Cramer–Rao bound based mean‐squared error and throughput analysis of superimposed pilots for semi‐blind multiple‐input multiple‐output wireless channel estimation

Summary The abundant benefits of Orthogonal Frequency‐Division Multiplexing and its high flexibility have resulted in its widespread applications in many telecommunication standards. One important parameter for improving wireless system's efficiency is the accurate estimation of channel state information. In the literatures, many techniques have been studied in order to estimate the channel state information. Nowadays, the techniques based on intelligent algorithms such as genetic algorithm and particle swarm optimization (PSO) have attracted attention of researchers. With a very low pilot overhead, these techniques are able to estimate the channel frequency response properly only using the received signals. Unfortunately, each of these techniques suffers a common weakness: they have a slow convergence rate. In this paper, a new intelligent and different method has been presented for channel estimation using learning automata, entitled LA estimator, where the learning automata are search agents, and each pair is responsible for searching 1 complex coefficient of the channel frequency response. This method can achieve an accurate channel estimation with a moderate computational complexity in comparison with GA and PSO estimators. Furthermore, with higher convergence rate, our proposed method is capable of providing the same performance as GA and PSO. For a 2‐path fast fading channel, simulation results demonstrate the robustness of our proposed scheme according to the bit error rate and the mean square error.

Section: Introductionmentioning

confidence: 99%

Channel estimation based on learning automata for OFDM systems

Salehi

Majidi

Neda

2018

“…Many estimation literatures have appeared, but most of them have the common assumption of the available perfect channel state information at the FC [2][3][4][5][6]. However, this assumption is not realistic, and there always exists estimation error in the case of channel estimation [7,8]. And the channel estimation error can degrade the estimation performance in practical occasion.…”

Section: Introductionmentioning

confidence: 99%

Power allocation in vector estimation systems with the impact of wireless channel uncertainty

Liu

et al. 2015

Motivated by the fact that wireless channel uncertainty always exists and influences the distributed estimation system, this paper proposes power allocation schemes for linear minimum mean square error estimation. We consider training-based vector systems and investigate how the power allocation ratio between training and transmitting is influenced by the system information, assuming that the sum of training and data transmitting power is fixed. We propose to use the average mean square error as the distortion measure so as to fulfill the statistical characteristics of channel estimation. We derive the closed-form solutions to the optimal power allocation ratio, which is the function of system parameters, such as the vector signal's size, channel covariance, and noise covariance. Monte Carlo simulations are carried out to verify the performance of the proposed methods. Simulation results show that (i) in vector estimation system, the power allocation set to be 0.5 is usually not optimal; (ii) compared with training based, the equal power allocation system, the newly proposed methods could significantly improve the estimation performance.

“…Adaptive system identification (ASI) includes many applications such as interference cancelation [1][2][3], spectral subtraction [4,5], wireless localization [6][7][8][9], channel equalization [10][11][12][13], and adaptive beamforming [14]. One of most popular algorithms is least mean square (LMS), which is proposed by Widrow et al [15].…”

Section: Introductionmentioning

confidence: 99%

Adaptive system identification using robust LMS/F algorithm

Gui

Peng

Adachi

2013

Adaptive system identification (ASI) problems have attracted both academic and industrial attentions for a long time. As one of the classical approaches for ASI, performance of least mean square (LMS) is unstable in low signal-to-noise ratio (SNR) region. On the contrary, least mean fourth (LMF) algorithm is difficult to implement in practical system because of its high computational complexity in high SNR region, and hence it is usually neglected by researchers. In this paper, we propose an effective approach to identify unknown system adaptively by using combined LMS and LMF algorithms in different SNR regions. Experiment-based parameter selection is established to optimize the performance as well as to keep the low computational complexity.