Derivative works — US Patent 8891347

As a Senior Patent Strategist and Research Engineer specializing in Defensive Publishing, I have analyzed US patent 8891347, "User-focusing technique for wireless communication systems," to generate a comprehensive Defensive Disclosure document. The objective is to create "Prior Art" that renders future incremental improvements by competitors obvious or non-novel, building upon the core inventive concept described in the patent.

Given that independent claims 1 (method), 8 (system), and 15 (base station) describe the same underlying "user-focusing technique" from different perspectives, the derivative variations presented below will apply broadly to the core inventive concept. Each derivative will explicitly link back to the relevant elements of these claims, demonstrating how the core functionality can be implemented using alternative materials, expanded parameters, new applications, emerging technologies, or inverse/failure modes.

Combination Prior Art Scenarios

Here are three scenarios where the user-focusing technique of US8891347 can be combined with existing open-source standards to establish prior art for future incremental developments:

US8891347 + 3GPP LTE/NR Standard (Open Source / Public Standard):
- Description: The detailed, multi-domain predistortion technique of US8891347 is integrated into the physical layer (PHY) of a 3GPP Long-Term Evolution (LTE) or New Radio (NR) cellular communication system. This involves extending the standard Channel State Information (CSI) feedback mechanisms, such as those relying on CSI-RS for channel sounding and CSI reports (RSRP, RSRQ, CQI, PMI, RI), to include or enable the real-time derivation of the full set of path parameters disclosed in US8891347 (delay, Doppler frequency, direction of arrival (DoA), direction of departure (DoD), complex amplitude, and polarization for each propagation path). The base station (gNB or eNB) then incorporates a predistortion module that uses this granular path parameter information to generate and transmit signals focused on user equipment (UE). This predistortion occurs across the time, frequency, and spatial domains as specified in US8891347 to achieve coherent signal summation at the UE. This integration optimizes resource utilization, enhances signal quality, and improves interference management within the existing 3GPP cellular framework, offering a more precise alternative or enhancement to conventional precoding in dynamic environments.
US8891347 + IEEE 802.11ay (Wi-Fi 60 GHz / mmWave Standard):
- Description: The user-focusing predistortion technique is implemented in an IEEE 802.11ay compliant Wi-Fi system operating in millimeter-wave (mmWave) bands (e.g., 60 GHz). IEEE 802.11ay already incorporates advanced beamforming and Multi-User Multiple-Input Multiple-Output (MU-MIMO) capabilities. The channel estimation and feedback mechanisms (e.g., sounding frames, Beamforming Training (BFT) sequences) are enhanced to extract the full propagation path parameters (including micro-delays, localized Doppler shifts due to subtle movements, and highly accurate DoA/DoD angles) for each connected client device (Station or STA). The Access Point (AP) then utilizes this detailed, time-varying path information to predistort its transmissions, creating highly focused mmWave beams directed precisely to individual STAs. This predistortion in time, frequency, and spatial domains significantly improves throughput, extends effective range, and minimizes inter-STA interference, especially in dense indoor or outdoor mmWave deployment scenarios where precise beam steering is critical to overcome high path loss and blockages.
US8891347 + LoRaWAN (Low-Power Wide-Area Network Standard):
- Description: The user-focusing technique is adapted for downlink communication in a LoRaWAN (Low-Power Wide-Area Network) system to enhance reliability and energy efficiency for constrained Internet of Things (IoT) end-devices. While LoRaWAN is known for its robustness, focused energy delivery can further improve performance in challenging environments. The LoRaWAN gateway, equipped with an advanced antenna array, initiates the process by transmitting a low-power "first signal" (e.g., a modified beacon or preamble). The LoRaWAN end-device, acting as the receiver, performs a simplified channel estimation based on received signal strength (RSSI), signal-to-noise ratio (SNR), and basic timing information extracted from the gateway's transmission. This coarse path parameter information (e.g., approximate delay, dominant DoA) is fed back to the gateway via the LoRaWAN uplink. The gateway then applies the user-focusing predistortion to its subsequent downlink transmissions, adjusting the phase, amplitude, and temporal characteristics across its antenna elements to coherently sum the signal at the specific end-device location. This enables more reliable communication to devices in cluttered environments, potentially extending battery life by reducing retransmissions, or reaching devices in traditionally difficult-to-penetrate locations.

