Efficient Carrier Frequency Offset Estimation Research Articles

Efficient Carrier Frequency Offset Estimation in Wireless Sensor Networks

The carrier frequency offset (CFO) estimation is a crucial problem in orthogonal frequency division multiplexing (OFDM) systems, especially for the enabling technologies in the Internet of Things, which demand stringent limitations of low cost, low power consumption, and wide ranges, like wireless sensor networks. In this letter, we propose an efficient CFO estimation method for OFDM systems with in- and quadrature-phase (IQ) imbalance, which combines the emerging multitask learning (MTL) with the channel residual energy (CRE) to reduce the average inferring time greatly. The MTL estimator can extract the correlated features between the CFO and the IQ imbalance so as to mitigate the intractable coupling effect. On the other hand, the CRE technique can guide the neural network and loss function design to improve the estimation accuracy and the training efficiency. Numerical results demonstrate that our proposal achieves high estimation accuracy while the inferring time is significantly reduced compared with other methods.

Efficient carrier frequency offset estimation algorithm for generalized frequency division multiplexing systems

This paper presents an efficient carrier frequency offset (CFO) estimation algorithm for generalized frequency division multiplexing (GFDM) systems. The proposed algorithm formats the preamble into multiple identical subsymbols, and then decimates the received signal in the time domain to estimate the fractional CFO through array signal processing. After compensating for the fractional CFO, the proposed algorithm uses a low-complexity searching process to estimate the integer CFO in a maximum likelihood. The proposed algorithm presents some distinctive features: (1) it provides the full range of frequency acquisition region (FAR), which was considerably larger than that provided by many existing algorithms whose FAR was limited to one subcarrier spacing; (2) the proposed algorithm can use a single subsymbol to estimate the integer CFO by using the searching process of stepsize 1, significantly reducing its computational complexity; and (3) the decimation of the received signal ensures the proposed algorithm a power gain in fractional CFO estimation, resulting in an improved estimation accuracy compared with conventional algorithms.

Maximum likelihood carrier frequency offset estimation algorithm with adjustable frequency acquisition region

This study presents a simple yet effective carrier frequency offset (CFO) estimation algorithm for orthogonal frequency division multiplexing (OFDM) systems. At the transmitter, the proposed algorithm uses null subcarriers to render the OFDM signal periodic in the time domain. At the receiver, these periodic time samples become CFO-bearing signals, which can be adopted to develop the maximum likelihood (ML) CFO estimation algorithm accordingly. In addition to providing reliable and efficient CFO estimation, the proposed algorithm has an adjustable acquisition region linearly proportional to the order of the null subcarrier insertion scheme.

Blind Frequency Synchronization in OFDM via Diagonality Criterion

In this paper, we address the problem of blind carrier frequency offset (CFO) estimation in orthogonal frequency-division multiplexing (OFDM) systems in the case of frequency-selective channels. CFO destroys the orthogonality between the carriers leading to nondiagonal signal covariance matrices in frequency domain. The proposed blind method enforces a diagonal structure by minimizing the power of nondiagonal elements. Hence, the orthogonality property inherent to OFDM transmission with cyclic prefix is restored. The method is blind since it does not require a priori knowledge of the transmitted data or the channel, and does not need any virtual subcarriers. A closed-form solution is derived, which leads to accurate and computationally efficient CFO estimation in multipath fading environments. Consistency of the estimator is proved and the convergence rate as a function of the sample size is analyzed as well. To assess the large sample performance, we derive the Cramer-Rao bound (CRB) for the blind CFO estimation problem. The CRB is derived assuming a general Gaussian model for the OFDM signal, which may be applied to both circular and noncircular modulations. Finally, simulation results on CFO estimation are reported using a realistic channel model