A Novel SIR to Channel-Quality Indicator (CQI) Mapping Method for HSDPA System

Ko, Kyungsu; Lee, Daewon; Lee, Moohong; Lee, Hwang Soo

doi:10.1109/vtcf.2006.299

Cited by 16 publications

(19 citation statements)

References 6 publications

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“…(2) is 0.1374. The mapping methods given in Table 1 in [25][26][27][28][29] are applied to measured data, and the NRMSEs are calculated and given in Table 2. Since the NRMSE values are very high, even higher than Eq.…”

Section: Analysis and Resultsmentioning

confidence: 99%

“…The relation between the dependent variables y i and Table 2. The NRMSEs of previous studies [25][26][27][28][29].…”

Section: Analysis and Resultsmentioning

confidence: 99%

“…Reference NRMSE [25] 0.4397 [26] 0.4357 [27] 0.3436 [28] 0.5115 [29] 0.3705 the independent variable x i is given in Eq. (4):…”

Section: Analysis and Resultsmentioning

confidence: 99%

“…However, there are no studies on a DC-HSDPA system. Moreover, the studies [25][26][27][28][29] given in Table 1 investigated the SIR-to-CQI mapping methods for AWGN and ITU test channels; so far there have not been any assessments based on real field measurements. This study aims to propose a novel SIR-to-CQI mapping method that represents the exact DC-HSDPA service environment based on real field measurements.…”

Section: Sir-to-cqi Mapping Methodsmentioning

confidence: 99%

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A novel empirical SIR-to-CQI mapping rule for DC-HSDPA systems

Kurnaz¹,

Engiz²,

Esenalp³

2017

Turk J Elec Eng & Comp Sci

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Abstract:This study aims to determine the mapping rule for signal-to-interference ratio (SIR) to channel quality indicator (CQI) based on DC-HSDPA real field measurements. The measurements were performed using the TEMS Investigation Tool at 85 different propagation mediums. The measurement results showed that the SIR-to-CQI mapping methods in the literature were insufficient to characterize actual radio environments; thus, proposal of a new empirical SIR-to-CQI mapping rule was aimed. This rule provides substantially better performance than the existing methods, and with this rule CQI can be generated from SIR with an accuracy of around 90% for DC-HSDPA systems.

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Section: Analysis and Resultsmentioning

confidence: 99%

“…The relation between the dependent variables y i and Table 2. The NRMSEs of previous studies [25][26][27][28][29].…”

Section: Analysis and Resultsmentioning

confidence: 99%

“…Reference NRMSE [25] 0.4397 [26] 0.4357 [27] 0.3436 [28] 0.5115 [29] 0.3705 the independent variable x i is given in Eq. (4):…”

Section: Analysis and Resultsmentioning

confidence: 99%

Section: Sir-to-cqi Mapping Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A novel empirical SIR-to-CQI mapping rule for DC-HSDPA systems

Kurnaz¹,

Engiz²,

Esenalp³

2017

Turk J Elec Eng & Comp Sci

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“…While the mapping between the CQI and the SIR is not specified in [4], this issue has been addressed in a number of proposals [11]- [13]. Recently, a mapping has been proposed in which the system throughput is maximized while the BLER constraint is relaxed [14].…”

Section: System Domainmentioning

confidence: 99%

A hybrid swarm intelligence algorithm for multiuser scheduling in HSDPA

Aydın¹,

Kwan²,

Leung³

et al. 2013

Applied Soft Computing

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Abstract. Multiuser scheduling is an important aspect in the performance optimization of a wireless network since it allows multiple users to access a shared channel efficiently by exploiting multiuser diversity. To perform efficient scheduling, channel state information (CSI) for users is required, and is obtained via their respective feedback channels. In this paper, a more realistic imperfect CSI feedback, in the form of a finite set of Channel Quality Indicator (CQI) values, is assumed as specified in the HSDPA standard. A mathematical model of the problem is developed for use in the optimization process. A hybrid heuristic approach based on particle swarm optimization and simulated annealing is used to solve the problem. Simulation results indicate that the hybrid approach outperforms individual implementations of both simulated annealing and particle swarm optimisation.

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