Optimized scheduling method and device for data transmission, equipment and storage medium

Abstract

The invention relates to an optimal scheduling method and device for data transmission, equipment and a storage medium, and the method comprises the steps: calculating a participation probability according to a local weight when a monitored channel state is that the channel is idle, randomly generating a pseudo-random number, and determining whether to participate in transmission or not based on the participation probability and the pseudo-random number; when determining to participate in sending, constructing a sending frame containing a coding rate and a transmitting power; the sending frame is sent to the receiving node, feedback information returned by the receiving node is received, and the feedback information is obtained by estimating a channel gain matrix by the receiving node based on at least one sending frame, calculating channel capacity based on the channel gain matrix and judging whether the sending frame can be decoded or not according to the system channel capacity; if the feedback information is decoding failure, the coding rate and the transmitting power are dynamically adjusted, and if the feedback information is decoding success, a next time slot node sending strategy fed back by the receiving node is received; compared with the prior art, the technical scheme of the invention can reasonably schedule node access and improve the channel utilization efficiency.

Description

Technical Field

[0001] This application relates to the technical field of wireless communication, and in particular to an optimized scheduling method, apparatus, device, and storage medium for data transmission. Background Technology

[0002] With the development of wireless communication technology, especially in typical scenarios with low bandwidth, high interference, and multiple concurrent nodes, such as underwater acoustic communication, low-power IoT LoRa, and UAV clusters, how to reasonably schedule node access, improve channel utilization efficiency, and enhance communication reliability in communication systems has become a core research challenge.

[0003] In such environments, the commonly used multi-node access method is the random access mechanism. The basic idea is that each node decides whether to transmit after detecting that the channel is idle. If a collision occurs, it waits for a backoff period and tries again. This type of mechanism has the advantages of simple implementation and no need for centralized control, so it is widely used in wireless ad hoc networks. In addition, at the physical layer, forward error correction coding and power control mechanisms are generally introduced to improve bit error rate performance and transmission reliability. Nodes can adjust the coding strength and transmission power according to the channel quality to adapt to the communication needs in different environments.

[0004] While the aforementioned methods have achieved some success in various communication scenarios, most MAC protocols rely solely on probabilistic backoff mechanisms, failing to perceive channel conditions and spatial differences between nodes. This leads to frequent access conflicts, particularly in scenarios with dense node density or drastic channel fluctuations. Furthermore, in multi-node concurrency, the receiver often only performs a "retransmit if it can't decode" approach, without analyzing spatial channel gain differences from different nodes or identifying which nodes are causing interference. This results in a lack of optimization basis for scheduling and limited system performance. Moreover, many protocols are designed with only fixed coding schemes or simple SNR threshold adjustments, failing to incorporate dynamic optimization based on feedback information. This inability to balance redundancy costs and bit error rates wastes resources and impacts throughput. Summary of the Invention

[0005] This application provides an optimized scheduling method, apparatus, device, and storage medium for data transmission, which can rationally schedule node access and improve channel utilization efficiency.

[0006] In a first aspect, this application provides an optimized scheduling method for data transmission, comprising: monitoring the channel state; when the channel state is idle, calculating the participation probability based on local weights and randomly generating a pseudo-random number; determining whether to participate in transmission based on the participation probability and the pseudo-random number; when determining to participate in transmission, constructing a transmission frame, wherein the transmission frame contains a coding rate and a transmit power; sending the transmission frame to a receiving node and receiving feedback information returned by the receiving node, wherein the feedback information is obtained by the receiving node estimating a channel gain matrix based on at least one received transmission frame, calculating the channel capacity based on the channel gain matrix, and determining whether the transmission frame is decodeable based on the system channel capacity; if the feedback information indicates decoding failure, dynamically adjusting the coding rate and the transmit power; if the feedback information indicates decoding success, receiving the next time slot node transmission strategy fed back by the receiving node.

[0007] In one possible implementation, the monitoring of the channel state specifically includes: periodically detecting the time slot signal in the time slot, and performing a fast Fourier transform on the detected time slot signal to obtain a frequency domain subcarrier; based on the frequency domain subcarrier, determining the received signal strength of the time slot, and converting the received signal strength into an instantaneous energy value; comparing the instantaneous energy value with a preset noise threshold, and if the instantaneous energy value is less than the noise threshold, determining that the channel state is idle; otherwise, confirming that the channel state is busy.

[0008] In one possible implementation, the step of calculating the participation probability based on local weights and randomly generating a pseudo-random number, and determining whether to participate in transmission based on the participation probability and the pseudo-random number, specifically includes: calculating the participation probability based on local weights, wherein the local weights include at least one of the queue length of the data to be transmitted, the priority level of the data group, and the historical collision count statistics; randomly generating a pseudo-random number, comparing the participation probability with the pseudo-random number, and determining to participate in transmission if the pseudo-random number is not greater than the participation probability.

[0009] In one possible implementation, constructing the transmission frame specifically includes: obtaining the current channel gain matrix fed back by the receiving node; determining the transmission power based on the current channel gain matrix; and determining the coding rate based on the coding redundancy length and the payload of the data to be transmitted; constructing a frame header field based on the coding rate and the transmission power; and dynamically encoding the payload to obtain coded data; and concatenating the frame header field and the coded data to obtain the transmission frame.

