A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

5G definition and standardization projects are well underway, and governing characteristics and major challenges have been identified. A critical network element impacting the potential performance of 5G networks is the backhaul, which is expected to expand in length and breadth to cater to the exponential growth of small cells while offering high throughput in the order of gigabit per second and less than 1 ms latency with high resilience and energy efficiency. Such performance may only be possible with direct optical fiber connections that are often not available country-wide and are cumbersome and expensive to deploy. On the other hand, a prime 5G characteristic is diversity, which describes the radio access network, the backhaul, and also the types of user applications and devices. Thus, we propose a novel, distributed, self-optimized, end-to-end user-cell-backhaul association scheme that intelligently associates users with candidate cells based on corresponding dynamic radio and backhaul conditions while abiding by users' requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator (DPI) of the end-to-end network performance such as capacity, latency, resilience, energy consumption, and so on. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul performance that conforms to the user requirements. Reinforcement learning is used at the radio cells to optimise the bias factors for each DPI in a way that maximise the system throughput while minimising the gap between the users' achievable and required end-to-end quality of experience (QoE). Preliminary results show considerable improvement in users' QoE and cumulative system throughput when compared with the state-of-the-art user-cell association schemes

Quantum three-body system in D dimensions

The independent eigenstates of the total orbital angular momentum operators for a three-body system in an arbitrary D-dimensional space are presented by the method of group theory. The Schr\"{o}dinger equation is reduced to the generalized radial equations satisfied by the generalized radial functions with a given total orbital angular momentum denoted by a Young diagram $[\mu,\nu,0,...,0]$ for the SO(D) group. Only three internal variables are involved in the functions and equations. The number of both the functions and the equations for the given angular momentum is finite and equal to $(\mu-\nu+1)$.Comment: 16 pages, no figure, RevTex, Accepted by J. Math. Phy

Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks

A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of things (IoT) networks. Different from the state-of-the-art approaches, the proposed approach simultaneously selects the best connectivity and processing unit (e.g., device, fog, and cloud) along with the percentage of data to be offloaded by jointly optimizing energy consumption, response-time, security, and monetary cost. The proposed scheme employs a reinforcement learning algorithm, and manages to achieve significant gains compared to deterministic solutions. In particular, the requirements of IoT devices in terms of response-time and security are taken as inputs along with the remaining battery level of the devices, and the developed algorithm returns an optimized policy. The results obtained show that only our method is able to meet the holistic multi-objective optimization criteria, albeit, the benchmark approaches may achieve better results on a particular metric at the cost of failing to reach the other targets. Thus, the proposed approach is a device-centric and context-aware solution that accounts for the monetary and battery constraints

Load Aware Self-Organising User-Centric Dynamic CoMP Clustering for 5G Networks

Coordinated multi-point (CoMP) is a key feature for mitigating inter-cell interference, improve system throughput and cell edge performance. However, CoMP implementation requires complex beamforming/scheduling design, increased backhaul bandwidth, additional pilot overhead and precise synchronisa-tion. Cooperation needs to be limited to a few cells only due to this imposed overhead and complexity. Hence, small CoMP clusters will need to be formed in the network. In this paper, we first present a self organising, user-centric CoMP clustering algorithm in a control/data plane separation architecture (CDSA), proposed for 5G to maximise spectral efficiency (SE) for a given maximum cluster size. We further utilise this clustering algorithm and introduce a novel two-stage re-clustering algorithm to reduce high load on cells in hotspot areas and improve user satisfaction. Stage-1 of the algorithm utilises maximum cluster size metric to introduce additional capacity in the system. A novel re-clustering algorithm is introduced in stage-2 to distribute load from highly loaded cells to neighbouring cells with less load for multi-user (MU) joint transmission (JT) CoMP case. We show that unsatisfied users due to high load can be significantly reduced with minimal impact on SE

