RRC State Handling for 5G

Hailu, Sofonias; Säily, Mikko; Tirkkonen, Olav

doi:10.1109/mcom.2018.1700957

Cited by 20 publications

(6 citation statements)

References 39 publications

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“…The process of HO in mm-wave communications requires that the UEs makes intra-frequency or inter-frequency measurements depending on its carrier frequency and that of the BS. These measurements increases as the number of mmwave BSs increases, thereby resulting in increased power consumption for the UEs [190]. This power consumption can be minimized by reducing the measurement gaps and ensuring that the UEs take measurements within the DRX cycle.…”

Section: H Device Power Consumptionmentioning

confidence: 99%

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

et al. 2021

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Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The stateof-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed. INDEX TERMS Handover, machine learning, mobility management, fifth generation.

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Section: H Device Power Consumptionmentioning

confidence: 99%

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

et al. 2021

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“…In order to meet the requirements of mMTC service for low power consumption and large connection, a new RRC state (RRC connected inactive) has been proposed in 5G systems. The high-level characteristics of RRC Inactive are similar to LTE's Light Connected [9]. This new state enables the UE to maintain a connection to the core network at all times and save the UE context so that it can quickly return to RRC Connected state.…”

Section: Overview Of Rrc Protocolmentioning

confidence: 99%

A Signaling Monitor Scheme of RRC Protocol in 5G Road Tester

Zhang

Cheng

Deng

2021

Communications and Networking

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Faced with the growth of massive data in the 5G network and the low query efficiency in the existing signaling synthesis algorithm, the traditional LTE/LTE-A signaling monitor scheme has been unable to satisfy with the new 5G network architecture. The Radio Resource Control (RRC) protocol is the core of the control plane, which manages and controls the wireless resources of the network. Based on this, a signaling monitor scheme of RRC protocol suitable for 5G road tester was proposed and the specific functions of its main submodule were introduced in detail. Additionally, this paper discussed an improved dynamic hash signaling synthesis algorithm based on AVL tree under a new network architecture. The tree structure is used to reduce the query time of traditional algorithm in the hash table, so as to quickly deal with hash collisions and improve the real time on the signaling synthesis. At present, the proposed scheme was applied to the real network testing of air interface data in the 5G road tester. The experimental results show that the improved algorithm can efficiently solve the issue of low efficiency of Call Detail Recording (CDR) synthesis and the average query time can be reduced by 49.3% and 33.1% compared with the two traditional algorithms. The signaling synthesis scheme described above achieves the expected effect and signaling messages in RRC protocol can be accurately decoded and monitored in real time.

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“…The state machine design for the 5G radio access network (RAN) contains numerous divergent and partially contradictory 5G use cases. For this reason, the mobility framework consisting of the state machine design is one of the important issues of the control plane [22]. The RRC layer processes the control plane signals between the UE and RAN on the radio interface and controls the UE's transition mobility to the cells.…”

Section: Rrc Statesmentioning

confidence: 99%

“…In performance analysis for small packet transmission in [22], it has been observed that the RRC inactive state has a superiority over 71%, 88%, and 79%, respectively, in terms of overhead, delay, and power consumption signals compared to LTE Idle state. In the study on 5G networks containing machine-type communication in [26], it appears that adopt- ing an optimal configuration of the RC Inactive state saves more than 200% latency reduction compared to the RRC Idle state and approximately 40% UE power savings compared to RRC Connected.…”

Section: Rrc Inactive Statementioning

confidence: 99%

A Comprehensive Survey on Mobility Management in 5G Heterogeneous Networks: Architectures, Challenges and Solutions

et al. 2020

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With the rapid increase in the number of mobile users, wireless access technologies are evolving to provide mobile users with high data rates and support new applications that include both human and machine-type communications. Heterogeneous networks (HetNets), created by the joint installation of macro cells and a large number of densely deployed small cells, are considered an important solution to deal with the increasing network capacity demands and provide high coverage to wireless users in future fifth generation (5G) wireless networks. Due to the increasing complexity of network topology in 5G HetNets with the integration of many different base station types, in 5G architecture mobility management has many challenges. Intense deployment of small cells, along with many advantages it provides, brings important mobility management problems such as frequent handover (HO), HO failure, HO delays, ping-pong HO and high energy consumption which will result in lower user experience and heavy signal loads. In this paper, we provide a comprehensive study on the mobility management in 5G HetNet in terms of radio resource control, the initial access and registration procedure of the user equipment (UE) to the network, the paging procedure that provides the location of the UE within the network, connected mode mobility management schemes and beam level mobility and beam management. Besides, this paper addresses the challenges and suggest possible solutions for the 5G mobility management.

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RRC State Handling for 5G

Cited by 20 publications

References 39 publications

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

A Signaling Monitor Scheme of RRC Protocol in 5G Road Tester

A Comprehensive Survey on Mobility Management in 5G Heterogeneous Networks: Architectures, Challenges and Solutions

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