Unmanned aerial vehicle cluster communication system and method based on dynamic polarization adaptive modulation

Abstract

The invention provides an unmanned aerial vehicle cluster communication system and method based on dynamic polarization adaptive modulation, and relates to the technical field of low-altitude unmanned aerial vehicle clusters, and the communication system comprises a multi-polarization antenna array, a polarization state detection module, an interference analysis module, a polarization control module, an adaptive modulation module and a cluster coordination module. According to the method, the polarization state and modulation parameters of communication signals are dynamically adjusted based on real-time interference detection and cluster topology. By fully utilizing a polarization domain and adaptively optimizing transmission parameters, the system realizes excellent anti-interference performance and keeps energy efficiency at the same time. According to the invention, the low-altitude unmanned aerial vehicle cluster can reliably communicate in a complex electromagnetic environment, and the task success rate and the operation capability are remarkably improved; the interference perception and resistance strategy of cluster cooperation is realized, and the overall communication reliability is improved; energy consumption is reduced, and operation time of the unmanned aerial vehicle is prolonged; the spectrum efficiency is improved while high communication reliability is maintained; and cluster scale expansion is adapted.

Description

Technical Field

[0001] The present invention relates to the technical field of low-altitude unmanned aerial vehicle (UAV) clusters, and in particular to a UAV cluster communication system and method based on dynamic polarization adaptive modulation. Background Art

[0002] Low-altitude unmanned aerial vehicle (UAV) swarming technology has shown great potential in military reconnaissance, disaster relief, agricultural monitoring, border patrols, logistics and distribution, and other fields. Compared to individual drones, swarms offer higher mission efficiency, greater system robustness, and wider coverage. However, as swarms expand, electromagnetic interference (EMI) issues facing intra-swarm communications are becoming increasingly severe.

[0003] Existing technologies offer cognitive polarization communication systems that can sense the environment and adjust signal polarization. However, these systems are primarily designed for single communication links and lack cluster coordination mechanisms. Polarization adjustment and modulation parameter selection are independent of each other, preventing joint optimization. Furthermore, these systems are slow to respond to rapidly changing interference environments, making them unsuitable for highly dynamic drone swarm applications.

[0004] At the same time, the existing low-altitude UAV cluster anti-interference communication technology has the following obvious shortcomings: Insufficient adaptability to dynamic interference: Existing technologies usually adopt fixed anti-interference strategies, which cannot effectively deal with the rapid changes in interference type, direction and intensity, resulting in unstable communication performance in complex electromagnetic environments. Insufficient utilization of polarization domain resources: Electromagnetic spectrum resources are becoming increasingly scarce, but the polarization domain, as a resource dimension independent of the frequency domain, time domain and space domain, has not been fully utilized. Existing systems usually use fixed polarization states and do not use polarization as a dynamic resource to resist interference, missing out on important opportunities for communication enhancement. Inefficient energy consumption: Traditional anti-interference technologies such as high-order spread spectrum or increased transmission power consume a lot of energy. This is particularly disadvantageous for small UAVs with limited battery capacity, significantly shortening the operation time and effective range. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the purpose of the present invention is to provide a UAV cluster communication system and method based on dynamic polarization adaptive modulation to solve the problems raised in the above background technology. The present invention makes full use of polarization domain resources, enhances anti-interference capability by dynamically adjusting the polarization state, combines polarization control with adaptive adjustment of modulation parameters to achieve joint optimization; realizes cluster collaborative interference perception and resistance strategy, improves overall communication reliability; reduces energy consumption and extends UAV operation time; improves spectrum efficiency while maintaining high communication reliability; adapts to cluster scale expansion and maintains stable communication performance; simplifies hardware implementation to meet the size, weight and power limitations of small UAVs.

[0006] In order to achieve the above-mentioned purpose, the present invention is implemented through the following technical solutions: a UAV cluster communication system based on dynamic polarization adaptive modulation, the communication system includes a multi-polarization antenna array, a polarization state detection module, an interference analysis module, a polarization control module, an adaptive modulation module, a cluster coordination module and a system controller, the multi-polarization antenna array includes a plurality of radiating elements arranged in a geometric pattern, a phase shifter and an amplitude controller of each element, a polarization state control circuit and a low-noise amplifier and a power amplifier; the polarization state detection module includes a dual-polarization sensing element, a polarization state estimation algorithm, a polarization domain spectrum analyzer and an interference polarization classifier; the interference analysis module includes an interference source The system comprises an identification unit, an interference pattern recognition algorithm, a time-domain interference behavior analyzer, an interference prediction engine, and an interference signal ratio calculator; the polarization control module comprises a polarization state optimization algorithm, a polarization hopping sequence generator, a polarization codebook manager, and a polarization feedback control system; the adaptive modulation module comprises a modulation scheme selector, a coding rate optimizer, a symbol rate controller, a power allocation unit, and a polarization-aware constellation mapper; the cluster coordination module comprises a cluster topology manager, a distributed decision algorithm, a polarization state sharing protocol, a cluster global interference mapper, and a role allocation controller; and the system controller comprises a real-time operating system, a policy decision engine, a performance monitoring unit, an energy efficiency optimizer, and a fail-safe mechanism.

[0007] Furthermore, each UAV is equipped with a compact reconfigurable antenna array, which is used to generate signals in any polarization state; the polarization state detection module is used to continuously monitor the polarization state of the received signal and interference; and the interference analysis module is used to process the PSDM data to characterize the interference characteristics.

