Derivative works — US Patent 8027326

Defensive Disclosure: US Patent 8027326 Derivatives

Patent Number: US8027326B2
Title: Method and system for high data rate multi-channel WLAN architecture
Current Date: April 26, 2026

This document outlines a series of derivative works and technical disclosures intended to act as prior art against future incremental improvements by competitors on the subject matter of US patent 8027326. The derivations are structured around core claims and explore alternative materials, expanded operational parameters, cross-domain applications, integration with emerging technologies, and inverse/failure modes.

Derivatives Based on Core Claims

The core independent claims of US8027326 focus on:

Claims 1 & 12 (Transmit Side): Generating and transmitting two adjacent OFDM signals over separate channels, filling the frequency gap with additional subcarriers to form an expanded bandwidth channel.
Claims 19 & 20 (Receive Side): Receiving such a wideband OFDM signal, decomposing it, and processing the received signals concurrently with adaptive anti-aliasing to mitigate adjacent channel waveform effects.

For brevity and to avoid redundancy, the derivatives below are described generically but are explicitly applicable to both the method (Claims 1, 19) and device (Claims 12, 20) aspects of the claims, with the 'device' descriptions implicitly requiring corresponding hardware or software modules.

1. Material & Component Substitution

This axis explores replacing key electronic components or materials with alternatives to achieve the same functional result in the multi-channel WLAN architecture.

Derivative 1.1: Tunable Micro-Electro-Mechanical Systems (MEMS) Filters for Channel Isolation

Enabling Description: Instead of fixed-frequency analog low-pass filters (LPFs) for channel separation, the system employs electrically tunable MEMS bandpass filters at the RF front-end of each parallel receive chain. Each MEMS filter comprises a micro-fabricated resonant structure (e.g., a cantilever or diaphragm) coated with piezoelectric or electrostatic actuation layers. The resonant frequency and bandwidth of these filters are dynamically adjusted via a digital control unit (DCU) that modulates a DC bias voltage or an RF control signal, allowing precise tuning to the center frequency of each adjacent channel (e.g., CH2 and CH3 in FIG. 3). This enables sharper roll-offs and adaptive suppression of out-of-band interference, improving the efficacy of the adaptive anti-aliasing in the digital domain. The Q-factor of these MEMS filters can exceed 1000, offering superior selectivity compared to conventional lumped-element filters, even for closely spaced "filled-gap" channels.

flowchart TD
    A[Wideband RF Input] --> B{MEMS Tunable Filter Bank}
    B -- Channel 1 Center Freq. --> C[Rx Chain 1 (Filtered RF)]
    B -- Channel 2 Center Freq. --> D[Rx Chain 2 (Filtered RF)]
    C --> E[ADC 1]
    D --> F[ADC 2]
    E --> G[Digital Processor 1]
    F --> H[Digital Processor 2]
    G & H --> I[Adaptive Anti-Aliasing]
    I --> J[Combined Data Output]
    K[DCU / Filter Control] --> B

Derivative 1.2: Gallium Nitride (GaN) Power Amplifiers for Wideband Transmission

Enabling Description: For the transmitting device (Claims 1, 12), the power amplifiers (PAs) responsible for amplifying the individual OFDM signals before summation (e.g., pre-power amplifier summation in FIG. 17) are implemented using Gallium Nitride (GaN) high-electron-mobility transistors (HEMTs). GaN PAs offer significantly higher power efficiency and linearity over a wider bandwidth compared to traditional Silicon (Si) or Gallium Arsenide (GaAs) PAs. This allows for greater output power with less spectral regrowth and intermodulation distortion when transmitting the combined, gap-filled wideband OFDM signal. The enhanced linearity of GaN PAs reduces the required PA back-off, enabling more efficient use of transmit power while still meeting stringent spectral mask requirements, particularly critical for "filled-gap" scenarios that push spectrum utilization boundaries.

graph TD
    A[OFDM Signal 1 (Digital)] --> B[DAC 1]
    C[OFDM Signal 2 (Digital)] --> D[DAC 2]
    B --> E[GaN PA 1]
    D --> F[GaN PA 2]
    E --> G[Combiner]
    F --> G
    G --> H[Antenna]
    I[Control Unit] --> E
    I --> F

