Broadcast emission signal optimization method based on multi-antenna cooperation

Abstract

The invention relates to a broadcast emission signal optimization method based on multi-antenna cooperation, and the method comprises the steps: constructing a multi-antenna cooperation broadcast emission system which comprises a central control unit, a distributed antenna array, an environment perception module and a terminal feedback module; real-time channel state information (CSI) is collected through the environment sensing module, and a dynamic propagation environment map is constructed; and optimizing a radiation pattern of the antenna array by adopting a three-dimensional beam forming algorithm based on the propagation environment map. According to the multi-antenna cooperative broadcast emission signal optimization method, a cooperative architecture of the central control unit, the distributed antenna array, the environment sensing module and the terminal feedback module is adopted, dynamic beam forming and power optimization are achieved, the coverage uniformity of broadcast signals is remarkably improved, and blind areas and overlapped interference areas are reduced; the energy utilization efficiency is improved, the energy waste caused by ineffective radiation is reduced, the self-adaptive capability of the system is enhanced, and the channel environment change can be responded in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of broadcast transmission signals, and in particular to a broadcast transmission signal optimization method based on multi-antenna collaboration. Background Art

[0002] A broadcast transmitter is a facility responsible for converting audio or video signals into electromagnetic waves and broadcasting them. A broadcast transmitter usually consists of a main transmitter and an antenna feeder system. The main transmitter is used to amplify the electrical signal converted from the original audio or video signal and convert it into a high-intensity high-frequency electrical oscillation signal. The antenna feeder system consists of a feeder and a transmitting antenna. The main transmitter is connected through the antenna feeder system to convert the high-frequency electrical oscillation into electromagnetic waves and radiate them to the outside world.

[0003] With the advancement of wireless communication technology, broadcast systems (such as digital television, emergency broadcasting, and connected car broadcasting) have increasingly stringent requirements for coverage, signal quality, and energy efficiency. Traditional broadcast transmission technologies primarily use single antennas or simple diversity antenna architectures, which suffer from uneven coverage, low energy efficiency, limited multipath fading mitigation, a lack of dynamic optimization mechanisms, and low spectrum resource utilization.

[0004] There have been some studies trying to improve broadcast transmission technology, for example:

[0005] Beamforming technology: A directional beam is formed by adjusting the phase and amplitude of the antenna array. However, existing solutions are mostly based on static optimization and cannot adapt to dynamic environmental changes.

[0006] Coordinated Multi-Point (CoMP): CoMP is used in cellular networks to enhance coverage. However, broadcast systems require higher synchronization accuracy and lower latency, which existing CoMP solutions cannot meet.

[0007] Cognitive radio technology: used for dynamic spectrum allocation, but has not yet been effectively applied to multi-antenna collaborative optimization in broadcast systems.

[0008] However, these technologies still fail to fundamentally solve the problems of coverage optimization, energy efficiency improvement, and anti-interference in broadcast systems. Therefore, a broadcast transmission signal optimization method based on multi-antenna collaboration is urgently needed. Summary of the Invention

[0009] To address the shortcomings of existing technologies, a broadcast transmission signal optimization method based on multi-antenna collaborative dynamic beamforming, environmental perception and distributed power optimization has effectively improved the performance of the broadcast system.

[0010] To achieve the above objectives, the present invention provides the following technical solution: a broadcast transmission signal optimization method based on multi-antenna collaboration, comprising the following steps:

[0011] Step S1: Construct a multi-antenna cooperative broadcast transmission system, including a central control unit, a distributed antenna array, an environment perception module, and a terminal feedback module;

[0012] Step S2: collecting real-time channel state information (CSI) through the environment perception module and constructing a dynamic propagation environment map;

[0013] Step S3: Based on the propagation environment map, a three-dimensional beamforming algorithm is used to optimize the radiation pattern of the antenna array;

[0014] Step S4: Based on the terminal feedback information, a distributed power optimization algorithm is used to dynamically adjust the transmit power of each antenna;

[0015] Step S5: Monitor the signal coverage quality in real time and adaptively adjust the beam and power allocation strategies according to network changes.

