Deep reinforcement learning-based optimization of multi-RAT handovers
Pavan Kumar Mangipudi, Satya Krishna Pothapragada, Alina Zare, Janise McNair
原文摘要
A major evolutionary goal and challenge for next-generation wireless systems is seamless multiple radio access technology (multi-RAT) mobility. In a multi-RAT environment, handover decisions must account for both user quality of service and network priorities, including signal quality, resource availability, energy consumption, latency, and security. Although WiFi/cellular mobility is well explored in research and standardization, many conventional handover mechanisms continue to rely primarily on received signal strength. Such decisions may overlook link delay, congestion, and the distinct operating characteristics of heterogeneous RATs. In this work, we present the Reinforced Flexible Multi-RAT Selection framework (ReFlexMRS), a configurable Deep Reinforcement Learning (DRL)-based approach for multi-criteria handover target selection. ReFlexMRS is evaluated using an SDN-enabled multi-RAT testbed. The decision problem is formulated as a Markov Decision Process with a finite action space and is addressed using a Deep Q-Network (DQN) with convolutional layers that extract relationships among network measurement features. The agent is trained offline from collected network observations using reward guided Q-value updates, then used to select handover targets during inference. A 5G-WiFi case study demonstrates that ReFlexMRS can trade a small amount of throughput for substantial improvements in link delay, handover stability, and WiFi offloading. Compared with RSSI-based selection, the proposed formulations retain approximately 90-95% of the baseline throughput while reducing total handovers by up to 18% and WiFi-to-5G handovers by up to 13%, while maintaining the required RSSI levels.
cs.NI