[0010] In one possible implementation, the receiving node estimates the channel gain matrix based on at least one received transmission frame, specifically including: the receiving node receiving at least one transmission frame transmitted by the transmitting node based on multiple receiving antenna channels, and extracting pilot symbols from the transmission frame corresponding to each receiving antenna channel; performing least squares estimation on the pilot symbols to obtain the gain vector corresponding to each receiving antenna channel, and integrating all the gain vectors to obtain the channel gain matrix.

[0011] In one possible implementation, the step of calculating the system channel capacity based on the channel gain matrix and determining whether the transmitted frame can be decoded based on the system channel capacity specifically includes: obtaining the node access state matrix and the channel noise covariance; substituting the channel noise covariance, the node access state matrix, and the channel gain matrix into a preset system channel capacity calculation formula to obtain the system channel capacity; comparing the system channel capacity with a preset capacity threshold; if the system channel capacity is not greater than the capacity threshold, then determining that the transmitted frame cannot be successfully decoded; if the system channel capacity is greater than the capacity threshold, then constructing a first receiving matrix based on the node access state matrix and the channel gain matrix; continuously sampling each receiving antenna channel to obtain the receiving vector corresponding to each receiving antenna channel; integrating all the receiving vectors to construct a second receiving matrix; constructing a linear equation based on the first receiving matrix and the second receiving matrix, and solving the linear equation; if the estimated value of the transmitted frame is obtained, then determining that the transmitted frame can be successfully decoded.

[0012] In one possible implementation, the next time slot node transmission strategy is as follows: the receiving node uses the effective information transmission volume per unit time as the objective function, enumerates and generates all node access state matrices, calculates the objective function value corresponding to each of the node access state matrices, determines the optimal node access state matrix based on the objective function value, and determines the node access state based on the node access state in the optimal node access state matrix.

[0013] Secondly, this application provides an optimized scheduling device for data transmission, comprising: a transmission detection module, a transmission frame construction module, a feedback information receiving module, and a dynamic adjustment module; wherein, the transmission detection module is used to monitor the channel state, and when the channel state is idle, calculate the participation probability based on local weights and randomly generate a pseudo-random number, and determine whether to participate in transmission based on the participation probability and the pseudo-random number; the transmission frame construction module is used to construct a transmission frame when it is determined to participate in transmission, wherein the transmission frame contains a coding rate and a transmit power; the feedback information receiving module is used to send the transmission frame to a receiving node and receive feedback information returned by the receiving node, wherein the feedback information is obtained by the receiving node estimating the channel gain matrix based on at least one received transmission frame, calculating the channel capacity based on the channel gain matrix, and determining whether the transmission frame is decodeable based on the system channel capacity; the dynamic adjustment module is used to dynamically adjust the coding rate and the transmit power if the feedback information indicates decoding failure, and receive the next time slot node transmission strategy fed back by the receiving node if the feedback information indicates decoding success.

[0014] Thirdly, embodiments of this application also provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0015] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the above-described method.

[0016] This application provides an optimized scheduling method, apparatus, device, and storage medium for data transmission, which has the following advantages compared with the prior art:

[0017] By monitoring the channel state, when the channel is idle, the participation probability is calculated based on local weights, and a pseudo-random number is randomly generated. Whether to participate in transmission is determined based on the participation probability and the pseudo-random number. When participation in transmission is determined, a transmission frame is constructed, containing a coding rate and transmit power. The transmission frame is sent to the receiving node, and feedback information is received from the receiving node. This feedback information is obtained by the receiving node estimating the channel gain matrix based on at least one received transmission frame, calculating the channel capacity based on the channel gain matrix, and determining whether the transmission frame is decodeable based on the system channel capacity. If the feedback information indicates decoding failure, the coding rate and transmit power are dynamically adjusted; if the feedback information indicates successful decoding, the transmission frame is received. The present invention describes the next time slot node transmission strategy fed back by the receiving node. Compared with the prior art, the technical solution of this application, in low bandwidth and high interference scenarios, allows the transmitting node to dynamically calculate the participation probability based on local weights and combine it with pseudo-random number decision-making for access. Compared with the traditional fixed probability backoff mechanism, this can significantly reduce the collision probability when nodes are dense and improve channel utilization. Moreover, the transmitted frame integrates dynamic coding rate and transmit power configuration. The receiving end models the channel capacity through the channel gain matrix and feeds back the decoding information, enabling the transmitting node to adjust the coding rate and transmit power in real time according to the feedback information. At the same time, the receiving end feeds back the next time slot node transmission strategy to the transmitting node based on the decoding judgment mechanism of channel capacity, avoiding the blind retransmission when the decoding fails in the existing method, making the scheduling strategy more targeted, and able to reasonably schedule node access, thereby improving channel utilization efficiency. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0021] Figure 1 This is a flowchart illustrating an embodiment of an optimized scheduling method for data transmission provided in this application;

[0022] Figure 2This is a schematic diagram of the structure of an embodiment of a data transmission optimization scheduling device provided in this application;

[0023] Figure 3 This is a schematic diagram of the structure of a computer device provided in this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] The following disclosure provides numerous different embodiments or examples for implementing various structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0026] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0027] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0028] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0029] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrases "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0030] Example 1, see Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of an optimized scheduling method for data transmission provided in this application, as shown below. Figure 1 As shown, the method includes steps 101-104, as detailed below:

[0031] Step 101: Monitor the channel status. When the channel status is idle, calculate the participation probability based on the local weight and randomly generate a pseudo-random number. Determine whether to participate in transmission based on the participation probability and the pseudo-random number.