[0016] Furthermore, the distributed antenna array adopts a conformal antenna structure, including but not limited to:

[0017] Uniform linear array (ULA), uniform area array (UPA) or cylindrical array;

[0018] The antenna unit spacing meets Where λ is the operating wavelength;

[0019] The antenna array supports dynamic reconfiguration and can adjust the array element activation mode according to environmental changes;

[0020] The working method of the environment perception module includes:

[0021] The feedback module deployed on the terminal collects received signal strength (RSSI), signal-to-noise ratio (SNR), and multipath delay information;

[0022] Use drones or ground sensor networks to assist in measuring the propagation environment;

[0023] Use machine learning algorithms to predict channel change trends and build a spatiotemporal joint channel model:

[0024] H(x,y,z,t)=aH hist (x,y,z)+(1-a)H real (x,y,z,t);

[0025] Among them, a is the historical data weight factor, H hist is the historical channel data, H real For real-time measurement data.

[0026] Furthermore, the three-dimensional beamforming algorithm includes:

[0027] Beam weight optimization based on spatial angles θ and Φ:

[0028]

[0029] Where H is the channel matrix, d is the target radiation pattern, and λ is the regularization coefficient;

[0030] Adaptive beam tracking technology is used to dynamically adjust the main lobe and null direction to suppress interference;

[0031] Supports multi-beam collaboration and covers different geographical areas at the same time.

[0032] Furthermore, the distributed power optimization algorithm includes:

[0033] Set up a multi-objective optimization problem:

[0034]

[0035] Constraints:

[0036]

[0037] Among them, P i is the transmission power of the i-th antenna, Coverage k is the coverage quality of the kth region, γ th is the signal-to-interference-and-noise ratio threshold;

[0038] Game theory or distributed ADMM (Alternating Direction Method of Multipliers) is used to solve the optimal power allocation.

[0039] Furthermore, the central control unit adopts a layered architecture, including:

[0040] Cloud global optimization layer: responsible for long-term coverage planning and resource allocation;

[0041] Edge computing layer: responsible for real-time beamforming and power adjustment;

[0042] Local antenna control layer: performs low-latency beam switching and power control

[0043] The working mode of the terminal feedback module includes:

[0044] The terminal device periodically reports the received signal quality indicator;

[0045] Use compressed sensing technology to reduce feedback overhead;

[0046] Supports event-triggered aperiodic feedback to cope with sudden channel changes.

[0047] Furthermore, it also includes dynamic spectrum management strategies, as follows:

[0048] monitoring spectrum usage by adjacent broadcast towers;

[0049] Adopt cognitive radio technology to dynamically adjust the operating frequency band to avoid co-frequency interference;

[0050] When spectrum resources are limited, non-orthogonal multiple access (NOMA) technology is used to improve spectrum efficiency.

[0051] Furthermore, the anti-interference enhancement strategy is also included, specifically including:

[0052] Use space-time coding (STC) technology to suppress multipath interference;

[0053] Use artificial intelligence algorithms to identify malicious interference sources and dynamically adjust the null direction;

[0054] In military or security-sensitive scenarios, frequency hopping and encrypted broadcast modes are supported.

[0055] Furthermore, a broadcast transmission system based on the method is also included, including:

[0056] Multi-antenna array module, a reconfigurable array composed of multiple antenna units (such as uniform array, cylindrical array, etc.), supports dynamic adjustment of radiation pattern;

[0057] The environment perception and channel estimation module collects channel state information (CSI) in real time and builds a dynamic propagation environment map;

[0058] Dynamic beamforming controller, which calculates the optimal beam weights based on channel status and coverage requirements;

[0059] Distributed power optimization unit dynamically adjusts the transmit power of each antenna to balance coverage and energy consumption;

[0060] The terminal feedback and collaboration management module collects signal quality feedback from terminal devices and coordinates multi-antenna collaboration strategies;

[0061] The central control and decision-making unit coordinates global resource allocation and formulates optimization strategies.

[0062] Specifically, the multi-antenna array module supports beam scanning, multi-beam collaboration and null control. The environmental perception and channel estimation module combines terminal feedback, drone measurement and AI prediction to improve channel estimation accuracy. The distributed power optimization unit adopts game theory or distributed optimization algorithm.

[0063] Compared with the existing technology, the technical solution of this application has the following beneficial effects:

[0064] 1. The present invention adopts a multi-antenna collaborative broadcast transmission signal optimization method, adopts a collaborative architecture of a central control unit, a distributed antenna array, an environmental perception module and a terminal feedback module, to achieve dynamic beamforming and power optimization, significantly improve the coverage uniformity of broadcast signals, reduce blind spots and overlapping interference areas; improve energy utilization efficiency, reduce energy waste caused by invalid radiation, enhance the system's adaptability, and be able to respond to changes in the channel environment in real time.

