OFDM Channel Estimation Based on Impulse Response Decimation: Analysis and Novel Algorithms

Abstract

Abstract-In this paper, OFDM data-aided channel estimation based on the decimation of the Channel Impulse Response (CIR) through the selection of the Most Significant Samples (MSS) is addressed. Our aim is to approach the Minimum Mean Square Error (MMSE) channel estimation performance, while avoiding the need for a-priori knowledge of channel statistics (KCS). The optimal set of samples is defined in the instantaneous and average senses. We derive lower bounds on the estimation mean-square error (MSE) performance for any MSS selection strategy. We show how MSS-based channel estimation can approach these MSE lower bounds. We introduce novel MSS strategies oriented towards instantaneous decimation (Instantaneous Energy Selection -IES), and windowed decimation (Average Energy Selection -AES). We also consider decimation via Threshold Crossing Selection (TCS), which we characterize analytically, to derive the optimum threshold in the minimum MSE sense. We also propose a sub-optimal method for threshold setting that does not require KCS. Finally, we provide numerical results in terms of both MSE estimation performance and Bit Error Rate (BER) of a coded OFDM system using the proposed channel estimators, to show that they indeed approach MMSE performance.Index Terms-OFDM, channel estimation, minimum mean square error (MMSE), impulse response, pilot tone, DVB, threshold selection.

Efficient time domain threshold for sparse channel estimation in OFDM system

“…Both theoretical analysis and simulation results show that the proposed method can achieve better performance in both BER and NMSE than the compared methods within a wide range of sparsity rate, has good spectral efficiency and moderate computational complexity. The proposed two-step threshold estimation technique is general, other threshold, like the suboptimal threshold proposed in [12] can be used in the same way as the universal threshold used in this paper.…”

“…In order to overcome the drawbacks of the above sparse channel estimation methods, recently, the theoretical optimal threshold is derived for sparse Rayleigh channel estimation in OFDM system [12], which relies on the accurate knowledge of the channel tap power delay profile and the received pilot energy to noise ratio. In order to make the threshold more robust to communication environment, a sub-optimal threshold is proposed by assuming a uniform power delay profile [12], which is proven to be robust with the true power delay profile and depends only on the received signal to noise ratio (SNR). This paper proposes a novel sparse channel estimation method with effective time-domain threshold.…”

A novel threshold based compressed channel sensing in OFDM system

“…the E b /N 0 gap is about 2dB for the same BER performance. Additionally, the proposed method slightly outperforms the LS estimator with sub-optimal threshold (SOT) [12] (the estimated number of channel tapsŜ is set to be the channel sparsity S) and 25% of pilots in the overall considered E b /N 0 , however, the spectral eciency of the proposed method is much better. Furthermore, without prior knowledge of either channel statistics and noise STD, the proposed method still maintains good performance on BER compared with sparsity adaptive matching pursuit (SAMP) method (step size s = 1) [23] with the prior knowledge of noise STD and 12.5% of pilots, OMP method with 6 taps (exact sparsity S in the rst channel model), the oracle estimator (constrained LS with known number of non-zero channel coecients and their positions) [24] and known channel CSI (known instantaneous channel frequency response).…”

“…There are generally two types of methods, the rst type requires the prior knowledge of channel statistics (power prole of channel impulse response (CIR) or sparsity level) [8,9], while the second one relies on the estimated noise power or 2 noise standard deviation (STD) [10,11]. [12] shows that threshold without prior requirement of channel statistics will benet wireless communication system. A two-step threshold is proposed in [11], which realizes eective sparse channel estimation within a wide range of channel sparsity without prior knowledge of both channel statistics and noise STD.…”

A Novel Transform for Secret Key Generation in Time-Varying TDD Channel under Hardware Fingerprint Deviation

“…The differential operation also implies the dependency of channel mobility in the LDD transform in (9) and (10). In the next section, we analyze these two problems and indications for transform parameter design in terms of MSE of receptions at Alice and Bob and the resulting ESER.…”