Abstract
This paper studies an energy-efficient beamforming and resource allocation for multi-access edge computing (MEC) systems consisting of multi-antenna access points (APs) and single-antenna users. We consider maximizing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">energy efficiency</i> (EE) of a MEC system, defined as the total computed bits per total energy consumption of the MEC system. To enhance the EE performance, we employ multiple antennas at APs to exploit multiplexing- or (receive) beamforming-gain in the uplink and perform download beamforming for transmitting computation results in the downlink. We consider both spatial-division multiple access (SDMA) based MEC system and time-division multiple access (TDMA) based MEC system and compare their EE performance. We formulate EE maximization problems for the SDMA-based and TDMA-based MEC systems, which are nonconvex and thus cannot be solved by standard convex optimization techniques. We first transform the problems by applying semidefinite relaxation (SDR). Then, we solve the relaxed problems for the SDMA-based MEC system and TDMA-based MEC system by using Dinkelbach method and difference-of-concave programming algorithm. We observe that the SDMA-based MEC system outperforms the TDMA-based MEC system in EE performance most of the cases. However, for the special case when i) only users’ energy consumption is counted (i.e., energy consumption of AP and MEC server is not considered) for EE, and ii) there is no minimum throughput requirement, we show that the TDMA-based MEC system outperforms the SDMA-based MEC system. Simulation results demonstrate that the proposed schemes significantly enhance the EE of MEC systems.
Highlights
With recent advancement of Internet of Things (IoT) and increasing popularity of mobile devices, including smart phones, wearable devices, and smart sensors, users expect to be able to run a wide-range of new applications such as face recognition, virtual reality, and unmanned driving on their devices
We observe that the spatial-division multiple access (SDMA)-based multi-access edge computing (MEC) system in general outperforms the time-division multiple access (TDMA)-based MEC system in EE performance
Under this non-negligible time model, we designed the timeline for the TDMA-based MEC system such that the MEC server can compute users’ tasks while it is receiving tasks offloaded by other users or transmitting computed results to them, which can improve the efficiency of the MEC system
Summary
With recent advancement of Internet of Things (IoT) and increasing popularity of mobile devices, including smart phones, wearable devices, and smart sensors, users expect to be able to run a wide-range of new applications such as face recognition, virtual reality, and unmanned driving on their devices. The authors in [20] investigated a systemwise EE maximization problem of a wireless powered MEC network, where non-orthogonal multiple access (NOMA) is employed for uplink task offloading These works on enhancing the EE of MEC systems are limited to the scenarios with a single-antenna AP, which cannot exploit the advantages of multiple antennas in offloading and downloading efficiency. Since EE is adversely affected by the energy consumption of the MEC server and AP during MEC computation and downloading, respectively, it may not be always optimal to reduce the computing and downloading time by increasing the power for MEC computation and downloading from AP Under this non-negligible time model, we designed the timeline for the TDMA-based MEC system such that the MEC server can compute users’ tasks while it is receiving tasks offloaded by other users or transmitting computed results to them, which can improve the efficiency of the MEC system. Cx×y denotes the space of x × y complex matrices. x denotes the Euclidean norm of a complex vector x, and |z| denotes the magnitude of a complex number z
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