A method for edge perception and task offloading based on a movable antenna

Abstract

This invention discloses a collaborative method for edge sensing and task offloading based on movable antennas, belonging to the field of wireless communication and sensing integration technology. The method is applied to base stations equipped with movable antenna arrays, enabling concurrent downlink environmental sensing and uplink multi-user computation offloading via the movable antenna arrays. It constructs a joint optimization problem centered on optimizing sensing performance while also considering communication constraints. An alternating optimization framework is employed to iteratively optimize receive beamforming, user transmit power, and transmit and receive antenna positions to achieve dynamic collaborative allocation of sensing and communication computing resources. This invention fully leverages spatial freedom, improves sensing accuracy and communication efficiency, and effectively supports the collaborative requirements of high-precision sensing and concurrent multi-task offloading in edge intelligence scenarios.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication and sensing integration technology, and particularly relates to a collaborative method for edge sensing and task offloading based on a movable antenna. Background Technology

[0002] With the widespread adoption of the Internet of Things (IoT) and smart terminal devices, edge sensing scenarios are increasingly demanding higher performance in terms of communication, sensing, and computing collaboration. Traditional intelligent networks typically design communication (such as data transmission) and sensing (such as radar and cameras) as independent modules. This separate architecture not only increases system hardware and scheduling complexity but can also lead to competition for spectrum and computing resources, resulting in increased transmission latency and reduced energy efficiency. To address this, Integrated Sensing and Communication (ISAC) technology has emerged. By sharing hardware platforms and spectrum resources, it deeply integrates wireless communication with environmental sensing functions, improving spectrum utilization while effectively reducing system power consumption and processing latency. Existing research has explored ISAC systems from the perspectives of waveform design, performance trade-offs, and beamforming. For example, it has proposed using radar pulse intervals to transmit communication signals in full-duplex ISAC to reduce sensing blind spots, or analyzed the trade-off between sensing detection probability and communication rate. These works have laid the theoretical foundation for the initial application of ISAC technology.

[0003] However, existing ISAC schemes still have significant limitations. On the one hand, most related research focuses on single-base station scenarios, which are limited by the signal coverage, available spectrum resources, and processing capabilities of a single base station, making it difficult to support the collaborative requirements of high data rates and high-precision positioning in intelligent sensing and large-scale disconnected scenarios. On the other hand, existing systems typically configure fixed-position antenna (FPA) arrays at the base station, and the positions of the antenna elements cannot be dynamically adjusted. This prevents the system from fully exploiting the degrees of freedom (DoFs) in the continuous spatial array region, thus limiting its overall performance in accurately sensing and efficiently communicating in complex and changing environments. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a collaborative method for edge sensing and task offloading based on a movable antenna, thereby resolving the issues present in the prior art.

[0005] To achieve the above objectives, this invention provides a collaborative method for edge sensing and task offloading based on a movable antenna, applied to a base station equipped with a movable antenna array, comprising:

[0006] The base station transmits downlink sensing signals through its movable transmitting antenna array, while simultaneously sensing targets in the environment and receiving uplink computing offload signals from multiple user devices.

[0007] Construct a joint optimization problem with the core objective of optimizing perception performance while satisfying user communication needs and system constraints;

[0008] The joint optimization problem is solved using an alternating optimization framework. By iteratively optimizing the receive beamforming, user transmit power, transmit antenna position, and receive antenna position, the coordination of sensing and multi-user computing offloading is achieved.

[0009] Preferably, the core objective of perception performance means that the objective function of the joint optimization problem is to minimize the Cramer-Rao lower bound for angle estimation of the objective.

[0010] Preferably, satisfying user communication needs and system constraints includes: the uplink communication rate of each user equipment is not lower than its task requirement rate, the transmit power of each user equipment does not exceed its maximum power budget, and the position of each movable antenna is located within a preset movable area and meets the minimum spacing constraint.

[0011] Preferably, the iterative optimization of the receive beamforming specifically involves: given other variables, solving a subproblem aimed at maximizing the signal-to-interference-plus-noise ratio (SINR) of each user equipment by using a positive semidefinite relaxation and Gaussian randomization method, thereby obtaining the optimal receive beamforming vector.

