A sensory and communication integrated resource allocation method based on multi-device collaboration

Abstract

The present invention specifically relates to a method for allocating integrated sensory resources based on multi-device collaboration, including: defining the bandwidth constraints of the ISAC device in time slot m based on the fixed bandwidth of the cellular user, the number of cellular users in time slot m, and the total bandwidth; defining the energy consumption constraints of the ISAC device in M ​​consecutive time slots based on the time the ISAC device executes the sensing mode and communication mode in each time slot, the transmission power of the ISAC device in a single time slot, and the total energy budget of the ISAC device in m time slots; defining the maximization discriminant gain problem based on each constraint, including the joint optimization of sensing power, transmission power, quantization gain, and communication bandwidth; optimizing the sensing power, transmission power, and quantization gain in the first stage based on predefined bandwidth conditions; and allocating the communication bandwidth in the second stage based on the optimization results of the first stage. This method can realize dynamic resource allocation and can realize integrated sensory resource allocation that adapts to multiple devices and multiple time slots.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a sensory-communication integrated resource allocation method based on multi-device collaboration, a storage medium, a computer program product, and an electronic device. Background Art

[0002] In related technologies, with the rise of Integrated Sensing and Communication (ISAC) technology, the field of wireless communications has gradually evolved from the traditional single communication function to the multi-functional integration of perception and communication. By sharing hardware modules and spectrum resources, ISAC technology can achieve the dual goals of environmental perception and information transmission without increasing additional spectrum consumption. Therefore, ISAC technology has shown great application potential in fields such as intelligent transportation, environmental monitoring and unmanned driving. However, although Integrated Sensing and Communication technology can theoretically achieve higher spectrum efficiency and system performance, it still faces the challenge of perception and communication resource allocation in practical applications. Existing research often regards perception and communication as two independent processes for allocation and optimization when designing resource allocation. This strategy ignores the coupling relationship between the two in time and space, resulting in limited improvement in system performance.

[0003] In addition, most existing ISAC technologies adopt resource allocation strategies based on single devices or static scenarios, which fail to fully consider the resource competition and dynamic change characteristics of multiple devices working together in complex environments.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0005] The present invention provides a sensory-communication integrated resource allocation method based on multi-device collaboration, a storage medium, a computer program product, and an electronic device, thereby overcoming the defects in the prior art to a certain extent.

[0006] Other features and advantages of the present invention will become apparent from the following detailed description, or may be learned in part by practice of the present invention.

[0007] According to a first aspect of the present invention, a method for allocating sensory and communication-integrated resources based on multi-device collaboration is provided, the method comprising:

[0008] Define the bandwidth constraint of the ISAC device at time slot m based on the fixed bandwidth B0 of the cellular user, the number of cellular users u(m) at time slot m, and the total bandwidth B;

[0009] Define the energy consumption constraint of the ISAC device in M ​​consecutive time slots based on the time the ISAC device executes the sensing mode and the communication mode in each time slot, the transmit power of the ISAC device in a single time slot, and the total energy budget of the ISAC device in m time slots.

[0010] The problem of maximizing the discriminant gain is defined based on various constraints, including the joint optimization of perception power, transmission power, quantization gain, and communication bandwidth.

[0011] Based on predefined bandwidth conditions, the sensing power, transmission power and quantization gain are optimized in the first stage; and in the second stage, the communication bandwidth is allocated based on the optimization results of the first stage.

[0012] In some exemplary embodiments, optimizing perceived power in a first stage includes:

[0013] Based on the number of action categories of the target to be identified and the posterior mean of the features under each action category, the ISAC device performs the sensing mode for a certain duration in time slot m and optimizes the sensing power in combination with the Lagrange multiplier.

[0014] In some exemplary embodiments, optimizing transmit power in the first stage includes:

[0015] The transmit power is optimized according to the channel gain of the ISAC device at time slot m, the bandwidth of the ISAC device at time slot m, and the Lagrange multiplier.

[0016] In some exemplary embodiments, optimizing the quantization gain in the first stage includes:

[0017] Based on the posterior distribution of the features obtained at time slot m, the number of action categories corresponding to the target to be identified and the performance requirements, the quantization gain is optimized in combination with the Lagrange multiplier.

[0018] In some exemplary embodiments, the method further comprises:

[0019] The Lagrange multiplier is updated based on the currently obtained optimization results of the perceived power, quantization gain, and transmit power, and the perceived power, quantization gain, and transmit power are iterated based on the updated Lagrange multiplier until convergence.

