Multi-base-station cooperative sensing networking planning method, device, medium and product

Abstract

The invention relates to the technical field of communication, and particularly provides a multi-base-station cooperative sensing networking planning method and device, a medium and a product. The multi-base-station cooperative sensing networking planning method comprises the following steps: constructing a multi-base-station sensing model in a multi-base-station cooperative sensing scene; the multi-base-station sensing model is used for describing the whole process that the signal is transmitted from each base station, reflected by the unmanned aerial vehicle and received by the base station; based on a multi-base-station sensing model, determining a mapping relation between the station spacing and the sensing performance index; and solving the perception performance boundary based on the mapping relation, and determining the target station spacing based on the perception performance boundary. According to the method, the problem of mutual interference between the base stations is solved through model construction, performance analysis and station spacing optimization, and the mutual interference between the base stations can be minimized while network coverage and performance are ensured, so that the sensing performance and communication quality of the whole network are improved.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to methods, devices, media and products for multi-base station cooperative sensing network planning. Background Technology

[0002] In traditional cellular communication systems, inter-site spacing, as a key network design metric, directly impacts communication performance, including signal strength, interference levels, and system capacity. In related technologies, to meet specific network requirements, researchers derive the relationship between communication network performance metrics such as spectral efficiency, uplink / downlink capacity, energy efficiency, and gain, and inter-site spacing. This establishes a mapping between inter-site spacing and communication performance, and based on this mapping, determines the inter-site spacing to ensure network performance meets practical needs while minimizing interference.

[0003] However, the relevant technologies did not take into account the mutual interference problem between base stations in multi-base station cooperative sensing scenarios, which limited the optimization of network performance and reduced the overall performance and efficiency of the network. Summary of the Invention

[0004] In view of this, exemplary embodiments of the present disclosure provide a multi-base station cooperative sensing network planning method, device, medium, and product to solve the problems existing in the related technologies.

[0005] One aspect of an exemplary embodiment of this disclosure provides a multi-base station cooperative sensing network planning method, the method comprising:

[0006] Construct a multi-base station sensing model for a multi-base station collaborative sensing scenario; the multi-base station sensing model is used to describe the entire process of signals being transmitted from each base station, reflected by the UAV, and received by the base station;

[0007] The mapping relationship between the distance between base stations and the sensing performance index is determined based on the multi-base station sensing model.

[0008] The sensing performance bound is solved based on the mapping relationship, and the target station spacing is determined based on the sensing performance bound.

[0009] In another aspect of exemplary embodiments of this disclosure, a computer device is provided, including a memory, a processor, and a computer program stored in the memory, the processor executing the computer program to implement the methods described in exemplary embodiments of this disclosure.

[0010] In another aspect of exemplary embodiments of this disclosure, a computer-readable storage medium is provided having a computer program / instructions stored thereon that, when executed by a processor, implements the methods described in exemplary embodiments of this disclosure.

[0011] In another aspect of exemplary embodiments of this disclosure, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the methods described in exemplary embodiments of this disclosure.

[0012] As will be described in detail below, a multi-base station cooperative sensing network planning method according to an embodiment of this disclosure constructs a multi-base station sensing model in a multi-base station cooperative sensing scenario. The multi-base station sensing model describes the entire process of signals being transmitted from each base station, reflected by a drone, and received by the base station. Based on the multi-base station sensing model, the mapping relationship between station spacing and sensing performance indicators is determined. Based on the mapping relationship, the sensing performance bound is solved, and the target station spacing is determined based on the sensing performance bound. Therefore, the multi-base station cooperative sensing network planning method provided by this disclosure solves the mutual interference problem between base stations through model construction, performance analysis, and optimization of station spacing. It can minimize mutual interference between base stations while ensuring network coverage and performance, thereby improving the sensing performance and communication quality of the entire network. Attached Figure Description

[0013] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0014] Figure 1 This is a flowchart illustrating an exemplary multi-base station cooperative sensing network planning method provided in this disclosure;

[0015] Figure 2 The present disclosure provides, as an example, curves showing the variation of radar SINR / joint detection probability and its first-order partial derivative with station spacing when the interference coefficient λ = 1.

[0016] Figure 3 The present disclosure provides, as an example, a curve showing the variation of the first-order partial derivative of the radar SINR / joint detection probability with the distance between stations when the interference coefficient λ = 0.5.

[0017] Figure 4 This is a flowchart illustrating an exemplary multi-base station cooperative sensing network planning method provided in this disclosure.

[0018] Figure 5 This is a schematic block diagram of the functional modules of a multi-base station cooperative sensing network planning device provided as an example in this disclosure;

[0019] Figure 6 This is a structural block diagram of an exemplary electronic device provided in this disclosure;

[0020] Figure 7 This is a schematic diagram of a computer program product provided as an example of this disclosure. Detailed Implementation

[0021] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0022] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0023] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc., used in this disclosure are only used to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0024] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0025] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0026] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0027] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0028] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understood that the above notification and user authorization process is merely illustrative and does not constitute a limitation on the implementation of this disclosure; other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0029] Before introducing the embodiments of this disclosure, the relevant terms involved in the embodiments of this disclosure are first defined as follows:

[0030] Korbeck-Leibler divergence: a measure of the difference between two probability distributions. In radar sensing, it can be used to compare the difference between the actually observed signal distribution and the hypothesized signal distribution.

[0031] Maximum ratio transmission precoding is a precoding technique used in multi-antenna wireless communication systems. It utilizes multiple antennas at the transmitting end to adjust the weights of the transmitted signals, thereby maximizing the signal gain for the target user and improving the signal reception quality.

[0032] Perception performance bound: The maximum value that a perception performance metric can achieve under given network configuration and conditions.

[0033] In traditional cellular communication systems, inter-site spacing, as a key network design metric, directly impacts communication performance, including signal strength, interference levels, and system capacity. In related technologies, to meet specific network requirements, researchers derive the relationship between communication network performance metrics such as spectral efficiency, uplink / downlink capacity, energy efficiency, and gain, and inter-site spacing. This establishes a mapping between inter-site spacing and communication performance, and based on this mapping, determines the inter-site spacing to ensure network performance meets practical needs while minimizing interference.

[0034] However, the relevant technologies did not take into account the mutual interference problem between base stations in multi-base station cooperative sensing scenarios, which limited the optimization of network performance and reduced the overall performance and efficiency of the network.

[0035] Therefore, to address the aforementioned issues, this exemplary embodiment provides a multi-base station cooperative sensing network planning method. First, it constructs a model considering path loss, radar cross-section, and antenna gain for useful echo signals and echo mutual interference between base stations and UAVs. Then, based on this model, it derives closed-loop expressions for radar SINR and joint detection probability with respect to station spacing, clarifying the impact of station spacing on the radar system's sensing performance. Finally, based on these closed-loop expressions, it solves for the sensing performance bound and the target station spacing, and uses a binary search algorithm to optimize the station spacing. This achieves reduced inter-station interference while meeting sensing performance requirements, and the target station spacing is determined according to actual network needs.

