In this letter, an angle adjustment method is proposed to improve the accuracy of the sampling frequency offset (SFO) estimation for the very high throughput wireless local area networks (WLANs). This angle adjustment can work together with existing least square (LS) and weighted least square (WLS) to achieve better system performance. Simulation results show that, the angle adjustment can help LS and WLS to get better pocket error rate (PER).

In this paper, we propose an improved weighted least square (IWLS) method to estimate and compensate phase variations utilizing pilots, for Orthogonal Frequency Division Multiplexing (OFDM) based very high throughput wireless local area networks (WLANs). The remaining phase is composed of the common phase error (CPE) and the sampling time offset (STO). For IWLS, the CPE maximum likelihood (ML) estimation is proposed to improve the CPE estimation accuracy, while the STO fitting is proposed to enhance the estimation of STO. With these two mechanisms, IWLS can improve phase estimation performance. Simulation results show that, compared to weighted least square (WLS) scheme, a better pocket error rate (PER) is achieved by using the proposed method, but with a comparable complexity.

In this paper, we propose a single-channel speech enhancement method for a push-to-talk enabled wireless communication device. The proposed method is based on adaptive weighted β-order spectral amplitude estimation under speech presence uncertainty and enhanced instantaneous phase estimation in order to achieve flexible and effective noise reduction while limiting the speech distortion due to different noise conditions. Experimental results confirm that the proposed method delivers higher voice quality and intelligibility than the reference methods in various noise environments.

In this paper, we present a new four parameter estimator of sampled sinusoidal signals that does not require iteration. Mathematically, the four parameters (frequency, phase, magnitude, and dc offset) of sinusoidal signals can be obtained when four data points are given. In general, the parameters have to be calculated with iteration since the equations are nonlinear. In this paper, we point out that the four parameters can be obtained analytically if the four data points given are measured using a fixed sampling interval. Analytical expressions for the four parameters are derived using the signal differences. Based on this analysis, we suggest an algorithm of estimating the four parameters from N data samples corrupted by noise without iteration. When comparing with the IEEE-1057 method which is based on the least-square method, the proposed algorithm does not require the initial guess of the parameters for iteration and avoid the convergence problem. Also, the number of required numerical operations for estimation is fixed if N is determined. As a result, the processing time of parameter estimation is much faster than the least-square method which has been confirmed by numerical simulations. Simulation results and the quantitative analysis show that the estimation error of the estimated parameters is less than 1.2 times the square root of the Cramer-Rao bounds when the signal to noise ratio is larger than 20dB.

We present the recovery of 2.5 Gb/s synchronous 16-point quadrature amplitude modulation data in real-time for an linewidth-times-symbol-duration ratio of 0.00048 after transmission over 1.6 km standard single mode fiber.

We present a BPSK coherent optical wireless link in a multiple-beam, multiple-aperture configuration. The data are recovered using the signal obtained by the coherent addition of a set of maximum likelihood optical phase estimates and a select-largest stage. The proposal offers higher performance than the combining methods commonly used in optical wireless systems with diversity transmission and coherent detection.

This paper considers the quantum query complexity of ε-biased oracles that return the correct value with probability only 1/2 + ε. In particular, we show a quantum algorithm to compute N-bit OR functions with O(/ε) queries to ε-biased oracles. This improves the known upper bound of O(/ε2) and matches the known lower bound; we answer the conjecture raised by the paper [1] affirmatively. We also show a quantum algorithm to cope with the situation in which we have no knowledge about the value of ε. This contrasts with the corresponding classical situation, where it is almost hopeless to construct a bounded error algorithm without knowing the value of ε.

In this Letter, linear least squares (LLS) techniques for phase estimation of real sinusoidal signals with known or unknown amplitudes are studied. It is proved that the asymptotic performance of the LLS approach attains Cramér-Rao lower bound. For the case of a single tone, a novel LLS algorithm with unit-norm constraint is derived. Simulation results are also included for algorithm evaluation.

In this Letter, estimation of the phase of a real sinusoid with known frequency in white Gaussian noise is addressed. Based on the Newton-Raphson iterative procedure, two simple realizations of exact maximum likelihood phase estimators for known and unknown amplitude are devised. Computer simulations are included to contrast the performance of the proposed algorithms with the approximate maximum likelihood estimate as well as Cramér-Rao lower bound for different phase values and signal-to-noise ratios.