Derivative works — US Patent 11974143

Defensive Disclosure: US11974143 - Method and Apparatus for Processing Bandwidth Intensive Data Streams Using Virtual Media Access Control and Physical Layers

Date: April 26, 2026

This document discloses derivative variations and extensions of the technology described in US Patent 11974143, aiming to establish prior art for future incremental improvements and render them obvious or non-novel. The focus is on expanding the scope of the disclosed virtual MAC and PHY layer functionalities, dynamic bandwidth allocation, and multi-transceiver management in wireless networking devices.

Derivatives of Core Claim 1

Claim 1 Summary (Recap from previous output): A method involving a processing interface forming virtual MAC/PHY layers to dynamically allocate bandwidth across multiple actual MAC/PHY interfaces and wireless transceivers operating in different frequency bands. It enables simultaneous transmit and receive operations, with bandwidth utilization transparently managed and non-blocking for other devices.

Axis 1: Material & Component Substitution

Derivative 1.1: GaN-based Multi-band Transceiver Modules with Optical Frontend Integration

Enabling Description: This derivative employs wireless transceivers where the RF power amplifiers and front-end modules are fabricated using Gallium Nitride (GaN) semiconductor technology. These GaN transceivers are designed to operate across a broader spectrum of frequency bands (e.g., extending into sub-THz ranges while maintaining current Wi-Fi bands) with enhanced power efficiency and linearity compared to traditional Silicon-based components. The processing interface, including the virtual MAC and virtual PHY, dynamically allocates these GaN-based transceivers. Furthermore, the system integrates a parallel optical transceiver (Li-Fi or free-space optical, FSO) frontend for extremely high-bandwidth, line-of-sight communication within a limited range. The virtual PHY layer dynamically switches or aggregates bandwidth between the GaN RF transceivers and the optical transceiver based on environmental conditions, data type, and proximity to the recipient. The virtual MAC is responsible for arbitrating data streams for transmission over either the RF or optical path, maintaining seamless connectivity.

Mermaid Diagram:

graph TD
    A[Application Interface] --> P[Processing Interface]
    P --> VMAC[Virtual MAC Interface]
    VMAC --> VPHY_RF[Virtual PHY (GaN RF)]
    VMAC --> VPHY_Optical[Virtual PHY (Optical)]
    VPHY_RF --> MAC1[Actual MAC 1 (GaN RF)]
    VPHY_RF --> MAC2[Actual MAC 2 (GaN RF)]
    VPHY_Optical --> MAC3[Actual MAC 3 (Optical)]
    MAC1 --> PHY1[Actual PHY 1 (GaN Transceiver)]
    MAC2 --> PHY2[Actual PHY 2 (GaN Transceiver)]
    MAC3 --> PHY3[Actual PHY 3 (Optical Transceiver)]
    PHY1 -- RF Link --> RECIP_RF1[Recipient RF]
    PHY2 -- RF Link --> RECIP_RF2[Recipient RF]
    PHY3 -- Optical Link --> RECIP_Optical[Recipient Optical]
    VPHY_RF -- Bandwidth Info --> VMAC
    VPHY_Optical -- Bandwidth Info --> VMAC

Derivative 1.2: Memristor-based Reconfigurable MAC/PHY Hardware with Software-Defined Radio (SDR) Core

Enabling Description: This variation implements the actual MAC and PHY layers using reconfigurable hardware platforms featuring memristor arrays for dynamic circuit reconfiguration and adaptive impedance matching. The core RF components are based on Software-Defined Radio (SDR) architectures, allowing for highly flexible waveform generation, modulation schemes, and frequency band adjustments through software. The processing interface (virtual MAC/PHY) directly controls the memristor-based reconfiguration and SDR parameters. This enables ultra-fine-grained control over transceiver characteristics, allowing for on-the-fly adjustment of antenna characteristics, filtering, and even the fundamental radio waveform itself to optimize for specific bandwidth requirements and channel conditions. The memristor arrays provide non-volatile, high-density, and low-power reconfigurability, facilitating rapid switching between diverse operational modes and frequency bands (e.g., dynamically reconfiguring a single physical radio to operate as distinct virtual radios across different bands).

Mermaid Diagram:

graph TD
    A[Application Interface] --> P[Processing Interface]
    P --> VMAC[Virtual MAC Interface]
    VMAC --> VPHY[Virtual PHY Interface]
    VPHY --> SDR_Core[SDR Core with Memristor Reconfiguration]
    SDR_Core -- Configures --> MAC_HW[Actual MAC Hardware (Reconfigurable)]
    SDR_Core -- Configures --> PHY_HW[Actual PHY Hardware (Reconfigurable)]
    MAC_HW --> PHY_HW
    PHY_HW -- Wireless Link --> Recipient[Recipient Device]
    SDR_Core -- Bandwidth & Config Info --> VPHY

Axis 2: Operational Parameter Expansion

Derivative 1.3: Deep-Space Communication Relay with Cryogenic, High-Frequency Transceivers

Enabling Description: This derivative applies the virtual MAC/PHY concept to deep-space communication networks, where wireless networking devices are deployed on spacecraft or planetary habitats. Transceivers are designed to operate at extremely high frequencies (e.g., 90 GHz to 300 GHz, also known as E-band and D-band) to achieve high data rates over vast distances, and are maintained at cryogenic temperatures (e.g., below 77K) to minimize thermal noise and maximize signal-to-noise ratio (SNR). The processing interface dynamically manages multiple such transceivers (e.g., dish antennas, phased arrays) for simultaneous uplink and downlink operations, adapting to variable link conditions such as planetary occultation, solar interference, and spacecraft orientation. The virtual MAC prioritizes data streams (e.g., telemetry, scientific data, crew communications) and allocates portions of the available bandwidth from different cryogenic transceivers, potentially across different frequency windows within the high-frequency bands, to meet mission-critical requirements.

