] Figure 1 This is a flowchart illustrating a method for allocating multi-service slice resources in a cognitive wireless network according to an embodiment of the present invention.

[0053] Figure 2 This is a graph showing the relationship between the proportion of spectrum demand in licensed frequency bands and the number of users.

[0054] Figure 3 This is a graph showing the relationship between power allocation and the number of users in unlicensed frequency bands.

[0055] Figure 4 This is a comparison chart of user throughput satisfaction rates under three different allocation schemes. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0057] It should be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0058] It should be emphasized that the term "including / comprises / has" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0059] Stackelberg game theory can improve player payoffs by studying their strategies and applying non-cooperative game theory. In multi-operator, multi-slice communication networks, optimizing resource allocation for users serving different slices is a pressing issue. Addressing the shortcomings of existing user resource allocation schemes, this invention proposes a dynamic allocation strategy algorithm for users and operators based on a simplified Vickrey-Clarke-Groves algorithm and interference pricing in a multi-operator, multi-service Stackelberg game, solving the multi-operator, multi-service resource allocation problem. This method can achieve higher user throughput and revenue.

[0060] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0061] Figure 1 This is a flowchart illustrating a method for allocating multi-service slice resources in a cognitive wireless network according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes steps S10 to S40.

[0062] Step S10: Establish the operator's revenue function in licensed frequency bands and revenue function in unlicensed frequency bands, and determine the operator's total revenue function based on the operator's revenue function in licensed frequency bands and revenue function in unlicensed frequency bands.

[0063] In a cognitive radio network based on the underlay model, consider M operators located at the same location serving N end users within their coverage area. Assume operator i... Within the coverage area of ​​the base station deployment, there are publicly available unlicensed frequency bands. End users can communicate using both the operator's licensed frequency band spectrum and the publicly available unlicensed frequency band spectrum. Assume the operator supports a total of L types of service slices in the unlicensed frequency bands, where l∈L={1,2,……,L} represents the service type. Assume the total bandwidth length of the l-th service slice owned by operator i is... In the licensed spectrum, assume all end users j, Selfishly prioritizing their own profits, companies obtain communication resources from both licensed and unlicensed frequency bands to support communication services. Furthermore, users are segmented from the perspective of operators and service types. Let j be the set of users providing spectrum slicing services for Class l services offered by operator i. End user j supports its transmission requirements by purchasing licensed spectrum. If end user j fails to obtain sufficient resources from licensed spectrum or if the profit from throughput is too high, end user j will also seek spectrum resources in unlicensed spectrum to further improve its service quality.

[0064] When operator i announces the unit spectrum slice price for supporting service type l services At that time, end user j competes for spectrum resources to support transmission requirements. Within the licensed frequency band, a simplified Vickrey-Clarke-Groves (VCG) algorithm is used to set a penalty mechanism, i.e., assuming that when end user j∈N... i,l The reported spectrum demand ratio is At that time, the actual allocated spectrum slice bandwidth was The set of revenues payable to operator i remains the same. This approach can incentivize end users to report their actual spectrum demand ratios based on their actual needs.

[0065] If end users are dissatisfied with the service in the licensed spectrum or wish to further increase their economic benefits, they can choose to transmit in the unlicensed band. However, excessively high transmit power from each end user will severely interfere with the transmission of other unlicensed users in the same subband, thus affecting the transmission quality of the entire unlicensed band. Therefore, a carrier-customized transmit power interference price (r) is adopted. i The transmit power of end users is constrained in a certain way. When end users wish to use unlicensed frequency bands for service transmission, they need to pay each operator i their respective interference management fee based on the transmit power.

[0066] Therefore, the revenue function for operators in licensed frequency bands is:

[0067]

[0068] The revenue function for the operator in the unlicensed frequency band is:

[0069]

[0070] The total revenue function obtained by operators in licensed and unlicensed frequency bands is W. i =W i,a +W i,b .

[0071] In the above function, L is the set of business types, and N... i,l The set of users providing spectrum slicing services for the l-th type of service to operator i. This is a summation operation for all operators' Class L service users, where M is the total number of operators, and W... i W represents the total revenue that operator i obtains in both licensed and unlicensed frequency bands. i,a For operator i's revenue in the licensed frequency band, W i,b For operator i's revenue in unlicensed frequency bands, The unit spectrum slice price for operator i serving type l services. The proportion of spectrum required for operator i's support service type l business purchased for user j. For operator i, the total length of spectrum slice bandwidth allocated to support Class l services is r. i (l) For interference management fees paid by users to operator i, g j p represents the transmission gain of user j in the unlicensed frequency band. j The transmit power allocated to user j in the unlicensed frequency band.

