## 6 Simulations Results and Discussion  
This section provides the simulation experiments for evaluating the proposed methods. Table 3 summarises the main simulation parameters with their values. The coverage region of each MEC-equipped BS is considered to be a circle with a radius of 150 m deployed on a regular grid network within an area of \(1000\times 1000\) m\({}^{2}\), as shown in Figure 3. The channel bandwidth of BS is set to be \(\omega=20\) MHz. The channel gain from the UE to BS is modelled as \(127+30\log d\) (km) dB [60]. All wireless communication parameters are set based on the 3 GPP specification [62]. According to [43], the one-second video size should be set to \(d_{sub}=0.62\) Mbits. Let us assume that each video is 60 s to 120 s long, and \(k_{m}\in\{60,\ldots,120\}\). According to the following definition, \(d_{m}=k_{m}d_{sub}\), the input data size is uniformly distributed \(d_{m}\sim U(37.2,74.4)\) Mbits. The same consideration holds for computation intensity, which is uniformly distributed within U(500, 1000) cycles/bits. Each MEC server \(m\) allocates its available computational CPU resource to its users (\(f_{m,n}\)), and the total used CPU should not reach the resource constraint of that MEC server (\(F_{n}\)). The MEC server \(n\) uniformly distributes the resource constraint \(F_{n}=20\) GHz to its UEs. For simplicity, it is assumed that the available CPU frequency on all BSs are equal and are set to 25 GHz. To ensure that execution of each sub-task is completed within its latency constraint, \(t_{m}\) is set to be 150 ms. After reaching the total execution time of a task \(T_{m,n}^{tot}=\sum_{k=1}^{k_{m}}t_{m,n}^{e}\), the MEC server deallocates the resource \(f_{m,n}\) and makes it available for reallocation to other users as soon as they request it. The number of tasks varies for different simulations. We consider a fixed number of BSs as \(N=49\), where all are active the entire time. It is also assumed that there is at least one BS to provide service to each UE. The transmit power of mobile UEs are \(p_{m}=0.5\) W, and the noise power is set to be \(\mathcal{N}_{\varphi^{2}}=2\times 10^{-13}\) W. The one time HO is \(\tau_{m}^{h}=5\) ms, and the battery capacity is \(J=1\) kJ. This section compares our two proposed methods, UE-BS and BS-learning algorithms, with the optimum offline solution as well as the two common benchmark algorithms known as time greedy and energy greedy. The performance of the proposed methods are evaluated and compared with the related study of [43]. Figure 4 compares the average time cost and total energy cost for all algorithms over different task sizes \(\mathcal{M}\). As seen in Figure 4a, both EMM-GSI and the proposed UE-BS algorithms have their time cost close to the optimum offline solution due to possessing BS-side information. However, EMM-LSI and the proposed BS-learning have additional costs due to sub-optimal BS selection and handover cost. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Radius of the BS coverage area \(R\) & \(150\) m \\ Availbe CPU frequency on BS \(F_{n}\) & \(25\) GHz \\ Channel bandwidth \(\omega\) & \(20\) MHz \\ Channel gain from UE to BS \(h_{m,n}\) & \(127+30\log{(km)}\) dB \\ Subtask size & \(d_{sub}=0.62\) Mbits \\ Input data size of each task \(d_{m}\) & \([37.2,74.4]\) Mbits \\ Computation intensity of each task \(c_{m}\) & \([500,1000]\) cycles/bit \\ Total available computation CPU for each task \(m\) by BS \(n\)\(f_{m,n}\) & \([0,F_{n}]\) \\ Computation deadline of each task \(t_{m}\) & \(150\) ms \\ Noise power \(\mathcal{N}_{\varphi^{2}}\) & \(2\times 10^{-13}\) W \\ UE transmission power \(p_{m}\) & \(0.5\) W \\ One-time handover delay \(\tau_{m}^{h}\) & \(5\) ms \\ Battery capacity & \(1\) kJ \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Employed simulation parameters with corresponding values. Figure 3: The location of BSs and users within simulation region.