An adaptive waveform design method for unmanned aerial vehicle (UAV) sensing and communication integration

Abstract

The present application belongs to the technical field of wireless communication, and particularly relates to a self-adaptive waveform design method for unmanned aerial vehicle (UAV) integrated communication and sensing. The waveform designed by the present application divides subcarriers into two categories, i.e. modulation subcarriers and optimization subcarriers, to realize the collaborative optimization of communication and sensing functions. In each transmission process, the symbols of the modulation subcarriers are generated by the in-phase and quadrature component modulation method based on the MDS code; and the symbols of the optimization subcarriers are generated by an adaptive method according to the symbols on the modulation subcarriers, aiming to simultaneously optimize the communication performance, sensing performance and peak to average power ratio (PAPR) of the waveform. The present application provides a feasible technical solution for the UAV integrated application scenario of communication and sensing, has high communication efficiency and sensing performance, and performs outstandingly in reducing PAPR, and can effectively improve the overall performance of the UAV communication system.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless communication, and particularly relates to a self-adaptive coding modulation waveform design and system for unmanned aerial vehicle integrated sensing and communication. BACKGROUND

[0002] With the continuous progress of technology and the increasing diversification of application needs, the unmanned aerial vehicle (UAV) industry is rapidly developing and becoming an important driving force in many fields such as modern logistics, agriculture, and environmental monitoring. In this context, the integrated sensing and communication (ISAC) technology provides important support for the sustainable development of the UAV industry by optimizing resource utilization, improving safety, and enhancing service efficiency. Therefore, it is particularly important to design a waveform suitable for unmanned aerial vehicle integrated sensing and communication.

[0003] In existing waveforms, orthogonal frequency division multiplexing (OFDM) and its variants have become a research hotspot in the field of integrated sensing and communication due to their strong anti-interference ability and efficient hardware implementation. In recent years, many research papers have conducted in-depth studies on OFDM-based integrated sensing and communication waveforms, and through power allocation, probability constellation shaping, and other technologies, further optimized the balance between the communication performance and sensing performance of the waveform. In 2022, F. Yarkin and J. Coon proposed a joint coding modulation OFDM (MDS-OFDM) waveform based on maximum distance separable code (MDS). Studies have shown that this waveform outperforms traditional OFDM waveforms in bit error rate (BER) performance and can achieve robust sensing performance. Therefore, the MDS-OFDM waveform has great potential in applications for unmanned aerial vehicle integrated sensing and communication and is worth further research and development.

[0004] Although MDS-OFDM shows outstanding potential in the field of UAV integrated communication and sensing, it inherits the problem of high peak to average power ratio (PAPR) inherent in OFDM waveform. High PAPR will cause distortion when the signal passes through a high power amplifier (HPA), thereby adversely affecting the performance of communication and sensing. This problem is particularly prominent in the scenario of UAV integrated communication and sensing, because the communication and sensing coverage of the UAV network is more extensive than that of the traditional ground network. Therefore, reducing the PAPR of MDS-OFDM is the key to ensuring its effectiveness in the application of UAV integrated communication and sensing.

[0005] Existing PAPR reduction techniques, such as clipping, partial subcarrier reservation and selective mapping, are effective to some extent, but also have some limitations. The clipping technique will change the amplitude of some subcarriers, which may affect the communication reliability of the waveform; while the partial subcarrier reservation technique and the selective mapping technique will reduce the spectral efficiency (SE) of the waveform. Given that the check information of the MDS code has occupied part of the frequency spectrum resource, further PAPR reduction methods that reduce SE may no longer be applicable to MDS-OFDM. Therefore, the design and optimization of low PAPR MDS-OFDM integrated communication and sensing waveform is still a topic that needs to be further studied. SUMMARY

[0006] The purpose of the present application is to address the problem of high PAPR of MDS-OFDM waveform, and to propose an adaptive MDS-OFDM integrated communication and sensing waveform that is more suitable for the scenario of UAV integrated communication and sensing.

