Multi-QoS-Aware Fair Scheduling for LTE

Long Term Evolution (LTE) is an all IP packet system in which guaranteeing QoS is a real challenge. The LTE aims to provide a maximum data rates of 100Mbps for downlink and 50Mbps for uplink when 20 MHz bandwidth is used. The scheduler takes different considerations into account such as throughput and fairness when deciding the allocation of the scarce radio resources. Therefore the LTE MAC scheduler should consider not only the throughput optimization but also the QoS differentiations in an effective manner. The objective of this project is to focuses on enhancing the performance of LTE downlink scheduling. This project work proposes and implements a, "Dynamic Multitraffic Scheduler" which uses dynamic bandwidth and Resource Blocks (RBs) allocations based on number of service classes. The proposed scheduler also considers the different users channel conditions and tries to create a balance between the Quality of service (QoS) guarantees and the multi-user diversity in a proportional fair manner. Round Robin (RR), Best Channel Quality Indicator (BCQI), Proportional Fair (PF) and Opportunistic scheduling are investigated and compared for both existing and proposed scheduler.

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“…The opportunistic algorithm work better when compared to the other algorithms namely RR, BCQI, PFS and opportunistic scheduling [10].…”

Section: Opportunistic Schedulingmentioning

confidence: 92%

Prioritized dynamic multitraffic scheduler for downlink in LTE

Sundari

Jayanthi

Gunasundari

2015

2015 2nd International Conference on Electronics and Communication Systems (ICECS)

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“…The cells schedule their UEs using Optimized Service Aware Scheduler (OSA) [21] [22] in this simulator. The OSA scheduler differentiates between different QoS classes mainly by defining several MAC QoS bearer types such as Guaranteed (GBR) and non-Guaranteed (nonGBR) Bit Rate.…”

Section: Comnets Lte-a System Level Simulatormentioning

confidence: 99%

Scalable and Self-sustained Algorithms for Femto-Cell Interference Mitigation

Palipana

Zaki

Toseef

et al. 2015

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Cellular networks are reaching their physical limits providing capacity that is almost near the Shannon theory. However, cellular usage is still increasing exponentially with hungry applications demanding higher data rates. As a result, designers are facing significant challenge in meeting the required demands. One of the promising solutions, being fostered by the 3GPP, is to increase the spectral efficiency through higher frequency reuse using smaller and denser network cells such as femto, pico and nano cells. One of the main challenges behind using smaller cells is managing interference, in this paper, we propose two novel solutions that alleviate the interference of femto-cells on macro-cell user equipment (MUEs). The solutions do not rely on any additional information exchange or signaling, nor do they rely on the backhaul and it's delay. The first proposal is Femto-cell Power Control Scheme (FPCS) that utilizes an analytical approach to adapt the femto base station's transmit power based on Channel Quality Indicator (CQI) reports from affected MUEs. The second method is Random Physical Resource Block Selection Scheme (RPSS) that allocates the femto-cell's resources from a random subset of Physical Resource Blocks (PRBs) so that the MUEs benefit from a reduced interference level. Our evaluations have shown that the two proposals do alleviate the femto-cell interference significantly, increasing the SINR and enhancing the end performance. To the best of our knowledge, no similar work exist in literature that addresses the femto-cell's interference without information exchange.

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“…This is done by configuring different QoS weights for different service classes [3]. Scheduling in LTE is normally done in two stages: a Time Domain Scheduler (TDS) and a Frequency Domain Scheduler (FDS).…”

Section: Problem Statementmentioning

confidence: 99%

Intelligent traffic enforcement for LTE backhaul

Zaki

Weerawardane

Hauth

et al. 2013

2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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There has been a lot of research focusing on optimizing the Long Term Evolution (LTE) networks performance. A large part of this research focused on how to optimize the LTE radio interface to efficiently distribute the scarce radio resources among the LTE users. Since LTE offers a diverse amount of services, the Quality of Service (QoS) differentiation is a major challenge. QoS is handled in LTE at different levels; the most important one is the radio scheduler. The QoS aware radio scheduler is designed to guarantee different service requirements. However, if the LTE radio interface is fully optimized towards QoS guarantees, what happen when the LTE transport network runs into congestion?Congestion can happen at any point in the network, and normally congested LTE backhaul tends to create large unfairness among the users, even among the ones with the same QoS class. The LTE transport congestion can occur at any router in the backhaul network. In this paper, we present two novel algorithms, which focus on the LTE transport congestion. The first algorithm is a congestion detection mechanism (CD) that identifies the transport network congestion as early as possible, and triggers the congestion control function. The second algorithm preserves and optimizes the QoS performance of each user over the transport to match the radio scheduler service differentiation, i.e., achieving fairness among the users in the same QoS demand and guaranteeing the enforced radio QoS weights for different service classes.

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Multi-QoS-Aware Fair Scheduling for LTE

Cited by 78 publications

References 3 publications

Prioritized dynamic multitraffic scheduler for downlink in LTE

Prioritized dynamic multitraffic scheduler for downlink in LTE

Scalable and Self-sustained Algorithms for Femto-Cell Interference Mitigation

Intelligent traffic enforcement for LTE backhaul

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