A communication and exploration integrated resource optimization method based on 5G macro base station

Abstract

The present invention discloses a method for optimizing the integrated resources of communication and detection based on 5G macro base stations. First, a 5G communication and detection integrated scenario is established, and system parameters are initialized. By analyzing the communication service and time-frequency resource allocation process, a communication service transmission model and a detection function task model are built. By combining the communication and detection models, a 5G communication and detection integrated two-dimensional scenario model is constructed. The energy efficiency of the communication system is selected as the objective function. Combined with the maximum possible detection coverage constraint condition, the integrated resource optimization problem of communication and detection of the 5G macro base station is established. Finally, the optimization problem is solved to achieve the integrated resource optimization of communication and detection of the 5G macro base station. The method of the present invention meets the detection requirements under the premise of completing the communication service, maximizes the optimization effect of the integrated resource of communication and detection by allocating time-frequency resources and spatial resources, and is significantly better than the detection function coverage before optimization in terms of detection function coverage. It can be applied to fields such as communication and detection integration.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated communication and detection, and specifically relates to a communication and detection integrated resource optimization method based on 5G macro base stations. Background Art

[0002] Integrated communication and detection technology can meet the multifunctional development needs of electronic devices in resource-constrained environments. It integrates communication and detection functions on the same platform, coordinates the allocation of system resources, and enables multifunctional coexistence and collaboration. This system achieves this by multiplexing communication and detection functions in terms of signals, channels, resources, and processing. Furthermore, with the continuous evolution of 5G technology, base station coverage is becoming denser, communication bandwidth is increasing, carrier frequency is increasing, and MIMO antennas are expanding. The frequency band of communication systems is similar to the operating range of radar. Thanks to its advantages in hardware resources and costs, integrated communication and detection technology for 5G has gradually become a broad and important research topic.

[0003] The paper “Wymeersch H, Mach T, et al. Positioning and sensing for vehicular safety applications in 5G and beyond. IEEE Communications Magazine, 2021, 59(11): 15-21” proposes a novel multi-beam framework based on a steerable analog antenna array, which allows for seamless integration of communication and detection functions under power or volume constraints. The paper “Interference measurement between 3.5GHz 5G system and radar. 2018 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2018: 1539-1541” applies the novel multi-beam framework based on a steerable analog antenna array to 5G base stations, leveraging the wide coverage and large-scale deployment advantages of 5G base stations to improve the scalability of the integrated communication and detection system. However, most of the above works do not provide specific solutions or implementation methods.

[0004] 5G-based research primarily focuses on indoor environments and short-range detection scenarios. This is due to the fact that as frequency increases, signal attenuation in the environment increases dramatically. Furthermore, due to the limitations of hardware such as power and antenna gain in small devices, long-distance, large-scale active monitoring is difficult. Implementing detection capabilities for macro base stations is an effective approach to address this problem, but currently, little research exists on sensing technologies for macro base stations. Significant research gaps remain in areas such as resource allocation, hardware system integration, and detection information processing. Summary of the Invention

[0005] To solve the above technical problems, the present invention proposes a method for optimizing communication and exploration integrated resources based on 5G macro base stations. For time-frequency resource allocation and spatial beam resource allocation, resource optimization algorithms based on game models and beam scheduling are proposed respectively. Through simulation, the improvement of the performance of integrated functional business requirements by the optimization algorithm is verified.

[0006] The technical solution adopted by the present invention is a method for optimizing communication and exploration integrated resources based on 5G macro base stations, and the specific steps are as follows:

[0007] S1. Establish an integrated 5G communication detection scenario and initialize system parameters;

[0008] S2. Analyze the communication service and time-frequency resource allocation process, build a communication service transmission model and a detection function task model, and combine the communication and detection models to construct a 5G communication and detection integrated two-dimensional scenario model;

[0009] S3. Select the communication system energy efficiency as the objective function and combine it with the maximum possible detection coverage constraint to establish the communication and detection integrated resource optimization problem of 5G macro base stations.

[0010] S4. Solve the optimization problem to achieve integrated communication and exploration resource optimization of 5G macro base stations.

[0011] Furthermore, the step S1 is specifically as follows:

[0012] The 5G communication detection integrated scenario is a scenario in which a macro base station, multiple mobile user devices and multiple micro base stations interact with each other.

[0013] The system parameters include: macro base station coverage range, macro base station altitude, number and location of mobile users and micro base stations, user requests, number of base station cache files, maximum number of files, file size, number and location of beam cells, average coverage radius of micro base stations, communication downlink SNR requirements, subcarrier spacing, detection speed resolution requirements, detection distance resolution requirements, and detection matching gain requirements.

[0014] In a macro base station coverage scenario model, within the coverage area of ​​a 5G macro base station, Ns micro-stations and N u The micro base station is equipped with a cache server and a wireless backhaul link that can communicate with the macro base station.

[0015] Furthermore, the step S2 is specifically as follows:

[0016] The communication service transmission model includes a service processing model, a service burst request model, a service response and access model, and a service transmission communication model.

[0017] The service processing model includes: a file request mechanism, a communication access mechanism, a cache mechanism and a method for calculating the downlink communication service volume of a macro base station.

[0018] During the downlink transmission process of a data-type service, a file transfer is divided into three processing stages: user request stage, response access stage, and response transmission stage, and a service burst request model, service response and access model, and service transmission communication model are constructed accordingly.

[0019] The detection function task model divides the low-altitude area covered by the entire macro base station into D-beam low-altitude detection task areas according to the direction of the antenna beam. At the same time, the amount of business that the macro base station needs to complete by transmitting an integrated beam to the low-altitude detection area indirectly quantitatively describes the amount of time-frequency resources consumed for detecting the area. When the detection performance of a beam cell can meet the minimum performance indicators of the detection requirements within the scanning period, it means that the low-altitude detection task area is covered by the detection.

[0020] Finally, the communication and detection models are combined to construct an integrated two-dimensional scenario model of 5G communication and detection. The scenario model includes both the communication model constructed by users, micro base stations, and collaborative cache networks, and the detection model based on beam cell segmentation.

