Derivative works — US Patent 9794797

Defensive Disclosure Document

Publication Date: April 26, 2026
Reference ID: DPD-20260426-9794797
Title: Derivative Implementations and Extensions of Game-Theoretic and Auction-Based Resource Allocation in Wireless and Other Networks
Keywords: Game Theory, VCG Auction, Beamforming, Phased Array, Metamaterial, Reconfigurable Intelligent Surface (RIS), Ad-Hoc Network, IoT, AI, Blockchain, Defensive Publication.

This document discloses novel methods, systems, and applications derived from the core principles of U.S. Patent 9,794,797. The following disclosures are intended to enter the public domain to serve as prior art for future patent applications in these and related fields.

Section 1: Derivatives of Game-Theoretic Antenna State Selection (Relates to Claims 1 & 8)

1.1. Metamaterial-Based Reconfigurable Intelligent Surface (RIS) for Wavefront Shaping

Axis: Material & Component Substitution
Enabling Description: An automated controller for a wireless node utilizes a metamaterial-based Reconfigurable Intelligent Surface (RIS) in place of a conventional phased array antenna. The RIS consists of a 2D array of passive unit cells, each loaded with a varactor diode. The controller applies a unique DC bias voltage vector to the diodes, altering their capacitance and inducing a specific phase shift pattern on a reflected RF signal. The "state" in the game-theoretic model is this high-dimensional voltage vector, which allows for complex wavefront shaping beyond simple beam steering, including null-steering and multi-beam formation. The controller's subjective value function is defined as V = w₁·SINR + w₂·(1/P_tx) - w₃·C, where w are weighting factors, SINR is the Signal-to-Interference-plus-Noise Ratio, P_tx is transmit power, and C is the computational cost. The controller employs a Nash Q-learning algorithm to converge on a stable voltage vector (state) by observing the actions of other network nodes.

Diagram:

stateDiagram-v2
    [*] --> Idle
    Idle --> Calculating: Network State Change Detected
    Calculating --> Applying_State: Nash Equilibrium Found
    Applying_State --> Monitoring: Voltage Vector Applied to RIS
    Monitoring --> Calculating: SINR Below Threshold or Peer State Change
    Monitoring --> Idle: Stable State
    Applying_State --> [*]: Communication Complete

1.2. Cryogenic Quantum-Entangled Communication Node

Axis: Operational Parameter Expansion
Enabling Description: A network node operates at cryogenic temperatures (< 4 Kelvin) where "antenna states" are quantum spin states of entangled photon pairs generated via spontaneous parametric down-conversion. The game-theoretic decision algorithm selects a polarization measurement basis (e.g., Rectilinear, Diagonal) for its half of the entangled pair, influenced by the measurement basis choices of other nodes communicated over a classical side-channel. The subjective value function aims to maximize quantum channel fidelity by minimizing the Quantum Bit Error Rate (QBER) and the latency penalty associated with entanglement swapping protocols across the network. The controller is implemented on a specialized FPGA running a quantum game theory model.

Diagram:

sequenceDiagram
    participant NodeA
    participant NodeB
    NodeA->>NodeB: Announce chosen measurement basis (Classical Channel)
    NodeB->>NodeA: Announce chosen measurement basis (Classical Channel)
    Note over NodeA, NodeB: Both nodes run game-theoretic model to confirm optimal basis
    NodeA->>NodeB: Transmit/Measure Entangled Photons (Quantum Channel)
    NodeB->>NodeA: Transmit/Measure Entangled Photons (Quantum Channel)

1.3. Autonomous Agricultural Swarm with Acoustic Dispersal

Axis: Cross-Domain Application (AgTech)
Enabling Description: A swarm of agricultural drones uses a game-theoretic model to co-optimize acoustic communication and pesticide dispersal. Each drone is equipped with a 128-element ultrasonic phased-array transducer operating at 40 kHz. The "directional state" is a combined acoustic beam pattern for both inter-drone communication and shaped acoustic fields that precisely target pesticide spray. The controller runs a cooperative multi-agent reinforcement learning (MARL) algorithm. The value function V = w₁·Coverage_Area - w₂·Pesticide_Drift - w₃·Acoustic_Interference is maximized. Drones share their intended state vectors (transducer phase/amplitude settings) and GPS locations to collaboratively compute optimal patterns for the entire swarm.

