Patient-Centric HetNets Powered by Machine Learning and Big Data Analytics for 6G Networks

Having a cognitive and self-optimizing network that proactively adapts not only to channel conditions, but also according to its users' needs can be one of the highest forthcoming priorities of future 6G Heterogeneous Networks (HetNets). In this paper, we introduce an interdisciplinary approach linking the concepts of e-healthcare, priority, big data analytics (BDA) and radio resource optimization in a multi-tier 5G network. We employ three machine learning (ML) algorithms, namely, naïve Bayesian (NB) classifier, logistic regression (LR), and decision tree (DT), working as an ensemble system to analyze historical medical records of stroke out-patients (OPs) and readings from body-attached internet-of-things (IoT) sensors to predict the likelihood of an imminent stroke. We convert the stroke likelihood into a risk factor functioning as a priority in a mixed integer linear programming (MILP) optimization model. Hence, the task is to optimally allocate physical resource blocks (PRBs) to HetNet users while prioritizing OPs by granting them high gain PRBs according to the severity of their medical state. Thus, empowering the OPs to send their critical data to their healthcare provider with minimized delay. To that end, two optimization approaches are proposed, a weighted sum rate maximization (WSRMax) approach and a proportional fairness (PF) approach. The proposed approaches increased the OPs' average signal to interference plus noise (SINR) by 57% and 95%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, nevertheless, the PF approach yielded higher SINRs for the OPs, better fairness and a lower margin of error

Maximum Achievable Sum Rate in Highly Dynamic Licensed Shared Access

In this paper, we propose a novel power allocation scheme that maximizes the sum spectral efficiency of the licensee in a dynamic Licensed Shared Access (LSA) system. In particular, our focus is on the time intervals in which the incumbent system is active in the spectrum. We derive an expression for the interference distribution of the licensee, e.g., a mobile network operator, utilizing a spectrum belonging to an airport incumbent under the LSA spectrum sharing. Formulating an optimization problem to maximize the sum spectrum efficiency subject to the interference threshold constraint at the licensee, we then show its convexity, and obtain its optimal solutions. We further investigate the impact of sum rate maximization on the fairness of network resource allocations. Simulation results show a significant gain in the achievable spectrum efficiency, especially during the intervals in which the incumbent system is active in the LSA band. This paper provides quantitative insights on the maximum achievable sum rate in an LSA system in which both the licensee and the incumbent systems are active at the same time

Patient-Centric Cellular Networks Optimization using Big Data Analytics

Big data analytics is one of the state-of-the-art tools to optimize networks and transform them from merely being a blind tube that conveys data, into a cognitive, conscious, and self-optimizing entity that can intelligently adapt according to the needs of its users. This, in fact, can be regarded as one of the highest forthcoming priorities of future networks. In this paper, we propose a system for Out-Patient (OP) centric Long Term Evolution-Advanced (LTE-A) network optimization. Big data harvested from the OPs' medical records, along with current readings from their body sensors are processed and analyzed to predict the likelihood of a life-threatening medical condition, for instance, an imminent stroke. This prediction is used to ensure that the OP is assigned an optimal LTE-A Physical Resource Blocks (PRBs) to transmit their critical data to their healthcare provider with minimal delay. To the best of our knowledge, this is the first time big data analytics are utilized to optimize a cellular network in an OP-conscious manner. The PRBs assignment is optimized using Mixed Integer Linear Programming (MILP) and a real-time heuristic. Two approaches are proposed, the Weighted Sum Rate Maximization (WSRMax) approach and the Proportional Fairness (PF) approach. The approaches increased the OPs' average SINR by 26.6% and 40.5%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, however, the PF approach reported higher SINRs for the OPs, better fairness and a lower margin of error

On the efficiency of dynamic licensed shared access for 5G/6G wireless communications