[0065] 2. This invention utilizes a conformal distributed antenna array (DAA) structure, employing uniform linear arrays (ULAs), uniform planar arrays (UPAs), or cylindrical arrays. This design supports dynamic reconfiguration to adapt to different application scenarios. By dynamically adjusting the array element activation mode, it optimizes beam directivity and improves signal quality. Through real-time channel modeling using an environmental perception module, combined with terminal feedback, drone-assisted measurements, and machine learning algorithms, a spatiotemporal joint channel model is constructed to accurately predict channel fading and multipath effects, optimize beamforming strategies, reduce signal fluctuations caused by environmental changes, and improve reception stability.

[0066] 3. This invention uses a distributed power optimization algorithm to establish a multi-objective optimization problem and employs game theory or the ADMM algorithm to dynamically adjust power allocation. This reduces total transmit power while ensuring coverage quality, significantly saving energy, balancing the power load across antennas, and extending device life. The terminal feedback module utilizes compressed sensing technology, supporting both periodic and event-triggered feedback mechanisms. This reduces data volume, reduces system feedback latency, improves real-time performance, and reduces wireless resource usage. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flow chart of the broadcast transmission signal optimization method based on multi-antenna collaboration of the present invention;

[0068] Figure 2 This is a system architecture diagram of the broadcast transmission signal optimization method based on multi-antenna collaboration of the present invention. DETAILED DESCRIPTION

[0069] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0070] See also Figure 1-2 The broadcast transmission signal optimization method based on multi-antenna collaboration in this embodiment includes the following steps:

[0071] Step S1: Construct a multi-antenna cooperative broadcast transmission system, including a central control unit, a distributed antenna array, an environment perception module, and a terminal feedback module. The distributed antenna array adopts a conformal antenna structure, including but not limited to:

[0072] Uniform linear array (ULA), uniform area array (UPA) or cylindrical array;

[0073] The antenna unit spacing meets Where λ is the operating wavelength;

[0074] The antenna array supports dynamic reconfiguration and can adjust the array element activation mode according to environmental changes;

[0075] The working methods of the environmental perception module include:

[0076] The feedback module deployed on the terminal collects received signal strength (RSSI), signal-to-noise ratio (SNR), and multipath delay information;

[0077] Use drones or ground sensor networks to assist in measuring the propagation environment;

[0078] Use machine learning algorithms to predict channel change trends and build a spatiotemporal joint channel model:

[0079] H(x,y,z,t)=aH hist (x,y,z)+(1-a)H real (x,y,z,t);

[0080] Among them, a is the historical data weight factor, H hist is the historical channel data, H real To measure data in real time;

[0081] The central control unit adopts a layered architecture, including:

[0082] Cloud global optimization layer: responsible for long-term coverage planning and resource allocation;

[0083] Edge computing layer: responsible for real-time beamforming and power adjustment;

[0084] Local antenna control layer: performs low-latency beam switching and power control

[0085] The terminal feedback module works as follows:

[0086] The terminal device periodically reports the received signal quality indicator;

[0087] Use compressed sensing technology to reduce feedback overhead;

[0088] Supports event-triggered aperiodic feedback to cope with sudden channel changes;

[0089] Step S2: Collect real-time channel state information (CSI) through the environment perception module and build a dynamic propagation environment map;

[0090] Step S3: Based on the propagation environment map, a three-dimensional beamforming algorithm is used to optimize the radiation pattern of the antenna array. The three-dimensional beamforming algorithm includes:

[0091] Beam weight optimization based on spatial angles θ and Φ:

[0092]

[0093] Where H is the channel matrix, d is the target radiation pattern, and λ is the regularization coefficient;

[0094] Adaptive beam tracking technology is used to dynamically adjust the main lobe and null direction to suppress interference;

[0095] Support multi-beam collaboration to cover different geographical areas simultaneously;

[0096] Step S4: Based on the terminal feedback information, a distributed power optimization algorithm is used to dynamically adjust the transmit power of each antenna. The distributed power optimization algorithm includes:

[0097] Set up a multi-objective optimization problem:

[0098]

[0099] Constraints:

[0100]

[0101] Among them, P i is the transmission power of the i-th antenna, Coverage k is the coverage quality of the kth region, γ th is the signal-to-interference-and-noise ratio threshold;

[0102] Use game theory or distributed ADMM (alternating direction method of multipliers) to solve the optimal power allocation;

[0103] Step S5: Monitor the signal coverage quality in real time and adaptively adjust the beam and power allocation strategies according to network changes.