[0020] In some exemplary embodiments, allocating the communication bandwidth in the second stage based on the optimization result of the first stage includes:

[0021] Based on bandwidth constraints and energy consumption constraints, combined with the communication bandwidth and communication time of each ISAC device at time slot m, the average bandwidth allocation result of the ISAC device in the time slot is calculated;

[0022] The communication bandwidth allocation result is optimized by combining the number of cellular users, the fixed bandwidth of cellular users, the average bandwidth allocation result, the difference between the transmission overhead and the maximum communication volume.

[0023] In some exemplary embodiments, the method further comprises:

[0024] The constraint condition for defining the transmission overhead of the ISAC device in time slot m includes: the transmission overhead is less than the maximum communication volume in the communication duration in one time slot.

[0025] In some exemplary embodiments, the method further comprises:

[0026] Each ISAC device responds to the sensing request of the edge server and executes the sensing mode to transmit a frequency modulated continuous wave (FMCW) signal during the sensing time interval; the sensing time interval includes M consecutive time slots;

[0027] The ISAC device receives the echo signal, extracts features from the echo signal, quantizes the extracted motion feature data corresponding to the target to be identified, and generates a corresponding quantized feature set; wherein the quantized feature set includes: the quantization gain and quantization distortion of the ISAC device at time slot m;

[0028] The ISAC device switches to a communication mode and sends the quantitative feature set to the edge server.

[0029] In some exemplary embodiments, the method further comprises:

[0030] The edge server receives the quantitative feature set fed back by each ISAC device; wherein each ISAC device shares the channel with the current cellular communication user through orthogonal frequency division multiple access;

[0031] Feature data integration processing is performed based on the quantized feature sets corresponding to multiple consecutive time slots to obtain the action recognition results corresponding to the target to be identified.

[0032] According to a second aspect of the present invention, a computer program product is provided, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned sensory communication integrated resource allocation method based on multi-device collaboration is implemented.

[0033] According to a third aspect of the present invention, there is provided a storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned sensory communication integrated resource allocation method based on multi-device collaboration.

[0034] According to a fourth aspect of the present invention, there is provided an electronic device, comprising:

[0035] processor; and

[0036] a memory for storing executable instructions of the processor;

[0037] Among them, the processor is configured to implement the above-mentioned sensory integration resource allocation method based on multi-device collaboration by executing the executable instructions.

[0038] The embodiment of the present invention provides a method for allocating integrated sensing and communication resources based on multi-device collaboration. The method defines the bandwidth constraints of the ISAC device in time slot m based on the fixed bandwidth of the cellular user, the number of cellular users in time slot m, and the total bandwidth. The method also defines the energy consumption constraints of the ISAC device in M ​​consecutive time slots based on the time the ISAC device executes sensing and communication modes in each time slot, the transmit power of the ISAC device in a single time slot, and the total energy budget of the ISAC device in m time slots. Based on these constraints, a maximum discriminant gain problem is defined, including the joint optimization of sensing power, transmit power, quantization gain, and communication bandwidth. Based on predefined bandwidth conditions, sensing power, transmit power, and quantization gain are optimized in the first phase. In the second phase, communication bandwidth is allocated based on the optimization results of the first phase. This method optimizes energy allocation, quantization gain, and bandwidth allocation through a two-stage resource allocation approach, achieving dynamic resource allocation. This method can achieve integrated sensing and communication resource allocation that adapts to multiple devices and multiple time slots, and can obtain the optimal resource allocation solution for each ISAC device in each time slot, thereby improving the reasoning accuracy of downstream tasks.

[0039] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings are incorporated into and constitute a part of this specification, illustrate embodiments consistent with the present invention, and together with the description, serve to explain the principles of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and it is clear that those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0041] Figure 1 A schematic diagram schematically illustrates an exemplary embodiment of the present invention, a method for allocating sensory and communication-integrated resources based on multi-device collaboration;

[0042] Figure 2 A schematic diagram schematically illustrates a method for an ISAC device to process a perception request of an edge server according to an exemplary embodiment of the present invention;

[0043] Figure 3 A schematic diagram schematically illustrates a method for an edge server to process a quantitative feature set according to an exemplary embodiment of the present invention;

