Biased Estimator for OFDMA

With Vaishnavi Adella (now at Qualcomm, India), Sai Charan Thoutam (now at Qualcomm, India)

Implemented the paper : Biased estimators with adaptive shrinkage targets for orthogonal frequency division multiple access channel estimation

Channel Estimation is the means of characterising channel effects-scattering, fading and power decay
Can be done using either decision feedback scheme or the known pilot symbols
Here : Using user specific pilots
Why is it crucial ? Huge transmit power spent on it Incorrect estimates lead to residual cancellation errors Necessary for high data rates

Channel statistics – known
Estimation methods such as modified least squares require wide band pilots
Finite or large number of pilots / wide band pilots
The two-dimensional (2D)-minimum mean square error (2D-MMSE) methods [1, 2] can be applied using the pilots in the RB. However, optimal MMSE estimator requires the knowledge of the channel statistics which are seldom known accurately at the receiver.
MVUE-Zero bias as constraint and acheive CRLB
ML estimate is biased in order to reduce the MSE.

2D-MMSE can be used when channel statistics is unknown
Assumption: Ideally band limited and time limited uniform scattering function Is that even possible ? Google says ‘the only time and band limited signal is zero’. A little more of research and we find: A time limited signal with most of its energy contained in the band is a good approximation for both time and band limited signal.
2D-MMSE : Performance degrades if the robust filter has finite number of taps [2]
JS-estimator used to bridge the gap between robust 2D MMSE and optimal MMSE

Applying biased estimation techniques for localised CFR estimation
Adaptively choosing a vector shrinkage target for the biased estimator that best reflects the time–frequency selectivity of the CFR using the aid of multiple hypothesis tests
Choosing the thresholds for the hypothesis tests such that the probability of incorrect choice of shrinkage targets is bounded.