Future 5th generation (5G) networks are expected to enable three key services -enhanced mobile broadband (eMBB), massive machine type communications (mMTC) and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project (3GPP) URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. The paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution (LTE) to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, the paper discusses the potential future research directions and challenges in achieving the URLLC requirements.
In this paper, we propose novel approaches using state-of-the-art machine learning techniques, aiming at predicting energy demand for electric vehicle (EV) networks. These methods can learn and find the correlation of complex hidden features to improve the prediction accuracy. First, we propose an energy demand learning (EDL)-based prediction solution in which a charging station provider (CSP) gathers information from all charging stations (CSs) and then performs the EDL algorithm to predict the energy demand for the considered area. However, this approach requires frequent data sharing between the CSs and the CSP, thereby driving communication overhead and privacy issues for the EVs and CSs. To address this problem, we propose a federated energy demand learning (FEDL) approach which allows the CSs sharing their information without revealing real datasets. Specifically, the CSs only need to send their trained models to the CSP for processing. In this case, we can significantly reduce the communication overhead and effectively protect data privacy for the EV users. To further improve the effectiveness of the FEDL, we then introduce a novel clustering-based EDL approach for EV networks by grouping the CSs into clusters before applying the EDL algorithms. Through experimental results, we show that our proposed approaches can improve the accuracy of energy demand prediction up to 24.63% and decrease communication overhead by 83.4% compared with other baseline machine learning algorithms.
This letter proposes two novel proactive cooperative caching approaches using deep learning (DL) to predict users' content demand in a mobile edge caching network. In the first approach, a (central) content server takes responsibilities to collect information from all mobile edge nodes (MENs) in the network and then performs our proposed deep learning (DL) algorithm to predict the content demand for the whole network. However, such a centralized approach may disclose the private information because MENs have to share their local users' data with the content server. Thus, in the second approach, we propose a novel distributed deep learning (DDL) based framework. The DDL allows MENs in the network to collaborate and exchange information to reduce the error of content demand prediction without revealing the private information of mobile users. Through simulation results, we show that our proposed approaches can enhance the accuracy by reducing the root mean squared error (RMSE) up to 33.7% and reduce the service delay by 36.1% compared with other machine learning algorithms.Index Terms-Mobile edge caching, deep learning, distributed deep learning, proactive and cooperative caching.
ultiple-input multiple-output (MIMO) communication is a proven technique to increase the throughput and reduce the energy consumption of a wireless network. The throughput and energy gains are realized by having a multi-antenna node simultaneously send/receive several data streams (spatial multiplexing) or send/receive one data stream from several antennas (spatial diversity), respectively. To exploit MIMO's spatial multiplexing and diversity gains, each wireless device (henceforth referred to as a node) has to be equipped with multiple antennas, which must be separated from each other by at least half of the operating wavelength. Small form-factor devices (e.g., mobile stations and sensors) are typically equipped with at most a few antennas. This limitation prevents such devices from efficiently taking advantage of MIMO gains.In cooperative communications, a group of nodes that lie within a certain proximity can cooperate in sending (receiving) a signal to (from) another group of nodes. Cooperative MIMO (CMIMO), sometimes referred to as distributed, virtual, or networked MIMO, is one type of cooperative communications, whereby several nodes, each equipped with one or more antennas, cooperate to emulate a multi-antenna node, also known as a virtual antenna array (VAA). CMIMO allows small devices to harvest MIMO gains, and moreover offers numerous advantages that are beyond what is typically expected from a real multi-antenna system. For instance, unlike real MIMO systems, CMIMO can flexibly select its distributed antennas to avoid having a low-rank channel gain matrix so that the spatial multiplexing gain can be better harvested (with high-rank channels).CMIMO has been shown to improve the network lifetime and throughput, and reduce the communication delay. Network lifetime is a critical performance metric in energy-constrained systems such as wireless sensor networks (WSNs). Thanks to CMIMO's higher energy efficiency, the lifetime of a WSN can be prolonged by several times [1] compared to IEEE Network • July/August 2013 48 0890-8044/13/$25.00 AbstractCooperative multiple-input multiple-output (CMIMO) is a form of cooperative communications. CMIMO emulates the functionality of multi-antenna systems by grouping wireless devices to operate as virtual multi-antenna nodes. Its main objectives are to boost network throughput, conserve energy, and improve network coverage.In this article, we discuss recent applications of CMIMO in contemporary wireless networks, including wireless sensor, mobile ad hoc, wireless LAN, cognitive, and cellular networks. We first review CMIMO techniques at the physical layer. We then focus on state-of-the-art approaches for realizing CMIMO at the network layer, and classify these approaches based on their objectives and application scenarios, and how they exploit CMIMO gains. We highlight several open issues that present challenges to practical deployment of CMIMO.
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