[0044] Figure 4 A schematic diagram schematically illustrates a multi-device collaborative human motion recognition system in an exemplary embodiment of the present invention;

[0045] Figure 5 Schematically illustrates a perceptual communication FMCW waveform diagram of an ISAC device in an exemplary embodiment of the present invention;

[0046] Figure 6 A schematic diagram schematically illustrates a variation trend of human motion recognition accuracy with data collection time in an exemplary embodiment of the present invention;

[0047] Figure 7 A schematic diagram schematically illustrates a variation trend of human motion recognition accuracy with network scale in an exemplary embodiment of the present invention;

[0048] Figure 8 A schematic diagram schematically illustrates the accuracy of human motion recognition under different bandwidth budgets in an exemplary embodiment of the present invention;

[0049] Figure 9 A schematic diagram schematically illustrates the accuracy of human motion recognition under different energy budgets in an exemplary embodiment of the present invention;

[0050] Figure 10 The figure schematically shows the composition of an electronic device in an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0051] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0052] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the blocks shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0053] In the related technologies, for the problem of integrated sensory and communication resource allocation in multi-device collaborative scenarios, the existing technologies still have significant deficiencies in the following aspects: First, the importance of perception data and communication requirements between multiple devices are different, and how to effectively allocate them under limited resources to improve the overall performance of the system is still a problem. Because the positions and perspectives of different devices have obvious differences in the perception accuracy of the target, if the perception contribution of each device is not taken into account when allocating resources, it will lead to resource waste of some devices or resource shortage of some devices. In addition, since the resource allocation results of different time slots will affect the resource availability and overall reasoning performance of subsequent time slots, the existing technologies lack systematic modeling and analysis methods in the joint optimization across time slots, resulting in the resource allocation strategy of each time slot being carried out independently, and the global optimum cannot be achieved. It can be seen that designing a sensory and communication integrated resource allocation method that can adapt to multiple devices and multiple time slots is the key to improving the performance of the ISAC system.

[0054] In response to the shortcomings and deficiencies of the prior art, this example embodiment provides a method for allocating sensory and communication-integrated resources based on multi-device collaboration. Below, the various steps of this method for allocating sensory and communication-integrated resources based on multi-device collaboration in this example embodiment will be described in more detail with reference to the accompanying drawings and examples.

[0055] In this example implementation, the integrated sensing and communication process of multiple ISAC devices can be modeled, formulating the problem as a joint sensing and communication resource allocation problem. This problem includes the resources required for each ISAC device in each time slot, namely, sensing power, transmission power, quantization gain, and communication bandwidth. Specifically, for an ISAC device, a time slot can be divided into three phases: sensing, computing, and communication. Correspondingly, corresponding models can be defined for the sensing and communication phases.

[0056] Specifically, during the ISAC's perception phase, the device may execute perception mode, transmitting a radio signal and receiving an echo from the target. The transmitted radio signal may be an FMCW wave. Features are extracted and quantified from the echo for transmission. The perception signal and its processing model are described below.

[0057] (1) Perception signal: During the perception time interval of each time slot, the ISAC device transmits a chirp sequence with a linear frequency increase as the perception signal, denoted as s k (t). At time slot m, the echo signal is:

[0058]

[0059] in, is the expected echo from the target, is the clutter signal through the jth path, J is the total number of reflected paths, and z(t) is the Gaussian noise in the receiver.

[0060] (2) Perception signal processing: echo At sampling rate f s Sampling and rearranging into a two-dimensional data matrix Where T chirp is the signal duration, T chirp f s is the length of the fast time dimension, and L is the length of the slow time dimension. In the slow time dimension, principal component analysis (PCA) is used to Extract independent N k Features.

[0061] Since all processing steps are linear, based on formula (1), the nth eigencomponent can be expressed as:

[0062]

[0063] in, and are the characteristic elements extracted from the direct echo and the clutter signal of the jth path, is the noise in the characteristic elements.

[0064] By normalizing the nth feature component to the perceptual power get:

[0065]

[0066] in, represents the normalized true features, represents the normalized clutter signal.

[0067] The true features after normalization Assuming it obeys a mixed Gaussian distribution, it can be expressed as Where D is the total number of categories in the task, and are the mean and variance of the d-th class distribution in the feature subspace at time slot m; this distribution is the prior distribution of the feature.

