AI-Driven Spectrum Management: Using Machine Learning and Agentic Intelligence for Dynamic Wireless Optimization

<p>Recent theoretical advances in machine learning enable a new approach for reformulation, self-learning, and specification of complex problems. We propose a novel architecture and a set of techniques that, together, can be regarded as an instantiation of SDN, knowledge-based software-defined communications, and machine learning for self-management. By combination of novel SDN controllers and novel self-learning algorithms, users change their operational parameters at runtime for profit maximization and/or resource conservation. The latter either optimally selects dynamically the communication resources for added constraints, or transmits continuously variable signals that achieve a desired target signal at the receiver even while the channels and the receiver location are unknown. All these techniques are context-aware and resort to state-of-the-art models of the channel/receiver environment but do not consult the operator’s centralized controller so as to allow for scalability.</p> <p>While cognitive networking has recently emerged as a highly promising paradigm for the future mobile networks, it is unclear whether global utility optimization of the network state via such techniques can be achieved. In addition, high idealization is imposed to the network elements regarding a comprehensive state knowledge which may be impractical and computationally prohibitive in reality. We exploit recent theoretical advances in distributed computation for novel datacenter management schemes that gradually localize non-redundant measurements of stochastic problems and converge to a unique global gradient. Social comparator structures are proposed to exploit this locality into the network elements while guaranteeing joint compact state-space convergence and near-optimality; as a dynamic mechanism to incentivize network elements via a new reward to unlike measurements. Feedback of the elements’ utility states is a prerequisite, thus allowing extension of the approach to cognitive networks.</p>