[0032] In one embodiment, when monitoring the channel state, the transmitting node periodically detects the time slot signal in the time slot and performs a Fast Fourier Transform on the detected time slot signal to obtain a frequency domain subcarrier; based on the frequency domain subcarrier, the received signal strength of the time slot is determined and converted into an instantaneous energy value; the instantaneous energy value is compared with a preset noise threshold; if the instantaneous energy value is less than the noise threshold, the channel state is determined to be idle; otherwise, the channel state is confirmed to be busy.

[0033] Specifically, in underwater acoustic communication scenarios, channel resources are scarce and signal propagation delays are large. Periodic detection can avoid the energy waste caused by continuous monitoring, while ensuring real-time perception of channel changes. Therefore, in this embodiment, the transmitting node periodically monitors its current time slot through a timed sampling mechanism. The monitoring period is usually synchronized with the system time slot division in order to extract the time slot signal.

[0034] Preferably, the transmitting node can also periodically detect the time-domain signal in the frequency-domain sub-channel and perform fast Fourier transform processing on the detected time-domain signal to obtain the frequency-domain subcarrier.

[0035] Specifically, performing a fast Fourier transform on a time-slotted signal in the time domain essentially converts the signal from the time domain to the frequency domain, making the energy distribution of each subcarrier visible and obtaining multiple equally spaced frequency domain subcarriers.

[0036] Specifically, when determining the received signal strength of the time slot based on the frequency domain subcarriers, the energy intensity corresponding to each frequency domain subcarrier is calculated, and the sum of the energy intensities of all frequency domain subcarriers is calculated to obtain the received signal strength of the time slot.

[0037] Specifically, the preset noise threshold P th The system dynamically sets the noise level based on the ambient background noise level, such as by determining the noise statistics during no-load conditions, if the instantaneous energy value P obs <P th This indicates that the current channel energy is mainly background noise, and the channel is determined to be idle; if the instantaneous energy value P obs ≥P th If the signal energy exceeds the noise level in the channel, it indicates that the channel is busy.

[0038] Preferably, to avoid misjudgment caused by instantaneous noise fluctuations, the transmitting node will also base its judgment on the instantaneous energy value P obtained from historical monitoring. obs Set a historical sliding window and use mean filtering or median filtering to eliminate sudden noise interference; for example, when the instantaneous energy value P in a certain time slot... obs Briefly exceeding the noise threshold P th However, most samples within the historical sliding window were below the noise threshold P. th Even if the system still determines that the channel is idle, based on this historical sliding window, it can further eliminate background noise interference, reduce the false judgment rate, and ensure the accuracy of subsequent access decisions.

[0039] In one embodiment, when the channel state is busy, it is directly determined that the current transmitting node will not participate in subsequent transmissions, a backoff strategy is executed, and the node waits for the next cycle.

[0040] In one embodiment, when the channel state is channel idle, the contention determination phase is entered.

[0041] In one embodiment, when the channel state is idle, the participation probability is calculated based on local weights, and a pseudo-random number is randomly generated. When determining whether to participate in transmission based on the participation probability and the pseudo-random number, the participation probability is calculated based on local weights, wherein the local weights include at least one of the queue length of the data to be transmitted, the priority level of the data packet, and the historical collision count statistics. A pseudo-random number is randomly generated, and the participation probability is compared with the pseudo-random number. If the pseudo-random number is not greater than the participation probability, then participation in transmission is determined.

[0042] Specifically, when calculating the participation probability based on local weights, the queue length of the data to be sent, the priority level of the data group, and the historical collision count are weighted, combined, and normalized to obtain the participation probability.

[0043] Specifically, the sending node generates a pseudo-random number r using a hardware random number generator or a software algorithm. i ∈[0,1].

[0044] Specifically, if the pseudo-random number is not greater than the participation probability, the sending node determines to participate in the transmission and sets the access status of the sending node. If the pseudo-random number is greater than the participation probability, the sending node determines not to participate in the transmission and sets the access status of the sending node.

[0045] Specifically, when the pseudo-random number is greater than the participation probability, the sending node determines not to participate in the sending and also executes the backoff strategy, waiting for the next cycle.

[0046] Preferably, since the number of sending nodes in the network includes at least one, when the number of sending nodes is multiple, a node access state matrix is ​​also generated for the access state of each sending node. If a sending node participates in sending, its state in the node access state matrix is ​​as follows:

[0047] In one embodiment, in wireless communication, if all transmitting nodes transmit arbitrarily, frequent collisions will occur, leading to data corruption. Therefore, this application employs a space-time random access control mechanism to control whether each transmitting node is allowed to transmit at a certain time. The transmitting node monitors whether the current channel is idle; if the channel is idle, the node calculates whether to participate in transmission according to an algorithm; the node access status is used... (participation) or (Non-participation) marking; This process is dynamic and continuously updates decisions based on channel feedback, which can effectively reduce the probability of transmission collisions and control the use of channel resources.