[0068] Since the interference object is assumed to have the same action category as the target, the normalized clutter Also obeys the mixed Gaussian distribution, expressed as The normalized noise has a mean of zero and a variance of Gaussian distribution.

[0069] After normalization, the features are further processed by a linear quantizer to generate a quantized version, thereby reducing communication overhead. Specifically, when using high quantization bits, the quantization result can be expressed as:

[0070]

[0071] in, and are the quantization gain and quantization distortion of ISAC device k at time slot m, and the quantization distortion Approximately has a mean of zero and a variance of Gaussian variables.

[0072] Exemplarily, a communication model corresponding to the communication mode executed by the ISAC device in the time slot may also be defined.

[0073] In communication mode, each ISAC device transmits its quantized feature set to the edge server. During this process, the ISAC device shares the channel with existing cellular communication users through orthogonal frequency division multiple access (OFDMA). Assume that the number of subcarriers is large enough, so the bandwidth allocation can be approximated as continuous. Let B be the total bandwidth and B0 be the fixed bandwidth allocated to each cellular user. Since the number of cellular users u(m) varies in different time slots, the total bandwidth required by all cellular users will also vary, which in turn causes the transmission bandwidth available for the ISAC device to vary. Let is the bandwidth allocated to ISAC device k in time slot m. Assume that the correlation channel is a frequency-flat channel with zero mean and variance Rayleigh distribution. is the channel gain from ISAC device k to the edge server at time slot m, then the communication rate is:

[0074]

[0075] in, is the transmit power of ISAC device k in time slot m, and N0 is the power of additive white Gaussian noise (AWGN).

[0076] On the edge server side, the features of ISAC device k are recovered from the quantized features as:

[0077]

[0078] Then, the transmission overhead of ISAC device k in time slot m can be calculated by the recovered feature set With normalized feature set It can be represented by the mutual information of:

[0079]

[0080] From formula (6) and formula (7), it can be seen that the quantization gain Determines the distortion and transmission overhead of the restored features. Specifically, a higher quantization gain leads to a higher transmission overhead and a lower quantization distortion. Therefore, while ensuring that the transmission overhead is less than the maximum communication volume, the quantization gain It is optimized.

[0081] For example, an action recognition model can be predefined for the edge server. By integrating features recovered over M consecutive time slots, the edge server predicts the target's action classification label, thereby achieving action recognition. We use the discriminant gain to evaluate the target's action recognition accuracy. This gain is defined as the Kullback–Leibler (KL) divergence between any two classes given a given feature.

[0082] After substituting formula (3) into formula (6), the recovered features can be rewritten as:

[0083]

[0084] Then, restore the features The probability density function can be expressed as:

[0085]

[0086] in and are the posterior mean and posterior variance of the features respectively.

[0087] For the restored features The discrimination gain between category d and category d′ is defined as:

[0088]

[0089] Here, KL[·||·] represents the KL divergence of one probability distribution with respect to another probability distribution.

[0090] This discriminant gain is extended to the feature sets of K ISAC devices and M time slots, recovering the feature The overall discriminant gain of Expressed as:

[0091]

[0092] As can be seen from Equations (10) and (11), the discriminant gain depends on the probability distribution of the recovered features, which is determined by both the perception and communication processes. In particular, the perception power affects the quality of the extracted features, see Equation (3). At the same time, the quantization gain is affected by the communication constraints, affecting the quality of the recovered features, see Equation (6). Therefore, the device needs to jointly optimize the resources of the perception and communication processes to maximize the action recognition accuracy, that is, the discriminant gain.

[0093] In this example implementation, reference Figure 1 As shown in FIG, the sensory integration resource allocation method based on multi-device collaboration specifically includes:

[0094] Step S11, defining a bandwidth constraint condition of the ISAC device at time slot m according to the fixed bandwidth of the cellular user, the number of cellular users at time slot m, and the total bandwidth;

[0095] Step S12: defining an energy consumption constraint for the ISAC device in M ​​consecutive time slots based on the time during which the ISAC device executes the sensing mode and the communication mode in each time slot, the transmit power of the ISAC device in a single time slot, and the total energy budget of the ISAC device in m time slots;

[0096] Step S13, defining a problem of maximizing the discrimination gain based on various constraints, including joint optimization of perception power, transmit power, quantization gain, and communication bandwidth;

[0097] Step S14: Based on the predefined bandwidth conditions, the sensing power, the transmission power and the quantization gain are optimized in the first phase; and based on the optimization results of the first phase, the communication bandwidth is allocated in the second phase.

