Abstract
We consider joint estimation of carrier frequency offset (CFO) and channel impulse response (CIR) for orthogonal frequency division multiplexing (OFDM) with pilot symbols. A new method based on compressed sensing is proposed. It has been shown that the CIR can be represented as a 1-block sparse signal by using a dictionary constructed by concatenating subspaces of CFO values taken from a search space. Recovery of both CFO and CIR is accomplished by the block orthogonal matching pursuit algorithm. The proposed method uses only one OFDM training block and does not require any initialization. The performance of the proposed method is compared against the well-established pilot based estimators: Moose, Classen, the maximum likelihood estimator, and the p-algorithm. Numerical results show that the performance of the proposed method does not depend on the value of the CFO. We also give worst-case upper bounds for the mean squared error of the CIR estimate for a sparse multipath channel.
Highlights
Orthogonal frequency division multiplexing (OFDM) has become a standard multi-carrier modulation technique for broadband wireless communication networks due to its resistance to interblock interference (IBI) caused by frequency-selective multipath fading channels
The Moose, Classen, and p-algorithm perform worse than the maximum likelihood estimate (MLE) and the block orthogonal matching pursuit (BOMP) methods since both carrier frequency offset (CFO) and channel impulse response (CIR) is changing for each OFDM block
6 Conclusions We introduced a novel compressive sensing (CS) based framework for the joint estimation of CFO and CIR in OFDM systems
Summary
Orthogonal frequency division multiplexing (OFDM) has become a standard multi-carrier modulation technique for broadband wireless communication networks due to its resistance to interblock interference (IBI) caused by frequency-selective multipath fading channels. The estimator in [9] is an approximate MLE since the received signal samples of the OFDM system are assumed to be Gaussian Both [6] and [9] require the secondorder statistics of the channel and noise. There exist numerous implementations of the CS-based block sparse signal recovery methods for the estimation of the CIR of the OFDM systems [13–15]. In these works, block sparsity is achieved by either assuming that the several channel instantiations are group-sparse, locations of the nonzero channel coefficients are same, [15] or concatenating multiple CIRs of different antennas with common support in a block sparse structure [13, 14]. We derive our proposed estimator by using the frequency domain model given in Eq (6), as detailed below
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