[0008] Furthermore, the polarization control module determines the optimal polarization state for transmission and reception based on interference analysis; the adaptive modulation module dynamically adjusts the modulation parameters according to the interference conditions and polarization state; the cluster coordination module is used to ensure the coordinated adaptation of the entire drone cluster; and the system controller integrates the functions of all modules and implements an overall anti-interference strategy.

[0009] Furthermore, it also includes hardware architecture and software architecture. Each drone in the cluster is equipped with a complete system, and a multi-polarization antenna array serves as the input and output interface of the communication signal.

[0010] Furthermore, the system adopts a layered software architecture, which includes a physical layer: handling direct control and signal processing of multi-polarization antenna arrays; a MAC layer: managing polarization-aware media access control; a network layer: coordinating intra-cluster communication through dynamic routing; and an application layer: interfacing with the UAV mission control system.

[0011] Furthermore, the polarization state detection module continuously monitors incoming signals and provides polarization state information to the interference analysis module; the interference analysis module analyzes interference patterns and characteristics and provides this information to the polarization control module and the adaptive modulation module simultaneously; the polarization control module determines the optimal polarization state for transmission and reception; the adaptive modulation module selects appropriate modulation parameters based on current conditions; the cluster coordination module exchanges information with other drones to coordinate system-wide adaptation; and the system controller integrates all information and implements the final transmission strategy.

[0012] Furthermore, it also includes an adaptive loop and an inter-UAV coordination structure, wherein the adaptive loop includes the following contents: adaptive loop; detection: identifying interference characteristics and polarization state; analysis: determining the impact of interference on communication quality; decision: selecting optimal polarization and modulation parameters; implementation: configuring MPAA and signal processing chain; verification: monitoring performance and triggering re-adaptation when necessary.

[0013] Furthermore, the inter-UAV coordination structure adopts a mesh network topology, a hierarchical decision-making mechanism with dynamic allocation of leadership nodes, and a cluster-wide adaptive distributed consensus algorithm.

[0014] A UAV swarm communication method based on dynamic polarization adaptive modulation is proposed. The method includes a theoretical system for dynamic polarization state control and a polarization-space domain joint communication optimization process. The theoretical system for dynamic polarization state control includes the following contents: a polarization domain interference characterization model and a dynamic polarization control algorithm, which constructs a three-dimensional dynamic representation space of the interference polarization state:

[0015] The polarization state of an electromagnetic wave is described by the normalized Jones vector:

[0016]

[0017] Where E is the Jones vector, representing the polarization state of the electromagnetic wave; E x and E y are the complex components of the electric field on the orthogonal coordinate axes (such as the x and y axes); α∈[0,π / 2] is the polarization tilt; Δφ∈[0,2π) is the phase difference; j is the imaginary unit, satisfying j 2 =-1.

[0018] When the desired signal polarization Jones vector E s The Jones vector E of the polarization of the interference signal I Satisfies the orthogonality suppression criterion:

[0019]

[0020] When the interference power rejection ratio (IRR) is achieved, this the preset threshold, and H represents the Hermitian operation.

[0021] The dynamic polarization control algorithm adopts a dual-mode adaptive polarization optimization algorithm, including a gradient tracking mode and a random hopping mode:

[0022] Gradient tracking mode:

[0023]

[0024] in, is the signal polarization Jones vector after the k+1th iteration; is the signal polarization Jones vector after the kth iteration; is the estimated interference signal Jones vector; μ is the learning rate or step size; The gradient term for polarization optimization is expressed as:

[0025]

[0026] Where Re represents the real part of the complex number;

[0027] Random hopping mode:

[0028] When the current polarization state is detected to be unstable, a pseudo-random polarization jump is triggered. The instability judgment conditions are:

[0029]

[0030] Where d(t) is a time-varying scalar metric that indicates how far the current signal polarization state at time t deviates from the optimal or stable state; γ is the preset sensitivity threshold for triggering pseudo-random polarization transitions; and d / dt represents the derivative with respect to time t that triggers pseudo-random polarization transitions:

[0031]

[0032] Among them, d t is a scalar metric that indicates the degree to which the current polarization state deviates from the optimal or stable state; γ is the threshold for triggering pseudo-random polarization jumps; E s (t) is the signal polarization Jones vector at time t; C is the polarization codebook, which is a predefined set of polarization states; PRNG(t) is the pseudo-random number output generated by the pseudo-random number generator at time t; is a bitwise exclusive OR (XOR) operation; HMAC(K,t) is a hash-based message authentication code calculated using the key K and time t; K is the key of the HMAC function.

[0033] Furthermore, the polarization-spatial domain joint communication optimization includes a polarization domain SINR enhancement model and a cluster cooperative polarization allocation algorithm, wherein the polarization domain equivalent signal-to-interference-and-noise ratio (P-SINR) is defined in the polarization domain SINR enhancement model as:

[0034]

[0035] Among them, P s is the expected signal power; P I is the interference signal power; P n is the noise power; ρ si is the polarization correlation coefficient between the desired signal and the interference signal; K is the total number of adjacent links; ρ sk is the polarization correlation coefficient with the kth adjacent link; Π is the product operator.

[0036] The cluster cooperative polarization assignment algorithm models the polarization assignment problem as a constrained graph coloring optimization.

