Method and system for dynamic allocation of underwater acoustic communication system resources

Abstract

The application relates to the technical field of underwater acoustic communication, and discloses a method and system for dynamically allocating resources of an underwater voice communication system. The method comprises the following steps: setting a multi-user priority strategy according to a formation modular grouping; adopting a hybrid access mechanism of fusion of time division multiple access and carrier sense multiple access / collision avoidance, dividing communication channels into inter-group time slots and intra-group time slots, and competing for access to idle sub-carrier clusters in the time slots through a CSMA / CA mechanism; predicting a future state of an underwater acoustic channel; based on the predicted future state, adaptively switching a modulation and coding scheme according to a preset signal-to-noise ratio threshold; distributing sub-carrier bands to users with the largest channel gain based on a channel gain matrix; and combining the multi-user priority strategy, the hybrid access mechanism and the modulation and coding scheme to jointly optimize and distribute sub-carriers, bit loading and transmission power. Through dynamic resource allocation and adaptive transmission optimization, the application effectively improves the resource utilization rate and transmission quality of underwater voice communication.

Description

Technical Field

[0001] This application relates to the field of underwater acoustic communication technology, specifically to a method and system for dynamic resource allocation in underwater voice communication systems. Background Technology

[0002] With the rapid development of marine development, underwater exploration, and marine rescue, the demand for underwater voice communication systems is increasing. In marine development, real-time, clear voice communication is essential for coordination between divers and between divers and the surface command center, ensuring the safety and efficiency of development tasks. In underwater exploration missions, voice interaction between exploration equipment and the control center provides timely feedback on exploration data, helping researchers adjust their exploration strategies. In marine rescue scenarios, voice communication between rescuers and those in distress is a matter of life and death; rapid and accurate information transmission can significantly improve the success rate of rescues.

[0003] The underwater environment differs significantly from the terrestrial environment. Seawater possesses high conductivity, high attenuation, and complex acoustic field characteristics. These factors lead to severe signal attenuation, large transmission delays, and significant multipath interference during underwater voice signal transmission. Therefore, reasonable resource allocation is crucial to ensuring the quality of underwater voice communication. The resources of an underwater voice communication system mainly include frequency band resources, power resources, and time resources. Currently, common resource allocation methods in underwater voice communication systems are mostly based on fixed allocation models, that is, allocating resources to each communication node according to preset rules, or using simple adaptive allocation methods that adjust resources based on a single channel parameter (such as signal strength).

[0004] Regarding frequency band allocation, traditional methods typically divide available underwater frequency bands into fixed channels and allocate them to different communication users. While this approach is simple and easy to implement, it cannot be flexibly adjusted according to actual communication needs and changes in channel conditions, easily leading to wasted or conflicting frequency band resources. In terms of power resource allocation, most methods adopt equal allocation or simple power adjustments based on distance, without fully considering channel attenuation characteristics and interference. This may result in insufficient signal strength in some areas, affecting communication quality, while other areas may have excessive power, causing energy waste and additional interference. Time resource allocation often uses fixed time slot allocation mechanisms, which are difficult to adapt to the real-time service needs of different communication nodes. When the traffic volume of some nodes increases sharply, communication congestion can easily occur, affecting the real-time performance and continuity of voice transmission.

[0005] Existing technologies mostly employ fixed resource allocation models, resulting in poor resource flexibility and low resource utilization. Whether it's frequency bands, power, or time resources, it's difficult to adjust them in a timely manner according to the dynamic changes in the underwater communication environment and the real-time service needs of different communication nodes. Due to the lack of accurate prediction and dynamic allocation mechanisms for resource demand, existing technologies often lead to some resources being idle while others are overused. Regarding frequency band resources, some bands may not be used by communication nodes for extended periods but are permanently reserved and cannot be allocated to nodes with needs. Regarding power resources, some communication nodes are allocated higher power according to fixed rules, but the actual communication distance is short, resulting in a significant waste of power, while some long-distance communication nodes cannot communicate normally due to insufficient power. In terms of time resource allocation, certain time slots are allocated to specific nodes that have no service needs during those slots, resulting in wasted time resources, while other nodes with significant service needs cannot use those time slots, further reducing overall resource utilization. Summary of the Invention

[0006] This application provides a method for dynamic resource allocation in an underwater voice communication system, which solves the technical problems of poor resource allocation flexibility and low resource utilization in real-time underwater voice communication for multiple divers in a formation under the condition of limited underwater acoustic spectrum resources.

[0007] This application is achieved through the following technical solution:

[0008] In a first aspect, a method for dynamic resource allocation in an underwater voice communication system includes the following steps:

[0009] A multi-user priority strategy for resource allocation in the underwater voice communication system is set based on modular grouping of the formation.

[0010] A hybrid access mechanism combining time division multiple access (TDMA) and carrier sense multiple access / collision avoidance is adopted, dividing the communication channel into inter-group time slots and intra-group time slots. Within the time slot, access to the idle subcarrier cluster is contested through the CSMA / CA mechanism.

