9,992 research outputs found
A BP-MF-EP Based Iterative Receiver for Joint Phase Noise Estimation, Equalization and Decoding
In this work, with combined belief propagation (BP), mean field (MF) and
expectation propagation (EP), an iterative receiver is designed for joint phase
noise (PN) estimation, equalization and decoding in a coded communication
system. The presence of the PN results in a nonlinear observation model.
Conventionally, the nonlinear model is directly linearized by using the
first-order Taylor approximation, e.g., in the state-of-the-art soft-input
extended Kalman smoothing approach (soft-in EKS). In this work, MF is used to
handle the factor due to the nonlinear model, and a second-order Taylor
approximation is used to achieve Gaussian approximation to the MF messages,
which is crucial to the low-complexity implementation of the receiver with BP
and EP. It turns out that our approximation is more effective than the direct
linearization in the soft-in EKS with similar complexity, leading to
significant performance improvement as demonstrated by simulation results.Comment: 5 pages, 3 figures, Resubmitted to IEEE Signal Processing Letter
On the Derivation of Optimal Partial Successive Interference Cancellation
The necessity of accurate channel estimation for Successive and Parallel
Interference Cancellation is well known. Iterative channel estimation and
channel decoding (for instance by means of the Expectation-Maximization
algorithm) is particularly important for these multiuser detection schemes in
the presence of time varying channels, where a high density of pilots is
necessary to track the channel. This paper designs a method to analytically
derive a weighting factor , necessary to improve the efficiency of
interference cancellation in the presence of poor channel estimates. Moreover,
this weighting factor effectively mitigates the presence of incorrect decisions
at the output of the channel decoder. The analysis provides insight into the
properties of such interference cancellation scheme and the proposed approach
significantly increases the effectiveness of Successive Interference
Cancellation under the presence of channel estimation errors, which leads to
gains of up to 3 dB.Comment: IEEE GLOBECOM 201
Directional Modulation via Symbol-Level Precoding: A Way to Enhance Security
Wireless communication provides a wide coverage at the cost of exposing
information to unintended users. As an information-theoretic paradigm, secrecy
rate derives bounds for secure transmission when the channel to the
eavesdropper is known. However, such bounds are shown to be restrictive in
practice and may require exploitation of specialized coding schemes. In this
paper, we employ the concept of directional modulation and follow a signal
processing approach to enhance the security of multi-user MIMO communication
systems when a multi-antenna eavesdropper is present. Enhancing the security is
accomplished by increasing the symbol error rate at the eavesdropper. Unlike
the information-theoretic secrecy rate paradigm, we assume that the legitimate
transmitter is not aware of its channel to the eavesdropper, which is a more
realistic assumption. We examine the applicability of MIMO receiving algorithms
at the eavesdropper. Using the channel knowledge and the intended symbols for
the users, we design security enhancing symbol-level precoders for different
transmitter and eavesdropper antenna configurations. We transform each design
problem to a linearly constrained quadratic program and propose two solutions,
namely the iterative algorithm and one based on non-negative least squares, at
each scenario for a computationally-efficient modulation. Simulation results
verify the analysis and show that the designed precoders outperform the
benchmark scheme in terms of both power efficiency and security enhancement.Comment: This manuscript is submitted to IEEE Journal of Selected Topics in
Signal Processin
Soft metrics and their Performance Analysis for Optimal Data Detection in the Presence of Strong Oscillator Phase Noise
In this paper, we address the classical problem of maximum-likelihood (ML)
detection of data in the presence of random phase noise. We consider a system,
where the random phase noise affecting the received signal is first compensated
by a tracker/estimator. Then the phase error and its statistics are used for
deriving the ML detector. Specifically, we derive an ML detector based on a
Gaussian assumption for the phase error probability density function (PDF).
Further without making any assumptions on the phase error PDF, we show that the
actual ML detector can be reformulated as a weighted sum of central moments of
the phase error PDF. We present a simple approximation of this new ML rule
assuming that the phase error distribution is unknown. The ML detectors derived
are also the aposteriori probabilities of the transmitted symbols, and are
referred to as soft metrics. Then, using the detector developed based on
Gaussian phase error assumption, we derive the symbol error probability (SEP)
performance and error floor analytically for arbitrary constellations. Finally
we compare SEP performance of the various detectors/metrics in this work and
those from literature for different signal constellations, phase noise
scenarios and SNR values
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