[0021] Furthermore, the step S3 is specifically as follows:

[0022] Set the total power required for the macro base station to complete the downlink service response to be P MS , the power P required to complete the downlink service between the micro base station and the connected mobile user S,Y ,The EE of the communication system describes the average energy consumption required to serve a unit file service request, and is expressed as:

[0023]

[0024] Among them, Φ u ={x} represents the location set of mobile user equipment in different beam cells, x∈Φ u represents the mobile users in different beam cells, k xRepresents the file request queue for each user.

[0025] Set the number of low-altitude detections that meet the detection performance to N Dcover , then the detection coverage R Dcover It is expressed as follows:

[0026]

[0027] Wherein, D represents the number of low-altitude detection task areas that are divided into the low-altitude area of ​​the entire macro base station coverage range according to the direction of the antenna beam.

[0028] The energy efficiency of the communication system is selected as the optimization target to establish an optimization model, and the communication and exploration integrated resource optimization problem based on 5G macro base stations is modeled as follows:

[0029]

[0030] Among them, EE represents the energy efficiency of the communication system, Indicates the number of files of all users, P MS Indicates the total power required by the macro base station to complete the downlink service response, P S,Y It represents the power required to complete the downlink service between the micro base station and the connected mobile user, R Dcover,max represents the maximum possible detection coverage, R Dcover Indicates the detection coverage.

[0031] Furthermore, the step S4 is specifically as follows:

[0032] S41. At the service and time-frequency resource allocation level, the base station response model to services is incorporated into the game theory model analysis to achieve optimal allocation of service resources.

[0033] After obtaining the scene model information, the optimization objectives and constraints are obtained through indirect calculation of two types of data: user request sequence and micro-station cache file directory. Resource allocation is mainly reflected in how to update the micro-station cache file. The optimization problem is transformed into the following expression

[0034]

[0035] in, Represents the cache file matrix expected by each micro-station, Represents the cache file matrix of each micro-station at the initial moment. Indicates the cache update file sent by the macro base station to the micro base station, the initial cache file directory and the updated cache file directory. Indicates the user request that will be responded to by the micro site, Indicates whether the user requested the file. It can be 0 to indicate no request.

[0036] The base station response problem model for services is brought into the game theory model analysis, and the optimization subject is transformed from the macro base station to each micro base station. That is, the micro base station acts as a player in the game, and requests the macro base station to update the cache file as the action selected in the game. An iterative method is used for optimization and solution.

[0037] Let the game be G, and the action in the jth game is given by The income is expressed as In each iteration, some micro-stations are selected to change their actions. The selected base stations are called active stations, and the remaining ones that do not change their actions are called passive stations. Let the set of active stations in a certain iteration be φ act , the set of passive stations led by the active station is φ neg (y).

[0038] A two-stage iterative algorithm is proposed. In the first stage, beam cells are used as players to optimize detection targets, at which time the cell detection task requirements are achieved as much as possible. In the second stage, reducing communication loss is used as the objective function to find a better response path, achieve Nash equilibrium, and realize the optimal allocation of service resources.

[0039] The two-stage iterative algorithm is specifically as follows:

[0040] A1. Initialization: At the beginning of each time slot, randomize the file cache of the micro-station and obtain the file request list of the mobile user as Ω s , let Ω s '=Ω s ;

[0041] A2. Phase 1:

[0042] A21, from Ω s 'Randomly select a small base station ζ to join φ act , and from Ω s 'Remove base station ζ and its associated passive station φ neg (ζ);

[0043] A22, return to step A21 until

[0044] A23. For all φ act Medium base station, set each file in each small base station to zero in turn, and get each base station action Make

[0045] A24. If there is no action that meets the conditions, go to step A3 and set Otherwise, let Ω s '=Ω sAnd return to step A21;

[0046] A3. Phase 2:

[0047] A31, from Ω s 'Randomly select a small base station ζ to join φ act , and from Ω s 'Remove base station ζ and its associated passive station φ neg (ζ);

[0048] A32, return to step A31 until

[0049] A33. For all φ act Medium base station, select each base station action Make Indicates all downlink requests responded to by the macro base station;

[0050] A34. Judgment If so, exit; otherwise, return to step A31.

[0051] S42. Propose a spatial resource allocation method based on beam scheduling, solve the optimization problem, and obtain the final detection function coverage, so as to achieve improved detection coverage at the cost of low communication loss;

[0052] First, for the beams in the uncovered area, analyze the beam access status of all downlink services in the coverage area of ​​the macro base station, and define c d,u is the beam-user association factor. If the device u requests a response and is accessed by the beam in area d, then c d,u =1, otherwise 0.

[0053] Here, u represents a user device or a micro base station.

[0054] At this time, the system energy efficiency EE is expressed as:

[0055]

[0056] Among them, EE represents the energy efficiency of the communication system, Indicates the number of files for all users. Indicates that the macro base station selects c d,x =1 area responds to the downlink power of communication services, It represents the total power consumed by the macro base station to complete the downlink service response, that is, the downlink power matrix P of all user communication service responses in the low-altitude detection mission area d∈{1:D} X,K Downlink power matrix P for cache update with micro base stations Y,K , P S,YIt represents the power required to complete the downlink service between the micro base station and the connected mobile user, R Dcover,max represents the maximum possible detection coverage, R Dcover Indicates the detection coverage.

[0057] Since the total power P required for the macro base station in EE to complete the downlink service response MS It is often much larger than the transmission power of the micro base station. Therefore, the optimization goal of maximizing EE is transformed into the optimization of minimizing the power consumption of the macro base station, as shown in the following formula:

[0058]

[0059] Among them, the beam-user matching factor c d,u Composition matrix k x represents the file request queue of each user, k y represents the file request queue of each micro-station, K th Indicates the set threshold value.

