Adaptive Beamforming System with Dynamic Interference Mapping and Power Optimization for Multi-Antenna Wireless Networks

Abstract

The present invention relates to an adaptive beamforming system for multi-antenna wireless networks, designed to optimize signal quality and power efficiency by dynamically adjusting beam direction and power allocation based on real-time data. The system includes an input module for receiving user location, interference, and environmental data; a dynamic interference mapping module that continuously identifies and maps interference sources; an adaptive beamforming control module for adjusting beam direction based on mapped interference and predicted user movements; and a power optimization module that manages power distribution based on user density and quality of service requirements. Additionally, a machine learning prediction module forecasts user movement and interference changes, enabling proactive adjustments. A real-time feedback loop refines the system’s performance continuously, enhancing resilience against interference and optimizing energy usage. This invention provides significant improvements in network performance, making it suitable for high-density, interference-prone, and energy-sensitive environments.

Specification

Description:"Adaptive Beamforming System with Dynamic Interference Mapping and Power Optimization for Multi-Antenna Wireless Networks"

FIELD OF INVENTION
[0001] The present invention relates to the field of wireless communication systems, specifically to adaptive beamforming methodologies for multi-antenna wireless networks. More particularly, it involves a system and method for dynamically optimizing beamforming by employing real-time interference mapping and power management techniques to enhance signal quality, network efficiency, and user experience in wireless communication environments. This invention addresses the need for improved beam direction control, interference mitigation, and energy efficiency in modern, high-capacity, multi-antenna network deployments.
BACKGROUND OF INVENTION
[0002] In recent years, the demand for high-speed, reliable wireless communication has surged, driven by the proliferation of data-intensive applications and the widespread adoption of mobile devices. To meet these demands, multi-antenna wireless communication systems, particularly those employing multiple-input multiple-output (MIMO) technology, have become foundational to next-generation networks, including 5G and beyond. MIMO systems leverage multiple antennas to enhance data throughput, improve link reliability, and accommodate higher user densities, making them essential in crowded and dynamic communication environments.
[0003] However, the benefits of multi-antenna systems are accompanied by significant challenges. Traditional beamforming techniques, which direct signals to intended users by controlling the phase and amplitude of antenna elements, are often limited by static interference suppression and power inefficiencies. These conventional systems lack the adaptability required to handle real-time changes in interference patterns, user mobility, and fluctuating environmental conditions. Consequently, existing beamforming systems can struggle to maintain optimal signal quality, frequently resulting in degraded performance and increased power consumption.
[0004] Moreover, as wireless networks expand to support diverse applications-ranging from low-latency services to high-bandwidth streaming-the need for adaptable, energy-efficient beamforming methodologies becomes increasingly critical. Current systems typically lack the ability to dynamically map interference sources and allocate power based on real-time network demands, resulting in inefficient resource utilization and reduced network performance.
[0005] The present invention addresses these challenges by introducing an adaptive beamforming system equipped with dynamic interference mapping and power optimization capabilities. This innovative approach enables real-time adjustments to beam direction and power levels, improving network resilience, reducing interference, and optimizing energy consumption. Through these advancements, the invention enhances the performance, efficiency, and scalability of multi-antenna wireless networks, particularly in high-density, interference-prone environments.
SUMMARY OF INVENTION
[0006] The present invention provides an adaptive beamforming system for multi-antenna wireless networks, designed to optimize signal quality and energy efficiency by dynamically mapping interference and adjusting power levels in real time. The system continuously monitors interference sources and environmental factors, leveraging a dynamic mapping process to identify and suppress interference from multiple directions. By incorporating power optimization algorithms, the system efficiently allocates power based on user location, network demand, and quality of service requirements.
[0007] This adaptive methodology allows for precise beam direction control and minimizes unnecessary power consumption, improving both signal reliability and overall network performance. The invention's unique capability to adjust to real-time changes in user position, interference patterns, and environmental conditions makes it especially beneficial for high-density communication networks, where demand for bandwidth and data throughput is high. This innovative approach ultimately enhances user experience, network scalability, and resource utilization in multi-antenna wireless systems, positioning it as a critical advancement for modern and future wireless communication infrastructures.