[0098] Specifically, the task-oriented ISAC design goal is to maximize the target's action recognition accuracy while meeting the communication needs of cellular users. Since the ISAC device shares the entire bandwidth with the cellular user, the bandwidth constraint of the ISAC device at time slot m is:

[0099]

[0100] Where B0 is the fixed bandwidth allocated to each cellular user to meet their communication needs, u(m) is the number of cellular users at time slot m, and B is the total bandwidth. Due to the changes in the number of existing cellular users, the available bandwidth of the ISAC device in different time slots also varies.

[0101] For each ISAC device, its energy consumption constraint within M consecutive time slots is:

[0102]

[0103] Among them, Ts and T c are the time of perception and communication in each time slot, which is a constant; E k is the total energy budget of ISAC device k in m time slots.

[0104] In addition, to ensure the successful transmission of the feature, the transmission overhead needs to be less than the communication duration T within one time slot. c The maximum communication volume in is expressed as:

[0105]

[0106] Under the above constraints, the problem of maximizing the discriminant gain is formulated as follows:

[0107]

[0108] Among them, the perceived power Transmit power Quantization gain and communication bandwidth Joint optimization is required. Due to the non-convexity of the objective function, solving this problem is challenging. To address this problem, we propose a new TSO (Two-Stage Offline Optimization Algorithm) algorithm for solving it.

[0109] Exemplarily, optimizing the perception power in the first stage includes optimizing the perception power in combination with Lagrange multipliers based on the number of action categories of the target to be identified, the posterior mean of the features under each action category, and the duration for which the ISAC device executes the perception mode in time slot m.

[0110] Exemplarily, optimizing the transmit power in the first stage includes optimizing the transmit power according to the channel gain of the ISAC device at time slot m, the bandwidth of the ISAC device at time slot m, and a Lagrange multiplier.

[0111] Exemplarily, optimizing the quantization gain in the first stage includes optimizing the quantization gain based on the posterior distribution of the features obtained at time slot m, the number of action categories corresponding to the target to be identified, and the performance requirements, in combination with Lagrange multipliers.

[0112] Exemplarily, the method further includes: updating the Lagrange multiplier based on the currently obtained optimization results of the perceived power, quantization gain and transmit power, and iterating the perceived power, quantization gain and transmit power based on the updated Lagrange multiplier until convergence.

[0113] Specifically, first, problem P1 is transformed into a convex optimization problem, and a two-stage optimization algorithm (TSO) is proposed to solve it. Since the objective function of P1 is a ratio in the form of summation, it can be transformed into an equivalent subtraction form. The converted problem is:

[0114]

[0115] in, is a variable transformation to make the constraints of P1 convex. Here, is an auxiliary variable.

[0116] function is defined as:

[0117]

[0118] To solve problem P1, we solve P2 given the auxiliary variables and update the auxiliary variables based on the solution to P2, performing alternating iterations until convergence. To solve problem P2, the first phase optimizes the perceived power, transmitted power, and quantization gain within a feasible bandwidth. In the second phase, based on these optimization results, the bandwidth is optimized.

[0119] Specifically, in the first stage, the power and quantization gain allocation is realized. Specifically, in problem P2, given a feasible communication bandwidth, the joint optimization problem of power allocation and quantization gain is:

[0120]

[0121] This is a convex optimization problem and the solution is as follows:

[0122] Theorem 1: The channel gain when ISAC device k is in time slot m is The optimal sensing power and transmission power for problem P2.1 are:

[0123]

[0124] Where D is the number of action categories, is the feature posterior mean under this category, N0 is the AWGN power, α k and β k，m is the Lagrange multiplier, α k ≥0 and β k,m ≥0,

[0125] Proof: Since problem P2.1 is convex, it can be solved by using the Karush-Kuhn-Tucker (KKT) condition.

[0126] As can be seen from Theorem 1, the optimal sensing power depends on the posterior distribution of the features acquired in that time slot and the characteristics of the human action recognition task, namely the number of action categories and the required performance. In particular, when the difference in the posterior mean of the features between the two categories is large, it indicates that the impact of the interfering object is small, making it easier to distinguish the two categories from the features. In this case, the required sensing power can be reduced. In addition, the optimal transmission power depends on the wireless channel state in that time slot. Since the total energy constraint of M consecutive time slots is given, the power allocation between time slots is interrelated. Therefore, it is necessary to jointly allocate sensing power and transmission power across all time slots.

