floading only. This phenomenon can be explained by the following. Firstly, in the real-world applications, processor clock speed a mobile device can reach MHz level. Thus, when the data size is relatively small, the pre- ferred choice is to compute locally. Furthermore, based on the channel gain between UEs and the BS, and also the available bandwidth, data offloading may not be the ideal choice since it may take a longer time and a higher energy for small data offloading than for small data computing locally. On the other hand, when the data size is large, offloading to more computation powerful MEC server can become a much better choice. Moreover, the energy Proposed scheme Offloading only Local computing only Required computing data size (bits) Figure 7.1 Performance comparison of different schemes. 7.4 Numerical Results X 106 Proposed scheme Binary offloading scheme Required computing data size (bits) X 104 Figure 7.2 Performance comparison of our proposed scheme and the binary offloading. decrease in local computing is more dramatic than the energy used in offloading when data size shrinks. According to the equation for local computing only, the computation local efficiency is Flocal The k which indicates that its efficiency is inversely proportional to the square of the data size, while the offloading has a much slower deceasing rate thanks to the log function. Compared with partial offloading, another MEC offloading scheme is the binary offloading, where each UE either completely offloads all the data to the MEC server or computes all the data locally. To compare its performance with our proposed scheme, we show the result in Figure 7.2. It can be seen that our proposed joint scheme outperforms the binary offloading in terms of computation efficiency, which indicates the superiority of the proposed algorithms. Figure 7.3 illustrates the trade-off between two strategies: data offloading and local computing in our proposed scheme. The vertical axis represents the number of data bits (in percentage) calculate