[0104] Specifically, it also includes dynamic spectrum management strategies, as follows:

[0105] monitoring spectrum usage by adjacent broadcast towers;

[0106] Adopt cognitive radio technology to dynamically adjust the operating frequency band to avoid co-frequency interference;

[0107] When spectrum resources are limited, non-orthogonal multiple access (NOMA) technology is used to improve spectrum efficiency.

[0108] Anti-interference enhancement strategies include:

[0109] Use space-time coding (STC) technology to suppress multipath interference;

[0110] Use artificial intelligence algorithms to identify malicious interference sources and dynamically adjust the null direction;

[0111] In military or security-sensitive scenarios, frequency hopping and encrypted broadcast modes are supported.

[0112] A method-based broadcast transmission system comprising:

[0113] Multi-antenna array module, a reconfigurable array composed of multiple antenna units (such as uniform array, cylindrical array, etc.), supports dynamic adjustment of radiation pattern;

[0114] The environment perception and channel estimation module collects channel state information (CSI) in real time and builds a dynamic propagation environment map;

[0115] Dynamic beamforming controller, which calculates the optimal beam weights based on channel status and coverage requirements;

[0116] Distributed power optimization unit dynamically adjusts the transmit power of each antenna to balance coverage and energy consumption;

[0117] The terminal feedback and collaboration management module collects signal quality feedback from terminal devices and coordinates multi-antenna collaboration strategies;

[0118] The central control and decision-making unit coordinates global resource allocation and formulates optimization strategies.

[0119] Specifically, the multi-antenna array module supports beam scanning, multi-beam coordination and null-steering control. The environmental perception and channel estimation module combines terminal feedback, drone measurement and AI prediction to improve channel estimation accuracy. The distributed power optimization unit adopts game theory or distributed optimization algorithm.

[0120] Example 1: Digital TV Broadcasting System Optimization

[0121] 1. System Deployment: An 8×8 conformal antenna array was deployed on the TV tower, using a uniform planar array (UPA) structure. The antenna unit spacing was set to 0.48λ (operating frequency 600 MHz). An environmental perception module was configured, including 10 distributed channel measurement nodes.

[0122] 2. Workflow:

[0123] 1) Update the environmental map every 15 minutes: Collect RSSI and SNR data fed back by terminals, supplement blind spot data through drone-assisted measurements, and use an LSTM neural network to predict channel status for the next 5 minutes.

[0124] 2) 3D beamforming: Divide the service area into 16 sub-areas, and calculate the optimal beam weight for each sub-area:

[0125] w_opt=(H HH +λ1) -1 ·H Hd ;

[0126] Dynamically adjust 3 main beams and 5 null directions.

[0127] 3. Power optimization: The initial total power is 8kW, which is reduced to 5.2kW after optimization. The power in the edge area is increased by 40%, and the power in the center area is reduced by 25%. The ADMM algorithm is used to achieve distributed optimization.

[0128] 4. Measured results: Coverage uniformity increased by 43%, user average signal-to-noise ratio increased by 9.2dB, and system energy consumption decreased by 38%.

[0129] Example 2: Implementation of emergency broadcast system

[0130] 1. System architecture: The main base station uses a 4×4 cylindrical antenna array, equipped with three drones as mobile relay nodes, and the terminal equipment integrates an emergency feedback module.

[0131] 2. Key technology implementation:

[0132] 1) Millisecond-level beam switching: 10 typical disaster scenario beam templates are pre-stored, and FPGA is used to quickly calculate beam weights, with a switching delay of less than 5ms.

[0133] 2) Dynamic priority management:

[0134] Establish three levels of emergency broadcast priority:

[0135] Level 1 (life safety): occupies 70% of resources;

[0136] Level 2 (disaster warning): occupies 25% of resources;

[0137] Level 3 (General Information): occupies 5% of resources.

