Sritam Parida,
Rosalin Pradhan,
Sidhartha Kumar Samal,
Bibhu Prasad Ganthia,
- Assistant Professor, Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India
- Assistant Professor, Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India
- Assistant Professor, Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India
- Assistant Professor, Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India
Abstract
Next-generation wireless communication systems operate under highly dynamic conditions characterized by nonlinear channel behaviour, time-varying interference, heterogeneous traffic, mobility, fading, and rapidly changing resource requirements. Conventional linear network models are often inadequate for representing the coupled relationships among channel quality, transmit power, bandwidth allocation, interference, latency, and energy consumption. This study develops a nonlinear mathematical modelling framework for adaptive optimization of wireless communication networks. The proposed formulation represents the network as a nonlinear dynamical system in which channel state, interference level, user demand, transmit power, and resource allocation evolve jointly over time. A nonlinear signal-to-interference-plus-noise ratio model is formulated and incorporated into achievable-rate, energy-efficiency, latency, and reliability equations. The resulting multi-objective optimization problem minimizes a composite network cost while simultaneously maximizing throughput and energy efficiency under power, bandwidth, interference, quality-of-service, and reliability constraints. A Lyapunov-based stability formulation is introduced to analyse the boundedness and convergence of the adaptive network state. Sensitivity analysis is additionally formulated to quantify the influence of channel gain, interference, transmit power, and user density on network performance. The mathematical framework provides a unified analytical basis for adaptive resource allocation and performance optimization in emerging wireless communication systems. The developed model is applicable to dense wireless networks, IoT communication, intelligent networks, and future high-capacity communication architectures where nonlinear interactions and dynamic operating conditions significantly influence network performance.
Keywords: Nonlinear mathematical modelling; wireless communication; adaptive optimization; interference modelling; resource allocation; energy efficiency; network stability; dynamic systems
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Recent Trends in Electronics Communication Systems
| Volume | 13 | |
| 02 | ||
| Received | 17/09/2026 | |
| Accepted | 18/09/2026 | |
| Published | 26/10/2026 | |
| Publication Time | 39 Days |