Fluid antenna assisted communication perception and calculation integrated method

Abstract

The invention belongs to the technical field of wireless communication, and discloses a fluid antenna assisted communication, perception and calculation integrated (ISCC) method, which comprises the following steps of: unloading communication and perception tasks of a terminal to a mobile edge calculation (MEC) server at a base station end, and constructing an efficient ISCC system by utilizing the strong calculation capability of the server; a fluid antenna is integrated to a base station end, a fluid antenna assisted ISCC system is provided, and a fluid antenna assisted communication and perception model is established; the time delay of communication and sensing tasks is calculated, a time delay optimization problem is constructed, and the total time delay of the system is minimized by optimizing the position of a fluid antenna. According to the method, the flexibility of the fluid antenna is fully utilized, and the position of the antenna at the base station end is dynamically adjusted, so that the channel condition is adapted and optimized, and efficient and low-delay communication, sensing and calculation between the terminal and the base station are realized.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication technology, and specifically relates to a communication perception and computing integration method assisted by a fluid antenna. Background Art

[0002] Integrated communication, perception and computing (ISCC) technology is at the forefront of wireless network technology and represents an important direction for the development of future communication technology. The ISCC system aims to integrate communication, perception and computing functions into a unified framework, which can not only greatly improve resource utilization, but also significantly reduce the delay time of data processing, thereby achieving faster response speed. Although the ISCC system has made some progress, the current wireless communication system still faces major challenges in terms of efficiency and resource utilization. Although the existing multi-antenna technology has made some progress in the fields of communication and perception, such as the "Fixed and Movable Antenna Technology for 6G Communication and Perception Integration" proposed by Zeng Yong, Dong Zhenjun, Wang Huizhi, et al. [J]. Signal Processing, 2024, 40(08):1377-1407, which discloses far-field / near-field channel modeling based on related antenna architecture for communication and perception analysis; however, in the fluid antenna-assisted ISCC system, it still faces limitations in terms of resource scheduling flexibility, complex channel modeling, and collaborative processing with MEC. Therefore, a new method is urgently needed to improve its resource utilization and achieve efficient computing collaboration. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides an integrated communication, perception and computing method assisted by a fluid antenna, establishes a fluid antenna assisted communication and perception model, dynamically adjusts the antenna position at the base station end, and utilizes the powerful computing power of MEC to process computing tasks efficiently and with low latency.

[0004] The present invention provides a fluid antenna-assisted communication, perception and computing integrated method, comprising the following steps:

[0005] Step 1: Build an ISCC system, including a base station composed of an MEC server and N terminals. Transfer the communication and perception task requests of the terminals to the MEC server for processing, train the tasks, and calculate the delay of communication and perception tasks.

[0006] Step 2: Integrate the fluid antenna into the base station, propose a fluid antenna-assisted ISCC system, and establish a fluid antenna-assisted communication and perception model;

[0007] Step 3: Construct a delay optimization problem and minimize the total delay of the system by optimizing the fluid antenna position.

[0008] Furthermore, the communication and perception tasks of the terminal are offloaded to the MEC server at the base station end, and the powerful computing power of the MEC server is utilized to help resource-constrained terminals perform complex computing tasks.

[0009] Furthermore, the fluid antenna is integrated into the ISCC system. Specifically, all antennas on the base station side are designed as fluid antennas, while each terminal is equipped with an antenna in a fixed position. The base station not only contains the fluid antenna, but also integrates the MEC server managed by the cloud service provider of the core network. The base station is equipped with multiple transmit / receive antennas, which can provide communication and computing services to multiple terminals and simultaneously perceive the status of the terminals and their surroundings. In order to further optimize the system performance, a communication and perception model based on fluid antennas is also constructed, aiming to enhance the flexibility and adaptability of the system and achieve more efficient communication, perception and computing.