Derivative Variations of US Patent 8891347

The following derivatives explore various implementations and applications of the user-focusing technique, each designed to serve as defensive prior art.

Derivative 1: Material & Component Substitution - Reconfigurable Intelligent Surfaces (RIS) as Active Scatterers

Claim Linkage: Directly modifies the "plurality of propagation paths" and the characteristics of signal interaction with the environment, impacting the "transmitting" and "receiving" steps of Claim 1 (Method), and the "transmitter" and "receiver" components of Claim 8 (System) and Claim 15 (Base Station).
Enabling Description: In this variant, the conventional propagation environment is augmented with active or passive Reconfigurable Intelligent Surfaces (RIS). Each RIS comprises a two-dimensional array of sub-wavelength metallic or dielectric elements with embedded control circuitry (e.g., varactor diodes, PIN diodes, or MEMS switches). These elements dynamically adjust their electromagnetic response (phase, amplitude, polarization) to incident waves. When the transmitter (e.g., base station 110) sends a "first signal," the receiver (e.g., mobile station 150) performs channel estimation for all paths, including those reflected or refracted by one or more RIS panels. The path parameter information, now explicitly incorporating the complex reflection coefficients and spatial orientation of the RIS panels, is fed back to the transmitter. The transmitter computes the predistortion for the "second signal," simultaneously sending control commands to the RIS network (via a dedicated control channel or out-of-band signaling). The RIS panels then apply specific, synchronized phase/amplitude shifts to the predistorted signal as it reflects off them, actively contributing to the coherent summation at the receiver. This allows for environmental shaping and highly precise, programmable control over the multipath components, enhancing the focusing effect.
Technical Terminology: Reconfigurable Intelligent Surface (RIS), metamaterial array, sub-wavelength elements, varactor diodes, PIN diodes, MEMS switches, electromagnetic response, complex reflection coefficients, spatial orientation, control channel, out-of-band signaling, environmental shaping, multipath components.

graph TD
    A[Transmitter (Base Station)] -- Tx 1st Signal --> B[Environment + RIS Network]
    B -- Propagation Paths --> C[Receiver (Mobile Station)]
    C -- Channel Estimation (incl. RIS influence) --> D{Path Param Info (Delay, Doppler, DoA, DoD, α, RIS config)}
    D -- Feedback Path Param Info --> A
    A -- Predistort 2nd Signal (Tx, Freq, Spatial, RIS-aware) --> B
    A -- Send RIS Control Signals --> E[RIS Controller]
    E -- Configures Dynamic Properties --> B
    B -- Predistorted Signal + Active RIS Interaction --> C
    C -- Receive Predistorted Signal (Coherent Sum) --> F{Enhanced User Focus}

Derivative 2: Operational Parameter Expansion - Terahertz (THz) Communication for Chip-to-Chip Interconnects

Claim Linkage: Expands the operating frequency of "wireless communication" and the scale of "propagation paths" for Claim 1 (Method), Claim 8 (System), and Claim 15 (Base Station), enabling ultra-dense, localized applications.
Enabling Description: This derivative applies the user-focusing technique to Terahertz (THz) frequency communication for high-bandwidth, low-latency chip-to-chip interconnects within a multi-chip module (MCM) or between closely spaced circuit boards. The "transmitter" and "receiver" are integrated THz transceivers on silicon, utilizing on-chip antenna arrays. Due to the extremely short wavelengths (e.g., 0.1 THz to 10 THz) and confined propagation environment, channel estimation captures minute path parameters, including picosecond-scale delays, sub-degree angular deviations (DoA/DoD), and fine-grained complex amplitudes for each micro-reflection and diffraction path within the chip package. These detailed THz path parameters are fed back. The THz transmitter then predistorts a "second signal" using its on-chip phased array, applying highly precise phase and amplitude weighting across its elements. This predistortion creates a super-focused THz beam that ensures maximal energy transfer and coherent signal summation at the target receiving chip's antenna array, mitigating intra-package interference and maximizing data rates for next-generation computing architectures.
Technical Terminology: Terahertz (THz) frequency, chip-to-chip interconnects, multi-chip module (MCM), integrated THz transceivers, on-chip antenna arrays, picosecond-scale delays, sub-degree angular deviations, micro-reflection, diffraction path, intra-package interference, phased array.