[0138] 3. Anti-interference measures: Gold sequence spread spectrum is used, with a processing gain of 23dB, adaptive nulling to align with the direction of the interference source, and support for 5MHz bandwidth frequency hopping.

[0139] 4. Measured performance: Coverage reliability in disaster scenarios reaches 99.7%, voice broadcast delay is less than 50ms, and anti-interference capability is improved by 15dB.

[0140] Example 3: Indoor venue broadcasting system

[0141] 1. Deployment plan: Distributed deployment of 8 4×4 antenna nodes, operating in the 2.4 GHz frequency band, using a fiber fronthaul architecture.

[0142] 2. Key technologies:

[0143] 1) 3D precise coverage: Establish a 3D electromagnetic model of the venue, with a vertical beam control accuracy of 0.5°, supporting inter-floor interference elimination,

[0144] 2) User density adaptation: Real-time monitoring of user distribution heat maps and dynamic adjustment of beam quantity and width:

[0145] Low density: 4 wide beams;

[0146] High density: 16 narrow beams.

[0147] 3. Performance indicators: Venue coverage uniformity > 95%, peak capacity supports 5,000 users, and co-channel interference is reduced by 18dB.

[0148] In summary, the present invention realizes dynamic beamforming and power optimization through a multi-antenna collaborative broadcast transmission signal optimization method, adopts a collaborative architecture of a central control unit, a distributed antenna array, an environmental perception module and a terminal feedback module, significantly improves the coverage uniformity of the broadcast signal, reduces blind spots and overlapping interference areas; improves energy utilization efficiency, reduces energy waste caused by invalid radiation, enhances the system's adaptability, and can respond to channel environment changes in real time. Through the conformal structure design of the distributed antenna array, a uniform linear array (ULA), a uniform planar array (UPA) or a cylindrical array is adopted to support dynamic reconfiguration and adapt to different application scenarios. By dynamically adjusting the array element activation mode, the beam directivity is optimized and the signal quality is improved. Through the real-time channel modeling of the environmental perception module, combined with terminal feedback, drone-assisted measurement and machine learning algorithms, a spatiotemporal joint channel model is constructed to accurately predict channel fading and multipath effects, optimize the beamforming strategy, reduce signal fluctuations caused by environmental changes, and improve reception stability. A three-dimensional beamforming algorithm optimizes beam weights based on spatial angle, supports multi-beam coordination and adaptive nulling control, and achieves precise regional coverage, preventing signal leakage into non-target areas, effectively suppressing co-channel interference, and improving the signal-to-interference-and-noise ratio (SINR). A distributed power optimization algorithm formulates a multi-objective optimization problem and dynamically adjusts power allocation using game theory or ADMM algorithms. This reduces total transmit power while ensuring coverage quality, significantly saving energy, balancing the power load across antennas, and extending device life. Compressed sensing technology in the terminal feedback module supports periodic and event-triggered feedback mechanisms. This reduces data volume, reduces system feedback latency, improves real-time performance, reduces wireless resource usage, and improves spectrum efficiency. A dynamic spectrum management strategy, combining cognitive radio and non-orthogonal multiple access (NOMA) technology, dynamically adjusts frequency bands to avoid co-channel interference and increase spectrum reuse, ensuring high-quality broadcast services even when spectrum resources are limited. A hierarchical central control unit, employing a three-tier architecture consisting of cloud-based global optimization, edge computing, and local control, achieves millisecond-level beam switching, meeting the requirements of low-latency applications, balancing computing loads, and improving system scalability.

[0149] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0150] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A broadcast transmission signal optimization method based on multi-antenna collaboration, characterized in that: The following steps are involved: Step S1: Construct a multi-antenna cooperative broadcast transmission system, including a central control unit, a distributed antenna array, an environment perception module, and a terminal feedback module; Step S2: collecting real-time channel state information (CSI) through the environment perception module and constructing a dynamic propagation environment map; Step S3: Based on the propagation environment map, a three-dimensional beamforming algorithm is used to optimize the radiation pattern of the antenna array; Step S4: Based on the terminal feedback information, a distributed power optimization algorithm is used to dynamically adjust the transmit power of each antenna; Step S5: Monitor the signal coverage quality in real time and adaptively adjust the beam and power allocation strategies according to network changes.

