Optimizing Information Freshness in Dual-Connection Cellular Networks for Real-Time Applications

Emerging cellular networks integrate diverse technologies like millimeter wave (mmWave) to deliver high capacity but face challenges like blockage sensitivity. Dual connections (DC) enhance throughput but ensuring information freshness (Age-of-Information, AoI) is crucial for real-time applications. This paper proposes a novel approach for AoI optimization in DC cellular networks (4G/5G) with TCP multipath transport. We address the stochastic nature of AoI and channel conditions with a combined random and stochastic optimization approach. Modeling the system as a multi-queue system with LTE and mmWave connections, we propose a difference learning-based algorithm that dynamically selects the optimal queue for each packet. This eliminates the need for statistical information like packet arrival rates, enabling real-time adaptation. Compared to existing methods, our algorithm significantly reduces AoI under various network conditions (over 40%), demonstrating its effectiveness for real-time applications.