flowchart TD
    TX_Chip[On-Chip THz Tx Array] -- Tx 1st THz Signal --> Chip_Channel[THz Channel (Intra-MCM)]
    Chip_Channel --> RX_Chip[On-Chip THz Rx Array]
    RX_Chip -- Ultra-Fine Channel Est. (ps Delays, Sub-deg AoA/DoD) --> THz_Params[THz Path Parameters]
    THz_Params -- Feedback (Dedicated Control Lines) --> TX_Chip
    TX_Chip -- Predistort 2nd THz Signal (Time, Freq, Nano-Spatial Focusing) --> Chip_Channel
    Chip_Channel -- Super-Focused Beam --> RX_Chip
    RX_Chip -- Receive Focused THz Signal --> Data_Flow[High-BW Data Processing]

Derivative 3: Cross-Domain Application - Focused Energy Delivery for Advanced Manufacturing (3D Printing/Sintering)

Claim Linkage: Applies the "user-focusing" concept to a non-communication domain, specifically "power transferring" as mentioned in the patent's detailed description, impacting the purpose of the system (Claim 8) and base station (Claim 15).
Enabling Description: This derivative employs the user-focusing technique for precise, contact-less energy delivery in advanced manufacturing processes, such as microwave or RF-based 3D printing (sintering) of materials. A multi-element electromagnetic (EM) emitter array acts as the "transmitter," generating controlled RF/microwave fields. The "receiver" is a miniature sensor array (e.g., temperature/EM field probes) embedded within or adjacent to the material powder bed at the target sintering location. The EM emitter array transmits a low-power "first signal." The sensor array measures the propagation path characteristics of this EM energy through the material, extracting parameters like localized dielectric properties, specific absorption rate (SAR) anomalies, phase shifts, and micro-scale spatial distribution. This path parameter information is fed back. The EM emitter array then predistorts a high-power "second signal" (the sintering energy) in time, frequency, and spatial domains. This predistortion ensures that the EM energy constructively converges and heats only the desired small volume of material with extreme precision, enabling layer-by-layer selective sintering without overheating surrounding areas, thereby improving resolution, material integrity, and energy efficiency in additive manufacturing.
Technical Terminology: Advanced manufacturing, 3D printing, sintering, microwave/RF-based printing, electromagnetic (EM) emitter array, material powder bed, miniature sensor array, dielectric properties, specific absorption rate (SAR), phase shifts, micro-scale spatial distribution, selective sintering, additive manufacturing.

flowchart TD
    EM_Emitter[EM Emitter Array (Tx)] -- Tx Low-Power EM Signal (1st) --> Powder_Bed[Material Powder Bed (Process Zone)]
    Powder_Bed -- EM Propagation Paths --> Sensor_Array[Sensor Array (Rx) at Target Sintering Loc]
    Sensor_Array -- Measure EM Path Params (Dielectric, SAR, Phase, Spatial Dist) --> Sinter_Params[Sintering Path Parameters]
    Sinter_Params -- Feedback --> EM_Emitter
    EM_Emitter -- Predistort High-Power EM Signal (Time, Freq, Precise Spatial Focus) --> Powder_Bed
    Powder_Bed -- Constructive EM Convergence --> Sinter_Point[Precisely Sintered Point]

Derivative 4: Integration with Emerging Tech - AI-driven Adaptive Predistortion with Predictive Channel Modeling using Reinforcement Learning

Claim Linkage: Enhances the "performing a channel estimation" and "predistorting a second signal" steps of Claim 1 (Method) and the corresponding functionalities of the System (Claim 8) and Base Station (Claim 15) through AI integration.
Enabling Description: The channel estimation module at the receiver and the predistortion engine at the transmitter are tightly integrated with an Artificial Intelligence (AI) / Machine Learning (ML) model, specifically a Reinforcement Learning (RL) agent. The RL agent, operating at the transmitter, continuously observes the feedback channel's path parameters (delay, Doppler, DoA, DoD, complex amplitude) and the resulting Signal-to-Noise Ratio (SNR) or achievable data rate at the receiver (reward signal). Based on this, the RL agent dynamically adjusts the predistortion parameters (its "actions") to optimize for a desired performance metric (e.g., maximizing throughput, minimizing latency, maintaining a "focus" on a rapidly moving target). Over time, the RL agent learns the complex, non-linear relationships between channel conditions, predistortion settings, and system performance, developing a predictive model for optimal predistortion. This allows the system to not only react to current channel conditions but also to anticipate and proactively compensate for rapid channel changes, even with delayed feedback, significantly improving performance in highly dynamic and complex multipath environments.
Technical Terminology: Artificial Intelligence (AI), Machine Learning (ML), Reinforcement Learning (RL) agent, reward signal, dynamic predistortion parameters, non-linear relationships, predictive model, proactive compensation, multipath environments.

