Intelligent Active Queue Management for Stabilized QoS Guarantees in 5G Mobile Networks

Jung, Soyi; Kim, Joongheon; Kim, Jae Hyun

doi:10.1109/jsyst.2020.3014231

Cited by 36 publications

(18 citation statements)

References 23 publications

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“…Respect to this tradeoff, the Lyapunov optimization theorybased drift-plus-penalty (DPP) algorithm [22], [38]- [42] can be used for optimizing the time-average utility function (i.e., energy consumption) subject to queue stability. Define the Lyapunov function L(Q[t]) = 1 2 (Q[t]) 2 , and let ∆(.)…”

Section: ) Two-stage Mobile Charging Matching/schedulingmentioning

confidence: 99%

Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles

et al. 2021

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In modern networks, the use of drones as mobile base stations (MBSs) has been discussed for coverage flexibility. However, the realization of drone-based networks raises several issues. One of critical issues is drones are extremely power-hungry. To overcome this, we need to characterize a new type of drones, so-called charging drones, which can deliver energy to MBS drones. Motivated by the fact that the charging drones also need to be charged, we deploy ground-mounted charging towers for delivering energy to the charging drones. We introduce a new energy-efficiency maximization problem, which is partitioned into two independently separable tasks. More specifically, as our first optimization task, two-stage charging matching is proposed due to the inherent nature of our network model, where the first matching aims to schedule between charging towers and charging drones while the second matching solves the scheduling between charging drones and MBS drones. We analyze how to convert the formulation containing non-convex terms to another one only with convex terms. As our second optimization task, each MBS drone conducts energy-aware time-average transmit power allocation minimization subject to stability via Lyapunov optimization. Our solutions enable the MBS drones to extend their lifetimes; in turn, network coverage-time can be extended.INDEX TERMS Cellular network, charging drone, coverage-time, mobile base station, scheduling.

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Section: ) Two-stage Mobile Charging Matching/schedulingmentioning

confidence: 99%

Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles

et al. 2021

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“…Such an approach permits the existence of bursty traffic during periods shorter than the interval value. Recent results on CoDel in cellular networks [21] [24] [26] show a latency reduction when adopted.…”

Section: A Bufferbloat At the Rlc Sublayer 1) Problem Descriptionmentioning

confidence: 99%

Preventing RLC Buffer Sojourn Delays in 5G

et al. 2021

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The 3rd Generation Partnership Project (3GPP) is investing a notable effort to mitigate the endogenous stack and protocol delays (e.g., introducing new numerology, through preemptive scheduling or providing uplink granted free transmission) to attain to the heterogeneous Quality of Service (QoS) latency requirements for which the fifth generation technology standard for broadband cellular networks (5G) is envisioned. However, 3GPP's goals may become futile if exogenous delays generated by the transport layer (e.g., bufferbloat) and the Radio Link Control (RLC) sublayer segmentation/reassembly procedure are not targeted. On the one hand, the bufferbloat specifically occurs at the Radio Access Network (RAN) since the data path bottleneck is located at the radio link, and contemporary RANs are deployed with large buffers to avoid squandering scarce wireless resources. On the other hand, a Resource Block (RB) scheduling that dismisses 5G's packet-switched network nature, unnecessarily triggers the segmentation procedure at sender's RLC sublayer, which adds extra delay as receiver's RLC sublayer cannot forward the packets to higher sublayers until they are reassembled. Consequently, the exogenously generated queuing delays can surpass 5G's stack and protocol endogenous delays, neutralizing 3GPP's attempt to reduce the latency. We address RLC's related buffer delays and present two solutions: (i) we enhance the 3GPP standard and propose a bufferbloat avoidance algorithm, and (ii) we propose a RB scheduler for circumventing the added sojourn time caused by the packet segmentation/reassembly procedure. Both solutions are implemented and extensively evaluated along with other state-of-the-art proposals in a testbed to verify their suitability and effectiveness under realistic conditions of use (i.e., by considering Modulation and Coding Scheme (MCS) variations, slices, different traffic patterns and off-the-shelf equipment). The results reveal current 3GPP deficits in its QoS model to address the bufferbloat and the contribution of the segmentation/reassembly procedure to the total delay.

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“…where X is a decision set, In(x(t), t) is queue arrival procedure with decision x(t) and time t, Out(x(t), t) is queue departure process with decision x(t) and time t, V is a trade-off factor, and x * is optimal decision, respectively. More details about this theory are discussed in [19]; and the various applications that implement the theory are in [20]- [28].…”

Section: B Lyapunov Optimization Frameworkmentioning

confidence: 99%