rocessing if needed. In this chapter, we assume the task-input bits are bit-wise independent and can be arbitrarily divided into different groups and exe- cuted by different entities in MEC system, e.g. parallel execution at the mobile and MEC server [199]. Partial offloading is used here. Thus the system can support data offloading and local computing simultaneously. 7.3 Problem Formulation 7.2.1 Data Offloading Denote the set of UEs as K = {1,2, ,K}. A UE can offload part of the computation bits to the server. To reduce the interference between different UEs, UEs doing offloading are allocated a portion of T and transmit sequentially, such as in the TDMA mode. Specifi- cally, let gk, Pk, and tk, respectively, represent the channel between the server and UE k, the transmission power, and time duration allocated to UE k. The total number of offloaded bits is rk = Blog2 Ak K, where o2 is the noise power and B is the system bandwidth. Under this mode, the corresponding energy consumption for UE k is where Pktk denotes the over-the-air information transmission energy consumption, and Pr is the constant circuit power for transmit signal processing, which is the same for all UEs. 7.2.2 Local Computing In addition to offloading, part of the bits can be computed locally by UEs. Let Ck be the number of computation cycles needed to process one bit of data for UE k. Clearly, each UE can compute the data throughout the entire block T. Furthermore, fk denotes the proces- sor's computing speed in the unit of cycles per second, and similar to [15], this speed holds constant. Therefore, the total number of bits locally computed is energy con- sumption of local computing is modeled as a function of the processor speed fk. Specifically, where Ek is the computation energy efficiency coefficient of the processor's chip [15, 197]. 7.3 Problem Formulation In this section, we form an optimization problem that maximizes the total computation energy efficiency among all UEs. Mathematically, the problem is expressed as follows. (7.1a) s.