[0072] Step S20: Establish the user's revenue function in the licensed frequency band and the revenue function in the unlicensed frequency band, and determine the user's total revenue function based on the user's revenue function in the licensed frequency band and the revenue function in the unlicensed frequency band.

[0073] The user's revenue function in the licensed frequency band is:

[0074]

[0075] The user's revenue function in the unlicensed frequency band is:

[0076]

[0077] The total revenue function obtained by users in licensed and unlicensed frequency bands is: U j =U j,a +U j,b .

[0078] in, For user j, the benefits in the licensed frequency band, For user j's revenue in the unlicensed frequency band, U j Let γ be the total revenue function obtained by user j in both licensed and unlicensed frequency bands; j Let J be the revenue coefficient for user j. The unit spectrum slice price for operator i serving type l services. The proportion of spectrum required for operator i's support service type l business purchased for user j. The total length of spectrum slice bandwidth allocated to operator i to support Class l services, R i,j For the spectrum efficiency of user j managed by operator i in the licensed frequency band, N i,l The set of users providing spectrum slicing services for the l-th type of service to operator i; For purchase The benefits gained from long-range spectrum transmission. This is to pay operator i for licensed frequency band spectrum slicing fees.

[0079] Let j be the spectral efficiency of user j in the unlicensed frequency band. z j p represents the magnitude of primary user interference experienced by user j in the unlicensed frequency band. -j g -j Let J be the sum of the interference experienced by user j in the unlicensed frequency band from users other than the primary user. M represents the total number of operators, g j p represents the transmission gain of user j in the unlicensed frequency band. j The transmit power allocated to user j in the unlicensed frequency band. This refers to the sub-band bandwidth of the unlicensed frequency band. The profit that user j can obtain by choosing to transmit in an unlicensed sub-band. The sum of interference fees that user j needs to pay to each operator for choosing to transmit in an unlicensed sub-band.

[0080] Step S30: Calculate the price of a unit spectrum slice allocated by the operator in the licensed frequency band to support a certain type of service, the interference price paid by the user to the operator in the unlicensed frequency band, the proportion of spectrum demand for the certain type of service purchased by the user from the operator, and the Nash equilibrium solution corresponding to the transmit power allocated by the user in the unlicensed frequency band, based on the operator's total revenue function and the user's total revenue function.

[0081] In this step, "a certain type of service" refers to one of multiple service types, such as service type l. Based on the total revenue function for the operator and the user determined in steps S10 and S20, the optimal allocation strategy that maximizes the revenue for both the operator and the user is further obtained. That is, while ensuring the user's communication quality and throughput requirements, the problem of maximizing the revenue for both the operator and the user is solved; specifically, the operator's total revenue W is calculated. i ρ at its maximum i and r i The calculation method is as follows:

[0082]

[0083]

[0084] r * ≥0;

[0085]

[0086] 1≥a * ≥0;

[0087] In the above formula, p * The set of transmit power configuration responses for end users predicted by operator i in unlicensed frequency bands, then This means that the transmit power of any end user j in any unlicensed sub-band is between 0 and p. max Between; r * ≥0 represents the set of interference prices r offered by all operators. * ={r1,r2,……,r n If each interfering price in} is non-negative, then r i This refers to the interference price offered by operator i, which can also be understood as the interference management fee paid by the user to operator i. This is the set of predicted interference prices made by operators other than operator i. ρ represents the set of unit spectrum slice prices for various services of operator i. i ≥0 indicates that the price of the slice given by operator i is non-negative; a * The set of responses to the number of licensed spectrum slices purchased by operator i for the predicted end users, 1 ≥ a * ≥0 indicates that the spectrum demand ratio of each user is between 0 and 1.