[0127] Theorem 2: The optimal quantization gain of ISAC device k in time slot m is:

[0128]

[0129] in,

[0130] Proof: Please refer to Theorem 1 for proof.

[0131] Theorem 2 shows that the optimal quantization gain depends on the posterior distribution of the features acquired in that time slot and the requirements of the overall human action recognition task, namely the number of action categories and performance requirements. Similarly, when the difference in the posterior mean of the features between the two categories is large, the required quantization gain is small, and vice versa.

[0132] After obtaining the optimal sensing power, quantization gain, and transmission power, the Lagrange multiplier is updated, completing one iteration. This iterative process continues until convergence, thus solving problem P2.1.

[0133] For example, allocating the communication bandwidth in the second stage based on the optimization result of the first stage includes:

[0134] Based on bandwidth constraints and energy consumption constraints, combined with the communication bandwidth and communication time of each ISAC device at time slot m, the average bandwidth allocation result of the ISAC device in the time slot is calculated;

[0135] The communication bandwidth allocation result is optimized by combining the number of cellular users, the fixed bandwidth of cellular users, the average bandwidth allocation result, the difference between the transmission overhead and the maximum communication volume.

[0136] Specifically, based on the optimization of perception power, transmission power, and quantization gain in the previous stage, the second stage can obtain the optimal communication bandwidth allocation between ISAC devices. The constraints of problem P2 show that the allocated bandwidth must meet the requirements for successful transmission of the characteristics. To ensure that a feasible solution exists for problem P2, problem P2.2 can be formulated as follows:

[0137]

[0138] This is a convex optimization problem that can be solved by Based on the bandwidth allocation results obtained in P2.2, the bandwidth will be updated according to Theorem 3.

[0139] Theorem 3: When there are u(m) cellular users sharing bandwidth B with the ISAC device in time slot m, the optimal bandwidth allocated to ISAC device k is:

[0140]

[0141] Where B0 is the fixed bandwidth allocated to each cellular user, is the total bandwidth obtained from Problem P2.2, Represents the difference between the required transmission overhead and the maximum communication volume.

[0142] From Theorem 3, we can see that when When , the solution to problem P2.2 is the optimal bandwidth allocation, that is, when When , more remaining bandwidth will be allocated to the ISAC device to improve the discrimination gain, i.e. Otherwise, the allocated bandwidth of the ISAC device will be reduced to meet the bandwidth constraint, i.e. In addition, when the difference The larger the value, the larger the bandwidth allocation update. In other words, when the maximum communication volume is much smaller than the required transmission overhead, more bandwidth will be allocated to the ISAC device. Figure 8 As shown in Figure 3, the accuracy of human action recognition varies under different bandwidth budgets.

[0143] Since the TSO algorithm is static, it needs to obtain information of all time slots in advance. The TSO algorithm can maximize the recognition accuracy in a dynamic environment by jointly optimizing resources in all time slots. Figure 9 As shown in Figure 3, the accuracy of human action recognition varies under different energy budgets.

[0144] Exemplary, reference Figure 2 As shown, the method further includes:

[0145] In step S21, each ISAC device responds to the sensing request of the edge server and executes a sensing mode, transmitting a frequency modulated continuous wave (FMCW) signal within a sensing time interval; the sensing time interval includes M consecutive time slots;

[0146] In step S22, the ISAC device receives the echo signal, extracts features from the echo signal, quantizes the extracted motion feature data corresponding to the target to be identified, and generates a corresponding quantized feature set; wherein the quantized feature set includes: the quantization gain and quantization distortion of the ISAC device at time slot m;

[0147] Step S23: The ISAC device switches to a communication mode and sends the quantitative feature set to the edge server.

[0148] Exemplary, reference Figure 3 As shown, the method further includes:

[0149] In step S31, the edge server receives the quantitative feature set fed back by each ISAC device; wherein each ISAC device shares a channel with the current cellular communication user via orthogonal frequency division multiple access;

[0150] Step S32 , performing feature data integration processing based on the quantitative feature sets corresponding to a plurality of consecutive time slots to obtain an action recognition result corresponding to the target to be recognized.