Derivative 1.3: Superconducting Analog-to-Digital Converters (SADCs) for Ultra-Low Noise Reception

Enabling Description: In the receive path (Claims 19, 20), for environments requiring extremely high sensitivity and dynamic range, the ADCs are replaced with Superconducting Analog-to-Digital Converters (SADCs) based on Josephson junctions, operating at cryogenic temperatures (e.g., 4 Kelvin). These SADCs exhibit virtually no thermal noise and extremely high effective number of bits (ENOB) (e.g., >16 bits at multi-GHz sampling rates), minimizing quantization noise and maximizing the fidelity of the received signals. This is particularly beneficial for signals with very low Signal-to-Noise Ratios (SNRs) or for highly complex modulation schemes where minor signal distortions can lead to significant error rates, providing pristine input for the adaptive anti-aliasing algorithms to further refine signal separation.

sequenceDiagram
    participant WR as Wideband RF Input
    participant LN as LNA (Cryogenic)
    participant SF as Superconducting Filters
    participant SADC1 as SADC (Channel 1)
    participant SADC2 as SADC (Channel 2)
    participant DP as Digital Processor & Anti-Aliasing

    WR->>LN: Receive signal
    LN->>SF: Amplified signal
    SF->>SADC1: Filtered Ch1 (Analog)
    SF->>SADC2: Filtered Ch2 (Analog)
    SADC1->>DP: Digitized Ch1 (Ultra-low noise)
    SADC2->>DP: Digitized Ch2 (Ultra-low noise)
    DP->>DP: Adaptive Anti-Aliasing
    DP->>DP: Demodulation/Decoding

2. Operational Parameter Expansion

This axis explores extending the operational limits of the multi-channel WLAN system to extreme scales or environmental conditions.

Derivative 2.1: Terahertz (THz) Multi-Channel Communication for Intra-Data Center Links

Enabling Description: The multi-channel architecture, including channel bonding and adaptive anti-aliasing (Claims 1, 12, 19, 20), is adapted for Terahertz (THz) frequencies (e.g., 100 GHz to 10 THz) for ultra-high data rate, short-range communication within data centers (e.g., rack-to-rack, server-to-server). The system utilizes compact plasmonic antennas and silicon-germanium (SiGe) or indium phosphide (InP) based transceivers operating in specific atmospheric absorption windows (e.g., 150-200 GHz, 280-350 GHz, 500-700 GHz). The "frequency gap" between adjacent THz channels might be orders of magnitude larger than in WLAN, but the principle of filling this gap with additional subcarriers and applying adaptive anti-aliasing for isolation remains critical due to the extremely wideband nature of individual channels and the need for spectral efficiency.

graph LR
    A[THz Source] --> B{THz Tx Array}
    B -- Ch1 (150GHz band) --> C[THz Tx Module 1]
    B -- Ch2 (180GHz band) --> D[THz Tx Module 2]
    C --> E[THz Wireless Link]
    D --> E
    E --> F{THz Rx Array}
    F --> G[THz Rx Module 1]
    F --> H[THz Rx Module 2]
    G & H --> I[Adaptive Anti-Aliasing (THz DSP)]
    I --> J[Ultra-High Data Rate Output]

Derivative 2.2: Deep-Space Communication with Dynamically Scaled Channels and Anti-Aliasing

Enabling Description: The multi-channel, gap-filled OFDM and adaptive anti-aliasing techniques are applied to deep-space communication systems, operating at extremely low signal-to-noise ratios (SNRs) and over vast distances. The "adjacent channels" are dynamically formed by splitting a very wide, available frequency band (e.g., Ka-band or optical frequencies) into narrower sub-bands, where the number and width of these "channels" (and thus the size of the "frequency gap") are adaptively scaled based on propagation conditions, distance, and available power budget. The adaptive anti-aliasing (Claims 19, 20) becomes paramount to extract desired signals from severe background noise and interstellar interference, leveraging advanced error-correction coding and iterative channel estimation.

