DRL-Enhanced Vehicular Edge Caching Addressing Content Dynamics and Complex Intersections

Edge caching is crucial for enhancing the performance of vehicular networks primarily by reducing service latency and improving data availability. However, existing research typically focuses only on unidirectional vehicle movement, which limits its application in complex scenarios, such as those in urban areas. To address this, we propose two novel edge caching strategies specifically tailored for the intricate vehicle movements and traffic signal controls at urban intersections, taking into account temporal variability of content popularity, making them more practical in the real world. The first caching strategy, based on dynamic programming (DP), is suited for scenarios with low traffic flow, providing an optimal solution and serving as a benchmark for evaluating the performance of the second strategy. This benchmark assesses how closely the second strategy approaches the optimal solution. The second strategy employs deep reinforcement learning (DRL) and is suitable for high traffic scenarios. Its performance, when compared with the DP approach in low traffic scenarios, demonstrates results that are near-optimal. Simulation outcomes indicate that the DRL strategy effectively adapts to changes in content popularity, significantly optimizing service latency and hit rates.