AP duplex mode optimization method for multi-station cooperative communication and inductance integrated system

Abstract

The invention discloses an AP (Access Point) duplex mode optimization method for a multi-station cooperative communication and inductance integrated system. The method comprises the following steps: initializing two-dimensional position distribution of APs; the method comprises the following steps: dividing time slots in a system based on network-assisted full duplex, wherein each time slot comprises a pilot frequency training stage and a communication sensing stage; constructing an uplink pilot frequency receiving signal model through uplink pilot frequency training, and constructing a downlink pilot frequency receiving signal model through downlink pilot frequency training; in a communication sensing stage, uplink and downlink communication and rate closed expressions are deduced from a communication perspective, and a target position estimation rate expression is deduced from a sensing perspective; a multi-target optimization problem with maximization of communication sum rate and perception position estimation rate as targets is established, and duplex mode optimization is realized by optimizing an AP mode distribution strategy. According to the method, the problem of conflict between communication and sensing performance is solved, and the sensing precision is improved while the communication quality is guaranteed.

Description

Technical Field

[0001] The present invention relates to an AP duplex mode optimization method for a multi-station cooperative interawareness integrated system, and belongs to the technical field of mobile communications. Background Art

[0002] As mobile communication technology advances toward the era of intelligent connectivity, mobile communication networks are being endowed with more capabilities. Amid the emergence of new intelligent applications such as smart cities, intelligent transportation, and unmanned factories, mobile communication systems, as widely deployed information infrastructure, will continue to integrate sensing functions based on integrated communication and perception technologies, gradually evolving into a unified information infrastructure integrating both perception and communication capabilities. Combining cellular-free networks with technologies such as massive MIMO, network-assisted full-duplex, and ultra-dense networking not only enables high-speed information transmission but also significantly enhances the system's perception accuracy.

[0003] Multi-station collaborative sensing technology uses multiple distributed nodes, some of which transmit signals while others receive the echo signals reflected from the sensing target. This technology achieves higher sensing accuracy and wider coverage, while avoiding full-duplex interference. Network-assisted full-duplex cell-free massive MIMO technology densely distributes multiple distributed antennas, each performing uplink or downlink communication, to support simultaneous access for uplink and downlink users, providing more efficient, lower-latency, and more reliable communication services.

[0004] The multi-station collaborative telepresence integrated system enables high-performance perception while delivering high-quality communication services, fully leveraging the advantages of mobile communication networks. The AP's uplink and downlink mode selection is closely related to both communication and perception performance. Choosing the right AP mode can provide more perception information and higher spatial diversity gain, potentially improving both communication and perception performance. Summary of the Invention

[0005] Technical problem: The present invention aims to improve the interawareness capability of a multi-station collaborative interawareness integrated system and optimize the AP uplink and downlink modes, and proposes an AP duplex mode optimization method for a multi-station collaborative interawareness integrated system.

[0006] Technical Solution: To achieve the above-mentioned purpose, the present invention provides an AP duplex mode optimization method for a multi-station collaborative interawareness integrated system, comprising the following steps:

[0007] Step S1, simulation preparation, initializing the two-dimensional position distribution of APs in the simulation;

[0008] Step S2: In the NAFD-based multi-station cooperative telepathy integrated system, the communication perception process can be divided into multiple time slots. Each time slot includes a pilot training phase and a communication perception phase. One time slot is equivalent to a complete communication perception process, including channel estimation, cross-link interference cancellation, uplink and downlink communication, and uplink and downlink perception. First, in the uplink pilot training phase, all users send uplink pilot signals to the AP. The AP performs uplink channel estimation based on the received pilot signals. The uplink channel estimation obtains the channel between the user and the AP. After receiving the pilot signal, the AP constructs a signal model as a mathematical expression of the uplink pilot received signal.

[0009] In step S3, during the downlink pilot training phase, all downlink APs transmit downlink pilot signals. Downlink users can estimate the channel between themselves and the downlink AP based on the received pilot signals. The channel estimation process is similar to the uplink pilot transmission phase and is not described here. The uplink AP can estimate the channel between APs to eliminate cross-link interference and improve the system communication rate. The uplink AP transmits the received pilot signal to the CPU via the backhaul link, and a mathematical expression of the downlink pilot signal received by the CPU is modeled.