Diagram:

flowchart TD
    A[Start Drone Operation] --> B{Sense Environment};
    B --> C[Receive State Vectors from Peer Drones];
    C --> D[Run MARL Game-Theoretic Model];
    D --> E{Calculate Optimal Acoustic State};
    E --> F[Apply Phase/Amplitude Vector to Transducer];
    F --> G[Disperse Pesticide & Communicate];
    G --> B;

1.4. AI-Driven Predictive Beamforming with Federated Learning and IoT Sensing

Axis: Integration with Emerging Tech (AI, IoT)
Enabling Description: The game-theoretic controller is a federated deep reinforcement learning (DRL) model, such as a Deep Q-Network (DQN), running on each node. To preserve privacy, nodes train their local model and share only encrypted model weight updates with neighbors via a gossip protocol. The state input to the DQN is a fusion of data from onboard IoT sensors: LIDAR point-clouds for detecting mobile obstructions, a thermal camera for identifying active electronic interferers, and a real-time RF spectrum analyzer. This rich environmental data allows the model to proactively select an antenna state to preemptively mitigate interference. The reward function is based on achieved data throughput and packet loss over a trailing time window.

Diagram:

flowchart LR
    subgraph Node
        A[LIDAR] --> D;
        B[Thermal Cam] --> D;
        C[RF Analyzer] --> D;
        D[Sensor Fusion] --> E(DQN Model);
        E --> F[Select Antenna State];
    end
    subgraph Federated Learning
        E -- Encrypted Weights --> G{Model Averaging};
        G -- Updated Weights --> E;
    end
    F --> H[RF Front-End];
    G <--> I[Peer Nodes];

1.5. Failsafe Isotropic Mode with Jamming Detection

Axis: The "Inverse" or Failure Mode
Enabling Description: The system incorporates a failsafe mode that reverts the antenna to a low-power, omnidirectional (isotropic) pattern. This mode is triggered by one of two conditions: 1) A hardware watchdog timer, which is periodically reset by the game-theoretic controller, expires, indicating a software failure. 2) A dedicated spectral analysis module detects wideband jamming when the noise floor across all monitored channels rises above a predefined threshold (e.g., -40 dBm) for a sustained period. Upon triggering, an RF switch bypasses the antenna's phase shifters, routing the signal to a simple dipole element for basic "limp-home" communication.

Diagram:

stateDiagram-v2
    state "Normal Operation" as Normal
    state "Failsafe Mode" as Failsafe
    [*] --> Normal
    Normal --> Normal: Game-Theoretic State Selection
    Normal --> Failsafe: Watchdog Timeout OR Jamming Detected
    Failsafe --> Normal: Manual Reset OR Jammer Removed
    note right of Normal
        High-throughput, directional
        communication.
    end note
    note left of Failsafe
        Low-power, omnidirectional
        emergency communication.
    end note

Section 2: Derivatives of VCG Auction for Antenna State Negotiation (Relates to Claim 14)

2.1. Hypersonic Vehicle Plasma Sheath Communication Window Auction

Axis: Operational Parameter Expansion
Enabling Description: On a hypersonic vehicle, a VCG auction manages communication through the plasma sheath that forms during atmospheric flight. The "antenna" is an array of surface electrodes and magnets that use magnetohydrodynamics (MHD) to create a temporary, localized "window" in the plasma. Onboard subsystems (telemetry, guidance, sensors) bid for control of the MHD system to create a window pointed at a target. Bids are valued based on data criticality. The auction, run on a radiation-hardened processor, ensures the most vital data is transmitted, thereby overcoming the signal blackout effect.

Diagram:

flowchart TD
    A[Start] --> B{Communication Window Required};
    B --> C[Onboard Systems Submit Bids];
    subgraph Bids
        C1[Guidance System: High Value]
        C2[Telemetry System: Medium Value]
        C3[Sensor Data: Low Value]
    end
    C --> D[VCG Auction on Rad-Hardened CPU];
    D --> E{Allocate Plasma Window to Winner};
    E --> F[Configure Electrode/Magnet Fields];
    F --> G[Transmit Critical Data];
    G --> B;

2.2. Smart Grid Power Routing via Polyphase Vector Auction

Axis: Cross-Domain Application (Energy/Consumer Electronics)
Enabling Description: In an electrical microgrid, intelligent power routers use a VCG auction to negotiate the "directional state" of power flow. The state is a vector representing power distribution on a polyphase system, controlled by solid-state transformers and silicon carbide (SiC) switches. Subsystems like EV chargers, battery storage, and solar inverters bid for power capacity over a PLC control channel. The VCG mechanism, run by the grid controller, ensures an efficient, stable allocation that maximizes grid utility and prevents faults. The payment calculated by the VCG auction is debited from the subsystem's utility account.

