An Accurate Model for EESM and its Application to Analysis of CQI Feedback Schemes and Scheduling in LTE

Donthi, Sushruth N.; Mehta, Neelesh B.

doi:10.1109/twc.2011.081011.102247

Cited by 90 publications

(64 citation statements)

References 37 publications

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“…We note that this is not a simple extension of [18], [20] because no tractable, natural extension to the bivariate case exists in the literature for the beta model of [18], and the generalized extreme value (GEV) and Pearson models of [20]. While the lognormal model proposed in [9] can be extended, we shall see that it is inaccurate.…”

Section: B Contributionsmentioning

confidence: 97%

“…However, only Monte Carlo simulation results are presented in [4], [16], [17]. The impact of the coarseness of the channel quality feedback on the downlink throughput of LTE is analyzed in [9]. In it, EESM-based feedback is used.…”

Section: A Related Literaturementioning

confidence: 99%

“…Its accuracy has been conclusively established in several prior works [5], [6]. It has been widely used in system-level simulations of LTE and WiMAX [7], [8] and to generate feedback to the base station (BS) [9].…”

mentioning

confidence: 99%

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Characterizing the Impact of Feedback Delays on Wideband Rate Adaptation

Francis

Mehta

2015

IEEE Trans. Wireless Commun.

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Abstract-In contemporary orthogonal frequency division multiplexing (OFDM) systems, such as Long Term Evolution (LTE), LTE-Advanced, and WiMAX, a codeword is transmitted over a group of subcarriers. Since different subcarriers see different channel gains in frequency-selective channels, the modulation and coding scheme (MCS) of the codeword must be selected based on the vector of signal-to-noise-ratios (SNRs) of these subcarriers. Exponential effective SNR mapping (EESM) maps the vector of SNRs into an equivalent flat-fading SNR, and is widely used to simplify this problem. We develop a new analytical framework to characterize the throughput of EESM-based rate adaptation in such wideband channels in the presence of feedback delays. We derive a novel accurate approximation for the throughput as a function of feedback delay. We also propose a novel bivariate gamma distribution to model the time evolution of EESM between the times of estimation and data transmission, which facilitates the analysis. These are then generalized to a multi-cell, multi-user scenario with various frequency-domain schedulers. Unlike prior work, most of which is simulation-based, our framework encompasses both correlated and independent subcarriers and various multiple antenna diversity modes; it is accurate over a wide range of delays.

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Section: B Contributionsmentioning

confidence: 97%

Section: A Related Literaturementioning

confidence: 99%

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Characterizing the Impact of Feedback Delays on Wideband Rate Adaptation

Francis

Mehta

2015

IEEE Trans. Wireless Commun.

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“…Even though the PRB is the smallest unit, the base station can allocate to each user; in order to limit the amount of signalling information, each MU is unable to feed back detailed information on each PRB, and thus the PRBs are generally grouped in subbands and only one value for such band is measured. This value is referred to as the effective SINR and is computed with the Effective Exponential SNR Mapping (EESM) formulation [16]:…”

Section: Network Modelmentioning

confidence: 99%

Adaptive CSI and feedback estimation in LTE and beyond: a Gaussian process regression approach

Chiumento

Bennis

Desset

et al. 2015

J Wireless Com Network

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The constant increase in wireless handheld devices and the prospect of billions of connected machines has compelled the research community to investigate different technologies which are able to deliver high data rates, lower latency and better reliability and quality of experience to mobile users. One of the problems, usually overlooked by the research community, is that more connected devices require proportionally more signalling overhead. Particularly, acquiring users' channel state information is necessary in order for the base station to assign frequency resources. Estimating this channel information with full resolution in frequency and in time is generally impossible, and thus, methods have to be implemented in order to reduce the overhead. In this paper, we propose a channel quality estimation method based on the concept of Gaussian process regression to predict users' channel states for varying user mobility profiles. Furthermore, we present a dual-control technique to determine which is the most appropriate prediction time for each user in order to keep the packet loss rate below a pre-defined threshold. The proposed method makes use of active learning and the exploration-exploitation paradigm, which allow the controller to choose autonomously the next sampling point in time so that the exploration of the control space is limited while still reaching an optimal performance. Extensive simulation results, carried out in an LTE-A simulator, show that the proposed channel prediction method is able to provide consistent gain, in terms of packet loss rate, for users with low and average mobility, while its efficacy is reduced for high-velocity users. The proposed dual-control technique is then applied, and its impact on the users' packet loss is analysed in a multicell network with proportional fair and maximum throughput scheduling mechanisms. Remarkably, it is shown that the presented approach allows for a reduction of the overall channel quality signalling by over 90 % while keeping the packet loss below 5 % with maximum throughput schedulers, as well as signalling reduction of 60 % with proportional fair scheduling.

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“…www.iosrjournals.org 16 | Page consist of n contagious PRBs with 2 ≤ n ≤ 8 [8]. 3GPP has standardised 15 MCS values for LTE.…”

Section: Channel Quality Indicator Feedback In Long Term Evolution (Lmentioning

confidence: 99%

Channel Quality Indicator Feedback in Long Term Evolution (LTE) System

Ibrahim¹

2014

IOSRJECE

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An Accurate Model for EESM and its Application to Analysis of CQI Feedback Schemes and Scheduling in LTE

Cited by 90 publications

References 37 publications

Characterizing the Impact of Feedback Delays on Wideband Rate Adaptation

Characterizing the Impact of Feedback Delays on Wideband Rate Adaptation

Adaptive CSI and feedback estimation in LTE and beyond: a Gaussian process regression approach

Channel Quality Indicator Feedback in Long Term Evolution (LTE) System

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