5G Backhaul Challenges and Emerging Research Directions: A Survey

5G is the next cellular generation and is expected to quench the growing thirst for taxing data rates and to enable the Internet of Things. Focused research and standardization work have been addressing the corresponding challenges from the radio perspective while employing advanced features, such as network densi cation, massive multiple-input-multiple-output antennae, coordinated multi-point processing, intercell interference mitigation techniques, carrier aggregation, and new spectrum exploration. Nevertheless, a new bottleneck has emerged: the backhaul. The ultra-dense and heavy traf c cells should be connected to the core network through the backhaul, often with extreme requirements in terms of capacity, latency, availability, energy, and cost ef ciency. This pioneering survey explains the 5G backhaul paradigm, presents a critical analysis of legacy, cutting-edge solutions, and new trends in backhauling, and proposes a novel consolidated 5G backhaul framework. A new joint radio access and backhaul perspective is proposed for the evaluation of backhaul technologies which reinforces the belief that no single solution can solve the holistic 5G backhaul problem. This paper also reveals hidden advantages and shortcomings of backhaul solutions, which are not evident when backhaul technologies are inspected as an independent part of the 5G network. This survey is key in identifying essential catalysts that are believed to jointly pave the way to solving the beyond-2020 backhauling challenge. Lessons learned, unsolved challenges, and a new consolidated 5G backhaul vision are thus presented

Case study on using the user-centric-backhaul scheme to unlock the realistic backhaul

The fifth generation of mobile networks (5G) is maturing fast and the target year 2020 is around the corner. However, the realistic backhaul network may not be ready for 5G arrival as it is likely to converge to 5G requirements at a slower pace than the radio counterpart. In this work, we develop a method that identifies pertinent backhaul upgrade stages that are ranked based on their associated cost. First, the User-centric- backhaul (UCB) scheme is employed to reveal the bottlenecks of the incumbent backhaul network, as perceived by users and holistic network. A multi- hop hybrid backhaul modelling framework is then employed to quantify possible rectifications that would deliver the highest improvement at the lowest cost. These are implemented and the results are verified following another usage of UCB. A case study is presented that demonstrates the strength of this method in enabling an effective and cost efficient evolution road map towards the 5G backhaul

Towards Continuous Subject Identification Using Wearable Devices and Deep CNNs

© 2020 IEEE. Subject identification has several applications. In transportation companies, knowing who is driving their vehicles might prevent theft or other ill-intended actions. On the other hand, privacy concerns, and the respective legislation, hinder the applicability of several traditional recognition techniques based on invasive technologies, such as video cameras. Moreover, in order to keep the driver's distractions to a minimum, this technologies must be non-disruptive, that is, they must be able to identify the subject seamlessly without them taking any action. In this context, we propose using deep learning applied to smart watch data for recognizing the person driving a vehicle based on a training set. Our proposal relies on the possibility of using transfer learning to avoid long training sessions for new drivers and to deliver a solution which can be deployed in practice. In this paper, we describe the convolutional neural network used in the solution and present results according to a real data-set collected by us, achieving accuracies ranging from 75 to 94%

Backhaul Aware User-Specific Cell Association Using Q-Learning

With the advent of network densification and the development of other radio interface technologies, the major bottleneck of future cellular networks is shifting from the radio access network to the backhaul. The future networks are expected to handle a wide range of applications and users with different requirements. In order to tackle the problem of downlink user-cell association, and allocate users to the best cell, an intelligent solution based on reinforcement learning is proposed. A distributed solution based on Q-Learning is developed in order to determine the best cell range extension offsets (CREOs) for each small cell (SC) and the best weights of each user requirement to efficiently allocate users to the most appropriate SC, based on both backhaul constraints and user demands. By optimizing both CREOs and user weights, a user-specific allocation can be achieved, resulting in a better overall quality of service. The results show that the proposed algorithm outperforms current solutions by achieving better user satisfaction, mitigating the total number of users in outage, and minimizing user dissatisfaction when satisfaction is not possible

Energy-efficient full-range oscillation analysis of parallel-plate electrostatically actuated MEMS resonators

This is the peer reviewed version of the following article: “Fargas Marques, A., Costa Castelló, R. (2017) Energy-efficient full-range oscillation analysis of parallel-plate electrostatically actuated MEMS resonators, 1-13.” which has been published in final form at [doi: 10.1007/s11071-017-3633-8]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."Electrostatic parallel-plate actuators are a common way of actuating microelectromechanical systems, both statically and dynamically. Nevertheless, actuation voltages and oscillations are limited by the nonlinearity of the actuator that leads to the pull-in phenomena. This work presents a new approach to obtain the electrostatic parallel-plate actuation voltage, which allows to freely select the desired frequency and amplitude of oscillation. Harmonic Balance analysis is used to determine the needed actuation voltage and to choose the most energy-efficient actuation frequency. Moreover, a new two-sided actuation approach is presented that allows to actuate the device in all the stable range using the Harmonic Balance Voltage.Peer ReviewedPostprint (author's final draft