Joint Beamforming and Computation Offloading for Multi-User Mobile-Edge Computing

Abstract

Mobile edge computing (MEC) is considered as an efficient method to relieve the computation burden of mobile devices. In order to reduce the energy consumption and time delay of mobile devices (MDs) in MEC, multiple users multiple input and multiple output (MU-MIMO) communications is considered to be applied to the MEC system. The purpose of this paper is to minimize the weighted sum of energy consumption and time delay of MDs by jointly considering the offloading decision and MU-MIMO beamforming problems. And the resulting optimization problem is a mixed-integer non-linear programming problem, which is NP-hard. To solve the optimization problem, a semidefinite relaxation based algorithm is proposed to solve the offloading decision problem. Then, the MU-MIMO beamforming design problem is handled with a newly proposed fractional programming method. Simulation results show that the proposed algorithms can effectively reduce the energy consumption and time delay of the computation offloading.

Multi-Access Edge Computing (MEC) Based on MIMO: A Survey

“…It is attempted to reduce downlink transmission power by using Multi-User Interference (MUI) of the downlink instead of suppression. Compared with the studies mentioned above, [29,44] also take into account the users' local computing capacity, and adaptively choose to calculate locally or completely offload the tasks to the BS. Different from binary offloading, partial offloading is another offload strategy, i.e., the computing task can be arbitrarily split into two parts and completed by the local and BS at the same time, which is more flexible.…”

“…In fact, both energy consumption and time delay are extremely important for MEC, so researchers usually jointly optimize the two aspects, and the most common objective function is to minimize the weighted sum of system energy consumption and time delay, to balance the QoE and power consumption [8,29,30,43,44,76].…”

“…On the basis of MEC optimization, beamforming, pilot estimation and other issues are taken into account to optimize MIMO performance so as to further achieve overall performance improvement. The offloading decision and beamforming are considered in [44]. The uplink transmission power allocation and downlink beamforming vector are optimized in [43], and [49] also takes backhaul bandwidth allocation into account based on [43].…”

“…Optimization problems in the field of MEC are usually non-convex or NP-hard [18], and optimization models are usually proved to be mixed integer non-linear programming problems [2,44], which are complex and very difficult to solve. The problems in MIMO-MEC are basically the same as those in MEC.…”

“…The distributed local search algorithm is used to solve the user association problem, and then the residual optimization problem is transformed into a rankconstrained D.C. problem, which is solved iteratively [33]. In [44], Quadratic Constrained Quadratic Programming (QCQP) is used to reconstruct the optimization problem, and the semi-definite relaxation (SDR) algorithm is used to solve the offloading decision problem. Based on the optimal offloading decision, the MU-MIMO beamforming design problem is transformed into a convex problem by using the fractional programming method, and then solved by an iterative algorithm.…”