[0088] Furthermore, each operator i will determine its own non-cooperative r iThe information is sent to a distributed spectrum controller, which then forwards the channel and pricing information to the end user. After receiving the channel and pricing information, end user j, based on their own needs and predictions of other users' reactions, determines the number of slices they wish to purchase and the transmit power of unlicensed frequency bands. Therefore, the calculation of p corresponds to maximizing user j's total revenue. j and a j The calculation method is as follows:

[0089]

[0090]

[0091]

[0092] In the above formula, st0≤p j ≤p max p here j To optimize the objective, let p represent the transmit power p of any end user j in any unlicensed sub-band. j Both are at 0 and p max between; This indicates that the total throughput obtained by user j in both licensed and unlicensed frequency bands should meet the minimum throughput standard. This represents the predicted set of the number of spectrum slices within the licensed frequency band purchased by users other than end user j. The set of unit spectrum slice prices for operator i; The set of unlicensed frequency band transmit power strategies predicted for other end users for end user j; r * The set of interference prices charged to operators.

[0093] Step S40: Use the calculated Nash equilibrium solutions as the optimal resource allocation strategy.

[0094] This multi-service slicing resource allocation method in cognitive wireless networks reduces user competition and interference while ensuring user communication quality and needs, thereby optimizing wireless resource allocation and alleviating spectrum shortages; it also solves the resource allocation problem between licensed and unlicensed frequency bands.

[0095] To better understand users' resource allocation strategies, backward induction can be used to analyze the problem. For example, [the following is an example:] It can be decomposed into two sub-constraints and Further, based on the two decomposed sub-constraints, we find the Nash equilibrium solutions for each.

[0096] Using non-cooperative game theory and the Debreu-Fan-Glicksberg theorem, we can obtain the following set of formulas for calculating Nash equilibrium solutions:

[0097]

[0098] Where (x) - =min{x, 1};

[0099]

[0100]

[0101] In the formulas above, The optimal unit spectrum slice price allocated by operator i within the licensed frequency band to support Class l services is the Nash equilibrium solution corresponding to the unit spectrum slice price allocated by operator i within the licensed frequency band to support Class l services; N is the total number of users. i,l The set of users for the spectrum slicing service of the type l service provided by operator i, γ j Let R be the revenue coefficient for user j. i,j For the spectrum efficiency of user j managed by operator i in the licensed frequency band, a -j Let a be the sum of the predicted number of spectrum slices purchased by users other than user j within the licensed frequency band. j The number of spectrum slices within the licensed frequency band purchased for user j.

[0102] The optimal spectrum demand ratio for operator i's supported service type l purchased by user j is the Nash equilibrium solution corresponding to the spectrum demand ratio for operator i's supported service type l purchased by user j; γ j Let R be the revenue coefficient for user j. i,j The spectrum efficiency of user j managed by operator i in the licensed frequency band. The unit spectrum slice price for operator i serving type l services, θ j Assign throughput coefficients to user j, η j For user j's throughput requirements, The total spectrum length of the type l service slice owned by operator i.

[0103] The optimal interference price paid by a user of type l service in the unlicensed frequency band to operator i is the Nash equilibrium solution corresponding to the interference price paid by the user to the operator in the unlicensed frequency band. Interference management fees paid by the user to operators other than operator i, and M represents the total number of operators. For the summation operation of all L-type service users of all operators, For the sub-band bandwidth of the unlicensed frequency band, z j p represents the magnitude of primary user interference experienced by user j in the unlicensed frequency band. -j g -j Let J be the sum of the interference experienced by user j in the unlicensed frequency band from users other than the primary user.

[0104] The optimal transmit power allocated to user j in the unlicensed frequency band, i.e., the Nash equalization solution corresponding to the transmit power allocated to user j in the unlicensed frequency band; g j For user j, the transmission gain in the unlicensed frequency band. The interference management fee paid by the user to operator K. θ is the sum of interference management fees paid by the user to all operators. j Assign throughput coefficients to user j, η j Let p be the throughput requirement of user j, and p be the transmit power. max This is the upper limit of the transmission power.

[0105] In practice, due to the operator's and The equilibrium solution is related to the interference pricing strategies of other operators, and the user's r i (l)* and The equilibrium solution is related to the revenue coefficients of other users. Without knowing the strategies of other operators or users, each end user only obtains some information about the resource allocation of other users through historical game information. Each operator and end user uses a distributed algorithm to adjust their strategy so that the calculated result approaches the Nash equilibrium solution. For example, this multi-service slice resource allocation method in a cognitive wireless network further includes the following steps: when an operator adjusts the unit slice pricing in licensed frequency bands and the interference price in unlicensed frequency bands, iteratively updating the user's spectrum demand ratio in licensed frequency bands and transmit power in unlicensed frequency bands; when a user adjusts the spectrum demand ratio in licensed frequency bands and the transmit power in unlicensed frequency bands, iteratively updating the operator's unit slice pricing in licensed frequency bands and interference price in unlicensed frequency bands.