[0151] Exemplary, reference Figure 4 As shown in Figure 2, consider a multi-device collaborative human action recognition system. In this system, K Each single-antenna device performs radar perception to obtain different perspectives of the target and transmits its observed features to an edge server. By fusing the received features, the edge server recognizes the target's movements. Assume that some unknown interfering objects move around the target, resulting in different perception qualities between devices and on different time slots. Each device is equipped with a dual-function radar and communication (DFRC) transceiver, called an integrated sensing and communication (ISAC) device. The ISAC device can switch between perception mode and communication mode in a time-division manner. This means that a time slot is divided into a perception time interval and a communication time interval. When a perception request is received from the edge server, the data collection process starts and continues M In each time slot, the ISAC device first performs a sensing mode. Specifically, it transmits a frequency modulated continuous wave (FMCW) signal during the sensing time interval. Figure 6 As shown in the figure, the accuracy of human action recognition varies with the duration of data collection. By processing the echoes, the ISAC device extracts and quantifies features that capture information about the target's actions. The ISAC device then switches to communication mode and transmits its quantified features to the edge server. After data collection is complete, the edge server aggregates all received features to identify the target's actions.

[0152] The DFRC transceiver uses shared beamforming front-end circuitry to flexibly switch between sensing mode and communication mode, thereby achieving the integrated fusion of sensing and communication. Figure 5 As shown in the figure, the Dual-Function Radar-Communication (DFRC) transceiver flexibly switches between the perception mode and the communication mode in a time-division manner by sharing the RF front-end circuit, thereby realizing the integration of perception and communication (ISAC). In the perception mode, the DFRC system transmits a Frequency Modulated Continuous Wave (FMCW) signal composed of multiple uplink ramp chirps. Subsequently, by processing the received radar echo signal, the ISAC device can obtain perception data containing the motion information of the perceived target. In the communication mode, the DFRC system adopts a digital modulation scheme (for example, Quadrature Amplitude Modulation (QAM)) for signal transmission. Through flexible mode switching, the DFRC transceiver can achieve high-precision detection of the perceived target while meeting the communication requirements, fully reflecting the versatility and resource utilization efficiency of the ISAC system. Reference Figure 7 As shown in Figure 3, the accuracy of human action recognition varies with the size of the network.

[0153] The method provided by the embodiment of the present invention can be applied to a multi-device collaborative human motion recognition system with dynamic interference and time-varying network status, and is composed of multiple devices with limited energy and bandwidth and edge servers. These devices obtain perception data from the received signals in continuous time periods and transmit it to the edge server to complete the motion recognition task of the mobile target. The solution of the present invention proposes a novel resource allocation problem, which optimizes the perception and communication resource allocation of multiple devices in multiple time periods, aiming to maximize the accuracy of human motion recognition. Specifically, the accuracy of human motion recognition is measured by the discriminant gain theory. A two-stage offline (TSO) algorithm is proposed to solve the resource allocation problem proposed by us, optimize energy allocation, quantization gain and bandwidth allocation, and dynamically allocate resources to maximize the accuracy of human motion recognition. Extensive numerical results show that the proposed algorithm outperforms the comparison algorithm in the overall performance of human motion recognition.

[0154] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention and are not intended to be limiting. It is readily understood that the processes illustrated in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0155] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0156] Figure 10 A schematic diagram of an electronic device suitable for implementing an embodiment of the present invention is shown.

[0157] It should be noted that Figure 10 The electronic device 1000 shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0158] like Figure 10 As shown, electronic device 1000 includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to the program stored in read-only memory (ROM) 1002 or the program loaded from storage portion 1008 into random access memory (RAM) 1003. Various programs and data required for system operation are also stored in RAM 1003. CPU 1001, ROM 1002 and RAM 1003 are connected to each other via bus 1004. Input / output (I / O) interface 1005 is also connected to bus 1004.

[0159] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, and the like; an output section 1007 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 1008 including a hard disk and the like; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. Removable media 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 1010 as needed, so that computer programs read therefrom can be installed into the storage section 1008 as needed.

[0160] In particular, according to an embodiment of the present invention, the process described below with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a storage medium, the computer program containing program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 1009 and / or installed from a removable medium 1011. When the computer program is executed by the central processing unit (CPU) 1001, the various functions defined in the system of the present application are performed.

[0161] It should be noted that the storage medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any storage medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on the storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0162] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0163] The units involved in the embodiments of the present invention may be implemented in software or hardware, and the units described may also be provided in a processor. In some cases, the names of these units do not limit the units themselves.