[0011] Predicting the future state of an underwater acoustic channel based on channel state information;

[0012] Based on the predicted future state, the modulation and coding scheme is adaptively switched according to a preset signal-to-noise ratio threshold;

[0013] Based on the channel gain matrix, subcarrier segments are allocated to the user with the largest channel gain. Combining the multi-user priority strategy, the hybrid access mechanism, and the modulation and coding scheme, subcarrier, bit loading, and transmit power are jointly optimized and allocated.

[0014] A further optimized solution is that the modular grouping of the formation includes a reconnaissance group, a demolition group, and a support group.

[0015] A further optimization scheme is that the signal-to-noise ratio threshold of the adaptive switching modulation and coding scheme is optimized and determined based on the bit error rate and system throughput criteria.

[0016] Further optimization schemes include modulation methods such as BPSK, QPSK, 8QAM, and 16QAM, and encoding methods such as convolutional codes and Turbo codes.

[0017] A further optimization scheme is that the joint optimization allocation adopts a multi-user joint resource allocation algorithm, allocating each subcarrier segment to the user with the largest channel gain on that subcarrier segment.

[0018] A further optimization scheme is to adopt the Chow algorithm for the multi-user joint resource allocation algorithm to maximize throughput under the constraint of total system power.

[0019] A further optimization scheme is to use a formation OFDM interleaving allocation method for subcarriers, with discrete interleaving distribution of subcarriers, and to update the channel status in real time in conjunction with pilot signals.

[0020] A further optimization scheme is that the prediction of the future state of the underwater acoustic channel based on channel state information specifically includes:

[0021] After completing the channel estimation, the receiver sends the estimated current channel state information to the transmitter through the feedback link.

[0022] The transmitting end inputs the received channel state information into the channel predictor to compensate for the transmission delay of the underwater acoustic channel and predict the future channel state for the next communication cycle.

[0023] Among them, the channel state information uses the signal-to-noise ratio as the core metric.

[0024] Secondly, this application provides a dynamic resource allocation system for an underwater voice communication system, used to implement the dynamic resource allocation method for an underwater voice communication system as described above, including:

[0025] The channel state prediction module is used to predict the future state of the underwater acoustic channel based on the received channel state information.

[0026] The priority processing module is used to generate and output user priority policies based on the modular grouping information of the formation.

[0027] The channel access control module is communicatively connected to the priority processing module and is used to perform hybrid access control that integrates TDMA and CSMA / CA, and output time slot allocation and user access permission information.

[0028] The modulation and coding dynamic selection module is communicatively connected to the channel state prediction module, and is used to adaptively switch the modulation and coding scheme based on the predicted future state, and output the modulation and coding selection command.

[0029] The joint resource allocation module is communicatively connected to the channel state prediction module, the priority processing module, the channel access control module, and the modulation and coding dynamic selection module, respectively. It is used to receive the future state, the user priority policy, the user access permission information, and the modulation and coding selection instruction, and to perform joint optimization allocation of subcarriers, bit loading, and transmit power based on the channel gain matrix.

[0030] Thirdly, this application provides a computer-readable storage medium storing a dynamic resource allocation program for an underwater voice communication system, wherein when the dynamic resource allocation program for an underwater voice communication system is executed by a processor, it implements the steps of the dynamic resource allocation method for an underwater voice communication system as described above.

[0031] Compared with the prior art, this application has the following advantages and beneficial effects:

[0032] By combining the multi-user priority strategy set by the modular grouping of the formation, the transmission priority of key instructions and important communications in collaborative tasks is effectively ensured, thus guaranteeing the reliability of critical services.

[0033] The hybrid access mechanism isolates communication between different groups through time slot division at the macro level, and flexibly allocates subcarrier resources through contention access at the micro level, thereby improving access efficiency while maintaining channel order.

[0034] Adaptive modulation and coding switching based on channel state enables the transmission strategy to match the current channel quality in real time, thus optimizing spectrum utilization efficiency.

[0035] By using joint optimization allocation guided by the channel gain matrix, subcarriers, bits, and power resources are precisely and dynamically allocated to the users who need them most, achieving a synergistic improvement in overall system throughput and communication quality under harsh underwater acoustic conditions.

[0036] By acquiring and predicting channel state information and combining it with a multi-user priority strategy for dynamic resource allocation, the resource utilization, communication reliability, and equipment endurance of the underwater voice communication system are effectively improved. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the exemplary embodiments of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0038] Figure 1 A block diagram illustrating the composition and working principle of an underwater voice communication system provided in this application embodiment;

[0039] Figure 2 A flowchart illustrating the underwater voice communication system resource dynamic allocation method provided in this application embodiment;

[0040] Figure 3 A time slot allocation diagram based on TDMA provided for embodiments of this application;

[0041] Figure 4 A schematic diagram of a single communication based on channel state estimation feedback provided in an embodiment of this application;

[0042] Figure 5 Bit error performance diagrams for four fixed modulation modes provided in embodiments of this application;

[0043] Figure 6 This application provides a graph showing the relationship between average user throughput under different modulation schemes in its embodiments.