[0012] Preferably, the iterative optimization of user transmit power specifically involves: given other variables, using convex optimization tools to solve a power control subproblem that ensures the signal-to-interference-plus-noise ratio of each user equipment meets its required rate, with the goal of minimizing the Cramer-Rao lower bound.

[0013] Preferably, the iterative optimization of the transmitting antenna position specifically involves: for each transmitting antenna, under the condition of satisfying its position constraints, obtaining its optimal position by solving a subproblem aimed at maximizing the fraction related to the antenna position, wherein some antenna positions have closed-form solutions.

[0014] Preferably, the iterative optimization of the receiving antenna position specifically involves: for each receiving antenna, under the condition of satisfying its position constraints, solving a subproblem aimed at maximizing the fraction related to the antenna position through a one-dimensional search or iterative method to obtain its optimal position.

[0015] Preferably, the base station adopts a full-duplex working mode, and concurrently transmits the downlink sensing signal and receives the uplink computing offload signal on the same time and frequency resources.

[0016] Preferably, the movable antenna array includes a transmitting uniform linear array composed of several movable antennas and a receiving uniform linear array composed of several movable antennas.

[0017] Preferably, the target is at least one of the user equipment, or other intelligent agents within the communication coverage area of ​​the base station.

[0018] Compared with the prior art, the present invention has the following advantages and technical effects:

[0019] This invention employs a movable antenna array as its core hardware architecture and constructs a joint optimization problem with the core objective of optimizing sensing performance while simultaneously satisfying communication and power constraints. It then uses an alternating optimization framework involving coverage and receiving beamforming, user power, and the positions of the transmitting and receiving antennas to solve the problem. This approach makes the position of the antenna elements a dynamically optimizable key variable, overcoming the inherent limitations of fixed-position antenna arrays in beamforming and spatial degree-of-freedom utilization. By iteratively optimizing the antenna positions, the system can proactively adapt to the environment and user distribution, more fully exploiting the degrees of freedom in continuous space. This results in more accurate environmental perception and more efficient multi-user communication within the same hardware scale, significantly improving the overall sensing-communication collaborative performance of the system.

[0020] This invention constructs an ISAC system framework supporting full-duplex operation, enabling base stations to concurrently perform downlink sensing and uplink computation offloading on the same time-frequency resources. It integrates sensing performance metrics (such as the Cramer-Rao lower bound) and communication service quality (user rate requirements) into a unified joint optimization problem for overall design. By using an alternating optimization framework to coordinate the allocation of key resources such as power, beam, and antenna location, this solution fundamentally solves the resource contention problem between communication and sensing modules in traditional separate designs, achieving deep sharing and efficient reuse of hardware and spectrum resources. This effectively reduces overall system power consumption and signal processing latency, realizing integrated and efficient collaboration of sensing and communication functions.

[0021] This invention explicitly supports multiple user devices simultaneously offloading uplink intelligent task computations, and ensures the minimum communication rate requirements of each user through constraints in the optimization model. By alternating user power control and receive beamforming optimization sub-steps within the optimization framework, the system can intelligently allocate power and space resources among multiple users, dynamically balancing the communication needs of different users with the system's sensing task requirements. This makes the system particularly suitable for edge computing scenarios with dense intelligent terminals, reliably supporting concurrent computation task offloading of multiple devices while ensuring high-precision environmental perception, significantly improving the base station's overall response capability and resource adaptation efficiency to heterogeneous service demands. Attached Figure Description

[0022] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0023] Figure 1 This is a schematic diagram of a movable antenna (MA) assisted FD-ISAC according to an embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram illustrating the convergence performance of different schemes in embodiments of the present invention;

[0025] Figure 3 This is a schematic diagram illustrating the relationship between the Cramér-Rao Bound (CRB) and the base station transmit power in an embodiment of the present invention.

[0026] Figure 4 This is a schematic diagram illustrating the relationship between the Cramér-Rao Bound (CRB) and the number of receiving antennas in an embodiment of the present invention.