stateDiagram
    state "Initialization: Low-Rate Link" as Init
    state "Channel State Estimation" as CSE
    state "Dynamic Channel Configuration" as DCC
    state "Wideband Data Transmission" as WDT
    state "Adaptive Anti-Aliasing Rx" as AAR
    state "Decoded Data Output" as DDO

    Init --> CSE: Establish initial link
    CSE --> DCC: Determine optimal channels/gaps
    DCC --> WDT: Transmit multi-channel, gap-filled data
    WDT --> AAR: Receive wideband data (low SNR)
    AAR --> DDO: Process & Output
    DDO --> CSE: Continuous channel monitoring
    AAR --> DCC: Feedback for channel adaptation
    WDT --> DCC: Link degradation detected

Derivative 2.3: Industrial Sensor Mesh Networks with Extreme Environmental Robustness

Enabling Description: The multi-channel WLAN architecture (Claims 1, 12, 19, 20) is deployed in extreme industrial environments (e.g., high-temperature furnaces, sub-zero refrigeration units, high-vibration machinery, chemical processing plants). Robust physical layer components are used, such as high-temperature tolerant ceramic antennas, hardened RF transceivers, and radiation-shielded digital processors. The "frequency gap" filling and adaptive anti-aliasing are critical for maintaining high data throughput and reliability despite severe ambient noise (electrical, acoustic, thermal) and unpredictable channel fluctuations caused by dynamic industrial processes or equipment movement. The system may dynamically adjust channel bandwidths and gap-filling subcarrier density based on real-time environmental sensor data (temperature, vibration, EMI).

graph TD
    A[Industrial Sensor 1] -- Ch1 Data --> B{Hardened Tx Module}
    C[Industrial Sensor 2] -- Ch2 Data --> B
    B -- Freq. Concatenated, Gap-Filled --> D[Harsh Environment Wireless Link]
    D --> E{Hardened Rx Module}
    E -- Ch1, Ch2 Decomposed --> F[Adaptive Anti-Aliasing Processor]
    F --> G[Industrial Control System]
    H[Environmental Sensors] --> B
    H --> E

3. Cross-Domain Application

This axis applies the core mechanisms of US8027326 to three unrelated industries.

Derivative 3.1: Autonomous Vehicle-to-Infrastructure (V2I) Communication (Automotive)

Enabling Description: The multi-channel WLAN architecture is adapted for high-data-rate V2I communication. Autonomous vehicles transmit sensor data (LiDAR, radar, camera, GPS) and receive command/control information from roadside units (RSUs). Two adjacent DSRC (Dedicated Short Range Communications, IEEE 802.11p) or 5G NR V2X channels (e.g., 5.9 GHz band) are bonded, and the frequency gap between them is filled with additional OFDM subcarriers to transmit critical, low-latency data (e.g., real-time traffic updates, hazard warnings, cooperative perception data). Adaptive anti-aliasing at the receiver (vehicle or RSU) is crucial to mitigate interference from other vehicles, infrastructure, or environmental factors (e.g., buildings, foliage) in dense urban environments or high-speed scenarios.

flowchart LR
    A[Autonomous Vehicle] -- Tx Multi-Ch V2X --> B(Roadside Unit - RSU)
    A -- Sensor Data (Lidar, Radar, Camera) --> C[Ch1 OFDM]
    A -- Vehicle Control Data --> D[Ch2 OFDM]
    C --> E[Gap-Fill Subcarriers]
    D --> E
    E --> F[Wideband V2X Tx]
    F -- Wireless Link --> G[Wideband V2X Rx]
    G --> H[Signal Separator]
    H -- Ch1 Rx --> I[Adaptive Anti-Aliasing 1]
    H -- Ch2 Rx --> J[Adaptive Anti-Aliasing 2]
    I & J --> K[RSU Processing & Control]

Derivative 3.2: Precision Agriculture Drone Swarm Data Relay (AgTech)