[0007] The technical solution of the present application is:

[0008] An adaptive waveform design method for UAV integrated communication and sensing, in which the UAV uses the same waveform to simultaneously achieve communication with users and sensing of unlicensed targets in the UAV integrated communication and sensing system, and the sensing platform is the UAV itself; the method comprises the following steps:

[0009] S1, divide the subcarriers into two types, defined as modulation subcarriers and optimization subcarriers, wherein the positions of the optimization subcarriers are used to carry additional information, and the symbols of the optimization subcarriers are generated adaptively according to the symbols on the modulation subcarriers, while the symbols of the modulation subcarriers are generated by a co-phase quadrature component modulation method based on maximum distance separable code;

[0010] S2, the input information bits are also divided into two types, which are defined as modulation bits and optimization bits respectively, the optimization bits are used to determine the position of the optimization sub-carrier, and the modulation bits are used to determine the symbol on the modulation sub-carrier;

[0011] S3, according to the settings of S1 and S2, the input bits are used to generate modulation symbols, and the transmission waveform is obtained after OFDM modulation.

[0012] Further, the specific method of S1 is:

[0013] Define that there are N sub-carriers in the system, and divide them into G=N / n' sub-blocks, each of which has n' sub-carriers, divide the n' sub-carriers into F groups in order, each group contains n=n' / F sub-carriers; divide the F groups into 1 optimization sub-carrier group and F-1 modulation sub-carrier groups.

[0014] Further, the specific method of S2 is:

[0015] Divide L input bits into G groups, each group contains l=L / G bits, and each group of bits determines the transmission symbol of an OFDM sub-block; divide the first l o =log2F bits in the l bits as optimization bits, which are used as index bits to determine the position of the optimization sub-carrier group, and the remaining (F-1)l m bits are modulation bits, which are used to determine the symbol on the F-1 modulation sub-carrier groups.

[0016] Further, the specific method of S3 is:

[0017] Define that the index bit stream b g =[b g (1),b g (2),…,b g (l o )]^T determines the position of the optimization sub-carrier group of the gth sub-block, g∈{1,2,…,G}:

[0018]

[0019] Where I g represents the position of the optimization sub-carrier within the gth sub-block;

[0020] The remaining (F-1)l m modulation bits are used to obtain the symbol on the F-1 modulation sub-carrier groups:

[0021] Because the symbol generation steps of the modulation sub-carrier groups are the same, here we take one group of modulation sub-carriers as an example to show the symbol generation process, where the number of bits carried by one group of modulation sub-carriers is l mFirstly, two M -dimensional Pulse Amplitude Modulation (PAM) constellations are generated, denoted as and Then, they are equally divided into K and R M -dimensional PAM constellations with index interval K and R, denoted as and respectively, where and are the in-phase and quadrature components, respectively. and are generated as follows:

[0022]

[0023] For a group of l m input bits carried by the modulation subcarrier, we first divide them into two parts, where the first l i bits are used for in-phase component modulation and the last l q bits are used for quadrature component modulation:

[0024]

[0025] The l and l bits are encoded by MDS, and the MDS code words are {c i (1), c i (2), …, c i (n)} and {c q (1), c q (2), …, c q (n)} respectively. After the MDS code is generated, we further convert each log2M bits of the l and l bits into their corresponding decimal numbers to obtain {d i (1), d i (2), …, d i (n)} and {d q (1), d q (2), …, d q (n)} respectively, so that the in-phase component s i = {s i (1), s i (2), …, s i (n)} and the quadrature component s q = {s q (1), s q (2), …, s q of the modulation subcarrier are obtained.​s

[0026]

[0027] where s i = [s i (1), s i (2), …, s i (n)] T and s q = [s q (1), s q (2), …, s q (n)] T represent the in-phase and quadrature components of a group of modulated subcarriers, respectively, and the final symbol s m on a group of modulated subcarriers is given by:

[0028] s m = s i + js q

[0029] The above modulation method is repeated F-1 times to obtain the symbols on F-1 groups of modulated subcarriers within the gth subblock. The above steps are then repeated G times to obtain the symbols on all modulated subcarriers within an OFDM symbol, and the symbols on the remaining optimized subcarriers are generated based on the symbols on the G(F-1) groups of modulated subcarriers;

[0030] The method of adaptively generating the symbols on the optimized subcarriers based on the symbols on the modulated subcarriers is as follows:

[0031] First, a discrete search method is used to determine the power of a group of optimized subcarriers with the goal of minimizing the BER. Specifically, we first generate multiple equally spaced power values, and simulate the BER of the system under these different power values, where the power value corresponding to the minimum BER is the sum of the powers of a group of optimized subcarriers, denoted as

[0032] Subsequently, we optimize the power of each optimized subcarrier with the goal of minimizing the peak-to-sidelobe level (PSL) of the waveform. In fact, the sum of the powers of each group of optimized subcarriers is Specifically, the PSL of the waveform is the Fourier transform of the power spectral density of the frequency-domain transmit signal vector. The problem of minimizing the PSL can be converted into a convex optimization problem, which can be efficiently solved by convex optimization toolboxes such as CVX, etc.