[0106] Specifically, when at least one operator adjusts its unit slice pricing in licensed frequency bands and its interference pricing in unlicensed frequency bands—that is, when not all operators' unit slice pricing and interference prices reach a Nash equilibrium—[the following occurs:] The step size is used to iteratively update the spectrum demand ratio of the certain type of service purchased by user j to support services; The transmit power allocated to user j in the unlicensed frequency band is updated iteratively with a step size. Where α j β is the iteration step size coefficient for the proportion of spectrum demand in the licensed frequency band. j Configure the iteration step size coefficient for unlicensed frequency band power transmission, U j,a For user j's revenue in the licensed frequency band, a j For, U j,b For user j's revenue in the unlicensed frequency band, p j The transmit power allocated to user j in the unlicensed frequency band; then Let be the first-order partial derivative of user j's revenue in the licensed frequency band with respect to the number of spectrum slices purchased by user j within the licensed frequency band. This is the first-order partial derivative of the benefit of user j in the unlicensed frequency band with respect to the transmit power allocated to user j in the unlicensed frequency band. The specific algorithm is shown in Algorithm 1.

[0107]

[0108]

[0109] Similarly, when at least one end user adjusts its spectrum demand ratio in licensed bands and its transmit power in unlicensed bands, in order to The unit spectrum slice price for the operator's supported service type l is updated iteratively with a step size; The interference management fee paid by the user to operator i is updated iteratively with a step size. Where λ i σ is the iteration step size coefficient for the price of licensed frequency band spectrum slices. i W represents the iteration step size coefficient for unlicensed frequency band interference prices. i,a For operator i's revenue in licensed frequency bands, The unit spectrum slice price for operator i serving type l services, W i,b For operator i's revenue in unlicensed frequency bands, This refers to the interference management fees paid by the user to operator i. Let be the first-order partial derivative of operator i's revenue in the licensed frequency band with respect to the unit spectrum slice price for operator i's service of class l services. This is the first-order partial derivative of the revenue of operator i in the unlicensed frequency band with respect to the interference management fee obtained by operator i for serving Class l services. The specific algorithm is shown in Algorithm 2:

[0110]

[0111]

[0112] Furthermore, based on Algorithm 1, θ can also be adjusted. j Perform iterative updates. When When established, the user's payoff depends only on the upper bound or the Nash equilibrium solution, and on θ. j Irrelevant; among them R is the sum of the spectrum proportions required by users of service l under operator i. j Let be the spectral efficiency of user j.

[0113] And because It is a monotonically increasing function. As a monotonically decreasing function, it can be seen from the user utility function and its concavity / convexity that when and When θ increases j to U j (θ j ′)≥U j (θ j );when and When θ decreases j to U j (θ″ j )≥U j (θ j ).

[0114] If it exists The interval, and At that time, user j's benefit is related to θ j Related, its profit function is For U j (θ j Differentiation yields This holds true consistently. Therefore, θ j The update can be performed iteratively using the following algorithm 3.

[0115]

[0116] This invention discloses a multi-service slice resource allocation method for cognitive wireless networks, considering the dynamic multi-service slice allocation problem involving multiple operators and multiple users in licensed and unlicensed frequency bands in the uplink. By studying the spectrum allocation mechanism for multiple operators in licensed frequency bands, the power control mechanism in unlicensed frequency bands, and the dynamic throughput allocation mechanism for multiple service users, it improves the economic benefits and communication quality for both operators and users without affecting the primary users in unlicensed frequency bands. The problem is decomposed into two sub-problems: licensed and unlicensed frequency bands. A simplified Vickrey-Clarke-Groves algorithm and an interference price are used to ensure fairness in user resource allocation, respectively. A Stackelberg game with multiple leaders and followers is used to solve the problem. The impact of the throughput allocation ratio of users in licensed and unlicensed frequency bands on Nash equilibrium and revenue is studied, and a distributed dynamic update algorithm is established to ensure that both operators and users maximize their revenue.

[0117] Correspondingly, the present invention also discloses a multi-service slice resource allocation system in a cognitive wireless network. The system includes a processor and a memory. The memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps of the method as described in any of the above embodiments.