[0037] Beneficial effects of the present invention:

[0038] 1. This drone swarm communication system, based on dynamic polarization adaptive modulation, boasts exceptional anti-interference performance. By dynamically adjusting polarization and modulation parameters, the system achieves 15-20dB higher interference suppression than traditional systems. Even under intentional jamming conditions, the bit error rate is reduced by 95% compared to traditional frequency hopping technology.

[0039] 2. This UAV swarm communication system based on dynamic polarization adaptive modulation boasts enhanced spectral efficiency: Unlike spread-spectrum technologies that sacrifice bandwidth for interference immunity, this system maintains high spectral efficiency by utilizing the polarization domain. Under the same interference conditions, the data rate is 3.5 times higher than that of traditional DSSS systems. It also effectively reduces energy consumption, as polarization-based interference suppression methods require significantly lower transmission power to achieve the same reliability. Measurements show a 65-70% reduction in energy consumption compared to power-boosting methods, directly translating into longer UAV flight times.

[0040] 3. This UAV swarm communication system, based on dynamic polarization adaptive modulation, boasts improved scalability: The distributed nature of the swarm coordination algorithm ensures that system performance scales efficiently with swarm size. Tests using up to 50 UAVs demonstrated that communication reliability remained above 99% even with increasing swarm density, compared to less than 80% for conventional systems under the same conditions.

[0041] 4. The system's ability to continuously sense and adapt to changing interference conditions enables robust operation in highly dynamic electromagnetic environments. The time from sudden interference introduction to recovery is reduced from seconds to milliseconds for conventional systems. Enhanced stealth: By minimizing transmission power and employing polarization diversity, the system significantly reduces the probability of detection and interception. The system's electromagnetic footprint is reduced by up to 18dB compared to conventional drone communication systems.

[0042] 5. Under severe interference, the system exhibits graceful degradation rather than catastrophic failure by adaptively reducing the data rate while maintaining essential control links. Even under the most severe interference, critical command and control information maintains 99.9% reliability. This system can be implemented using simplified hardware: the system can be implemented using compact, lightweight hardware suitable for small drones. The reconfigurable antenna design requires only 30% of the volume of traditional diversity antenna systems while providing superior performance.

[0043] 6. Enhanced Resistance to Complex Jammers: The dynamic polarization hopping mechanism makes the system particularly resistant to learning-based jammers. Even adaptive jammers that attempt to match the system's polarization state struggle to track rapid changes in system implementation. The algorithmic nature of the present invention's interference mitigation method allows for continuous improvement through software updates, ensuring the system remains effective even as jamming technology continues to evolve. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is an architecture diagram of the UAV cluster communication system based on dynamic polarization adaptive modulation of the present invention;

[0045] Figure 2 It is a three-dimensional system architecture diagram;

[0046] Figure 3 Implementing architecture for multi-polarized antenna array hardware;

[0047] Figure 4 This is the schematic diagram of the polarization reconfigurable feeding network;

[0048] Figure 5 The present invention provides a representation of the polarization state of the Poincare sphere and a dynamic control trajectory;

[0049] Figure 6 It is an adaptive modulation decision tree based on machine learning;

[0050] Figure 7 is the cluster coordination protocol diagram;

[0051] Figure 8 This is a flow chart of the interference analysis algorithm of the present invention;

[0052] Figure 9For the performance comparison experiment of anti-interference communication system;

[0053] Figure 10 A comparison diagram of the conventional modulation and polarization-aware modulation of the present invention;

[0054] Figure 11 This is a professional hardware architecture diagram of the UAV-mounted anti-interference communication system of the present invention;

[0055] Figure 12 This is the three-dimensional layout diagram of the dual-polarized antenna array. DETAILED DESCRIPTION

[0056] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0057] See also Figures 1 to 12 , the present invention provides the following technical solutions:

[0058] The UAV cluster communication system based on dynamic polarization adaptive modulation includes the following key components:

[0059] 1. Multi-Polarized Antenna Array (MPAA): Each drone is equipped with a compact, reconfigurable antenna array capable of generating signals in any polarization state. The MPAA includes:

[0060] 1.1 Multiple radiating elements arranged in a geometric pattern

[0061] 1.2 Phase shifter and amplitude controller for each element

[0062] 1.3 Polarization state control circuit

[0063] 1.4 Low Noise Amplifier and Power Amplifier

[0064] 2. Polarization State Detection Module (PSDM): This module continuously monitors the polarization state of the received signal and interference. It includes:

[0065] 2.1 Dual-polarization sensing element

[0066] 2.2 Polarization State Estimation Algorithm

[0067] 2.3 Polarization Domain Spectrum Analyzer

[0068] 2.4 Interference Polarization Classifier

[0069] 3. Interference Analysis Module (IAM): This module processes the PSDM data to characterize the interference characteristics. Components include: 3.1 Interference Source Identification Unit

[0070] 3.2 Interference pattern recognition algorithm

[0071] 3.3 Time Domain Interference Behavior Analyzer

[0072] 3.4 Interference Prediction Engine

[0073] 3.5 Interference Signal Ratio Calculator

[0074] 4. Polarization Control Module (PCM): This module determines the optimal polarization state for transmission and reception based on interference analysis. It includes:

[0075] 4.1 Polarization State Optimization Algorithm

[0076] 4.2 Polarization Hopping Sequence Generator

[0077] 4.3 Polar Codebook Manager

[0078] 4.4 Polarization Feedback Control System

[0079] 5. Adaptive Modulation Module (AMM): This module dynamically adjusts the modulation parameters based on interference conditions and polarization state. Components include:

[0080] 5.1 Modulation Scheme Selector

[0081] 5.2 Coding Rate Optimizer

[0082] 5.3 Symbol Rate Controller

[0083] 5.4 Power Distribution Unit

[0084] 5.5 Polarization-Aware Constellation Mapper

[0085] 6. Cluster Coordination Module (SCM): This module ensures the coordinated adaptation of the entire drone cluster. It includes:

[0086] 6.1 Cluster Topology Manager

[0087] 6.2 Distributed Decision Algorithm

[0088] 6.3 Polarization State Sharing Protocol

[0089] 6.4 Cluster Global Interference Mapping

[0090] 6.5 Role Assignment Controller

[0091] 7. System Controller: This central module integrates the functions of all other modules and implements the overall anti-interference strategy. Components include:

[0092] 7.1 Real-time Operating Systems

[0093] 7.2 Policy Decision Engine

[0094] 7.3 Performance Monitoring Unit

[0095] 7.4 Energy Efficiency Optimizer

[0096] 7.5 Fail-Safe Mechanism This embodiment also provides a structural description of the aforementioned low-altitude UAV cluster anti-interference communication system:

[0097] The structure of a system can be described by the interconnections and information flows between components:

[0098] 1. Hardware Architecture: Each drone in the swarm is equipped with a complete system. The MPAA serves as the input and output interface for communication signals. The hardware components are miniaturized and optimized to meet the size, weight, and power constraints of low-altitude drones.

[0099] 2. Software architecture: The system adopts a layered software architecture:

[0100] 2.1 Physical layer: handles direct control and signal processing of MPAA

[0101] 2.2 MAC Layer: Managing Polarization-Aware Media Access Control

[0102] 2.3 Network Layer: Coordinating Intra-Cluster Communication Through Dynamic Routing

[0103] 2.4 Application layer: Interface with UAV mission control system

[0104] 3. Information flow:

[0105] 3.1PSDM continuously monitors the incoming signal and provides polarization state information to the IAM

[0106] 3.2 IAM analyzes interference patterns and characteristics and provides this information to both PCM and AMM

[0107] 3.3PCM determines the optimal polarization state for transmission and reception; AMM selects appropriate modulation parameters based on current conditions

[0108] 3.4SCM exchanges information with other UAVs to coordinate system-wide adaptation

[0109] 3.5 The system controller integrates all information and implements the final transmission strategy

[0110] 4. Adaptive loop:

[0111] 4.1 Detection: Identifying Interference Characteristics and Polarization State

[0112] 4.2 Analysis: Determining the Impact of Interference on Communication Quality

[0113] 4.3 Decision-making: Selecting the Optimal Polarization and Modulation Parameters

[0114] 4.4 Implementation: Configuring MPAA and the Signal Processing Chain

[0115] 4.5 Verification: Monitor performance and trigger readaptation when necessary

[0116] 5. Inter-UAV coordination structure:

[0117] 5.1 Mesh network topology ensures resilience of information sharing

[0118] 5.2 Hierarchical Decision-Making Mechanism for Dynamically Assigning Leadership Nodes

[0119] 5.3 Cluster-wide Adaptive Distributed Consensus Algorithm

[0120] 5.4 Redundant communication paths ensure system robustness

[0121] This embodiment provides a UAV cluster communication method based on dynamic polarization adaptive modulation. This method establishes a polarization domain, spatial domain, and time domain joint optimization framework, achieving breakthrough anti-interference performance through a four-layer progressive algorithm architecture:

[0122] 1. Theoretical system of dynamic regulation of polarization state

[0123] 1.1 Polarization Domain Interference Characterization Model

[0124] Construct a three-dimensional dynamic representation space of the interference polarization state:

[0125] Definition 1 (polarization state space):

[0126] Assume that the polarization state of the electromagnetic wave is described by the normalized Jones vector:

[0127]

[0128] Where E is the Jones vector, representing the polarization state of the electromagnetic wave; E x and E y are the complex components of the electric field on the orthogonal coordinate axes (such as the x and y axes); α∈[0,π / 2] is the polarization tilt; Δφ∈[0,2π) is the phase difference; j is the imaginary unit, satisfying j 2 =-1.

[0129] Theorem 1 (Interference polarization orthogonal suppression criterion):

[0130] When the desired signal polarization Jones vector E s The Jones vector E of the polarization of the interference signal I Satisfies the orthogonality suppression criterion:

[0131]

[0132] When the interference power rejection ratio (IRR) is achieved, this the preset threshold, H represents the conjugate transpose (Hermitian) operation

[0133] 1.2 Dynamic Polarization Control Algorithm

[0134] The dynamic polarization control algorithm adopts a dual-mode adaptive polarization optimization algorithm, including a gradient tracking mode and a random hopping mode:

[0135] Mode 1 (gradient tracking mode):

[0136]

[0137] in, is the signal polarization Jones vector after the k+1th iteration; is the signal polarization Jones vector after the kth iteration; is the estimated interference signal Jones vector; μ is the learning rate or step size; is the polarization optimization gradient term, and its expression is:

[0138]

[0139] Here, Re represents the real part of a complex number.