[0060] Relaxing the beam-user matching factor converts the above problem into a convex problem, that is, c d,u The value range is changed from {0,1} to any real value in [0,1], and the Lagrange dual method is chosen to solve the problem. Then the above optimization problem can be written as:

[0061]

[0062] We choose to use the Lagrange dual method to solve the problem, and its Lagrange function is written as:

[0063]

[0064] Among them, λ u ≥0,υ d ≥0 are Lagrange multipliers, corresponding to st1 and st2 of the optimization problem respectively. The Lagrange dual function of Equation (7), ignoring the fixed power part between the macro base station and the micro base station, is written as:

[0065]

[0066] Among them, F d,u (c d,u ,λ u ,υ d ) is each c d,u The corresponding Lagrange dual function:

[0067]

[0068] in, Indicates that the macro base station selects c d,u =1 area responds to the downlink power of user communication services.

[0069] Finally, we get the Lagrange dual problem:

[0070]

[0071] According to the KKT conditions, solve F d,u (c d,u ,λ u ,υ d ) for c d,u The partial derivative of , we can get:

[0072]

[0073] In order to obtain the minimum EE, the beam-user matching factor is defined as follows:

[0074]

[0075] in,

[0076]

[0077] The matching result c obtained by relaxing the problem d,u Further converted into a binary integer, that is, the solution to the problem before relaxation. Then the optimal beam-user access factor solution is obtained, considering its Lagrange multiplier υ d Related, the gradient descent method is used to update υ d , and convergence is guaranteed, expressed as:

[0078]

[0079] in,[·] + =max{0,·}, represents the amount of business that needs to be completed by the macro base station transmitting an integrated beam to the low-altitude detection mission area d, δ (t) represents the update step size greater than zero in the t-th iteration, and the formula should satisfy:

[0080]

[0081] Among them, g t represents the iterative gradient direction, Q * It represents the optimal solution of the dual problem, and Q represents the current iterative solution of the dual problem.

[0082] For the task area within the detection coverage range, when the device u requests a response to the access beam that is not from this beam cell, but is accessed by the beam of other beam cells, if the currently accessed beam cell is the beam cell that illuminates the task area within the detection coverage range, the detection coverage rate will be improved at the cost of low communication energy efficiency loss, and the integrated communication and detection resource optimization of the 5G macro base station will be completed.

[0083] Beneficial effects of the present invention: The method of the present invention first establishes a 5G communication and detection integration scenario and initializes system parameters. By analyzing the communication service and time-frequency resource allocation process, a communication service transmission model and a detection function task model are built. The two models of communication and detection are combined to construct a 5G communication and detection integration two-dimensional scenario model. The energy efficiency of the communication system is selected as the objective function. Combined with the maximum possible detection coverage constraint, the communication and detection integration resource optimization problem of the 5G macro base station is established. Finally, the optimization problem is solved to achieve the communication and detection integration resource optimization of the 5G macro base station. The method of the present invention meets the detection requirements on the premise of completing the communication service, maximizes the optimization effect of the communication and detection integration resources through the allocation of time-frequency resources and spatial resources, and is significantly better than the detection function coverage before optimization in terms of detection function coverage. It can be applied to fields such as communication and detection integration. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 This is a flow chart of a method for optimizing communication and exploration integrated resources based on 5G macro base stations according to the present invention.

[0085] Figure 2 This is a diagram of a single macro base station coverage scenario used in an embodiment of the present invention.

[0086] Figure 3 This is a diagram of the integrated two-dimensional scene model of 5G communication detection used in an embodiment of the present invention.

[0087] Figure 4 It is an iterative convergence curve diagram in an embodiment of the present invention.

[0088] Figure 5 3 is a comparison chart of the detection function coverage results in the embodiment of the present invention and before optimization. DETAILED DESCRIPTION

[0089] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0090] The method of the present invention is mainly verified by computer simulation experiments, and all steps and conclusions are verified to be correct on Matlab2021a.

[0091] like Figure 1 As shown in FIG, a flow chart of a method for optimizing communication and exploration integrated resources based on a 5G macro base station of the present invention is shown, and the specific steps are as follows:

[0092] S1. Establish an integrated 5G communication detection scenario and initialize system parameters;

[0093] S2. Analyze the communication service and time-frequency resource allocation process, build a communication service transmission model and a detection function task model, and combine the communication and detection models to construct a 5G communication and detection integrated two-dimensional scenario model;

[0094] S3. Select the communication system energy efficiency as the objective function and combine it with the maximum possible detection coverage constraint to establish the communication and detection integrated resource optimization problem of 5G macro base stations.

[0095] S4. Solve the optimization problem to achieve integrated communication and exploration resource optimization of 5G macro base stations.

[0096] In this embodiment, step S1 is specifically as follows:

[0097] The 5G communication detection integrated scenario is a scenario in which a macro base station, multiple mobile user devices and multiple micro base stations interact with each other.

[0098] The system parameters include: macro base station coverage range, macro base station altitude, number and location of mobile users and micro base stations, user requests, number of base station cache files, maximum number of files, file size, number and location of beam cells, average coverage radius of micro base stations, communication downlink SNR requirements, subcarrier spacing, detection speed resolution requirements, detection distance resolution requirements, and detection matching gain requirements.

[0099] like Figure 2 The single macro base station coverage scenario diagram shown and Figure 3 As shown in the integrated two-dimensional scenario model of 5G communication detection, in this embodiment, the macro base station is located at the origin of the rectangular coordinate system. Within the coverage range of 1000m of the macro base station, the number of mobile users waiting for service requests that obey the two-dimensional homogeneous Poisson point process distribution is 300 and the number of micro base stations is 40. The micro base stations are equipped with cache servers and wireless backhaul links that can communicate with the macro base station.

[0100] In this embodiment, step S2 is specifically as follows:

[0101] The communication service transmission model includes a service processing model, a service burst request model, a service response and access model, and a service transmission communication model.

[0102] The service processing model includes: a file request mechanism, a communication access mechanism, a cache mechanism and a method for calculating the downlink communication service volume of a macro base station.

[0103] During the downlink transmission of a data-based service, a file transfer is divided into three processing stages: user request stage, response access stage, and response transmission stage. A service burst request model, service response and access model, and service transmission communication model are constructed accordingly. The details are as follows:

[0104] 1. Business burst request model;

[0105] In the user request phase, consider that each user makes random and sudden file requests, set the total number of core data files as F, and record the total set of files as i represents the i-th file, and each file is assumed to be of fixed size L. Let f i Represents file popularity, which represents the probability of each file being requested. Considering the preference of data files and the known popularity, the probability of a file being requested usually follows the Zipf distribution:

[0106]

[0107] Among them, γ represents the skewness of popularity distribution. The larger γ is, the more obvious the preference of the file is. When γ is 0, it means that all files have equal probability of being requested.