[0033] Finally, we determine the optimized phase of the subcarriers with the goal of minimizing the PAPR of the waveform. Specifically, when the power of the waveform satisfies the normalization condition, optimizing the PAPR of the waveform is equivalent to optimizing the infinity norm of the waveform, and the optimization problem can also be converted into a convex optimization problem and solved by using the convex optimization toolbox.

[0034] The beneficial effects of the present application are that the present application can significantly reduce the PAPR of the MDS-OFDM waveform while maintaining reliable communication and perception, so that the distortion of the signal after passing through the HPA is significantly reduced, and therefore it is more suitable for future unmanned aerial sensing integrated scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 Waveform generation and system design schematic diagram of the present application;

[0036] Figure 2 BER comparison diagram of the present application, the traditional OFDM waveform and the MDS-OFDM waveform under non-ideal HPA;

[0037] Figure 3 PSL comparison diagram of the present application, the traditional OFDM waveform and the MDS-OFDM waveform;

[0038] Figure 4 PAPR comparison diagram of the present application, the traditional OFDM waveform and the MDS-OFDM waveform. DETAILED DESCRIPTION

[0039] The effectiveness and practicability of the present application are proved below by combining the drawings and simulation examples:

[0040] A kind of adaptive coding modulation waveform design method for unmanned aerial sensing integration of the present application. As shown in Figure 1As shown, in the UAV sensing integrated system, the UAV adopts the same waveform to realize the communication with the user and the sensing of the unauthorized target at the same time, wherein the sensing platform is the UAV itself. In the waveform, the subcarriers are divided into two types, which are called modulation subcarriers and optimization subcarriers. In each transmission, the symbols of the modulation subcarriers are generated by a kind of MDS-coded in-phase quadrature modulation (MDS-IQM) method based on MDS code; the symbols of the optimization subcarriers are adaptively generated according to the symbols on the modulation subcarriers, aiming to jointly optimize the communication performance, the sensing performance and the PAPR of the waveform. In order to further improve the SE of the waveform, the positions of the optimization subcarriers are used to carry additional information. Therefore, the input bits are divided into two parts, one part of the bits is called index bits, which are used to determine the positions of the optimization subcarriers; the other part is modulation bits, which are used to determine the symbols on the modulation subcarriers. The communication receiver recovers the information bits from the received waveform; the UAV realizes the sensing of the unauthorized target according to the echo of the transmitted waveform.

[0041] The present application comprises the following steps:

[0042] S1, divide the N subcarriers of one OFDM symbol into G subblocks in sequence, each subblock contains n'=N / G subcarriers; then divide the n' subcarriers in each subblock into F groups in sequence, each group contains n=n' / F subcarriers; the F groups of subcarriers in one subblock are further divided into two types, one group is the optimization subcarriers, and the remaining F-1 groups are the modulation subcarriers;

[0043] S2, divide the L input bits into G groups, each group contains l=L / G bits; the l information bits of each group are further divided into two types, the first l o =log2F index bits are used to determine the positions of the optimization subcarrier groups, and the remaining (F-1)l m bits are used to determine the symbols on the F-1 groups of modulation subcarriers, wherein l m =(n-1)log2KR+2nlog2M is the number of bits carried by one group of modulation subcarriers, K and R are the modulation orders of the in-phase component and the quadrature component respectively, and M is the dimension of the PAM constellation. Based on the above relationship, the number of information bits carried by one subblock can be obtained as formula 1:

[0044] l=l o +(F-1)l m =log2F+(F-1)((n-1)log2KR+2nlog2M) (Formula 1)

[0045] S3. Determine the position of the optimized subcarrier based on the index bits of each sub-block. Taking the g-th sub-block as an example (g∈{1,2,…,G}), let the input bit be b. g =[b g (1),b g (2),…,b g (lo)] T Then the optimal subcarrier group position of the g-th sub-block (denoted as I) g The calculation formula for ) is as follows: Formula 2:

[0046]

[0047] MDS-IQM is performed on the modulated subcarriers of group G(F-1). Taking one modulated subcarrier group as an example, the MDS-IQM process is as follows:

[0048] First, two Pulse Amplitude Modulation (PAM) constellations, KM and RM dimensional, are generated. Then, the symbols of these two constellations are divided into K and R M-dimensional PAM constellations, respectively, according to index intervals K and R, denoted as […]. and in Used for in-phase component modulation Used for quadrature component modulation. Then, l m The bits are divided into two parts, where the first l bits are... i One bit is used for in-phase component modulation, and then l q One bit is used for quadrature component modulation, l i and l q The calculation formulas are as follows: Formula 3 and Formula 4:

[0049]

[0050] Subsequently, and Each bit is encoded using MDS, so that... For example, the specific encoding process of MDS encoding is as follows: First, ... Converting each log2K bit in the formula to its corresponding decimal number is done using the same formula as in Formula 2. The resulting n-1 decimal numbers are denoted as {c i (1),c i (2),…,c i (n-1)}, these n-1 numbers are the first n-1 codewords of the MDS code used for in-phase modulation. The nth codeword is the check codeword, calculated using the following formula 5:

[0051] c i (n)=K-(c i (1)+ci (2)+…+c i (n-1))modK (Formula 5)

[0052] Where mod represents the modulo operation. The process of generating MDS codes from 1 bit is similar; simply replace K with R, where l1 q The n-dimensional MDS code corresponding to each bit is denoted as {c q (1),c q (2),…,c q (n)}. Then we will and Each log2M bit is converted to its corresponding decimal number using the same formula as in Formula 2. The resulting n-dimensional result is denoted as {d}. i (1),d i (2),…,d i (n)} and {d q (1),d q (2),…,d q (n)}. PAM constellation based on in-phase and quadrature components. and MDS codeword {c i (1),c i (2),…,c i (n)} and {c q (1),c q (2),…,c q (n)} and {d i (1),d i (2),…,d i (n)} and {d q (1),d q (2),…,d q (n)}, the in-phase and quadrature components of a set of modulated subcarriers are generated by the following formula 6:

[0053]

[0054] Where s i =[s i (1),s i (2),…,s i (n)] T and s q =[s q (1),s q (2),…,s q (n)] T These represent the in-phase and quadrature components of a set of modulated subcarriers, respectively. According to Formula 6, the symbol s of a set of modulated subcarriers...m The final result can be generated by the following formula 7:

[0055] s m =s i +js q (Formula 7)

[0056] The design of the symbols for the modulated subcarrier group is adaptively optimized to jointly optimize the waveform's BER, PSL, and PAPR, where PSL is a metric for waveform sensing performance. Specifically, we first determine the optimal power sum of a set of optimized subcarriers using a discrete search method, aiming to minimize the BER. Then, under the constraint of the optimal power sum, we optimize the power of each subcarrier within the set of optimized subcarriers using convex optimization, with the objective of minimizing the PSL. Finally, we determine the phase of the optimized subcarriers with the objective of minimizing the PAPR.

[0057] The final generated N-dimensional symbol string is subjected to OFDM modulation, up-conversion, and radio frequency amplification, and finally the waveform is transmitted.

[0058] The communication receiver first performs down-conversion and OFDM demodulation on the received waveform, and then recovers the information bits through signal detection methods, such as maximum likelihood detection, zero-breaking detection, and minimum mean square error detection.

[0059] The UAV first performs down-conversion and OFDM demodulation on the received echo, and then calculates the information of the sensed target (such as distance and speed) based on the received echo. The sensing algorithm can adopt the classic matched filtering algorithm as well as the efficient 2D Fourier transform algorithm.

[0060] The simulation parameters are set as follows: Consider an OFDM symbol containing 256 subcarriers; the communication channel is considered to be a free-space large-scale fading propagation channel close to the UAV scenario, and a non-ideal HPA is considered. Traditional OFDM uses 8QAM modulation, and the modulation order of MDS-OFDM is n=2, K=2, R=2, M=2. In the proposed A-MDS-OFDM-IM waveform, G=32, and the modulation order of MDS-IQM is consistent with that of MDS-OFDM.

[0061] Depend on Figure 2 It can be seen that in non-ideal HPA and UAV communication scenarios, the BER performance of the present invention is significantly better than that of the comparison waveform, and this advantage is particularly obvious under high signal-to-noise ratio.

[0062] Depend on Figure 3As can be seen, the PSL performance of this invention is close to that of the comparative waveform. In other words, this invention significantly improves the communication reliability of the system while ensuring minimal loss of sensing performance. Therefore, this invention achieves a trade-off between the communication and sensing performance of the waveform, making it a feasible integrated sensing waveform.