[0027] Figure 5 This is a schematic diagram showing the relationship between the Cramér-Rao Bound (CRB) and the size of the movable area of ​​the antenna in an embodiment of the present invention. Detailed Implementation

[0028] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0029] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0030] Example 1

[0031] This embodiment provides a collaborative method for edge sensing and task offloading based on a movable antenna, applied to a base station equipped with a movable antenna array, including:

[0032] Step 1: The base station transmits downlink sensing signals through its movable transmitting antenna array, simultaneously sensing targets in the environment and receiving uplink computing offload signals from multiple user devices;

[0033] Furthermore, the base station adopts a full-duplex working mode, concurrently transmitting the downlink sensing signal and receiving the uplink computing offload signal on the same time and frequency resources.

[0034] Furthermore, the movable antenna array includes a transmitting uniform linear array composed of several movable antennas and a receiving uniform linear array composed of several movable antennas.

[0035] Furthermore, the target is at least one of the user equipment, or other intelligent agents within the communication coverage area of ​​the base station.

[0036] Specifically, such as Figure 1 As shown, this embodiment considers a full-duplex integrated sensing and communication (FD-ISAC) mode assisted by a movable antenna (MA), where each base station includes a... A uniform linear array (ULA) of multiple transmitters (MA) and a band The base station receives the ULA of each MA to fully explore the degrees of freedom in a continuous spatial region. Uplink intelligent task computation is offloaded for each single-antenna user, and downlink signals are continuously transmitted using the same time-frequency resources to sense a single target. Specifically, each user Send a signal Used for uplink communication. The average transmit power of each user should not exceed the maximum power budget, so the user transmit power should satisfy formula (1).

[0037] (1)

[0038] in, For user transmit power, This is the user's maximum transmit power.

[0039] In this example of channel modeling, this embodiment models the channel between the BS and the user as follows: in, For complex channel gain coefficients, For the set of complex numbers, It refers to the number of receiving antennas. This is the base station's reception guidance vector for users.

[0040] In order to detect targets, the base station... Each snapshot continuously transmits sensing signals. Assume... Within each snapshot, the channel of the target base station link remains unchanged, and the base station in the... The transmission signal of a snapshot can be written as , This refers to the number of transmitting antennas, and the corresponding average power is... . This represents the expectation operation. Meanwhile, the base station transmit steering vector and receive steering vector used for target sensing are defined as follows:

[0041] (2)

[0042] (3)

[0043] in, It is the angle of the target relative to the base station. Indicates the carrier wavelength. Indicates user Angle of arrival to the base station Indicates transpose. For the launch guidance vector, To receive the guide vector, let Then the base station Target The channel of the base station link is The perceived channel coefficients are determined by... Given, among which Parameters related to radar cross-sectional area, among which dB is the free path loss at a reference distance of 1m. This represents the distance between the base station and the sensing target. Therefore, the base station in the [missing information]... The received signal for each snapshot is:

[0044] (4)

[0045] in, It is additive white Gaussian noise at the base station receiver. For communication signals, In order to sense the echo signal, For the base station in the A snapshot of the sensing signal emitted. This is its average noise power. Therefore, the base station in The received signals within each snapshot are:

[0046] (5)

[0047] Among them, the signal correlation quantities under different receiving scenarios are and the received noise are To achieve uniform sensing performance across arbitrary departure and arrival angles, The general requirement is that it is a full-rank matrix (this requirement) ), Make An omnidirectional beam pattern is generated in the angular domain to uniformly scan targets in all possible directions.

[0048] To restore the signal for uplink users, this embodiment utilizes a series of receive beamforming vectors. The received signal from the base station is processed. To ensure the physical feasibility of the receiver, the received beamforming vector needs to meet certain conditions. .

[0049] Therefore, users The SINR is:

[0050] (6)

[0051] in, , .user The maximum number of bits of information transmitted without error through the channel per unit time is:

[0052] (7)

[0053] in, It refers to transmission bandwidth.

[0054] To ensure the data transmission performance of each user's uplink communication, Formula (8) should be satisfied:

[0055] (8)

[0056] in, User The number of bits that need to be transmitted per unit of time.