[0010] Step S4: In the communication perception phase, from a communication perspective, the downlink signal sent by the mth AP can be modeled as the downlink AP transmit signal; the signal received by the lth user can be modeled as the downlink user receive signal; and the receive signal of the mth AP can be modeled as the uplink AP receive signal. Through rigorous mathematical calculations and analysis, closed-form expressions for the corresponding uplink and downlink communication rates can be obtained.

[0011] Step S5: In the communication perception phase, from the perspective of perception, for the t-th target, a mathematical expression of its position estimation rate can be obtained;

[0012] In step S6, a multi-objective optimization problem is established based on the expressions of uplink and downlink communication rates and target position estimation rates derived in steps S4 and S5; the communication rate and the perceived position estimation rate are maximized by finding the best AP mode allocation strategy.

[0013] in,

[0014] The mathematical expression of the uplink pilot received signal in step S2 is:

[0015]

[0016] In formula (1), Y up,m represents the pilot signal received at the mth AP, represents the pilot signal sent by the kth uplink user, p pis the normalized signal-to-noise ratio of the pilot signal, represents additive white Gaussian noise, h mk is the aggregate channel from the mth AP to the kth user, N represents the number of antennas, K represents the number of users, τ up Indicates the length of the uplink pilot symbol, Indicates the phase of the pilot signal sent by the kth uplink user.

[0017] The mathematical expression of the downlink pilot reception signal in step S3 is:

[0018]

[0019] In formula (2), Y dp Represents the downlink pilot signal received by the CPU, represents the pilot signal sent by the mth AP, represents additive white Gaussian noise, Indicates the channel between APs, M ul Indicates the number of uplink APs, M dl Indicates the number of downlink APs, τ dp Indicates the length of the downlink pilot symbol, represents the phase of the pilot signal sent by the mth downlink AP, represents the downlink noise power of the system, and I represents the identity matrix.

[0020] The mathematical expression of the downlink signal sent by the mth AP in step S4 is:

[0021]

[0022] In formula (3), p dl,k represents the communication power of the kth downlink user, m represents the beamforming matrix of the communication signal, s dl,k For the communication signal transmitted to the kth downlink user, p s,t represents the sensing signal power allocated to the t-th sensing target, represents the beamforming matrix of the sensing signal, It represents the sensing signal sent by the mth AP to sense the tth target, satisfying K dl represents the number of downlink users, and T represents the number of sensing targets.

[0023] The mathematical expression of the downlink user received signal received by the lth user in step S4 is:

[0024]

[0025] In formula (4), xd,m represents the downlink transmission binary allocation vector, h ml Indicates the channel between the downlink user and the AP, s ul,u Indicates the uplink data signal of the uplink user, p ul,u represents the u-th uplink user data transmission power, represents the downlink additive white Gaussian noise interference, M ul Indicates the number of uplink APs, x m Indicates the downlink signal sent by the mth AP, M represents the total number of APs, represents the downlink noise power of the system, h I,u,U represents the aggregate channel from the u-th uplink user to the k-th downlink user.

[0026] The mathematical expression of the uplink signal received by the mth AP in step S4 is:

[0027]

[0028] In formula (5), x u,m represents the mth item of the uplink transmission binary allocation vector, h mk Indicates the channel between the uplink user and the AP, s ul,k Indicates the uplink data signal of the uplink user, represents the uplink additive white Gaussian noise interference, K ul Indicates the number of uplink users, x d,i represents the i-th item of the downlink transmission binary allocation vector, represents the aggregate channel from the i-th AP to the m-th AP, Indicates the uplink noise power of the system.

[0029] The closed-form expression of the uplink communication sum rate in step S4 is:

[0030]

[0031] In formula (6), is the SINR of the uplink user received signal, τ dp represents the downlink pilot symbol length, τ up represents the uplink pilot symbol length, and τ represents the coherent block symbol length.