[0036] In this embodiment, a multi-base station cooperative sensing network planning method is proposed. Consider a cooperative sensing system containing two base stations, within which a low-altitude unmanned aerial vehicle (UAV) is located at a certain altitude. Both base stations in the system adopt a passive sensing mode (A transmits, A receives), simultaneously sending sensing signals to the UAV and receiving echo signals from the UAV carrying parameters such as distance, angle, and speed. These parameters are then estimated, and finally, data fusion is performed to achieve cooperative sensing.

[0037] Assume that the transmit and receive antennas of both base stations are uniform planar arrays. The size of the transmit antenna array is N. t =N t，x ×N t，z The size of the receiving antenna array is N r =N r，x ×N r，z N t，x and N t，z N represents the number of transmit antenna arrays parallel to the x-axis and z-axis, respectively. r，x and N r，z These represent the number of receiving antenna arrays parallel to the x-axis and z-axis, respectively.

[0038] In this system, two base stations can operate on the same frequency band or be frequency-divided. When the two base stations operate on the same frequency, the echo interference considered is co-channel interference; when the two base stations are frequency-divided, the echo interference considered is adjacent-channel interference. Therefore, an interference coefficient λ can be introduced to measure the interference intensity level. When the interference is co-channel interference, λ = 1; when the interference is adjacent-channel interference, λ ∈ (0, 1).

[0039] In low-altitude economic scenarios, the information transmission link between drones and base stations is a line-of-sight environment. In a three-dimensional coordinate system, assume that the two base stations are located at (0, 0, h) respectively. BS ) and (0, d, h BS ), where d represents the station spacing, h BS Let Δh represent the base station altitude. Assume the drone's flight altitude is h, and let Δh = hh.BS The position coordinates of the UAV are (Δhtan(φ1), αd, h), where α∈(0,1) represents the position of the UAV relative to the base station. This indicates the azimuth angle of the drone relative to base station 1.

[0040] For example, Figure 1 This is a flowchart illustrating an exemplary multi-base station cooperative sensing network planning method provided in this disclosure, as shown below. Figure 1 As shown, the specific steps may include:

[0041] Step S110: Model the useful echo signals and echo mutual interference received by base station 1 and base station 2, as follows:

[0042]

[0043] in, and These represent the data symbols transmitted by base station 1 and base station 2, respectively. and These represent the transmission precoding for base station 1 and base station 2, respectively; Represents additive white Gaussian noise; a1, a2, b1, and b2 represent coefficients related to path loss, radar cross section, and antenna gain; θ i and φ i These represent the elevation angle and azimuth angle of the UAV relative to base station i, respectively; and Indicates the channel of the relevant link; a t (θ, φ) and a r (θ, φ) are the transmit steering vector and the receive steering vector, respectively, and can be expressed as:

[0044]

[0045] Where, for i∈{t, r}, the following condition is satisfied:

[0046] Furthermore, the path loss can be determined using the basic free-space transmission loss of the radar system. The basic free-space transmission loss can be expressed as:

[0047] PL(d1,d2)=103.4+20logf+20logd1+20logd2-10logσdB

[0048] Where f represents the system frequency; σ represents the radar cross section; d1 represents the straight-line distance for the radar to receive and transmit signals; and d2 represents the straight-line distance for the radar to transmit and return signals.

[0049] Based on this, a1, a2, b1, and b2 can be represented as follows:

[0050]

[0051] in, G = G t G r This indicates the total antenna gain.

[0052] Based on this, by comprehensively considering signal, interference, noise, path loss, and radar parameters, the communication link between the base station and the UAV can be accurately modeled, providing an accurate foundation for subsequent data fusion and collaborative sensing. Furthermore, by distinguishing between co-channel and adjacent-channel interference and introducing an interference coefficient, the interference intensity at different operating frequencies can be effectively managed and evaluated, thereby optimizing communication quality.

[0053] Step S120: Derive the closed-loop expression for the sensing performance of the multi-base station cooperative sensing system with respect to the distance between base stations.

[0054] The objective of step S120 is to derive the mathematical relationship, i.e., a closed-form expression, between the sensing performance and the inter-station spacing of a multi-base station cooperative sensing system. Specific steps may include:

[0055] Step S121: Determine the relationship between radar signal-to-interference-plus-noise ratio and station spacing.

[0056] The radar signal-to-interference-plus-noise ratio (SINR) is a fundamental metric for measuring radar sensing performance and is related to the Coulbeck-Leibler dispersion between observation densities. Given the potential performance degradation caused by thermal noise and inter-base station interference, SINR takes into account the impact of these factors on sensing performance.

[0057] To improve radar SINR, maximum ratio transmission precoding can be used, expressed as:

[0058]

[0059] Wherein, P1 and P2 are the transmission powers of base station 1 and base station 2, respectively.

[0060] Based on the useful echo and mutual interference model from step S110, and using the identity... The radar SINR at base station 1 and base station 2 can be initially represented as:

[0061]

[0062] in, All are matrices of rank 1.

[0063] Consider A r， 1 and A r，2 eigenvalue decomposition and satisfy And Λ1=Λ2=diag{1,0,…,0}. Therefore, radar SINR can be further expressed as:

[0064]

[0065] Where U2, U1, and Λ2 represent the results of eigenvalue decomposition; Represents the identity matrix.

[0066] also:

[0067]

[0068] Based on the above derivation, the final expressions for the radar SINR at base station 1 and base station 2 can be expressed as:

[0069]

[0070] Step S122: Determine the detection probability and the false alarm probability.

[0071] Detection probability and false alarm probability, as important indicators for evaluating radar detection, can be generalized to measure the perception performance of cooperative sensor networks. Detection probability is the probability that a radar system correctly detects a target when it is present, while false alarm probability is the probability that a radar system incorrectly detects a target when it is not present.

[0072] In multi-base station cooperative sensing networks, joint detection probability can be used to establish a mapping relationship between inter-base station distance metrics and sensing performance metrics. Joint detection probability refers to the probability that the system correctly detects a target when multiple base stations cooperate.

[0073] Consider using a likelihood ratio detector to jointly detect UAVs within a perception block containing L symbols. The likelihood ratio detector performs joint detection of UAVs within a perception block containing multiple symbols. The likelihood ratio is the ratio of the likelihood of the target existence hypothesis to the likelihood of the target non-existence hypothesis, given the observed data.

[0074] For base station 1, the detection problem can be formulated as a binary hypothesis testing problem as follows:

[0075]

[0076] Where Y1 = [y1(1), ..., y1(L)], N = [n(1), ..., n(L)], s1 = [s 1，1 , ..., s1，L ], s2=[s 2，1 , ..., s 2，L ]; This indicates that the target does not exist; This indicates the presence of the target. The echo interference term b1G1(θ1, φ1, θ2, φ2)w2s2 and the additive white Gaussian noise term N are both considered as interference by base station 1.