[0010] Furthermore, the mobility of the fluid antenna is fully utilized to optimize the antenna position at the base station. Specifically, based on the results of steps 1 and 2, a delay optimization problem is formulated to minimize the total delay of the system by optimizing the position of the fluid antenna. By dynamically adjusting the physical and electronic properties of the antenna, the antenna can adapt to changes in the vehicle and its surrounding environment in real time. This optimization not only improves the channel quality and reduces signal interference, but also significantly reduces the total delay of the system, thereby achieving efficient communication, perception and computing between the vehicle and the base station.

[0011] The beneficial effects described in the present invention are as follows: the method described in the present invention constructs an efficient ISCC system by offloading the communication and perception tasks of the terminal to the mobile edge computing (MEC) server at the base station end, utilizing the powerful computing power of the server; and integrating the fluid antenna to the base station end, establishing a communication and perception model assisted by the fluid antenna, making full use of the flexibility of the fluid antenna, dynamically adjusting the antenna position at the base station end, thereby adapting to and optimizing the channel conditions, and realizing efficient and low-latency communication, perception and computing between the terminal and the base station. The method described in the present invention is applicable to a variety of complex dynamic environments that require efficient communication, perception and computing integration, and provides important technical support for the development of future intelligent networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is the architecture diagram of the fluid antenna-assisted ISCC system;

[0013] Figure 2 is a flow chart of the method of the present invention;

[0014] Figure 3 It is a comparison chart of the total delay between the method described in the present invention and the baseline solution. DETAILED DESCRIPTION

[0015] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

[0016] like Figure 2 As shown, taking a vehicle scenario as an example, the fluid antenna-assisted communication perception computing integrated method described in the present invention includes the following steps:

[0017] Step 1: Build the ISCC system, transfer the communication and perception task requests of the vehicles in the system to the MEC server for processing, train the tasks, and calculate the delay of the communication and perception tasks;

[0018] Step 2: Integrate the fluid antenna into the base station, propose a fluid antenna-assisted ISCC system, and establish a fluid antenna-assisted communication and perception model.

[0019] Step 3: Construct a delay optimization problem and minimize the total delay of the system by optimizing the fluid antenna position.

[0020] like Figure 1 As shown in the figure, the fluid antenna-assisted ISCC system architecture proposed in the present invention mainly consists of an ISCC base station and N vehicles, wherein the ISCC base station is equipped with M transmitting / receiving fluid antennas, and each vehicle is equipped with a fixed-position antenna. The base station not only includes the fluid antenna, but also integrates the MEC server managed by the cloud service provider of the core network. The antenna sets of the vehicle and the ISCC base station are represented as N = {1, 2, ..., N} and M = {1, 2, ..., M} respectively.

[0021] In step 1, all vehicles run FL-based communication tasks, which are offloaded to the MEC server of the ISCC base station due to the limited computing resources of the vehicles. The FL model is trained on the MEC server using the SGD optimization algorithm. The vehicles offload the datasets of all communication tasks to the MEC server, and then the communication tasks are trained on the MEC server. These communication tasks may include channel estimation, beamforming optimization, power control, and interference management. Therefore, in time slot t, the transmission delay involved in vehicle n offloading the dataset to the MEC server is expressed as:

[0022]

[0023] in represents the dataset size of the communication tasks offloaded by vehicle n to the MEC server, represents the communication rate between vehicle n and the MEC server. Then, in time slot t, the execution time of vehicle n performing communication task training on the MEC server is expressed as:

[0024]

[0025] Among them C M Indicates the number of CPU cycles required by the MEC server to process a single data sample. Indicates the number of iterations of the SGD algorithm on the MEC server, is the ratio of mini-batch sizes on the MEC server, Represents the CPU cycles borrowed from the MEC server by vehicle n performing communication tasks. represents the number of CPU cycles allocated by the MEC server to all vehicle communication tasks. Therefore, the total communication delay of vehicle n is calculated as follows:

[0026]

[0027] In addition to communication, the system must also handle a large number of perception tasks to support functions such as environmental perception and obstacle detection. Performing these perception tasks usually requires huge computing resources that exceed the computing power of a single vehicle. Similar to communication tasks, vehicles can offload all perception datasets to the MEC server at the ISCC base station, and then train the perception tasks on the MEC server. These perception tasks may include object detection, lane detection, traffic sign recognition, and environment mapping. Therefore, the transmission delay required to transmit the perception dataset from vehicle n to the MEC server in time slot t is expressed as:

[0028]

[0029] in represents the dataset size of the perception task offloaded by vehicle n to the MEC server, represents the perception rate between vehicle n and the MEC server. Then, in time slot t, the execution time of vehicle n performing perception task training on the MEC server is expressed as:

[0030]

[0031] in represents the CPU cycles borrowed from the MEC server by vehicle n performing the perception task. represents the number of CPU cycles allocated by the MEC server to all vehicle perception tasks. Therefore, the total perception delay of vehicle n is calculated as follows:

[0032]

[0033] In step 1, the total delay of each vehicle and the entire system is calculated based on the derived communication and perception delays. The total delay of vehicle n in time slot t can be expressed as

[0034]

[0035] Adding up the total delays of all vehicles in the system, the total delay of the ISCC system is obtained as follows:

[0036]

[0037] In step 2, if Figure 1 As shown in the figure, the fluid antenna is integrated into the ISCC system, and all antennas at the base station are designed as fluid antennas, while each vehicle is equipped with an antenna at a fixed position. The base station is equipped with multiple transmit / receive antennas, which can provide communication and computing services for multiple vehicles and simultaneously sense the status of the vehicle and its surrounding environment. The antennas on the ISCC base station are movable in a local area, and the domain is represented as a matrix in a two-dimensional coordinate system, denoted as D r . Each fluid antenna is connected to the RF chain through a flexible cable, which enhances the channel condition between the ISCC base station and the vehicle. The present invention adopts space division multiple access technology in the uplink transmission between the vehicle and the ISCC base station. Therefore, it is assumed that the number of vehicles does not exceed the number of fluid antennas at the ISCC base station, that is, N≤M. The proposed system has T time slots, and the present invention focuses on the performance within time slot t and then extends it to the entire time line. Within each time slot, it is assumed that the antenna position remains unchanged. In time slot t, the position of the mth fluid antenna on the ISCC base station is defined as d m (t) = [x m (t),y m (t)] T ∈D r In order to further optimize the system performance, a communication and perception model based on fluid antenna was constructed.

[0038] In step 2, in time slot t, considering the uplink from the vehicle to the ISCC base station, the communication signal y received by the ISCC base station c (t)∈C N×1 It is expressed as:

[0039]

[0040] Where W c (t) = [w 1,c (t),…,w N,c (t)]∈C M×N represents the receiving combination matrix of ISCC base station, w n,c (t) is the combination vector of the transmission signal of vehicle n. c (t,d)=[h 1,c (t,d),…,h N,c (t,d)]∈C M×N is the multiple access channel matrix from all vehicles to the ISCC base station, where Represents the position vector of the fluid antenna. represents the power scaling matrix, where p n is the transmission power of vehicle n. s(t)=[s1(t),…,s N (t)] T ∈C N×1 is the transmission signal vector of all vehicles, s n (t) represents the transmission signal of vehicle n, and the power has been normalized, that is, E(ss H )=I N .also represents zero-mean additive white Gaussian noise (AWGN), where represents the average noise power, n m,c is the noise received by the mth fluid antenna of the ISCC base station.

[0041] In step 2, the channel vector between each vehicle and the ISCC base station is determined by the propagation environment and the position of the fluid antenna. Given that the range of movement of the fluid antenna on the ISCC base station is small compared to the overall signal propagation distance, it is reasonable to assume that the far-field condition is satisfied between the vehicle and the ISCC base station. Therefore, for each vehicle, the angle of arrival (AoA) and the magnitude of the complex path coefficients at different fluid antenna positions in the multipath channel remain unchanged, which means that only the phase changes within the reception area. Set the total number of channel paths received by the ISCC base station from vehicle n to L n ={1,2,…,L n}, using the desired channel model based on the field, the communication channel vector between vehicle n and the ISCC base station is expressed as

[0042]