[0044] Figure 7 Simulation diagrams of the bit error rate for the two encoding methods provided in the embodiments of this application;

[0045] Figure 8 This is a multi-user resource allocation framework diagram provided in the embodiments of this application;

[0046] Figure 9 A diagram illustrating the OFDM interleaved subcarrier resource allocation model provided in this application embodiment;

[0047] Figure 10 A comparison chart of the bit error rate performance of three algorithms provided in the embodiments of this application;

[0048] Figure 11 This diagram illustrates the user carrier bit and power allocation for the MC-Chow resource allocation algorithm 3 provided in this embodiment. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this application are only for explaining this application and are not intended to limit this application.

[0050] First, some of the technical terms used in this application will be explained to help those skilled in the art understand this application.

[0051] ACK: Acknowledgment, confirmation character;

[0052] BER: Bit Error Rate;

[0053] BPSK: Binary Phase Shift Keying;

[0054] CC: Convolutional Code;

[0055] CSI: Channel State Information;

[0056] CSMA / CA: Carrier Sense Multiple Access with Collision Avoidance;

[0057] MCS: Modulation and Coding Scheme;

[0058] OFDM: Orthogonal Frequency Division Multiplexing;

[0059] PDF: Probability Density Function;

[0060] QPSK: Quadrature Phase Shift Keying;

[0061] SER: Symbol Error Rate;

[0062] SNR: Signal to Noise Ratio;

[0063] TDMA: Time Division Multiple Access;

[0064] Turbo: Turbo Code, Turbo code;

[0065] 8QAM: 8-ary Quadrature Amplitude Modulation;

[0066] 16QAM: 16-ary Quadrature Amplitude Modulation;

[0067] Chow: Chow Algorithm;

[0068] Fischer: Fischer Algorithm, Fischer algorithm;

[0069] Hughes-Hartogs: Hughes-Hartogs Algorithm, Hughes-Hartogs algorithm;

[0070] MATLAB: MATrix LABoratory, Matrix Laboratory;

[0071] Bellhop: Bellhop Acoustic Channel Model.

[0072] like Figure 1 As shown, the underwater voice communication system mainly consists of a transmitting module, a channel propagation module, and a receiving module. The transmitting module collects voice signals through external sensors such as underwater microphones and converts them into easily processed digital signals via analog-to-digital conversion. Since the original digital signal has a large data volume, it is difficult to meet the system's high-speed real-time transmission requirements. Therefore, voice coding compression is used to eliminate signal redundancy, forming a low-bit-rate digital signal. The data stream after channel coding is called a symbol. To improve channel utilization and the system's anti-interference and anti-fading capabilities, the symbol is modulated and loaded into a waveform adapted to the underwater acoustic channel propagation, forming a signal with limited bandwidth. This signal is then transmitted via a power amplifier to drive the transmitting transducer, achieving long-distance transmission in the form of sound waves.

[0073] When acoustic signals propagate in underwater acoustic channels, they are significantly attenuated due to sound absorption and scattering. Simultaneously, signal distortion occurs due to system electrical noise and environmental noise, and multipath interference severely impacts transmission quality. Upon receiving the signal, the receiving module performs preprocessing. The measurement amplifier automatically adjusts its gain and filters the signal based on the signal-to-noise ratio, amplifying the signal to a range suitable for subsequent digital processing. Subsequent processing corresponds to that of the transmitting module, including demodulation, channel error correction decoding, and voice decompression. Finally, after digital-to-analog conversion and filtering amplification, an audible voice signal is output.

[0074] Firstly, such as Figure 2As shown, this application provides a method for dynamic resource allocation in an underwater voice communication system, comprising the following steps:

[0075] Step S1: Set a multi-user priority strategy for underwater voice communication system resource allocation based on the modular grouping of the formation;

[0076] Step S2: A hybrid access mechanism combining time division multiple access and carrier sense multiple access / collision avoidance is adopted. The communication channel is divided into inter-group time slots and intra-group time slots. Within the time slot, access to the idle subcarrier cluster is contested through the CSMA / CA mechanism.

[0077] Step S3: Predict the future state of the underwater acoustic channel based on channel state information;

[0078] Step S4: Based on the predicted future state, adaptively switch the modulation and coding scheme according to the preset signal-to-noise ratio threshold;

[0079] Step S5: Based on the channel gain matrix, subcarrier segments are allocated to the user with the largest channel gain. Combining the multi-user priority strategy, the hybrid access mechanism, and the modulation and coding scheme, the subcarrier, bit loading, and transmit power are jointly optimized and allocated.