[0048] Step 102: When it is determined that participation in transmission is required, a transmission frame is constructed, wherein the transmission frame contains the coding rate and the transmit power.

[0049] In one embodiment, when constructing a transmission frame, the current channel gain matrix fed back by the receiving node is obtained, the transmission power is determined based on the current channel gain matrix, and the coding rate is determined based on the coding redundancy length and the payload of the data to be transmitted; based on the coding rate and the transmission power, a frame header field is constructed, and the payload is dynamically encoded to obtain coded data; the frame header field and the coded data are concatenated to obtain the transmission frame.

[0050] Specifically, the transmitting node obtains the channel gain matrix H through feedback from the receiving end. This matrix is ​​estimated by the receiving end through pilot symbols, such as the least squares method, and represents the spatial channel gain from each transmitting node to the receiving end.

[0051] Specifically, the coding redundancy length and the payload of the data to be transmitted are input into the coding rate calculation formula to calculate the coding rate. The coding redundancy length can be dynamically configured. The coding rate calculation formula is as follows:

[0052]

[0053] In the formula, R c L is the coding rate. p For the effective load, L c This is the length of the encoding redundancy.

[0054] Specifically, the transmission power is dynamically determined by the received current channel gain matrix to maximize the receiver capacity.

[0055] Specifically, the frame header field consists of a synchronization identifier, a sequence number, a coding rate, and a transmit power. By encapsulating these four sets of parameters into the frame header field, it is convenient for the receiving end to extract and parse them later.

[0056] Specifically, the payload is dynamically encoded using adjustable LDPC or convolutional coding to obtain encoded data.

[0057] Specifically, the payload is the raw bit data to be transmitted.

[0058] Step 103: Send the transmitted frame to the receiving node and receive feedback information returned by the receiving node, wherein the feedback information is obtained by the receiving node estimating the channel gain matrix based on at least one received transmitted frame, calculating the channel capacity based on the channel gain matrix, and determining whether the transmitted frame is decodeable based on the system channel capacity.

[0059] In one embodiment, if multiple transmitting nodes participate in transmitting during a certain receiving period, each transmitting node sends its corresponding transmitting frame to the receiving node, so that the receiving node's multiple receiving antenna channels receive the transmitting frames sent by the multiple transmitting nodes respectively.

[0060] Specifically, when multiple sending nodes send frames to a receiving node, the multiple sending frames constitute a complete data matrix: X = [x1; x2; ...; x...]. k ], where k is the total number of sending nodes.

[0061] In one embodiment, when a receiving node estimates the channel gain matrix based on at least one received transmission frame, the receiving node receives at least one transmission frame transmitted by a transmitting node based on multiple receiving antenna channels, and extracts pilot symbols from the transmission frames corresponding to each receiving antenna channel; performs least squares estimation on the pilot symbols to obtain the gain vector corresponding to each receiving antenna channel, and integrates all the gain vectors to obtain the channel gain matrix.

[0062] Specifically, in scenarios with multiple transmitting nodes, each transmitting node has a different location and transmission path. Therefore, the signals received by the receiving node have spatial gain differences, and these differences constitute the channel gain matrix of the receiving antenna channel.

[0063] Specifically, during the system initialization phase or each frame reception cycle, the receiving node performs training sequence listening based on the receiving node fusion estimation and channel state awareness mechanism; each transmitting node inserts a preset pilot symbol in its frame header, which is globally known in the system; the receiving node estimates the gain vector of each receiving antenna channel using the least squares estimation or least mean square error estimation method based on the pilot symbol, thereby obtaining the gain vector corresponding to each transmitting node, and integrating the gain vectors corresponding to all transmitting nodes to obtain the channel gain matrix.

[0064] Preferably, for a system with multiple receive antenna channels, the gain vector corresponding to each transmit node is: h i =[h i1 ,h i2 ,…,h iT ].

[0065] Preferably, the channel gain matrix is ​​formed by stacking the gain vectors corresponding to all transmitting nodes.

[0066] In one embodiment, when calculating the system channel capacity based on the channel gain matrix and determining whether the transmitted frame can be decoded based on the system channel capacity, the node access state matrix and the channel noise covariance are obtained. The channel noise covariance, the node access state matrix, and the channel gain matrix are substituted into a preset system channel capacity calculation formula to obtain the system channel capacity. The system channel capacity is compared with a preset capacity threshold. If the system channel capacity is not greater than the capacity threshold, it is determined that the transmitted frame cannot be successfully decoded. If the system channel capacity is greater than the capacity threshold, a first receiving matrix is ​​constructed based on the node access state matrix and the channel gain matrix. Each receiving antenna channel is continuously sampled to obtain the receiving vector corresponding to each receiving antenna channel. All the receiving vectors are integrated to construct a second receiving matrix. A linear equation is constructed based on the first receiving matrix and the second receiving matrix, and the linear equation is solved. If the estimated value of the transmitted frame is obtained, it is determined that the transmitted frame can be successfully decoded.