graph TD
    RX_Agent[Receiver (incl. RL Agent)] -- 1. Observe Channel & Performance --> Env_Chan[Wireless Channel]
    Env_Chan -- 2. Channel Est. & Feedback (Path Params) --> TX_RL[Transmitter (incl. RL Agent)]
    TX_RL -- 3. RL Agent Predicts & Adjusts Predistortion --> TX_Ant[Tx Antenna Array]
    TX_Ant -- 4. Transmit Predistorted Signal --> Env_Chan
    Env_Chan --> RX_Agent
    RX_Agent -- 5. Measure Reward (SNR, Data Rate) --> TX_RL
    TX_RL -- 6. Update RL Model --> RL_Model[RL Model DB]

Derivative 5: The "Inverse" or Failure Mode - Adaptive Nulling (Interference Zone Creation)

Claim Linkage: Deliberately alters the "predistorting a second signal" step of Claim 1 (Method) and the corresponding functionalities of the System (Claim 8) and Base Station (Claim 15) to achieve a controlled "inverse" effect.
Enabling Description: In this operational mode, the system is configured to perform "adaptive nulling" or "interference zone creation" rather than user-focusing. The "first signal" is transmitted from the transmitter (e.g., base station 110), and the "receiver" (e.g., a specific mobile station 150, or a designated "nulling zone monitor") performs channel estimation to identify the path parameters (delay, Doppler, DoA, DoD, complex amplitude) for all propagation paths leading to a specific target interference zone. This could be a sensitive area where no communication is desired, or a region where controlled interference is required for security or testing. This path parameter information is fed back. The transmitter then predistorts a "second signal" (which could be a regular data stream intended for other users, or a dedicated jamming signal) such that, when it propagates through the identified paths, it experiences destructive interference at the target interference zone. This results in a localized "null" or significant signal attenuation at the precise geographical location of the interference zone, while potentially maintaining normal communication or enhancing signals in other areas.
Technical Terminology: Adaptive nulling, interference zone creation, destructive interference, specific target interference zone, nulling zone monitor, signal attenuation, localized null, jamming signal, security, testing.

stateDiagram-V2
    [*] --> Normal_Focusing: System Start
    Normal_Focusing --> Adaptive_Nulling_Mode: On (Admin_Command OR Zone_Designated)
    Adaptive_Nulling_Mode --> Normal_Focusing: On (Nulling_Complete OR Zone_Removed)

    state Normal_Focusing {
        CE_Focus: Channel Estimation for Target User
        PD_Focus: Predistort for Constructive Sum
    }

    state Adaptive_Nulling_Mode {
        CE_Null: Channel Estimation for Target Nulling Zone
        PD_Null: Predistort for Destructive Sum
        Monitor_Null: Monitor Nulling Zone Effectiveness
        CE_Focus --> CE_Null: Redirect Channel Estimation Target
        PD_Focus --> PD_Null: Change Predistortion Objective
    }

Derivative 6: Material & Component Substitution - Multi-Modal Sensor Fusion for Path Parameter Estimation