2. The broadcast transmission signal optimization method based on multi-antenna collaboration according to claim 1, characterized in that: The distributed antenna array adopts a conformal antenna structure, including but not limited to: Uniform linear array (ULA), uniform area array (UPA) or cylindrical array; The antenna unit spacing meets Where λ is the operating wavelength; The antenna array supports dynamic reconfiguration and can adjust the array element activation mode according to environmental changes; The working method of the environment perception module includes: The feedback module deployed on the terminal collects received signal strength (RSSI), signal-to-noise ratio (SNR), and multipath delay information; Use drones or ground sensor networks to assist in measuring the propagation environment; Use machine learning algorithms to predict channel change trends and build a spatiotemporal joint channel model: H(x,y,z,t)=aH hist (x,y,z)+(1-a)H real (x,y,z,t); Among them, a is the historical data weight factor, H hist is the historical channel data, H real For real-time measurement data.

3. The broadcast transmission signal optimization method based on multi-antenna collaboration according to claim 1, characterized in that: The three-dimensional beamforming algorithm includes: Beam weight optimization based on spatial angles θ and Φ: Where H is the channel matrix, d is the target radiation pattern, and λ is the regularization coefficient; Adaptive beam tracking technology is used to dynamically adjust the main lobe and null direction to suppress interference; Supports multi-beam collaboration and covers different geographical areas at the same time.

4. The broadcast transmission signal optimization method based on multi-antenna collaboration according to claim 1, characterized in that: The distributed power optimization algorithm includes: Set up a multi-objective optimization problem: Constraints: Among them, P i is the transmission power of the i-th antenna, Coverage k is the coverage quality of the kth region, γ th is the signal-to-interference-and-noise ratio threshold; Game theory or distributed ADMM (Alternating Direction Method of Multipliers) is used to solve the optimal power allocation.

5. The broadcast transmission signal optimization method based on multi-antenna collaboration according to claim 1, characterized in that: The central control unit adopts a layered architecture, including: Cloud global optimization layer: responsible for long-term coverage planning and resource allocation; Edge computing layer: responsible for real-time beamforming and power adjustment; Local antenna control layer: performs low-latency beam switching and power control The working mode of the terminal feedback module includes: The terminal device periodically reports the received signal quality indicator; Use compressed sensing technology to reduce feedback overhead; Supports event-triggered aperiodic feedback to cope with sudden channel changes.

6. The broadcast transmission signal optimization method based on multi-antenna collaboration according to claim 1, characterized in that: It also includes dynamic spectrum management strategies, as follows: monitoring spectrum usage by adjacent broadcast towers; Adopt cognitive radio technology to dynamically adjust the operating frequency band to avoid co-frequency interference; When spectrum resources are limited, non-orthogonal multiple access (NOMA) technology is used to improve spectrum efficiency.

7. The broadcast transmission signal optimization method based on multi-antenna collaboration according to claim 1, characterized in that: The above also includes anti-interference enhancement strategies, specifically including: Use space-time coding (STC) technology to suppress multipath interference; Use artificial intelligence algorithms to identify malicious interference sources and dynamically adjust the null direction; In military or security-sensitive scenarios, frequency hopping and encrypted broadcast modes are supported.

8. The broadcast transmission signal optimization method based on multi-antenna collaboration according to claim 1, characterized in that: Also included is a broadcast transmission system based on the method according to any one of claims 1 to 7, comprising: Multi-antenna array module, a reconfigurable array composed of multiple antenna units (such as uniform array, cylindrical array, etc.), supports dynamic adjustment of radiation pattern; The environment perception and channel estimation module collects channel state information (CSI) in real time and builds a dynamic propagation environment map; Dynamic beamforming controller, which calculates the optimal beam weights based on channel status and coverage requirements; Distributed power optimization unit dynamically adjusts the transmit power of each antenna to balance coverage and energy consumption; The terminal feedback and collaboration management module collects signal quality feedback from terminal devices and coordinates multi-antenna collaboration strategies; The central control and decision-making unit coordinates global resource allocation and formulates optimization strategies.

9. The broadcast transmission signal optimization method based on multi-antenna collaboration according to claim 8, characterized in that: The multi-antenna array module supports beam scanning, multi-beam coordination and null steering control. The environmental perception and channel estimation module combines terminal feedback, drone measurement and AI prediction to improve channel estimation accuracy. The distributed power optimization unit adopts game theory or distributed optimization algorithm.

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