[0067] Specifically, the preset system channel capacity calculation formula is as follows:

[0068]

[0069] In the formula, C is the system channel capacity, σ 2 Let I be the channel noise covariance, and let H be the channel gain matrix, Λ be the node access state matrix, and Λ be the channel noise covariance matrix. T H is the transpose of the node access state matrix. T This is the transpose of the channel gain matrix.

[0070] Specifically, by setting a capacity threshold C min If the system channel capacity C > C min If the probability of successful decoding of the access state matrix of the node is high, then the node is considered to have a low probability of successful decoding of the access state matrix, and the transmitted frame cannot be successfully decoded.

[0071] Specifically, if the node is considered to have a high probability of successfully decoding the access state matrix, it is further determined whether the data in the sent frame can be successfully decoded.

[0072] Specifically, when constructing the first receiving matrix based on the node access state matrix and the channel gain matrix, since multiple transmitting nodes participate in transmission during one receiving cycle, each transmitting a frame of size x. i The total signal received by the multiple receiving antenna channels of the receiving node through channel superposition is denoted as: Alternatively, in matrix form: y = HΛX + N, where N is the noise matrix, and this matrix form is used as the first receiving matrix.

[0073] Specifically, each receiving antenna channel is continuously sampled to obtain the corresponding receiving vector. When integrating all the receiving vectors to construct the second receiving matrix, each receiving antenna channel is sampled at L consecutive sampling points to obtain the receiving vector y at time t. (t) ∈R T And concatenate all received vectors column-wise to obtain the second received matrix Y for the entire time period, which is Y = [y (1) y (2) …y (L) ]∈R T×L R is the set of real numbers.

[0074] Specifically, the received matrix Y is the result of the transmitted data matrix X being superimposed with the noise matrix N after being transmitted through the channel HΛ. Decoding is essentially the process of inferring the data matrix X from the received matrix Y. Therefore, a linear equation Y≈HΛX is constructed based on the first and second received matrices. The decoding process is abstracted into solving an inverse problem: whether there exists a solution such that Y≈HΛX holds. If so, it is determined that the transmitted frame can be successfully decoded.

[0075] Preferably, a CRC checksum is usually added during the encoding of the transmitted frame. After decoding, the checksum is recalculated for the estimated value of the transmitted frame in the estimated data matrix and compared with the checksum bit at the end of the frame. If the calculated value is consistent with the received value, the decoding is considered successful.

[0076] Preferably, if the transmitted data matrix X is known, the estimated data matrix and the number of bit errors in the data matrix are calculated. If the number of bit errors is less than 10... -4 If so, then it is considered successful.

[0077] In one embodiment, based on the above decoding determination, if the receiving node successfully decodes, it returns ACK feedback information to the sending node to indicate successful decoding; if the receiving node fails to decode, it returns NACK feedback information to the sending node to indicate decoding failure.

[0078] In one embodiment, during each communication cycle, the receiving node receives a mixed data matrix from multiple transmitting nodes. Due to the different locations and transmission paths of each node, the received signals exhibit spatial gain differences, which constitute the channel gain matrix of the receiving antenna channel. The signals in these receiving antenna channels are sampled and analyzed to establish the current receiving matrix, and the system channel capacity is estimated based on this matrix. If the current system channel capacity is less than the transmitting load, collisions, interference, or channel degradation may occur. The system feeds back this estimation result to the transmitting end, thus providing a basis for access control and parameter adjustment. The accuracy of this module directly affects the effectiveness of the scheduling strategy and is the core source of the communication system's adaptive capability.

[0079] Step 104: If the feedback information indicates decoding failure, dynamically adjust the coding rate and the transmission power; if the feedback information indicates decoding success, receive the next time slot node transmission strategy fed back by the receiving node.

[0080] In one embodiment, if the feedback information indicates a decoding failure, the coding rate and the transmission power are dynamically adjusted while counting the number of decoding failures. When the number of decoding failures exceeds a preset threshold, the coding rate and the transmission power are dynamically adjusted.

[0081] Preferably, the bit error rate is also obtained, and when the bit error rate exceeds a preset bit error rate threshold, the coding rate and the transmission power are dynamically adjusted.

[0082] Specifically, when dynamically adjusting the coding rate and the transmit power, historical transmission performance indicators are statistically analyzed, wherein the historical transmission performance indicators include, but are not limited to, the coding redundancy length L. c Number of retransmissions R tx Bit error rate (BER), current transmit power (P) i If the average retransmission count is high, the current coding redundancy length is increased to reduce the coding rate and increase the current transmit power. If the channel is good, the current coding redundancy length is decreased to increase the coding rate. This is because a large average retransmission count indicates insufficient current coding redundancy, and increasing the coding redundancy length L... c To reduce the coding rate, if the coding redundancy length L c If the error rate is already high and decoding still fails, it indicates poor channel quality. In this case, increase the transmit power. If excessive transmit power is used without effectively reducing bit errors, it indicates low redundancy efficiency. In this case, appropriately reduce the coding redundancy length L. c Try a higher data rate.

