Carrier aggregation-based communication and sensing integrated speed and distance measurement method

Abstract

The invention discloses a carrier aggregation-based flux-sensing integrated speed and distance measurement method, and the method comprises the steps: carrying out the precise positioning of the fusion information of target positioning through employing high and low frequency OFDM flux-sensing integrated signals; the objective of the invention is to solve the problems of limited sensing performance, low estimation precision in a low signal-to-noise ratio (SNR) scene, high ambiguity of a high frequency band and high calculation complexity caused by scarcity of spectrum resources of an existing ISAC system. Specifically, the method comprises the following steps: firstly, designing a two-dimensional lattice point pilot frequency structure, and setting a pilot frequency in a time-frequency domain according to a high and low frequency subcarrier interval multiple so as to realize effective energy accumulation and noise suppression; and a coarse positioning-fine estimation framework is provided, target coarse positioning is completed by using a low-frequency-band wide coverage characteristic, and target parameter fine estimation is realized by combining a high-frequency-band high-resolution characteristic. According to the scheme, the distance measurement root-mean-square error is reduced by more than one order of magnitude in a low signal-to-noise ratio scene, and the speed measurement root-mean-square error is reduced by 50%; in addition, based on a multi-band carrier aggregation technology, the ranging and speed measurement performance in a dynamic target scene is superior to that of a single band.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of communication sensing integration technology (Integrated Sensing And Communications, ISAC) in wireless communication, and particularly relates to a carrier aggregation-based integrated sensing and communication speed and distance measurement method. BACKGROUND

[0002] With the rapid development of 6G wireless networks, emerging applications such as autonomous driving, smart cities, and industrial Internet of Things have put forward the demand for integration and intelligence of wireless services, which not only requires high-throughput communication to support data-intensive services, but also requires high-precision sensing to realize environmental cognition and dynamic decision-making. Communication sensing integration (ISAC) technology, with the inherent similarity of communication and sensing systems in transceivers, signal processing chains, and channel characteristics, realizes the "double use" of wireless resources, effectively reduces hardware complexity and spectrum fragmentation, and becomes a key paradigm to meet the stringent requirements of 6G scenarios.

[0003] Orthogonal Frequency Division Multiplexing (OFDM) has become the mainstream waveform of ISAC systems due to its high spectrum utilization and good compatibility with existing 5G infrastructure. However, ISAC systems face the problem of spectrum resource scarcity and fragmentation, which severely restricts the sensing performance. Carrier aggregation (CA) technology can expand the effective bandwidth by aggregating different frequency bands (including in-band continuous, in-band non-continuous, and inter-band non-continuous frequency bands), providing "virtual frequency bands" for ISAC systems to improve sensing performance, but existing CA-ISAC related research still has shortcomings:

[0004] 1. Defects in pilot design: Existing solutions mostly use traditional pilot structures such as comb-shaped and block-shaped, which are easily disturbed by noise in low signal-to-noise ratio (SNR) scenarios, resulting in poor sensing performance. Some pilot signal processing methods based on compressed sensing (CS) can improve performance, but the computational complexity is extremely high, making it difficult to apply in engineering.

[0005] 2. Inadequate use of frequency bands: Existing system models only consider single distance or speed estimation scenarios, and do not fully utilize the heterogeneity characteristics of aggregated frequency bands (low-frequency bands cover a wide range, and high-frequency bands have high resolution), which cannot balance sensing coverage and estimation accuracy, and there is a trade-off between coverage and accuracy.

[0006] 3. Limited estimation performance: In low SNR scenarios, the root mean square error (RMSE) of distance and speed estimation of single frequency band or traditional pilot schemes is large, which cannot meet the demand for high-precision sensing of 6G.

[0007] Therefore, there is an urgent need for a CA-ISAC distance and velocity estimation method that can optimize pilot structure, make full use of multi-band complementary characteristics, and balance coverage and accuracy. Summary of the Invention

[0008] The purpose of this invention is to overcome the problems of low SNR performance, difficulty in balancing coverage and accuracy, and high computational complexity in the existing CA-ISAC system for distance and velocity estimation. It provides an enhanced method based on lattice pilot design and coarse positioning-fine estimation (CLFE) framework to achieve joint distance and velocity estimation with wide coverage, high accuracy, and low complexity.

[0009] To achieve the above objectives, the technical solution of the present invention is as follows:

[0010] 1. System Model Construction:

[0011] The CA-ISAC system includes a multi-band base station (BS) and a dynamic vehicle target. The base station transmits signals to the vehicle, and after reflection from the vehicle, the base station processes the received echo signals. The transmitted signals are aggregated from two discontinuous frequency bands: the low-frequency band (LF, 5.9 GHz) and the high-frequency band (HF, 24 GHz); the definition of the first... (1 represents HF; 2 represents LF) Band parameters: carrier frequency Subcarrier spacing Number of subcarriers Number of OFDM symbols in one frame Total length of OFDM symbol (Including the cyclic prefix CP).