PRIOR ART OF INVENTION
[0008] Existing beamforming techniques in multi-antenna wireless networks primarily focus on static interference suppression and limited adaptability to real-time environmental changes. Traditional systems rely on pre-defined algorithms that adjust beam direction based on the average interference and noise levels, often without the capability to account for dynamic interference patterns or fluctuations in user location. As a result, these systems can suffer from signal degradation and reduced efficiency, particularly in dense network environments where interference sources are frequently shifting and unpredictable.
[0009] Several prior art solutions attempt to address these issues through basic interference cancellation and power control methods. For example, some adaptive beamforming systems utilize feedback mechanisms to adjust beam directions based on user feedback or channel state information (CSI). However, these methods often fall short in high-mobility or rapidly changing interference scenarios, as the feedback mechanisms introduce latency, and the system's response time may be insufficient to maintain optimal performance.
[0010] Additionally, existing systems commonly apply uniform power distribution across beams or rely on coarse adjustments that do not account for real-time user density, service type, or specific network demands. This leads to excessive energy consumption, as power is frequently allocated without regard to the varying requirements of individual users or services. Consequently, these systems struggle to balance energy efficiency with high-quality service delivery, especially in heterogeneous networks that support multiple types of traffic with differing quality of service (QoS) requirements.
[0011] Another area of prior art includes systems that employ machine learning or predictive models to enhance beamforming adaptability. However, these approaches are typically limited in scope, focusing on isolated aspects such as user trajectory prediction or interference detection, without integrating these functionalities into a unified, adaptive beamforming framework. Furthermore, existing machine learning-based systems often lack the computational efficiency required for real-time adaptation, making them impractical for high-density, high-mobility scenarios.
[0012] Prior art in the field of adaptive beamforming for multi-antenna wireless networks lacks a cohesive, real-time approach to dynamic interference mapping and power optimization. These limitations highlight the need for an advanced system capable of adaptive, interference-aware beamforming with energy-efficient power management to meet the demands of modern wireless networks. The present invention addresses these shortcomings by introducing a comprehensive, real-time adaptive beamforming solution that combines interference mapping with dynamic power optimization, offering substantial improvements in signal quality, network resilience, and energy efficiency.
[0013] The present invention offers several key advantages over prior art in adaptive beamforming for multi-antenna wireless networks, addressing limitations in real-time adaptability, interference management, and power efficiency:
[0014] Unlike prior systems with limited interference suppression, this invention features real-time, dynamic interference mapping. By continuously monitoring the interference landscape and adjusting beam directions accordingly, the system maintains high-quality signal transmission even in environments with fluctuating interference sources. This results in improved network resilience and minimized signal degradation.
[0015] Real-Time Power Optimization: Prior art often applies static or uniform power allocation across beams, leading to inefficient energy use. The present invention integrates a real-time power optimization algorithm that allocates power based on specific user needs, location, and network demand. This dynamic approach not only reduces unnecessary energy consumption but also enhances battery life for mobile devices and lowers operational costs for network operators.
[0016] Adaptive Beam Control with Low Latency: Existing systems may experience latency in adapting to changes due to slow feedback mechanisms. The present invention minimizes latency by using a predictive, machine-learning-based algorithm that rapidly adjusts beam direction and power levels based on environmental changes and user mobility. This adaptability supports high-mobility users, such as those in vehicular or urban settings, ensuring a consistent and reliable connection.
[0017] User Behavior Prediction for Proactive Adaptation: By incorporating a behavior prediction module, this system forecasts user movement and proactively adjusts beams, a feature not found in conventional systems. This forward-looking approach significantly reduces interruptions and enhances the user experience, especially in networks with high mobility or complex environments.
[0018] Enhanced Energy Efficiency in High-Density Networks: Traditional systems struggle to maintain both energy efficiency and performance in densely populated network areas. The present invention addresses this by dynamically scaling beam power and direction according to real-time user density and quality of service (QoS) requirements. This not only conserves energy but also ensures that users receive optimal service quality without compromising network resources.
[0019] Self-Healing Beam Pathways for Increased Reliability: Unlike conventional systems with single-path beamforming, this invention introduces self-healing beam pathways with redundancy, allowing for automatic rerouting in case of signal degradation or blockage. This feature is particularly advantageous in urban or indoor environments where obstructions may frequently interfere with the signal path.
