Millimetre wave frequency band as a candidate spectrum for 5G network architecture : a survey

In order to meet the huge growth in global mobile data traffic in 2020 and beyond, the development of the 5th Generation (5G) system is required as the current 4G system is expected to fall short of the provision needed for such growth. 5G is anticipated to use a higher carrier frequency in the millimetre wave (mm-wave) band, within the 20 to 90 GHz, due to the availability of a vast amount of unexploited bandwidth. It is a revolutionary step to use these bands because of their different propagation characteristics, severe atmospheric attenuation, and hardware constraints. In this paper, we carry out a survey of 5G research contributions and proposed design architectures based on mm-wave communications. We present and discuss the use of mm-wave as indoor and outdoor mobile access, as a wireless backhaul solution, and as a key enabler for higher order sectorisation. Wireless standards such as IEE802.11ad, which are operating in mm-wave band have been presented. These standards have been designed for short range, ultra high data throughput systems in the 60 GHz band. Furthermore, this survey provides new insights regarding relevant and open issues in adopting mm-wave for 5G networks. This includes increased handoff rate and interference in Ultra-Dense Network (UDN), waveform consideration with higher spectral efficiency, and supporting spatial multiplexing in mm-wave line of sight. This survey also introduces a distributed base station architecture in mm-wave as an approach to address increased handoff rate in UDN, and to provide an alternative way for network densification in a time and cost effective manner

The Novel Applications of Deep Reservoir Computing in Cyber-Security and Wireless Communication

This chapter introduces the novel applications of deep reservoir computing (RC) systems in cyber-security and wireless communication. The RC systems are a new class of recurrent neural networks (RNNs). Traditional RNNs are very challenging to train due to vanishing/exploding gradients. However, the RC systems are easier to train and have shown similar or even better performances compared with traditional RNNs. It is very essential to study the spatio-temporal correlations in cyber-security and wireless communication domains. Therefore, RC models are good choices to explore the spatio-temporal correlations. In this chapter, we explore the applications and performance of delayed feedback reservoirs (DFRs), and echo state networks (ESNs) in the cyber-security of smart grids and symbol detection in MIMO-OFDM systems, respectively. DFRs and ESNs are two different types of RC models. We also introduce the spiking structure of DFRs as spiking artificial neural networks are more energy efficient and biologically plausible as well

Empirical Rates Characterization of Wearable Multi-Antenna Terminals for First-Responders

Empirical characterization of the achievable rates for a wearable multi-antenna terminal shows the potential advantages of deploying a large number of antennas at the user end. We focus on the challenges and requirements of the broadband communication in future emergency communication systems, specifically addressing the outdoor-to-indoor propagation scenario, where the first responder is within an underground area such as a garage or basement. The measurement campaign undertaken characterizes the flat fading multiple-input multiple-output (MIMO) channel matrices at 3.5 GHz for a maximum of M = 30 antennas deployed at the base station (BS), and N = 12 wearable antennas at the user. The achievable rates are obtained for two transmission strategies that account for the different levels of channel knowledge. In both cases, all the MIMO processing is carried out at the BS.This work was supported in part by the Spanish Government under Project MIMOTEX (TEC2014-61776-EXP), Project CIES (RTC-2015-4213-7), and Project TERESA-ADA (TEC2017-90093-C3-2-R) (MINECO/AEI/FEDER, UE), and in part by the Chilean Government through projects CONICYT under Grant Proyecto Basal FB0821, Grant Fondecyt Iniciación 11171159, and Grant VRIEA-PUCV 039.462/2017.Publicad