e important feature of MEC is performing computation offloading, which leverages the powerful MEC servers in proximity and sends the computation-intensive tasks to MEC servers for processing. It can help overcome the physical limitations of local small devices. Current research involves two categories of offloading: binary [15] and partial [126-173, 178, 180-194, 196-220]. Binary offloading executes the task as a whole, either locally or in the MEC server, while partial offloading assumes the task can be partitioned into two parts, one for local processing and one for offloading. Even though the former is easier in implementation, for a very large dataset, partial offloading can help reduce the latency and energy consumption on the local devices more effectively. Previous works either target on minimizing the total energy consumption or maximizing total computed bits. Energy-efficient communication has received tremendous industrial 5G and Beyond Wireless Communication Networks, First Edition. Haijian Sun, Rose Qingyang Hu, and Yi Qian. © 2024 John Wiley & Sons Ltd. Published 2024 by John Wiley & Sons Ltd. 7 Mobile Edge Computing in 5G Wireless Networks and academic attention in various systems such as multi-hop and heterogeneous net- works [78]. By applying energy efficiency as the performance metric, QoS can be obtained, together with a reduction on energy consumption [209]. Energy efficiency defined in traditional communication systems in bits transmitted per Joule is an important metric to evaluate the overall system energy consumed. However, in the new communications systems, there exist a large number of computation-constrained and power-limited devices (such as IoT devices) that will need to support delay-critical yet computation-intensive tasks. Offloading through communications to MEC servers in order to compute the tasks timely becomes critically important to meet the short delay budget requirement, while communications throughput requirements may become secondary. To capture the efficiency of energy us