[0118] In one embodiment, consider an uplink cellular network with three operators and N randomly distributed users supporting two types of services. The loss function between the base station and the users is 28.5 + 20 * log10(d). The N users are randomly distributed at a distance of 200-500 meters from the base station. The bandwidth of the licensed frequency band for each type of service of each operator is 10 MHz. The simulation parameters are shown in Table 1.

[0119] Table 1: Simulation Parameters

[0120] Simulation parameters Parameter value Business license frequency band bandwidth 10MHz Unlicensed frequency band bandwidth 30MHz bandwidth 25kHz Path loss model 28.5 + 20 * log10(d) upper limit of transmission power 4W User revenue coefficient / (MBps) [3,3.05]

[0121] For the above simulation parameters, d in 28.5+20*log10(d) is the distance from the user to the base station, in kilometers.

[0122] During execution, the user's channel state data can be used as input to Algorithm 1 and Algorithm 2, and the Nash equilibrium solution can be obtained iteratively through Algorithm 1, Algorithm 2 and Algorithm 3.

[0123] Furthermore, the services were divided into high-throughput services and low-throughput services, and a comparison was made between users of these two types of services in terms of the proportion of licensed frequency band demand, unlicensed frequency band power configuration, and allocation coefficients. For example... Figure 2 As shown, Service 1 represents high-throughput service users, and Service 2 represents low-throughput service users. Figure 2 This demonstrates the consistency in the average demand ratio of the two types of service users for licensed frequency bands. The result indicates that when only throughput is limited while parameters such as user revenue coefficients remain consistent, users maintain nearly identical interest in licensed frequency bands. Figure 2 This reflects a pattern where user demand for licensed spectrum initially increases and then decreases as the number of users grows. This is because, with more users, their initial consideration is to increase their bids to secure sufficient spectrum. At this point, users will choose a higher spectrum percentage than their actual needs, sacrificing some revenue to guarantee adequate spectrum. However, once the number of users exceeds capacity, excessive competition means that even higher spectrum bids yield only a small amount of spectrum. Therefore, after some negotiation, users choose to lower their spectrum demand percentage. Users with low revenue coefficients consistently maintain a lower spectrum demand percentage than users with high revenue coefficients.

[0124] Figure 3 The diagram illustrates the differences in average power configuration between two types of service users in unlicensed frequency bands. Here, y=3 and y=2.5 represent the user's revenue coefficient. Furthermore, the curves for both service types coincide when y=3 and when y=2.5. The results indicate that as the number of users increases within a reasonable range, Service 1 users, due to their greater throughput demand, configure higher transmit power in unlicensed frequency bands. Simultaneously, with the increase in users, interference from similar service users in unlicensed frequency bands increases, requiring users to configure higher power to meet throughput requirements. Service 2 users, with lower throughput demand, can obtain sufficient throughput in licensed frequency bands; therefore, power configuration in unlicensed frequency bands only affects their revenue. Thus, within a reasonable user number range, the second type of user will choose to reduce transmit power to maximize their revenue. Users with low revenue coefficients consistently maintain lower transmit power than users with high revenue coefficients.

[0125] like Figure 4 As shown, Model 1 is a non-cooperative Stackelberg strategy, lacking both a dynamic allocation strategy for licensed frequency bands and a dynamic throughput allocation strategy for users. Model 2 is also a non-cooperative Stackelberg strategy, but lacks a dynamic allocation strategy for licensed and unlicensed frequency bands. Comparing the algorithm in this invention with the two models above, it can be seen that as the number of users increases, the throughput satisfaction of different types of users in the optimized model proposed in this invention is consistently superior to the other two Stackelberg models, achieving a more reasonable resource allocation and accommodating more users in the same system to ensure transmission quality. The dynamic allocation strategy for users can reduce interference from unlicensed frequency band users, resulting in higher throughput.