[0080] This embodiment ensures the reliability and real-time performance of critical commands through modular grouping and multi-user priority strategies; it effectively improves channel utilization efficiency and reduces access conflicts by adopting a hybrid access mechanism; it significantly enhances communication robustness and spectral efficiency in time-varying, multipath underwater acoustic channels by combining channel state prediction and adaptive modulation and coding; and finally, through joint resource optimization based on channel gain and priority, it achieves efficient, dynamic, and fair allocation of limited communication resources in complex underwater environments, thereby improving the overall throughput, energy efficiency, and task adaptability of the system while meeting the target bit error rate requirements.

[0081] In one embodiment, step S1: setting a multi-user priority strategy for underwater voice communication system resource allocation based on formation modular grouping specifically includes the following steps:

[0082] Step S11: Adopting a modular grouping architecture, the diver team is divided into three functional units: reconnaissance, demolition, and support. A hierarchical priority mechanism is established based on mission criticality. More specifically, in the diver team topology setting, medium-sized mission teams generally consist of around 10 people, usually not exceeding 12 people, to avoid increasing exposure risk due to redundancy. The team exhibits a "modular" characteristic, that is, it is divided into reconnaissance (2-4 people), demolition (4-6 people), and support (1-2 people) according to the functional modules of mission execution. Specifically, the reconnaissance team has 4 people, the demolition team has 4 people, and the support team has 2 people, with 1 team leader assigned to each team.

[0083] Step S12: Establish a hierarchical priority mechanism based on task criticality. The priority mechanism is as follows:

[0084] The support group has the highest priority when sending emergency instructions and has the ability to preempt channels; specifically, the support group is responsible for monitoring the entire channel and generally does not send information, but in case of an emergency (such as sending a retreat instruction), it can preempt one of the channels, interrupt the current communication, and send instruction information with priority.

[0085] Group leaders have the second highest priority when performing inter-group communication, and support high-power (e.g., 2km) long-distance transmission. Group leaders can use high power for inter-group communication. Ordinary group members use low-power transmission mode for intra-group communication, with the lowest priority. That is, members in the group use low-power signals for intra-group communication.

[0086] The reconnaissance and demolition teams are responsible for core mission execution and are given priority in real-time communication resources, while the support team usually receives one-way monitoring signals, but can trigger the highest priority mechanism in emergencies.

[0087] In one embodiment, step S2 involves employing a hybrid access mechanism that combines Time Division Multiple Access (TDMA) with Carrier Sense Multiple Access / Collision Avoidance (CSMA / CA), dividing the communication channel into inter-group time slots and intra-group time slots. Within each time slot, access to an idle subcarrier cluster is contested using a CSMA / CA mechanism. This specifically includes the following steps:

[0088] Step S21: Based on the initial parameters of the system's distributed network, TDMA technology is used to divide the communication channel into inter-group time slots and intra-group time slots, establishing a time slot framework. Specifically, the system adopts a decentralized distributed structure, where each user node can obtain information about its neighboring nodes at any given time based on the routing table and independently run channel access and resource allocation algorithms. Figure 3 As shown, the communication channel is divided into two fixed time slots, inter-group time slot and intra-group time slot, by TDMA (Time Division Multiple Access). The inter-group time slot is dedicated to high-power communication between groups, while the intra-group time slot is used for internal communication among group members. This fixed division method effectively avoids mutual interference.

[0089] Step S22: Based on the above time slot framework, competitive access to idle subcarrier clusters is achieved in each time slot through the CSMA / CA mechanism. Collision detection is completed through channel listening and ACK confirmation procedures to determine the final access user list. Specifically, within each time slot, user nodes compete to access idle subcarrier clusters based on the CSMA / CA (Carrier Sense Multiple Access / Collision Avoidance) mechanism, that is, within the same group of user time slots, they compete to access idle subcarrier clusters according to the CSMA / CA mechanism. Preferably, channel listening is performed before data transmission, and transmission is started only when the channel is idle. During transmission, the ACK (Acknowledgment character) confirmation feedback mechanism is used for collision detection. The transmission is considered successful only when the sender receives the ACK sent by the other party.

[0090] Step S23: Perform differentiated processing based on the final access user list: After the contention phase and resource allocation phase are completed, users who successfully access the network confirm resource allocation by broadcasting a transmission format frame containing information on successful users and channel allocation details, while users who fail to access the network execute a backoff and retransmission mechanism and retry access by reselecting a backoff value.

