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Vehicular Named Data Network (VNDN) is considered a strong paradigm to deploy in vehicular applications. In VNDN, each node has its cache, but due to limited cache, it directly affects the performance in a highly dynamic environment, which requires massive and fast content delivery. To reduce these issues, the cooperative caching plays an efficient role in VNDN. Most studies regarding cooperative caching focus on content replacement and caching algorithms and implement these methods in a static environment rather than a dynamic environment. In addition, few existing approaches addressed the cache diversity and latency in VNDN. This paper proposes a Dynamic Cooperative Cache Management Scheme (DCCMS) based on social and popular data, which improves the cache efficiency and implements it in a dynamic environment. We designed a two-level dynamic caching scheme, in which we choose the right caching node that frequently communicates with other nodes, keep the copy of the most popular content, and distribute it with the requester’s node when needed. The main intention of DCCMS is to improve the cache performance in terms of reducing latency, server load, cache hit ratio, average hop count, cache utilization, and diversity. The simulation results show that our proposed DCCMS scheme improves the cache performance than other state-of-the-art approaches.

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The frequent handover and handover failure problems obviously degrade the QoS of mobile users in the terrestrial segment (e.g., cellular networks) of satellite-terrestrial integrated networks (STINs). And the traditional handover decision methods rely on the historical data and produce the training cost. To solve these problems, the deep reinforcement learning- (DRL-) based handover decision methods are used in the handover management. In the existing DQN-based handover decision method, the overestimates of DQN method continue. Moreover, the current handover decision methods adopt the greedy strategy which lead to the load imbalance problem in base stations. Considering the handover decision and load imbalance problems, we proposed a load balancing-based double deep Q-network (LB-DDQN) method for handover decision. In the proposed load balancing strategy, we define a load coefficient to express the conditions of loading in each base station. The supplementary load balancing evaluation function evaluates the performance of this load balancing strategy. As the selected basic method, the DDQN method adopts the target Q-network and main Q-network to deal with the overestimate problem of the DQN method. Different from joint optimization, we input the load reward into the designed reward function. And the load coefficient becomes one handover decision factor. In our research, the handover decision and load imbalance problems are solved effectively and jointly. The experimental results show that the proposed LB-DDQN handover decision method obtains good performance in the handover decision. Moreover, the access of mobile users becomes more balancing and the throughput of network is also increased.

The traditional handover decision methods depend on the handover threshold and measurement reports, which cannot efficiently resolve the frequent handover issue and ping-pong effect in 5G (5 generation) ultradense networks. To reduce the unnecessary handover and improve the QoS (quality of service), combine with the analysis of dwell time, we propose a state aware-based prioritized experience replay (SA-PER) handover decision method. First, the cell dwell time is computed by the geometrical analysis of real-time locations of mobile users in cellular networks. The constructed state aware sequence including SINR, load coefficient, and dwell time is normalized by max-min normalization method. Then, the handover decision problem in 5G ultradense networks is formalized as a discrete Markov decision process (MDP). The random sampling and small batch sampling affect the performance of deep reinforcement learning methods. We adopt the prioritized experience replay (PER) method to resolve the learning efficiency problems. The state space, action space, and reward functions are designed. The normalized state aware decision matrix inputs the DDQN (double deep Q-network) method. The competitive and collaborative relationships between vertical handover and horizontal handover in 5G ultradense networks are mainly discussed. And the high average network throughput and long average cell dwell time make sure of the communication quality for mobile users.