he iteration process for three variables, we analyze the com- plexity in a sequential way. Firstly, Pk and fk have a linear complexity with the user number K. The updating of Lagrange variables is of O(K²) complexity since the total number of variables is 3K + 1. Here O(x) means the upper bound for the complexity grows with order X. Finally, auxiliary variables Nk and Bk have a complexity independent of K. Thus, our proposed algorithm has a total complexity in O(K³). 7.4 Numerical Results In this section, we present our simulation results of the joint offloading and computation scheme. The parameters are set as follows. The system bandwidth is B = 200 kHz, block 7 Mobile Edge Computing in 5G Wireless Networks length T = 1s, total number of UEs K = 2, Ck = 103 cycles needed for one bit raw data pro- cessing, the chip computing efficiency Ek = = 10-24 and static circuit power pr = 50 mW. The channels between the MEC server and each UE are modeled as the joint effect of large-scale and small-scale fading, with gk/o2 = Gkhk, G1 = 7, and G2 = 3. hk is the unitary Gaussian random variable. Lastly, the maximum computation capacity of each UE is set equally as fmax = 109 Hz. Eth = Eth = 2 Joule. All the results are averaged over different random channel realizations. In Figure 7.1, we present the comparison results among three schemes, namely, the pro- posed scheme in this chapter, offloading only scheme, and local computing only scheme. We set L1 = L2 and W1 = W2 = 1, which means the minimum required data bits for all UEs are the same. In Figure 7.1, the computation efficiency of all the schemes decreases with the increase of the minimum required data bits. This suggests that the energy required to compute grows faster than the growth of the data bits. It is evident that our proposed algo- rithm outperforms other schemes. Additionally, we notice that when the data size is small, the proposed scheme's performance is closer to that of local computing only; when the data size grows, the performance will approach that of of