Mermaid Diagram:

graph TD
    A[Application Interface (Spacecraft Apps)] --> P[Processing Interface (Onboard)]
    P --> VMAC[Virtual MAC Interface]
    VMAC --> VPHY[Virtual PHY Interface]
    VPHY --> TR1[Cryogenic Transceiver 1 (E/D-band)]
    VPHY --> TR2[Cryogenic Transceiver 2 (E/D-band)]
    TR1 -- Deep Space Link --> Ground_Station1[Ground Station 1]
    TR2 -- Deep Space Link --> Ground_Station2[Ground Station 2]
    VPHY -- Resource Data --> VMAC

Derivative 1.4: Nanoscale Intra-body Network for Biomedical Data Streaming

Enabling Description: This derivative envisions the wireless networking device operating at the nanoscale within a biological system (e.g., human body). Wireless transceivers are molecular or photonic nanodevices operating at ultra-low power levels and communicating via molecular signals, acoustic waves, or localized electromagnetic fields in the THz gap (0.1-10 THz) or far-infrared. The processing interface, potentially located on a larger implanted microchip, forms virtual MAC and PHY layers to manage these numerous nanoscale transceivers. The virtual MAC allocates bandwidth for streaming physiological data (e.g., glucose levels, neural activity, drug delivery status) from multiple points within the body to a central collector. The operational parameters include extremely low signal-to-noise ratios, high attenuation, and dynamic biological interference. The virtual PHY intelligently selects optimal communication channels (e.g., specific molecular signals or acoustic frequencies) and dynamically aggregates the limited bandwidths of many nanoscale transceivers to meet the cumulative data stream requirements, while maintaining non-blocking utilization for other nanodevices.

Mermaid Diagram:

graph TD
    A[Application Interface (Biomedical Sensors)] --> P[Processing Interface (Implanted Microchip)]
    P --> VMAC[Virtual MAC Interface]
    VMAC --> VPHY[Virtual PHY Interface]
    VPHY --> NanoTR1[Nanoscale Transceiver 1]
    VPHY --> NanoTR2[Nanoscale Transceiver 2]
    VPHY --> NanoTR_N[Nanoscale Transceiver N]
    NanoTR1 -- Intra-body Link --> Collector[Central Data Collector]
    NanoTR2 -- Intra-body Link --> Collector
    NanoTR_N -- Intra-body Link --> Collector
    VPHY -- Health Data --> VMAC

Axis 3: Cross-Domain Application

Derivative 1.5: Automated Port & Logistics Management (Industrial Automation)

Enabling Description: In a large-scale automated port or logistics hub, the wireless networking device (e.g., a central control unit or a mobile robotic gateway) manages communication for hundreds of autonomous guided vehicles (AGVs), robotic cranes, and sensor arrays. The application layer includes tasks like real-time AGV control, cargo tracking, and security surveillance. The actual transceivers (e.g., Wi-Fi 6E, mmWave, private 5G NR) are deployed on AGVs, cranes, and fixed infrastructure, operating across diverse spectrums to handle high-bandwidth video feeds, low-latency control commands, and intermittent sensor data. The processing interface, with its virtual MAC and PHY, dynamically aggregates and allocates transceiver bandwidths. For instance, a critical AGV requiring high-bandwidth, low-latency control during a complex maneuver might be allocated dedicated bandwidth from multiple mmWave transceivers, while background cargo tracking data uses aggregated Wi-Fi 6E channels. Simultaneous transmit/receive operations are essential for real-time fleet coordination.

Mermaid Diagram:

graph TD
    A[Logistics Control Apps] --> P[Processing Interface (Control Unit)]
    P --> VMAC[Virtual MAC]
    VMAC --> VPHY[Virtual PHY]
    VPHY --> TR_AGV1[AGV Transceiver 1 (mmWave/5G)]
    VPHY --> TR_Crane1[Crane Transceiver 1 (Wi-Fi 6E)]
    VPHY --> TR_SensorArray[Sensor Array Transceiver (LoRa/Wi-Fi)]
    TR_AGV1 -- Wireless Link --> AGV_Fleet[AGV Fleet]
    TR_Crane1 -- Wireless Link --> Robotic_Cranes[Robotic Cranes]
    TR_SensorArray -- Wireless Link --> Env_Sensors[Environmental Sensors]
    VPHY -- Bandwidth Allocation --> VMAC

Derivative 1.6: Agricultural Field Monitoring and Drone Swarm Control (AgTech)

Enabling Description: This application involves a central agricultural gateway managing a network of IoT crop sensors, automated irrigation systems, and a swarm of agricultural drones. The applications demand high-resolution imagery and video from drones, real-time soil and weather data from sensors, and control signals for irrigation. The wireless networking device (gateway) uses multiple transceivers (e.g., sub-GHz LoRa for long-range sensor data, Wi-Fi HaLow for medium-range, and dedicated 5.8 GHz or 2.4 GHz for drone control/telemetry). The virtual MAC/PHY dynamically assigns bandwidth. For example, when a drone is transmitting high-definition multispectral imagery, it is allocated multiple channels across different 5.8 GHz radios. Simultaneously, low-bandwidth sensor data continues to be collected over LoRa. The system constantly monitors channel conditions (e.g., interference from other farm equipment, weather effects) to reallocate receive bandwidth transparently, as described in Claim 20.