[0091] In one embodiment, such as Figure 4 As shown, in the channel state information of step S3, the channel transfer function and signal-to-noise ratio (SNR) are commonly used for short-term channel quality estimation and prediction, while long-term estimation uses bit error rate and frame error rate. In practical applications, they can be flexibly combined according to the channel state. In this application, the signal-to-noise ratio is used as the core metric for channel state information. Step S3: Predicting the future state of the underwater acoustic channel based on the channel state information, specifically includes the following steps:

[0092] Step S31: The receiver performs channel estimation using the pilot signal to obtain the channel coefficients. And based on the channel coefficients Compared with the original test signal Calculate the ideal received signal :

[0093] Equation (1)

[0094] Step S32: Based on the ideal received signal The channel state information, with signal-to-noise ratio (SNR) as the core metric, is calculated from the actual received signal y(n). The calculation formula is as follows:

[0095] Equation (2)

[0096] Step S33: Send the signal-to-noise ratio to the transmitting end via the feedback link;

[0097] Step S34: The transmitting end inputs the received channel state information to the channel predictor. The channel predictor, based on historical and current channel state information, uses an adaptive prediction algorithm of time series analysis, linear prediction, or Kalman filtering to compensate for the transmission delay of the underwater acoustic channel and predict the future channel state for the next communication cycle.

[0098] In one embodiment, step S4: Based on the predicted future state, adaptively switching the modulation and coding scheme according to a preset signal-to-noise ratio threshold, specifically includes the following steps:

[0099] Step S41: Based on real-time estimation of channel state information (such as signal-to-noise ratio SNR), and combined with a pre-set SNR switching threshold (e.g., determined based on the performance curves of different modulation schemes BPSK, QPSK, 8QAM, 16QAM, and coding schemes Turbo codes or convolutional codes) according to bit error rate and throughput criteria, the current SNR is compared with the preset threshold vector to obtain the comparison result. Specifically, the receiver selects a matching modulation and coding scheme based on the comparison between the SNR obtained from channel estimation and the threshold value. When constructing an adaptive modulation and coding system for underwater acoustic communication, in the context of user-internal adaptive multi-mode modulation and coding schemes and switching criteria, a suitable modulation and coding scheme needs to be selected based on the actual application scenario and requirements, while balancing the code rate and error correction capability. Setting the threshold value too high will cause low-order modulation to be used even when the channel quality is good, reducing system throughput; setting the threshold value too low will increase the transmission bit error rate and affect communication effectiveness. Multi-mode modulation schemes include BPSK, QPSK, 8QAM, and 16QAM, corresponding to information bit rates of {1, 2, 3, 4}.

[0100] Channel gain region is constructed using channel state information (CSI). Based on the modulation switching threshold range, the user sub-channel state is quantized into a five-state set S{0, 1, 2, 3, 4}, where 0 indicates that the sub-channel is unavailable. The channel state space B is defined as shown in equation (3).

[0101] Equation (3)

[0102] The modulation scheme switching threshold is determined based on the bit error rate and throughput criteria. System throughput is defined as the expected number of correctly received bits at the receiver when a data packet of length L uses a fixed modulation scheme m. Based on the underwater acoustic channel model, the average number of correctly transmitted bits for user u within one OFDM symbol period on a single subcarrier is:

[0103] Equation (4)

[0104] in It depends on the modulation scheme on each subcarrier. It is the actual average signal-to-noise ratio of the user sub-channel. The probability density function (PDF) has a signal-to-noise ratio of When using MQAM modulation, the symbol error rate of the system is used This can be approximated as:

[0105] Equation (5)

[0106] When using MQAM modulation, , It represents BPSK.

[0107] The modulation mode switching relationship is expressed as shown in equation (6).

[0108] Equation (6)

[0109] in, Indicates the selected modulation and coding scheme. Indicates the boundary values ​​of the signal-to-noise ratio. This represents the signal-to-noise ratio at time k.

[0110] Figure 5 The following are bit error rate performance diagrams for four fixed modulation modes provided in the embodiments of this application. Figure 6 The comparison of average throughput under different modulation modes is presented. Figure 7 Simulation results of the bit error rate for two encoding methods are presented to provide a basis for threshold setting.

[0111] Step S42: Dynamically select the corresponding modulation and coding scheme (MCS) based on the comparison results. For example, automatically switch to the matching MCS mode when the signal-to-noise ratio falls within a specific range. Specifically, the multi-mode coding scheme includes convolutional codes (CC) and Turbo codes. The simulation uses (2, 1, 5) convolutional codes. Turbo codes are more suitable for low signal-to-noise ratio environments, but their complexity is higher than that of convolutional codes. When the signal-to-noise ratio is good, choosing convolutional codes can reduce system complexity while ensuring performance. Table 1 summarizes the signal-to-noise ratio thresholds based on bit error rate and throughput criteria, as follows:

[0112] Table 1. Signal-to-noise ratio threshold table for adaptive modulation and coding scheme switching algorithms

[0113]

[0114] In the table, (-, 9) means less than 9, and [19, -) means greater than or equal to 19.

