Adaptive Radio Communications System Using Distributed Waveform Processing
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Solution Overview
Problem
Software-defined radios (SDRs) face limitations in processing power and availability due to their monolithic, stove-piped design, making them inadequate for handling increasingly complex waveforms, and they lack the ability to dynamically scale system resources such as transceivers, processors, and storage.
Innovation Solution
A radio frequency communications system utilizing a network of transceivers, waveform processor entities, and application processor entities connected via a network fabric, where digital signal processing is performed software-defined, allowing dynamic scaling and distribution of processing tasks across multiple nodes, eliminating the need for specialized hardware like FPGAs or ASICs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If a monolithic SDR design is used, then device simplicity is maintained, but processing power and availability are insufficient for complex waveforms
Solution Approach 1:
The patent segments the SDR system into multiple independent waveform processor entities that can be distributed across different nodes. Each processor entity handles specific waveform processing tasks, allowing the system to scale processing power by adding more entities rather than increasing the complexity of a single monolithic device.
Solution Approach 2:
The patent transitions from a single-node processing architecture to a multi-node distributed architecture, adding the dimension of spatial distribution. This allows the system to achieve higher processing capacity through parallel processing across multiple nodes while maintaining software-defined flexibility.
2Adaptability or versatility
If specialized hardware like FPGAs or ASICs is used, then processing capability is enhanced, but system adaptability and scalability are reduced
Solution Approach 1:
The patent implements universal waveform processor entities that can execute multiple waveform processing functions through software rather than hardware specialization. Each processor entity can be configured to handle different waveform types and processing requirements, providing both adaptability and sufficient processing capability through software-defined functionality.
Solution Approach 2:
The system dynamically adjusts processing parameters and resource allocation based on the complexity of waveforms being processed. The grid-computing module can allocate additional processor entities or adjust processing parameters to match the requirements of different waveforms, enabling the system to adapt to varying processing needs without requiring specialized hardware for each waveform type.
3Productivity
If system resources are fixed, then device simplicity is maintained, but the ability to handle increasingly complex waveforms is limited
Solution Approach 1:
The patent implements dynamic resource allocation where the grid-computing module can add, remove, or reallocate waveform processor entities based on the processing requirements of current waveforms. This dynamic scaling allows the system to handle increasingly complex waveforms by provisioning additional processing resources only when needed, rather than maintaining fixed oversized infrastructure.
Solution Approach 2:
The system employs self-service resource management where the grid-computing module automatically monitors processing requirements and allocates appropriate resources without manual intervention. The system can autonomously determine when additional processor entities are needed and provision them accordingly, reducing the complexity of manual resource management while maintaining high productivity.
4Reliability
If a distributed architecture is implemented, then processing power and availability are improved, but system complexity increases
Solution Approach 1:
The patent implements local quality by having each waveform processor entity operate independently with its own processing capabilities, while the grid-computing module provides centralized coordination. This distribution of functionality ensures that individual node failures do not compromise overall system availability, while the standardized interface between nodes simplifies network fabric complexity.
Data Source
AI summary
An illustrative adaptive radio communications system comprises a cluster of waveform and application processor entities coupled and a plurality of transceivers. The transceivers convert radio frequency (RF) signals into digital in-phase and quadrature (I/Q) data, which is sent to the waveform processor entities via a network fabric. The waveform processor entities perform low-level waveform processing and the application processor entities perform high-level, distributed signal processing. The system and related methods are capable of processing multiple programmable waveforms of varying complexity.