User Equipment Velocity Research Articles

Machine-learning-aided method for optimizing beam selection and update period in 5G networks and beyond

Finding the optimal beam pair and update time in 5G systems operating at mmWave frequencies is time-intensive and resource-demanding. This intricate procedure calls for the proposal of more intelligent approaches. Therefore, this work proposes a machine learning-based method for optimizing beam pair selection and its update time. The method is structured around three main modules: spatial characterization of beam pair service areas, training of a machine learning model using collected beam pair data, and an algorithm that uses the decision function of the trained model to compute the optimal update time for beam pairs based on the spatial position and velocity of user equipment. When the machine learning model is deployed in a network with a single gNB equipped with a 8×8\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$8\ imes 8$$\\end{document} UPA and one UE equipped with a 1×2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1\ imes 2$$\\end{document} UPA in an mmWave scenario simulated in NS3, improvements in SINR and throughput up to 407%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$407\\%$$\\end{document}, were observed. Improvements are gathered because of a reduction of 85.7%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$85.7\\%$$\\end{document} in beam pair selections because of an increase of approximately 1543%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1543\\%$$\\end{document} in the effective time between successive beam pair searches. This method could offer real-time optimization of the beam pair procedures in 5G networks and beyond.

Robust Handover Optimization Technique with Fuzzy Logic Controller for Beyond 5G Mobile Networks.

Mobility management is an essential process in mobile networks to ensure a high quality of service (QoS) for mobile user equipment (UE) during their movements. In fifth generation (5G) and beyond (B5G) mobile networks, mobility management becomes more critical due to several key factors, such as the use of Millimeter Wave (mmWave) and Terahertz, a higher number of deployed small cells, massive growth of connected devices, the requirements of a higher data rate, and the necessities for ultra-low latency with high reliability. Therefore, providing robust mobility techniques that enable seamless connections through the UE’s mobility has become critical and challenging. One of the crucial handover (HO) techniques is known as mobility robustness optimization (MRO), which mainly aims to adjust HO control parameters (HCPs) (time-to-trigger (TTT) and handover margin (HOM)). Although this function has been introduced in 4G and developed further in 5G, it must be more efficient with future mobile networks due to several key challenges, as previously illustrated. This paper proposes a Robust Handover Optimization Technique with a Fuzzy Logic Controller (RHOT-FLC). The proposed technique aims to automatically configure HCPs by exploiting the information on Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and UE velocity as input parameters for the proposed technique. The technique is validated through various mobility scenarios in B5G networks. Additionally, it is evaluated using a number of major HO performance metrics, such as HO probability (HOP), HO failure (HOF), HO ping-pong (HOPP), HO latency (HOL), and HO interruption time (HIT). The obtained results have also been compared with other competitive algorithms from the literature. The results show that RHOT-FLC has achieved considerably better performance than other techniques. Furthermore, the RHOT-FLC technique obtains up to 95% HOP reduction, 95.8% in HOF, 97% in HOPP, 94.7% in HOL, and 95% in HIT compared to the competitive algorithms. Overall, RHOT-FLC obtained a substantial improvement of up to 95.5% using the considered HO performance metrics.

Adaptive Pilot Interval Optimization for Intelligent Reflecting Surface-Aided Communication Systems

Channel estimation plays a crucial role in intelligent reflecting surface (IRS)-aided communication systems, in which the channel state information must be acquired to configure the optimal IRS reflection coefficient. However, using large numbers of IRS elements induce large channel estimation overheads, which cause crucial problems in systems utilizing IRSs. In this study, we investigate the optimal pilot interval design for IRS-aided communication systems to reduce the channel estimation overheads. The channel coherent time depends on user equipment (UE) velocity. Therefore, the proposed method aims to find a pilot interval that balances accuracy of the IRS reflection coefficient with system throughput by considering UE velocity. In the proposed method, we identify the relationship between the mobility of the UE and complex spatial correlation of the channel matrix, and the transmission capacity in a time slot is formulated to maximize the achievable sum rate. Numerical results from simulations show that the proposed method adaptively adjusts the pilot interval under various communication environments.