[0057] Step 2: Construct a joint optimization problem with the core objective of optimizing perception performance while satisfying user communication needs and system constraints;

[0058] Furthermore, the core objective of perception performance means that the objective function of the joint optimization problem is to minimize the Cramer-Rao lower bound for angle estimation of the objective.

[0059] Furthermore, satisfying user communication needs and system constraints includes: the uplink communication rate of each user equipment is not lower than its task requirement rate, the transmit power of each user equipment does not exceed its maximum power budget, and the position of each movable antenna is located within a preset movable area and meets the minimum spacing constraint.

[0060] Specifically, the main objective of this embodiment is to optimize sensing performance while ensuring user transmission rate and power constraints. Therefore, this optimization problem can be modeled as follows:

[0061] (9)

[0062] Where C3, C4, C5, and C6 represent the spacing constraints between the location of the base station MA and the antenna. Indicates the user's maximum transmit power. This indicates the position of the m-th transmitting antenna. This indicates the position of the nth receiving antenna. This indicates the maximum range of movement of the movable antenna. This represents the minimum distance between any two antennas.

[0063] Step 3: The joint optimization problem is solved using an alternating optimization framework. By iteratively optimizing the receive beamforming, user transmit power, transmit antenna position, and receive antenna position, the coordination of sensing and multi-user computing offloading is achieved.

[0064] Furthermore, the iterative optimization of receive beamforming specifically involves solving a subproblem aimed at maximizing the signal-to-interference-plus-noise ratio (SIR) of each user equipment, given other variables, through a positive semidefinite relaxation and Gaussian randomization method, to obtain the optimal receive beamforming vector.

[0065] Furthermore, the iterative optimization of user transmit power specifically involves: given other variables, using convex optimization tools to solve a power control subproblem that aims to minimize the Cramer-Rao lower bound, with the constraint that the signal-to-interference-plus-noise ratio of each user equipment meets its required rate.

[0066] Furthermore, the iterative optimization of the transmitting antenna position is specifically as follows: for each transmitting antenna, under the condition of satisfying its position constraints, its optimal position is obtained by solving a subproblem with the objective of maximizing the fraction related to the antenna position, where some antenna positions have closed-form solutions.

[0067] Furthermore, the iterative optimization of the receiving antenna position specifically involves: for each receiving antenna, under the condition of satisfying its position constraints, solving a subproblem aimed at maximizing the fraction related to the antenna position through a one-dimensional search or iterative method to obtain its optimal position.

[0068] Specifically, due to the coupling between the aforementioned optimization variables, this problem is a non-convex optimization problem, making it difficult to obtain the optimal solution. This embodiment employs an alternating optimization algorithm to iteratively solve the optimization scheme, which specifically includes:

[0069] (1) Optimization of receiving beamforming;

[0070] Given a transmit power, the antenna position vectors of the transmitting MA array and the receiving MA array are... Under these conditions, the subproblem of receiving beamforming design can be expressed as:

[0071] (10)

[0072] in, , And because These are independent problems, and the optimization problem is equivalent to solving K subproblems. The optimization problem can be described as follows:

[0073] (11)

[0074] make and At this point, the optimization problem (11) can be transformed into:

[0075] (12)

[0076] Then, the optimal value is obtained through semidefinite relaxation techniques. Then, Gaussian randomization is used to recover the rank-one constraint. .

[0077] (2) User power control;

[0078] Given the receive beamforming vector, the antenna position vector of the transmitting MA array and the antenna position vector of the receiving MA array... Under these conditions, the power control subproblem can be expressed as:

[0079] (13)

[0080] in, For the optimization problem (13), the existing CVX toolbox can be used to solve it.

[0081] (3) Optimization of transmitting antenna position;

[0082] According to equation (9), minimizing CRB is equivalent to maximizing it. This embodiment will optimize the position of each transmitting antenna one by one, given that each... One transmitting antenna location The optimization solution process is similar; the following will take the solution for the position of the i-th transmitting antenna as an example. Simplify to only contain The function, that is:

[0083] (14)

[0084] in, This is a constant term. Since ignoring the constant term does not affect the optimal solution, the subproblem of optimizing the transmit antenna location becomes:

[0085] (15)

[0086] Assumption ,make ,constraint and It can be written as:

[0087] (16)

[0088] Therefore, the closed-form solution for the position of the transmitting antenna can be obtained as follows:

[0089] (17)

[0090] in, express and Distance between the two More distant values ​​and .

