LTE/Wi-Fi Coexistence in Unlicensed Bands Based on Dynamic Transmission Opportunity

With the rapid proliferation of smart devices, the demand for more licensed spectrum bands arises. Due to the scarcity of the licensed spectrum, the 3rd Generation Partnership (3GPP) has recently deployed Long Term Evolution (LTE) networks using the Licensed Assisted Access (LAA) scheme over unlicensed bands. On the other hand, the Wi-Fi technology is the main technology that operates over these unlicensed bands. Thus, the major concern is to attain a fair coexistence mechanism between these coexisting technologies (i.e., LTE and Wi- Fi). In this paper, we focus on the downlink scenario under different traffic loads to study the effect of the maximum Transmission Opportunity (TxOP) period for LTE-LAA in the performance of LTE-LAA/Wi-Fi coexistence. A dynamic TxOP period method is proposed to provide better fairness and higher total aggregated throughputs for the coexisting networks based on the Hybrid Automatic Repeat Request (HARQ) reports. The novelty of this work is that the existing HARQ reports are exploited to update the TxOP period for LAA in a dynamic manner. We show that the TxOP period plays a key role in the coexistence between LTE-LAA and Wi-Fi networks over unlicensed bands. The simulation results show that the proposed dynamic TxOP method improves the fairness and achieves higher total aggregated throughputs for both coexisting networks as compared to the static TxOP period used by the standard Category 4 LBT (Cat 4 LBT) method defined by 3GPP

Deep Learning-based Fingerprinting for Outdoor UE Positioning Utilising Spatially Correlated RSSs of 5G Networks

Outdoor user equipment (UE) localisation has attracted a significant amount of attention due to its importance in many location-based services. Typically, in rural and open areas, global navigation satellite systems (GNSS) can provide an accurate and reliable localisation performance. However, in urban areas GNSS localisation accuracy is significantly reduced due to shadowing, scattering and signal blockages. In this work, the UE positioning assisted by deep learning in 5G and beyond networks is investigated in an urban area environment. We study the impact of utilising the spatial correlation in the received signal strengths (RSSs) on the UE positioning accuracy and how to utilise such correlation with deep learning algorithms to improve the localisation accuracy. Numerical results showed the importance of utilising the spatial correlation in the RSS to improve the prediction accuracy for all of the considered models. In addition, the impact of varying the number of access points (APs) transmitters on the localisation accuracy is also investigated. Numerical results showed that a lower number of APs may be sufficient when not considering uncertainties in RSS measurements. Moreover, we study how much the degrading effect of RSS uncertainty can be compensated for by increasing the number of APs.Peer reviewe