[0032] The closed expression of the downlink communication sum rate in step S4 is:

[0033]

[0034] In formula (7), is the SINR of the downlink user received signal.

[0035] The mathematical expression of the position estimation rate of the t-th target in step S5 is:

[0036]

[0037] In formula (8), CRLB loc,t is the CRLB of the position estimate, represents the uncertainty of the target position.

[0038] The first optimization goal of the multi-objective optimization problem in step S6 is to maximize the communication sum rate, which is expressed as

[0039]

[0040] In formula (9), P D Indicates the downlink AP power constraint, x u represents the upstream binary allocation vector, x d represents the downstream binary allocation vector, represents the downlink traversal achievable rate of the lth downlink user, The uplink ergodic achievable rate of the u-th uplink user, p dl,l Indicates the data power sent by the downlink AP to the lth user, represents the beamforming matrix of the communication signal, p s,t represents the sensing signal power allocated to the t-th sensing target, represents the beamforming matrix of the sensing signal, P U Indicates the maximum power constraint of uplink users, I M Represents the unit matrix.

[0041] The second optimization goal of the multi-objective optimization problem in step S6 is to maximize the perceptual performance, which is expressed as

[0042]

[0043] represents the position estimation rate of the t-th target;

[0044] The multi-objective optimization problem in step S6 is expressed as

[0045]

[0046] Beneficial effect: The AP mode optimization method provided by the present invention based on maximizing communication speed and perception position estimation rate can provide better perception performance while ensuring communication quality, thereby further improving system performance.

[0047] The AP mode optimization method of the present invention based on maximizing communication speed and perception position estimation rate can enable an AP with both perception and communication performance to perform better uplink and downlink mode configuration, and determine the spatial position of the perception target more accurately while ensuring communication quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a step diagram of the system access point mode optimization method for multi-station collaborative interawareness integration of the present invention;

[0049] Figure 2 This is a schematic diagram of the placement of APs without a cellular architecture according to the present invention;

[0050] Figure 3 The cumulative probability distribution diagram of the objective function values ​​of the algorithm proposed in this invention and the benchmark solution;

[0051] Figure 4 The present invention obtains the communication performance and perception performance corresponding to the AP mode by proposing an algorithm. DETAILED DESCRIPTION

[0052] To further understand the content of the present invention, a method for optimizing access point modes for a multi-station collaborative synaesthesia integrated system of the present invention is described in detail with reference to the accompanying drawings and specific embodiments.

[0053] like Figure 1 As shown, the steps of the system access point mode optimization method for multi-station collaborative interawareness integration of the present invention are:

[0054] Step 1: Simulation preparation: Initialize the two-dimensional position distribution of APs in the simulation, such as Figure 2 The two-dimensional coordinates are (0, -200), (100, -173), (173, -100), (200, 0), (173, 100), (100, 173), (0, 200), (-100, 173), (-173, 100), (-200, 0), (-173, -100), (-100, -173), and the unit is meter (m).

[0055] Step 2: In the NAFD-based multi-station collaborative telepathy integrated system, the communication perception process can be divided into multiple time slots. Each time slot includes a pilot training phase and a communication perception phase. One time slot is equivalent to a complete communication perception process, including channel estimation, cross-link interference cancellation, uplink and downlink communication, and uplink and downlink perception. First, in the uplink pilot training phase, all users send uplink pilot signals to the AP. The AP performs uplink channel estimation based on the received pilot signals. The uplink channel estimation obtains the channel between the user and the AP. After receiving the pilot signal, the AP constructs a signal model as a mathematical expression of the uplink pilot received signal.

[0056]

[0057] In formula (1), Y up,m represents the pilot signal received at the mth AP, represents the pilot signal sent by the kth uplink user, p p is the normalized signal-to-noise ratio of the pilot signal, represents additive white Gaussian noise, h mk is the aggregate channel from the mth AP to the kth user.

[0058] Step 3: During the downlink pilot training phase, all downlink APs transmit downlink pilot signals. Downlink users can estimate the channel between themselves and the downlink AP based on the received pilot signals. The channel estimation process is similar to the uplink pilot transmission phase and is not detailed here. Uplink APs can estimate the channel between APs to eliminate cross-link interference and improve the system communication rate. The uplink APs transmit the received pilot signals to the CPU via the backhaul link, and a mathematical expression for the downlink pilot signal received by the CPU is modeled.