[0108] Assume that each user randomly requests one or more files at a certain time. At most K files can be requested during this time. At the start of a certain time slot, the file request queue is expressed as:

[0109]

[0110] in, Indicates whether the user requested the file, and can be 0 to indicate no request. u ={x} represents the location set of mobile user equipment in different beam cells, x∈Φ u Indicates mobile users in different beam cells.

[0111] The probability of sending the number of requested files follows a Poisson process:

[0112]

[0113] Where N(t) represents the total number of request files sent within the time (0, t), τ represents the time difference, P represents the probability that the number of request files sent is k, and the parameter λ of the Poisson distribution represents the number of request files sent per unit time.

[0114] Then suppose the request set of all users at the start of a time slot is expressed as

[0115] 2. Business response and access model;

[0116] The macro base station obtains and caches data through optical fiber. All files in the micro site, and the micro site is set to be able to cache only M files, and M<F.

[0117] When a file is requested by a mobile user, if a nearby micro base station has cached the file, the file request will be responded to by the micro base station, otherwise it will be responded to by the macro base station.

[0118] Assume R y Represents the coverage radius of the micro-station, when ||yx||<R y When , user x meets the conditions for sending a file request to micro-station y. The candidate user set of micro-station y Expressed as:

[0119]

[0120] Candidate request set for microsite y Expressed as:

[0121]

[0122] in, Represents the file collection cached by micro-station y. Since a file request can only be responded to by one base station, the micro-station will Select a portion of the request Respond to user file requests that require a macro base station response It is composed of all user requests excluding the requests responded by the micro-station, and is expressed as:

[0123]

[0124] Among them, Φ s ={y} represents the location set of micro stations.

[0125] On the other hand, the cache file downlink request of the macro base station and the micro base station Expressed as:

[0126]

[0127] in, Indicates whether the microsite requests the file. It can be 0 to indicate no request.

[0128] All downlink requests responded by the macro base station It can be expressed as:

[0129]

[0130] 3. Business transmission communication model;

[0131] For the downlink communication response of service request user x, its received signal-to-noise ratio (SNR) is expressed as:

[0132]

[0133] Among them, p x Indicates the transmit power of the base station in response to user x's service. Its value is closed-loop controlled by combining the channel status information (CSI) and the detection power requirement. x represents the channel attenuation, D d (θ x ) represents the antenna directional gain of the corresponding antenna port in the beam cell d where user x is located, θ x Indicates the antenna direction angle of the antenna port corresponding to user x, represents the noise estimate.

[0134] Then, based on the downlink SNR threshold value that meets the requirements of the integrated function, the downlink power matrix P for each user's communication service response is calculated. X,K , and the downlink power matrix P for caching and updating micro stations Y,K , respectively expressed as:

[0135]

[0136] The total power required by a macro base station to complete downlink service response is mainly composed of two parts: the downlink transmit power for micro base stations and the downlink transmit power for mobile users, which can be expressed as:

[0137] P MS =P X,K +P Y,K (29)

[0138] The detection task model divides the low-altitude area within the macro base station's coverage area into D-beam low-altitude detection task areas based on the antenna beam direction. Furthermore, the volume of traffic required to transmit an integrated beam to the low-altitude detection area indirectly quantitatively describes the time-frequency resources consumed for detection within that area. When the detection performance of a beam cell meets the minimum performance requirements within a scanning cycle, the low-altitude detection task area is considered covered.

[0139] Taking into account the channel multipath attenuation and target echo multipath reflection attenuation problems, the area that the base station beam can directly cover is represented by S to represent the entire macro base station low-altitude coverage area, and according to the beam cell mapping method mentioned above, it is divided into D beam low-altitude detection areas S = {S1, S2, ... S D}.

[0140] Among them, S iRepresents the space of each beam cell. At the same time, mobile users and micro base stations in different beam cells can also be divided into different zones, which are represented as follows:

[0141]

[0142] Correspondingly, the macro base station responds to the business use set transmitted to each low-altitude detection mission area express, It includes the cache update amateur and communication services requested by micro-stations and mobile users, which can be expressed as Thus, all service requests that the macro base station needs to respond to in a certain low-altitude detection mission area can be obtained, which can be expressed as:

[0143]

[0144] at the same time, It also indicates the amount of business that needs to be completed by the macro base station transmitting an integrated beam to the low-altitude detection mission area d, indirectly and quantitatively describing the amount of time-frequency resources consumed in detecting the area.

[0145] The range resolution of the detection range-velocity radar image obtained in the low-altitude detection mission area d is:

[0146]

[0147] Where c represents the speed of light, S d It represents the number of downlink transmission subcarriers, and Δf represents the subcarrier spacing.

[0148] The velocity resolution is:

[0149]

[0150] Among them, R d Indicates the number of OFDM symbols transmitted, T s Indicates the symbol length, f c Indicates the carrier frequency.

[0151] The processing gain is:

[0152] gain o =10log 10 (S d ·R d ) (35)

[0153] Set Δd th , Δv th 、gain thThey are the minimum performance indicators that meet the detection requirements. When the detection performance of a beam cell can meet the distance resolution, velocity resolution, and system gain indicators within the scanning period, it means that the low-altitude detection mission area is covered by the detection.

[0154] For a macro base station communication system with fixed modulation mode and communication parameters, the detection performance index requirement can be converted into downlink transmission time-frequency resource S d 、R d Constraints are further transformed into the number of responses to the downlink services of the macro base station. The threshold value is set to K th :

[0155]

[0156] Set the total power required for the macro base station to complete the downlink service response to be P MS , the power P required to complete the downlink service between the micro base station and the connected mobile user S,Y ,Energy Efficiency (EE) of the communication system describes the average energy consumption required to serve a unit file service request, which is expressed as:

[0157]

[0158] Among them, Φ u ={x} represents the location set of mobile user equipment in different beam cells, x∈Φ u represents the mobile users in different beam cells, k x Represents the file request queue for each user.

