Towards Zero Data Interruption Time with Enhanced Synchronous Handover

Gimenez, Lucas Chavarria; Michaelsen, Per Henrik; Pedersen, Klaus I.; Kolding, Troels; Nguyen, Huan Cong

doi:10.1109/vtcspring.2017.8108504

Cited by 16 publications

(8 citation statements)

References 7 publications

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“…10, we can see that the handover policy learned with images achieves a higher or equal data rate as compared to the policy learned without images. A realistic scenario where we can benefit from the gain is exemplified by combining Agile-Link [41] as the beam search method and make-before-break [49] as the handover procedure. In such a scenario, the service interruption subjected by a beam alignment is under 1 ms with a 128 size array, and by the other handover procedure would be tens of milliseconds.…”

Section: ) Resultsmentioning

confidence: 99%

Handover Management for mmWave Networks With Proactive Performance Prediction Using Camera Images and Deep Reinforcement Learning

Koda

Nakashima

Yamamoto

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

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For millimeter-wave networks, this paper presents a paradigm shift for leveraging time-consecutive camera images in handover decision problems. While making handover decisions, it is important to predict future long-term performance-e.g., the cumulative sum of time-varying data rates-proactively to avoid making myopic decisions. However, this study experimentally notices that a time-variation in the received powers is not necessarily informative for proactively predicting the rapid degradation of data rates caused by moving obstacles. To overcome this challenge, this study proposes a proactive framework wherein handover timings are optimized while obstacle-caused data rate degradations are predicted before the degradations occur. The key idea is to expand a state space to involve timeconsecutive camera images, which comprises informative features for predicting such data rate degradations. To overcome the difficulty in handling the large dimensionality of the expanded state space, we use a deep reinforcement learning for deciding the handover timings. The evaluations performed based on the experimentally obtained camera images and received powers demonstrate that the expanded state space facilitates (i) the prediction of obstacle-caused data rate degradations from 500 ms before the degradations occur and (ii) superior performance to a handover framework without the state space expansion.

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Section: ) Resultsmentioning

confidence: 99%

Handover Management for mmWave Networks With Proactive Performance Prediction Using Camera Images and Deep Reinforcement Learning

Koda

Nakashima

Yamamoto

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

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“…The approach has been investigated for only femtocells. [135] Inter BS data forwarding and make-over-handover…”

Section: [80] Transmission Power Optimizationmentioning

confidence: 99%

“…This approach can reduce handover latency while reducing of signal overhead. In [135], QoS, CQI and other parameter based data utilization is proposed among eNBs to reduce X2 latency, processing and reconfiguration delays. Additionally, makebefore-handover is proposed for low latency 5G services for no data interruption.…”

Section: ] Transmission Power Optimizationmentioning

confidence: 99%

A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

Parvez

Rahmati

Güvenç

et al. 2018

IEEE Commun. Surv. Tutorials

769

395

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The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: RAN, core network, and caching. We also present a general overview of 5G cellular networks composed of software defined network (SDN), network function virtualization (NFV), caching, and mobile edge computing (MEC) capable of meeting latency and other 5G requirements.

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“…Hence, the mobility events that may trigger a HO are handled by the BS the UE is connected to and the core network (CN). In the typical break-before-make HO used in LTE, the UE can experience, at the radio level, typical data interruption times of 15-50 ms, but delays of hundred of milliseconds can also occur [26]. Since the UE cannot communicate with the BS during the HO interruption time, some HO types have been proposed to solve this problem.…”

Section: A Mobility With Scmentioning

confidence: 99%

Fast Data Recovery for Improved Mobility Support in Multiradio Dual Connectivity

2022

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Data aggregation is one of the crucial features of the 3GPP Multi-Radio Dual Connectivity (MR-DC) technology. However, mobility events and radio link failures, which may occur during the data aggregation, may pose challenges in meeting the latency, reliability, and throughput key performance indicators (KPIs). Unlike single connectivity, the user equipment (UE) in MR-DC operation can experience such events in either of the two base stations (BSs) serving the UE with MR-DC. In typical MR-DC deployments, these events occur more frequently in the BS acting as the secondary node (SN) since the SN operates at a higher frequency band. In this paper, we show that handovers (HOs) and signal blockage events that occur at the SN can create out-of-order data reception or losses at the UE's Packet Data Control Protocol (PDCP) layer, making the application stop receiving data for up to hundreds of milliseconds. Thus, challenging to meet the KPIs defined for such application. To mitigate this effect, we propose an intelligent and efficient mechanism that operates in the transmitting PDCP layer and significantly minimizes the data interruption periods suffered by the application when the UE aggregates data and HOs or failures of the SN occur. We use LTE/NR testbed experiments to show that the proposed mechanism achieves a high and stable aggregate throughput with near-zero interruption time and data reliability of at least five nines without transport layer retransmissions. The experiments are conducted for saturated TCP traffic and under link quality variations based on traces extracted from a Nokia-proprietary system-level simulator.

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Towards Zero Data Interruption Time with Enhanced Synchronous Handover

Cited by 16 publications

References 7 publications

Handover Management for mmWave Networks With Proactive Performance Prediction Using Camera Images and Deep Reinforcement Learning

Handover Management for mmWave Networks With Proactive Performance Prediction Using Camera Images and Deep Reinforcement Learning

A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

Fast Data Recovery for Improved Mobility Support in Multiradio Dual Connectivity

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