[0020] Integrated Machine Learning for Interference and Resource Management: While some prior art utilizes machine learning, it is often limited to isolated functions. This invention integrates machine learning to simultaneously manage interference, power allocation, and beam direction, offering a comprehensive approach to adaptive beamforming. This unified methodology optimizes resource allocation across all elements, leading to improved network scalability and operational efficiency.
[0021] Improved Service Quality Across Heterogeneous Traffic: Existing systems often lack the flexibility to adjust service levels for different types of traffic. The present invention's multi-layer beam allocation allows it to allocate beams with varying priorities, ensuring high-priority applications (e.g., video conferencing) receive stronger, focused beams, while lower-priority services maintain broader beams. This enhances the quality of service for diverse applications within the same network area.
BRIEF DESCRIPTION OF DRAWINGS
[0022] The accompanying drawings illustrate the embodiments of systems, methods, and other aspects of the disclosure. Any person with ordinary skills in the art will appreciate that the illustrated element boundaries (e.g., boxes, groups of boxes, or other shapes) in the figures represent an example of the boundaries. In some examples, one element may be designed as multiple elements, or multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component in another and vice versa. Furthermore, the elements may not be drawn to scale.
[0023] Various embodiments will hereinafter be described in accordance with the appended drawings, which are provided to illustrate, not limit, the scope, wherein similar designations denote similar elements, and in which:
[0024] Figure 1 illustrates the structural and functional interconnections between the primary components of the adaptive beamforming system. The system begins with an Input Module (100), which collects real-time data through Location Sensors (101), Interference Sensors (102), and Environmental Condition Sensors (103). This data flows into the Dynamic Interference Mapping Module (104), where the Signal Processing Unit (105) and Interference Mapping Software (106) analyze and map interference sources.
[0025] The Adaptive Beamforming Control Module (107) utilizes this interference data, alongside inputs from the Machine Learning Prediction Module (110), which includes Predictive Algorithms (112) and a Data Processing Unit (113) with Training Data Storage (114), to adjust beam direction and enhance signal focus. The control module further incorporates Beamforming Algorithms (108) and a Digital Signal Processor (111) to execute real-time adjustments in the Antenna Array (109).
[0026] The Power Optimization Module (115), comprising a Power Control Unit (116), Power Amplifiers (117), and Energy Management Software (118), dynamically manages power distribution based on inputs from the prediction module and environmental data. A Real-Time Feedback Loop (119), featuring Feedback Sensors (120) and a Control Loop Processor (121), continuously monitors system performance and feeds back updates to refine system responses.
[0027] Finally, the Output to Antenna Array (122) component, through its Control Interface (123) and Antenna Drivers (124), implements the adjusted beam direction and power settings on the Antenna Array (109). This interconnected arrangement enables the system to adaptively manage beam direction and power optimization, maintaining efficient and reliable wireless communication in dynamic environments.
DETAILED DESCRIPTION OF INVENTION
[0028] The present disclosure is best understood with reference to the detailed figures and description set forth herein. Various embodiments have been discussed with reference to the figures. However, those skilled in the art will readily appreciate that the detailed descriptions provided herein with respect to the figures are merely for explanatory purposes, as the methods and devices may extend beyond the described embodiments. For instance, the teachings presented and the needs of a particular application may yield multiple alternative and suitable approaches to implement the functionality of any detail described herein. Therefore, any approach may extend beyond certain implementation choices in the following embodiments.
[0029] References to "one embodiment," "at least one embodiment," "an embodiment," "one example," "an example," "for example," and so on indicate that the embodiment(s) or example(s) may include a particular feature, structure, characteristic, property, element, or limitation but that not every embodiment or example necessarily includes that particular feature, structure, characteristic, property, element, or limitation. Further, repeated use of the phrase "in an embodiment" does not necessarily refer to the same embodiment.
[0030] Methods of the present invention may be implemented by performing or completing manually, automatically, or a combination thereof, selected steps or tasks. The term "method" refers to manners, means, techniques, and procedures for accomplishing a given task including, but not limited to, those manners, means, techniques, and procedures either known to or readily developed from known manners, means, techniques, and procedures by practitioners of the art to which the invention belongs. The descriptions, examples, methods, and materials presented in the claims and the specification are not to be constructed as limiting but rather as illustrative only. Those skilled in the art will envision many other possible variations within the scope of the technology described herein.