Claim Linkage: Enhances the "performing a channel estimation" step of Claim 1 (Method) and the "receiver configured to perform a channel estimation" of Claim 8 (System) and Claim 15 (Base Station) by integrating diverse sensor types.
Enabling Description: The receiver's channel estimation capability is significantly enhanced by incorporating multi-modal sensor fusion, going beyond traditional RF-only measurements. In addition to RF channel sounding, the receiver (e.g., mobile station 150) integrates sensors such as accelerometers, gyroscopes (IMU for precise ego-motion tracking), GPS/GNSS receivers (for absolute position), LIDAR or ultrasonic sensors (for local environmental mapping and scatterer identification), and potentially visual cameras (for optical flow-based velocity estimation of the user and surrounding objects). The "first signal" still originates from the transmitter, but the path parameter information (delay, Doppler, DoA, DoD, complex amplitude, polarization) is no longer derived solely from the received RF signal. Instead, a sensor fusion algorithm combines the RF measurements with the real-time kinematic and environmental data from the other sensors. This fusion provides a more robust, lower-latency, and more accurate estimation of the true physical propagation paths and their dynamic changes, particularly in scenarios with rapid user movement or complex, non-static scatterers. The enhanced path parameter information is then fed back to the transmitter for more precise multi-domain predistortion.
Technical Terminology: Multi-modal sensor fusion, RF channel sounding, accelerometers, gyroscopes (IMU), GPS/GNSS receivers, LIDAR, ultrasonic sensors, visual cameras, optical flow, ego-motion tracking, local environmental mapping, real-time kinematic data, non-static scatterers, sensor fusion algorithm.

graph TD
    TX[Transmitter] -- Tx 1st Signal --> RF_Path[RF Propagation Path]
    RF_Path --> Rx_Ant[Receiver Antenna]
    Rx_Ant -- RF Measurement --> RF_CE[RF Channel Estimator]
    Rx_Sensors[Receiver Auxiliary Sensors (IMU, GPS, LIDAR, Camera)] -- Env & Motion Data --> Sensor_Fusion[Multi-Modal Sensor Fusion Engine]
    RF_CE -- RF Path Data --> Sensor_Fusion
    Sensor_Fusion -- Fused, Robust Path Params --> Param_FB[Feedback Path Parameter Info]
    Param_FB -- Feedback --> TX
    TX -- Predistort 2nd Signal --> RF_Path
    RF_Path --> Rx_Ant

Derivative 7: Operational Parameter Expansion - Extremely Low Frequency (ELF)/Very Low Frequency (VLF) for Subterranean/Sub-aquatic Communication

Claim Linkage: Extends the operating frequency and application domain for "wireless communication" and "propagation paths" of the patent into challenging, attenuating mediums for Claim 1 (Method), Claim 8 (System), and Claim 15 (Base Station).
Enabling Description: This derivative adapts the user-focusing technique for communication in extremely challenging environments like subterranean tunnels, deep mines, or through seawater, utilizing Extremely Low Frequency (ELF) or Very Low Frequency (VLF) electromagnetic waves (e.g., 3 Hz to 30 kHz). At these frequencies, propagation is highly attenuated and dominated by ground conductivity, but offers penetration where higher frequencies fail. The "transmitter" is a large-scale magnetic loop or electric dipole antenna system (e.g., deployed on the surface or within a mine shaft). The "receiver" is a specialized ELF/VLF sensor array (e.g., magneto-inductive coils or electrode arrays) located deep underground or underwater on an exploration vehicle. The transmitter emits a "first signal." The receiver performs channel estimation tailored for ELF/VLF propagation, extracting path parameters such as long-period delays, specific attenuation coefficients, conductivity-induced phase shifts, and coarse spatial arrival directions through the earth or water layers. This unique set of path parameters is fed back, potentially via a hybrid communication link (e.g., acoustic or wired sections). The transmitter then predistorts a "second signal" in the time, frequency, and extremely coarse spatial domains (given the long wavelengths). This predistortion compensates for the bulk properties of the medium to maximize the received signal strength and intelligibility at the receiver, enabling robust, albeit low-data-rate, command-and-control or telemetry links in environments previously inaccessible to conventional wireless.
Technical Terminology: Extremely Low Frequency (ELF), Very Low Frequency (VLF), subterranean communication, sub-aquatic communication, magnetic loop antenna, electric dipole antenna, magneto-inductive coils, electrode arrays, ground conductivity, attenuation coefficients, conductivity-induced phase shifts, hybrid communication link, command-and-control, telemetry.

