Performance Evaluation of Bandwidth and QoS Aware LTE Uplink Scheduler

Marwat, Safdar Nawaz Khan; Weerawardane, Thushara; Zaki, Yasir; Goerg, Carmelita; Timm‐Giel, Andreas

doi:10.1007/978-3-642-30630-3_26

Cited by 14 publications

(7 citation statements)

References 10 publications

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“…The OPNET simulations are performed under the parameter settings illustrated in Table 1. Since the ECG data requires uplink radio resources, the Bandwidth and QoS Aware (BQA) LTE uplink scheduler proposed in [24] is used for resource allocation. …”

Section: Simulation Settings and Resultsmentioning

confidence: 99%

“…The voice user results, however, do not reveal any substantial impact of increasing the ECG traffic load within the LTE cell. The cause that voice traffic is not influenced by the increase in load is that the LTE uplink scheduler [24] gives strict priority to delay sensitive voice traffic. However, the priority of ECG data traffic is higher than other traffic classes.…”

Section: Video Traffic Model (Priority 2)mentioning

confidence: 99%

See 1 more Smart Citation

Addressing the Challenges of E-Healthcare in Future Mobile Networks

Marwat

Pötsch

Zaki

et al. 2013

Lecture Notes in Computer Science

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Section: Simulation Settings and Resultsmentioning

confidence: 99%

Section: Video Traffic Model (Priority 2)mentioning

confidence: 99%

Addressing the Challenges of E-Healthcare in Future Mobile Networks

Marwat

Pötsch

Zaki

et al. 2013

Lecture Notes in Computer Science

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“…The downlink and uplink MAC schedulers are designed as in [19] and [23], respectively. We compare three scenarios.…”

Section: Qos Performance Under Different Load Situationsmentioning

confidence: 99%

Congestion-Aware Handover in LTE Systems for Load Balancing in Transport Network

Marwat¹,

Meyer

Weerawardane

et al. 2014

ETRI J

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Long‐Term Evolution employs a hard handover procedure. To reduce the interruption of data flow, downlink data is forwarded from the serving eNodeB (eNB) to the target eNB during handover. In cellular networks, unbalanced loads may lead to congestion in both the radio network and the backhaul network, resulting in bad end‐to‐end performance as well as causing unfairness among the users sharing the bottleneck link. This work focuses on congestion in the transport network. Handovers toward less loaded cells can help redistribute the load of the bottleneck link; such a mechanism is known as load balancing. The results show that the introduction of such a handover mechanism into the simulation environment positively influences the system performance. This is because terminals spend more time in the cell; hence, a better reception is offered. The utilization of load balancing can be used to further improve the performance of cellular systems that are experiencing congestion on a bottleneck link due to an uneven load.

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“…Multiclass based algorithms take into account the flow classes to perform the scheduling decision-making granting efficient results for all classes [14][15] [3]. These algorithms are built to prioritise RT flows without neglecting NRT ones.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation by pondering parameters for uplink system in LTE Networks

Iturralde

Martin

Yahiya

2013

38th Annual IEEE Conference on Local Computer Networks

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Long Term Evolution (LTE) Networks were proposed for serving a multitude of high-speed data-rate services. For such systems, the Quality of Service (QoS) at the wireless part needs to be guaranteed by using smart, fast and strong resource allocation mechanisms. In order to build such algorithms, several parameters such as channel conditions, packet delays, queue length sizes and flow bitrates should be used to compute a maximization metric. Most authors compute such metric by obtaining the product of the afore-mentioned parameters. We consider this fact as a huge mistake. In this paper, we analyse the relevance of each parameter and give them different pounds. We propose a new resource allocation algorithm that presents a low complexity level. It works in a heterogeneous service scenario real time (RT) and non-Real Time (NRT). In order to evaluate the performance of our algorithm, several QoS constraints such as throughput, Packet Loss Ratio (PLR) and Fairness Index (FI) are used.

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Performance Evaluation of Bandwidth and QoS Aware LTE Uplink Scheduler

Cited by 14 publications

References 10 publications

Addressing the Challenges of E-Healthcare in Future Mobile Networks

Addressing the Challenges of E-Healthcare in Future Mobile Networks

Congestion-Aware Handover in LTE Systems for Load Balancing in Transport Network

Resource allocation by pondering parameters for uplink system in LTE Networks

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