Simplified pilot-aided weighted least square phase estimation method for OFDM-based WLANs

Abstract

In this paper, we propose a simplified weighted least square (SWLS) to estimate phase variations utilizing pilots, for Orthogonal Frequency Division Multiplexing (OFDM) based very high throughput wireless local area networks (WLANs). For SWLS, the common phase error (CPE) maximum likelihood (ML) estimation and the angle boundary treatment are improved to enhance the performance of phase estimation, while the combined scheme of pair pilots is used to reduce the complexity. Simulation results show that, compared to weighted least square (WLS) scheme, a similar pocket error rate (PER) is achieved by using the SWLS method, but more than 40 percent of complexity is reduced.

A novel method for gear gravimetric wear prediction based on improved particle swarm optimization and non-stationary random process probability distribution fitting

“…Stimulated by the data shown in Table 6 and the method shown in Figure 2, this paper proposes using straight line of regression based on the least square method to fit gearwheel gravimetric loss data sequence, when the first-order difference sequence of Table 6 is based on the stationary random sequence suppose. The least square method (Zhou et al , 2019; Zhou et al , 2007; Chen et al , 2019) is a usual method that is used by this paper to define the fitting line, and all the prediction future gearwheel gravimetric loss data points are on the least square line. Suppose that there are m ( m ≥ 2) gearwheel gravimetric loss data points {n(i)}(i=1,2,⋯,m) by current moment, undetermined least square line equation is y = kt + b , k and b are undetermined coefficients and the problem is min⁡∑i=1m[ki+b−n(i)]2.…”

Successive Interference Cancellation-Based Weighted Least-Squares Estimation of Carrier and Sampling Frequency Offsets for WLANs

“…where ang l e is the error of angle introduced by l e . According to [19], the WLS estimation of l c  and l  can be derived as (14).…”

“…A number of CFO and SFO estimation algorithms have been studied throughout the years. These studied algorithms can be classified into two types: blind algorithms that do not use pilot symbols [7][8][9] and data-aided (DA) [10][11][12][13][14][15][16][17][18][19][20] algorithms using pilot symbols. Because of their simple form and computational convenience, DA methods have received more attention and are considered in this paper.…”

“…In our previous studies, simplified WLS and SFO fitting were proposed in [19] and [20], respectively. The simplified WLS and SFO fitting were based on existing WLS and followed the traditional calculation mechanism, where the CFO and the SFO were calculated simultaneously based on the phase difference between the received signal and the expected pilot.…”