[0164] It should be noted that, as another aspect, the present application also provides a storage medium, which can be included in an electronic device; or it can exist independently without being installed in the electronic device. The above storage medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the following embodiments. For example, the electronic device can implement the following Figure 1 The individual steps of the method are shown.

[0165] In one embodiment, the present application provides a computer program product, including a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0166] Furthermore, the above-described figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention and are not intended to be limiting. It is readily understood that the processes illustrated in the above-described figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0167] Other embodiments of the present invention will readily occur to those skilled in the art after considering the specification and practicing the invention herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the claims.

[0168] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof, which is limited only by the appended claims.

Claims

1. A sensory integration resource allocation method based on multi-device collaboration, characterized in that: The method comprises: Define the bandwidth constraint of the ISAC device at time slot m based on the fixed bandwidth of the cellular user, the number of cellular users at time slot m, and the total bandwidth; Define the energy consumption constraint of the ISAC device in M ​​consecutive time slots based on the time the ISAC device executes the sensing mode and the communication mode in each time slot, the transmit power of the ISAC device in a single time slot, and the total energy budget of the ISAC device in m time slots. The problem of maximizing the discriminant gain is defined based on various constraints, including the joint optimization of perception power, transmission power, quantization gain, and communication bandwidth. Based on predefined bandwidth conditions, the perception power, transmission power, and quantization gain are optimized in the first phase. In the second phase, based on the optimization results of the first phase, the communication bandwidth is allocated. This includes: calculating the average bandwidth allocation result of the ISAC device in the time slot m based on bandwidth constraints and energy consumption constraints, combined with the communication bandwidth and communication time of each ISAC device in time slot m; and optimizing the communication bandwidth allocation result based on the number of cellular users, the fixed bandwidth of cellular users, the average bandwidth allocation result, and the difference between transmission overhead and maximum communication volume.

2. The method according to claim 1, characterized in that In the first stage, the perceived power is optimized, including: Based on the number of action categories of the target to be identified and the posterior mean of the features under each action category, the ISAC device performs the sensing mode for a certain duration in time slot m and optimizes the sensing power in combination with the Lagrange multiplier.

3. The method according to claim 1, characterized in that In the first stage, the transmit power is optimized, including: The transmit power is optimized according to the channel gain of the ISAC device at time slot m, the bandwidth of the ISAC device at time slot m, and the Lagrange multiplier.

4. The method according to claim 1, wherein In the first stage, the quantization gain is optimized, including: Based on the posterior distribution of the features obtained at time slot m, the number of action categories corresponding to the target to be identified and the performance requirements, the quantization gain is optimized in combination with the Lagrange multiplier.

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: The Lagrange multiplier is updated based on the currently obtained optimization results of the perceived power, quantization gain, and transmit power, and the perceived power, quantization gain, and transmit power are iterated based on the updated Lagrange multiplier until convergence.

6. The method according to claim 1, characterized in that The method further comprises: The constraint condition for defining the transmission overhead of the ISAC device in time slot m includes: the transmission overhead is less than the maximum communication volume in the communication duration in one time slot.

7. The method according to claim 1, characterized in that The method further comprises: Each ISAC device responds to the sensing request of the edge server and executes the sensing mode to transmit a frequency modulated continuous wave (FMCW) signal during the sensing time interval; the sensing time interval includes M consecutive time slots; The ISAC device receives the echo signal, extracts features from the echo signal, quantizes the extracted motion feature data corresponding to the target to be identified, and generates a corresponding quantized feature set; wherein the quantized feature set includes: the quantization gain and quantization distortion of the ISAC device at time slot m; The ISAC device switches to a communication mode and sends the quantitative feature set to the edge server.

8. The method according to claim 7, characterized in that The method further comprises: The edge server receives the quantitative feature set fed back by each ISAC device; wherein each ISAC device shares the channel with the current cellular communication user through orthogonal frequency division multiple access; Feature data integration processing is performed based on the quantized feature sets corresponding to multiple consecutive time slots to obtain the action recognition results corresponding to the target to be identified.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the sensory communication integrated resource allocation method based on multi-device collaboration described in any one of claims 1 to 8 is implemented.

10. An electronic device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; The processor is configured to execute the sensory communication integrated resource allocation method based on multi-device collaboration according to any one of claims 1 to 8 by executing the executable instructions.

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