Multi-Agent Automation of Reconfigurable Intelligent Surface (RIS) Control

This workflow automates the real-time control of passive meta-surface arrays (RIS) to solve coverage gaps and interference without deploying new cell sites. It eliminates manual RF re-planning cycles by dynamically creating virtual cells or signal nulls based on live UE distribution and traffic demand. The operational upside comes from capital avoidance, reduced energy consumption versus active radios, and the ability to reshape coverage in minutes versus months. Implementation integrates with the RAN Intelligent Controller (RIC), ingesting performance management and location data to calculate optimal phase-shift configurations for thousands of RIS elements.

Implementation requires a digital twin for safe simulation of RIS configurations before live deployment, ensuring no service degradation. The orchestrator agent, built on frameworks like LangGraph, manages the optimization loop, while a validator agent checks configurations against policy and physical constraints. Controls include a human review gate for major pattern changes and continuous monitoring for anomaly-triggered rollbacks. Integration points are the RIC via O-RAN A1/E2 interfaces and element management systems for direct hardware control, creating a closed-loop, zero-touch automation layer that redefines RAN coverage planning.

This workflow automates the real-time optimization of Reconfigurable Intelligent Surfaces (RIS), a passive 6G technology that redirects signals to fill coverage holes or suppress interference. Manual tuning is impossible at scale; a custom orchestration layer translates network KPIs—like SINR or throughput degradation—into precise phase-shift commands for thousands of RIS elements. The operational upside is capital efficiency: RIS units are low-cost, but their value is unlocked only through continuous, AI-driven adjustment that reduces the need for new cell sites and lowers energy consumption by optimizing signal paths.

Implementation integrates with the RAN Intelligent Controller (RIC) via rApp/xApps and the Service Management and Orchestration (SMO) framework. The Coverage Optimization and Interference Nulling agents operate on digital twin simulations before issuing commands, with a human approval gate for major network-wide reconfigurations. Observability is built into the orchestration layer, logging all decisions and environmental outcomes to a time-series database for performance auditing and model retraining, ensuring the system adapts to new deployment patterns and maintains strict control over the physical layer.

Deploying RIS control requires a phased, risk-managed integration into the live network. Phase 1 establishes a digital twin and offline simulation layer, ingesting geospatial, propagation, and live KPI data to train and validate multi-agent policies without operational impact. This sandboxed environment, built on frameworks like Ray or LangGraph, allows agents to learn coordination for beamforming and null-steering, with all decisions logged for safety review before any live commands are issued to the physical RIS hardware via a secured API gateway.

Phase 2 operates agents in a shadow mode, where proposed RIS configurations are validated against the digital twin and compared to baseline network performance, requiring a statistical performance gain before enabling live control. The final phase implements a gated live control loop, where an orchestrator agent issues commands through a policy engine that enforces safety constraints (e.g., never creating coverage holes). Exceptions or low-confidence decisions are routed to a human review queue in the NOC. Integration points include the RIC (RAN Intelligent Controller) for UE context, GIS systems for topology, and element managers for the RIS hardware itself, creating a closed-loop system that monetizes passive infrastructure.

Deploying autonomous RIS control in a live network introduces significant operational risk. A phased, governance-first rollout is critical. The workflow begins with a digital twin simulation, where agents propose RIS phase-shift configurations to optimize coverage or null interference. These proposals are validated against network KPIs and safety constraints in the simulated environment before any live command is issued. This sandboxed experimentation layer is the primary control, preventing untested configurations from affecting customer service or creating new interference patterns.

Implementation requires tight integration with the RAN Intelligent Controller (RIC) via standardized O-RAN interfaces (A1, E2) for observability and control. Each RIS unit is treated as a software-defined asset. The policy controller acts as a guardrail, enforcing rules on maximum beam steering deviation, interference thresholds, and fallback states. A human-in-the-loop console provides override capability and audit trails for every configuration change. This architecture ensures the transformative potential of RIS—creating virtual cells, boosting capacity, and reducing energy use—is realized without compromising network stability.

A custom RIS control workflow automates the continuous optimization of passive meta-surface arrays to dynamically steer beams, create coverage nulls, and form virtual cells. This eliminates the manual, static planning cycles of traditional RAN expansion, directly reducing capital expenditure on new physical sites and lowering site energy use by optimizing signal paths. The operational upside comes from real-time adaptation to user mobility and traffic hotspots, improving spectral efficiency and user throughput without deploying additional active radio hardware. Implementation requires tight integration with the RAN Intelligent Controller (RIC), real-time user equipment (UE) measurement ingestion, and digital twin simulation for safe policy testing.

Implementation integrates the orchestrator with the O-RAN RIC via standardized A1/E2 interfaces, ingesting live metrics from the Near-Real-Time RIC. Each specialized agent (coverage, interference, energy) proposes configuration changes, which are validated against safety policies and simulated in a digital twin before deployment to the physical RIS units via a vendor API. Critical controls include human-in-the-loop approval gates for major topology changes, continuous monitoring for unintended coverage holes, and automated rollback triggers based on KPI degradation. This architecture enables zero-touch optimization, turning RIS into a software-defined layer for coverage and capacity, directly impacting network OpEx and CapEx.