[0083] Specifically, the transmitting node dynamically adjusts physical layer parameters, including transmit power and coding strength, based on feedback information from the receiving node. When the channel condition is good and the error rate is low, the transmitting node can reduce coding strength and transmit power to reduce coding redundancy, thereby increasing the proportion of effective data and improving transmission efficiency. In cases of poor channel quality or frequent collisions, the node can increase coding strength or increase transmit power to reduce the bit error rate and retransmission frequency. This adjustment process is dynamically completed based on real-time feedback information, reflecting the system's ability to perceive channel changes and its self-optimization capability.

[0084] In one embodiment, if the feedback information indicates successful decoding, the receiving node receives the next time slot node transmission strategy. The next time slot node transmission strategy is determined by the receiving node using the effective information transmission volume per unit time as the objective function, enumerating and generating all node access state matrices, calculating the objective function value corresponding to each of the node access state matrices, determining the optimal node access state matrix based on the objective function value, and determining the node access state based on the node access state in the optimal node access state matrix.

[0085] Specifically, the objective function is as follows:

[0086] f mac =L p / ((L p +L h +L c )×R tx );

[0087] In the formula, f mac L represents the effective information transmission volume per unit time. p For the effective load, L h For frame header fields, L c R is the length of the coding redundancy. tx This represents the number of retransmissions.

[0088] Specifically, based on the system channel capacity calculated above, the receiving node constructs a fitting expression for the system channel capacity and the number of retransmissions, wherein the fitting expression is as follows:

[0089] R tx ≈1+α·exp(-β·C);

[0090] In the formula, R tx Let denot be the number of retransmissions, C be the system channel capacity, and α and β be the fitting parameters.

[0091] Specifically, by substituting the number of retransmissions into the objective function, the objective function value can be calculated.

[0092] Specifically, when determining the transmission strategy for the next time slot node, all feasible node access state matrices are generated by enumeration or using a heuristic algorithm. For each node access state matrix, its corresponding channel gain matrix, noise power, and coding redundancy length are determined. Based on the channel gain matrix and the node access state matrix, the system channel capacity corresponding to each node access state matrix is ​​calculated. Based on the system channel capacity, the number of retransmissions is calculated. Based on the number of retransmissions and the coding redundancy length, the objective function value corresponding to each node access state matrix is ​​calculated. For all objective function values, the optimal solution among all feasible node access state matrices is determined, resulting in the optimal node access state matrix. The node access states in the optimal node access state matrix are then output. As the next time slot node transmission strategy, it is used to indicate which nodes should transmit and which nodes should remain silent in the next scheduling cycle, so that the receiver can decode it and the overall MAC layer transmission efficiency is maximized.

[0093] Specifically, the optimal solution in the state matrix for all feasible node accesses is as follows:

[0094]

[0095] In the formula, Λ * The optimal node access state matrix, Λ i Let i be the access state matrix for the i-th node.

[0096] Specifically, this application abstracts the access state of each node into a diagonal node access state matrix. The node access state matrix is ​​used as the core variable for scheduling optimization, which clearly reflects whether each node transmits at a certain moment. By enumerating or optimizing the system capacity and load function through different combinations of node access state matrices, the joint optimal selection of the set of transmitting nodes and physical parameters is achieved, providing a unified form of expression and computational basis for subsequent scheduling strategies.

[0097] Specifically, the objective function is the amount of effective information transmitted per unit time, taking into account factors such as effective payload length, coding redundancy, frame header overhead, and retransmission count. Using this function as the optimization objective, based on the current channel capacity estimate and node status, the set of nodes participating in transmission is dynamically selected; that is, which nodes in the node access state matrix are determined to be in the access state matrix. Set to 1 to indicate which nodes are connected. By employing methods such as combinatorial optimization and heuristic search, the core issues of which nodes should send data and how they should send data are addressed. This maximizes the overall network throughput while ensuring communication reliability, achieving optimal global resource scheduling. Unlike traditional empirical or heuristic scheduling methods, this optimization function provides clear objectives and evaluation criteria for system design, giving the invention advantages such as strong interpretability, tunability, and portability. It is particularly suitable for communication systems with high performance requirements and dynamic environments.

[0098] Example 2, see Figure 2 , Figure 2 This is a schematic diagram of an embodiment of a data transmission optimization scheduling device provided in this application. Corresponding to the above-described data transmission optimization scheduling method, this application also provides a data transmission optimization scheduling device. This data transmission optimization scheduling device includes modules for executing the above-described data transmission optimization scheduling method, and can be configured in terminals such as desktop computers, tablet computers, and laptops. Specifically, the data transmission optimization scheduling device includes a transmission detection module 201, a transmission frame construction module 202, a feedback information receiving module 203, and a dynamic adjustment module 204.

[0099] The transmission detection module 201 is used to monitor the channel status. When the channel status is idle, it calculates the participation probability based on the local weight and randomly generates a pseudo-random number. Based on the participation probability and the pseudo-random number, it determines whether to participate in transmission.

[0100] The transmission frame construction module 202 is used to construct a transmission frame when it is determined to participate in transmission, wherein the transmission frame includes a coding rate and a transmit power.

[0101] The feedback information receiving module 203 is used to send the transmitted frame to the receiving node and receive the feedback information returned by the receiving node. The feedback information is obtained by the receiving node estimating the channel gain matrix based on at least one received transmitted frame, calculating the channel capacity based on the channel gain matrix, and determining whether the transmitted frame is decodeable based on the system channel capacity.