[0126] As can be seen from the above embodiments, this invention starts with the problem of spectrum and power resource allocation for multi-operator, multi-service slices, and proposes a method for multi-service slice resource allocation in cognitive wireless networks with the goal of maximizing the respective benefits of operators and users. This method addresses the dynamic multi-service slice allocation problem in licensed and unlicensed frequency bands for multiple operators and users in the uplink. By studying the spectrum allocation mechanism for multiple operators in licensed frequency bands, the power control mechanism in unlicensed frequency bands, and the dynamic throughput allocation mechanism for multi-service users, it improves the economic benefits and communication quality of both operators and users without affecting the primary users in unlicensed frequency bands. Furthermore, the overall problem is decomposed into two sub-problems: licensed and unlicensed frequency bands. The simplified Vickrey-Clarke-Groves (VCG) algorithm and the setting of interference prices are used to ensure the fairness of user resource allocation, respectively. A Stackelberg game with multiple leaders and multiple followers is used to solve the problem. The method also proposes a mechanism for the impact of the throughput allocation ratio of users in licensed and unlicensed frequency bands on Nash equilibrium and benefits, and sets up a distributed dynamic update algorithm to ensure that both operators and users maximize their respective benefits.

[0127] In addition, the invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method as described in any of the above embodiments.

[0128] Those skilled in the art will understand that the exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the desired tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0129] It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0130] In this invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or in place of features of other embodiments.

[0131] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for allocating multi-service slice resources in a cognitive wireless network, characterized in that, The method includes: Establish the operator's revenue function in licensed frequency bands and revenue function in unlicensed frequency bands, and determine the operator's total revenue function based on the operator's revenue function in licensed frequency bands and revenue function in unlicensed frequency bands; Establish the user's revenue function in licensed frequency bands and revenue function in unlicensed frequency bands, and determine the user's total revenue function based on the user's revenue function in licensed frequency bands and revenue function in unlicensed frequency bands; The Nash equilibrium solution is calculated based on the operator's total revenue function and the user's total revenue function, corresponding to the price per unit spectrum slice for a certain type of service allocated by the operator within the licensed frequency band, the interference price paid by the user to the operator in the unlicensed frequency band, the proportion of spectrum demand for the certain type of service purchased by the user from the operator's support services, and the transmit power allocated by the user in the unlicensed frequency band. The formula for calculating the Nash equilibrium solution corresponding to the price per unit spectrum slice for a certain type of service allocated by the operator within the licensed frequency band is as follows: ； ； in, For operators Support allocated within licensed frequency bands The optimal unit spectrum slice price for similar services For operators The provided first The set of users of spectrum slicing services for similar businesses. This represents the total number of operators. For users The profit coefficient, For operators Managed users Spectral efficiency in licensed frequency bands, In addition to users In addition, the sum of the predicted number of spectrum slices purchased by other users within the licensed frequency band. For users The number of spectrum slices within the licensed frequency band purchased; The formula for calculating the Nash equilibrium solution corresponding to the spectrum demand ratio of a certain type of service purchased by the user from the operator is as follows: ； ； in, For users Purchased operator Support services The optimal spectrum requirement ratio for similar services For users The profit coefficient, For operators Managed users Spectral efficiency in licensed frequency bands, In addition to users In addition, the sum of the predicted number of spectrum slices purchased by other users within the licensed frequency band. For operators Serve Price per unit spectrum slice for similar services For users Throughput allocation coefficient, To meet user A's throughput requirements, For operators The provided first The set of users of spectrum slicing services for similar businesses. This represents the total number of operators. For operators Distribution support Total length of spectrum slice bandwidth for similar services The formula for calculating the Nash equilibrium solution corresponding to the interference price paid by the user to the operator in the unlicensed frequency band is as follows: ； in, For the first time in unlicensed frequency bands Users of similar services to the operator The optimal interference price to pay. For users other than operators Interference management fees paid by other operators besides [the main operators] This represents the total number of operators. For all operators Summation operations for similar business users For users The profit coefficient, For sub-band bandwidth of unlicensed frequency bands, For users The magnitude of primary user interference received in unlicensed frequency bands For users The sum of interference from users other than the primary user in the unlicensed frequency band; The formula for calculating the Nash equalization solution corresponding to the user's transmit power in the unlicensed frequency band is as follows: ； in, For users The optimal transmit power allocated in unlicensed frequency bands. For users The profit coefficient, For sub-band bandwidth of unlicensed frequency bands, For users Transmission gain in unlicensed frequency bands, This represents the total number of operators. This is the sum of interference management fees paid by the user to all operators. For users The magnitude of primary user interference received in unlicensed frequency bands For users The sum of interference from users other than the primary user in the unlicensed frequency band. For users Transmission gain in unlicensed frequency bands, For users Throughput allocation coefficient, For users throughput requirements, This is the upper limit of the transmission power; Each calculated Nash equilibrium solution is taken as the optimal allocation strategy.