[0077] After signal vectorization, the testing problem can be further represented as:

[0078]

[0079] in, u1=vec{a1A1(θ1,φ1)w1s1}, u2=vec{b1G1(θ1,φ1,θ2,φ2)w2s2}, n s =vec{b1G1(θ1,φ1,θ2,φ2)w2s2+N},,

[0080]

[0081] Based on this, the likelihood ratio detector can be expressed as:

[0082]

[0083] Where κ represents the threshold, which is the false alarm probability P in constant false alarm detection. FA The target value is determined; and They represent In the assumption and assumptions The probability density functions under these conditions can be specifically expressed as follows:

[0084]

[0085] After the logarithmic operation, the logarithmic likelihood ratio function can be expressed as:

[0086]

[0087] In the assumption and assumptions The following distributions are respectively:

[0088]

[0089] Based on this, the false alarm probability and detection probability can be calculated as follows:

[0090]

[0091] in, The function is Q-function. The detection threshold k is given by the following formula:

[0092]

[0093] Among them, Q -1 (x) is the inverse Q-function. Therefore, the detection probability of base station 1 can be expressed as:

[0094]

[0095] Using identities And based on the assumption and P can be D，1 Rewritten in a simpler form as follows:

[0096]

[0097] Similarly, the detection probability of base station 2 can be obtained as follows:

[0098]

[0099] Therefore, with a constant false alarm probability, the detection probability is directly related to the radar SINR. The higher the radar SINR, the higher the detection probability. Furthermore, and These two issues arise because mutual interference occurs only when the drone is present, which is fundamentally different from interference that is always present.

[0100] In a multi-base station collaborative sensing scenario, two base stations simultaneously detect the drone. If either base station successfully detects the drone, it is considered successfully detected. Therefore, the joint detection probability can be expressed as:

[0101] P D =1-(1-P) D，1 )·(1-P D，2 )

[0102] Based on this, step S120 aims to derive a closed-form expression relating sensing performance and inter-station spacing in a multi-base station cooperative sensing system. First, the radar signal-to-interference-plus-noise ratio (SINR) is defined as a fundamental indicator of radar sensing performance, taking into account the effects of thermal noise and inter-base station interference. By employing maximum ratio transmission precoding (MRC), combined with eigenvalue decomposition and identities, the radar SINR expressions for base stations 1 and 2 are derived. Next, the scheme introduces detection probability and false alarm probability as important indicators of radar detection performance, and proposes the concept of joint detection probability to establish a mapping relationship between inter-base station spacing and sensing performance indicators. Using a likelihood ratio detector, joint detection of UAVs is performed within a sensing block containing multiple symbols, and the false alarm probability and detection probability are calculated. Finally, the scheme determines the conditions for successful detection of UAVs in a multi-base station cooperative sensing scenario through the calculation of the joint detection probability.

[0103] Step S130: Solve for the perception performance bound and the target station spacing based on interference and performance cooperation.

[0104] In step S130, the corresponding sensing performance bound is calculated based on the sensing performance index derived in step S120. Then, based on actual network requirements, the target station spacing based on interference and performance cooperation is calculated. The results are then analyzed. Specific steps may include:

[0105] Step S131: Derive the first-order partial derivatives of the radar SINR and joint detection probability with respect to the station spacing d, defining the following four expressions:

[0106]

[0107] The first partial derivative of radar SINR with respect to the station spacing d is calculated by the following formula:

[0108]

[0109] Define the following four expressions:

[0110]

[0111]

[0112] According to the chain rule for finding the derivative of a composite function, the first partial derivative of the joint detection probability with respect to the station spacing d is calculated by the following formula:

[0113]

[0114] Step S132: Use the binary search algorithm to solve for the sensing performance boundary and its corresponding station spacing.

[0115] Based on the closed-form expression for sensing performance with respect to station spacing derived in step S120, a conversion relationship between sensing indices and station spacing indices was established, and the first-order partial derivative of sensing performance with respect to station spacing was derived to study the changing trend of sensing performance with respect to station spacing. The specific steps for solving the sensing performance bound and its corresponding station spacing using a binary search may include:

[0116] Step S1321: Initialize the error tolerance ε and the upper bound of the station spacing d up The lower bound d of the station spacing lp .

[0117] Step S1322: Calculate the midpoint d of the station spacing. m :d m =(d up +d lp ) / 2.

[0118] Step S1323: In d m Evaluate perceived performance metrics at d. If at d m If the performance at point d meets the performance bound, then stop the search. m This is the target station spacing.

[0119] If in d m The performance at a given point does not satisfy the performance bound; therefore, the interval is updated based on the sign of the first-order partial derivative.

[0120] If the first-order partial derivative is positive, it indicates that the performance increases with the increase of the station spacing; therefore, the lower bound is updated to d. m .

[0121] If the first-order partial derivative is negative, it indicates that the performance decreases as the station spacing increases; therefore, the upper bound is updated to d. m .

[0122] Specifically, calculation or like or Let d up =d m Otherwise, let d lp =d m .

[0123] If |d up -d lp If |≤∈, terminate the program and output the target station spacing d. opt =d m and perception performance boundary or Otherwise, return to step S1322.

[0124] Step S133: Based on actual network requirements, solve for the target station spacing based on interference and performance cooperation.

[0125] When planning a multi-base station collaborative sensing integrated network, it is necessary to minimize inter-site interference while meeting sensing performance requirements. Assume the sensing performance requirement threshold is μ, and the sensing performance requirement μ is less than the sensing performance bound. Define the function g(d) = SINR(d) - μ or g(d) = P D (d)-μ. The specific steps for solving the target station spacing based on interference and performance cooperation using the improved binary search algorithm may include:

[0126] Step S1331: Solve the interval [d] lp d up Refine the interval [d] by taking a positive integer n. 1p d up Divide the sample into n equal parts, and calculate Δd = (d... up -d lp ) / n, d i =iΔd, 1≤i≤n.

[0127] Step S1332: From the first subinterval [d i d i+1 ], i = 0, begin the judgment: if g(d i ) = 0 or g(d i+1 ) = 0, thus obtaining d i or d i+1 As an approximate solution d i+0.5 Otherwise, proceed with the subsequent judgment. If g(d) i )·g(d i+1 If ) < 0, then in the interval [d i d i+1 The approximate solution d is obtained using binary search. i+0.5 Otherwise, continue to check if g(d) is satisfied. i )·g(d i+2 ) < 0, until the entire interval [d] is completed. lp d up If the condition is met, exit the loop.

[0128] Step S1333: Select an approximate solution d i+0.5 Neighboring points or With d i+2 The binary search method is applied in pairs.

[0129] Step S1334: Continue executing steps S1332 and S1333 and determine whether two solutions satisfying the condition can be found. If so, output the result; otherwise, let Δd ← 0.5Δd.

[0130] Step S1335: Continue executing steps S1332 and S1333 until two solutions that meet the conditions are found, and take the larger solution as the station spacing that meets the network requirements.

