Augmenting TCP Communication Efficiency in Cognitive Radio Networks Using Iterative Dimensional Neural Optimization

Abstract

The increasing interest in data-driven applications in the dynamic wireless settings has further urged the requirement of efficient bandwidth exploitation and fair load distribution in cognitive radio (CR) networks. Conventional TCP communication is exposed to serious difficulties in these networks because of the heterogeneity in the spectrum, uncertain activity by primary users, and changing channel conditions. To overcome these issues, this work introduces a new Iterative Dimensional Neural Optimization (IDNO) paradigm capable of optimizing TCP performance using adaptive, cross-layer optimization. The main scientific contribution of IDNO is its Transformer-augmented Efficiency Prediction Model, which can precisely predict network capacity based on past channel information and instantaneous feedback from lower network layers. This predictive model supports IDNO's dynamic and iterative optimization of its key parameters such as relay node selection, power allocation, and frame size for maximum TCP rate with the promise of zero interference and high-efficiency utilization of spectrum resources. IDNO is empirically validated via simulations and experimental work. IDNO shows improvements as high as 51% when simulated under optimal laboratory-like situations, whereas 30% improves when under natural operating conditions in realistic settings. These findings prove the resilience and versatility of IDNO in coping with the dynamic characteristic of CR networks. In addition, the scheme attains an accuracy of throughput prediction of 2% error, exceeding traditional optimization techniques. With iterative optimization integrated with predictive modeling, IDNO builds a robust and effective solution towards enhancing TCP communication in spectrum-sharing networks, providing contributions to advances in spectrum efficiency, network reliability, and energy-efficient transmission strategy.

The IDNO paradigm enhances TCP communication in cognitive radio (CR) networks by addressing spectrum heterogeneity, primary user interference, and dynamic channel conditions. The Transformer-Augmented Efficiency Prediction Model predicts network capacity using historical data and real-time feedback. IDNO optimizes relay node selection, power allocation, and frame size through an iterative process, ensuring zero interference and high efficiency. Performance results demonstrate a 51% improvement in optimal conditions, 30% in real-world settings, and ≤ 2% error in throughput prediction, contributing to spectrum efficiency, network reliability, and energy-efficient transmission strategies in CR networks.