Mermaid Diagram:

graph TD
    A[AgTech Apps (Drone Control, Sensor Mgmt)] --> P[Processing Interface (Farm Gateway)]
    P --> VMAC[Virtual MAC]
    VMAC --> VPHY[Virtual PHY]
    VPHY --> TR_Drone[Drone Transceiver (5.8GHz)]
    VPHY --> TR_Sensor[Sensor Transceiver (LoRa/HaLow)]
    VPHY --> TR_Irrigation[Irrigation System Transceiver (Wi-Fi)]
    TR_Drone -- Wireless Link --> Drone_Swarm[Agricultural Drone Swarm]
    TR_Sensor -- Wireless Link --> Crop_Sensors[IoT Crop Sensors]
    TR_Irrigation -- Wireless Link --> Irrigation_Sys[Automated Irrigation]
    VPHY -- Resource Status --> VMAC

Derivative 1.7: Crowd-Sourced Public Safety and Emergency Response (Smart City/Public Safety)

Enabling Description: In urban environments during emergencies, a mobile or temporary base station (wireless networking device) dynamically establishes a resilient network. It leverages diverse wireless transceivers including commercial cellular bands (LTE/5G), Wi-Fi (2.4/5/6 GHz), Citizens Broadband Radio Service (CBRS), and dedicated public safety bands (e.g., FirstNet). Applications include live video streaming from first responders, emergency alerts to citizens' devices, and data offload from congested commercial networks. The virtual MAC/PHY dynamically allocates and aggregates these diverse transceivers to ensure critical communications have priority and sufficient bandwidth. For instance, high-definition video from a responder's body camera might be allocated bandwidth across available FirstNet and CBRS channels, while general public alerts utilize less congested Wi-Fi channels. The system is designed to identify and utilize any available spectrum portion, even fragmented ones, to maintain network access during infrastructure failures.

Mermaid Diagram:

graph TD
    A[Emergency Response Apps] --> P[Processing Interface (Mobile Base Station)]
    P --> VMAC[Virtual MAC]
    VMAC --> VPHY[Virtual PHY]
    VPHY --> TR_Cellular[Cellular Transceiver (5G/LTE)]
    VPHY --> TR_WiFi[Wi-Fi Transceiver (2.4/5/6GHz)]
    VPHY --> TR_PS[Public Safety Transceiver (FirstNet/CBRS)]
    TR_Cellular -- Wireless Link --> Responder_Devices[First Responder Devices]
    TR_WiFi -- Wireless Link --> Public_Devices[Citizen Devices]
    TR_PS -- Wireless Link --> Command_Center[Command Center]
    VPHY -- Traffic & Resource --> VMAC

Axis 4: Integration with Emerging Tech

Derivative 1.8: AI-Driven Predictive Bandwidth Allocation and Cognitive Spectrum Management

Enabling Description: The processing interface's virtual MAC and PHY layers are enhanced with an Artificial Intelligence (AI) engine, specifically a Deep Reinforcement Learning (DRL) agent. This DRL agent continuously monitors network traffic patterns, application bandwidth requirements, transceiver performance metrics, and real-time environmental RF conditions (e.g., interference, fading, channel occupancy) using IoT sensors. The AI engine predicts future bandwidth demands and channel availability, proactively optimizing transceiver resource allocation, frequency band selection, and power levels. It learns optimal strategies for aggregating multiple transceivers and dynamically configuring variable duplex links (as described in the patent) to maximize throughput and minimize latency across the entire network, even before congestion or interference occurs. The feedback loop from the virtual PHY to the virtual MAC now includes AI-generated recommendations for resource management.

Mermaid Diagram:

graph TD
    A[Application Interface] --> P[Processing Interface]
    P --> VMAC[Virtual MAC]
    P --> AI_Engine[AI Engine (DRL)]
    VMAC --> VPHY[Virtual PHY]
    VPHY --> Actual_MAC_PHY[Actual MAC/PHY Interfaces & Transceivers]
    Actual_MAC_PHY -- Performance Metrics & RF Conditions --> VPHY
    VPHY -- Bandwidth Info & Environmental Data --> AI_Engine
    AI_Engine -- Predictive Allocation & Config --> VMAC
    Actual_MAC_PHY -- Wireless Links --> Recipient[Recipient Devices]

Derivative 1.9: IoT Sensor-Augmented Context-Aware Network with Real-time Environmental Feedback

Enabling Description: The wireless networking device incorporates a dense array of integrated IoT environmental sensors (e.g., temperature, humidity, atmospheric pressure, air quality, motion detectors, acoustic sensors, and RF spectrum analyzers). These sensors provide real-time, fine-grained context about the immediate physical environment where the wireless transceivers operate. The virtual PHY layer continuously collects this sensor data. The virtual MAC uses this enriched environmental feedback to make highly informed decisions for bandwidth allocation and transceiver configuration. For example, high humidity might impact mmWave performance, prompting dynamic shifting to lower frequency bands or aggregation of more Wi-Fi transceivers. Acoustic sensors could detect heavy machinery operation causing interference, triggering an immediate reallocation of spectrum away from the affected bands. This goes beyond simple resource availability, incorporating the physical context for adaptive optimization.

Mermaid Diagram:

graph TD
    A[Application Interface] --> P[Processing Interface]
    P --> VMAC[Virtual MAC]
    VMAC --> VPHY[Virtual PHY]
    VPHY --> Actual_MAC_PHY[Actual MAC/PHY Interfaces & Transceivers]
    Actual_MAC_PHY -- Wireless Links --> Recipient[Recipient Devices]
    VPHY -- Transceiver Status --> VMAC
    IoT_Sensors[Integrated IoT Environmental Sensors] -- Real-time Data --> VPHY
    VPHY -- Environmental Context --> VMAC

Derivative 1.10: Blockchain-Enabled Verifiable Spectrum Allocation and Resource Trading

Enabling Description: The processing interface, specifically the virtual MAC, integrates with a distributed ledger technology (DLT) or blockchain network. This blockchain serves as an immutable record for spectrum allocation, bandwidth usage rights, and agreements between multiple networking devices or service providers. When the virtual MAC allocates portions of bandwidth from actual transceivers, these allocations are recorded as transactions on the blockchain, providing transparency and verifiability. This allows for dynamic, real-time trading or sharing of spectrum resources between different wireless networking devices (e.g., multiple access points in a shared environment) based on smart contracts. The virtual PHY reports actual utilization, which is then validated against blockchain records. This enables a dynamic marketplace for spectrum, allowing a device to acquire temporary additional bandwidth or offload unused capacity, with all transactions cryptographically secured.

Mermaid Diagram:

graph TD
    A[Application Interface] --> P[Processing Interface]
    P --> VMAC[Virtual MAC]
    P --> Blockchain_Module[Blockchain Module]
    VMAC --> VPHY[Virtual PHY]
    VPHY --> Actual_MAC_PHY[Actual MAC/PHY Interfaces & Transceivers]
    Actual_MAC_PHY -- Wireless Links --> Recipient[Recipient Devices]
    VPHY -- Bandwidth Usage Data --> Blockchain_Module
    Blockchain_Module -- Spectrum Rights & Agreements --> VMAC
    VMAC -- Allocation Transactions --> Blockchain_Module

Axis 5: The "Inverse" or Failure Mode

Derivative 1.11: Redundant Low-Power Failover Mode for Critical Infrastructure

Enabling Description: This derivative implements a specialized "failover" mode within the wireless networking device for scenarios of primary power loss or critical transceiver failure. The processing interface includes dedicated, low-power processing logic (e.g., an independent microcontroller with minimal firmware) and a subset of actual transceivers (e.g., a single sub-GHz LoRa or narrowband IoT radio) powered by a backup battery or energy harvesting system. In the event of primary system failure, the virtual MAC/PHY seamlessly transitions to this low-power failover mode. The application layer switches to critical-only functions (e.g., emergency alerts, minimal telemetry). The virtual MAC aggregates the remaining low-power transceivers (or reconfigures a single one for multiple tasks) to ensure a guaranteed minimum data rate for essential communications, prioritizing emergency data streams. The system maintains continuous, albeit reduced, connectivity, transparently informing connected recipients of the degraded state.

Mermaid Diagram:

graph TD
    P[Processing Interface (Primary)] --> VMAC_P[Virtual MAC (Primary)]
    VMAC_P --> VPHY_P[Virtual PHY (Primary)]
    VPHY_P --> TR_Full_Power[Full Power Transceivers]
    TR_Full_Power -- High Bandwidth Link --> Recipients_Full[Normal Operation Recipients]

    P -- Failure Detected --> Failover_Logic[Failover Logic]
    Failover_Logic --> VMAC_LP[Virtual MAC (Low Power)]
    Failover_Logic --> VPHY_LP[Virtual PHY (Low Power)]
    VMAC_LP --> App_LP[Critical Apps (Low Power)]
    VPHY_LP --> TR_Low_Power[Low Power Transceiver(s)]
    TR_Low_Power -- Emergency Link --> Recipients_LP[Emergency Recipients]

    TR_Full_Power -- Status --> Failover_Logic

Derivative 1.12: Degraded Performance "Eco-Mode" with Prioritized Service

Enabling Description: This derivative introduces an "Eco-Mode" for the wireless networking device, which is activated either manually, on a schedule, or automatically when power constraints are detected (e.g., battery level drops, grid power instability). In Eco-Mode, the processing interface's virtual MAC and PHY actively reduce power consumption by dynamically deactivating non-essential transceivers, lowering transmit power, or reducing sampling rates. The virtual MAC reprioritizes application bandwidth requirements, satisfying critical applications (e.g., security monitoring) with a guaranteed minimum bandwidth from a reduced set of transceivers, while non-critical applications (e.g., large file downloads) experience significantly lower throughput or are temporarily paused. This is achieved transparently to higher layers by the virtual MAC's intelligent resource allocation, similar to the existing mechanism but with an explicit constraint on energy budget. The virtual PHY dynamically reconfigures the remaining active transceivers to operate at optimal efficiency points rather than peak performance.

Mermaid Diagram:

graph TD
    A[Application Interface] --> P[Processing Interface]
    P --> VMAC[Virtual MAC]
    VMAC --> VPHY[Virtual PHY]
    VPHY --> TR1_Active[Transceiver 1 (Active)]
    VPHY --> TR2_Active[Transceiver 2 (Active)]
    VPHY --> TR3_Inactive[Transceiver 3 (Inactive - Eco Mode)]
    TR1_Active -- Link --> Recipient1[Recipient 1 (Prioritized)]
    TR2_Active -- Link --> Recipient2[Recipient 2 (Reduced)]

    P -- Power Constraint --> Eco_Mode_Ctrl[Eco Mode Controller]
    Eco_Mode_Ctrl -- Optimize/Deactivate --> VPHY
    Eco_Mode_Ctrl -- Prioritize --> VMAC

Derivatives of Core Claim 20

Claim 20 Summary (Recap from previous output): Focuses on dynamic, transparent reallocation of receive bandwidth for a selected wireless transceiver if its identified portion becomes unavailable or if more bandwidth becomes available, without requiring recipient disassociation.

Axis 1: Material & Component Substitution

Derivative 2.1: Reconfigurable Intelligent Surface (RIS)-Assisted Receive Path Optimization

Enabling Description: This derivative integrates Reconfigurable Intelligent Surfaces (RIS) as a core component of the receive path. An RIS is an array of passive, low-power metamaterial elements whose reflection/refraction properties can be dynamically controlled (e.g., phase shifts). The virtual PHY layer, in conjunction with localized sensing modules, detects changes in the receive signal path (e.g., blockage, interference, mobility of recipient). Instead of solely reallocating frequency bands, the virtual PHY computes optimal phase shifts for the RIS elements to steer the incoming signal, create virtual line-of-sight paths, or suppress interference, thereby effectively identifying a "new portion" of bandwidth by enhancing the quality and availability of the existing spectrum. If RIS optimization is insufficient, the virtual PHY then proceeds to traditional frequency reallocation across other transceivers. This re-optimization happens transparently and without requiring the recipient to re-associate.

Mermaid Diagram:

graph TD
    R[Second Recipient] -- Wireless Signal --> RIS[Reconfigurable Intelligent Surface]
    RIS --> Rx_Antenna[Receive Antenna Array]
    Rx_Antenna --> Actual_PHY[Actual PHY Layer]
    Actual_PHY --> VPHY[Virtual PHY Interface]
    VPHY --> VMAC[Virtual MAC Interface]
    VMAC --> P[Processing Interface]
    VPHY -- RIS Control & Feedback --> RIS
    VPHY -- Bandwidth Info & Status --> VMAC
    P -- App Data --> A[Application Layer]

Derivative 2.2: Photonic Integrated Circuit (PIC) based Tunable Receiver Front-End

Enabling Description: This derivative utilizes a Photonic Integrated Circuit (PIC) for the tunable receiver front-end of the wireless transceiver. The PIC incorporates optical components (waveguides, modulators, filters, detectors) that allow for ultra-wideband signal reception and highly selective frequency filtering. When the virtual PHY detects a change in the availability of a receive bandwidth portion, it sends control signals to the PIC to rapidly reconfigure its optical filters and demodulators. This enables instantaneous switching between different frequency subsets (e.g., dynamically filtering out interference or tuning into a newly available clean channel) across a very broad spectrum, achieving the "identifying at least one new portion of bandwidth" step with optical precision and speed. The PIC can also perform analog-to-digital conversion and beamforming functions in the optical domain before converting to electrical signals for further processing.

Mermaid Diagram:

graph TD
    R[Second Recipient] -- Wireless Signal --> RF_Antenna[RF Antenna]
    RF_Antenna --> Opto_Conv[RF-to-Optical Converter]
    Opto_Conv --> PIC_Rx[Photonic Integrated Circuit Receiver]
    PIC_Rx --> Actual_PHY[Actual PHY Layer]
    Actual_PHY --> VPHY[Virtual PHY Interface]
    VPHY --> VMAC[Virtual MAC Interface]
    VMAC --> P[Processing Interface]
    VPHY -- PIC Control Signals --> PIC_Rx
    VPHY -- Bandwidth Info & Status --> VMAC
    P -- App Data --> A[Application Layer]

Axis 2: Operational Parameter Expansion

Derivative 2.3: Atmospheric Free-Space Optical (FSO) Link Adaptation for Hyperspectral Data

Enabling Description: This derivative extends the concept to atmospheric Free-Space Optical (FSO) communication links, which are highly susceptible to environmental factors like fog, rain, scintillation, and atmospheric absorption. The "wireless transceiver" here includes an FSO optical transponder capable of operating across multiple optical wavelengths (hyperspectral channels). The virtual PHY layer continuously monitors atmospheric conditions (e.g., using integrated lidar, humidity sensors, turbulence meters) and the SNR of each optical channel. If a specific wavelength channel's receive bandwidth degrades (e.g., due to dense fog), the virtual PHY dynamically identifies and switches to or aggregates other less affected optical wavelength channels, or even shifts to a backup RF channel if optical conditions become prohibitive. This reallocation of "new portions" of receive bandwidth across the hyperspectral optical domain or to alternative RF is transparent to the data application and maintains continuous reception of, for example, high-resolution satellite imagery or scientific data.

Mermaid Diagram:

graph TD
    SAT[Satellite (Sender)] -- FSO Link (Multi-wavelength) --> FSO_Rx[FSO Receiver (Hyperspectral)]
    FSO_Rx --> Env_Sensors[Atmospheric Sensors]
    FSO_Rx --> Actual_PHY[Actual PHY Layer]
    Actual_PHY --> VPHY[Virtual PHY Interface]
    VPHY --> VMAC[Virtual MAC Interface]
    VMAC --> P[Processing Interface]
    Env_Sensors -- Atmospheric Data --> VPHY
    VPHY -- Wavelength Reallocation --> FSO_Rx
    VPHY -- Link Status --> VMAC
    P -- Data Stream --> A[Application Layer]

Derivative 2.4: Sub-Terahertz (THz) Band Dynamic Spatial Multiplexing for Edge Computing

Enabling Description: This derivative applies the receive reallocation to sub-Terahertz (THz) wireless communication, which offers extremely high bandwidth but suffers from high path loss and sensitivity to blockage. The wireless networking device is an edge computing node equipped with multiple THz transceivers, each capable of highly directional beamforming and spatial multiplexing. The virtual PHY layer continuously assesses the receive channel quality and potential blockages for incoming THz data streams (e.g., from nearby high-density sensors or augmented reality devices). If a current spatial path or THz frequency sub-band becomes unavailable or degraded, the virtual PHY transparently identifies a "new portion" of bandwidth by either: 1) shifting to an alternative THz frequency sub-band, or 2) dynamically reconfiguring beamforming on an alternative THz transceiver to establish a new spatial path for reception, or 3) aggregating portions of bandwidth from multiple THz transceivers operating in different spatial directions or frequencies. This ensures continuous, ultra-high-speed data reception for edge applications.

Mermaid Diagram:

graph TD
    S[Sender (e.g., AR Device)] -- THz Beam --> THz_TR_A[THz Transceiver A]
    S -- THz Beam --> THz_TR_B[THz Transceiver B]
    THz_TR_A --> Actual_PHY_A[Actual PHY A]
    THz_TR_B --> Actual_PHY_B[Actual PHY B]
    Actual_PHY_A --> VPHY[Virtual PHY Interface]
    Actual_PHY_B --> VPHY
    VPHY --> VMAC[Virtual MAC Interface]
    VMAC --> P[Processing Interface (Edge Node)]
    P -- App Data --> A[Application Layer]
    VPHY -- Spatial/Freq Reconfig --> THz_TR_A
    VPHY -- Spatial/Freq Reconfig --> THz_TR_B
    VPHY -- Receive Status --> VMAC

Axis 3: Cross-Domain Application

Derivative 2.5: Real-time Medical Imaging Data Ingestion (Healthcare)

Enabling Description: In a hospital environment, a wireless networking device (e.g., a central imaging gateway) receives massive, continuous data streams from portable medical imaging devices (e.g., wireless ultrasound, portable MRI units, high-resolution endoscopic cameras). These devices operate in various hospital-approved frequency bands (e.g., dedicated Wi-Fi 6E channels, possibly mmWave for local high-throughput). The virtual MAC/PHY manages these diverse receive requirements. If a particular transceiver's receive capacity for a real-time ultrasound stream is impacted by interference (e.g., from other medical equipment or network congestion), the processing interface transparently identifies a "new portion" of bandwidth. This could involve dynamically shifting the ultrasound stream to another available Wi-Fi 6E channel, aggregating receive capacity from a previously underutilized mmWave transceiver, or even utilizing a temporary, less congested cellular band if available, all without interrupting the live image acquisition from the portable device.

Mermaid Diagram:

graph TD
    IMG_DEV[Medical Imaging Device] -- Wireless Link --> TR1_Rx[Transceiver 1 (Rx)]
    IMG_DEV -- Wireless Link --> TR2_Rx[Transceiver 2 (Rx)]
    TR1_Rx --> Actual_PHY1[Actual PHY 1]
    TR2_Rx --> Actual_PHY2[Actual PHY 2]
    Actual_PHY1 --> VPHY[Virtual PHY Interface]
    Actual_PHY2 --> VPHY
    VPHY --> VMAC[Virtual MAC Interface]
    VMAC --> P[Processing Interface (Imaging Gateway)]
    P -- Processed Image Data --> A[Medical Imaging Apps]
    VPHY -- Rx Channel Status --> VMAC
    VMAC -- Reallocate Rx BW --> VPHY

Derivative 2.6: Autonomous Underwater Vehicle (AUV) Swarm Communication with Dynamic Acoustic/Optical Channels (Oceanography/Defense)

Enabling Description: A command vessel acts as a wireless networking device, receiving data from a swarm of AUVs in a dynamic underwater environment. AUVs communicate via a combination of acoustic modems (long-range, low-bandwidth, prone to multipath) and blue-green laser optical links (short-range, high-bandwidth, line-of-sight dependent). The virtual MAC/PHY on the command vessel manages these heterogeneous receive channels. If an AUV's acoustic link degrades due to changing water conditions or distance, the virtual PHY transparently identifies and switches to an available optical channel if the AUV comes within range, or reallocates more receive bandwidth from another acoustic modem (e.g., operating at a different frequency or with advanced signal processing) to maintain critical telemetry and sensor data reception. The system must adapt to unpredictable factors like marine life interference, thermoclines, and turbidity, ensuring continuous data flow without requiring AUV re-association.

Mermaid Diagram:

graph TD
    AUV1[AUV 1] -- Acoustic Link --> Acoustic_Rx1[Acoustic Receiver 1]
    AUV1 -- Optical Link --> Optical_Rx1[Optical Receiver 1]
    AUV2[AUV 2] -- Acoustic Link --> Acoustic_Rx2[Acoustic Receiver 2]
    Acoustic_Rx1 --> Actual_PHY_A1[Actual PHY (Acoustic)]
    Optical_Rx1 --> Actual_PHY_O1[Actual PHY (Optical)]
    Acoustic_Rx2 --> Actual_PHY_A2[Actual PHY (Acoustic)]
    Actual_PHY_A1 --> VPHY[Virtual PHY Interface]
    Actual_PHY_O1 --> VPHY
    Actual_PHY_A2 --> VPHY
    VPHY --> VMAC[Virtual MAC Interface]
    VMAC --> P[Processing Interface (Command Vessel)]
    P -- Data Analysis --> A[Oceanography/AUV Ops]
    VPHY -- Channel Quality --> VMAC
    VMAC -- Reallocate Channel --> VPHY

Derivative 2.7: Disaster Relief Network with Adaptive Mesh and Opportunistic Spectrum (Humanitarian Aid)

Enabling Description: In a disaster zone with damaged infrastructure, a mobile base station acts as the wireless networking device, establishing an ad-hoc mesh network. It uses diverse transceivers: satellite transceivers (for backhaul), long-range Wi-Fi (e.g., Wi-Fi HaLow), and opportunistic scanning of unlicensed bands (e.g., ISM bands). Applications include emergency communications, search and rescue coordination, and providing internet access to affected populations. The virtual MAC/PHY dynamically reallocates receive bandwidth. If a satellite link degrades (e.g., due to weather), the system transparently shifts critical voice traffic to aggregated Wi-Fi HaLow mesh links while continuously scanning for and utilizing any temporarily available, unlicenced spectrum. The virtual PHY intelligently identifies "new portions" of bandwidth by adapting to transient clear channels or by forming directional links with other mesh nodes that have better reception, without forcing connected client devices (e.g., responders' radios, survivors' smartphones) to re-establish connections.

Mermaid Diagram:

graph TD
    SAT_GW[Satellite Gateway] -- Satellite Link --> SAT_TR_Rx[Satellite Transceiver (Rx)]
    MESH_NODE1[Mesh Node 1] -- Wi-Fi HaLow --> HaLow_TR_Rx1[HaLow Transceiver (Rx) 1]
    MESH_NODE2[Mesh Node 2] -- Opportunistic Link --> Opp_TR_Rx1[Opportunistic Transceiver (Rx) 1]
    SAT_TR_Rx --> Actual_PHY_SAT[Actual PHY (Satellite)]
    HaLow_TR_Rx1 --> Actual_PHY_HALOW[Actual PHY (HaLow)]
    Opp_TR_Rx1 --> Actual_PHY_OPP[Actual PHY (Opportunistic)]
    Actual_PHY_SAT --> VPHY[Virtual PHY Interface]
    Actual_PHY_HALOW --> VPHY
    Actual_PHY_OPP --> VPHY
    VPHY --> VMAC[Virtual MAC Interface]
    VMAC --> P[Processing Interface (Mobile Base Station)]
    P -- Emergency Data --> A[SAR/Aid Apps]
    VPHY -- Spectrum Scan Data --> VMAC
    VMAC -- Reconfigure Rx --> VPHY