[0131] Step S134: Analyze the results and provide heuristic conclusions.

[0132] In the simulation, due to symmetry, the UAV position parameter α∈(0, 0.5) is taken. At this time, the radar SINR is SINR1. The curves showing the variation of radar SINR / joint detection probability with station spacing for interference coefficients λ=1 and λ=0.5 are given below. Figure 2 and Figure 3 As shown.

[0133] Figure 2 The present disclosure provides, as an example, curves showing the variation of radar SINR / joint detection probability and its first-order partial derivative with station spacing when the interference coefficient λ = 1. Figure 3 The present disclosure provides, as an example, a curve showing the variation of the first-order partial derivative of the radar SINR / joint detection probability with the distance between stations when the interference coefficient λ = 0.5.

[0134] like Figure 2 and Figure 3 As shown, the relationship between the perception performance bound and the UAV position parameters and interference coefficient is first studied. It can be seen that both radar SINR and joint detection probability increase and then decrease with the increase of inter-station spacing, indicating the existence of an extreme point in perception performance. The smaller the UAV position parameter α, that is, the closer the UAV is to the base station, the larger the perception performance bound, and the larger the corresponding inter-station spacing. The smaller the interference coefficient λ, that is, the smaller the interference intensity, the larger the perception performance bound, and the smaller the corresponding inter-station spacing.

[0135] Next, the relationship between the optimal inter-station spacing and the UAV position parameters and interference coefficient based on interference and performance cooperation is investigated. By selecting a certain network demand threshold, the inter-station spacing can be determined to reduce inter-station interference while meeting the demand. The smaller the UAV position parameter α, the smaller the interference coefficient λ, and the higher the overall curve of perception performance changing with inter-station spacing, the larger the inter-station spacing based on interference and performance cooperation.

[0136] Based on this, we first define the rate of change of the sensing performance index with respect to the inter-station spacing by deriving the first-order partial derivatives of the radar SINR and joint detection probability with respect to the inter-station spacing. Next, we use a binary search algorithm to solve for the sensing performance bound and its corresponding inter-station spacing. By initializing the error tolerance and the inter-station spacing limit, we calculate the sensing performance index at the midpoint and update the search interval according to the sign of the first-order partial derivative until we find the optimal inter-station spacing that satisfies the performance bound. Finally, based on the actual network requirements, we further refine the solution interval and use an improved binary search algorithm to determine the optimal inter-station spacing.

[0137] One or more technical solutions provided in the exemplary embodiments of this disclosure first construct a model of useful echo signals and echo mutual interference between a base station and a UAV, considering path loss, radar cross-section, and antenna gain. Then, based on the model, closed-form expressions for radar SINR and joint detection probability with respect to station spacing are derived, clarifying the impact of station spacing on the radar system's sensing performance. Finally, based on the closed-form expressions, the sensing performance bound and target station spacing are solved, and a binary search algorithm is used to optimize the station spacing to reduce inter-station interference while meeting sensing performance requirements, and the target station spacing is determined according to actual network needs.

[0138] Therefore, the multi-base station cooperative sensing network planning method provided in the exemplary embodiments of this disclosure can accurately assess the impact of inter-base station spacing on sensing performance. By providing a method for flexibly adjusting the inter-base station spacing, it can minimize inter-base station interference while meeting sensing performance requirements, thereby improving the overall performance and reliability of the network.

[0139] Based on the above embodiments, this disclosure also provides a multi-base station cooperative sensing network planning method. Figure 4 This is a flowchart illustrating an exemplary multi-base station cooperative sensing network planning method provided in this disclosure, as follows: Figure 4 As shown, the method may include the following steps:

[0140] Step S410: Construct a multi-base station sensing model in a multi-base station cooperative sensing scenario; the multi-base station sensing model is used to describe the entire process of signals being transmitted from each base station, reflected by the UAV, and received by the base station.

[0141] In this embodiment, the multi-base station sensing model can describe the entire process of a signal being transmitted from each base station, reflected by the UAV, and finally received by the base station. The multi-base station sensing model comprehensively considers the signal propagation path, environmental factors, the reflection characteristics of the UAV, and the receiving capabilities of the base stations, thereby capturing the propagation characteristics of the signal in space, including path loss, signal attenuation, and interference.

[0142] Step S420: Determine the mapping relationship between the distance between base stations and the sensing performance indicators based on the multi-base station sensing model.

[0143] In this embodiment, after establishing a multi-base station sensing model, the data in the model is analyzed to deduce how the inter-base station spacing affects key performance indicators, such as signal-to-interference-plus-noise ratio (SINR), detection probability, and false alarm probability. This mapping relationship helps to understand the specific impact of different inter-base station spacing configurations on network performance, providing network designers with a theoretical basis for adjusting inter-base station spacing to optimize performance.

[0144] Step S430: Solve for the sensing performance bound based on the mapping relationship, and determine the target station spacing based on the sensing performance bound.

[0145] In this embodiment, based on the mapping relationship between station spacing and sensing performance indicators, the sensing performance indicators are derived, and the corresponding sensing performance bounds are solved. Then, based on actual network requirements, the target station spacing based on interference and performance cooperation is solved. The results are then analyzed.

[0146] For example, the target station spacing can be found within a preset performance requirement threshold using a binary search algorithm.

[0147] Based on this, the multi-base station sensing model considers not only the direct path of the signal but also potential multipath effects and interference sources, providing a foundation for understanding and quantifying mutual interference between base stations. Furthermore, the multi-base station sensing model establishes the mapping relationship between inter-base station spacing and sensing performance indicators, involving the analysis of how inter-base station spacing affects the signal-to-interference-plus-noise ratio (SINR) and other key performance indicators. Through this mapping relationship, network planners can predict the level of mutual interference under different inter-base station spacing configurations and optimize base station layout accordingly to reduce the impact of mutual interference on signal quality. Finally, performance bounds are used to determine the optimal distance between base stations while meeting specific performance requirements. This method minimizes mutual interference between base stations while ensuring network coverage and performance, thereby improving the overall sensing performance and communication quality of the network.

[0148] Based on the above embodiments, in another embodiment provided in this disclosure, the multi-base station sensing model includes a useful echo signal sub-model and an echo interference sub-model, and the above step S410 may include:

[0149] Acquire transmission path loss, channel information, additive white Gaussian noise, and the relative position of the UAV to the base station;

[0150] Based on relative position information, transmission path loss, and channel information, a useful echo signal sub-model and an echo interference sub-model are constructed respectively.

[0151] A multi-base station sensing model is constructed based on the useful echo signal sub-model, the echo interference sub-model, and additive white Gaussian noise.