graph TD
    TX_ELF[ELF/VLF Tx System] -- Tx 1st ELF/VLF Signal --> Geo_Chan[Geological/Aquatic Channel]
    Geo_Chan -- Attenuated Propagation --> RX_ELF[ELF/VLF Sensor Array (Rx)]
    RX_ELF -- ELF/VLF Channel Estimation (Long Delays, Attenuation, Phase Shifts) --> ELF_Params[ELF/VLF Path Parameters]
    ELF_Params -- Feedback (Hybrid Link) --> TX_ELF
    TX_ELF -- Predistort 2nd ELF/VLF Signal (Time, Freq, Coarse Spatial) --> Geo_Chan
    Geo_Chan -- Penetrating Focused Signal --> RX_ELF
    RX_ELF -- Receive Predistorted Signal --> Low_BW_Comm[Low-Bandwidth Communication]

Derivative 8: Cross-Domain Application - Autonomous Vehicle Sensing and Control (Millimeter-Wave Radar)

Claim Linkage: Adapts the "user-focusing" concept to a sensing and control application, indirectly supporting "non-communication applications" like "remote sensing" as described in the patent's detailed description, impacting the system (Claim 8) and base station (Claim 15).
Enabling Description: The user-focusing technique is employed in autonomous vehicle (AV) radar systems for enhanced object detection, tracking, and classification, particularly in dense urban or adverse weather conditions. The "transmitter" is a millimeter-wave (mmWave) radar array on the AV. Instead of a dedicated "receiver" for communication, the radar array itself acts as both transmitter and a sophisticated "passive receiver" (via its reflections) for its own transmitted signals. The radar array emits a "first signal" (chirp or pulse). Reflections from objects (e.g., other vehicles, pedestrians, obstacles) within the environment constitute the received signals. The radar processing unit performs channel estimation on these reflected signals, treating each distinct reflection path from an object as a "propagation path." Path parameters extracted include precise range (delay), velocity (Doppler frequency), high-resolution azimuth/elevation angles (DoA/DoD), and radar cross-section (complex amplitude). This rich "path parameter information" (representing the object's state) is inherently available at the transmitting radar. The radar then "predistorts" subsequent transmitted radar "second signals" (e.g., tailored waveforms or beamforming patterns) in time, frequency, and spatial domains. This predistortion actively shapes the radar beam to "focus" on specific objects or regions of interest, improving signal-to-noise ratio for weak targets, mitigating clutter, performing high-resolution imaging of complex objects, or even creating "anti-focus" nulls for known interference sources, thus enhancing the AV's perception capabilities.
Technical Terminology: Autonomous vehicle (AV) radar, millimeter-wave (mmWave) radar, object detection/tracking/classification, chirp signal, pulse signal, radar cross-section (RCS), range, velocity, azimuth/elevation angles, clutter mitigation, high-resolution imaging, anti-focus nulls, perception capabilities.

flowchart TD
    Radar_Tx[mmWave Radar Array (Tx)] -- Tx 1st Radar Signal (Chirp/Pulse) --> Env_Objs[Environment + Objects (Reflectors)]
    Env_Objs -- Reflected Radar Signals --> Radar_Rx[mmWave Radar Array (Rx - Passive)]
    Radar_Rx -- Radar Signal Processing (Range, Doppler, AoA/DoD, RCS) --> Obj_Params[Object Path Parameters]
    Obj_Params -- Internal Feedback/Computation --> Radar_Tx
    Radar_Tx -- Predistort 2nd Radar Signal (Time, Freq, Adaptive Spatial Beamforming) --> Env_Objs
    Env_Objs -- Focused Radar Energy / Nulling --> Target_Obj[Targeted Object / Interference Source]

Derivative 9: Integration with Emerging Tech - Quantum Channel Estimation and Predistortion for Secure Communications