[0012] 2. Lattice Pilot Design and Signal Processing:

[0013] Based on the high and low frequency band subcarrier spacing ratio ( (For pilot insertion intervals), the system transmits signals in the time domain (OFDM symbol dimension) and frequency domain (subcarrier dimension) according to intervals. Arrange pilots and define the pilot position matrix.

[0014]

[0015] in , , For subcarrier index, OFDM symbol index; time-domain signal of a single frequency band. With the system's total transmitted signal They are respectively:

[0016]

[0017]

[0018] wherein is the th subcarrier in the th OFDM symbol in the th frequency band, is the CP length of the th frequency band. The CA-ISAC system receives the time domain echo signal of the th frequency band, and obtains the frequency domain echo signal and the channel frequency response by Fourier transform. The channel frequency response after extracting the pilot is multiplied by .

[0019] Inverse fast Fourier transform (IFFT) is performed on each pilot symbol index ( is the pilot symbol index set, ), and the modulus value is averaged to obtain the range spectrum of the th frequency band :

[0020]

[0021] wherein is the range unit index;

[0022] Fast Fourier transform (FFT) is performed on each pilot subcarrier index ( is the pilot subcarrier index set, ), and the modulus value is averaged to obtain the Doppler spectrum of the th frequency band :

[0023]

[0024] wherein is the velocity unit index;

[0025] Finally, weights (HF frequency band weight) and (LF frequency band weight) are introduced to fuse the range spectrum and the Doppler spectrum of the high and low frequency bands respectively to obtain the fused range spectrum and the fused Doppler spectrum​​ :

[0026]

[0027]

[0028] 3. Coarse positioning-fine estimation (CLFE) processing

[0029] Because the low-frequency band subcarrier spacing is small and the carrier frequency is low, it has a larger maximum unambiguous range and maximum unambiguous speed , which is used for coarse positioning; while the high-frequency band has a larger bandwidth and a higher carrier frequency, which has a higher range resolution and speed resolution , which is used for fine estimation;

[0030] Among them, the maximum unambiguous range, speed and resolution calculation formula is as follows:

[0031]

[0032]

[0033]

[0034]

[0035] The speed of light is .

[0036] The peak index of the fusion range spectrum and the peak index of the fusion Doppler spectrum of the LF band are used to calculate the coarse range and coarse speed :

[0037]

[0038]

[0039] Based on the LF resolution, the coarse estimation interval is defined to ensure that the real target parameters fall within the HF search range;

[0040] ​​

[0041]

[0042] The LF rough estimation interval is mapped to the search index range of the HF frequency band, the calculation complexity is reduced, and the index mapping of distance and speed is:

[0043]

[0044]

[0045]

[0046]

[0047] Wherein , is the upper and lower bounds of , is the upper and lower bounds of

[0048] The local IFFT / FFT is performed in the above HF index range, and the peak index of the fused distance spectrum The peak index of the fused Doppler spectrum The final fine estimation result is calculated:

[0049]

[0050]

[0051] The beneficial effects of the present application are:

[0052] 1) Excellent SNR performance and outstanding noise suppression ability: the lattice pilot realizes effective energy accumulation through time-frequency two-dimensional distribution, combined with multi-band spectrum fusion strategy, significantly improves the noise suppression ability. In the extreme low SNR scene of SNR=-15dB, the distance estimation RMSE of the present application is reduced by more than 60% compared with the traditional comb pilot scheme, and the speed estimation RMSE is reduced by more than 50%, which meets the sensing demand in harsh environment.

[0053] ​​​​2) Breakthrough performance trade-off: CLFE framework cleverly uses the large non-fuzzy range of LF band to achieve wide coverage and coarse positioning, and combines with the high resolution of HF band to achieve high-precision fine estimation. The final distance estimation accuracy can reach sub-millimeter level, and the coverage range is expanded by more than 4 times compared with single HF band, effectively solving the contradiction between coverage and accuracy.

[0054] 3) Low computational complexity and low engineering implementation cost: By LF coarse positioning, the HF search range is reduced to 1 / 10-1 / 5 of the full frequency band, avoiding the large amount of calculation of traditional two-dimensional FFT of full frequency band; at the same time, the lattice pilot does not need the complex reconstruction algorithm of compressed sensing (CS), realizing the reduction of complexity. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 The figure is a schematic diagram of the CA-ISAC system model of the application, in which the base station aggregates the 5.9GHz low frequency band and the 24GHz high frequency band, transmits signals through the lattice pilot, and performs coarse positioning-fine estimation processing after receiving the target echo;

[0056] Figure 2 The figure is a distance estimation RMSE comparison chart of a fixed target (R=120m, v=20m / s) under different SNRs, showing the performance difference between the lattice pilot scheme of the application and the traditional pilot scheme of high and low frequency bands;

[0057] Figure 3 The figure is a speed estimation RMSE comparison chart of a fixed target (R=120m, v=20m / s) under different SNRs, showing the advantages of the lattice pilot scheme of the application;