[0152] In this embodiment, the useful echo signals and echo mutual interference received by base station 1 and base station 2 are modeled and represented as follows:

[0153]

[0154] in, and These represent the data symbols transmitted by base station 1 and base station 2, respectively. and These represent the transmission precoding for base station 1 and base station 2, respectively; Represents additive white Gaussian noise; a1, a2, b1, and b2 represent coefficients related to path loss, radar cross section, and antenna gain; θ i and φ i These represent the elevation angle and azimuth angle of the UAV relative to base station i, respectively; and Indicates the channel of the relevant link; a t (θ, φ) and a r (θ, φ) are the transmit steering vector and the receive steering vector, respectively, and can be expressed as:

[0155]

[0156] Where, for i∈{t, r}, the following condition is satisfied:

[0157] Furthermore, the path loss can be determined using the basic free-space transmission loss of the radar system. The basic free-space transmission loss can be expressed as:

[0158] PL(d1, d2)=103.4+20logf+20log d1+20log d2-10logσdB

[0159] Where f represents the system frequency; σ represents the radar cross section; d1 represents the straight-line distance for the radar to receive and transmit signals; and d2 represents the straight-line distance for the radar to transmit and return signals.

[0160] Based on this, a1, a2, b1, and b2 can be represented as follows:

[0161]

[0162] in, G = G t G r This indicates the total antenna gain.

[0163] Based on this, by comprehensively considering signal, interference, noise, path loss, and radar parameters, the communication link between the base station and the UAV can be accurately modeled, providing an accurate foundation for subsequent data fusion and collaborative sensing. Furthermore, by distinguishing between co-channel and adjacent-channel interference and introducing an interference coefficient, the interference intensity at different operating frequencies can be effectively managed and evaluated, thereby optimizing communication quality.

[0164] Based on the above embodiments, in another embodiment provided in this disclosure, the sensing performance indicators include radar signal-to-interference-plus-noise ratio and joint detection probability, and step S420 may include:

[0165] The mapping relationship between radar signal-to-interference-plus-noise ratio and station spacing is determined based on a multi-base station sensing model;

[0166] A mapping relationship between joint detection probability and station spacing is established using likelihood ratio detectors and radar signal-to-interference-plus-noise ratio.

[0167] In this embodiment, by constructing a multi-base station sensing model, the entire process of a signal being transmitted from a base station, reflected by a drone, and received by the base station can be simulated and analyzed. In the multi-base station sensing model, the inter-base station spacing is a key parameter that directly affects the signal propagation path and strength. By adjusting the inter-base station spacing and calculating the corresponding SINR, the mapping relationship between SINR and inter-base station spacing can be determined. This mapping relationship indicates how the signal-to-interference-plus-noise ratio (SIR) changes under different inter-base station spacing configurations, thus reflecting changes in signal quality and system detection capabilities.

[0168] Using a likelihood ratio detector, the probability of a UAV correctly detecting a target under a given SINR condition can be evaluated. The likelihood ratio detector compares the probabilities of observations under the assumptions of target presence and absence to determine whether a target is detected. By incorporating the relationship between SINR and inter-station spacing, and combining it with the likelihood ratio detector, a mapping relationship between joint detection probability and inter-station spacing can be established. This mapping relationship shows how the joint detection probability of the system detects a target changes when multiple base stations cooperate under different inter-station spacings, reflecting the cooperative detection performance of the network.

[0169] The mapping relationship between SINR and inter-station spacing can be used to assess the impact of different inter-station spacings on signal quality and detection capabilities. This helps ensure that while meeting coverage requirements, signal reception quality is maximized and interference is minimized.

[0170] The mapping relationship between joint detection probability and inter-station spacing further provides the specific impact of different inter-station spacing configurations on system detection performance in a multi-base station cooperative environment. This helps the system understand how to improve detection accuracy and reduce false alarm rate by adjusting the inter-station spacing, thereby improving the overall network's sensing performance and reliability.

[0171] Based on this, by combining the two mapping relationships, the performance of multi-base station cooperative sensing networks can be optimized more accurately by adjusting the inter-base station spacing. By precisely controlling the inter-base station spacing, network coverage and efficiency can be maximized while ensuring communication quality, or the deployment cost of base stations can be minimized while ensuring performance.

[0172] Based on the above embodiments, in another embodiment provided in this disclosure, the determination of the mapping relationship between radar signal-to-interference-plus-noise ratio and inter-station distance based on the multi-base station sensing model may include:

[0173] The initial mapping relationship between radar signal-to-interference-plus-noise ratio and inter-station distance is determined by using maximum ratio transmission precoding and a multi-base station sensing model.

[0174] The initial mapping relationship is derived by eigenvalue decomposition, and the mapping relationship between radar signal-to-interference-plus-noise ratio and station spacing is determined.

[0175] In this embodiment, the radar signal-to-interference-plus-noise ratio (SINR) is a fundamental indicator for measuring radar sensing performance and is related to the Kourbek-Leibler dispersion between observation densities. Given the potential performance degradation caused by thermal noise and inter-base station interference, SINR takes into account the impact of thermal noise and inter-base station interference on sensing performance.

[0176] To improve radar SINR, maximum ratio transmission precoding can be used, expressed as:

[0177]

[0178] Wherein, P1 and P2 are the transmission powers of base station 1 and base station 2, respectively.

[0179] Based on the useful echo and mutual interference model from step S110, and using the identity... The radar SINR at base station 1 and base station 2 can be initially represented as:

[0180]

[0181] in, All are matrices of rank 1.

[0182] Consider A r，1 and A r，2 eigenvalue decomposition and satisfy And Λ1=Λ2=diag{1,0,…,0}. Therefore, radar SINR can be further expressed as:

[0183]

[0184] Where U2, U1, and Λ2 represent the results of eigenvalue decomposition; Represents the identity matrix.

[0185] also:

[0186]

[0187] Based on the above derivation, the final expressions for the radar SINR at base station 1 and base station 2 can be expressed as:

[0188]

[0189] Based on this, maximum ratio transmission precoding technology can effectively improve radar SINR and enhance signal transmission efficiency and quality. Secondly, utilizing a multi-base station sensing model allows for a comprehensive consideration of signal propagation characteristics and mutual interference among multiple base stations, thus more accurately establishing the relationship between SINR and inter-base station spacing. Furthermore, the application of eigenvalue decomposition makes the initial mapping relationship more precise.

[0190] Based on the above embodiments, in another embodiment provided in this disclosure, the joint detection probability includes the detection probability and the false alarm probability. The above-mentioned use of a likelihood ratio detector and radar signal-to-interference-plus-noise ratio to establish the mapping relationship between the joint detection probability and the inter-station spacing may include:

[0191] The likelihood ratio detector threshold is determined based on the quantile function;

[0192] Within a perception block containing multiple symbols, a likelihood ratio detector is used to jointly detect UAVs. Based on the likelihood ratio detector threshold and the radar signal-to-interference-plus-noise ratio, the mapping relationship between the detection probability and false alarm probability of the base station and the distance between the base stations is obtained.

[0193] Based on the mapping relationship between detection probability, false alarm probability and station spacing, the mapping relationship between joint detection probability and station spacing is determined.

