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Table 1 Some important parameters

From: A reinforcement learning-based computing offloading and resource allocation scheme in F-RAN

Parameters Value
M The number of FAPs
N The number of UEs
K The number of DCNs
\({{t}_{n}}\) The task of UE n
\({{B}_{n}}\) The number of CPU cycles
\({{D}_{n}}\) The size of the task data
\({{d}_{n}}\) Offloading decision vector
\(\varvec{P}\) Network topology matrix
\({{d}_{FAP}}\) The maximal distance of FAP
\(\varvec{Y}\) The willingness matrix of DCN
\(f_{n}^{l}\) The computational capacity of UE n
\(z{}_{n}\) The energy consumption in per CPU cycle of UE n
\(\rho _{n}^{t}\) The weight factors of latency
\(\rho _{n}^{e}\) The weight factors of energy
\({{f}_{n,m}}\) The allocated computational resource to UE n in FAP m
\({{f}_{k}}\) The computational capacity of DCN k
\({{T}_{c}}\) The round-trip transmission delay
\(f_{n}^{Cloud}\) The allocated computational resource to UE n at the cloud server
\({{f}^{FAP}}\) The computational resource of FAP
\({{f}^{Cloud}}\) The computational resource of cloud server
\({{C}_{m}}\) The maximum accessible number of FAP
T The steps in each training epoch
\(\varvec{S}\) The state matrix
\(\varvec{C}\) The optimal caching matrix
\({{N}_{m}}\) The number of UEs who offload their tasks to FAP m