Enabling Description: The multi-channel WLAN system facilitates high-throughput data relay between a swarm of agricultural drones and a central ground station. Each drone captures high-resolution imagery (multispectral, thermal), soil sensor data, and crop health metrics. The ground station (Claims 19, 20) receives wideband OFDM signals by bonding multiple adjacent ISM band channels (e.g., 2.4 GHz or 5.8 GHz) or even unlicensed 60 GHz channels for short-range links. The frequency gaps are dynamically filled with subcarriers to prioritize and multiplex critical data streams (e.g., disease detection alerts) while concurrently relaying less time-sensitive data. Adaptive anti-aliasing is vital to handle interference from other farm equipment, natural terrain features causing multipath, and environmental factors (e.g., weather conditions impacting signal propagation).

graph TD
    A[Ag Drone 1 (Sensor Data)] -- Ch1 Tx --> B{Drone Swarm Data Aggregator}
    C[Ag Drone 2 (Image Data)] -- Ch2 Tx --> B
    D[Ag Drone N] -- ChX Tx --> B
    B -- Multi-Ch, Gap-Filled OFDM --> E[Wireless Link to Ground Station]
    E --> F[Ground Station Rx]
    F --> G[Signal Separator]
    G -- Ch1 Rx --> H[Anti-Aliasing Processor]
    G -- Ch2 Rx --> I[Anti-Aliasing Processor]
    H & I --> J[Crop Health Analytics / Farm Management System]

Derivative 3.3: In-Flight Entertainment (IFE) and Cabin Connectivity (Aerospace)

Enabling Description: The multi-channel WLAN architecture is deployed within an aircraft cabin to provide high-speed internet access and video-on-demand services to passengers, as well as operational data for aircraft systems. Multiple adjacent unlicensed channels (e.g., 60 GHz Wi-Fi or next-generation 6 GHz U-NII bands) are bonded to create a robust, high-capacity wireless backbone within the cabin. The "frequency gap" between these channels is filled with subcarriers dedicated to high-priority operational data (e.g., real-time aircraft diagnostics, crew communications) to ensure minimal latency and dedicated bandwidth. Adaptive anti-aliasing (Claims 19, 20) is essential at access points and passenger devices to overcome significant multipath interference within the confined metallic cabin environment and signal attenuation due to passenger bodies and luggage.

sequenceDiagram
    participant P as Passenger Device
    participant AP as Cabin Access Point
    participant GW as Aircraft Gateway (Rx)
    participant SAT as Satellite Link (Tx)

    P->>AP: Request HD Stream (Ch1 Data)
    AP->>AP: Aggregate (Ch1 & Ch2 Control)
    AP->>AP: Fill Gap Subcarriers
    AP->>GW: Wideband OFDM Transmission
    GW->>GW: Decompose Wideband Signal
    GW->>GW: Apply Adaptive Anti-Aliasing
    GW->>SAT: Processed Data (Uplink)
    SAT->>P: Downlink (Internet)

4. Integration with Emerging Tech

This axis integrates the multi-channel WLAN architecture with AI, IoT, and Blockchain.

Derivative 4.1: AI-Driven Dynamic Spectrum Optimization and Anti-Aliasing

Enabling Description: An Artificial Intelligence (AI) agent, specifically a Reinforcement Learning (RL) model, is integrated into both the transmitter and receiver (Claims 1, 12, 19, 20). The AI observes real-time channel conditions (SNR, interference, traffic load), network topology, and spectral mask compliance. On the transmit side, the RL agent dynamically optimizes the number, placement, and modulation scheme of gap-filling subcarriers (e.g., FIG. 20, 21), as well as power allocation across channels, to maximize throughput while minimizing ACI and adhering to regulatory masks. On the receive side, the AI agent dynamically tunes the parameters (e.g., tap weights, filter coefficients) of the adaptive anti-aliasing filter (e.g., FIG. 45, 54, 55, 56) and selects the optimal anti-aliasing algorithm based on the detected interference characteristics, adapting faster and more precisely than traditional LMS algorithms.