[0031] The present invention relates to an adaptive beamforming system designed to enhance wireless communication performance in multi-antenna networks by dynamically optimizing beam direction and power distribution in response to real-time changes in interference patterns, user locations, and environmental conditions. Referring to Figure 1, the system is composed of multiple interconnected modules and components, each performing specific functions to ensure adaptive, interference-aware beamforming and efficient power utilization.
[0032] The system begins with an Input Module (100), which continuously collects data necessary for real-time adaptive control. This module comprises Location Sensors (101), Interference Sensors (102), and Environmental Condition Sensors (103). Location Sensors (101) monitor the positions of users within the network coverage area, while Interference Sensors (102) detect and measure interference signals from neighboring transmitters and devices. Environmental Condition Sensors (103) capture additional data on environmental factors, such as temperature and obstacles, that may influence signal quality. The information gathered by the Input Module (100) is then transmitted to the Dynamic Interference Mapping Module (104) and Machine Learning Prediction Module (110) for further processing.
[0033] The Dynamic Interference Mapping Module (104), which consists of a Signal Processing Unit (105) and Interference Mapping Software (106), processes the interference data received from Interference Sensors (102). The Signal Processing Unit (105) analyzes the interference levels, identifying sources and their respective intensities across the coverage area. This processed information is then used by Interference Mapping Software (106) to generate a real-time map of interference zones. The mapped data is subsequently transmitted to the Adaptive Beamforming Control Module (107), enabling precise adjustments to beam direction.
[0034] The Adaptive Beamforming Control Module (107) leverages data from the Dynamic Interference Mapping Module (104) and Machine Learning Prediction Module (110) to adjust the direction of signal beams. The Machine Learning Prediction Module (110) comprises Predictive Algorithms (112), a Data Processing Unit (113), and Training Data Storage (114). This module utilizes historical and real-time data processed by Predictive Algorithms (112) to forecast user movement and potential interference changes. These predictions enable the Adaptive Beamforming Control Module (107) to make proactive adjustments, ensuring minimal interruption and optimal signal quality.
[0035] Within the Adaptive Beamforming Control Module (107), Beamforming Algorithms (108) and a Digital Signal Processor (DSP) (111) work in conjunction to calculate and implement optimal phase and amplitude settings across antenna elements in the Antenna Array (109). The Antenna Array (109), consisting of multiple antenna elements, is directed toward intended users while avoiding interference sources, as determined by the beamforming calculations.
[0036] Power management is handled by the Power Optimization Module (115), which consists of a Power Control Unit (116), Power Amplifiers (117), and Energy Management Software (118). This module receives data from the Machine Learning Prediction Module (110) and Environmental Condition Sensors (103), allowing it to allocate power dynamically based on user density and Quality of Service (QoS) requirements. The Power Control Unit (116) manages power distribution across beams, while Power Amplifiers (117) boost signal strength where needed. The Energy Management Software (118) ensures that power is used efficiently, balancing energy consumption with communication quality.
[0037] A Real-Time Feedback Loop (119), which includes Feedback Sensors (120) and a Control Loop Processor (121), continuously monitors system performance and environmental changes. Feedback Sensors (120) gather updated information on user positions, interference levels, and other environmental factors, which is processed by the Control Loop Processor (121). This feedback loop ensures that the system remains adaptive, refining adjustments in the Dynamic Interference Mapping Module (104), Adaptive Beamforming Control Module (107), and Power Optimization Module (115).
[0038] Finally, the Output to Antenna Array (122) component, through the Control Interface (123) and Antenna Drivers (124), implements the beam direction and power adjustments calculated by the system. The Control Interface (123) sends control signals to the Antenna Drivers (124), which adjust each antenna element in the Antenna Array (109) according to the calculated beam settings and power levels, thereby executing the system's real-time adjustments to maintain optimal performance.
[0039] In sum, the invention achieves a comprehensive adaptive beamforming and power optimization process by integrating real-time data from the Input Module (100), predictive capabilities from the Machine Learning Prediction Module (110), and responsive adjustments from the Adaptive Beamforming Control Module (107) and Power Optimization Module (115). This adaptive methodology allows for enhanced signal quality, interference mitigation, and efficient energy utilization, providing significant advantages over traditional static beamforming systems. The real-time feedback enabled by Feedback Loop (119) ensures that the system can continually adjust to dynamic changes, making it particularly suitable for modern, high-density, and interference-prone wireless communication environments..
