I. System Core Parameters and Architecture Overview

Guangzhou Sigtenna’s 6.4–7.2 GHz digital-control active multi-beam lens antenna is a next-generation high-frequency communication solution specifically developed for the evolution of 5G-Advanced high-frequency bands and the large-scale commercialization iteration trend of 6G. It features lightweight design, high performance, and low cost. Leveraging a proprietary metamaterial physical innovation architecture and digital-analog hybrid precise control technology, the product offers multi-dimensional core advantages including ultra-high antenna gain, ultra-low system energy consumption, minimal deployment cost, full-scenario intelligent adaptation, and lightweight rapid deployment. It is widely adaptable to mainstream networking scenarios such as 6G high-frequency wide-area coverage, AI-RAN intelligent macro base stations, and distributed small cells. Compared with traditional 6G AAU equipment, this product possesses significant generational competitive differentiation in terms of technical advancement, engineering deployability, and long-term evolution scalability.

The product operates over the 6.4–7.2 GHz frequency band, precisely matching the mainstream commercial high-frequency band planning for 6G around 6 GHz. The equipment supports **120° ultra-wide horizontal sector coverage**, incorporating **eight groups of high-precision directional narrow beams**, each beam covering a refined **15°** interval. The subdivided sectors adopt a **dual-beam stacking gain design**, combined with a **±45° dual-polarization architecture** to form an efficient and stable 2TR transceiver mechanism. Leveraging the inherent electromagnetic control advantages of the metamaterial lens, the native antenna gain reaches **27 dBi**, with beam isolation **>30 dB** and cross-polarization ratio **>20 dB**. Thanks to its superior physical-layer anti-interference performance and ultra-high gain characteristics, it establishes a highly reliable communication foundation for 6G high-frequency challenging scenarios characterized by weak coverage, high interference, and fast fading.

At the system architecture level, the product pioneers a **digital-domain + analog-domain dual-coding fusion architecture**, effectively breaking through the technical bottlenecks of traditional antennas, such as fixed beam patterns, single control methods, and insufficient dynamic adaptation capabilities. It enables real-time beam reconfiguration, adaptive beamforming, and fine-grained resource scheduling across the entire network. Leveraging a proprietary multi-layer lens stacking technology, the equipment provides industry-leading hardware elastic scalability. Based on the basic architecture of **120° wide coverage and 8 beams**, it can flexibly achieve **4TR/6TR/8TR** gradient MIMO networking per **15°** subdivided sector according to dynamic changes in user traffic and service tidal effects, precisely adapting to light-load, medium-load, and heavy-load full-gradient service scenarios. Coupled with a full-process AI intelligent management and control system, it provides real-time channel awareness, scene feature recognition, intelligent power scheduling, and link adaptive coordination. This fundamentally solves the pain points of traditional high-frequency equipment, such as redundant computing power, high energy consumption, fixed parameters, weak scene adaptation, and inflexible capacity expansion.

II. Multi-Dimensional Comparative Analysis with 6G 256TR/512TR AAU Technology

Current mainstream 6G high-frequency networking solutions center on **256TR/512TR ultra-large array AAUs**, which rely on a massive number of active RF channels to achieve large capacity transmission. Inherent drawbacks include fixed TR array size, non-adjustable MIMO specifications, and rigid scene adaptation. Furthermore, they introduce a series of commercialization challenges: high hardware stacking costs, high operational energy consumption, heavy baseband computational load, complex O&M debugging, and high technology iteration costs. Relying on five core technological breakthroughs—metamaterial physical innovation, multi-layer lens elastic expansion, digital-analog dual-coding fusion architecture, physical passive beamforming, and AI lightweight intelligent scheduling—Sigtenna’s digital-control lens antenna builds a differentiated technology system characterized by **extremely simple hardware architecture, ultimate transmission performance, ultra-low operating energy consumption, highly intelligent adaptation, and elastic capacity expansion**. This creates a clear generational technology gap compared with traditional large-TR-count AAUs. A comprehensive quantitative comparative analysis is provided below.