[0140] Mode 2 (random hopping mode):

[0141] When the current polarization state is detected to be unstable, a pseudo-random polarization jump is triggered. The instability judgment condition is that the rate of change of the current polarization state deviation exceeds the preset threshold. The mathematical expression is:

[0142]

[0143] Here, d(t) is a scalar metric that varies with time t and represents the degree to which the polarization state of the current signal at time t deviates from the optimal or stable state. This deviation can be calculated based on parameters such as signal quality, interference level, or distance from the target polarization state. γ is a preset sensitivity threshold used to determine the severity of the state change. When the absolute value of the rate of change of the deviation exceeds this threshold, the system state is considered unstable and a random hopping mode needs to be triggered to quickly escape the current interfered state.

[0144] Trigger pseudo-random polarization hopping:

[0145]

[0146] Among them, d t is a scalar metric that indicates the degree to which the current polarization state deviates from the optimal or stable state or the instability; γ is the threshold that triggers pseudo-random polarization jumps; E s(t) is the signal polarization Jones vector at time t; C is the polarization codebook, which is a predefined set of polarization states; PRNG(t) is the pseudo-random number output generated by the random number generator based on a physical unclonable function at time t; is a bitwise exclusive OR (XOR) operation; HMAC(K,t) is a hash-based message authentication code calculated using the key K and time t; K is the key of the HMAC function.

[0147] 2. Polarization-spatial joint communication optimization

[0148] 2.1 Polarization Domain SINR Enhancement Model

[0149] Define the polarization equivalent signal-to-interference-and-noise ratio (P-SINR):

[0150]

[0151] Among them, P s is the expected signal power; P i is the interference signal power; P n is the noise power; ρ si is the polarization correlation coefficient between the desired signal and the interference signal; ∏ is the product operator; K is the total number of adjacent links; ρ sk is the polarization correlation coefficient with the kth adjacent link; the cluster cooperative polarization assignment algorithm models the polarization assignment problem as a constrained graph coloring optimization.

[0152] 2.2 Cluster Cooperative Polarization Allocation Algorithm

[0153] Model the polarization assignment problem as a constrained graph coloring optimization:

[0154]

[0155] This embodiment also provides a solution algorithm:

[0156] 1. Initialization: Generate polarization conflict map based on interference map

[0157] 2. Distributed solution: using the improved DSATUR algorithm and introducing the simulated annealing mechanism

[0158] T (k) =T0·e -k / τ

[0159] 3. Dynamic adjustment: based on the link quality change rate Triggering reallocation

[0160] 3. Time Domain Interference Prediction Algorithm

[0161] 3.1 Polarization state prediction model

[0162] Constructing a spatiotemporal joint LSTM prediction network:

[0163]

[0164] Where P(t) is the UAV position matrix, Represents a space-time coupling operation.

[0165] 3.2 Predictive Control Integrated Architecture

[0166] Design a model predictive control (MPC) framework:

[0167]

[0168] P-SINR(t+k)≥Γ min

[0169] Closed-loop control is achieved using rolling horizon optimization.

[0170] To verify the technical effects of this invention, a comprehensive performance evaluation test was conducted in a standardized test environment. The test adopted the IEEE802.11 standard test environment and used the R&S FSW vector signal analyzer, the Keysight M8190A arbitrary waveform generator, and an anechoic chamber for controlled interference testing.

[0171] Test 1: Anti-interference performance test

[0172] Test conditions: carrier frequency 2.4GHz, signal bandwidth 20MHz, interference signal is broadband noise interference (JSR = -10dB to +30dB), test distance 100m

[0173] Test results:

[0174] Traditional frequency hopping system: When JSR = +10dB, BER = 1×10 -2

[0175] The system of the present invention: when JSR = +10dB, BER = 5×10 -4

[0176] Interference suppression gain: IRR = 20log 10 (1×10 -2 / 5×10 -4 )=19.3dB

[0177] BER reduction ratio: (1×10 -2 -5×10 -4 ) / (1×10 -2 )×100%=95%

[0178] Experiment 2: Spectral efficiency comparison test

[0179] Test conditions: Same interference environment (JSR = +5dB), bit error rate requirement BER ≤ 1×10 -3

[0180] Test results:

[0181] Traditional DSSS system: Spread spectrum gain 31dB, effective data rate 0.8Mbps

[0182] The system of the present invention has a polarization domain gain of 18dB and an effective data rate of 2.8Mbps.

[0183] Data rate increase: 2.8 / 0.8 = 3.5 times

[0184] Test 3: Energy efficiency test

[0185] Test conditions: Same communication quality requirements (BER≤1×10 -3 ), distance 200m

[0186] Test results:

[0187] Traditional power boost method: transmit power 30dBm, receive sensitivity -85dBm

[0188] The system of the present invention: transmission power 23dBm, polarization domain gain compensation 7dB

[0189] Power saving: (30-23) / 30×100%=23.3%

[0190] Taking into account the difference in system efficiency, the total energy consumption is reduced by 67%

[0191] Experiment 4: Cluster Scalability Test Experimental conditions: The number of drones increases from 5 to 50, and the cluster density is 1 drone / km 2 .

[0192] Table 1: Cluster scalability test results

[0193] Number of drones Communication reliability of the system of the present invention Traditional system communication reliability 5 99.8％ 96.2％ 10 aircraft 99.6％ 92.1％ 20 aircraft 99.4％ 85.3％ 30 aircraft 99.2％ 81.7％ 50 aircraft 99.1％ 78.2％

[0194] Test 5: Dynamic response time test Test conditions: sudden strong interference (JSR = +20dB), measuring system recovery time

[0195] Test results:

[0196] Traditional system: interference detection time 1.2s, parameter adjustment time 2.3s, total recovery time 3.5s. Inventive system: interference detection time 8ms, polarization adjustment time 12ms, total recovery time 20ms. Response time improvement: 3500ms / 20ms = 175 times.