[0102] The dynamic adjustment module 204 is used to dynamically adjust the coding rate and the transmission power if the feedback information indicates decoding failure, and to receive the next time slot node transmission strategy fed back by the receiving node if the feedback information indicates decoding success.

[0103] In one embodiment, the transmission detection module 201 is used to monitor the channel state, specifically including: periodically detecting the time slot signal in the time slot, and performing fast Fourier transform processing on the detected time slot signal to obtain a frequency domain subcarrier; obtaining the received signal strength of the frequency domain subcarrier, and converting the received signal strength into an instantaneous energy value; comparing the instantaneous energy value with a preset noise threshold, and if the instantaneous energy value is less than the noise threshold, determining that the channel state is idle; otherwise, confirming that the channel state is busy.

[0104] In one embodiment, the sending detection module 201 is used to calculate the participation probability based on local weights and randomly generate a pseudo-random number. The determination of whether to participate in sending is based on the participation probability and the pseudo-random number. Specifically, this includes: calculating the participation probability based on local weights, wherein the local weights include at least one of the queue length of the data to be sent, the priority level of the data group, and the historical collision count statistics; randomly generating a pseudo-random number; comparing the participation probability with the pseudo-random number; and determining whether to participate in sending if the pseudo-random number is not greater than the participation probability.

[0105] In one embodiment, the transmission frame construction module 202 is used to construct a transmission frame, specifically including: obtaining the current channel gain matrix fed back by the receiving node; determining the transmission power based on the current channel gain matrix; and determining the coding rate based on the coding redundancy length and the payload of the data to be transmitted; constructing a frame header field based on the coding rate and the transmission power; and dynamically encoding the payload to obtain encoded data; and concatenating the frame header field and the encoded data to obtain the transmission frame.

[0106] In one embodiment, the receiving node in the feedback information receiving module 203 estimates the channel gain matrix based on at least one received transmission frame, specifically including: the receiving node receiving at least one transmission frame sent by the transmitting node based on multiple receiving antenna channels, and extracting pilot symbols from the transmission frame corresponding to each receiving antenna channel; performing least squares estimation on the pilot symbols to obtain the gain vector corresponding to each receiving antenna channel, and integrating all the gain vectors to obtain the channel gain matrix.

[0107] In one embodiment, the receiving node in the feedback information receiving module 203 calculates the system channel capacity based on the channel gain matrix, and determines whether the transmitted frame can be decoded based on the system channel capacity. Specifically, this includes: obtaining the node access state matrix and the channel noise covariance; substituting the channel noise covariance, the node access state matrix, and the channel gain matrix into a preset system channel capacity calculation formula to obtain the system channel capacity; comparing the system channel capacity with a preset capacity threshold; if the system channel capacity is not greater than the capacity threshold, then it is determined that the transmitted frame cannot be successfully decoded; if the system channel capacity is greater than the capacity threshold, then a first receiving matrix is ​​constructed based on the node access state matrix and the channel gain matrix; continuously sampling each receiving antenna channel to obtain the receiving vector corresponding to each receiving antenna channel; integrating all the receiving vectors to construct a second receiving matrix; constructing a linear equation based on the first receiving matrix and the second receiving matrix, and solving the linear equation; if the estimated value of the transmitted frame is obtained, then it is determined that the transmitted frame can be successfully decoded.

[0108] In one embodiment, the next time slot node transmission strategy is as follows: the receiving node uses the effective information transmission amount per unit time as the objective function, enumerates and generates all node access state matrices, calculates the objective function value corresponding to each of the node access state matrices, determines the optimal node access state matrix based on the objective function value, and determines the node access state based on the node access state in the optimal node access state matrix.

[0109] The aforementioned data transmission optimization scheduling device can implement the data transmission optimization scheduling method of the above method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here.

[0110] like Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of a computer device provided in this application; it includes a processor 111, a communication interface 112, a memory 113 and a communication bus 114, wherein the processor 111, the communication interface 112 and the memory 113 communicate with each other through the communication bus 114, and the memory 113 is used to store computer programs.

[0111] In one embodiment of this application, when the processor 111 executes the program stored in the memory 113, it implements the optimized scheduling method for data transmission provided in any of the foregoing method embodiments.

[0112] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program may be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0113] Therefore, embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the optimized scheduling method for data transmission as provided in any of the foregoing method embodiments.

[0114] The storage medium is a physical, non-transient storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), magnetic disk, or optical disk, or any other physical storage medium capable of storing program code. The computer-readable storage medium can be non-volatile or volatile.

[0115] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0117] The steps in the methods of this application embodiment can be adjusted, merged, or deleted according to actual needs. The units in the apparatus of this application embodiment can be merged, divided, or deleted according to actual needs. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0118] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0119] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0120] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Since these modifications and variations fall within the scope of the claims and their equivalents, this application also intends to include these modifications and variations.