[0159] Set the number of low-altitude detections that meet the detection performance to N Dcover , then the detection coverage R Dcover It is expressed as follows:

[0160]

[0161] Wherein, D represents the number of low-altitude detection task areas that are divided into low-altitude areas within the entire macro base station coverage range according to the direction of the antenna beam.

[0162] The EE indicator directly measures the energy consumption required to meet the needs of cell communication services. It also potentially includes the access efficiency between macro base stations and users and micro base stations, the utilization efficiency of cache files at each site, and the communication efficiency of the backhaul link. It is the result of the request, access, and transmission of the communication function model. Dcover , describes the proportion of low-altitude detection mission areas that meet the detection performance requirements within the coverage area of ​​the macro base station.

[0163] Finally, combining the communication and detection models, based on the communication model, the downlink signal waveform of the 5G macro base station carries communication information, and mobile users interact with the macro base station and micro base station for data. The macro base station antenna port transmits the time domain waveform of the signal, and transmits it in the fixed beam direction through the base station physical antenna group corresponding to the antenna port. After transmission, the signal is reflected by the target, and the echo is received by the base station. Since the transmitting and receiving antennas are in the same position and work in full-duplex mode, self-interference cancellation technology is used to reduce the impact of self-interference between transmission and reception. The target echo signal can be processed by the base station communication uplink receiving information processing system to obtain relevant status information of the target. While meeting the performance of the original communication system, the detection function is realized. An integrated two-dimensional scenario model for 5G communication detection is constructed. The scenario model includes both the communication model constructed by users, micro base stations, and collaborative cache networks, and the detection model based on beam cell segmentation.

[0164] In this embodiment, step S3 is specifically as follows:

[0165] For communication functions, it is required to meet the communication service requests of each mobile user, while requiring the macro base station to bear as little downlink traffic as possible to reduce the overall power loss of the system. For detection tasks, it is required to ensure the maximum possible detection coverage under objective conditions. Therefore, we consider how to allocate downlink resources of macro base stations, while minimizing the traffic volume undertaken by macro base stations while ensuring the requirements of various detection tasks as much as possible. We select the energy efficiency of the communication system as the optimization goal to establish an optimization model, and establish the integrated communication and detection resource optimization problem based on 5G macro base stations. The model is expressed as follows:

[0166]

[0167] Among them, EE represents the energy efficiency of the communication system, Indicates the number of files of all users, P MS Indicates the total power required by the macro base station to complete the downlink service response, P S,Y It represents the power required to complete the downlink service between the micro base station and the connected mobile user, R Dcover,max represents the maximum possible detection coverage, R Dcover Indicates the detection coverage.

[0168] In this embodiment, step S4 is specifically as follows:

[0169] S41. At the service and time-frequency resource allocation level, the base station response model to services is incorporated into the game theory model analysis to achieve optimal allocation of service resources.

[0170] After obtaining the scene model information, the optimization objectives and constraints can be obtained through indirect calculation through two types of data: user request sequence and micro-station cache file directory. Therefore, resource allocation is mainly reflected in how to update the micro-station cache file.

[0171] In this embodiment, the optimization problem is transformed into the following expression:

[0172]

[0173] in, Represents the cache file matrix expected by each micro-station, Represents the cache file matrix of each micro-station at the initial moment. Indicates the cache update file sent by the macro base station to the micro base station, the initial cache file directory and the updated cache file directory. Indicates the user request that will be responded to by the micro site, Indicates whether the user requested the file. It can be 0 to indicate no request.

[0174] Considering the above optimization problem, we can analyze that when a macro base station updates the cache file of a micro cell, it not only affects the access of related mobile users within the base station's coverage area, but also affects the cache file utilization of overlapping base stations. Operations on a micro cell within a beam cell also affect the detection performance of surrounding beam cells. Game models are particularly suitable for analyzing such interactive problems.

[0175] In order to apply this problem to the game model, the optimization subject is transformed from the macro base station to each micro base station. That is, the micro base station acts as a player in the game and requests the macro base station to update the cache file as the action selected in the game. An iterative method is used for optimization and solution.

[0176] Let the game be G, and the action in the jth game is given by The income is expressed as In each iteration, when a micro-station changes its action, the micro-stations that overlap with it will also change their states. This is because each user request will only be responded to and connected by one base station. Therefore, in each iteration, only some micro-stations will be selected to change their actions. The selected base stations are called active stations, and the remaining ones that do not change their actions are called passive stations. Let the set of active stations in a certain iteration be φ act , the set of passive stations led by the active station is φ neg (y).

[0177] Considering that the detection constraints are difficult to reach when the problem is initialized, a two-stage iterative algorithm is proposed.

[0178] In the first stage, beam cells are used as players to optimize detection targets, aiming to meet the cell detection task requirements as much as possible. In the second stage, reducing communication loss is used as the objective function to find a better response path, achieve Nash equilibrium, and achieve optimal allocation of business resources.

[0179] The two-stage iterative algorithm is specifically as follows:

[0180] A1. Initialization: At the beginning of each time slot, randomize the file cache of the micro-station and obtain the file request list of the mobile user as Ω s , let Ω s '=Ω s ;

[0181] A2. Phase 1:

[0182] A21, from Ω s 'Randomly select a small base station ζ to join φ act , and from Ω s 'Remove base station ζ and its associated passive station φ neg (ζ);

[0183] A22, return to step A21 until

[0184] A23. For all φ act Medium base station, set each file in each small base station to zero in turn, and get each base station action Make

[0185] A24. If there is no action that meets the conditions, go to step A3 and set Otherwise, let Ω s '=Ω s And return to step A21;

[0186] A3. Phase 2:

[0187] A31, from Ω s 'Randomly select a small base station ζ to join φ act , and from Ω s 'Remove base station ζ and its associated passive station φ neg (ζ);

[0188] A32, return to step A31 until

[0189] A33. For all φ act Medium base station, select each base station action Make Indicates all downlink requests responded to by the macro base station;

[0190] A34. Judgment If so, exit; otherwise, return to step A31.