(i) Cost Dimension: Full-Dimension Cost Reduction via Minimalist Architecture, Avoiding Large-Array Premium

Traditional 256TR/512TR AAUs adopt a fully active large-scale array architecture, relying on a huge number of TR RF chains, high-precision phase shifters, massive dedicated digital beamforming chips, and associated baseband pre-processing modules. The number of active components and customization level are high; hardware BOM cost grows exponentially with array size. For a 512TR ultra-large array architecture, hardware investment remains extremely high. At the engineering deployment level, the equipment is bulky and heavy, requiring reinforced base station structures, high-power power supplies, independent cooling systems, and complex cabling modifications—lengthy construction processes and long deployment cycles. Meanwhile, due to severe high-frequency signal attenuation, traditional AAUs have limited single-site coverage, necessitating dense site deployment to meet full-area coverage and capacity requirements, further increasing capital expenditure (CAPEX). At the iteration and upgrade level, the equipment has deep hardware-software coupling and a closed architecture; upgrading from 5G to 6G or changing frequency bands requires complete replacement, making legacy reuse impossible, thus very high iteration costs. At the O&M level, the large number of active components leads to many failure points, requiring regular beam calibration, channel fault diagnosis, and algorithm parameter adjustments—continuously accumulating long-term labor maintenance and equipment renewal operational expenses (OPEX). The superposition of multiple costs makes it difficult to commercialize traditional large-TR-array AAUs at scale.

Sigtenna’s antenna innovatively adopts a **passive metamaterial lens + minimalist active unit deep integration architecture**, completely overturning the traditional AAU’s “performance through hardware stacking, high cost for large arrays” approach, achieving quantified cost reduction across all dimensions. At the hardware level, the system requires only **two 8TR RRUs** to complete **120°** full-sector coverage and large-capacity networking, reducing the number of active components by **more than 60%** compared with a 512TR ultra-large array solution. Using a proprietary 3D photonic crystal metamaterial mass-production process, which eliminates the need for large-scale custom active components, hardware BOM cost is directly reduced by **more than 50%**. At the same time, thanks to the lens antenna’s core advantage of **27 dBi ultra-high native physical gain**, the equipment does not require specially developed, custom 6G high-frequency high-power RF chips. This avoids multi-billion-yuan NRE R&D investment, 2–3 year long R&D cycles, and the low mass-production yield (less than 40%) of high-end RF chips, greatly reducing R&D expenses and shortening project implementation timelines—achieving cost reduction and efficiency improvement from both hardware and R&D sources.

Traditional 256TR/512TR AAUs, to compensate for high-frequency transmission attenuation and ensure basic coverage and capacity indicators, must carry a large number of high-spec, high-power, multi-channel dedicated RF chips. Such 6G high-frequency chips rely on special semiconductor materials and nanometer-level precision processes, requiring massive R&D investment and a full cycle of 2–3 years, with mass-production yield below 40%. This not only significantly raises hardware procurement costs but also carries high R&D failure risk and project delay risk. Moreover, the supply chain for high-end high-frequency RF chips is constrained, with procurement lead times of 3–6 months and unstable material supply, severely restricting the large-scale construction progress of 6G high-frequency networking, and indirectly increasing project time and management costs.

Across the full lifecycle dimensions of engineering deployment, generation iteration, O&M energy consumption, and computing power support, the quantified cost reduction advantages of Sigtenna’s solution are prominent. At the deployment level: lightweight equipment reduces volume by **30%** and weight by **50%**, supporting minimalist deployment methods such as wall mounting, pole mounting, and ceiling mounting; construction labor time is shortened by **more than 60%**, and labor deployment cost reduced by **more than 50%**. Meanwhile, single-site coverage radius is increased by **more than 60%**, allowing one unit to replace 2–3 traditional high-frequency sites, greatly reducing the number of networking sites, and further reducing overall networking CAPEX by **30%–40%**. At the iteration and upgrade level: the equipment adopts a decoupled hardware-software design, enabling **100% reuse** of existing 8TR RRU equipment; upgrading from 5G to 6G only requires upgrading lens accessory components and the AI management module, reducing network generational upgrade costs by **more than 70%**. At the O&M level: the passive lens structure is calibration-free and maintenance-free, combined with AI full-process fault self-diagnosis and automated O&M, reducing equipment failure rate by **more than 40%** and O&M labor costs by **more than 70%**, saving 3,000–5,000 CNY per site annually. At the energy consumption and computing power level: baseline energy consumption is reduced by **more than 40%**, with an additional **15%–20%** reduction in wasteful energy consumption through AI intelligent energy-saving optimization; baseband computing load is reduced by **more than 90%**, eliminating the need for additional baseband hardware expansion, and spectrum efficiency is improved by **15%–20%**. Based on comprehensive full-dimension calculation, Sigtenna’s solution achieves **more than 50%** reduction in total cost of ownership (TCO), making it the only high-quality commercial technology route that combines low cost, short cycle, easy mass production, and large-scale deployment potential for current 6G high-frequency scenarios.

Full-Dimension Cost Quantification Comparison Quick-View Table

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(ii) Summary of Core Causes for Cost Differences

The ability of Sigtenna’s antenna to achieve quantified full-dimension cost reduction stems from three generational technology differences that fundamentally eliminate the cost maladies of traditional 6G AAUs. **First, architecture difference drives cost reduction:** completely abandoning the traditional 6G approach of “increasing TR count to improve performance,” leveraging metamaterial physical properties to replace massive active components and digital computing power, streamlining system architecture from the hardware foundation and eliminating array stacking premiums. **Second, performance premium offsets incremental costs:** with top-tier physical performance including 27 dBi ultra-high gain, >30 dB ultra-high isolation, and >20 dB high polarization purity, single-site coverage, anti-interference performance, and transmission stability are comprehensively upgraded, enabling larger capacity and wider coverage with fewer devices, effectively diluting per-bit traffic operating costs. **Third, full-lifecycle minimalist O&M reduces costs:** leveraging the dual advantages of passive structure maintenance-free operation and AI full-automated O&M, it completely revolutionizes the traditional high-frequency manual O&M model, greatly compressing long-term OPEX, creating a mature value loop of “low-cost one-time deployment, low-burden long-term operation.”

(iii) Energy Consumption Dimension: Physical Energy Saving + AI Precision Control, Solving the 6G High Energy Consumption Bottleneck

Traditional 256TR/512TR AAUs rely on fully active arrays running continuously at full load. Massive numbers of RF, phase-shifting, and digital computing modules work uninterrupted for long periods, and redundant units cannot be shut down during traffic troughs, resulting in a very large baseline energy consumption. Moreover, their beamforming and interference suppression functions rely entirely on pure digital algorithm iteration, with massive matrix operations generating huge wasteful power consumption. Long-term per-site electricity costs remain high, which does not align with the green, low-carbon, energy-efficient industrial development direction for 6G, and is a core energy bottleneck restricting large-scale commercial deployment of 6G networks.

Sigtenna’s antenna adopts a dual-coding operation mechanism of **“analog domain for basic coverage, digital domain for precise control”** combined with a multi-layer lens stacked elastic architecture to achieve layered energy saving and on-demand capacity expansion. The passive metamaterial lens independently completes basic beamforming and full-area signal coverage with **zero energy consumption**. Active units are activated only on demand in fine-grained scheduling or capacity expansion scenarios, eliminating wasteful power output. Baseline energy consumption is reduced by **more than 40%** compared with traditional large-TR arrays. The proprietary AI intelligent energy-saving scheduling algorithm perceives user density, terminal distribution, and traffic tidal variations in real time, boosting power during peaks to ensure capacity and reducing power during troughs to cut waste, further reducing **15%–20%** of wasteful energy consumption, highly aligned with 6G’s core green and low-carbon networking trends.

(iv) Computing Power & Operational Efficiency: Hardware-Level Load Reduction, Eliminating Redundant Computing Overhead

Traditional 6G large-TR-count AAUs rely entirely on pure digital beamforming for beamforming, interference suppression, and beam scanning. The baseband side must perform real-time massive channel matrix operations, precoding solutions, and multi-user interference cancellation. In high-frequency multi-beam networking scenarios, baseband computing load increases by **more than 70%**, leading to high system latency and low resource utilization efficiency. The complex digital algorithm architecture further increases equipment debugging, upgrade, and O&M difficulty, resulting in high failure rates and weak operational stability, creating a vicious cycle of **“high computing investment, low performance gain.”** Additionally, due to weak physical-layer anti-interference capability, traditional equipment must occupy **15%–20%** of air interface resources for interference calibration, further compressing effective spectrum utilization space.

Sigtenna’s antenna’s core breakthrough lies in **replacing massive digital computation with physical beamforming**. Leveraging the precise electromagnetic control characteristics of proprietary 3D photonic crystal metamaterials, the hardware natively achieves **15°** precise narrow beamforming, high-isolation beam division, and polarization interference suppression, without requiring baseband equipment to perform complex iterative computations. This directly reduces baseband computing load by **more than 90%**, freeing valuable computing resources to focus on high-value 6G core services such as AI intelligent scheduling, communication-sensing coordination, and capacity optimization. With native physical advantages of **>30 dB beam isolation** and **>20 dB cross-polarization ratio**, the equipment does not need to consume air interface resources for interference calibration, improving effective spectrum utilization by **15%–20%**. Combined with the full-process AI automated O&M mechanism, manual intervention cost is reduced by **more than 80%**, achieving a comprehensive leap in system response speed, operational stability, and spectrum resource utilization—fundamentally solving the core weaknesses of traditional AAUs: computing redundancy and low operational efficiency.

(v) Transmission & Networking Performance: Physical-Level Anti-Interference, Suitable for High-Quality High-Frequency Transmission

Traditional 256TR/512TR AAUs rely entirely on backend digital algorithm compensation for interference suppression and beam control, with weak physical-layer fundamental performance. Typical beam isolation is **<20 dB** and cross-polarization ratio only 10–15 dB. In high-frequency multi-beam networking scenarios, issues such as beam crosstalk, side-lobe leakage, and polarization interference are prominent, leading to degraded system signal-to-noise ratio and increased bit error rate, unable to fully support 6G’s core requirements for ultra-high speed, low latency, and high reliability. Moreover, digital beam control is easily affected by wireless environment, terminal mobility, and multipath fading, resulting in parameter adjustment lag and link instability, making it difficult to adapt to dynamic networking scenarios such as dense urban areas and complex indoor-outdoor叠加.

Sigtenna’s 6.4–7.2 GHz high-frequency antenna has four industry-leading native physical core advantages, outperforming traditional large-TR-array AAUs comprehensively: **First, ultra-high physical gain:** native lens antenna gain of 27 dBi, effectively counteracting rapid 6G high-frequency signal attenuation, significantly improving single-site coverage radius and penetration in complex scenarios. **Second, ultra-high beam isolation:** beam isolation >30 dB, beam leakage suppressed below -30 dB, enabling co-frequency beam interference-free reuse. **Third, ultra-high polarization purity:** cross-polarization ratio >20 dB, effectively suppressing polarization crosstalk, ensuring orthogonality and reliability of MIMO multi-stream transmission. **Fourth, excellent side-lobe suppression:** eliminating cross-area spurious interference and signal leakage. Empowered by these four advantages, the system achieves a bit error rate as low as **10⁻⁸**, transmission rate fluctuation controlled within **3 dB**, and single-site coverage radius increased by **more than 60%**, fully meeting the complex networking requirements of 6G: ultra-high speed, low latency, high reliability, and wide coverage.

 (vi) Intelligent Adaptation: Solving Core Pain Points for 6G AI-RAN Macro Base Stations, Enabling Computing-Link Adaptive Coordination

The 6G AI-RAN architecture centers on AI intelligent scheduling, Massive MIMO adaptive optimization, and full-scenario self-adaptation. However, traditional 256TR/512TR AAUs are constrained by poor physical-layer performance, leading to three core bottlenecks that severely limit large-scale AI-RAN deployment: **First, high computing cost and limited intelligent gain for MIMO multi-user pairing:** insufficient beam isolation causes severe multi-user crosstalk, requiring massive baseband computing for channel orthogonalization and interference cancellation; physical-layer defects cannot be compensated by algorithms, diluting AI scheduling gains. **Second, weak scenario adaptation capability:** parameter optimization relies on offline simulation data, which deviates significantly from real network complex scenarios; beam and power parameter updates lag, resulting in insufficient dynamic adaptation. **Third, imbalance between computing and link coordination:** dynamic variables such as channel fast fading, user mobility, and traffic tidal effects are complexly coupled, making AI algorithm iteration difficult and causing serious mismatch between computing investment and performance gain, leading to poor overall networking economics.

Sigtenna’s lens antenna, through a three-in-one innovation system of **hardware-level physical performance + digital-analog dual-coding architecture + full-process AI intelligent management**, fundamentally breaks the technical barriers of traditional AI-RAN, redefining lightweight and efficient networking logic for 6G intelligent macro base stations, achieving minimal computing overhead, ultimate scene adaptation, and ultimate performance gain, fully compatible with next-generation network evolution systems like AI-RAN and O-RAN.

First, solving MIMO pairing computing redundancy and limited intelligent gain.The device’s **>30 dB** ultra-high beam isolation physically eliminates inter-beam crosstalk, eliminating the need for baseband to perform complex channel matrix calculations and interference risk assessment, supporting minimalist orthogonal pairing of multiple beams and users, greatly reducing millisecond-level massive computing overhead. The accompanying AI intelligent power scheduling strategy dynamically allocates 320W total power according to user count and traffic load per beam, precisely optimizing transmission quality in beam overlap regions and maximizing MIMO pairing gain. Compared with traditional AAUs, AI pairing algorithm development difficulty and deployment cost are reduced by **more than 60%**, completely solving the pain point of **“high computing input, low intelligent return”** of traditional architectures. Combined with AI channel prediction and link adaptation mechanisms, channel fluctuation risks are avoided in advance, greatly improving overall network stability.

Second, solving scene adaptation disconnect and parameter iteration lag.Abandoning the traditional AI-RAN’s fixed optimization model that relies on offline simulation parameters, it leverages real-time scene awareness to dynamically collect real data such as user density, mobility characteristics, and traffic tidal patterns, completing parameter self-adaptive optimization through AI autonomous learning iteration, highly suitable for complex macro base station environments with multiple superimposed scenes. Combined with the rapid reconfiguration capability of digital-analog dual coding, the control response speed for beam shape, coverage range, and power allocation is improved by **more than 80%** compared with traditional AAUs, enabling real-time tracking of channel changes and scene fluctuations, truly achieving **“deploy and adapt, dynamic and optimize”**, completely solving the industry pain points of parameter lag and insufficient scene adaptability in traditional intelligent networks.

Third, breaking down barriers to computing and link adaptive coordination.The device’s excellent physical anti-interference and beam stability characteristics greatly weaken the impact of dynamic variables such as channel fast fading, multipath interference, and anglejitter on the system, eliminating the need for complex interference suppression, channel calibration, and parameter iteration algorithms. Only lightweight AI scheduling is needed to achieve fine-grained intelligent management and control of the entire network. This effectively ends the vicious cycle of **“continuously increasing computing investment, marginally decreasing performance gain”** in traditional AI-RAN, freeing valuable computing resources from ineffective interference cancellation and channel correction work, concentrating them on enabling high-value scenarios such as user experience optimization, network capacity improvement, and communication-sensing coordination, greatly lowering the barrier to large-scale commercialization of 6G AI-RAN.

Empowered by multiple cutting-edge technological innovations, Sigtenna’s antenna achieves **MIMO scheduling gain of 60%–80%**, with scheduling efficiency **12–15 times** that of traditional 6G AAUs. It fully realizes the four-dimensional coordinated optimization capabilities of **precise channel awareness, intelligent scene recognition, adaptive computing matching, and dynamic power reconfiguration**, highly aligned with the lightweight, intelligent, efficient, and green evolution direction of 6G AI-RAN networks.

6G AI-RAN Macro Base Station Scenario Technical Comparison Summary Table

Traditional 256TR/512TR AAUs are pure communication-dedicated devices with a closed and rigid hardware architecture, single-function, supporting only basic data transmission, unable to natively adapt to the core technology evolution requirement of **6G integrated sensing and communication (ISAC)**. The equipment has weak frequency band adaptability; generation iteration and band upgrades require complete equipment replacement, with high iteration costs and long construction cycles. Moreover, large-array equipment is bulky and has constrained deployment conditions, making it difficult to adapt to the 6G trends of distributed, lightweight, high-density small cell networking, resulting in narrow long-term technology iteration space.

Sigtenna’s antenna is deeply developed for 6G long-term evolution architecture, natively embedding **integrated communication and sensing capabilities**. Relying on a dual-function fusion architecture of **passive beam wide-area sensing + active beam precise communication**, it achieves ISAC functions such as environmental detection, terminal positioning, signal monitoring, and interference source tracing without adding any additional hardware modules, precisely aligning with 6G’s core technology direction. The 6.4–7.2 GHz high-frequency band can smoothly interface with future evolution to higher bands such as terahertz, reserving multi-generation communication upgrade interfaces, achieving **“one deployment, multi-generation reuse”**. The lightweight, miniaturized form factor perfectly adapts to 6G distributed small cells and edge node high-density deployment, fully compatible with next-generation open network architectures such as O-RAN and AI-RAN, supporting operators in achieving low-cost, smooth, and efficient large-scale upgrade from 5G to 6G.

III. Summary of Core Technical Advantages of Guangzhou Sigtenna Antenna System

Based on multi-dimensional analysis of hardware parameters, architectural innovation, computing optimization, energy control, AI intelligence empowerment, and 6G long-term evolution adaptability, along with quantitative comparison results with traditional 256TR/512TR large-array AAUs, Sigtenna’s 6.4–7.2 GHz digital-control active multi-beam lens antenna completely breaks the traditional 6G communication equipment industry pain points of **“high performance inevitably high cost, high intelligence inevitably high energy consumption, high computing inevitably high redundancy”**, building a comprehensive, high-barrier, deployable system of core technical advantages, which can be summarized into six core dimensions.

1. Ultimate Physical Performance, Industry-Leading High-Frequency Anti-Interference Capability

Leveraging proprietary 3D photonic crystal metamaterial underlying technology innovation, the equipment achieves native physical gain of **27 dBi**, together with **>30 dB** ultra-high beam isolation, **>20 dB** high cross-polarization ratio, and excellent side-lobe suppression. It physically eliminates beam crosstalk, polarization interference, and spurious leakage, reducing high-frequency transmission loss by **more than 30%** compared with traditional 6G AAUs. The ultra-high gain effectively counteracts rapid high-frequency signal attenuation, significantly improving single-site coverage radius and penetration in complex scenarios. Key indicators such as spectrum reuse efficiency, transmission stability, and dynamic scene adaptability comprehensively surpass those of 256TR/512TR large-array solutions, making it the optimal physical-layer infrastructure solution for current 6G high-frequency networking.