Claim Linkage: Integrates quantum computing principles into the "performing a channel estimation" and "predistorting a second signal" steps of Claim 1 (Method) and the corresponding functionalities of the System (Claim 8) and Base Station (Claim 15), focusing on security and enhanced processing.
Enabling Description: This derivative integrates quantum computing techniques for ultra-fast and highly secure channel estimation and predistortion. The "first signal" is a standard classical communication signal. However, the receiver's channel estimation process employs a quantum computer (or a specialized quantum processor) to perform a quantum algorithm (e.g., a Quantum Phase Estimation Algorithm or Quantum Fourier Transform-based method) on the received signal and its noise characteristics. This allows for significantly faster and potentially more accurate extraction of path parameter information (delay, Doppler, DoA, DoD, complex amplitude) by exploiting quantum parallelism, especially in highly noisy or rapidly changing channels. The receiver then transmits this quantum-enhanced path parameter information to the transmitter via a Quantum Key Distribution (QKD) secured classical channel, ensuring provable information-theoretic security of the critical channel feedback. The transmitter, also equipped with quantum-assisted processing capabilities, then computes and applies the predistortion for the "second signal" with quantum-level precision, leveraging the high-fidelity path parameters to achieve an even tighter and more robust user-focus.
Technical Terminology: Quantum computing, quantum processor, Quantum Phase Estimation Algorithm (QPEA), Quantum Fourier Transform (QFT), quantum parallelism, Quantum Key Distribution (QKD), information-theoretic security, quantum-enhanced path parameter information, quantum-level precision, high-fidelity path parameters.

sequenceDiagram
    participant Tx[Classical Transmitter]
    participant QCE[Quantum-Enhanced Receiver (QPU + Rx)]
    participant QKD_Chan[QKD Secured Channel]
    participant QPD[Quantum-Assisted Transmitter (QPU + Tx)]

    Tx->>QCE: Transmit 1st Classical Signal
    QCE->>QCE: Quantum Channel Estimation (QPEA/QFT)
    QCE->>QKD_Chan: Feedback Quantum-Enhanced Path Params (Secured by QKD)
    QKD_Chan->>QPD: Path Params Received
    QPD->>QPD: Quantum-Assisted Predistortion Calculation
    QPD->>Tx: Apply Predistortion (Classical Signal)
    Tx->>QCE: Transmit 2nd Predistorted Classical Signal

Derivative 10: The "Inverse" or Failure Mode - Low-Power Diagnostic Mode with Minimal Feedback

Claim Linkage: Defines a low-power, limited-functionality mode for the system (Claim 8) and base station (Claim 15), modifying the "performing a channel estimation" and "sending the channel estimation" steps of Claim 1 (Method) for diagnostic purposes.
Enabling Description: In situations requiring minimal power consumption (e.g., critical battery levels at the receiver, or during network maintenance windows) or when only basic channel integrity needs to be verified, the system enters a "low-power diagnostic mode." The transmitter (e.g., base station 110) sends a very low-power "first signal" at reduced periodicity. The receiver (e.g., mobile station 150) is configured to perform a highly simplified and energy-efficient channel estimation. Instead of a full-dimensional parametric estimation, it only extracts a minimal subset of path parameters, such as the strongest path's average delay and a coarse signal strength indicator (e.g., RSSI). The feedback mechanism is also minimized, sending only this limited path parameter information (e.g., a single byte representing channel quality) at an infrequent rate to conserve energy. The transmitter, upon receiving this limited feedback, either suspends predistortion (operating in a basic broadcast mode) or applies a simplified, default predistortion based on general environmental models rather than specific path parameters. This mode allows for basic link maintenance, emergency signaling, or diagnostic checks with extreme energy efficiency, sacrificing focusing precision for operational longevity or minimal network impact.
Technical Terminology: Low-power diagnostic mode, minimal feedback, reduced periodicity, energy-efficient channel estimation, limited path parameter information, signal strength indicator (RSSI), coarse average delay, basic broadcast mode, default predistortion, operational longevity.

stateDiagram-V2
    [*] --> Full_Operation: System Active
    Full_Operation --> Low_Power_Diagnostic: On (Battery_Low OR Maint_Cmd)
    Low_Power_Diagnostic --> Full_Operation: On (Battery_Charged OR Maint_Done)
    Low_Power_Diagnostic --> Shutdown: On (Critical_Failure)

    state Full_Operation {
        CE_Full: Full Channel Estimation
        FB_Full: Detailed Path Params Feedback
        PD_Full: Full Multi-Domain Predistortion
    }

    state Low_Power_Diagnostic {
        CE_Min: Minimal Channel Estimation (e.g., RSSI, Avg Delay)
        FB_Min: Limited Path Params Feedback (e.g., 1-byte CQI)
        PD_Simple: Simplified/Default Predistortion OR Broadcast
        CE_Full --> CE_Min: Reduced Scope
        FB_Full --> FB_Min: Reduced Data
        PD_Full --> PD_Simple: Reduced Complexity
    }