System Requirements. Channel Model Prediction: FP6 IST SURFACE D2.2 v2.0

Research output: Other contribution

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Abstract

The SURFACE (Self Configurable Air Interface) project aims at studying and evaluating the performances of a novel generalised air interface capable of self-reconfiguring in order to satisfy global network QoS (Quality of Service) requirements. It considers Multiple Input Multiple Output (MIMO) technologies as an option. In this deliverable, we aim at extending existing SISO or MIMO simplified channel models that are suited for designing channel predictors. The document first presents the WINNER's SCME channel model, which will be the reference channel for testing predictors. Afterwards it introduce the SURFACE channel model, which is derived from the WINNER's SCME, with parameters adapted to other workpackages requirements. Several simplified channel models and predictors are then introduced. Two of them seemed particularly relevant for our research work, namely the original ESPRIT algorithm and a modified version by Andersen et al, as having limited computer power requirements. So we investigated and tested them thoroughly. Actually, these two predictors are only for SISO channels, so we extended them into MIMO models. After several tests which revealed rather good performances, and low quantization feedback robustness, these two MIMO predictors were merged into a single one, taking best of both. Finally, we will investigate the topic of rank prediction of the MIMO channel matrix. Actually, the whole matrices will be predicted by the MIMO predictor, and then their rank will be assessed.
Original languageEnglish
Publication statusPublished - 2008

Fingerprint

Air
Quality of service
Feedback
Testing

Keywords

  • Feedback quantization
  • Rank prediction
  • Channel prediction
  • Channel model
  • MIMO channel

Cite this

@misc{e6f949d243f4447a8246d473b547b2aa,
title = "System Requirements. Channel Model Prediction: FP6 IST SURFACE D2.2 v2.0",
abstract = "The SURFACE (Self Configurable Air Interface) project aims at studying and evaluating the performances of a novel generalised air interface capable of self-reconfiguring in order to satisfy global network QoS (Quality of Service) requirements. It considers Multiple Input Multiple Output (MIMO) technologies as an option. In this deliverable, we aim at extending existing SISO or MIMO simplified channel models that are suited for designing channel predictors. The document first presents the WINNER's SCME channel model, which will be the reference channel for testing predictors. Afterwards it introduce the SURFACE channel model, which is derived from the WINNER's SCME, with parameters adapted to other workpackages requirements. Several simplified channel models and predictors are then introduced. Two of them seemed particularly relevant for our research work, namely the original ESPRIT algorithm and a modified version by Andersen et al, as having limited computer power requirements. So we investigated and tested them thoroughly. Actually, these two predictors are only for SISO channels, so we extended them into MIMO models. After several tests which revealed rather good performances, and low quantization feedback robustness, these two MIMO predictors were merged into a single one, taking best of both. Finally, we will investigate the topic of rank prediction of the MIMO channel matrix. Actually, the whole matrices will be predicted by the MIMO predictor, and then their rank will be assessed.",
keywords = "Feedback quantization , Rank prediction, Channel prediction, Channel model, MIMO channel",
author = "Jo{\"e}l Vanderpypen and Laurent Schumacher",
year = "2008",
language = "English",
type = "Other",

}

TY - GEN

T1 - System Requirements. Channel Model Prediction

T2 - FP6 IST SURFACE D2.2 v2.0

AU - Vanderpypen, Joël

AU - Schumacher, Laurent

PY - 2008

Y1 - 2008

N2 - The SURFACE (Self Configurable Air Interface) project aims at studying and evaluating the performances of a novel generalised air interface capable of self-reconfiguring in order to satisfy global network QoS (Quality of Service) requirements. It considers Multiple Input Multiple Output (MIMO) technologies as an option. In this deliverable, we aim at extending existing SISO or MIMO simplified channel models that are suited for designing channel predictors. The document first presents the WINNER's SCME channel model, which will be the reference channel for testing predictors. Afterwards it introduce the SURFACE channel model, which is derived from the WINNER's SCME, with parameters adapted to other workpackages requirements. Several simplified channel models and predictors are then introduced. Two of them seemed particularly relevant for our research work, namely the original ESPRIT algorithm and a modified version by Andersen et al, as having limited computer power requirements. So we investigated and tested them thoroughly. Actually, these two predictors are only for SISO channels, so we extended them into MIMO models. After several tests which revealed rather good performances, and low quantization feedback robustness, these two MIMO predictors were merged into a single one, taking best of both. Finally, we will investigate the topic of rank prediction of the MIMO channel matrix. Actually, the whole matrices will be predicted by the MIMO predictor, and then their rank will be assessed.

AB - The SURFACE (Self Configurable Air Interface) project aims at studying and evaluating the performances of a novel generalised air interface capable of self-reconfiguring in order to satisfy global network QoS (Quality of Service) requirements. It considers Multiple Input Multiple Output (MIMO) technologies as an option. In this deliverable, we aim at extending existing SISO or MIMO simplified channel models that are suited for designing channel predictors. The document first presents the WINNER's SCME channel model, which will be the reference channel for testing predictors. Afterwards it introduce the SURFACE channel model, which is derived from the WINNER's SCME, with parameters adapted to other workpackages requirements. Several simplified channel models and predictors are then introduced. Two of them seemed particularly relevant for our research work, namely the original ESPRIT algorithm and a modified version by Andersen et al, as having limited computer power requirements. So we investigated and tested them thoroughly. Actually, these two predictors are only for SISO channels, so we extended them into MIMO models. After several tests which revealed rather good performances, and low quantization feedback robustness, these two MIMO predictors were merged into a single one, taking best of both. Finally, we will investigate the topic of rank prediction of the MIMO channel matrix. Actually, the whole matrices will be predicted by the MIMO predictor, and then their rank will be assessed.

KW - Feedback quantization

KW - Rank prediction

KW - Channel prediction

KW - Channel model

KW - MIMO channel

M3 - Other contribution

ER -