Self-Evolving Spectrum Sensing Framework for 7G Networks Using Deep Reinforcement Learning
Abstract
With the advent of seventh-generation (7G) wireless systems, the spectrum environment is extremely dynamic and heterogeneous, and traditional methods of sensing do not offer reliable and efficient performance. This paper introduces a self-evolving spectrum sensing system to enable adaptive and intelligent spectrum access based on a deep reinforcement-based learning paradigm. The framework depicts the sensing process as a sequence decision problem where an autonomous agent is able to continually refine its policy as it engages with the environment. The multi-objective reward formulation is designed to maximize the combination of the detection accuracy, false alarms, energy usage, and using the spectrum. Moreover, an adaptive representation of state mechanism is also introduced to indicate the temporal changes and short-term changes in the spectrum occupancy. The self-evolution strategy proposed adjusts learning parameters and decision policies in a dynamic manner that gives a robust operating in a non-stationary environment. The overall analysis of the experiment shows that the structure achieves detection probability of 97.1, false alarm rate reduced to minimum of 3.8, spectral usage maximized to above 92 and convergence rate is quicker as compared to the existing techniques. These results confirm the appropriateness of the proposed method in overcoming the issues of the next-generation wireless systems.