[0063] Depend on Figure 4 As can be seen, the PAPR of the present invention is significantly lower than that of the comparative waveform. Therefore, the waveform proposed in this invention can achieve less signal distortion, which is of great help in ensuring the communication and sensing reliability of the system. It can play its advantages in a wide-coverage UAV network and become a feasible and effective solution.

Claims

1. An adaptive waveform design method for UAV sensing integration, wherein in a UAV sensing integration system, the UAV uses the same waveform to simultaneously communicate with the user and perceive unauthorized targets, and the sensing platform is the UAV itself; characterized in that, The method includes the following steps: S1. Subcarriers are divided into two types, defined as modulation subcarriers and optimized subcarriers. The position of the optimized subcarrier is used to carry additional information. The symbol of the optimized subcarrier is adaptively generated based on the symbol on the modulation subcarrier, while the symbol of the modulation subcarrier is generated by the in-phase quadrature component modulation method based on maximum distance divisible codes. The specific method is as follows: The definition system has Subcarriers, and divided into There are 10 sub-blocks, and each sub-block has 10 sub-blocks. Subcarriers, The subcarriers are divided into several subcarriers in sequence. Groups, each group contains Subcarriers; The group is divided into 1 group of optimized subcarriers and Group modulation subcarrier; S2. The input information bits are also divided into two types, defined as modulation bits and optimization bits. Optimization bits are used to determine the position of the optimization subcarrier, and modulation bits are used to determine the symbol on the modulation subcarrier. The specific method is as follows: Will Each input bit is divided into Groups, each group contains bits, will The first bit of the bits One bit is defined as the optimization bit, used as an index bit to determine the position of the optimized subcarrier group, and the remaining bits are... These bits are modulation bits, used to determine... Symbols on modulated subcarriers; S3. Based on the settings of S1 and S2, a modulation symbol is generated using the input bits, and the transmitted waveform is obtained after OFDM modulation. The specific method is as follows: Defined by indexed bitstream Determine the first Optimized subcarrier group positions for each sub-block : ， in Representing the Optimize the position of subcarriers within each sub-block; From the remainder Each modulation bit is obtained Symbols on modulated subcarriers: The modulation method for a set of modulated subcarriers is as follows: first, two... and The pulse amplitude modulation constellation is denoted as follows: and Then and According to index interval and Divided into equal parts One and indivual The constellation VPAM, denoted as and ,in Used for in-phase component modulation Used for quadrature component tuning; and The generation method is as follows: ， ， For a set of modulated subcarriers carrying The input bits are divided into two parts, where the first part is the first bit. One bit is used for in-phase component modulation, then... One bit is used for quadrature component modulation: ， ， right and Each bit is encoded using MDS, resulting in the following MDS codeword: and After the MDS code is generated, and Each bit Each bit is converted to its corresponding decimal number to obtain and Thus, the in-phase component of the modulated subcarrier is obtained. and orthogonal components , respectively represented as: ， in and These represent the in-phase and quadrature components of a set of modulated subcarriers, respectively, and the final symbol of the set of modulated subcarriers. for: ， Repeat the modulation method of a set of modulation subcarriers Next, get the first Within each sub-block Symbols on the modulated subcarrier; then the symbols on the first... The modulation process of each sub-block is repeated. This process yields the symbols on all modulated subcarriers within an OFDM symbol; the symbols on the remaining optimized subcarriers will then be determined based on this... Symbol generation on modulated subcarriers; The method for adaptively generating optimized subcarrier symbols based on the symbols on the modulated subcarrier is: First, a set of optimized subcarrier powers is determined using a discrete search method with the objective of minimizing the BER (Power Error Rate). Specifically, multiple equally spaced power values ​​are generated, and the BER of the system under these different power values ​​is obtained through simulation. The power value corresponding to the minimum BER is the sum of the powers of the optimized subcarriers, denoted as . ; The power of each optimized subcarrier is optimized with the objective of minimizing the peak sidelobe level (PSL) of the waveform. The sum of the powers of each group of optimized subcarriers is... The PSL of the waveform is the Fourier transform of the power spectral density of the transmitted signal vector in the frequency domain. The problem of minimizing the PSL is transformed into a convex optimization problem, which can then be solved using the convex optimization toolbox. The phase of the optimized subcarrier is determined with the goal of minimizing the PAPR of the waveform. Specifically, when the power of the waveform satisfies the normalization condition, the PAPR of the optimized waveform is equivalent to the infinite norm of the optimized waveform. This optimization problem is also transformed into a convex optimization problem and solved using the convex optimization toolbox.

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