Fast and Robust Parametric Estimation of Jointly Sparse Channels

Abstract

If P antennas measure a K-multipath channel with N uniformly sampled measurements per channel, the algorithm possesses an OpKP N log N q complexity and an OpKP N q memory footprint instead of OpP N 3 q and OpP N 2 q for the direct implementation, making it suitable for K ! N . The sparsity is estimated online based on the PER, and the algorithm therefore has a sense of introspection being able to relinquish sparsity if it is lacking.The estimation performances are tested on field measurements with synthetic AWGN, and the proposed algorithm outperforms non-sparse reconstruction in the medium to low SNR range (ď 0dB), increasing the rate of successful symbol decodings by 1{10 th in average, and 1{3 rd in the best case. The experiments also show that the algorithm does not perform worse than a nonsparse estimation algorithm in non-sparse operating conditions, since it may fall-back to it if the PER criterion does not detect a sufficient level of sparsity.The algorithm is also tested against a method assuming a "discrete" sparsity model as in Compressed Sensing (CS). The conducted test indicates a trade-off between speed and accuracy.

Compensation Estimation Method for Fast Fading MIMO-OFDM Channels Based on Compressed Sensing

“…4 illustrate the MSE and BER performance of different schemes under the fast fading channel with the mobile speed of 200 km/h in a 66  MIMO system, respectively. The joint estimation methods of IJEP [5], FRI-PERK [13] and MIMO TFT-OFDM are also evaluated for comparison. It could be seen that for the mobile channel, the proposed method yields better performance compared with IJEP, FRI-PERK and MIMO TFT-OFDM.…”

“…Based on the definition of equation 20 (31) Conventional methods assume that the channels don't include time and frequency constraints. The following optimization problem is used to channel reconstruction [13]- [17]:…”

An Iterative Receiver for OFDM With Sparsity-Based Parametric Channel Estimation

“…The corresponding update of q(α l ) is the maximizer of (27), which remains as in (15). The criterion in (29) is the same as that obtained in [31]. 4) Update of Channel Parameter Estimates: The channel parameters (ρ, η, β) are estimated as the values that minimize the RBFE.…”

“…The channel estimation procedure alternates between two stages: In the activation stage (at line 7) one of the inactive components is activated and its multipath delay and coefficient are calculated. The activation criterion (29) determines if the component should stay activated. In the second stage (starting at line 20), all active components are sequentially refined.…”

“…In the second stage (starting at line 20), all active components are sequentially refined. Again, the criterion (29) determines if a component should be deactivated. The channel estimation procedure thus iteratively adds, updates and possibly removes components until the stopping criterion is fulfilled.…”