2018/09/19 by Mahsa Salmani, Salmani, Mahsa, Timothy N. Davidson +1
Computer Science · Engineering · #IoT and Edge/Fog Computing #Age of Information Optimization #Advanced Wireless Communication Technologies
paper · pdf · doi:10.48550/arxiv.1809.07453
The mobile edge computing framework offers the opportunity to reduce the\nenergy that devices must expend to complete computational tasks. The extent of\nthat energy reduction depends on the nature of the tasks, and on the choice of\nthe multiple access scheme. In this paper, we first address the uplink\ncommunication resource allocation for offloading systems that exploit the full\ncapabilities of the multiple access channel (FullMA). For indivisible tasks we\nprovide a closed-form optimal solution of the energy minimization problem when\na given set of users with different latency constraints are offloading, and a\ntailored greedy search algorithm for finding a good set of offloading users.\nFor divisible tasks we develop a low-complexity algorithm to find a stationary\nsolution. To highlight the impact of the choice of multiple access scheme, we\nalso consider the TDMA scheme, which, in general, cannot exploit the full\ncapabilities of the channel, and we develop low-complexity optimal resource\nallocation algorithms for indivisible and divisible tasks under that scheme.\nThe energy reduction facilitated by FullMA is illustrated in our numerical\nexperiments. Further, those results show that the proposed algorithms\noutperform existing algorithms in terms of energy consumption and computational\ncost.\n