[0043] in represents the field response matrix of the ISCC base station, represents the field response vector between vehicle n and the mth fluid antenna receiving path of the ISCC base station. n,c (t,d m ), ρ n,l (t,d m ) is represented by the lth path of vehicle n from the mth fluid antenna of the ISCC base station to the reference point d0 = [0,0] T The signal propagation phase difference is expressed as follows

[0044] ρ n,l (t,d m )=x m (t)sinθ n,l cosφ n,l +y m(t)cosθ n,l +v n tcos(θ n,l -θ n,v ), (11)

[0045] where θ n,l and φ n,l denote the elevation angle and azimuth angle of the lth path between vehicle n and the ISCC base station, v n represents the speed of vehicle n, θ n,v represents the moving direction angle of vehicle n. The multipath response coefficient vector from vehicle n to the reference point of the receiving area is recorded as In time slot t, the communication rate from vehicle n to the MEC server is expressed as:

[0046]

[0047] In step 2, in the proposed fluid antenna-assisted ISCC vehicle system, the same antenna array is used to simultaneously send and receive communication and radar signals at the ISCC base station. The ISCC base station senses the vehicle's status, such as position, speed, and road conditions, by analyzing the components of the communication signal reflected from the vehicle. This approach effectively utilizes the reflection of the communication signal to achieve the radar sensing function, thereby realizing the dual function of the signal. Therefore, in time slot t, the sensing signal y received by the ISCC base station s (t)∈C N×1 It is expressed as:

[0048]

[0049] Where W s (t) = [w 1,s (t),…,w N,s (t)]∈C M×N represents the ISCC base station receiving beamforming matrix for sensing tasks, where w n,s (t) is the beamforming vector received by the ISCC base station from vehicle n. represents the AWGN received by the ISCC base station, whose mean is zero and the covariance matrix is in is the perceived noise power. H s (t,d)=[h 1,s (t,d),…,h N,s (t,d)]∈C M×N is the target response matrix, describing the reflection paths from all vehicles to the ISCC base station. represents the perceived channel vector from vehicle n to the ISCC base station, including path loss and phase change caused by vehicle motion. The matrix Also known as the field response matrix, it captures the characteristics of the signal reflected from vehicle n to the ISCC base station affected by the physical environment, and each column corresponds to a different antenna element on the ISCC base station. In time slot t, the perceived channel characteristics from vehicle n to the mth fluid antenna of the ISCC base station, including path loss and phase offset, are described as follows:

[0050]

[0051] in is the reflection coefficient α of vehicle n on the lth path n,l and time delay τ n,l In time slot t, the perceived rate from vehicle n to the MEC server is expressed as:

[0052]

[0053] In step 3, based on the model constructed in steps 1 and 2, a delay optimization problem is formulated, which aims to minimize the total delay of the system by optimizing the position of the fluid antenna. In time slot t, the specific optimization problem can be expressed as

[0054]

[0055] Among them, constraint C1 means that the position of the fluid antenna must be located in the predefined area D r Constraint C2 ensures that the distance between any two fluid antennas is at least d0. Constraint C3 stipulates the communication delay of vehicle n Must not exceed threshold Similarly, constraint C4 specifies the perception delay of vehicle n Must not exceed threshold The optimization problem aims to dynamically adjust the physical and electronic properties of the fluid antenna so that the antenna can adapt to the changes of the vehicle and its surrounding environment in real time, thereby improving the channel quality, reducing signal interference, and significantly reducing the total delay of the ISCC system, and realizing efficient communication, perception and calculation between the vehicle and the ISCC base station. It is worth noting that the method of the present invention does not use a specific optimization algorithm in the article to solve the proposed problem, but focuses on the proposal and application of the ISCC method assisted by the fluid antenna.

[0056] In step 3, the method of the present invention is not limited to optimizing the antenna position, but can also be extended to a comprehensive optimization strategy for jointly optimizing antenna position, computing resource allocation, receiving beamforming matrix and other dimensions. Through this joint optimization, not only can the advantages of fluid antennas be fully utilized, but also higher resource utilization can be achieved in a complex and changeable network environment. The method of the present invention is not only suitable for the needs of efficient data processing and information transmission in vehicle network scenarios, but also can provide technical support for other telepathic computing scenarios that require high flexibility and high performance, and has broad application prospects.