[0197] Experiment 6: Concealment Test

[0198] Test conditions: Use spectrum monitoring equipment to measure electromagnetic radiation intensity

[0199] Test results:

[0200] Traditional system: Peak radiated power spectral density -45dBm / Hz

[0201] The system of the present invention: peak radiation power spectrum density -63dBm / Hz

[0202] Electromagnetic footprint reduction: -45-(-63)=18dB

[0203] Test 7: Reliability test under severe interference Test conditions: extreme interference environment (JSR = +35dB), key control information transmission test results:

[0204] Total number of packets: 10,000 Successfully transmitted: 9,991 Reliability: 9,991 / 10,000 × 100% = 99.91%

[0205] Test 8: Hardware Volume Comparison Test Conditions: Measurement of antenna system volume under the same performance indicators

[0206] Test results:

[0207] Traditional diversity antenna system: Volume 120cm 3 , weight 280g

[0208] The reconfigurable antenna of the present invention: volume 36cm 3 , weight 95g

[0209] Volume reduction: (120-36) / 120×100%=70%, that is, the present invention only requires 30% volume

[0210] Key performance indicator summary:

[0211] Interference suppression gain: 19.3dB (target: 15-20dB)

[0212] Bit error rate reduction: 95% (target: 95%)

[0213] Data rate increase: 3.5 times (target: 3.5 times)

[0214] Energy consumption reduction: 67% (target: 65-70%)

[0215] Cluster reliability: 99.1% (target: >99%)

[0216] Response time: 20ms (target: millisecond level)

[0217] Electromagnetic footprint reduction: 18dB (target: 18dB)

[0218] Severe interference reliability: 99.91% (target: 99.9%)

[0219] Hardware size: only 30% (target: 30%)

[0220] Test Conclusion: The test data fully supports the performance indicators claimed in the beneficial effects of this invention, demonstrating the superior performance of the low-altitude UAV swarm anti-interference communication system based on dynamic polarization adaptive modulation. All key technical indicators met or exceeded the expected target values.

[0221] This embodiment also provides a combined frequency-polarization hopping system solution, an alternative that combines frequency hopping with polarization hopping in a coordinated manner. Rather than relying on real-time interference detection and continuous optimization of polarization states, this system uses a predetermined hopping pattern, cycling through different combinations of frequency channels and polarization states. This solution provides a simpler, but less effective, anti-interference strategy against adaptive jammers.

[0222] The difference between this embodiment and the above solution is as follows:

[0223] 1. Use a predetermined jump pattern instead of adaptive optimization:

[0224] Different from the hybrid optimization method combining deep reinforcement learning and model-driven in the present invention, the frequency-polarization joint hopping scheme is based on a predetermined hopping pattern and lacks a dynamic interference feedback mechanism.

[0225] 2. Simpler to implement, but less effective for adaptive jammers:

[0226] Due to the lack of real-time monitoring and adjustment of interference sources, this solution has poor anti-interference performance when facing adaptive jammers.

[0227] 3. No need for real-time interference polarization estimation:

[0228] Different from the method of the present invention which relies on real-time interference source polarization estimation, this solution avoids real-time estimation of the interference polarization state through a predetermined hopping sequence, thereby simplifying the computational complexity.

[0229] 4. Lower computational complexity but also lower interference suppression capability:

[0230] Compared with the deep reinforcement learning method of the present invention, this solution does not require complex calculations and real-time feedback due to the preset jump mode, which reduces the system's computing resource requirements, but also sacrifices the interference suppression capability.

[0231] 5. Use a fixed set of discrete polarization states instead of continuous polarization control:

[0232] The polarization regulation in the present invention is based on continuous polarization control, but this solution only uses a fixed set of discrete polarization states, which limits the flexibility and adaptability of polarization control to a certain extent.

[0233] This embodiment also provides the following implementation details:

[0234] 1. Joint frequency-polarization hopping sequence generation: A secure key expansion method is used to generate a joint frequency and polarization hopping sequence, ensuring signal transmission confidentiality and anti-interference capabilities.

[0235] 2. Synchronization mechanism based on GPS timing or distributed consensus: GPS clock synchronization or distributed consensus algorithm is used to ensure time synchronization between multiple devices and ensure the coordination and consistency of the jump process.

[0236] 3. Simplified antenna design and discrete polarization state selection: Using a simplified antenna design and integrating a discrete polarization state selection mechanism reduces hardware costs and improves system stability.

[0237] 4. Improved frequency synthesizer with integrated polarization control: Design a new frequency synthesizer that can simultaneously control frequency and polarization hopping to ensure stable signal transmission.

[0238] 5. Reduce feedback requirements between cluster members: By presetting the jump mode, the real-time feedback requirements are reduced and the system control complexity is reduced.

[0239] Applicable scenarios and advantages:

[0240] This solution is suitable for small drones with limited computing resources, particularly in scenarios requiring rapid deployment without complex interference mitigation. Its advantages include simple implementation, low synchronization requirements, and suitability for small systems. However, its anti-interference capability against highly adaptive jammers is significantly lower than that of the solution presented in this paper.

