3,100 research outputs found
EM based channel estimation in an amplify-and-forward relaying network
Cooperative communication offers a way to obtain spatial diversity in a wireless network without increasing hardware demands. The different cooperation protocols proposed in the literature [1] are often studied under the assumption that all channel state information is available at the destination. In a practical scenario, channel estimates need to be derived from the broadcasted signals. In this paper, we study the Amplify-and-Forward protocol and use the expectation-maximization (EM) algorithm to obtain the channel estimates in an iterative way. Our results show that the performance of the system that knows the channels can be approached at the cost of an increased computational complexity. In case a small constellation is used, a low complexity approximation is proposed with a similar performance
EM based channel estimation in an amplify-and-forward relaying network
Cooperative communication offers a way to obtain spatial diversity in a wireless network without increasing hardware demands. The different cooperation protocols proposed in the literature [1] are often studied under the assumption that all channel state information is available at the destination. In a practical scenario, channel estimates need to be derived from the broadcasted signals. In this paper, we study the Amplify-and-Forward protocol and use the expectation-maximization (EM) algorithm to obtain the channel estimates in an iterative way. Our results show that the performance of the system that knows the channels can be approached at the cost of an increased computational complexity. In case a small constellation is used, a low complexity approximation is proposed with a similar performance
Decentralized Estimation over Orthogonal Multiple-access Fading Channels in Wireless Sensor Networks - Optimal and Suboptimal Estimators
Optimal and suboptimal decentralized estimators in wireless sensor networks
(WSNs) over orthogonal multiple-access fading channels are studied in this
paper. Considering multiple-bit quantization before digital transmission, we
develop maximum likelihood estimators (MLEs) with both known and unknown
channel state information (CSI). When training symbols are available, we derive
a MLE that is a special case of the MLE with unknown CSI. It implicitly uses
the training symbols to estimate the channel coefficients and exploits the
estimated CSI in an optimal way. To reduce the computational complexity, we
propose suboptimal estimators. These estimators exploit both signal and data
level redundant information to improve the estimation performance. The proposed
MLEs reduce to traditional fusion based or diversity based estimators when
communications or observations are perfect. By introducing a general message
function, the proposed estimators can be applied when various analog or digital
transmission schemes are used. The simulations show that the estimators using
digital communications with multiple-bit quantization outperform the estimator
using analog-and-forwarding transmission in fading channels. When considering
the total bandwidth and energy constraints, the MLE using multiple-bit
quantization is superior to that using binary quantization at medium and high
observation signal-to-noise ratio levels
Cross-Correlation-Function-Based Multipath Mitigation Method for Sine-BOC Signals
Global Navigation Satellite Systems (GNSS) positioning accuracy indoor and urban canyons environments are greatly affected by multipath due to distortions in its autocorrelation function. In this paper, a cross-correlation function between the received sine phased Binary Offset Carrier (sine-BOC) modulation signal and the local signal is studied firstly, and a new multipath mitigation method based on cross-correlation function for sine-BOC signal is proposed. This method is implemented to create a cross-correlation function by designing the modulated symbols of the local signal. The theoretical analysis and simulation results indicate that the proposed method exhibits better multipath mitigation performance compared with the traditional Double Delta Correlator (DDC) techniques, especially the medium/long delay multipath signals, and it is also convenient and flexible to implement by using only one correlator, which is the case of low-cost mass-market receivers
Eigenvalue-based Cyclostationary Spectrum Sensing Using Multiple Antennas
In this paper, we propose a signal-selective spectrum sensing method for
cognitive radio networks and specifically targeted for receivers with
multiple-antenna capability. This method is used for detecting the presence or
absence of primary users based on the eigenvalues of the cyclic covariance
matrix of received signals. In particular, the cyclic correlation significance
test is used to detect a specific signal-of-interest by exploiting knowledge of
its cyclic frequencies. The analytical threshold for achieving constant false
alarm rate using this detection method is presented, verified through
simulations, and shown to be independent of both the number of samples used and
the noise variance, effectively eliminating the dependence on accurate noise
estimation. The proposed method is also shown, through numerical simulations,
to outperform existing multiple-antenna cyclostationary-based spectrum sensing
algorithms under a quasi-static Rayleigh fading channel, in both spatially
correlated and uncorrelated noise environments. The algorithm also has
significantly lower computational complexity than these other approaches.Comment: 6 pages, 6 figures, accepted to IEEE GLOBECOM 201
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