2. Digital-Analog Dual-Coding Architecture, Balancing Flexible Control and Ultra-Low Energy Consumption

The innovative **“digital-domain + analog-domain” dual-coding fusion architecture**, combined with proprietary multi-layer lens stacking expansion technology and **120° wide sector 8-beam** refined layout, forms the unique technical advantage of **“analog domain for stable coverage, digital domain for precise control, multi-layer stacking for expansion”**. The system supports **4TR/6TR/8TR** multi-specification gradient MIMO flexible expansion per **15°** subdivided sector, allowing TX/RX channel allocation according to real-time network capacity pressure, precisely matching light-load, medium-load, and heavy-load full-scenario service requirements. It overcomes both the rigidity and weak dynamic control of pure passive antennas and the high energy consumption, high complexity, and fixed hardware of traditional large-TR fully active architectures, achieving **reconfigurable beams, schedulable power, expandable capacity, and adaptive scenes**, with comprehensive energy consumption and scene flexibility at industry-leading levels.

3. Full-Dimension Cost Reduction and Efficiency Improvement, Suitable for Large-Scale Commercial Deployment

Leveraging four core capabilities—**minimalist active architecture, metamaterial multi-layer elastic expansion, proprietary metamaterial low-cost mass production, and 100% reuse of existing equipment**—it achieves extreme cost reduction across the entire lifecycle: hardware BOM, engineering deployment, iteration upgrade, O&M energy consumption, and computing support. Quantitative calculation shows **total cost of ownership (TCO) reduced by more than 50%** compared with traditional 6G AAUs. Unlike traditional AAUs with fixed TR scale, no elastic expansion, severe resource waste in small scenarios, and high whole-equipment iteration costs, Sigtenna’s antenna supports **4TR/6TR/8TR gradient MIMO flexible on-demand configuration**, precisely matching hardware resources to real network capacity without redundancy. This greatly simplifies system architecture and algorithm deployment difficulty, reduces long-term O&M pressure, completely solves the industry pain point of traditional 6G large-array solutions **“excellent performance but too costly for commercial scale, difficult to deploy at scale”**, possessing strong engineering value and industrial popularization potential.

4. Extreme Computing Load Reduction, Reconstructing 6G Efficient Operation System

Pioneering the mechanism of **physical beamforming replacing massive digital computation**, it completely breaks free from traditional AAUs’ dependence on massive baseband computing power, reducing baseband computing load by **more than 90%**, effectively solving problems of traditional equipment such as computing redundancy, high system latency, and wasteful air interface resource consumption. The freed abundant computing resources can be fully allocated to 6G core services such as AI intelligent scheduling, enhanced communication-sensing coordination, and efficient large-scale MIMO networking, comprehensively improving overall network operation efficiency and resource utilization, reconstructing a minimalist, efficient, low-consumption computing operation system for 6G.

5. Deep AI Empowerment, Achieving Full-Scene Adaptive Intelligent Networking

Deeply integrating a full-process AI intelligent management and control system, it precisely solves the three core pain points of 6G AI-RAN macro base stations: **computing redundancy waste, weak scene adaptation, and unbalanced computing gain**. It builds a minimalist intelligent closed loop of **“physical-layer performance load reduction + AI precise intelligent empowerment”**. Achieving four-dimensional coordinated optimization—**precise channel awareness, intelligent scene recognition, adaptive computing matching, and dynamic power reconfiguration**—it adapts to various complex dynamic networking scenarios without complex algorithm iteration or massive computing support. MIMO scheduling gain far exceeds traditional intelligent solutions, completely ending the vicious cycle of computing investment versus performance gain, perfectly adapting to the evolution trends of open and intelligent networks such as 6G AI-RAN and O-RAN.

6. Forward-Looking Adaptation to 6G Evolution, Long-Term Technology Iteration Capability

The product natively supports the three core development directions of 6G: **integrated sensing and communication, high-frequency band evolution iteration, and distributed intelligent networking**. It is fully compatible with next-generation network architectures such as O-RAN and AI-RAN, supporting low-cost, smooth, and efficient upgrade from 5G to 6G. Compared with traditional 256TR/512TR AAUs, which have single function, difficult iteration, and poor scalability, Sigtenna’s antenna offers a longer technology lifecycle and broader ecosystem adaptation space, continuously adapting to subsequent 6G technology iterations and scenario innovation needs, effectively helping China’s 6G industry achieve low-cost, high-quality, sustainable large-scale construction and development.