https://cis.temple.edu/~wu/research/publications/Publication_files/10_bare_jrnl_compsoc.pdf
6.1 Basic Setting at runs a Linux operating system with E5-2620 CPU, NVIDIA RTX5000 GPU, 128Gb memory, and a 2Tb hard disk. We choose the Social LSTM model to predict the future trajectories of users which can achieve an average accuracy of over 70%. We used
https://cis.temple.edu/~jiewu/research/publications/Publication_files/Distributed_Deep_Multi-Agent_Reinforcement_Learning_for_Cooperative_Edge_Caching_in_Internet-of-Vehicles.pdf
2 Power of background noise -95 Bm Size of requested contents [0.5, 1.5] MB 2.4GHz Intel Xeon E5-2650 processor and 256GB RAM. The main parameters are listed in Table II. For performance comparison, the following four bench-mark caching methods are introduced: