Research Area: Edge Computing
The rapid development of mobile information industry has led to the emergence of various mobile applications in areas such as industrial automation, health care or transportation. These applications often require heavy computations with strict latency requirement, which may surpass the processing abilities of the mobile devices. A promising technology to support such applications is mobile edge computing (MEC), which places edge servers in the vicinity of mobile devices to enable computation offloading. MEC has the potential to substantially augment the computation capacities of the mobile devices. However, computation offloading requires to transfer the input data from the mobile devices to edge servers, resulting in extra transmission latency and energy consumption. Meanwhile, the limited computing power at the edge servers needs to be shared by multiple users, which may in turn lead to non-negligible computing time. Moreover, the effect of the available computing and communication resources is coupled in MEC systems, and thus, a joint allocation of these two resources is of significance. In this thesis we propose centralized and decentralized algorithms for effective resource allocation for various MEC systems that adopt orthogonal multiple access (OMA) or non-orthogonal multiple access (NOMA) for computation offloading.
In the thesis, we study the problem of transmission energy minimization for multi-cell MEC systems, now employing OMA. We first consider the scenario, where each user only offloads to its nearest base station. We transform the network-wide resource allocation into a convex optimization problem, and propose a distributed algorithm that achieves optimal solution. We then investigate the more general scenario, where each user can offload to multiple base stations for parallel processing. We first show that the joint resource allocation problem is non-convex. We study the complexity of optimizing a part of the system parameters, and based on these results propose an iterative algorithm that converges to a local optimum.
Name of the Researcher: Zeng, Ming
Name of the Supervisor(s): Fodor, Viktória, Karlsson, Gunnar
Year of Completion: 2020
University: KTH
Thesis Link: Home Page Url