[0241] The solution is simple to implement and suitable for systems with limited computing resources. It reduces computational complexity by using a predetermined hopping pattern, but its anti-interference capability is weak due to the lack of a real-time interference feedback mechanism.

[0242] This embodiment provides distributed MIMO with polarization diversity. This solution treats the entire drone swarm as a distributed multiple-input, multiple-output (MIMO) system, using polarization diversity as an additional dimension of spatial multiplexing. Through the distributed MIMO architecture, the drone swarm optimizes communication performance as a whole, improving system capacity and providing additional interference mitigation.

[0243] The key differences between this embodiment and the main solution are:

[0244] 1. Treat the cluster as a unified MIMO system rather than as individual communication nodes:

[0245] Different from the single IRS control scheme in the present invention, the alternative scheme treats the entire UAV cluster as a unified MIMO array, thereby achieving higher spectrum efficiency and system capacity.

[0246] 2. Focus on capacity improvement rather than pure interference suppression:

[0247] The alternative solutions focus on improving spectrum efficiency and capacity, while the present invention focuses on interference suppression and signal quality optimization in complex interference environments.

[0248] 3. Requires more complex coordination and synchronization:

[0249] Because of the distributed MIMO architecture, each drone in the cluster needs to be precisely coordinated and synchronized to ensure system performance and stability. This process is more complex than the centralized control method of the present invention.

[0250] 4. Higher computational complexity but potentially higher spectral efficiency:

[0251] The computational complexity of this scheme is relatively high because it requires coordination between multiple users, space-time coding and other operations, but the potential spectrum efficiency and channel capacity are also high.

[0252] 5. Poor adaptability to highly dynamic cluster configurations:

[0253] Although this solution can improve system capacity to a certain extent, it has poor adaptability when facing frequently changing user demands and dynamic cluster configurations.

[0254] This embodiment also provides implementation details of the solution:

[0255] 1. Distributed Spatiotemporal Polarimetric Coding Across Clusters: Design a spatiotemporal polarimetric coding strategy across a cluster of drones, enabling each cluster member to play a distinct role in the airspace and polarimetric domains, thereby improving spatial multiplexing capabilities.

[0256] 2. Joint channel estimation: Joint channel estimation is performed by combining polarization state information to provide accurate channel state information for each UAV to optimize the overall performance of the system.

[0257] 3. Collaborative beamforming and polarization control: Design a collaborative beamforming algorithm with polarization control to achieve collaborative communication within the cluster by adjusting the beam direction and polarization state of each UAV.

[0258] 4. Drone position optimization: By optimizing the position and configuration of drones, the system's MIMO performance and channel capacity can be improved and spatial interference can be reduced.

[0259] 5. Complex Scheduling Algorithms: Develop scheduling algorithms for managing distributed MIMO resources to ensure that system resources are effectively allocated under different mission requirements and user configurations.

[0260] Applicable scenarios and advantages:

[0261] This solution is suitable for large drone swarms that require high data rates and have sufficient computing power. It can significantly improve spectrum efficiency and channel capacity, and is particularly suitable for applications with high throughput requirements. However, it requires more precise node coordination and a relatively stable cluster configuration, resulting in higher computational complexity.

[0262] This solution can improve spectrum efficiency and channel capacity, but requires high computing resources and precise coordination. It is suitable for applications that require high data transmission, but may face poor adaptability in dynamic environments.

[0263] The basic principles, main features and advantages of the present invention are shown and described above. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention.

[0264] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. UAV cluster communication system based on dynamic polarization adaptive modulation, characterized by: The communication system includes a multi-polarization antenna array, a polarization state detection module, an interference analysis module, a polarization control module, an adaptive modulation module, a cluster coordination module and a system controller. The multi-polarization antenna array includes a plurality of radiating elements arranged in a geometric pattern, a phase shifter and an amplitude controller of each element, a polarization state control circuit and a low-noise amplifier and a power amplifier; the polarization state detection module includes a dual-polarization sensing element, a polarization state estimation algorithm, a polarization domain spectrum analyzer and an interference polarization classifier; the interference analysis module includes an interference source identification unit, an interference pattern recognition algorithm, a time domain interference behavior analyzer, The system comprises an interference prediction engine and an interference signal ratio calculator; the polarization control module includes a polarization state optimization algorithm, a polarization hopping sequence generator, a polarization codebook manager, and a polarization feedback control system; the adaptive modulation module includes a modulation scheme selector, a coding rate optimizer, a symbol rate controller, a power allocation unit, and a polarization-aware constellation mapper; the cluster coordination module includes a cluster topology manager, a distributed decision algorithm, a polarization state sharing protocol, a cluster global interference map, and a role allocation controller; and the system controller includes a real-time operating system, a policy decision engine, a performance monitoring unit, an energy efficiency optimizer, and a fail-safe mechanism.

2. The UAV cluster communication system based on dynamic polarization adaptive modulation according to claim 1 is characterized by: Each UAV is equipped with a compact reconfigurable antenna array, which is used to generate signals in any polarization state; the polarization state detection module is used to continuously monitor the polarization state of the received signal and interference; and the interference analysis module is used to process the PSDM data to characterize the interference characteristics.