[0059]

[0060] In formula (2), Y dp Represents the downlink pilot signal received by the CPU, represents the pilot signal sent by the mth AP, represents additive white Gaussian noise, Indicates the channel between APs.

[0061] Step 4: In the communication perception phase, from a communication perspective, the downlink signal sent by the mth AP can be modeled as the downlink AP transmit signal; the signal received by the lth user can be modeled as the downlink user receive signal; and the receive signal of the mth AP can be modeled as the uplink AP receive signal. Through rigorous mathematical calculations and analysis, closed-form expressions for the corresponding uplink and downlink communication rates can be obtained.

[0062] The closed-loop expression for the uplink communication sum rate is:

[0063]

[0064] In formula (3), is the SINR of the uplink user received signal.

[0065] The closed-loop expression for the downlink communication sum rate is:

[0066]

[0067] In formula (4), is the SINR of the downlink user received signal.

[0068] Step 5: In the communication perception phase, from the perspective of perception, for the t-th target, the mathematical expression of its position estimation rate can be obtained;

[0069]

[0070] In formula (5), CRLB loc,t CRLB for position estimation.

[0071] In step 6, a multi-objective optimization problem is established based on the expressions of uplink and downlink communication rates and target position estimation rates derived in steps 4 and 5. The optimal AP mode allocation strategy is found to maximize the communication rate and perception position estimation rate.

[0072] The first optimization goal of the multi-objective optimization problem is to maximize the communication sum rate, which is expressed as

[0073]

[0074] In formula (6), P D Indicates the downlink AP power constraint.

[0075] The second optimization goal of the multi-objective optimization problem is to maximize the perceptual performance, which is expressed as

[0076]

[0077] The multi-objective optimization problem is expressed as

[0078]

[0079] In summary, this invention employs a multi-objective optimization approach to rationally configure the uplink and downlink operating modes of access points in a multi-station collaborative telepathy system. Using communication rate and target perception position estimation rate as performance indicators for communication and perception, respectively, this improves the system's perception accuracy while maintaining the required communication rate, thus enhancing resource allocation and possessing practical significance.

[0080] Anything not described in detail in the present invention is well known to those skilled in the art.

[0081] The above is a schematic description of the present invention and its embodiments, which is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs a structure and embodiment similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A method for optimizing AP duplex mode for a multi-station collaborative interawareness integrated system, characterized in that: The method comprises the following steps: Step S1, simulation preparation, initializing the two-dimensional position distribution of APs in the simulation; Step S2: In the NAFD-based multi-station cooperative synaesthesia integrated system, the communication perception process is divided into multiple time slots, each of which includes a pilot training phase and a communication perception phase; During the uplink pilot training phase, the user sends an uplink pilot signal to the AP, and the AP builds an uplink pilot reception signal model. Step S3: In the downlink pilot training phase, the downlink AP sends a pilot signal, the uplink AP estimates the inter-AP channel and transmits it to the CPU to build a downlink pilot reception signal model; Step S4: In the communication perception phase, the AP downlink signal, user received signal, and AP uplink received signal are modeled from a communication perspective, and closed-form expressions for uplink and downlink communication and rates are derived. Step S5, in the communication perception stage, deriving a mathematical expression of the target position estimation rate from the perception perspective; Step S6, based on the communication sum rate expression of S4 and the position estimation rate expression of S5, a multi-objective optimization problem of maximizing the communication sum rate and the perception position estimation rate is established, and solved by optimizing the AP mode allocation strategy.

2. The method according to claim 1, characterized in that The uplink pilot reception signal model in step S2 is: In formula (1), Y up,m represents the pilot signal received at the mth AP, represents the pilot signal sent by the kth uplink user, p p is the normalized signal-to-noise ratio of the pilot signal, represents additive white Gaussian noise, h mk is the aggregate channel from the mth AP to the kth user, N represents the number of antennas, K represents the number of users, τ up Indicates the length of the uplink pilot symbol, Indicates the phase of the pilot signal sent by the kth uplink user.