[0121] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An optimized scheduling method for data transmission, characterized in that, include: Monitor the channel status. When the channel status is idle, calculate the participation probability based on local weights and randomly generate a pseudo-random number. Determine whether to participate in transmission based on the participation probability and the pseudo-random number. When it is determined that participation in transmission is to be undertaken, a transmission frame is constructed, wherein the transmission frame contains the coding rate and the transmit power; The transmitting frame is sent to the receiving node, and feedback information is received from the receiving node. The feedback information is obtained by the receiving node estimating the channel gain matrix based on at least one received transmitting frame, calculating the channel capacity based on the channel gain matrix, and determining whether the transmitting frame is decodeable based on the system channel capacity. If the feedback information indicates decoding failure, the coding rate and the transmission power are dynamically adjusted; if the feedback information indicates decoding success, the next time slot node transmission strategy fed back by the receiving node is received.

2. The method as described in claim 1 above, characterized in that, The monitoring channel status specifically includes: The time slot signals in the time slots are periodically detected, and the detected time slot signals are processed by fast Fourier transform to obtain frequency domain subcarriers; Based on the frequency domain subcarrier, the received signal strength of the time slot is determined, and the received signal strength is converted into an instantaneous energy value; The instantaneous energy value is compared with a preset noise threshold. If the instantaneous energy value is less than the noise threshold, the channel state is determined to be idle; otherwise, the channel state is confirmed to be busy.

3. The method as described in claim 1 above, characterized in that, The step of calculating the participation probability based on local weights, randomly generating pseudo-random numbers, and determining whether to participate in transmission based on the participation probability and the pseudo-random numbers specifically includes: The participation probability is calculated based on local weights, wherein the local weights include at least one of the following: the queue length of the data to be sent, the priority level of the data group, and the historical collision count statistics. A pseudo-random number is randomly generated. The participation probability is compared with the pseudo-random number. If the pseudo-random number is not greater than the participation probability, then participation in sending is determined.

4. The method as described in claim 1, characterized in that, The construction of the transmission frame specifically includes: Obtain the current channel gain matrix fed back by the receiving node, determine the transmission power based on the current channel gain matrix, and determine the coding rate based on the coding redundancy length and the effective payload of the data to be transmitted; Based on the coding rate and the transmit power, a frame header field is constructed, and the payload is dynamically encoded to obtain encoded data; The frame header field and the encoded data are concatenated to obtain the transmission frame.

5. The method as described in claim 1, characterized in that, The receiving node estimates the channel gain matrix based on at least one of the received transmitted frames, specifically including: The receiving node receives the transmission frame sent by at least one transmitting node based on multiple receiving antenna channels, and extracts pilot symbols from the transmission frame corresponding to each receiving antenna channel. The pilot symbols are estimated by least squares to obtain the gain vector corresponding to each receiving antenna channel. All the gain vectors are integrated to obtain the channel gain matrix.

6. The method as described in claim 5 above, characterized in that, The step of calculating the system channel capacity based on the channel gain matrix and determining whether the transmitted frame is decodeable based on the system channel capacity specifically includes: Obtain the node access state matrix and the channel noise covariance, and substitute the channel noise covariance, the node access state matrix and the channel gain matrix into the preset system channel capacity calculation formula to obtain the system channel capacity; The system channel capacity is compared with a preset capacity threshold. If the system channel capacity is not greater than the capacity threshold, it is determined that the transmitted frame cannot be successfully decoded. If the system channel capacity is greater than the capacity threshold, then a first receiving matrix is ​​constructed based on the node access state matrix and the channel gain matrix; Each receiving antenna channel is continuously sampled to obtain the receiving vector corresponding to each receiving antenna channel. All the receiving vectors are integrated to construct a second receiving matrix. A linear equation is constructed based on the first and second receiving matrices, and the linear equation is solved. If the estimated value of the transmitted frame is obtained, it is determined that the transmitted frame can be successfully decoded.

7. The method as described in claim 6 above, characterized in that, The next time slot node transmission strategy is as follows: the receiving node uses the effective information transmission volume per unit time as the objective function, enumerates and generates all node access state matrices, calculates the objective function value corresponding to each of the node access state matrices, determines the optimal node access state matrix based on the objective function value, and determines the node access state based on the node access state in the optimal node access state matrix.

8. An optimized scheduling device for data transmission, characterized in that, include: The module includes a transmission detection module, a transmission frame construction module, a feedback information receiving module, and a dynamic adjustment module. The transmission detection module is used to monitor the channel status. When the channel status is idle, it calculates the participation probability based on the local weight and randomly generates a pseudo-random number. Based on the participation probability and the pseudo-random number, it determines whether to participate in transmission. The transmission frame construction module is used to construct a transmission frame when it is determined that participation in transmission is required, wherein the transmission frame includes a coding rate and a transmit power; The feedback information receiving module is used to send the transmitted frame to the receiving node and receive the feedback information returned by the receiving node. The feedback information is obtained by the receiving node estimating the channel gain matrix based on at least one received transmitted frame, calculating the channel capacity based on the channel gain matrix, and determining whether the transmitted frame is decodeable based on the system channel capacity. The dynamic adjustment module is used to dynamically adjust the coding rate and the transmission power if the feedback information indicates decoding failure, and to receive the next time slot node transmission strategy fed back by the receiving node if the feedback information indicates decoding success.

9. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, can implement the method as described in any one of claims 1-7.

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