Experimental Evaluation of Decision Criteria for WLAN Handover: Signal Strength and Frame Retransmission

Tsukamoto, Kazuya; Yamaguchi, Takeshi; Kashihara, Shigeru; Oie, Yuji

doi:10.1007/11890348_19

Cited by 10 publications

(13 citation statements)

References 5 publications

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“…That is, in a mobile environment, MNs need to promptly and reliably detect wireless link condition by exploiting the lower layer (below layer 2) information. Furthermore, our practical experiments in [4] proved that the number of frame retries on the MAC layer has the potential to detect the wireless link degradation during movement because packets over wireless inevitably experience frame retries before being treated as packet loss.…”

Section: Related Workmentioning

confidence: 93%

“…It also cannot detect the degradation due to radio interference. Furthermore, in [4], we showed that the information of the MAC layer, frame retry, has a potential to serve as a significant metric. In this section, we describe two HO metrics employed in our new proposed method.…”

Section: Ho Decision Metricsmentioning

confidence: 96%

“…The authors in [3] proposed an SCTP based HO scheme for VoIP using a Mean Opinion Score (MOS [9]) as an HO decision metric. This HO mechanism [4]. Therefore, the existing study could execute unnecessary HOs due to some factors in the wired network, such as temporal congestion (not in the wireless network).…”

Section: Related Workmentioning

confidence: 99%

“…Many HO technologies employ Received Signal Strength (RSS) on PHY layer as an HO decision metric. However, according to our practical experiments [4], RSS is very difficult for an MN to properly detect deterioration in communication quality because RSS fluctuates abruptly due to distance and interfering objects. It also cannot detect the degradation due to radio interference.…”

Section: Ho Decision Metricsmentioning

confidence: 99%

“…That is, we can detect the occurrence of packet loss in advance by utilizing frame retries. Moreover, unlike the RSS, frame retries can promptly and reliably detect the wireless link degradation due to not only reduction of RSS but also radio interference and collisions [4]. Therefore, frame retries allows an MN to detect wireless link condition properly.…”

Section: A Frame Retriesmentioning

confidence: 99%

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Seamless VoWLAN handoff management based on estimation of AP queue length and frame retries

Niswar

Horiuchi

Kashihara

et al. 2009

2009 IEEE International Conference on Pervasive Computing and Communications

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Switching a communication path from one Access Point (AP) to another in inter-domain WLANs is a critical challenge for delay-sensitive applications such as Voice over IP (VoIP) because communication quality during handoff (HO)is more likely to be deteriorated. To maintain VoIP quality during HO, we need to solve many problems. In particular, in bi-directional communication such as VoIP, an AP becomes a bottleneck with the increase of VoIP calls. As a result, packets queued in the AP buffer may experience a large queuing delay or packet losses due to buffer overflow, thereby causing the degradation of VoIP quality for the Mobile Nodes (MNs) side. To avoid this degradation, MNs need to appropriately and autonomously execute HO in response to the change in wireless network condition, i.e., the deterioration of wireless link quality and the congestion state at the AP. In this paper, we propose an HO decision strategy estimating AP queue length at an MN and exploiting frame retries to maintain VoIP quality during HO. I. INTRODUCTIONWireless LAN (WLAN, IEEE802.11a/b/g) has been the dominant wireless network and is extensively deployed today. Meanwhile, there is a huge demand for Voice over IP (VoIP) service over WLANs. However, delivering VoIP over WLANs (VoWLANs) has many challenges because VoIP is a delay and packet loss sensitive application. In some metropolitan areas, WLANs (WiFi hotspots) have already provided Internet connectivity to many mobile nodes (MNs) everywhere. In such an environment, the MNs are likely to traverse several WLANs with different IP subnets during a VoIP call because the coverage of individual WLAN is relatively small. Consequently, VoWLAN quality could be drastically degraded due to the severe wireless network condition caused by the increase of the number of the MN and the movement of MN. Therefore, to maintain VoWLANs quality, MNs need to appropriately and autonomously execute handoffs (HOs) in response to the wireless network condition.In a mobile environment, typically, two main factors degrade VoWLAN quality: (1) degradation of wireless link quality and (2) congestion at an access point (AP). First, as an MN freely moves across WLANs, the communication quality degrades due to the fluctuation of wireless link condition. Second, as VoIP is a bi-directional communication, an AP becomes a bottleneck with the increase of VoIP calls. That is, VoIP packets to MNs are liable to experience a large queuing delay or packet loss due to buffer overflow in the AP buffer because each MN and AP has almost the same priority level of frame transmission by following the CSMA/CA scheme.

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Section: Related Workmentioning

confidence: 93%

Section: Ho Decision Metricsmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

Section: Ho Decision Metricsmentioning

confidence: 99%

Section: A Frame Retriesmentioning

confidence: 99%

See 3 more Smart Citations

Seamless VoWLAN handoff management based on estimation of AP queue length and frame retries

Niswar

Horiuchi

Kashihara

et al. 2009

2009 IEEE International Conference on Pervasive Computing and Communications

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Handover management based on the number of data frame retransmissions for VoWLAN

Kashihara

Oie

2007

Computer Communications

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Artificial Intelligence-Based Handoff Management for Dense WLANs: A Deep Reinforcement Learning Approach

Han

Lei

et al. 2019

IEEE Access

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So far, the handoff management involved in the wireless local area network (WLAN) has mainly fallen into the handoff mechanism and the decision algorithm. The traditional handoff mechanism generates noticeable delays during the handoff process, resulting in discontinuity of service, which is more evident in dense WLANs. Inspired by software-defined networking (SDN), prior works put forward many seamless handoff mechanisms to ensure service continuity. With respect to the handoff decision algorithm, when to trigger handoff and which access point to reconnect to, however, are still tricky problems. In this paper, we first design a self-learning architecture applicable to the SDN-based WLAN frameworks. Along with it, we propose DCRQN, a novel handoff management scheme based on deep reinforcement learning, specifically deep Q-network. The proposed scheme enables the network to learn from actual users' behaviors and network status from scratch, adapting its learning in time-varying dense WLANs. Due to the temporal correlation property, the handoff decision is modeled as the Markov decision process (MDP). In the modeled MDP, the proposed scheme depends on the real-time network statistics at the time of decisions. Moreover, the convolutional neural network and the recurrent neural network are leveraged to extract fine-grained discriminative features. The numerical results through simulation demonstrate that DCRQN can effectively improve the data rate during the handoff process, outperforming the traditional handoff scheme.

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Experimental Evaluation of Decision Criteria for WLAN Handover: Signal Strength and Frame Retransmission

Cited by 10 publications

References 5 publications

Seamless VoWLAN handoff management based on estimation of AP queue length and frame retries

Seamless VoWLAN handoff management based on estimation of AP queue length and frame retries

Handover management based on the number of data frame retransmissions for VoWLAN

Artificial Intelligence-Based Handoff Management for Dense WLANs: A Deep Reinforcement Learning Approach

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