2. The method for allocating multi-service slice resources in a cognitive wireless network according to claim 1, characterized in that, The revenue function for the operator in the licensed frequency band is: ； The revenue function for the operator in the unlicensed frequency band is: ； Where L is the set of business types, For operators The provided first The set of users for spectrum slicing services of the same type, where M is the total number of operators. For operators Revenue from licensed frequency bands, For operators Revenue from unlicensed frequency bands, For operators Serve Price per unit spectrum slice for similar services For users Purchased operator services The proportion of spectrum demand for similar services For operators Distribution support Total length of spectrum slice bandwidth for similar services For users to operators Interference management fees paid For users Transmission gain in unlicensed frequency bands, For users The transmission power allocated in unlicensed frequency bands, For all operators Summation operations for similar business users.

3. The method for allocating multi-service slice resources in a cognitive wireless network according to claim 1, characterized in that, The user's revenue function in the licensed frequency band is: ； in, For users Revenue from licensed frequency bands, For users The profit coefficient, For operators Serve Price per unit spectrum slice for similar services For users Purchased operator services The proportion of spectrum demand for similar services For operators Distribution support Total length of spectrum slice bandwidth for similar services For operators Managed users Spectral efficiency in licensed frequency bands, For operators The provided first The set of users of spectrum slicing services for similar businesses; The user's revenue function in the unlicensed frequency band is: ； in, For users Revenue from unlicensed frequency bands, For users The profit coefficient, For users Spectral efficiency in unlicensed frequency bands , For users The magnitude of primary user interference received in unlicensed frequency bands For users The sum of interference from users other than the primary user in unlicensed frequency bands, where M is the total number of operators. For users to operators Interference management fees paid For users Transmission gain in unlicensed frequency bands, For users The transmission power allocated in unlicensed frequency bands, This refers to the sub-band bandwidth of the unlicensed frequency band.

4. The method for allocating multi-service slice resources in a cognitive wireless network according to claim 1, characterized in that, The method further includes: When operators adjust the unit slice pricing in licensed frequency bands and the interference price in unlicensed frequency bands, the user's spectrum demand ratio in licensed frequency bands and transmit power in unlicensed frequency bands are iteratively updated. When users adjust the proportion of spectrum demand in licensed frequency bands and the transmit power in unlicensed frequency bands, the operator's unit slice pricing in licensed frequency bands and interference pricing in unlicensed frequency bands are iteratively updated.

5. The method for allocating multi-service slice resources in a cognitive wireless network according to claim 4, characterized in that, When operators adjust their unit slice pricing in licensed frequency bands and interference pricing in unlicensed frequency bands, the system iteratively updates the user's spectrum demand ratio in licensed frequency bands and transmit power in unlicensed frequency bands, including: by The step-size iterative update of the user's purchased operator support services The proportion of spectrum required by the business; by The transmit power allocated to the user in the unlicensed frequency band is updated iteratively with a step size. in, The step size coefficient for the proportional iteration of spectrum demand in the licensed frequency band. Configure iteration step size coefficients for power transmission in unlicensed frequency bands. For users The benefits to users in licensed frequency bands The first partial derivative of the number of spectrum slices purchased within the licensed frequency band. For users The benefits to users in unlicensed frequency bands The first-order partial derivative of the transmit power allocated in the unlicensed frequency band.

6. The method for allocating multi-service slice resources in a cognitive wireless network according to claim 4, characterized in that, When the user adjusts the proportion of spectrum demand in licensed frequency bands and the transmit power in unlicensed frequency bands, the operator's unit slice pricing in licensed frequency bands and interference pricing in unlicensed frequency bands are iteratively updated, including: by The step size is used to iteratively update the frequency bands allocated by the operator within the licensed bands. The price per unit spectrum slice for similar services; by The step size is used to iteratively update the interference price paid by the user to the operator in the unlicensed frequency band; in, The step size coefficient for the iterative pricing of licensed frequency band spectrum slices. This represents the iteration step size coefficient for unlicensed frequency band interference prices. For operators Revenue from licensed spectrum for operators Serve Price per unit spectrum slice for similar services , For operators Revenue from unlicensed spectrum for operators Serve The first partial derivative of the interference management fee obtained by the business.

7. A multi-service slice resource allocation system in a cognitive wireless network, the system comprising a processor and a memory, characterized in that, The memory stores computer instructions, and the processor executes the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps of the method as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 6.

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