The licensed shared access (LSA) is a spectrum licensing scheme authorizing additional new users (the licensees) to dynamically share the same spectrum with the old users (the incumbents). Contained in the terms of the spectrum usage authorization is a set of strict protective measures for the incumbent system which introduce extra restrictions on the licensee operations. Such measures imply that the licensee’s access to the spectrum can be revoked or restricted at any time which may result in the degradation of critical performance metrics of the latter. Addressing this issue and the accompanying challenges as we enter the 5G zettabytes era motivates the research problems addressed in this thesis. A vertical LSA spectrum sharing involving a mobile network operator (MNO) as the licensee and two categories of incumbent including the aeronautical telemetry, and a group of terrestrial public and ancillary wireless services is adopted in this thesis. Firstly, an analytical examination of the uplink and downlink licensee’s transmit power, when its spectrum access right is revoked (i.e., the limited transmit power) is done. Then a power allocation scheme that maximizes the energy efficiency (EE) of the licensee when it is operating with limited transmit power is proposed. Simulation results reveal the impact of the LSA spectrum access revocation on the allowable transmit power of the licensee as a function of the effect of different interference propagation path and the transmission direction. A comparison of the proposed optimal power allocation method with the equal power allocation (EPA) method further shows considerable improvement in the achievable EE of the licensee. Furthermore, in the LSA, the achievable spectrum efficiency (SE) of the licensee is limited by the interference threshold constraint set by the incumbent’s protective measures. Consequent on this, we propose an SE maximization of the licensee’s system subject to the incumbent interference threshold constraint. Furthermore, the LSA band spectral utilization was characterised as a function of the licensee’s achievable SE and the statistics of the LSA spectrum availability. The obtained results provide quantitative insights for practical system design and deployment of the LSA system, especially when compared to the results obtained in the maximization of the EE. In particular, the effect of variations in critical operational parameters throws up interesting network design trade-off challenge, worthy of consideration. This informs the subsequent multi objective optimization of the EE-SE trade-off investigated next. Interestingly, the obtained results indicate that with careful selection of the licensee eNodeB coverage radius, transmit power, and number of user equipment per eNodeB coverage area, one can engineer the best possible trade-off between the spectrum and energy efficiency in practical LSA deployment. A major LSA feature is guaranteeing predictable quality of service (QoS) for both the incumbent and the licensee systems. In terrestrial implementation, the reduction in the achievable data rate caused by the incumbents’ protective measures, may violate guaranteed QoS in the licensee system. To address this issue, we propose a LSA - based hybrid aerialterrestrial system with drone base station (D-BS). Simulation results show that using the proposed scheme, the licensee, when operating under the incumbents’ imposed restrictions, is able to achieve the QoS data rate requirements of the users on its network. In conclusion, the findings in this research indicates that the dynamic LSA is a practically viable solution to the spectrum management requirements of the emerging vertical wireless technologies in 5G and beyond

Green Licensed-Shared Access

In this paper, we present a novel licensed-shared access (LSA) spectrum-sharing scheme as an energy efficient way to increase the spectral utility of a network. We consider a twotier network where a small cell network offers offloading services to a macro network to improve its quality of service. In return, the small cells are rewarded for their cooperation with a number of licenses to operate in the spectrum owned by the macro network. The short link distances and diversity offered by the small cell network to offload the macro user help enhance the total spectral and energy utilization of the network. For effective functioning of the scheme, it is important to determine the division between the fraction of small cells that provide offloading services and those which obtain the licenses. In this paper, we present a comprehensive mathematical model of this LSA-based offloading and evaluate the optimal division between the offloading and licensing small cells. Our analysis shows that such spectrum sharing can lead to commendable gains in the spectral utilization of the network while assuring the desired quality of service for both macro and small network. An intelligent determination of the fraction of offloading small cell network results in efficient energy use for offloading. We test our scheme within the network parameters of the city of Leeds, U.K. Our results indicate that the spectral utility of Leeds can be improved by more than 12×, and the energy efficiency by 16× through our proposed scheme