Abstract
To support the application of IoT and smart city, high data-rate wireless transmission is required. To meet the demand of high data-rate, the techniques of multiple antennas and mobile edge computing (MEC) networks have been proposed in order to enhance the data transmission rate significantly. However, there still exist lots of challenges array signal processing assisted MEC networks. In this paper, we propose an intelligent framework of offloading strategy for MEC networks assisted by array signal processing, where one user with multiple antennas has some computational tasks. These tasks can be computed by the user itself which however has limited computational capability, or computed by the near-by computational access points (CAPs) which has a powerful computational capability at the cost of wireless transmission. We consider the system cost by jointly taking into account the computational price, the energy consumption and the latency. By minimizing the system cost, we propose an intelligent offloading strategy based on ant colony optimization (ACO) algorithm, where the ants randomly visit the CAPs in order to obtain the final results. To further enhance the MEC network performance, the array signal processing is utilized at the user, where either the maximum ratio transmission (MRT) or selection combining (SC) is used to assist the data transmission from the user to CAPs. Simulation results with MRT and SC are finally demonstrated to verify the effectiveness of the proposed ACO-based offloading strategy and array signal processing schemes.
Highlights
In recent years, the research and applications of smart city have attracted much attention, since it can help city planning, transportation planning, detecting unusual accidents, and so on [1]–[3]
The system cost was a linear combination of the computational price, energy consumption and latency
Based on the system cost, the intelligent offloading strategy was designed through the ant colony optimization (ACO) algorithm
Summary
The research and applications of smart city have attracted much attention, since it can help city planning, transportation planning, detecting unusual accidents, and so on [1]–[3]. We propose an intelligent offloading strategy for MEC networks, where one user equipped with multiple antennas has some intensive computational tasks. If the n-th sub-task is computed by the CAPm with xnm = 0 for m ∈ [1, M ], the uplink and downlink transmission time are given by tnUmL lnUL CmUL (5). We can obtain the latency which CAPm requires to calculate the assigned sub-tasks, given by xnm(tnc0omp), m=0 In this equation, m = 0 indicates that the latency of user’s local CPU to complete the assigned sub-tasks is given by T0; while m > 0 represents that the sub-tasks are offloaded to the near-by CAPs, where the associated transmission latency and computing latency are given by Tm. In this work, we consider. We will describe the ACO-based offloading strategy design for the considered MEC networks
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