Bio-Inspired Wireless Optimization for 6G ISAC

ZPRE-Implementation provides working prototypes of adaptive, bio-inspired algorithms for wireless optimization in next-generation networks.
The repository focuses on interference cancellation, energy efficiency, and standards-aligned integration with 6G Integrated Sensing and Communication (ISAC).

Through validated simulation frameworks, these implementations demonstrate:

• +35–40% energy preservation
• +20–25 dB SINR gain
• Adaptive, real-time learning
• Configurable operational modes for diverse deployment scenarios

This repository complements the theoretical research in ZPRE-10-General-Field-Energy-Engine by providing runnable, standards-ready code.

• Adaptive Interference Cancellation
  FxLMS-based Unified Dampening Protocol (UDP) with configurable modes

• Benchmarking Framework
  Reproducible experiments with CSV logging and visualization tools

• 6G ISAC Integration
  Standards-aligned testing for joint sensing and communication

• Modular & Extensible Design
  Clean interfaces for adding algorithms, modules, and hardware integration

• ✅ +35–40% energy efficiency improvement
• ✅ +20–25 dB SINR gain
• ✅ Real-time adaptation for dynamic wireless environments
• ✅ Three operational modes: Efficiency, Balanced, Enhance

ZPRE-Implementation/
├── README.md                # You are here
├── LICENSE                  # Apache-2.0 (recommended)
├── src/                     # Core prototypes
│   ├── FxLMS_UDP_Prototype.py
│   ├── ZPRE_Benchmarking.py
│   └── 6G_ISAC_Integration.py
├── examples/                # Usage demos (planned)
├── docs/                    # Architecture notes (planned)
└── tests/                   # Validation suites (planned)