[0040] Figure 1 provides a detailed overview of the interconnected modules and components within the adaptive beamforming system designed for multi-antenna wireless communication networks. Each module is structured to perform a distinct function, contributing to the system's adaptive, real-time response to user location, interference patterns, and environmental changes.
[0041] Components and Interconnections
1. Input Module (100)
o Location Sensors (101): Track user positions within the network's coverage area, providing location data that is critical for targeting beams accurately.
o Interference Sensors (102): Detect and measure interference signals from neighboring transmitters and devices, enabling the system to identify potential sources of signal disruption.
o Environmental Condition Sensors (103): Capture data on environmental variables such as temperature, obstacles, and other factors that can influence signal propagation.
[0042] The Input Module (100) serves as the starting point, continuously collecting essential real-time data. This data is then fed into the Dynamic Interference Mapping Module (104) and Machine Learning Prediction Module (110) to enable adaptive system adjustments.
2. Dynamic Interference Mapping Module (104)
o Signal Processing Unit (105): Analyzes interference signals detected by Interference Sensors (102), identifying the intensity and source location of interference within the coverage area.
o Interference Mapping Software (106): Generates a real-time map of interference zones based on data processed by the Signal Processing Unit (105). This map identifies areas with high interference, allowing the system to adjust beam directions accordingly.
[0043] The Dynamic Interference Mapping Module (104) provides crucial data to the Adaptive Beamforming Control Module (107), helping to avoid interference zones when directing signal beams.
3. Adaptive Beamforming Control Module (107)
o Beamforming Algorithms (108): Calculate optimal phase and amplitude settings for each antenna element within the Antenna Array (109), enabling the system to form and steer signal beams accurately.
o Antenna Array (109): A collection of multiple antenna elements that can adjust signal direction and intensity based on control instructions from the beamforming algorithms.
o Digital Signal Processor (DSP) (111): Executes the beamforming algorithms, processing data from the Interference Mapping Software (106) and Prediction Module (110) to dynamically control the antenna elements.
[0044] The Adaptive Beamforming Control Module (107) utilizes interference data from the Dynamic Interference Mapping Module (104) and predictions from the Machine Learning Prediction Module (110) to adjust beam direction and ensure precise targeting of users while avoiding interference.
4. Machine Learning Prediction Module (110)
o Predictive Algorithms (112): Use historical and real-time data to forecast user movement and anticipate changes in interference patterns, allowing the system to adjust proactively.
o Data Processing Unit (113): Processes incoming data from the Input Module (100) and refines it for use by the predictive algorithms.
o Training Data Storage (114): Stores historical data that allows the predictive algorithms to improve their accuracy over time.
[0045] The Machine Learning Prediction Module (110) generates predictive data that enhances the adaptability of both the Adaptive Beamforming Control Module (107) and the Power Optimization Module (115), enabling proactive rather than reactive adjustments.
5. Power Optimization Module (115)
o Power Control Unit (116): Allocates power to the beams, adjusting power levels based on real-time user density and Quality of Service (QoS) requirements.
o Power Amplifiers (117): Boost signal power where necessary to ensure sufficient signal strength for high-priority users or in areas with high interference.
o Energy Management Software (118): Balances energy consumption across the antenna array, minimizing power usage without compromising communication quality.
[0046] The Power Optimization Module (115) receives data from the Prediction Module (110) and Environmental Condition Sensors (103), dynamically adjusting power levels across beams to conserve energy and improve efficiency. This module ensures that power is directed where it is most needed, based on current network demands.
6. Real-Time Feedback Loop (119)
o Feedback Sensors (120): Monitor real-time updates on user positions, interference levels, and environmental factors, providing continuous feedback on system performance.
o Control Loop Processor (121): Processes feedback data and relays it to the Interference Mapping Module (104), Beamforming Control Module (107), and Power Optimization Module (115), allowing these modules to fine-tune their operations in real time.
[0047] The Real-Time Feedback Loop (119) ensures that the system can continually adapt to changing conditions by feeding real-time updates into the processing modules, helping to maintain optimal performance.