Axis 4: Integration with Emerging Tech

Derivative 2.8: Machine Learning-Enabled Adaptive Interference Mitigation and Spectrum Shaping

Enabling Description: The virtual PHY layer incorporates a Machine Learning (ML) model (e.g., a Convolutional Neural Network or Recurrent Neural Network) trained on vast datasets of RF interference patterns, channel characteristics, and successful receive reallocations. This ML model operates in real-time to analyze incoming RF signals and spectral data from the transceivers. When a receive channel degrades or new interference appears, the ML model rapidly predicts the optimal "new portion" of bandwidth for reception. This could involve not just frequency hopping, but also dynamic waveform shaping, adaptive equalization, or advanced beamforming parameters on available transceivers to actively mitigate specific interference types. The ML model allows the virtual PHY to transparently adapt receive operations with unprecedented speed and precision, learning from past successful reconfigurations to maintain seamless data reception.

Mermaid Diagram:

graph TD
    R[Second Recipient] -- Wireless Signal --> TR_Rx[Receive Transceiver(s)]
    TR_Rx --> Actual_PHY[Actual PHY Layer]
    Actual_PHY --> VPHY[Virtual PHY Interface]
    VPHY --> ML_Engine[ML Engine (Interference Mitigation)]
    ML_Engine -- Optimal Rx Config --> VPHY
    VPHY -- Rx Channel Status & Raw Signal --> ML_Engine
    VPHY --> VMAC[Virtual MAC Interface]
    VMAC --> P[Processing Interface]
    P -- App Data --> A[Application Layer]

Derivative 2.9: Digital Twin-Assisted Predictive Receive Channel Optimization

Enabling Description: This derivative implements a "Digital Twin" of the entire wireless networking environment, including all transceivers, their physical locations, surrounding obstacles, known interference sources, and predicted traffic loads. The Digital Twin is a continuously updated virtual model. The processing interface, particularly the virtual MAC and virtual PHY, feeds real-time telemetry from actual transceivers and IoT sensors into the Digital Twin. When a receive bandwidth portion shows signs of degradation or a new demand arises, the Digital Twin is used to run high-fidelity simulations of various reallocation strategies. It can predict the performance of moving a receive stream to a "new portion" of bandwidth (different frequency, different transceiver, different antenna configuration) before the actual change is implemented. This predictive capability allows for proactive and highly optimized transparent reallocation of receive resources, minimizing disruption and maximizing efficiency.

Mermaid Diagram:

graph TD
    R[Second Recipient] -- Wireless Signal --> TR_Rx[Receive Transceiver(s)]
    TR_Rx --> Actual_PHY[Actual PHY Layer]
    Actual_PHY --> VPHY[Virtual PHY Interface]
    VPHY --> Digital_Twin[Digital Twin (Network Model)]
    VPHY -- Real-time Telemetry --> Digital_Twin
    Digital_Twin -- Predictive Opt. --> VPHY
    VPHY --> VMAC[Virtual MAC Interface]
    VMAC --> P[Processing Interface]
    P -- App Data --> A[Application Layer]

Derivative 2.10: Edge Computing-Assisted Micro-reallocation of Receive Resources

Enabling Description: The wireless networking device offloads computationally intensive tasks related to spectrum sensing, interference analysis, and optimal receive path calculation to a co-located or nearby edge computing node. The virtual PHY layer, instead of performing all complex analytics locally, sends raw spectral data and channel state information to the edge compute for rapid processing. The edge node, with its greater computational resources, quickly identifies "new portions" of available receive bandwidth, considering highly localized and transient channel conditions. It then returns optimized transceiver configuration parameters (e.g., specific frequency channels, antenna settings, demodulation schemes) to the virtual PHY for immediate implementation. This allows for extremely rapid, fine-grained, and adaptive micro-reallocation of receive resources, ensuring ultra-low-latency data reception for demanding edge applications without impacting client association.

Mermaid Diagram:

graph TD
    R[Second Recipient] -- Wireless Signal --> TR_Rx[Receive Transceiver(s)]
    TR_Rx --> Actual_PHY[Actual PHY Layer]
    Actual_PHY --> VPHY[Virtual PHY Interface]
    VPHY -- Raw Spectrum Data --> Edge_Compute[Edge Computing Node]
    Edge_Compute -- Optimized Rx Config --> VPHY
    VPHY --> VMAC[Virtual MAC Interface]
    VMAC --> P[Processing Interface]
    P -- App Data --> A[Application Layer]

Axis 5: The "Inverse" or Failure Mode

Derivative 2.11: Guaranteed Minimum Receive Bandwidth for Critical Safety Alerts (Fail-Safe)

Enabling Description: This derivative introduces a "fail-safe" mode for receiving critical safety alerts or commands. The virtual MAC is programmed with a list of prioritized critical data streams (e.g., evacuation orders, system shutdown commands). If the primary receive bandwidth for these streams becomes entirely unavailable (e.g., all high-bandwidth transceivers fail or are heavily jammed), the virtual PHY initiates a low-power, wide-area scan using a dedicated, hardened transceiver (e.g., a robust, low-data-rate paging or satellite receiver). The system actively searches for any functional receive channel or frequency subset, even severely degraded ones, to establish a minimal-data-rate link for these critical streams. The virtual MAC prioritizes processing resources to recover and decode these streams, overriding all other receive activities. This "new portion of bandwidth" might be extremely small and previously ignored, but it is specifically identified and utilized for fail-safe operation, without requiring the sender of the critical alert to re-associate.

Mermaid Diagram:

graph TD
    S_Crit[Critical Sender] -- Primary Link --> TR_Rx_Primary[Primary Receive Transceiver(s)]
    TR_Rx_Primary --> Actual_PHY_P[Actual PHY (Primary)]
    Actual_PHY_P --> VPHY[Virtual PHY Interface]

    VPHY -- Failure Detected --> Fail_Safe_Logic[Fail-Safe Logic]
    Fail_Safe_Logic -- Activate Scavenge --> VPHY
    VPHY --> TR_Rx_Hardened[Hardened Low-Power Transceiver]
    TR_Rx_Hardened -- Scavenged Link --> S_Crit

    VPHY --> VMAC[Virtual MAC Interface]
    VMAC --> P[Processing Interface]
    P -- Critical Alerts --> A[Safety Applications]
    VPHY -- Scavenged BW Info --> VMAC

Derivative 2.12: Passive "Listen-Only" Mode for Spectrum Analysis and Interference Mapping

Enabling Description: This derivative implements a "listen-only" or passive monitoring mode. When receive bandwidth is not required for active data streams, or during periods of low activity, the processing interface's virtual PHY automatically switches into this mode. It continuously scans and maps the local RF spectrum across all available transceivers, identifying active channels, interference sources, and quiet bands. While not actively receiving data for an application, this process is continuously "identifying new portions of bandwidth availability" by building a detailed real-time spectral awareness database. This information is fed back to the virtual MAC. When a new application bandwidth requirement arises, the virtual MAC can instantly draw upon this pre-scanned and mapped "available bandwidth portions" knowledge for rapid, optimal allocation, minimizing the time needed for initial channel discovery and improving the efficiency of the first data reception, effectively pre-empting the need for reactive reallocation.

Mermaid Diagram:

graph TD
    TR_Rx_All[All Receive Transceivers] --> Actual_PHY_All[All Actual PHY Layers]
    Actual_PHY_All --> VPHY[Virtual PHY Interface]
    VPHY -- Spectrum Scan Data --> Spectrum_DB[Spectrum Awareness Database]
    Spectrum_DB --> VPHY
    VPHY -- Available BW Map --> VMAC[Virtual MAC Interface]
    VMAC --> P[Processing Interface]
    P -- On-Demand App Data --> A[Application Layer]
    A -- BW Request --> VMAC
    VMAC -- Initial BW Allocation --> VPHY

Combination Prior Art Scenarios with Open-Source Standards

Here are at least three combination prior art scenarios where US11974143 could be combined with existing open-source standards to demonstrate obviousness of future improvements:

US11974143 + OpenFlow/SDN (Software-Defined Networking):
- Description: The virtual MAC and virtual PHY layers described in US11974143 (FIG. 1, 3, 8) act as local controllers and data plane elements, respectively, within a wireless networking device. Combining this with an OpenFlow-enabled Software-Defined Networking (SDN) architecture would involve the virtual MAC/PHY layers exposing their resource allocation capabilities and bandwidth availability information to a centralized OpenFlow controller. The OpenFlow controller, using standard OpenFlow protocols, could then orchestrate the dynamic allocation of multiple wireless transceiver resources (both transmit and receive) across multiple virtual MAC/PHY-enabled networking devices (e.g., access points, relays) within a larger SDN-managed wireless network. The controller would dynamically push flow rules and resource assignments to the virtual MAC, instructing it to allocate specific frequency subsets from selected transceivers to meet application-specific QoS, enhancing network-wide spectrum efficiency and dynamic routing. The transparent reallocation described in Claim 20 would then be orchestrated by the central controller, informed by device-level virtual PHY feedback.
- Reference Standard: OpenFlow Specification (e.g., OpenFlow Switch Specification v1.5.0), widely available as an open standard for SDN.
US11974143 + Linux Kernel Networking Stack (e.g., Netfilter/iproute2):
- Description: The processing interface, virtual MAC, and virtual PHY concepts of US11974143 could be implemented as extensions or modules within the Linux kernel's networking stack. The kernel's existing multi-queue networking interfaces, software-defined radio frameworks (e.g., using cfg80211 or custom driver modules), and traffic control mechanisms (tc command with Netfilter/conntrack) would be leveraged. The virtual MAC logic would integrate with kernel-level packet scheduling and flow classification, while the virtual PHY would directly interface with multi-radio hardware drivers. Dynamic bandwidth allocation (Claim 1) and transparent receive reallocation (Claim 20) would be managed at the kernel level, using existing Netlink interfaces for user-space applications to declare bandwidth requirements. This allows for fine-grained control over actual MAC/PHY hardware resources, including the ability to bond multiple physical interfaces (e.g., using teamd or bonding drivers) for aggregated bandwidth, and to isolate frequency subsets using kernel-level filtering.
- Reference Standard: The Linux kernel source code and its networking stack components (e.g., net/mac80211, drivers/net/wireless, net/ipv4/netfilter/, iproute2 utilities).
US11974143 + O-RAN (Open Radio Access Network) Architecture:
- Description: The virtual MAC and virtual PHY concepts align directly with the disaggregated and virtualized functions in the O-RAN architecture. The wireless networking device, as an O-RAN compliant Radio Unit (O-RU) or Distributed Unit (O-DU), would implement the actual MAC/PHY layers. The virtual MAC/PHY functionality could reside within the O-DU or even in a higher-layer O-RAN Centralized Unit (O-CU). The O-RAN Near-Real-time RIC (RAN Intelligent Controller) and Non-Real-time RIC would interact with the virtual MAC to optimize resource allocation across multiple O-RUs, utilizing the E2 interface for control messages. The virtual PHY would provide detailed radio resource status via O-RAN interfaces (e.g., O1, A1). This combination enables dynamic spectrum sharing, multi-band aggregation (Claim 1), and proactive traffic steering based on AI/ML applications (xApps/rApps) running on the RIC, ensuring transparent and adaptive receive reallocation (Claim 20) across a heterogeneous O-RAN deployment.
- Reference Standard: O-RAN Alliance specifications (e.g., O-RAN Fronthaul Specification, O-RAN Overall Architecture Technical Specification), publicly available.