[0115] Threshold values ​​corresponding to these signal-to-noise ratios , Combining different optimization criteria yields different values, which can be used to form a signal-to-noise ratio switching threshold vector. When the estimated signal-to-noise ratio of the sub-channel occupied by user u In At that time, the average modulation rate of the sub-channel is ,in This indicates that no transmission will occur, and the allocation of internal user resources will be implemented using a bit power allocation algorithm.

[0116] like Figure 8 As shown, the multi-user resource allocation framework constructed in this application is a dynamic closed-loop system with a resource scheduler at its core. The scheduler makes centralized scheduling decisions in real time based on channel state information (CSI) fed back by each user, differentiated service requirements, and historical status. The system configures independent subcarrier scheduling and bit power allocation modules for K users in parallel, achieving precise and personalized allocation of OFDM subcarriers and transmit power. The scheduled multi-user data converges in the OFDM modulation module and is transmitted through a shared channel; after demodulation and separation at the receiving end, the link quality information of each user is reported to the scheduler in real time as CSI via the feedback path, thus forming a complete closed loop of "perception-decision-allocation-evaluation-optimization," achieving adaptive optimization of system resources.

[0117] Within this framework, the selection of dynamic resource allocation algorithms must comprehensively consider both time complexity and bit error rate performance. While the commonly used Hughes-Hartogs algorithm offers the best bit error rate performance, it suffers from high time complexity and poor real-time performance. In contrast, the Chow and Fischer algorithms significantly reduce complexity while maintaining good performance, offering superior real-time performance, and their allocation results better reflect time-varying channel characteristics. Specifically, the Hughes-Hartogs algorithm has high computational complexity and is suitable for scenarios with lower real-time requirements; the Chow algorithm allocates based on channel performance margins, has low complexity, and is suitable for systems where channel capacity is the rate allocation criterion under transmit power constraints; the Fischer algorithm allocates based on the error probability minimization criterion, pre-determining the allocation rate and then adjusting it, making it suitable for high-speed wireless transmission. In terms of time complexity, the Fischer and Chow algorithms are similar, both significantly lower than the Hughes-Hartogs algorithm. The Chow and Fischer algorithms offer better real-time performance, and their allocation results better reflect channel variation characteristics. Therefore, this application, combined with a prediction mechanism under non-ideal feedback, prioritizes the Chow algorithm for resource allocation analysis.

[0118] In one embodiment, step S5: Based on the channel gain matrix, subcarrier segments are allocated to the user with the highest channel gain. Combining the multi-user priority strategy, the hybrid access mechanism, and the modulation and coding scheme, a joint optimization allocation of subcarriers, bit loading, and transmit power is performed, specifically including the following steps:

[0119] Step S51: Based on the preset multi-user priority strategy, generate an initial subcarrier allocation scheme by prioritizing the allocation of high-priority users to subcarrier clusters with high channel gain;

[0120] Specifically, the multi-user resource allocation model divides the frequency band into multiple user sub-channels, each containing a cluster of subcarriers. Users occupy subcarriers in two ways: continuous carrier clusters and interleaved carrier clusters. Interleaved allocation avoids the risk of all carriers falling into deep fading during continuous allocation. The system allocates subcarriers based on the maximum eigenvalue of the channel gain matrix, effectively obtaining multi-user diversity gain. During resource allocation, the channel gain matrix of each user in different subcarrier segments is used as the utility matrix R(M×N). Singular value decomposition is used to obtain the sub-channel eigenvalues, and subcarrier segments are allocated to the user with the highest gain, ensuring that the sum of sub-channel gains for each user is maximized. To ensure fairness, when a user's subcarrier segment count reaches the allocation limit, they are no longer included in subsequent allocations. Subcarriers are allocated using an OFDM formation interleaving method, with discrete interleaved subcarrier distribution. The channel state is updated in real-time using pilot signals, such as... Figure 9 As shown.

[0121] Step S52: Using the initial subcarrier allocation scheme as input, and combining the channel access control strategy of TDMA and CSMA / CA hybrid, resources are dynamically adjusted through time slot monitoring and collision detection mechanisms to generate optimized time slot allocation results;

[0122] Step S53: Using the time slot allocation result as input, and based on the adaptive modulation and coding scheme, adjust the subcarrier loading parameters through the bit power allocation algorithm to generate an adaptive bit and power matching scheme.

[0123] Specifically, the adaptive allocation algorithm adjusts system resources based on the real-time channel status, and the parameter adjustments determine the optimal values ​​based on the optimization model and constraints. Common optimization models include the following three categories:

[0124] a) Given the target system rate, minimize energy consumption, as shown in equation (7);

[0125]

[0126] Equation (7)

[0127]

[0128] b) Maximize the system transmission rate under the constraints of bit error rate and total power, as shown in Equation (8);

[0129]

[0130] Equation (8)

[0131]

[0132] c) Minimize the bit error rate under power and system rate constraints, as shown in Equation (9). This ensures the flexibility and efficiency of resource allocation; based on this, a multi-user joint resource allocation algorithm, such as the aforementioned Chow algorithm, is adopted to maximize throughput under the total system power constraint.