3. The method according to claim 1, characterized in that The downlink pilot reception signal model in step S3 is: In formula (2), Y dp Represents the downlink pilot signal received by the CPU, represents the pilot signal sent by the mth AP, represents additive white Gaussian noise, Indicates the channel between APs, M ul Indicates the number of uplink APs, M dl Indicates the number of downlink APs, τ dp Indicates the length of the downlink pilot symbol, represents the phase of the pilot signal sent by the mth downlink AP, represents the downlink noise power of the system, and I represents the identity matrix.

4. The method according to claim 1, wherein The downlink signal model sent by the mth AP in step S4 is: In formula (3), p dl,k To represent the communication power of the kth downlink user, represents the beamforming matrix of the communication signal, s dl,k For the communication signal transmitted to the kth downlink user, p s,t represents the sensing signal power allocated to the t-th sensing target, represents the beamforming matrix of the sensing signal, It represents the sensing signal sent by the mth AP to sense the tth target, satisfying K dl represents the number of downlink users, and T represents the number of sensing targets.

5. The method according to claim 1, wherein The downlink user reception signal model received by the lth user in step S4 is: In formula (4), x d,m represents the downlink transmission binary allocation vector, h ml Indicates the channel between the downlink user and the AP, s ul,u Indicates the uplink data signal of the uplink user, p ul,u represents the u-th uplink user data transmission power, represents the downlink additive white Gaussian noise interference, M ul Indicates the number of uplink APs, x m Indicates the downlink signal sent by the mth AP, M represents the total number of APs, represents the downlink noise power of the system, h I,u,l represents the aggregate channel from the u-th uplink user to the k-th downlink user.

6. The method according to claim 1, characterized in that The uplink reception signal model received by the mth AP in step S4 is: In formula (5), x u,m represents the mth item of the uplink transmission binary allocation vector, h mk Indicates the channel between the uplink user and the AP, s ul,k Indicates the uplink data signal of the uplink user, represents the uplink additive white Gaussian noise interference, K ul Indicates the number of uplink users, x d,i represents the i-th item of the downlink transmission binary allocation vector, represents the aggregate channel from the i-th AP to the m-th AP, Indicates the uplink noise power of the system.

7. The method according to claim 1, characterized in that The closed-form expression of the uplink communication sum rate in step S4 is: In formula (6), is the SINR of the uplink user received signal, τ dp represents the downlink pilot symbol length, τ up represents the uplink pilot symbol length, and τ represents the coherent block symbol length.

8. The method according to claim 1, characterized in that The closed-form expression of the downlink communication sum rate in step S4 is: In formula (7), is the SINR of the downlink user received signal, τ dp represents the downlink pilot symbol length, τ up represents the uplink pilot symbol length, and τ represents the coherent block symbol length.

9. The method according to claim 1, characterized in that The mathematical expression of the position estimation rate of the t-th target in step S5 is: In formula (9), CRLB loc,t is the CRLB of the position estimate, represents the uncertainty of the target position, τ dp represents the downlink pilot symbol length, τ up represents the uplink pilot symbol length, and τ represents the coherent block symbol length.

10. The method according to claim 1, characterized in that The first optimization goal of the multi-objective optimization problem in step S6 is to maximize the communication sum rate, which is expressed as In formula (9), P D Indicates the downlink AP power constraint, x u represents the upstream binary allocation vector, x d represents the downstream binary allocation vector, represents the downlink traversal achievable rate of the lth downlink user, The uplink ergodic achievable rate of the u-th uplink user, p dl,l Indicates the data power sent by the downlink AP to the lth user, represents the beamforming matrix of the communication signal, p s,t represents the sensing signal power allocated to the t-th sensing target, represents the beamforming matrix of the sensing signal, P U Indicates the maximum power constraint of uplink users, I M represents the unit matrix; The second optimization goal of the multi-objective optimization problem in step S6 is to maximize the perceptual performance, which is expressed as represents the position estimation rate of the t-th target; The multi-objective optimization problem in step S6 is expressed as

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