[0091] (4) Optimization of receiving antenna position;

[0092] Similarly, the following will be based on the first... Each receiving antenna position Taking the solution as an example. Simplify to only contain The function, that is:

[0093] (18)

[0094] in, This is a constant term. Therefore, the subproblem for optimizing the receiver antenna position is:

[0095] (19)

[0096] constraint and It can be written as:

[0097] (20)

[0098] Therefore, the optimization problem (19) can be approximated as:

[0099] (twenty one)

[0100] The optimization problem (19) can be solved by iteratively solving the optimization problem (21) to obtain its local optimum. And the optimization problem (9) can be solved by iteratively solving the above four optimization subproblems.

[0101] Based on this AO algorithm, the solution to this non-convex problem is obtained, and the relevant simulation results are shown in the figure. Figure 2 , Figure 3 , Figure 4 , Figure 5 As shown.

[0102] Figure 2 The convergence performance of the proposed scheme and the benchmark scheme in this embodiment is presented. From Figure 2 As can be seen, with the increase of the number of iterations, the CRB of the proposed scheme in this embodiment gradually decreases and eventually tends to a stable value. In addition, the CRB of the proposed scheme in this embodiment is smaller than that of the benchmark scheme. This is because the proposed scheme in this embodiment optimizes both the transmit antenna position and the receive antenna position, while the benchmark scheme optimizes at most one of them.

[0103] Figure 3 The changes in CRB (Center for Receiving Noise Ratio) of a base station under different base station transmit powers are presented. As the base station transmit power increases, the CRB decreases. It can be seen that the CRB is inversely proportional to the base station transmit power. From a physical perspective, an increase in base station transmit power indicates an increase in the transmitted sensing signal energy, and the signal power is relatively stronger than the noise, thus increasing the signal-to-noise ratio. Furthermore, since the CRB is negatively correlated with the signal-to-noise ratio, an increase in the signal-to-noise ratio will decrease the CRB.

[0104] Figure 4 The relationship between CRB and the number of receiving antennas is presented. Simulation results show that CRB decreases as the number of receiving antennas increases. This is because increasing the number of receiving antennas leads to higher spatial diversity gain and information utilization. On the one hand, more receiving antennas can collect signals from different spatial locations, thereby acquiring more independent channel samples, which reduces noise due to spatial randomness. On the other hand, the superposition of multiple antennas allows the effective signal to carry richer information about parameters such as angle and distance, thus increasing the trace of the Fisher information matrix. Since CRB is the inverse of the Fisher information matrix, the larger the information content of the matrix, the smaller the CRB value corresponding to its inverse matrix. It can be seen that the number of receiving antennas directly affects the denominator of CRB; increasing the number of receiving antennas causes the denominator to increase, thereby reducing CRB.

[0105] Figure 5 The relationship between CRB and the size of the antenna's movable region is presented. The results show that CRB decreases as the size of the antenna's movable region increases. This is because the expanded spatial coverage of the antenna provides the antenna array with more spatial degrees of freedom, allowing for more flexible adjustment of the phase difference of the signal arriving at different antennas, thus enabling more accurate parameter estimation using richer phase gradient information. Simultaneously, as the size of the antenna's movable region increases, the system approaches its resolution limit, and the change in CRB gradually decreases.

