Fuzzy logic-driven nonlinear optimization for resource allocation in 6G networks

Abstract

Since the last-generation (6G) wireless networks strive to sustain ultra-dense, ultra-low-latency, and ultra-high-diversity systems, an essential challenge is how to manage resource scheduling in a realtime under uncertainty. Conventional deterministic optimization techniques hit their limit when they are confronted by the factors of non-stationary channel operation, dynamic user mobility, non predictable quality-of-service (QoS) needs, and distributed network control. Using fuzzy logic to drive nonlinear optimization of resource allocation in 6G networks is a proposed novel framework displayed in the paper. The suggested model incorporates fuzzy sets and nonlinear principles of optimality in humanlike reasoning and ambiguity to reshape policies of resources distributions in complicated wireless settings. The resource allocation problem is modeled as a fuzzy-constrained nonlinear program in which delay, QoS satisfaction and channel stability are considered as fuzzy parameters with membership functions related to them. The major contributions can be summarized as four: (1), development of a fuzzy module to model soft constraint of latency and throughput, (2), a nonlinear utility maximization resource allocation function which involves scalable service-level defuzzification, (3), convergence and existence of a solution as proved through the fuzzy variational inequality, (4), simulation and analysis of soft-constrained fuzzy optimization under numerous conditions of fading, mobility and user load. Python simulations on ISO/ITU-standard 6G use cases present significant performance gains with respect to conventional convex and heuristic plans: more than 27.4 improvement in spectral use, 18.2 improvement in packet latency violation, and 21.5 improvement on user fairness. Moreover, the model is robust, both in situations of partial observability, and in conditions of asymmetric information and the model works well under uncertainty in specifying drop rates and retransmission overhead. This collaborative way of working the sides of fuzzy reasoning structures, nonlinear optimization solver, and exploits of models of future 6 G systems offers a mathematically sound and practically competent design of adaptive intelligent wireless networks. The paper is a foundation in the future research on fuzzy cooperative game theory, hierarchically partitioned RIS-aided systems and the use of learning aids in fuzzy controllers to optimize wireless systems in the light of uncertainty.