Intelligent handover decision scheme using double deep reinforcement learning

Mollel, Michael S.; Abubakar, Attai Ibrahim; Öztürk, Metin; Kaijage, Shubi; Kisangiri, Michael; Zoha, Ahmed; Imran, Muhammad Ali; Abbasi, Qammer H.

doi:10.1016/j.phycom.2020.101133

Cited by 36 publications

(20 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One example would be the techniques used for regulating mobility control parameters, known as handover self-optimization functions [50,51,[53][54][55][56][57][58]. Another example is the mechanisms used for making handover decisions, known as handover decision algorithms [59][60][61][62][63]. Protocols are also applied for rerouting data or voice calls to the new routing path, known as mobile routing protocols [64,65].…”

Section: Figure2 Ultra-dense and Overlapping Hetnets With Multi-typmentioning

confidence: 99%

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

et al. 2020

View full text Add to dashboard Cite

Ensuring a seamless connection during the mobility of various User Equipment (UE) will be one of the major challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. Several key determinants will significantly contribute to numerous mobility challenges. One of the most important determinants is the use of millimeter waves (mm-waves) as it is characterized by high path loss. The inclusion of various types of small coverage Base Stations (BSs), such as Picocell, Femtocell and drone-based BSs is another challenge. Other issues include the use of Dual Connectivity (DC), Carrier Aggregation (CA), the massive growth of mobiles connections, network diversity, the emergence of connected drones (as BS or UE), ultra-dense network, inefficient optimization processes, central optimization operation, partial optimization, complex relation in optimization operations, and the use of inefficient handover decision algorithms. The relationship between these processes and diverse wireless technologies can cause growing concerns in relation to handover associated with mobility. The risk becomes critical with high mobility speed scenarios. Therefore, mobility issues and their determinants must be efficiently addressed. This paper aims to provide an overview of mobility management in 5G networks. The work examines key factors that will significantly contribute to the increase of mobility issues. Furthermore, the innovative, advanced, efficient, and smart handover techniques that have been introduced in 5G networks are discussed. The study also highlights the main challenges facing UEs' mobility as well as future research directions on mobility management in 5G networks and beyond.

show abstract

Section: Figure2 Ultra-dense and Overlapping Hetnets With Multi-typmentioning

confidence: 99%

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Although the beam switching for a phased-array antenna can be carried out almost instantaneous (50 ns) [68], increase in training overhead caused by beam switching results in the beamforming delay [69]. In order to make data-driven decisions by analysing information about vehicle movement and surrounding environments as well as channel quality, ML is applied to find best beams [50], [51], to manage beam switching [52]- [54], and to alleviate the training overheads [55]- [59]. Moreover, ML is adopted to effectively coordinate BS and intelligent reflecting surface (IRS) [60] and to manage increase of computational complexity of UE-BS association in a scenario of multiple moving UE and multiple BSs [61].…”

Section: Ml-based Mobility Management In Mmwave Networkmentioning

confidence: 99%

Beam-Based Mobility Management in 5G Millimetre Wave V2X Communications: A Survey and Outlook

Kose¹,

Lee

Foh

et al. 2021

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

It is envisaged that 5G can enable many vehicular use cases that require high capacity, ultra-low latency and high reliability. To support this, 5G proposes the use of dense small cells technology as well as and highly directional mmWave systems deployment, among many other new advanced communication technologies, to boost the network capacity, reduce latency and provide high reliability. In such systems, enabling vehicular communication, where the nodes are highly mobile, requires robust mobility management techniques to minimise signalling cost and interruptions during frequent handovers. This presents a major challenge that communication system engineers need to address to realise the promise of 5G systems for V2X and similar applications. In this paper, we provide an overview of recent progresses in the development of handover and beam management techniques in 5G communication systems. We conduct a critical appraisal of current research on beam level and cell level mobility management in 5G mmWave networks considering the ultra-reliable and low-latency communication requirements within the context of V2X applications. We also provide an insight into the open challenges and the emerging trends as well as the possible evolution beyond the horizon of 5G.

show abstract

“…A user association strategy in an mmWave ultra-dense network was proposed to select the optimal base station that maximizes the user-BS association duration [15]. Offline double deep reinforcement learning was utilized for the handover decision by mapping the SNR values to the UE trajectory.…”

Section: B User Association For Reducing Handovermentioning

confidence: 99%

Fuzzy Logic and Accelerated Reinforcement Learning-Based User Association for Dense C-RANs

Rodoshi

Kim

Choi³

2021

IEEE Access

View full text Add to dashboard Cite

Cloud radio access network (C-RAN) is a potential mobile network architecture providing seamless connectivity to users with high data rates by integrating it with the small-cell technology of 5G mobile communication systems. In C-RAN, base station functionality is divided into a baseband unit (BBU) and remote radio head (RRH); the BBUs from multiple sites are centralized and virtualized using cloud computing and virtualization techniques. Frequent handovers occur in the network, which results in control message flooding and repeated service outages because of the dense deployment of short-range RRHs and user mobility. It is necessary to optimize handover control parameters before the handover and re-associate the user with an RRH to minimize unnecessary handovers in the network. Traditional handover schemes rely on signal strengths of RRHs, which cause a large number of unnecessary handovers when a user moves within the coverage of multiple-overlapped RRHs. This study investigates the handover in C-RAN by carefully optimizing the handover control parameter and selecting the target RRH for handover. Our main goal is associating the user with an RRH such that association after the handover remains possible for as long as possible while maintaining the quality of service (QoS) requirements of the users. We have used fuzzy logic to optimize the handover control parameter and a reinforcement learning-based algorithm to select the target RRH. A key component of the proposed RL-based scheme is using an acceleration technique for the faster convergence of the algorithm. Numerical results show that the proposed scheme can significantly reduce the number of handovers while ensuring QoS requirements.

show abstract

Intelligent handover decision scheme using double deep reinforcement learning

Cited by 36 publications

References 35 publications

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

Beam-Based Mobility Management in 5G Millimetre Wave V2X Communications: A Survey and Outlook

Fuzzy Logic and Accelerated Reinforcement Learning-Based User Association for Dense C-RANs

Contact Info

Product

Resources

About