[0194] In this embodiment, detection probability and false alarm probability, as important indicators for evaluating radar detection, can be generalized to measure the perception performance of cooperative sensor networks. Detection probability is the probability that the radar system correctly detects a target when the target is present, while false alarm probability is the probability that the radar system incorrectly detects a target when the target is not present.

[0195] In multi-base station cooperative sensing networks, joint detection probability can be used to establish a mapping relationship between inter-base station distance metrics and sensing performance metrics. Joint detection probability refers to the probability that the system correctly detects a target when multiple base stations cooperate.

[0196] Consider using a likelihood ratio detector to jointly detect UAVs within a perception block containing L symbols. The likelihood ratio detector performs joint detection of UAVs within a perception block containing multiple symbols. The likelihood ratio is the ratio of the likelihood of the target existence hypothesis to the likelihood of the target non-existence hypothesis, given the observed data.

[0197] For base station 1, the detection problem can be formulated as a binary hypothesis testing problem as follows:

[0198]

[0199] Where Y1 = [y1(1), ..., y1(L)], N = [n(1), ..., n(L)], s1 = [s 1，1 , ..., s 1，L ], s2=[s 2，1 , ..., s 2，L ]; This indicates that the target does not exist; This indicates the presence of the target. The echo interference term b1G1(θ1, φ1, θ2, φ2)w2s2 and the additive white Gaussian noise term N are both considered as interference by base station 1.

[0200] After signal vectorization, the testing problem can be further represented as:

[0201]

[0202] in, u1=vec{a1A1(θ1,φ1)w1s1}, u2=vec{b1G1(θ1,φ1,θ2,φ2)w2s2}, n s =vec{b1G1(θ1,φ1,θ2,φ2)w2s2+N},

[0203]

[0204] Based on this, the likelihood ratio detector can be expressed as:

[0205]

[0206] Where κ represents the threshold, which is the false alarm probability P in constant false alarm detection. FA The target value is determined; and They represent In the assumption and assumptions The probability density functions under these conditions can be specifically expressed as follows:

[0207]

[0208] After the logarithmic operation, the logarithmic likelihood ratio function can be expressed as:

[0209]

[0210] In the assumption and assumptions The following distributions are respectively:

[0211]

[0212] Based on this, the false alarm probability and detection probability can be calculated as follows:

[0213]

[0214] in, The function is Q-function. The detection threshold k is given by the following formula:

[0215]

[0216] Among them, Q -1 (x) is the inverse Q-function. Therefore, the detection probability of base station 1 can be expressed as:

[0217]

[0218] Using identities And based on the assumption and P can be D，1 Rewritten in a simpler form as follows:

[0219]

[0220] Similarly, the detection probability of base station 2 can be obtained as follows:

[0221]

[0222] Therefore, with a constant false alarm probability, the detection probability is directly related to the radar SINR. The higher the radar SINR, the higher the detection probability. Furthermore, and These two issues arise because mutual interference occurs only when the drone is present, which is fundamentally different from interference that is always present.

[0223] In a multi-base station collaborative sensing scenario, two base stations simultaneously detect the drone. If either base station successfully detects the drone, it is considered successfully detected. Therefore, the joint detection probability can be expressed as:

[0224] P D =1-(1-P) D，1 )·(1-P D，2 )

[0225] Based on this, the detection probability and false alarm probability are introduced as important indicators for evaluating radar detection performance, and the concept of joint detection probability is proposed to establish a mapping relationship between the inter-station spacing index and the perception performance index. Using a likelihood ratio detector, UAVs are jointly detected within a perception block containing multiple symbols, and the false alarm probability and detection probability are calculated. Finally, the scheme determines the conditions for successful UAV detection in multi-base station cooperative perception scenarios through the calculation of the joint detection probability.

[0226] Based on the above embodiments, in another embodiment provided in this disclosure, the above-mentioned solution of the sensing performance bound based on the mapping relationship and determination of the target station spacing based on the sensing performance bound may include:

[0227] Based on the mapping relationship, the sensing performance boundary and the station spacing interval are solved by a binary search algorithm.

[0228] Obtain actual network requirements and set a threshold for perceived performance requirements;

[0229] Based on the perception performance requirement threshold, a binary search algorithm is used to determine whether the perception performance requirement is met within the station spacing interval.

[0230] While meeting the sensing performance requirements, the nearest point of the approximate solution is selected to perform a bisection method to obtain the target station spacing.

[0231] In this embodiment, when planning a multi-base station cooperative sensing integrated network, it is necessary to minimize inter-site interference while meeting sensing performance requirements. Assume the sensing performance requirement threshold is μ, and the sensing performance requirement μ is less than the sensing performance bound. Define the function g(d) = SINR(d) - μ or g(d) = P D (d)-μ. The specific steps for solving the target station spacing based on interference and performance cooperation using the improved binary search algorithm may include:

[0232] Step S1331: Solve the interval [d] 1p d up Refine the interval [d] by taking a positive integer n. 1p d up Divide the sample into n equal parts, and calculate Δd = (d... up -d lp ) / n, d i =iΔd, 1≤i≤n.

[0233] Step S1332: From the first subinterval [d i d i+1 ], i = 0, begin the judgment: if g(d i ) = 0 or g(d i+1 ) = 0, thus obtaining d i or d i+1 As an approximate solution di+0.5 Otherwise, proceed with the subsequent judgment. If g(d) i )·g(d i+1 If ) < 0, then in the interval [d i d i+1 The approximate solution d is obtained using binary search. i+0.5 Otherwise, continue to check if g(d) is satisfied. i )·g(d i+2 ) < 0, until the entire interval [d] is completed. lp d up If the condition is met, exit the loop.

[0234] Step S1333: Select an approximate solution d i+0.5 Neighboring points or With d i+2 The binary search method is applied in pairs.

[0235] Step S1334: Continue executing steps S1332 and S1333 and determine whether two solutions satisfying the condition can be found. If so, output the result; otherwise, let Δd ← 0.5Δd.

[0236] Step S1335: Continue executing steps S1332 and S1333 until two solutions that meet the conditions are found, and take the larger solution as the station spacing that meets the network requirements.

[0237] Step S134: Analyze the results and provide heuristic conclusions.

[0238] Based on this, we first define the rate of change of the sensing performance index with respect to the inter-station spacing by deriving the first-order partial derivatives of the radar SINR and joint detection probability with respect to the inter-station spacing. Next, we use a binary search algorithm to solve for the sensing performance bound and its corresponding inter-station spacing. By initializing the error tolerance and the inter-station spacing limit, we calculate the sensing performance index at the midpoint and update the search interval according to the sign of the first-order partial derivative until we find the optimal inter-station spacing that satisfies the performance bound. Finally, based on the actual network requirements, we further refine the solution interval and use an improved binary search algorithm to determine the optimal inter-station spacing.