[0191] S42. Propose a spatial resource allocation method based on beam scheduling, solve the optimization problem, and obtain the final detection function coverage, so as to achieve improved detection coverage at the cost of low communication loss;

[0192] For the beams not covered by the detection, the access status of the beams corresponding to all the downlink services responded within the coverage of the macro base station is considered, and the beams are reallocated to increase the amount of time and frequency resources in the areas where the detection resources are insufficiently covered. The optimization goal is to reduce the communication power loss caused by this behavior. A spatial resource allocation method based on beam scheduling is proposed, and the optimization goal of maximizing EE can be converted into the optimization of minimizing the power consumption of the macro base station. The Lagrangian dual method is chosen to solve the problem, and the final detection function coverage is obtained, so that the detection coverage is improved at the cost of low communication loss.

[0193] After analysis, it is found that the method of allocating only business and time-frequency resources cannot fully achieve low-altitude mission area detection coverage. The objective reason is that some beam areas respond to limited business requests, that is, To address this issue, it is necessary to increase downlink traffic to beam cells with insufficient coverage, thereby increasing resource allocation to specific beam cells. Furthermore, a spatial resource allocation method based on beam scheduling is proposed. By balancing detection coverage with base station communication loss, the integrated detection and communication performance of the 5G macro base station system is optimized.

[0194] First, to detect beams in uncovered areas, it is necessary to analyze the beam access status of all downlink services within the coverage area of ​​the macro base station. According to the previous communication service transmission model and detection function task model, the number of beams is usually less than the number of communication requesting devices. Each beam will match several requesting devices, so we define c d,u is the beam-user association factor. If the device u requests a response and is accessed by the beam in area d, then c d,u =1, otherwise 0.

[0195] Here, u represents a user device or a micro base station.

[0196] At this time, the system energy efficiency EE is expressed as:

[0197]

[0198] Among them, EE represents the energy efficiency of the communication system, Indicates the number of files for all users. Indicates that the macro base station selects c d,x=1 area responds to the downlink power of communication services, It represents the total power consumed by the macro base station to complete the downlink service response, that is, the downlink power matrix P of all user communication service responses when the beam low-altitude detection task area d∈{1:D} X,K Downlink power matrix P for cache update with micro base stations Y,K , P S,Y It represents the power required to complete the downlink service between the micro base station and the connected mobile user, R Dcover,max represents the maximum possible detection coverage, R Dcover Indicates the detection coverage.

[0199] Further analysis, considering the total power P required for the macro base station in EE to complete the downlink service response MS It is often much larger than the transmission power of the micro base station. Therefore, the optimization goal of maximizing EE can be transformed into the optimization of minimizing the power consumption of the macro base station, as shown in the following formula:

[0200]

[0201] Among them, the beam-user matching factor c d,u Composition matrix k x represents the file request queue of each user, k y represents the file request queue of each micro-station, K th Indicates the set threshold value.

[0202] Because c d,u The above optimization problem is a relatively complex non-convex problem and is not easy to solve. Therefore, the beam-user matching factor is relaxed and the above problem is transformed into a convex problem, that is, c d,u The value range is changed from {0,1} to any real value in [0,1], and the Lagrange dual method is chosen to solve the problem. Then the above optimization problem can be written as:

[0203]

[0204] According to the above analysis, the optimization problem is a convex optimization problem. The Lagrangian dual method is chosen to solve the problem. Its Lagrangian function can be written as:

[0205]

[0206] Among them, λ u ≥0,υ d ≥0 are Lagrange multipliers, corresponding to st1 and st2 of the optimization problem respectively. The Lagrange dual function of Equation (43), ignoring the fixed power part between the macro base station and the micro base station, can be written as:

[0207]

[0208] Among them, F d,u (c d,u ,λ u ,υ d ) is each c d,u The corresponding Lagrange dual function:

[0209]

[0210] in, Indicates that the macro base station selects c d,u =1 area responds to the downlink power of user communication services.

[0211] Finally, we get the Lagrange dual problem:

[0212]

[0213] According to the KKT conditions, solve F d,u (c d,u ,λ u ,υ d ) for c d,u The partial derivative of , we can get:

[0214]

[0215] In order to obtain the minimum EE, the beam-user matching factor is defined as follows:

[0216]

[0217] in,

[0218]

[0219] The matching result c obtained by relaxing the problem o,u Further converted into a binary integer, that is, the solution of the problem before relaxation. Thus, the optimal beam-user access factor solution is obtained, considering its Lagrange multiplier υ d Therefore, the gradient descent method is used to update υ d , and guarantee convergence, can be expressed as:

[0220]

[0221] in,[·] + =max{0,·},δ (t) It represents the update step size greater than zero in the t-th iteration. The choice of the step size has a significant impact on the final solution effect. The formula should satisfy the following:

[0222]

[0223] Among them, g t represents the iterative gradient direction, Q * It represents the optimal solution of the dual problem, and Q represents the current iterative solution of the dual problem.

[0224] For the task area within the detection coverage range, when the device u requests a response to the access beam that is not from this beam cell, but is accessed by the beam of other beam cells, if the currently accessed beam cell is the beam cell that illuminates the task area within the detection coverage range, the detection coverage rate will be improved at the cost of low communication energy efficiency loss, and the integrated communication and detection resource optimization of the 5G macro base station will be completed.

[0225] In this embodiment, the method of the present invention is further verified based on a Matlab simulation example.

[0226] In the simulation example, the corresponding values ​​of various parameters related to the coverage range of the macro base station, the number of mobile users and micro base stations are shown in Table 2.

[0227] Table 2

[0228]

[0229] Figure 4 It is an iterative convergence curve diagram in an embodiment of the present invention. With respect to the allocation of time-frequency resources, the method of the present invention analyzes the access and transmission relationship between micro base stations, users, and macro base stations based on game theory, constructs the time-frequency resource optimization problem into a potential game model, and uses a two-stage iterative algorithm to find a better reaction path to achieve Nash equilibrium and realize the optimal allocation of burst cache services. Then, based on the idea of ​​using suboptimal communication quality beam access to meet the resource-scarce low-altitude detection mission area, a beam-scheduled airspace resource allocation method is proposed to achieve improved detection coverage at the cost of low communication loss.

