Network Optimization for Dynamic Spectrum Allocation in Rural Cognitive Radio Networks Using Adaptive Genetic Algorithms in Television White Space Band.

The extension of reliable broadband into rural environments and an undertaking fundamentally limited by the severe free-space path loss of high-frequency architectures are requirements to close the identified digital divide in the globe. An optimized Dynamic Spectrum Allocation (DSA) model for Cognitive Radio Networks (CRNs) operating within the sub-1 GHz Ultra-High Frequency (UHF) Television White Space (TVWS) bands was developed in this study to address the limitations. Through the design and deployment of an Adaptive Genetic Algorithm (GA), the non-convex, NP-hard nature of multi-objective resource allocation was targeted. Initial localized allocation approaches revealed a 35% network failure rate caused by localized "spectral blindness" and unmitigated Secondary User (SU) co-channel interference and this challenge was resolved by engineering a Network Optimization model. The AI's evolutionary output was grounded in physical constraints. This was actualized due to the explicit incorporation of the deterministic Hata-Okumura open-area propagation model using the advanced multi-objective fitness scalarization. Robust and stable average network fitness of 0.0471 was achieved because of the successful elimination of the interference-induced failures states by Simulation. Absolute compliance with the IEEE 802.22 wireless standard was demonstrated by the global GA by maintaining a strict Primary User (PU) protection threshold of -114 dBm. It was revealed among others that GAs functioning under memory constraints is mathematically viable for scalable, low-power embedded hardware. Also, GAs successfully balanced maximum Shannon-Hartley throughput with aggressive energy minimization.