Towards Specialized Wireless Networks Using an ML-Driven Radio Interface

TL;DR

The paper addresses the need for specialized, cognition-enabled wireless networks (SpecNets) to meet diverse future applications. It proposes MLDRs as AI/ML-driven radios capable of autonomously configuring protocols through context‑aware learning, and outlines a modular architecture plus an agent‑based control loop that can operate across edge and cloud layers. Three use cases—bespoke industrial networks, data‑driven THz waveform design, and coexistence of competing networks—illustrate how MLDR interfaces enable tailored performance via multi‑objective optimization and reinforcement learning. An initial ns‑3 based evaluation using a multi‑armed bandit agent shows significant gains in throughput, latency, and fairness over standard IEEE 802.11 configurations, demonstrating autonomous adaptability across varied scenarios with rapid convergence. The work highlights the potential of SpecNets to deliver customized, scalable, and efficient wireless solutions for industry and emerging communications frontiers while outlining a roadmap for broader AI‑native networking research and deployment.

Abstract

Future wireless networks will need to support diverse applications (such as extended reality), scenarios (such as fully automated industries), and technological advances (such as terahertz communications). Current wireless networks are designed to perform adequately across multiple scenarios so they lack the adaptability needed for specific use cases. Therefore, meeting the stringent requirements of next-generation applications incorporating technology advances and operating in novel scenarios will necessitate wireless specialized networks which we refer to as SpecNets. These networks, equipped with cognitive capabilities, dynamically adapt to the unique demands of each application, e.g., by automatically selecting and configuring network mechanisms. An enabler of SpecNets are the recent advances in artificial intelligence and machine learning (AI/ML), which allow to continuously learn and react to changing requirements and scenarios. By integrating AI/ML functionalities, SpecNets will fully leverage the concept of AI/ML-defined radios (MLDRs) that are able to autonomously establish their own communication protocols by acquiring contextual information and dynamically adapting to it. In this paper, we introduce SpecNets and explain how MLDR interfaces enable this concept. We present three illustrative use cases for wireless local area networks (WLANs): bespoke industrial networks, traffic-aware robust THz links, and coexisting networks. Finally, we showcase SpecNets' benefits in the industrial use case by introducing a lightweight, fast-converging ML agent based on multi-armed bandits (MABs). This agent dynamically optimizes channel access to meet varying performance needs: high throughput, low delay, or fair access. Results demonstrate significant gains over IEEE 802.11, highlighting the system's autonomous adaptability across diverse scenarios.

Figures (9)

• Figure 1: Conceptual MLDR architecture bellalta2024towards
• Figure 2: The SpecNet triangle for bespoke industrial networks. Operators specify their requirements as a point within this triangle, thereby distributing the importance of each of the three conflicting requirements.
• Figure 3: Unit simplex as a triangle in the SpecNets objectives' space.
• Figure 4: The channel allocation problem in an example deployment. Default (static) configuration (a); dynamic (MLDR-based) configuration (b).
• Figure 5: Integration of the MAB agent into the ns-3 simulator established with shared memory through the ns3-ai library. The simulator provides the agent with network performance metrics, while the agent returns the updated network configuration at each control period.