[0106] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for edge-aware and task offloading coordination based on movable antennas, applied to a base station equipped with a movable antenna array, characterized in that, include: The base station transmits downlink sensing signals through its movable transmitting antenna array, while simultaneously sensing targets in the environment and receiving uplink computing offload signals from multiple user devices. Construct a joint optimization problem with the core objective of optimizing perception performance while satisfying user communication needs and system constraints; The joint optimization problem is: ； Where C3, C4, C5, and C6 represent the spacing constraints between the location of the base station MA and the antenna. For user transmission power, User The number of bits that need to be transmitted per unit of time. Indicates the user's maximum transmit power. This indicates the position of the m-th transmitting antenna. This indicates the position of the nth receiving antenna. This indicates the maximum range of movement of the movable antenna. This represents the minimum distance between any two antennas; The joint optimization problem is solved using an alternating optimization framework. By iteratively optimizing the receive beamforming, user transmit power, transmit antenna position, and receive antenna position, the coordination of sensing and multi-user computing offloading is achieved. Receiver beamforming optimization: Given a transmit power, the antenna position vectors of the transmitting MA array and the receiving MA array are... Under these conditions, the subproblem of receiving beamforming design is expressed as follows: ； in, , And because These are independent problems, and the optimization problem is equivalent to solving K subproblems. The optimization problem is expressed as: ； make and The optimization problem then becomes: ； Then, the optimal value is obtained through semidefinite relaxation techniques. Then, Gaussian randomization is used to recover the rank-one constraint. ; User power control optimization: Given the receive beamforming vector, the antenna position vector of the transmitting MA array and the antenna position vector of the receiving MA array... Under these conditions, the power control subproblem can be formulated as follows: ； in, For optimization problems, the existing CVX toolkit is used for solving them; Optimization of transmit antenna position: Minimizing CRB is equivalent to maximizing Therefore, the position of each transmitting antenna will be optimized one by one. Since the optimization process for the positions of m transmitting antennas is similar, the following will focus on the position of the mth transmitting antenna. Taking the solution as an example, Simplify to only contain The function, that is: ； in, As a constant term, the subproblem for optimizing the transmit antenna position is: ； Assumption ,make ,constraint and Written as: ； The closed-form solution for the position of the transmitting antenna is: ； in, express and Distance between the two More distant values ​​and ; Optimization of receiver antenna position: With the first Each receiving antenna position Taking the solution as an example, Simplify to only contain The function, that is: ； in, As a constant term, the subproblem for optimizing the receiving antenna position is: ； constraint and Written as: ； Therefore, the optimization problem can be approximated as: 。 2. The method according to claim 1, characterized in that, The objective function of the joint optimization problem is to minimize the Cramero lower bound for angle estimation of the objective.

3. The method according to claim 1, characterized in that, The requirements for meeting user communication needs and system constraints include: the uplink communication rate of each user device is not lower than its task requirement rate, the transmit power of each user device does not exceed its maximum power budget, and the position of each movable antenna is located within a preset movable area and meets the minimum spacing constraint.

4. The method according to claim 1, characterized in that, The iterative optimization of receive beamforming specifically involves solving a subproblem aimed at maximizing the signal-to-interference-plus-noise ratio (SINR) of each user equipment, given other variables, through a positive semidefinite relaxation and Gaussian randomization method, to obtain the optimal receive beamforming vector.

5. The method according to claim 2, characterized in that, The iterative optimization of user transmit power specifically involves: given other variables, using convex optimization tools to solve a power control subproblem that ensures the signal-to-interference-plus-noise ratio of each user equipment meets its required rate, with the objective of minimizing the Cramer-Rao lower bound.

6. The method according to claim 1, characterized in that, The iterative optimization of the transmitting antenna position is as follows: for each transmitting antenna, under the condition of satisfying its position constraints, the optimal position is obtained by solving a subproblem that aims to maximize the fraction related to the antenna position. Some antenna positions have closed-form solutions.

7. The method according to claim 1, characterized in that, The iterative optimization of the receiving antenna position is as follows: for each receiving antenna, under the condition of satisfying its position constraints, a subproblem aimed at maximizing the fraction related to the antenna position is solved by a one-dimensional search or iterative method to obtain its optimal position.

8. The method according to claim 1, characterized in that, The base station adopts a full-duplex working mode, and concurrently transmits the downlink sensing signal and receives the uplink computing offload signal on the same time and frequency resources.

9. The method according to claim 1, characterized in that, The movable antenna array includes a transmitting uniform linear array composed of several movable antennas and a receiving uniform linear array composed of several movable antennas.

10. The method according to claim 1, characterized in that, The target is at least one of the user equipment, or other intelligent agents within the communication coverage area of ​​the base station.

Images