This paper introduces a low-dropout regulator (LDO) with a quick transient response to the load and no off-chip capacitance. The LDO in this work powers digital modules in system-on-chip (SoC). It has low output voltage variation and fast recovery time during load changes. This paper proposed a novel tri-loop regulation method. The basic loop is a conventional feedback loop for LDO to ensure output voltage accuracy. The transient-enhanced loop is an improved design based on the flipped voltage follower (FVF) that compensates for the effect of removing off-chip capacitance on circuit stability. The introduced Miller capacitor is used to form the AC-coupled loop, further ensuring pole separation. It is worth mentioning that this paper introduces 2 FVFs for building basic loop and transient-enhanced loop. In addition, a bandgap reference (BGR) with a self-starting circuit is designed in this paper. The LDO described in this paper operates at 5V. The LDO is based on a 0.18 um CMOS process. The dropping and rising voltages are merely 24.5 mV and 25 mV when the load current (I ) fluctuates between 1 and 50 mA. And the corresponding settling times are 2.5 us and 1.9 us respectively. The LDO described in this paper has an output of 1.8 V and the load regulation (LDR) is as low as 6.68 uV/mA.

Mobile edge computing (MEC) is a promising technique to support the emerging delay-sensitive and compute-intensive applications for user equipment (UE) by means of computation offloading. However, designing a computation offloading algorithm for the MEC network to meet the restrictive requirements towards system latency and energy consumption remains challenging. In this paper, we propose a joint user-association, task-partition, and resource-allocation (JUTAR) algorithm to solve the computation offloading problem. In particular, we first build an optimization function for the computation offloading problem. Then, we utilize the user association and smooth approximation to simplify the objective function. Finally, we employ the particle swarm algorithm (PSA) to find the optimal solution. The proposed JUTAR algorithm achieves a better system performance compared with the state-of-the-art (SOA) computation offloading algorithm due to the joint optimization of the user association, task partition, and resource allocation for computation offloading. Numerical results show that, compared with the SOA algorithm, the proposed JUTAR achieves about 21% system performance gain in the MEC network with 100 pieces of UE.