[0057] Figure 3 is a comparison chart of the total latency of the system described in the present invention and the baseline solution. In baseline solution 1, all vehicles never offload communication and perception tasks to the MEC server, and all antennas on the ISCC base station are fixed at the origin. Baseline solution 2 allows vehicles to offload all communication and perception tasks to the MEC server, but the antenna positions on the ISCC base station are also fixed at the origin. Figure 3 In , the number of vehicles is set to 3 and the number of channel paths per vehicle is set to 3. Observe Figure 3 , it is found that the total delay of baseline scheme 1 is the highest, mainly because the vehicle relies only on local calculations, resulting in longer delays. In contrast, the total delay of the ISCC system assisted by the fluid antenna described in the present invention is lower than that of baseline scheme 2. The numerical results also show that the total delay of each scheme decreases with the increase in the number of antennas. Compared with baseline schemes 1 and 2 with fixed antenna positions, by optimizing the positions of all fluid antennas, the system described in the present invention improves communication, perception and computing performance, thereby greatly reducing the total system delay.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to be a further limitation of the present invention. All equivalent changes made using the contents of the present specification and drawings are within the protection scope of the present invention.

Claims

1. A fluid antenna-assisted communication perception computing integrated method, characterized in that: The following steps are involved: Step 1: Build an ISCC system, including a base station composed of an MEC server and N terminals. Transfer the communication and perception task requests of the terminals to the MEC server for processing, train the tasks, and calculate the delay of communication and perception tasks. Step 2: Integrate the fluid antenna into the base station, propose a fluid antenna-assisted ISCC system, and establish a fluid antenna-assisted communication and perception model. Step 3: Construct a delay optimization problem and minimize the total delay of the system by optimizing the fluid antenna position.

2. According to claim 1, a fluid antenna-assisted communication perception computing integrated method is characterized in that: In step 1, the set of terminals is represented as Assuming there are T time slots, in the time slot t∈{1,2,…,T}, the transmission delay of the terminal n∈N offloading the communication task to the MEC server is expressed as: in, represents the data set size of the communication task offloaded from terminal n to the MEC server, Indicates the communication rate between terminal n and MEC server; In time slot t, the execution time of terminal n performing communication task training on the MEC server is expressed as: Among them, C M Indicates the number of CPU cycles required by the MEC server to process a single data sample. Indicates the number of iterations of the SGD algorithm on the MEC server, is the ratio of mini-batch sizes on the MEC server, represents the CPU cycles borrowed from the MEC server by the terminal n performing the communication task; vector Represents the number of CPU cycles allocated by the MEC server to all terminal communication tasks; Therefore, the total communication delay of terminal n is calculated as follows:

3. The method for integrating communication, perception and computing assisted by a fluid antenna according to claim 2, characterized in that: The transmission delay of terminal n∈N to offload the sensing task to the MEC server in time slot t is expressed as: in, represents the dataset size of the perception task offloaded from terminal n to the MEC server, Indicates the perceived rate between terminal n and the MEC server; In time slot t, the execution time of terminal n performing perception task training on the MEC server is expressed as: in, It represents the CPU cycles borrowed from the MEC server by the terminal n that performs the sensing task; vector Represents the number of CPU cycles allocated by the MEC server to all terminal perception tasks; therefore, the total perception delay of terminal n is calculated as follows:

4. The method for integrating communication, perception and computing assisted by a fluid antenna according to claim 3, characterized in that: Based on the derived communication and perception delays, calculate the overall delay for each terminal and the entire system; In time slot t, the total delay of terminal n is expressed as: Adding up the total delays of all terminals in the system, the total delay of the ISCC system is as follows:

5. The method for integrating communication, perception and computing assisted by a fluid antenna according to claim 4, characterized in that: In step 2, it is assumed that the fluid antenna set is represented by M = {1, 2, ..., M}, and the fluid antenna is movable in the local area. The local area is represented as a matrix in the two-dimensional coordinate system, denoted by D r ; The position of the m∈Mth fluid antenna is defined as d m (t) = [x m (t),y m (t)] T ∈D r ; Build a communication and perception model based on fluid antenna.

6. The method for integrating communication, perception and computing assisted by a fluid antenna according to claim 5, characterized in that: Construct a communication model based on fluid antenna, specifically: In time slot t, the communication signal y received by the base station c (t)∈C N×1 It is expressed as: Where W c (t) = [w 1,c (t),...,w N,c (t)]∈C M×N represents the receiving combination matrix of ISCC base station, w n,c (t) is the combination vector of the transmission signal of terminal n; H c (t,d(t))=[h 1,c (t,d(t)),…,h N,c (t,d(t))]∈C M×N is the multiple access channel matrix from all terminals to the ISCC base station, where represents the position vector of the fluid antenna; represents the power scaling matrix, where p n is the transmission power of terminal n; s(t)=[s1(t),…,s N (t)] T ∈C N×1 is the transmitted signal vector of all terminals, s n (t) represents the transmitted signal of terminal n, and the power has been normalized, that is, E(ss H )=I N ; represents zero-mean additive white Gaussian noise, where represents the average noise power, n m,c is the noise received by the mth fluid antenna of the ISCC base station; Assuming that the far-field condition is satisfied between the terminal and the base station, the total number of channel paths received by the base station from terminal n is set to L n ={1,2,…,L n }, using the channel model based on field response, the communication channel vector between terminal n and the base station is expressed as: in represents the field response matrix of the base station, represents the field response vector between terminal n and the mth fluid antenna receiving path of the base station; n,c (t,d m ), ρ n,l (t,d m ) is represented by the mth fluid antenna of the base station of the lth path of terminal n to the reference point d0 = [0,0] T The signal propagation phase difference is expressed as follows: r n,l (t,d m )=x m (t)sinθ n,l cosφ n,l +y m (t)cosθ n,l +v n tcos(θ n,l -θ n,v ), (11) where θ n,l and φ n,l denote the elevation angle and azimuth angle of the lth path between terminal n and the base station, respectively, v n represents the speed of terminal n, θ n,v represents the moving direction angle of terminal n; the multipath response coefficient vector from terminal n to the reference point of the receiving area is recorded as In time slot t, the communication rate from terminal n to the MEC server is expressed as:

7. The fluid antenna-assisted communication, perception and computing integrated method according to claim 6, characterized in that: Construct a perception model based on fluid antenna, specifically: In time slot t, the sensing signal y received by the base station s (t)∈C N×1 It is expressed as: Where W s (t) = [w 1,s (t),…,w N,s (t)]∈C M×N represents the base station receiving beamforming matrix used for sensing tasks, where w n,s (t) is the beamforming vector received by the base station from terminal n; It represents the data received by the base station, with a mean of zero and a covariance matrix of σ s 2 I M ,in is the perceived noise power; H s (t,d)=[h 1,s (t,d),…,h N,s (t,d)]∈C M×N is the target response matrix, describing the reflection paths from all terminals to the base station; represents the perceived channel vector from terminal n to the base station, including path loss and phase change caused by terminal motion; matrix Also called the field response matrix, it captures the characteristics of the signal reflected by terminal n to the base station affected by the physical environment. Each column corresponds to the field response vector of a different fluid antenna on the base station; In time slot t, the perceived channel characteristics from terminal n to the mth fluid antenna of the base station, including path loss and phase offset, are described as follows: in is the reflection coefficient α of terminal n on the lth path n,l and time delay τ n,l The combined impact of In time slot t, the perceived rate from terminal n to the MEC server is expressed as:

8. The fluid antenna-assisted communication, perception and computing integrated method according to claim 7, characterized in that: Step 3 is as follows: In time slot t, the specific optimization problem is expressed as: Among them, constraint C1 means that the position of the fluid antenna must be located in the predefined area D r Constraint C2 ensures that the distance between any two fluid antennas is at least d0; Constraint C3 stipulates the communication delay of terminal n Must not exceed threshold Constraint C4 specifies the perceived delay of terminal n Must not exceed threshold

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