[0230] Figure 5 The following figure compares the detection coverage results of the proposed method with those before and after optimization. This graph shows how detection coverage changes with the number of iterations, demonstrating the impact and gain of resource allocation on detection coverage before and after. The solid line demonstrates the significant improvement in detection coverage using the spatial beam reallocation method. These two figures demonstrate that the proposed method significantly improves detection coverage at the expense of minimal reduction in communication EE, achieving a performance trade-off between detection and communication integration.

[0231] In summary, the method of the present invention first establishes a model for 5G macro base station scenarios and systems in conjunction with a wireless cache network; in order to measure the effect of integrated communication and detection resource optimization, the energy efficiency of the communication system is selected as the objective function; finally, a resource optimization method for joint collaborative wireless cache network technology and a spatial resource allocation method based on beam scheduling are proposed, which solves the problem of insufficient effective detection function coverage caused by the randomness and burstiness of communication services.

[0232] Those skilled in the art will appreciate that the embodiments described herein are intended to aid the reader in understanding the principles of the present invention, and it should be understood that the scope of the present invention is not limited to such specific descriptions and embodiments. Various modifications and variations are readily apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims.

Claims

1. A method for optimizing communication and exploration integrated resources based on a 5G macro base station, comprising the following steps: Step S1: Establish a 5G communication detection integrated scenario and initialize system parameters; Step S2: Analyze the communication service and time-frequency resource allocation process, build a communication service transmission model and a detection function task model, and combine the communication and detection models to construct a 5G communication and detection integrated two-dimensional scenario model; Step S3: Select the communication system energy efficiency as the objective function, combine it with the maximum possible detection coverage constraint, and establish the communication and detection integrated resource optimization problem of the 5G macro base station; Step S4: solving the optimization problem to achieve integrated communication and exploration resource optimization of the 5G macro base station; The step S4 is specifically as follows: S41. At the service and time-frequency resource allocation level, the base station response model to services is incorporated into the game theory model analysis to achieve optimal allocation of service resources. After obtaining the scene model information, the optimization objectives and constraints are obtained through indirect calculation of two types of data: user request sequence and micro-station cache file directory. Resource allocation is mainly reflected in how to update the micro-station cache file. The optimization problem is transformed into the following expression in, Represents the cache file matrix expected by each micro-station, Represents the cache file matrix of each micro-station at the initial moment; Indicates the cache update file sent by the macro base station to the micro base station, the initial cache file directory and the updated cache file directory. Indicates the user request that will be responded to by the micro site, Indicates whether user x has requested the file, and can be 0 to indicate no request; represents the candidate user set of micro-station y, Represents the candidate request set of microsite y Select some requests to respond to, Φ s represents the location set of micro-stations; R Dcover,max represents the maximum possible detection coverage, R Dcover represents the detection coverage; The base station response problem model is incorporated into the game theory model for analysis. The optimization subject is transformed from the macro base station to each micro base station. That is, the micro base station acts as a player in the game, and requests the macro base station to update the cache file as the action selected in the game. The optimization solution is solved by iterative methods. Let the game be G, and the action in the jth game is given by The income is expressed as In each iteration, some micro-stations are selected to change their actions. The selected base stations are called active stations, and the remaining ones that do not change their actions are called passive stations. Let the set of active stations in a certain iteration be φ act , the set of passive stations led by the active station is φ neg (y); A two-stage iterative algorithm is proposed. In the first stage, beam cells are used as players to optimize detection targets, at which point the cell detection task requirements are achieved as much as possible. In the second stage, the objective function is to reduce communication loss, find a better response path, achieve Nash equilibrium, and achieve optimal allocation of service resources. The two-stage iterative algorithm is specifically as follows: A1. Initialization: At the beginning of each time slot, randomize the file cache of the micro-station and obtain the file request list of the mobile user as Ω s , let Ω s '=Ω s ; A2. Phase 1: A21, from Ω s 'Randomly select a small base station ζ to join φ act , and from Ω s 'Remove base station ζ and its associated passive station φ neg (ζ); A22, return to step A21 until A23. For all φ act Medium base station, set each file in each small base station to zero in turn, and get each base station action Make A24. If there is no action that meets the conditions, go to step A3 and set Otherwise, let Ω s '=Ω s And return to step A21; A3. Phase 2: A31, from Ω s 'Randomly select a small base station ζ to join φ act , and from Ω s 'Remove base station ζ and its associated passive station φ neg (ζ); A32, return to step A31 until A33. For all φ act Medium base station, select each base station action Make Indicates all downlink requests responded to by the macro base station; A34. Judgment If yes, exit, otherwise return to step A31; S42. Propose a spatial resource allocation method based on beam scheduling, solve the optimization problem, and obtain the final detection function coverage, so as to achieve improved detection coverage at the cost of low communication loss; First, for the beams in the uncovered area, analyze the beam access status of all downlink services in the coverage area of ​​the macro base station, and define c d,u is the beam-user association factor. If the device u requests a response and is accessed by the beam in area d, then c d,u =1, otherwise 0; Where u represents user equipment x or micro base station y; At this time, the system energy efficiency EE is expressed as: Where EE represents the energy efficiency of the communication system, Φ u ={x} represents the location set of mobile user equipment in different beam cells, x∈Φ u represents the mobile users in different beam cells, k x Represents the file request queue for each user, Indicates the number of files for all users. Indicates that the macro base station selects c d,x =1 area responds to the downlink power of the communication service, D represents the number of low-altitude detection task areas divided into the low-altitude area of ​​the entire macro base station coverage according to the direction of the antenna beam, It represents the total power consumed by the macro base station to complete the downlink service response, that is, the downlink power matrix P of all user communication service responses in the low-altitude detection mission area d∈{1:D} X,K Downlink power matrix P for cache update with micro base stations Y,K , P S,Y It represents the power required to complete the downlink service between the micro base station and the connected mobile user, P MS Indicates the total power required by the macro base station to complete the downlink service response, R Dcover,max represents the maximum possible detection coverage, R Dcover represents the detection coverage; Indicates the user file request that needs to be responded to by the macro base station, which is composed of all user requests excluding the part responded to by the micro base station; Indicates the cache file downlink request of the macro base station and the micro base station. Indicates whether the microsite requests the file, and can be 0 to indicate no request; Since the total power P required for the macro base station in EE to complete the downlink service response MS It is often much larger than the transmission power of the micro base station. Therefore, the optimization goal of maximizing EE is transformed into the optimization of minimizing the power consumption of the macro base station, as shown in the following formula: Among them, the beam-user matching factor c d,u Composition matrix k x represents the file request queue of each user, k y represents the file request queue of each micro-station, K th Indicates the set threshold value; Relaxing the beam-user matching factor converts the above problem into a convex problem, that is, c d,u The value range is changed from {0,1} to any real value in [0,1], and the Lagrange dual method is chosen to solve the problem. Then the above optimization problem can be written as: We choose to use the Lagrange dual method to solve the problem, and its Lagrange function is written as: in, represents the amount of business that needs to be completed by the macro base station transmitting an integrated beam to the low-altitude detection mission area d, λ u ≥0,υ d ≥0 are Lagrange multipliers, corresponding to st1 and st2 of the optimization problem respectively. The Lagrange dual function of Equation (4), ignoring the fixed power part between the macro base station and the micro base station, is written as: Among them, F d,u (c d,u ,λ u ,υ d ) is each c d,u The corresponding Lagrange dual function: in, Indicates that the macro base station selects c d,u =1 area responds to the downlink power of user communication services; Finally, we get the Lagrange dual problem: According to the KKT conditions, solve F d,u (c d,u ,λ u ,υ d ) for c d,u The partial derivative of , we can get: In order to obtain the minimum EE, the beam-user matching factor is defined as follows: in, The matching result c obtained by relaxing the problem d,u Further converted into a binary integer, that is, the solution of the problem before relaxation; then the optimal beam-user access factor solution is obtained, considering its difference with the Lagrange multiplier υ d Related, the gradient descent method is used to update υ d , and convergence is guaranteed, expressed as: in,[·] + =max{0,·}, represents the amount of business that needs to be completed by the macro base station transmitting an integrated beam to the low-altitude detection mission area d, δ (t) represents the update step size greater than zero in the t-th iteration, and the formula should satisfy: Among them, g t represents the iterative gradient direction, Q * represents the optimal solution to the dual problem, and Q represents the current iterative solution to the dual problem; For the task area within the detection coverage range, when the device u requests a response to the access beam that is not from this beam cell, but is accessed by the beam of other beam cells, if the currently accessed beam cell is the beam cell that illuminates the task area within the detection coverage range, the detection coverage rate will be improved at the cost of low communication energy efficiency loss, and the integrated communication and detection resource optimization of the 5G macro base station will be completed.