[0133]

[0134] Equation (9)

[0135]

[0136] In the above expression, Indicates the total bit rate of the system. Indicates the total transmission power constraint. Indicates the first Number of loaded bits per subcarrier segment Indicates the first The required power for each subcarrier segment Indicates a given bit error rate requirement;

[0137] Step S54: Using the bit and power matching scheme as input, the joint resource allocation algorithm is adopted to globally adjust the parameters through the constraint optimization model to generate the final system-level resource allocation strategy.

[0138] Figure 10 The comparison of bit error rate performance of the three algorithms verifies the overall performance advantage of this algorithm. Specific simulation results show that, under the capacity maximization optimization model, the comparison of bit error rate performance of the three algorithms with varying signal-to-noise ratio (SNR) indicates that: in low SNR environments, the bit error rate performance of the three algorithms is similar; as the SNR increases, the Chow algorithm and the Hughes-Hartogs algorithm significantly outperform the Fischer algorithm in bit error rate performance, and their performance tends to be consistent, but the high complexity of the Hughes-Hartogs algorithm limits its practical application. When the SNR reaches 17 dB, the system bit error rate approaches 10%. −4 The transmission performance is good.

[0139] To verify the effectiveness of the algorithm, typical shallow sea channel parameters generated based on the Bellhop underwater acoustic channel model were used in the MATLAB simulation. The number of subcarrier clusters was set to 64, and power normalization was performed. Figure 11The results of bit power allocation based on this framework and the Chow algorithm in a three-user scenario are presented, effectively demonstrating the feasibility and performance of the proposed method. Further analysis shows that the carriers of different users are interleaved, making full use of channel characteristics: more bits are loaded when the channel gain is good, and less or no allocation is made when the gain is poor; carriers with high channel gain only need to be allocated less power to meet transmission requirements, achieving reasonable energy allocation under power constraints.

[0140] Figure 11 (a) is a 3-user channel gain diagram under the MC-Chow resource allocation algorithm in the embodiments of this application;

[0141] Figure 11 (b) is a 3-user carrier bit allocation diagram under the MC-Chow resource allocation algorithm in the embodiments of this application;

[0142] Figure 11 (c) is a power allocation diagram for 3 users under the MC-Chow resource allocation algorithm in the embodiments of this application.

[0143] This application achieves dynamic optimization of system resources through the synergistic effect of steps S1 to S5 described above. Specifically, firstly, based on the multi-user priority strategy set by the modular grouping of the formation, the priority transmission of critical mission instructions is effectively ensured, thus accurately adapting to multi-diver collaborative operation scenarios. Secondly, the adopted TDMA and CSMA / CA integrated hybrid access mechanism isolates communication between different groups through time slot division at the macro level, and flexibly allocates subcarrier resources using a contention-based access method at the micro level, significantly improving the overall channel resource utilization efficiency. Furthermore, by combining adaptive modulation and coding technology based on channel state prediction with a joint resource allocation strategy guided by channel gain, the system can dynamically match the time-varying characteristics of the underwater acoustic channel, significantly reducing system transmit power while ensuring stable communication quality. Finally, the system effectively reduces energy consumption, extends equipment endurance, and enhances adaptability to complex underwater communication environments while improving spectral efficiency.

[0144] Secondly, this application provides a dynamic resource allocation system for an underwater voice communication system, used to implement the dynamic resource allocation method for an underwater voice communication system as described above, including:

[0145] The channel state prediction module is used to predict the future state of the underwater acoustic channel based on the received channel state information.

[0146] The priority processing module is used to generate and output user priority policies based on the modular grouping information of the formation.

[0147] The channel access control module is communicatively connected to the priority processing module and is used to perform hybrid access control that integrates TDMA and CSMA / CA, and output time slot allocation and user access permission information.

[0148] The modulation and coding dynamic selection module is communicatively connected to the channel state prediction module, and is used to adaptively switch the modulation and coding scheme based on the predicted future state, and output the modulation and coding selection command.

[0149] The joint resource allocation module is communicatively connected to the channel state prediction module, the priority processing module, the channel access control module, and the modulation and coding dynamic selection module, respectively. It is used to receive the future state, the user priority policy, the user access permission information, and the modulation and coding selection instruction, and to perform joint optimization allocation of subcarriers, bit loading, and transmit power based on the channel gain matrix.

[0150] Thirdly, embodiments of this application provide a dynamic resource allocation device for an underwater voice communication system. The dynamic resource allocation device for an underwater voice communication system can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0151] In this embodiment, the underwater voice communication system resource dynamic allocation device may include a processor, a memory, a communication interface, and a communication bus.