flowchart TD
    A[Wireless Network Environment] --> B{AI Agent (RL Controller)}
    B -- Tx Policy (Subcarrier/Power) --> C[Multi-Channel Tx (Claims 1, 12)]
    C -- Wideband OFDM --> D[Wireless Channel]
    D -- Rx Feedback (CSI, Interference) --> B
    D --> E[Multi-Channel Rx (Claims 19, 20)]
    E -- Anti-Aliasing Parameters --> B
    E --> F[Decoded Data]

Derivative 4.2: IoT Sensor-Augmented Adaptive Anti-Aliasing

Enabling Description: The receiver's adaptive anti-aliasing function (Claims 19, 20) is augmented by a network of local Internet of Things (IoT) sensors. These sensors (e.g., dedicated spectrum analyzers, environmental monitors for temperature/humidity, accelerometer for vibration) provide real-time contextual information about the local RF environment, physical obstructions, and potential interference sources. This IoT data is fed to a centralized processing unit that pre-processes and informs the adaptive anti-aliasing algorithms. For example, if a nearby microwave oven (detected by an RF emission sensor) is active, the system can proactively adjust anti-aliasing filter parameters to suppress that specific interference profile before it significantly degrades the signal, improving resilience and speed of adaptation.

graph LR
    A[Wideband OFDM Rx] --> B{Signal Separator}
    B -- Ch1, Ch2 --> C[Adaptive Anti-Aliasing Core]
    D[IoT Local Spectrum Sensors] -- Real-time Interference Profile --> E[Contextual Awareness Module]
    F[IoT Environmental Sensors] -- Ambient Conditions --> E
    E --> C
    C --> G[Cleaned Data Output]

Derivative 4.3: Blockchain-Verified Channel State Information for Secure Multi-Channel Operations

Enabling Description: For critical multi-channel WLAN applications (e.g., industrial control, defense communications), the channel state information (CSI) and adaptive anti-aliasing filter coefficients (Claims 19, 20) are periodically recorded and verified on a permissioned blockchain. Each time the adaptive anti-aliasing algorithm updates its parameters based on channel estimation (e.g., from Long Syncs, as mentioned in the patent), a cryptographic hash of these parameters, along with a timestamp and the identity of the modifying node, is committed to the blockchain. This distributed ledger provides an immutable audit trail, ensuring the integrity and authenticity of the channel estimates and anti-aliasing configurations, protecting against malicious injection of false CSI or filter settings that could degrade performance or enable eavesdropping.

sequenceDiagram
    participant Tx as Multi-Channel Transmitter
    participant Rx as Multi-Channel Receiver
    participant BC as Blockchain Network

    Tx->>Rx: Transmit Wideband OFDM (with Long Syncs)
    Rx->>Rx: Estimate Channel State (CSI)
    Rx->>Rx: Compute Anti-Aliasing Filter Coefficients
    Rx->>Rx: Generate Hash(CSI, Coefficients)
    Rx->>BC: Commit Hash to Blockchain
    BC->>BC: Validate & Record Transaction
    Rx->>Rx: Apply Anti-Aliasing & Decode Data
    alt Malicious Activity Detected
        BC->>Rx: Alert: CSI/Coeff. Mismatch
        Rx->>Rx: Revert to Last Verified State
    end

5. The "Inverse" or Failure Mode

This axis describes versions of the invention designed to fail safely or operate in limited-functionality/low-power modes.

Derivative 5.1: Low-Power, Single-Channel Fallback with Deactivated Gap-Filling

Enabling Description: The wireless device (Claims 12, 20) incorporates a low-power mode where it deactivates the second radio chain and ceases transmission/reception of gap-filling subcarriers. When battery power falls below a threshold, or signal quality on one channel drops below a configurable error rate, the system automatically transitions from wideband dual-channel operation to a robust single-channel legacy mode (e.g., 802.11a/g). In this mode, only the primary channel's OFDM signal is processed, and the adaptive anti-aliasing circuit may be partially or entirely bypassed or reconfigured for a simpler, lower-power filtering scheme, as adjacent channel interference is no longer a concern. This conserves energy and ensures a basic, reliable communication link when full performance is not sustainable or required.