Diagram:

classDiagram
    GridController {
        +runVCGAuction()
    }
    PowerRouter {
        -SiC_Switches
        +setPowerVector()
    }
    BiddingSubsystem {
        +String name
        +float bidValue
        +submitBid()
    }
    GridController "1" -- "N" PowerRouter : Controls
    GridController "1" -- "N" BiddingSubsystem : Manages
    BiddingSubsystem --|> PowerRouter : Bids for resources from
    class EV_Charger
    class Battery_Storage
    class Solar_Inverter
    BiddingSubsystem <|-- EV_Charger
    BiddingSubsystem <|-- Battery_Storage
    BiddingSubsystem <|-- Solar_Inverter

2.3. Blockchain-Based Decentralized VCG Auction for Spatial Channels

Axis: Integration with Emerging Tech (Blockchain)
Enabling Description: The VCG auction for antenna states is implemented as a smart contract on a private, permissioned blockchain (e.g., Hyperledger Fabric). Each wireless node is a participant on the blockchain. Bids for spatial channels are submitted as transactions to the smart contract, which autonomously executes the VCG algorithm, determines winners, and calculates payments in a native token. This creates an immutable, auditable, and decentralized record of resource allocation, removing the need for a trusted central auctioneer and preventing Sybil attacks, as only nodes with sufficient token balances can participate.

Diagram:

sequenceDiagram
    actor User as Node
    participant Wallet
    participant SmartContract as VCG Auction
    participant Ledger
    User->>Wallet: Create Bid Transaction
    Wallet->>SmartContract: submitBid(value, state)
    SmartContract->>Ledger: Record Bid
    loop Bidding Period
    end
    User->>SmartContract: executeAuction()
    SmartContract->>SmartContract: Calculate Winners & Payments
    SmartContract->>Ledger: Record Auction Result
    SmartContract-->>Wallet: Transfer Token Payments

2.4. Graceful Degradation of Auction Mechanism Under Load

Axis: The "Inverse" or Failure Mode
Enabling Description: The system is designed to gracefully degrade its auction mechanism under high computational load or in a low-power state. A system daemon monitors CPU load and battery level. If a critical threshold is crossed (e.g., load > 90% or battery < 15%), it broadcasts a control message to switch from the computationally complex VCG auction to a simple, low-overhead first-price sealed-bid auction. While less economically efficient and not strategy-proof, this lightweight mode ensures the network remains operational for resource allocation. The system reverts to VCG mode when resources are restored.

Diagram:

stateDiagram-v2
    state "VCG Auction Mode" as VCG
    state "First-Price Auction Mode" as FirstPrice
    [*] --> VCG
    VCG --> FirstPrice: CPU Load > 90% OR Battery < 15%
    FirstPrice --> VCG: CPU Load < 50% AND Battery > 30%
    note right of VCG
        Computationally complex.
        Economically efficient.
        Strategy-proof.
    end note
    note left of FirstPrice
        Low overhead.
        Sub-optimal allocation.
        Ensures basic operation.
    end note

Section 3: Combination Prior Art Scenarios with Open-Source Standards

Combination with IEEE 802.11ax (Wi-Fi 6) and OpenWrt: A game-theoretic decision process is implemented as a software package in the OpenWrt open-source firmware. The package controls the beamforming (BF) feedback mechanisms defined in the 802.11ax standard. An access point running this package treats each connected client as a player, using their Channel State Information (CSI) reports to inform its value function. The AP controller then selects the optimal multi-user MIMO transmission state to maximize aggregate network throughput by applying the game-theoretic method via the standardized control frames of Wi-Fi 6.
Combination with 5G NR and O-RAN (Open Radio Access Network): A VCG auction is implemented as an "xApp" within the O-RAN Alliance's Radio Access Network Intelligent Controller (RIC). The xApp subscribes to RAN data (UE channel quality, traffic load) from multiple gNodeB base stations via the standardized E2 interface. It runs a VCG auction to allocate massive MIMO beams to competing User Equipments (UEs), with bids derived from the QoS requirements of the UE's network slice. The RIC sends control messages back to the gNodeBs to implement the winning beam configurations, using open interfaces for cross-vendor interoperability.
Combination with LoRaWAN and The Things Network: In a dense LoRaWAN deployment managed by The Things Network open-source stack, a VCG auction manages gateway antenna selection for gateways fitted with multiple directional antennas. End-devices submit bids for a "guaranteed reception slot" from a specific directional antenna as part of their uplink payload. The network server runs a VCG auction to allocate these slots, prioritizing devices with critical data who bid higher. The payment is a reduction in the device's "fair use" data credit, optimizing reception probability for critical sensors.