7. Output to Antenna Array (122)
o Control Interface (123): Communicates final beam direction and power settings to the Antenna Drivers (124).
o Antenna Drivers (124): Execute adjustments on the Antenna Array (109) based on commands received from the Control Interface (123).
[0048] The Output to Antenna Array (122) module applies the calculated adjustments to the physical antenna array, implementing the final beamforming and power settings determined by the other modules. This ensures that the antenna array directs beams accurately toward users while minimizing interference and maintaining energy efficiency.
[0049] System Workflow and Data Flow
[0050] In Figure 1, the Input Module (100) gathers initial data, which flows into the Dynamic Interference Mapping Module (104) for interference analysis and the Machine Learning Prediction Module (110) for predictive adjustments. The Adaptive Beamforming Control Module (107) uses the mapped interference data and predictions to adjust beam direction in the Antenna Array (109), while the Power Optimization Module (115) manages power allocation based on real-time user needs and environmental conditions.
[0051] Feedback from the Real-Time Feedback Loop (119) continuously updates the Interference Mapping (104), Beamforming Control (107), and Power Optimization (115) modules, allowing for dynamic refinements. Finally, the Output to Antenna Array (122) module implements the calculated beam direction and power settings, maintaining an optimal signal for users and reducing interference.
[0052] This interconnected system structure enables adaptive, real-time beamforming and power optimization, enhancing the performance of multi-antenna wireless networks by adapting to user location, interference patterns, and environmental conditions.
[0053] The present invention, an adaptive beamforming system with dynamic interference mapping and power optimization, has several applications across various fields and industries. Here are some key applications:
[0054] 1. 5G and 6G Wireless Networks: In next-generation wireless networks, the demand for high-speed, high-capacity data transmission is critical. The adaptive beamforming system can improve user experience by directing signals precisely to users while reducing interference. This enhances network coverage, data throughput, and latency, particularly in dense urban areas or crowded environments.
[0055] 2. Internet of Things (IoT) and Smart Cities: IoT and smart city applications rely on numerous interconnected devices that communicate wirelessly. The system's ability to dynamically manage interference and power can optimize network performance in densely populated sensor environments, ensuring reliable communication and efficient power usage across thousands of devices.
[0056] 3. Autonomous Vehicles and V2X (Vehicle-to-Everything) Communication: Autonomous vehicles require fast, reliable data exchange with each other and with infrastructure. This invention can be applied to Vehicle-to-Everything (V2X) communication to maintain robust connectivity even in high-interference zones. By adapting beam direction and power, it can improve safety and data accuracy in real-time vehicle interactions.
[0057] 4. Military and Defense Communications: Secure and interference-resistant communication is vital in military applications. This adaptive beamforming system can be used to ensure reliable, interference-resistant links between devices, vehicles, or personnel in complex and dynamic environments. Its predictive module also provides proactive adjustments to avoid potential disruptions in critical missions.
[0058] 5. Satellite Communications and Ground Stations: In satellite communication systems, maintaining accurate beam direction is crucial for reliable signal transmission. This invention can be used in satellite ground stations or onboard satellites to optimize signal reception, reduce interference from other satellites, and manage power efficiently, which is essential for conserving energy in space systems.
[0059] 6. Wireless Mesh and Ad-Hoc Networks: Wireless mesh and ad-hoc networks, often deployed in remote or temporary setups, benefit from adaptable systems that can manage interference and optimize power usage. This invention can improve the connectivity and resilience of these networks by continuously adjusting beams and power levels to match the real-time needs of users and network conditions.
[0060] 7. Public Safety and Emergency Response Networks: During emergencies, reliable communication between responders is essential, often under challenging conditions. This system can ensure that public safety communications remain interference-free and efficiently powered, providing robust connectivity for emergency teams working in high-interference or densely populated areas.
[0061] 8. Industrial and Manufacturing Environments: In industrial settings, such as factories with automated machinery and sensors, minimizing interference and managing power efficiently are critical. The adaptive beamforming system can enhance connectivity within industrial IoT networks, ensuring consistent data flow between devices and optimizing power usage in areas with high electromagnetic interference.
[0062] 9. High-Density Public Venues (Stadiums, Airports, and Convention Centers): High-density environments like stadiums, airports, and convention centers experience significant network strain and interference. This invention can optimize connectivity for large numbers of users by dynamically adjusting beam direction and power, improving network reliability and user experience.