[0058] Figure 4 The figure is a distance estimation RMSE comparison chart of a random distance (R=0-1000m) and random speed (v=0-35m / s) target under different SNRs, showing the performance of the CLFE framework;

[0059] Figure 5 The figure is a speed estimation RMSE comparison chart of a random distance (R=0-1000m) and random speed (v=0-35m / s) target under different SNRs, further verifying the effectiveness of the CLFE framework. DETAILED DESCRIPTION

[0060] The following combines the figures and simulation examples to prove the effectiveness and practicality of the application:

[0061] This embodiment constructs a CA-ISAC system based on the system parameters shown in Table 1, and the specific parameters are as follows:

[0062] Table 1 Parameter settings

[0063]

[0064] Example 1: Target positioning based on lattice pilot signal generation and spectrum fusion

[0065] According to generate a two-dimensional lattice pilot, insert it into the time-frequency domain specified position of the OFDM symbol; generate high and low frequency band OFDM signals, superimpose and transmit after up-conversion; receive the echo signal, down-convert, remove the CP and perform FFT to obtain the frequency domain signal ; calculate , get ; calculate , and fusion spectrum , , set , , extract the peak value index after fusion, calculate , ;

[0066] Example 2: Target positioning based on CLFE processing framework

[0067] The sending signal and receiving signal process is the same as example 1, but when performing target positioning, first extract peak value index , calculate ; extract peak value index , calculate ; set , ; get and by mapping, perform local IFFT / FFT on the high frequency band signal, extract , , calculate and .

[0068] According to Figures 2-3 , in the low SNR scene (-30dB to -20dB), the distance estimation RMSE of this method is reduced by 1-2 orders of magnitude than the traditional high frequency band block pilot, and the speed estimation RMSE is reduced by more than 50%;

[0069] From Figures 4-5 , in the dynamic target parameter scene (R=0-1000m, v=0-35m / s), the distance estimation RMSE converges to sub-meter level, and the speed estimation RMSE is reduced by 1-2 orders of magnitude in low SNR, which is better than the single frequency band scheme;

[0070] The above performance proves that the application can provide obvious accuracy better than the single-band positioning algorithm by using the lattice type pilot and the coarse positioning and fine estimation processing method, and is a feasible and effective scheme.

Claims

1. A carrier aggregation-based integrated sensing and ranging method for speed and distance measurement, characterized in that, comprising the steps of: S1, Construct a carrier aggregation-based integrated sensing and communication system (CA-ISAC): The system includes one multi-band communication and sensing integrated base station (BS) and one dynamic vehicle traveling on the ground. The base station transmits signals to the vehicle, and after the signals are reflected by the vehicle, the base station processes the received echo signals. The transmitted signals aggregate two non-continuous frequency band signals, which are low frequency (LF) 5.9 GHz and high frequency (HF) 24 GHz signals, respectively. The signal parameters of the first frequency band are defined as follows: carrier frequency , subcarrier spacing , number of subcarriers , number of OFDM symbols in a frame , total length of OFDM symbols including cyclic prefix , , 1 corresponds to a high frequency band, and q corresponds to a low frequency band. S2, design two-dimensional grid pilot and generate CA-OFDM signal: set pilot insertion interval , is high frequency subcarrier spacing, is low frequency subcarrier spacing, system transmission signal in time-frequency domain is inserted pilot according to interval Insert pilot, define grid pilot position matrix : ， wherein is a subcarrier index, is an OFDM symbol index, , ; time domain signal of a single frequency band and the total system transmission signal are respectively: ， ， wherein is the is the is the is the is the is the S3, echo signal processing: the CA-ISAC system receives the first The frequency domain echo signal is obtained by Fourier transform of the frequency domain echo signal The channel frequency response Respectively: ， ， wherein is a channel gain, is a round trip delay, is a target true distance, is a Doppler frequency, is a target true velocity, is a light speed, is an additive white Gaussian noise (AWGN) matrix, is a normalized noise; multiplying a channel frequency response with extracts a grid pilot signal , performing an inverse fast Fourier transform (IFFT) in a frequency domain on obtains a range profile, performing a fast Fourier transform (FFT) in a time domain obtains a Doppler profile; defining a high frequency band pilot symbol index set , , a low frequency band pilot symbol index set , , , ; calculating a range profile and a Doppler profile of each frequency band: ， ， Introducing high frequency band weight with low frequency band weight , calculating final fusion distance spectrum with fusion Doppler spectrum : ， ， wherein, , satisfies , and dynamically adjusts according to different scenarios; S4, coarse positioning-fine estimation processing: based on the wide coverage characteristics of the LF band, the coarse distance is calculated by with the peak index , coarse speed : ， ， Defining coarse estimate intervals , Mapping search index ranges to HF frequency bands: ， ， ， ， wherein , is the upper and lower bounds of , is the upper and lower bounds of The IFFT / FFT operation is performed within the index range, and the final fine estimation result is calculated by and the peak index. ， ， wherein, is the peak index of the HF local range profile, is the peak index of the HF local Doppler profile.

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