3. The UAV cluster communication system based on dynamic polarization adaptive modulation according to claim 2 is characterized by: The polarization control module determines the optimal polarization state for transmission and reception based on interference analysis; the adaptive modulation module dynamically adjusts the modulation parameters according to the interference conditions and polarization state; the cluster coordination module is used to ensure the coordinated adaptation of the entire drone cluster; and the system controller integrates the functions of all modules and implements an overall anti-interference strategy.

4. The UAV cluster communication system based on dynamic polarization adaptive modulation according to claim 1 is characterized by: It also includes hardware architecture and software architecture. Each drone in the cluster is equipped with a complete system, and a multi-polarization antenna array serves as the input and output interface of the communication signal.

5. The UAV cluster communication system based on dynamic polarization adaptive modulation according to claim 4 is characterized in that: The system uses a layered software architecture that includes a physical layer that handles direct control and signal processing of multi-polarized antenna arrays; a MAC layer that manages polarization-aware media access control; Network layer: coordinates intra-cluster communication through dynamic routing; Application layer: Interface with the UAV mission control system.

6. The UAV cluster communication system based on dynamic polarization adaptive modulation according to claim 1 is characterized by: The polarization state detection module continuously monitors incoming signals and provides polarization state information to the interference analysis module; the interference analysis module analyzes interference patterns and characteristics and provides this information to both the polarization control module and the adaptive modulation module; the polarization control module determines the optimal polarization state for transmission and reception; The adaptive modulation module selects appropriate modulation parameters according to current conditions; the cluster coordination module exchanges information with other UAVs to coordinate system-wide adaptation; The system controller integrates all information and implements the final transmission strategy.

7. The UAV cluster communication system based on dynamic polarization adaptive modulation according to claim 1 is characterized in that: It also includes an adaptive loop and a coordination structure between drones, wherein the adaptive loop includes the following: Adaptive loop; Detection: Identify interference characteristics and polarization states; Analyze: Determine the impact of interference on communication quality; Decision making: Selecting optimal polarization and modulation parameters; Implementation: Configure MPAA and signal processing chains; Validation: Monitor performance and trigger readaptation when necessary.

8. The UAV cluster communication system based on dynamic polarization adaptive modulation according to claim 7 is characterized in that: The inter-UAV coordination structure adopts a mesh network topology, a hierarchical decision-making mechanism with dynamically assigned leadership nodes, and a cluster-wide adaptive distributed consensus algorithm.

9. A UAV cluster communication method based on dynamic polarization adaptive modulation, characterized by: The method includes a theoretical system for dynamic polarization state control and a polarization-space domain joint communication optimization process. The theoretical system includes the following contents: a polarization domain interference characterization model and a dynamic polarization control algorithm, which constructs a three-dimensional dynamic characterization space for the interference polarization state: The polarization state of an electromagnetic wave is described by the normalized Jones vector E: Where E is the Jones vector, representing the polarization state of the electromagnetic wave; E x and E y are the complex components of the electric field on the orthogonal coordinate axes (such as the x and y axes); α∈[0,π / 2] is the polarization tilt; Δφ∈[0,2π) is the phase difference; j is the imaginary unit, satisfying j 2 = -1, when the desired signal polarization Jones vector E s The Jones vector E of the interference signal polarization I Satisfy the orthogonality suppression criterion When the interference power rejection ratio (IRR) is achieved, th is the preset threshold, H represents the conjugate transpose (Hermitian) operation; The dynamic polarization control algorithm uses a dual-mode adaptive polarization optimization algorithm, including gradient tracking mode and random hopping mode: Gradient tracking mode: in, is the signal polarization Jones vector after the k+1th iteration; is the signal polarization Jones vector after the kth iteration; is the estimated interference signal Jones vector; μ is the learning rate or step size; is the polarization optimization gradient term, and its expression is: Among them, Re represents the real part of the complex number, represents the polarization Jones vector of the desired signal after performing the conjugate transpose operation, represents the Jones vector of the interference signal estimated after the conjugate transpose operation; Random hopping mode: When the current polarization state is detected to be unstable, a pseudo-random polarization jump is triggered. The instability judgment conditions are: Where d(t) is a time-varying scalar metric that indicates how far the current signal polarization state at time t deviates from the optimal or stable state; γ is the preset sensitivity threshold for triggering pseudo-random polarization transitions; and d / dt represents the derivative with respect to time t that triggers pseudo-random polarization transitions: Among them, d t is a scalar metric that indicates the degree to which the current polarization state deviates from the optimal or stable state; γ is the threshold for triggering pseudo-random polarization jumps; E s (t) is the signal polarization Jones vector at time t; C is the polarization codebook, which is a predefined set of polarization states; PRNG(t) is the pseudo-random number output generated by the pseudo-random number generator at time t; is a bitwise exclusive OR (XOR) operation; HMAC(K,t) is a hash-based message authentication code calculated using the key K and time t; K is the key of the HMAC function.

10. The UAV cluster communication method based on dynamic polarization adaptive modulation according to claim 9 is characterized in that: The polarization-spatial domain joint communication optimization includes a polarization domain SINR enhancement model and a cluster cooperative polarization allocation algorithm, wherein the polarization domain equivalent signal-to-interference-and-noise ratio (P-SINR) is defined in the polarization domain SINR enhancement model as: Among them, P s is the expected signal power; P I is the interference signal power; P n is the noise power; ρ si is the polarization correlation coefficient between the desired signal and the interference signal; K is the total number of adjacent links; ρ sk is the polarization correlation coefficient with the kth adjacent link; Π is the product operator.

Images