[0063] 10. Wireless Broadband in Rural and Remote Areas: Providing reliable wireless connectivity in rural and remote areas can be challenging due to interference from environmental factors and limited infrastructure. This invention can enhance broadband coverage in these areas by adjusting beams based on terrain and minimizing power usage, making it a cost-effective solution for remote connectivity.
[0064] 11. Research and Development in Wireless Technology: This system has applications in research facilities working on advanced wireless communication technologies, such as beamforming algorithms and machine learning for predictive network management. The invention's combination of adaptive beamforming and machine learning prediction provides a robust testbed for exploring new wireless communication paradigms.
[0065] 12. Augmented Reality (AR) and Virtual Reality (VR) Applications: AR and VR applications require high-bandwidth, low-latency wireless connections to provide an immersive experience. This system can dynamically direct signals to AR/VR devices, reduce interference, and optimize power, ensuring consistent performance in real-time, data-heavy applications.
[0066] 13. Healthcare and Medical Device Networks: In healthcare environments, reliable wireless communication is critical for patient monitoring, telemedicine, and data exchange between medical devices. This invention can manage interference from numerous devices, maintain secure connections, and optimize power usage, enhancing connectivity in hospitals and healthcare facilities.
, Claims:We Claim:
1. An adaptive beamforming system for multi-antenna wireless networks, comprising:
• an input module (100) configured to receive real-time data on user locations, interference patterns, and environmental conditions;
• a dynamic interference mapping module (104) operatively connected to the input module (100), configured to analyze and map interference sources;
• an adaptive beamforming control module (107) configured to adjust beam direction based on data received from the dynamic interference mapping module (104) and user locations; and
• a power optimization module (115) configured to dynamically allocate power based on user density and quality of service requirements.
2. The system of claim 1, wherein the adaptive beamforming control module (107) comprises beamforming algorithms (108) and a digital signal processor (DSP) (111), the beamforming algorithms (108) being configured to calculate phase and amplitude settings to steer beams in real-time toward intended users via the antenna array (109).
3. The system of claim 1, further comprising a machine learning prediction module (110) configured to predict user movement and changes in interference patterns, wherein the machine learning prediction module (110) provides predictive data to the adaptive beamforming control module (107) and the power optimization module (115) for proactive adjustments.
4. The system of claim 1, wherein the power optimization module (115) further comprises a power control unit (116), power amplifiers (117), and energy management software (118), the power control unit (116) being configured to adjust power levels across beams based on real-time user density and environmental conditions.
5. The system of claim 3, wherein the machine learning prediction module (110) includes predictive algorithms (112) and a data processing unit (113), the predictive algorithms (112) being configured to use historical and real-time data to forecast user trajectory and interference shifts to enable proactive beam adjustments.
6. The system of claim 1, further comprising a real-time feedback loop (119) with feedback sensors (120) and a control loop processor (121), wherein the real-time feedback loop (119) is configured to provide continuous system updates based on changes in environmental conditions, interference levels, and user positions.
7. The system of claim 1, wherein the adaptive beamforming control module (107) is operatively connected to an antenna array (109), the antenna array (109) comprising multiple antenna elements configured to adjust phase and amplitude in response to control signals from the adaptive beamforming control module (107).
8. The system of claim 1, wherein the dynamic interference mapping module (104) further comprises a signal processing unit (105) and interference mapping software (106), the signal processing unit (105) being configured to analyze interference data and the interference mapping software (106) being configured to generate a real-time map of interference zones for beam direction adjustments.
9. The system of claim 1, further comprising an output to antenna array module (122), wherein the output to antenna array module (122) includes a control interface (123) and antenna drivers (124), the control interface (123) being configured to transmit calculated adjustments in beam direction and power to the antenna drivers (124).
10. A method for adaptive beamforming in multi-antenna wireless networks, as illustrated in Figure 1, comprising:
• receiving real-time data on user locations, interference patterns, and environmental conditions via an input module (100);
• mapping interference sources and levels based on the received interference data using a dynamic interference mapping module (104);
• adjusting beam direction using an adaptive beamforming control module (107) based on the mapped interference and user location data;
• predicting user movement and interference changes using a machine learning prediction module (110); and
• dynamically optimizing power allocation for beams using a power optimization module (115) based on user density and quality of service requirements.

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