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 -