[0239] Based on the above embodiments, in another embodiment provided in this disclosure, the above-mentioned method of solving the sensing performance boundary and the station-to-station spacing interval based on the mapping relationship and using a binary search algorithm may include:

[0240] Based on the mapping relationship, the first-order partial derivatives of the sensing performance index with respect to the station spacing are established; the first-order partial derivatives are used to characterize the changing trend of the sensing performance index with respect to the station spacing.

[0241] Initialize the error tolerance, the upper bound of the station spacing, and the lower bound of the station spacing;

[0242] Calculate the sensing performance indicators at the midpoint of the station spacing interval;

[0243] The station spacing interval is updated based on the sign of the first-order partial derivative.

[0244] In this embodiment, the first-order partial derivatives of the radar SINR and joint detection probability with respect to the station spacing d are derived, and the following four expressions are defined:

[0245]

[0246] The first partial derivative of radar SINR with respect to the station spacing d is calculated by the following formula:

[0247]

[0248] Define the following four expressions:

[0249]

[0250]

[0251] According to the chain rule for finding the derivative of a composite function, the first partial derivative of the joint detection probability with respect to the station spacing d is calculated by the following formula:

[0252]

[0253] Furthermore, based on the closed-form expression for sensing performance with respect to station spacing derived above, a conversion relationship between sensing indices and station spacing indices was established, and the first-order partial derivative of sensing performance with respect to station spacing was derived to study the changing trend of sensing performance with respect to station spacing. The specific steps for solving the sensing performance bound and its corresponding station spacing using a binary search may include:

[0254] Initialize error tolerance ε and upper bound of station spacing d up The lower bound d of the station spacing lp .

[0255] Calculate the midpoint d of the station spacing m :d m =(d up +d lp ) / 2.

[0256] In d m Evaluate perceived performance metrics at d. If at d m If the performance at point d meets the performance bound, then stop the search. m This is the target station spacing.

[0257] If in d m The performance at a given point does not satisfy the performance bound; therefore, the interval is updated based on the sign of the first-order partial derivative.

[0258] If the first-order partial derivative is positive, it indicates that the performance increases with the increase of the station spacing; therefore, the lower bound is updated to d. m .

[0259] If the first-order partial derivative is negative, it indicates that the performance decreases as the station spacing increases; therefore, the upper bound is updated to d. m .

[0260] Specifically, calculation or like or Let d up =d m Otherwise, let d lp =d m .

[0261] If |d up -d lp If |≤∈, terminate the program and output the target station spacing d. opt =d m and perception performance boundary or Otherwise, return to recalculate the sensing performance index at the midpoint of the station spacing.

[0262] Based on this, since the first-order partial derivative provides sensitivity information about the perceived performance index as a function of station spacing, the optimization process can more accurately capture performance change trends. Secondly, by initializing error tolerance and defining well-defined station spacing limits, the algorithm can efficiently narrow the search range and quickly approach the optimal solution. Furthermore, calculating the perceived performance index at the midpoint allows the algorithm to accurately evaluate performance at each step, ensuring the accuracy of the optimization process. Finally, updating the station spacing interval based on the sign of the first-order partial derivative not only improves search efficiency but also ensures that the algorithm iterates towards performance improvement, thus finding the optimal station spacing configuration while meeting performance requirements.

[0263] One or more technical solutions provided in the exemplary embodiments of this disclosure first construct a model of useful echo signals and echo mutual interference between a base station and a UAV, considering path loss, radar cross-section, and antenna gain. Then, based on the model, closed-form expressions for radar SINR and joint detection probability with respect to station spacing are derived, clarifying the impact of station spacing on the radar system's sensing performance. Finally, based on the closed-form expressions, the sensing performance bound and target station spacing are solved, and a binary search algorithm is used to optimize the station spacing to reduce inter-station interference while meeting sensing performance requirements, and the target station spacing is determined according to actual network needs.

[0264] Therefore, the multi-base station cooperative sensing network planning method provided in the exemplary embodiments of this disclosure can accurately assess the impact of inter-base station spacing on sensing performance. By providing a method for flexibly adjusting the inter-base station spacing, it can minimize inter-base station interference while meeting sensing performance requirements, thereby improving the overall performance and reliability of the network.

[0265] The foregoing primarily describes the solutions provided by exemplary embodiments of this disclosure. It is understood that, in order to achieve the above functions, the electronic device includes corresponding hardware structures and / or software modules for performing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware 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 disclosure.

[0266] The exemplary embodiments of this disclosure can divide the electronic device into functional units according to the above method examples. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into a single processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in the exemplary embodiments of this disclosure is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0267] In the case of dividing each function into corresponding functional modules, an exemplary embodiment of this disclosure provides a multi-base station cooperative sensing network planning device, which can be a server or a chip applied to a server. Figure 5 This is a schematic block diagram of the functional modules of a multi-base station cooperative sensing network planning device provided as an example of this disclosure. Figure 5 As shown, the multi-base station cooperative sensing network planning device 500 includes:

[0268] The data acquisition module 510 is used to construct a multi-base station perception model in a multi-base station cooperative perception scenario; the multi-base station perception model is used to describe the entire process of signals being transmitted from each base station, reflected by the UAV, and received by the base station.

[0269] Data processing module 520 is used to determine the mapping relationship between the distance between base stations and the sensing performance index based on the multi-base station sensing model;

[0270] The data processing module 520 is also used to solve the sensing performance boundary based on the mapping relationship, and to determine the target station spacing based on the sensing performance boundary.

[0271] In another embodiment provided in this disclosure, the multi-base station sensing model includes a useful echo signal sub-model and an echo interference sub-model; the data processing module 520 is further configured to acquire transmission path loss, channel information, additive white Gaussian noise, and the relative position information of the UAV relative to the base station; construct a useful echo signal sub-model and an echo interference sub-model based on the relative position information, transmission path loss, and channel information respectively; and construct the multi-base station sensing model based on the useful echo signal sub-model, the echo interference sub-model, and additive white Gaussian noise.

[0272] In another embodiment provided in this disclosure, the sensing performance indicators include radar signal-to-interference-plus-noise ratio (SINR) and joint detection probability; the data processing module 520 is further configured to determine the mapping relationship between radar SINR and inter-station distance based on the multi-base station sensing model; and to establish the mapping relationship between joint detection probability and inter-station distance using a likelihood ratio detector and radar SINR.

[0273] In another embodiment provided in this disclosure, the data processing module 520 is further configured to determine the initial mapping relationship between radar signal-to-interference-plus-noise ratio and inter-station distance through maximum ratio transmission precoding and the multi-base station sensing model; and to determine the mapping relationship between radar signal-to-interference-plus-noise ratio and inter-station distance by deriving the initial mapping relationship through eigenvalue decomposition.

[0274] In another embodiment provided in this disclosure, the joint detection probability includes the detection probability and the false alarm probability; the data processing module 520 is further configured to determine the likelihood ratio detector threshold based on the quantile function; within a perception block containing multiple symbols, the likelihood ratio detector is used to perform joint detection of the UAV, and the mapping relationship between the base station's detection probability and false alarm probability and the inter-station distance is obtained based on the likelihood ratio detector threshold and the radar signal-to-interference-plus-noise ratio; based on the mapping relationship between the detection probability and false alarm probability and the inter-station distance, the mapping relationship between the joint detection probability and the inter-station distance is determined.