Addressing the Curse of Mobility in Massive MIMO With Prony-Based Angular-Delay Domain Channel Predictions

Massive MIMO is widely touted as an enabling technology for 5th generation (5G) mobile communications and beyond. On paper, the large excess of base station (BS) antennas promises unprecedented spectral efficiency gains. Unfortunately, during the initial phase of industrial testing, a practical challenge arose which threatens to undermine the actual deployment of massive MIMO: user mobility-induced channel Doppler. In fact, testing teams reported that in moderate-mobility scenarios, e.g., 30 km/h of user equipment (UE) speed, the performance drops up to 50% compared to the low-mobility scenario, a problem rooted in the acute sensitivity of massive MIMO to this channel Doppler, and not foreseen by many theoretical papers on the subject. In order to deal with this “curse of mobility”, we propose a novel form of channel prediction method, named Prony-based angular-delay domain (PAD) prediction, which is built on exploiting the specific angle-delay-Doppler structure of the multipath. In particular, our method relies on the high angular-delay resolution which arises in the context of 5G. Our theoretical analysis shows that when the number of base station antennas and the bandwidth are large, the prediction error of our PAD algorithm converges to zero for any UE velocity level, provided that only two accurate enough previous channel samples are available. Moreover, when the channel samples are inaccurate, we propose to combine the PAD algorithm with a denoising method for channel estimation phase based on the subspace structure and the long-term statistics of the channel observations. Simulation results show that under a realistic channel model of 3GPP in rich scattering environment, our proposed method is able to overcome this challenge and even approaches the performance of stationary scenarios where the channels do not vary at all.

Towards Studying the Two-Tier Intra-Frequency X2 Handover Based on Software-Defined Open LTE Platform

The next generation cellular systems are rapidly evolving from a homogeneous macrocell deployment to a heterogeneous deployment of macrocells overlaid with femtocells, which is referred to as heterogeneous networks (HetNets). In macro-femto HetNets, the handover issue is more important than that in macrocell networks. On one hand, more frequent handovers are triggered because the coverage range of the femtocell is small, and the multiple femtocells are overlaid with macrocells. On the other hand, some schemes, such as load balancing, aimed at improving network performance, will also cause frequent handover in macro-femto HetNets. Therefore, the study on handover is of great importance, especially in macro-femto HetNets. In this paper, based on the software-defined open long-term evolution (LTE) platform, we propose an analytical model to study the two-tier intra-frequency X2 handover in realistic macrocell-femtocell HetNets scenario. Specifically, we first construct the software-defined open LTE platform. Based on the open LTE platform, we then characterize the relation between the handover failure and the ping-pong rates in a macro-femto HetNets scenario as a function of the relevant system parameters such as the time-to-trigger, user equipment velocity, etc. Finally, we derive the handover failure and ping-pong rates in closed-form expressions. The handover experiment results have verified the accuracy of our analytical derivations, which shed new light on key aspects of the handover process in macro-femto HetNets.

Performance Analysis of Handover Strategy in Femtocell Network

Femtocells, as known as HeNB is the tremendous network technology in the Long Term Evolution (LTE) network in order to fulfill the upcoming demand of high data rates. However, Femtocells deployment may cause the incidence of frequent and unnecessary handover due to the movement of the user. As Femtocells coverage area is very small and deployed randomly, there are many possible targets Femtocells for handover. This paper analyzed on the performance analysis of handover strategy in Femtocells network under Hybrid Access Mode to minimize the unnecessary handover. The handover strategy for three different threshold stay time with considering the velocity of user equipment (UE) in the mobility are analyzed. The simulation results showed that the proposed algorithm minimized the number of handover and decreased the unnecessary handover probability. Simulation results indicate that the proposed algorithm has a better performance as compare with the traditional strategy

A Cost-Based Adaptive Handover Hysteresis Scheme to Minimize the Handover Failure Rate in 3GPP LTE System

We deal with a cost-based adaptive handover hysteresis scheme for the horizontal handover decision strategies, as one of the self-optimization techniques that can minimize the handover failure rate (HFR) in the 3rd generation partnership project (3GPP) long-term evolution (LTE) system based on the network-controlled hard handover. Especially, for real-time operation, we propose an adaptive hysteresis scheme with a simplified cost function considering some dominant factors closely related to HFR performance such as the load difference between the target and serving cells, the velocity of user equipment (UE), and the service type. With the proposed scheme, a proper hysteresis value based on the dominant factors is easily obtained, so that the handover parameter optimization for minimizing the HFR can be effectively achieved. Simulation results show that the proposed scheme can support better HFR performance than the conventional schemes.