2. The method for optimizing communication and exploration integrated resources based on a 5G macro base station according to claim 1, characterized in that: The step S1 is specifically as follows: The 5G communication detection integrated scenario is a scenario where a macro base station, multiple mobile user devices and multiple micro base stations interact with each other. The system parameters include: macro base station coverage range, macro base station altitude, number and location of mobile users and micro base stations, number of user requests and base station cache files, maximum number of files, file size, number and location of beam cells, average coverage radius of micro base stations, downlink communication SNR requirement, subcarrier spacing, detection speed resolution requirement, detection distance resolution requirement, and detection matching gain requirement; In a macro base station coverage scenario model, within the coverage area of ​​a 5G macro base station, N s micro-stations and N u The micro base station is equipped with a cache server and a wireless backhaul link that can communicate with the macro base station.

3. The method for optimizing communication and exploration integrated resources based on a 5G macro base station according to claim 1, characterized in that: The step S2 is specifically as follows: The communication service transmission model includes the service processing model, service burst request model, service response and access model, and service transmission communication model; The service processing model includes: a file request mechanism, a communication access mechanism, a cache mechanism, and a method for calculating the downlink communication traffic volume of a macro base station; In the downlink transmission process of a data-type service, a file transfer is divided into three processing stages: user request stage, response access stage, and response transmission stage. Correspondingly, a service burst request model, a service response and access model, and a service transmission communication model are constructed. The detection function task model divides the low-altitude area covered by the entire macro base station into D-beam low-altitude detection task areas based on the antenna beam direction. At the same time, the amount of business that the macro base station needs to complete by transmitting an integrated beam to the low-altitude detection task area indirectly quantitatively describes the amount of time and frequency resources consumed for detection in this area. When the detection performance of a beam cell can meet the minimum performance index of the detection requirement within the scanning period, it means that the low-altitude detection task area is covered by detection; Finally, the communication and detection models are combined to construct an integrated two-dimensional scenario model of 5G communication and detection. The scenario model includes both the communication model constructed by users, micro base stations, and collaborative cache networks, and the detection model based on beam cell segmentation.

4. The method for optimizing communication and exploration integrated resources based on a 5G macro base station according to claim 1, characterized in that: The step S3 is specifically as follows: Set the total power required for the macro base station to complete the downlink service response to be P MS , the power P required to complete the downlink service between the micro base station and the connected mobile user S,Y ,The EE of the communication system describes the average energy consumption required to serve a unit file service request, and is expressed as: Among them, Φ u ={x} represents the location set of mobile user equipment in different beam cells, x∈Φ u represents the mobile users in different beam cells, k x Represents the file request queue for each user; Set the number of low-altitude detections that meet the detection performance to N Dcover , then the detection coverage R Dcover It is expressed as follows: Where D represents the number of low-altitude detection task areas that are divided into the low-altitude area of ​​the entire macro base station coverage range according to the antenna beam direction; The energy efficiency of the communication system is selected as the optimization target to establish an optimization model, and the communication and exploration integrated resource optimization problem based on 5G macro base stations is modeled as follows: Among them, EE represents the energy efficiency of the communication system, Indicates the number of files of all users, P MS Indicates the total power required by the macro base station to complete the downlink service response, P S,Y It represents the power required to complete the downlink service between the micro base station and the connected mobile user, R Dcover,max represents the maximum possible detection coverage, R Dcover Indicates the detection coverage.

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