In the last decade, several subspace-based algorithms for blind channel identification in zero-padded systems were introduced. These subspace-based methods are attractive because highly accurate estimates of the channel can be obtained by using only a few received blocks. However, they do not work when applied to the zero-padded orthogonal frequency division multiplexing systems (ZP-OFDM) with virtual carriers. In this paper, we propose two improvements on an earlier subspace-based blind channel estimation method for such systems. Firstly, we introduce a simple noise weighting approach. Unlike most of the earlier methods, the proposed weighting matrix is diagonal and it is independent of the channel noise variance and SNR. By doing so, the performance of previous work is significantly enhanced. Secondly, we extend the blind estimation method to ZP-OFDM systems with virtual carriers. Simulations are carried out to demonstrate the merits of the proposed method.

Recently, a blind subspace channel estimation algorithm in which using only a few received OFDM blocks is proposed. Based on this earlier work, we introduce a novel subspace-based blind carrier frequency offset (CFO) estimation method for OFDM systems. The CFO is obtained by solving the nullspace of the proposed rank-reduced matrix and the CFO estimate is given in closed form. Moreover, we do not make the assumption that the modulation symbols are white or constant-modulus. Simulation results show that the proposed method performs better than the existing methods.

The estimation of carrier frequency offset (CFO) is an important issue for OFDM systems. Many CFO estimation methods have been proposed in the past. In particular, two ESPRIT-based methods were introduced for blind CFO estimations. These ESPRIT-based methods can provide satisfactory performance at a reasonable implementation cost. In this paper, we propose a least-squares (LS) algorithm for improving the performance of these ESPRIT-based methods. Simulation results show that the proposed LS algorithm, although very simple, can significantly improve the accuracy of CFO estimation.

Carrier frequency offset (CFO) and in-phase and quadrature-phase (I/Q) imbalance are two of the common front-end impairments in low-cost communication devices. It is known that CFO can cause significant performance degradation in multi-carrier modulation (MCM) systems. Also, the existence of the I/Q imbalance usually reduces the accuracy of CFO estimation. In this paper, we propose a new data-aided scheme for the joint estimation of CFO and I/Q imbalance using simple matrix formulation. The proposed algorithms utilize only the periodicity of the generalized periodic pilot (GPP). They do not need to know the channel impulse response and the exact values of the training sequence. Moreover, our method has a low complexity and its performance compares favorably with the existing methods.

Carrier frequency offset (CFO) and in-phase/quadrature-phase (I/Q) imbalance are two major impairments of the wideband direct-conversion receivers. They can cause significant performance degradation in multi-carrier modulation systems. In this paper, we propose a new two-step algorithm for estimating the parameters of both impairments based on generalized periodic pilot (GPP). Simulation results show that the proposed method has a good performance. Furthermore, the complexity of our algorithm is lower than the existing methods.

The estimation of carrier frequency offset (CFO) is an important topic in broadband communication systems. It is known that the accuracy of CFO estimation will be affected by the existence of the in-phase and quadrature-phase (I/Q) imbalance. This paper proposes a novel algorithm for CFO estimation in the presence of I/Q imbalance. The estimation problem is solved by using a simple matrix approach. It exploits only the periodicity of the training sequence, and it does not need the information about the channel impulse response and the exact values of training sequence. Simulation results show that our method is robust to both the noise and the various values of CFO, and it compares favorably with the existing methods.