Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin

Abstract

In this work, we consider a mobile edge computing system with both ultra-reliable and lowlatency communications services and delay tolerant services. We aim to minimize the normalized energy consumption, defined as the energy consumption per bit, by optimizing user association, resource allocation, and offloading probabilities subject to the quality-of-service requirements. The user association is managed by the mobility management entity (MME), while resource allocation and offloading probabilities are determined by each access point (AP). We propose a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server. From the pre-trained deep neural network (DNN), the MME can obtain user association scheme in a real-time manner. Considering that real networks are not static, the digital twin monitors the variation of real networks and updates the DNN accordingly. For a given user association scheme, we propose an optimization algorithm to find the optimal resource allocation and offloading probabilities at each AP. Simulation results show that our method can achieve lower normalized energy consumption with less computation complexity compared with an existing method and approach to the performance of the global optimal solution.

When 5G Meets Deep Learning: A Systematic Review

“…Three papers proposed deep learning-based solutions to optimize the energy consumption in 5G networks [60][61][62][63]. The works proposed by [60,61] focused on NOMA systems.…”

“…In [61], a similar strategy was used, where a deep learning model was used to find the approximated optimal joint resource allocation strategy to minimize energy consumption. In [62], a deep learning model was used in the MME for user association taking into account the behavior of access points in the offloading scheme. In [63], the authors proposed a deep learning model to allocate carriers in multi-carrier power amplifier (MCPA) dynamically, taking into account the energy efficiency.…”

Energy-Efficient Offloading and Resource Allocation for Mobile Edge Computing Enabled Mission-Critical Internet-of-Things Systems

“…More specifically, the authors in [12] studied the offloading decision for MEC systems supporting single-carrier time division multiple access. Meanwhile, in [13], [14], the resource allocation for MEC systems supporting OFDMA was analyzed with short packet transmission consideration. In particular, a deep learning based approach was utilized in [13] to allocate online resources for MEC users to minimize the maximum normalized energy consumption of each user.…”

“…Meanwhile, in [13], [14], the resource allocation for MEC systems supporting OFDMA was analyzed with short packet transmission consideration. In particular, a deep learning based approach was utilized in [13] to allocate online resources for MEC users to minimize the maximum normalized energy consumption of each user. In addition, the optimal resource management of OFDMA-MEC system for ultra-reliable low-latency communication (URLLC) was studied in [14].…”

Resource Allocation for Multi-User Downlink URLLC-OFDMA Systems

“…However, the algorithm proposed in [19] assumes that the channel gains of different sub-carriers are identical which may not be a realistic assumption for broadband wireless channels. Moreover, the resource allocation algorithms proposed in [19] are based on a simplified version of the general expression for the achievable rate for FBT [17]. Furthermore, the existing MEC designs, such as [13], [20], do not take into account the size of the computation result of the tasks and do not consider the communication resources consumed for downloading of the processed data by the users.…”