stateDiagram
    state "High-Performance Wideband Mode" as HPWM
    state "Low-Power Single-Channel Mode" as LPSCM

    HPWM --> LPSCM: Battery < Threshold / Channel Degradation
    LPSCM --> HPWM: Battery > Threshold / Channel Recovery

    HPWM: Dual-Channel Tx/Rx
    HPWM: Gap-Filling Active
    HPWM: Full Adaptive Anti-Aliasing

    LPSCM: Single-Channel Tx/Rx
    LPSCM: Gap-Filling Deactivated
    LPSCM: Basic Filtering / Reduced Anti-Aliasing

Derivative 5.2: Diagnostic Aliasing Characterization Mode

Enabling Description: The wireless device (Claims 12, 20) includes a specialized diagnostic mode where the adaptive anti-aliasing function (Claims 19, 20) is intentionally disabled or selectively degraded. This allows the system to capture and analyze the raw, unmitigated aliasing effects due to adjacent channel waveforms (e.g., FIG. 36, 37, 38). By transmitting known test patterns across the two channels (Claim 1, 12) with and without gap-filling, the receiver can precisely characterize the spectral leakage and inter-subcarrier interference before anti-aliasing is applied. This mode is invaluable for system calibration, debugging, and for developing more sophisticated adaptive anti-aliasing algorithms by providing ground-truth data on interference profiles without active compensation.

flowchart TD
    A[Wideband OFDM Rx] --> B{Signal Separator}
    B -- Ch1, Ch2 Raw --> C{Aliasing Characterization Module}
    C -- Disable/Degrade --> D[Adaptive Anti-Aliasing]
    C --> E[Raw Aliasing Data Capture]
    E --> F[Diagnostic Analysis & Report]
    D --> G[Decoded Data (Optional)]

Derivative 5.3: Adaptive Fault Tolerance through Selective Subcarrier Disablement

Enabling Description: In the event of detected hardware failure (e.g., one radio chain partially failing, or an ADC exhibiting excessive noise) or persistent, unmitigable interference on specific subcarriers, the transmitting device (Claims 1, 12) can dynamically disable or 'null' problematic subcarriers, particularly those used for gap-filling. The system communicates these disabled subcarrier indices to the receiver (Claims 19, 20) via a robust control channel. The receiver's adaptive anti-aliasing and decoding logic then adapts to ignore or treat these nulled subcarriers as empty, preventing them from corrupting valid data. This ensures graceful degradation of the link rather than catastrophic failure, maintaining a reduced but functional data rate. This also applies to the adaptive anti-aliasing, where certain filter taps or weights associated with compromised subcarriers can be de-emphasized or zeroed out.

classDiagram
    class MultiChannelTransmitter {
        +OFDMSignalGenerator[]
        +SubcarrierAllocator
        +FaultDetector
        +ControlChannel
        +Transmit(OFDMSignal[])
    }
    class MultiChannelReceiver {
        +SignalSeparator
        +AdaptiveAntiAliaser
        +Decoder
        +ControlChannel
        +Receive(WidebandOFDM)
    }
    class FaultDetector {
        +DetectHardwareFailure()
        +IdentifyProblematicSubcarriers()
    }
    class SubcarrierAllocator {
        +Allocate(data, gap_fill_strategy)
        +DisableSubcarriers(indices)
    }
    class AdaptiveAntiAliaser {
        +Apply(received_signals)
        +AdaptToDisabledSubcarriers(indices)
    }
    MultiChannelTransmitter "1" -- "1" FaultDetector : <<uses>>
    MultiChannelTransmitter "1" -- "1" SubcarrierAllocator : <<uses>>
    MultiChannelTransmitter "1" -- "1" ControlChannel : <<uses>>
    MultiChannelReceiver "1" -- "1" SignalSeparator : <<uses>>
    MultiChannelReceiver "1" -- "1" AdaptiveAntiAliaser : <<uses>>
    MultiChannelReceiver "1" -- "1" ControlChannel : <<uses>>

Combination Prior Art Scenarios with Open-Source Standards

Here are three scenarios combining the principles of US8027326 with existing open-source standards to establish prior art for future incremental improvements.

1. US8027326 + Openwifi (Open-Source 802.11 Full Stack Implementation)

Scenario: An implementation of the multi-channel, gap-filled OFDM transmission and adaptive anti-aliasing reception described in US8027326 using the Openwifi framework. Openwifi is an open-source software-defined radio (SDR) project providing a full-stack 802.11 implementation on platforms like Ettus USRPs.
Combination: A developer leverages Openwifi's existing 802.11a/g PHY and MAC layers. They modify the OFDM signal generation module (e.g., the IFFT block in the transmitter, FIG. 16) to concatenate two 20 MHz 802.11a-like channels and insert additional subcarriers into the frequency gap using custom DSP functions within the SDR. On the receive side (e.g., FIG. 33), two parallel Openwifi receiver instances are configured to process the adjacent channels. The adaptive anti-aliasing is implemented as a custom module in the digital signal processing (DSP) pipeline (e.g., before the FFT, or a frequency-domain equalizer based on FIG. 54 or 55), exchanging cancellation coefficients between the two parallel receive paths to mitigate aliasing. The Openwifi MAC layer is extended to signal the use of bonded, gap-filled channels and to exchange anti-aliasing training sequences.
Result: A fully functional, open-source demonstration of high data rate multi-channel WLAN with gap-filling and adaptive anti-aliasing, available for public scrutiny and implementation.

2. US8027326 + GNU Radio (Software Defined Radio Toolkit)

Scenario: Utilizing GNU Radio, a free & open-source software development toolkit that provides signal processing blocks to implement software radios, to implement and simulate the multi-channel, gap-filled OFDM system with adaptive anti-aliasing.
Combination: A GNU Radio flowgraph is constructed. On the transmit path, two independent OFDM signal generators (based on existing 802.11-like modules in GNU Radio) are used, with their outputs up-converted and spectrally combined, and a custom 'gap-filling' block inserting additional subcarriers into the intermediate frequency range. This combined signal is then transmitted via an SDR hardware (e.g., HackRF, BladeRF). On the receive path, a wideband receiver samples the RF spectrum, and the digitized signal is fed into two parallel GNU Radio signal processing chains, each performing channel decomposition (e.g., down-conversion, filtering with a customizable FIR filter). An adaptive filter block (e.g., using GNU Radio's built-in adaptive algorithms or a custom Python block implementing LMS or RLS) is connected between the two chains to perform real-time anti-aliasing, subtracting interference components based on estimated cross-channel leakage.
Result: A reproducible, open-source, software-defined implementation and simulation environment for the core inventive concepts, demonstrating their feasibility and operability using off-the-shelf SDR hardware.

3. US8027326 + O-RAN Alliance Specifications (Open Radio Access Network)

Scenario: Integration of the multi-channel, gap-filled OFDM approach with adaptive anti-aliasing within an Open Radio Access Network (O-RAN) architecture, specifically within the O-RU (O-RAN Radio Unit) and O-DU (O-RAN Distributed Unit).
Combination: The O-RU, acting as the physical layer front-end (similar to the radio chains in Claims 12, 20), implements the parallel transmit and receive functionality for two adjacent channels. The digital baseband processing (OFDM signal generation, subcarrier mapping, DAC/ADC interfacing) for both the gap-filling subcarriers (Tx) and the adaptive anti-aliasing (Rx) is offloaded to the O-DU. The O-DU (acting as the processor in Claims 12, 20) orchestrates the channel bonding and anti-aliasing algorithms, dynamically adjusting parameters based on network-wide traffic and interference conditions reported via O-RAN's open interfaces (e.g., E2 interface to an intelligent controller, near-RT RIC). The O-RU implements the necessary filtering (e.g., 6th order LPFs as in FIG. 11) and provides the raw I/Q samples to the O-DU for centralized, adaptive anti-aliasing processing.
Result: An open, disaggregated radio access network architecture that incorporates the high data rate multi-channel techniques, leveraging open interfaces and centralized intelligence to manage spectrum, interference, and resource allocation.