In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.

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Federated Learning in Mobile Edge Networks: A Comprehensive Survey

A mobile terminal including a body; a touchscreen provided to a front and extending to side of the body and configured to display content; and a controller configured to detect one side of the body comes into contact with one side of an external terminal, display a first area on the touchscreen corresponding to a contact area of the body and the external terminal and a second area including the content, receive an input of moving the content displayed in the second area to the first area, display the content in the first area, and share the content in the first area with the external terminal.

Mobile terminal and method for controlling the same

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL

A review of applications in federated learning

The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: 1) RAN; 2) core network; and 3) caching. We also present a general overview of major 5G cellular network elements such as software defined network, network function virtualization, caching, and mobile edge computing capable of meeting latency and other 5G requirements.

A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

A wireless device includes a transceiver including an antenna arrangement with at least two antennas, a communication processor configured to control communications of the wireless device with at least one further wireless device included in a network of wireless devices based on data relating to mutual connections between wireless devices included in the network, a beamforming controller configured to control a configuration of the at least two antennas to steer at least one beam for transmission of data based on beamforming information.

Methods and devices for wireless communications in device-to-device networks

Embodiments include apparatuses, methods, and systems that may test a UE for its idle period distribution. A test system may identify a set of bins in which a union of the set of bins may be equal to a contention window, wherein each individual bin of the set of bins may have an associated probability. A first bin of the set of bins may have a first associated probability, and a second bin of the set of bins may have a second associated probability that is larger than the first associated probability. Each individual idle period may be assigned to a corresponding bin of the set of bins. A UE may have a pass status or a failure status based on the individual idle periods assigned to the corresponding bin of the set of bins, and the associated probability for the bin. Other embodiments may also be described and claimed.

Testing of user equipments for idle periods distribution

This paper proposes a novel load distribution aware soft frequency reuse (LDA-SFR) scheme for inter-cell interference mitigation and performance optimization in next generation wireless networks. Our proposed scheme aims to provide a solution to effectively achieve inter-cell interference mitigation while maintaining high spectrum efficiency to all users in the cell. The proposed scheme consists of two novel algorithms: edge bandwidth reuse and centre bandwidth compensation. Using the edge bandwidth reuse algorithm, cell-edge users can take advantage of uneven traffic load and user distributions within each cell to expand their resource allocations. The center bandwidth compensation algorithm, on the other hand, provides a protection mechanism for cell-center users to avoid exhaustive edge bandwidth extension. Applying LDA-SFR to an LTE network and comparing its performance against that of existing soft frequency reuse (SFR) and adaptive soft frequency reuse (ASFR) schemes indicates that LDA-SFR is superior as it achieves fairness between cell-edge users and cell-center users in terms of average throughput improvement.

Load Distribution Aware Soft Frequency Reuse for Inter-Cell Interference Mitigation and Throughput Maximization in LTE Networks

The issue of finding a three-tiered spectrum access scheme in the so-called innovation band from 3550 to 3700 MHz for incumbent applications and protection against other priority access licenses (PAL) and general authorized access (GAA) systems has been addressed by the federal communications commission (FCC). The PAL and GAA systems are citizens broadband radio service devices (CBSDs) connected to a spectrum access system (SAS) which controls the spectrum access. The challenge lies in creating an approach, where the CBSDs are able to fulfil interference mitigation (IM) among each other. In this paper, we present the design and results of a hybrid centralized and distributed medium access control (HMAC) scheme for SAS with efficient and self-organized spectrum sharing schemes using machine learning algorithms based on game theory (GT) and reinforcement learning (RL) for GAA CBSDs in an unlicensed spectrum. The system improvement shows how the SAS can assists decision-making algorithms in a minimum intrusive way to fulfill fair interference and channel occupancy compared with state-of-the-art (SoA) schemes, where the SAS uses an interference graph (IG) to solve the channel allocation problem. Moreover, the suggested schemes support the distributed carrier sense multiple access/collision avoidance (CSMA/CA) mechanism to handle adjacent-channel and cochannel interference (ACI, CCI), which opens new perspective for decentralized systems in a SAS context and beyond 5G.

Concept Design of Medium Access Control for Spectrum Access Systems in 3.5 GHz

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Methods and devices for wireless communications in device-to-device networks

Testing of user equipments for idle periods distribution

Load Distribution Aware Soft Frequency Reuse for Inter-Cell Interference Mitigation and Throughput Maximization in LTE Networks

Concept Design of Medium Access Control for Spectrum Access Systems in 3.5 GHz