[0152] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0153] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of the underwater voice communication system's resource dynamic allocation device, as well as interfaces used for interconnecting the underwater voice communication system's resource dynamic allocation device with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0154] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0155] The processor can be a general-purpose processor, which can call the underwater voice communication system resource dynamic allocation program stored in the memory and execute the underwater voice communication system resource dynamic allocation method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the underwater voice communication system resource dynamic allocation program is called can refer to the various embodiments of the underwater voice communication system resource dynamic allocation method of this application, and will not be repeated here.

[0156] Fourthly, embodiments of this application also provide a readable storage medium.

[0157] The present application stores a dynamic resource allocation program for an underwater voice communication system on a readable storage medium, wherein when the dynamic resource allocation program for an underwater voice communication system is executed by a processor, it implements the steps of the dynamic resource allocation method for an underwater voice communication system as described above.

[0158] The method implemented when the underwater voice communication system resource dynamic allocation procedure is executed can be referred to in the various embodiments of the underwater voice communication system resource dynamic allocation method of this application, and will not be repeated here.

[0159] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above description is only a specific embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for dynamic resource allocation in an underwater voice communication system, characterized in that, Includes the following steps: A multi-user priority strategy for resource allocation in the underwater voice communication system is set based on modular grouping of the formation. A hybrid access mechanism combining time division multiple access (TDMA) and carrier sense multiple access / collision avoidance is adopted, dividing the communication channel into inter-group time slots and intra-group time slots. Within the time slot, access to the idle subcarrier cluster is contested through the CSMA / CA mechanism. Predicting the future state of an underwater acoustic channel based on channel state information; Based on the predicted future state, the modulation and coding scheme is adaptively switched according to a preset signal-to-noise ratio threshold; Based on the channel gain matrix, subcarrier segments are allocated to the user with the highest channel gain. Combining the multi-user priority strategy, the hybrid access mechanism, and the modulation and coding scheme, subcarriers, bit loading, and transmit power are jointly optimized and allocated.

2. The underwater voice communication system resource dynamic allocation method according to claim 1, characterized in that, The modular grouping of the formation includes reconnaissance group, demolition group, and support group.

3. The underwater voice communication system resource dynamic allocation method according to claim 1, characterized in that, The signal-to-noise ratio threshold of the adaptive switching modulation and coding scheme is determined by optimizing the bit error rate and system throughput criteria.

4. The underwater voice communication system resource dynamic allocation method according to claim 1, characterized in that, The modulation methods include BPSK, QPSK, 8QAM and 16QAM, and the encoding methods include convolutional codes and Turbo codes.

5. The underwater voice communication system resource dynamic allocation method according to claim 1, characterized in that, The joint optimization allocation adopts a multi-user joint resource allocation algorithm, which allocates each subcarrier segment to the user with the largest channel gain on that subcarrier segment.

6. The underwater voice communication system resource dynamic allocation method according to claim 4, characterized in that, The multi-user joint resource allocation algorithm adopts the Chow algorithm to maximize throughput under the constraint of total system power.

7. The underwater voice communication system resource dynamic allocation method according to claim 4, characterized in that, The subcarriers are allocated using an OFDM array interleaving method, with discrete interleaving of the subcarriers and real-time updates of the channel status in conjunction with pilot signals.

8. The underwater voice communication system resource dynamic allocation method according to claim 1, characterized in that, The prediction of the future state of the underwater acoustic channel based on channel state information specifically includes: After completing the channel estimation, the receiver sends the estimated current channel state information to the transmitter through the feedback link. The transmitting end inputs the received channel state information into the channel predictor to compensate for the transmission delay of the underwater acoustic channel and predict the future channel state for the next communication cycle. Among them, the channel state information uses the signal-to-noise ratio as the core metric.

9. A dynamic resource allocation system for an underwater voice communication system, used to implement the dynamic resource allocation method for an underwater voice communication system as described in any one of claims 1 to 8, characterized in that, include: The channel state prediction module is used to predict the future state of the underwater acoustic channel based on the received channel state information. The priority processing module is used to generate and output user priority policies based on the modular grouping information of the formation. The channel access control module is communicatively connected to the priority processing module and is used to perform hybrid access control that integrates TDMA and CSMA / CA, and output time slot allocation and user access permission information. The modulation and coding dynamic selection module is communicatively connected to the channel state prediction module, and is used to adaptively switch the modulation and coding scheme based on the predicted future state, and output the modulation and coding selection command. The joint resource allocation module is communicatively connected to the channel state prediction module, the priority processing module, the channel access control module, and the modulation and coding dynamic selection module, respectively. It is used to receive the future state, the user priority policy, the user access permission information, and the modulation and coding selection instruction, and to perform joint optimization allocation of subcarriers, bit loading, and transmit power based on the channel gain matrix.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a dynamic resource allocation program for an underwater voice communication system, wherein when the dynamic resource allocation program for an underwater voice communication system is executed by a processor, it implements the steps of the dynamic resource allocation method for an underwater voice communication system as described in any one of claims 1 to 8.

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