[0275] In another embodiment provided in this disclosure, the data processing module 520 is further configured to: solve the sensing performance boundary and the station spacing interval based on the mapping relationship using a binary search algorithm; obtain the actual network requirements and set a sensing performance requirement threshold; determine whether the sensing performance requirements are met within the station spacing interval using a binary search algorithm based on the sensing performance requirement threshold; and, if the sensing performance requirements are met, select a neighboring point of the approximate solution to perform a binary search to obtain the target station spacing.

[0276] In another embodiment provided in this disclosure, the data processing module 520 is further configured to establish a first-order partial derivative of the sensing performance index with respect to the station spacing based on the mapping relationship; the first-order partial derivative is used to characterize the changing trend of the sensing performance index with respect to the station spacing; initialize the error tolerance, the upper bound of the station spacing, and the lower bound of the station spacing; calculate the sensing performance index at the midpoint of the station spacing interval; and update the station spacing interval according to the sign of the first-order partial derivative.

[0277] Exemplary embodiments of this disclosure also provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the electronic device to perform a method according to an embodiment of this disclosure.

[0278] Exemplary embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to embodiments of this disclosure.

[0279] Figure 6 The present disclosure provides an exemplary structural block diagram of an electronic device. The structural block diagram of an electronic device 600 that can serve as a server or client of the present disclosure is now described, and it is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0280] like Figure 6 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the electronic device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0281] Multiple components in electronic device 600 are connected to I / O interface 605, including: input unit 606, output unit 607, storage unit 608, and communication unit 609. Input unit 606 can be any type of device capable of inputting information to electronic device 600. Input unit 606 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 607 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 608 may include, but is not limited to, disks and optical discs. Communication unit 609 allows electronic device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0282] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above. The various methods described above can all be implemented as computer software programs, which are tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 600 via ROM 602 and / or communication unit 609.

[0283] Figure 7 The present disclosure provides an exemplary computer program product. An exemplary embodiment of the present disclosure also provides a computer program product 700, including a computer program 701, wherein the computer program 701, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of the present disclosure.

[0284] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0285] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0286] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0287] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0288] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0289] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0290] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this disclosure are performed, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center integrating one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video disc (DVD); or it can be a semiconductor medium, such as a solid-state drive (SSD).

[0291] Although this disclosure has been described in conjunction with specific features and embodiments, it will be apparent that various modifications and combinations can be made therein without departing from the spirit and scope of this disclosure. Accordingly, this specification and drawings are merely exemplary illustrations of the disclosure as defined by the appended claims and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this disclosure. It is obvious that those skilled in the art can make various alterations and modifications to this disclosure without departing from its spirit and scope. Thus, this disclosure is also intended to include any such modifications and modifications that fall within the scope of the claims of this disclosure and their equivalents.

Claims

1. A multi-base station cooperative sensing network planning method, characterized in that, The method includes: Construct a multi-base station sensing model for a multi-base station collaborative sensing scenario; the multi-base station sensing model is used to describe the entire process of signals being transmitted from each base station, reflected by the UAV, and received by the base station; The mapping relationship between the distance between base stations and the sensing performance index is determined based on the multi-base station sensing model. The sensing performance bound is solved based on the mapping relationship, and the target station spacing is determined based on the sensing performance bound.

2. The method according to claim 1, characterized in that, The multi-base station sensing model includes a useful echo signal sub-model and an echo interference sub-model; the construction of the multi-base station sensing model under the multi-base station cooperative sensing scenario includes: Acquire transmission path loss, channel information, additive white Gaussian noise, and the relative position of the UAV to the base station; Based on relative position information, transmission path loss, and channel information, a useful echo signal sub-model and an echo interference sub-model are constructed respectively. The multi-base station sensing model is constructed based on the useful echo signal sub-model, the echo interference sub-model, and additive white Gaussian noise.

3. The method according to claim 1, characterized in that, The sensing performance indicators include radar signal-to-interference-plus-noise ratio and joint detection probability; determining the mapping relationship between inter-station spacing and sensing performance indicators based on the multi-base station sensing model includes: The mapping relationship between radar signal-to-interference-plus-noise ratio and station spacing is determined based on the multi-base station sensing model. A mapping relationship between joint detection probability and station spacing is established using likelihood ratio detectors and radar signal-to-interference-plus-noise ratio.

4. The method according to claim 3, characterized in that, Determining the mapping relationship between radar signal-to-interference-plus-noise ratio and inter-station distance based on the multi-base station sensing model includes: The initial mapping relationship between radar signal-to-interference-plus-noise ratio and station spacing is determined by using maximum ratio transmission precoding and the multi-base station sensing model. The initial mapping relationship is derived by eigenvalue decomposition, and the mapping relationship between radar signal-to-interference-plus-noise ratio and station spacing is determined.

5. The method according to claim 3, characterized in that, The joint detection probability includes the detection probability and the false alarm probability; the step of establishing a mapping relationship between the joint detection probability and the inter-station distance using a likelihood ratio detector and radar signal-to-interference-plus-noise ratio includes: The likelihood ratio detector threshold is determined based on the quantile function; Within a perception block containing multiple symbols, a likelihood ratio detector is used to jointly detect UAVs. Based on the likelihood ratio detector threshold and the radar signal-to-interference-plus-noise ratio, the mapping relationship between the detection probability and false alarm probability of the base station and the distance between the base stations is obtained. Based on the mapping relationship between the detection probability and false alarm probability and the station spacing, the mapping relationship between the joint detection probability and the station spacing is determined.

6. The method according to claim 1, characterized in that, The step of solving the sensing performance bound based on the mapping relationship and determining the target station spacing based on the sensing performance bound includes: Based on the mapping relationship, the sensing performance boundary and the station spacing interval are solved by a binary search algorithm; Obtain actual network requirements and set a threshold for perceived performance requirements; Based on the aforementioned sensing performance requirement threshold, a binary search algorithm is used to determine whether the sensing performance requirement is met within the station spacing interval. While meeting the sensing performance requirements, the nearest point of the approximate solution is selected to perform a bisection method to obtain the target station spacing.

7. The method according to claim 6, characterized in that, The step of solving for the sensing performance boundary and the inter-station spacing interval using a binary search algorithm based on the mapping relationship includes: Based on the mapping relationship, the first-order partial derivatives of the sensing performance index with respect to the station spacing are established; the first-order partial derivatives are used to characterize the changing trend of the sensing performance index with respect to the station spacing. Initialize the error tolerance, the upper bound of the station spacing, and the lower bound of the station spacing; Calculate the sensing performance indicators at the midpoint of the station spacing interval; The station spacing interval is updated based on the sign of the first-order partial derivative.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method of claim 1.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the method of claim 1.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the method of claim 1.

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