3,019 research outputs found
Estimation of phase noise in oscillators with colored noise sources
In this letter we study the design of algorithms for estimation of phase
noise (PN) with colored noise sources. A soft-input maximum a posteriori PN
estimator and a modified soft-input extended Kalman smoother are proposed. The
performance of the proposed algorithms are compared against those studied in
the literature, in terms of mean square error of PN estimation, and symbol
error rate of the considered communication system. The comparisons show that
considerable performance gains can be achieved by designing estimators that
employ correct knowledge of the PN statistics
Calculation of the Performance of Communication Systems from Measured Oscillator Phase Noise
Oscillator phase noise (PN) is one of the major problems that affect the
performance of communication systems. In this paper, a direct connection
between oscillator measurements, in terms of measured single-side band PN
spectrum, and the optimal communication system performance, in terms of the
resulting error vector magnitude (EVM) due to PN, is mathematically derived and
analyzed. First, a statistical model of the PN, considering the effect of white
and colored noise sources, is derived. Then, we utilize this model to derive
the modified Bayesian Cramer-Rao bound on PN estimation, and use it to find an
EVM bound for the system performance. Based on our analysis, it is found that
the influence from different noise regions strongly depends on the
communication bandwidth, i.e., the symbol rate. For high symbol rate
communication systems, cumulative PN that appears near carrier is of relatively
low importance compared to the white PN far from carrier. Our results also show
that 1/f^3 noise is more predictable compared to 1/f^2 noise and in a fair
comparison it affects the performance less.Comment: Accepted in IEEE Transactions on Circuits and Systems-I: Regular
Paper
Optimal state estimation for cavity optomechanical systems
We demonstrate optimal state estimation for a cavity optomechanical system
through Kalman filtering. By taking into account nontrivial experimental noise
sources, such as colored laser noise and spurious mechanical modes, we
implement a realistic state-space model. This allows us to obtain the
conditional system state, i.e., conditioned on previous measurements, with
minimal least-square estimation error. We apply this method for estimating the
mechanical state, as well as optomechanical correlations both in the weak and
strong coupling regime. The application of the Kalman filter is an important
next step for achieving real-time optimal (classical and quantum) control of
cavity optomechanical systems.Comment: replaced with published version, 5+12 page
Oscillator Phase Noise and Small-Scale Channel Fading in Higher Frequency Bands
This paper investigates the effect of oscillator phase noise and channel
variations due to fading on the performance of communication systems at
frequency bands higher than 10GHz. Phase noise and channel models are reviewed
and technology-dependent bounds on the phase noise quality of radio oscillators
are presented. Our study shows that, in general, both channel variations and
phase noise can have severe effects on the system performance at high
frequencies. Importantly, their relative severity depends on the application
scenario and system parameters such as center frequency and bandwidth. Channel
variations are seen to be more severe than phase noise when the relative
velocity between the transmitter and receiver is high. On the other hand,
performance degradation due to phase noise can be more severe when the center
frequency is increased and the bandwidth is kept a constant, or when
oscillators based on low power CMOS technology are used, as opposed to high
power GaN HEMT based oscillators.Comment: IEEE Global Telecommun. Conf. (GLOBECOM), Austin, TX, Dec. 201
Receiver Algorithm based on Differential Signaling for SIMO Phase Noise Channels with Common and Separate Oscillator Configurations
In this paper, a receiver algorithm consisting of differential transmission
and a two-stage detection for a single-input multiple-output (SIMO) phase-noise
channels is studied. Specifically, the phases of the QAM modulated data symbols
are manipulated before transmission in order to make them more immune to the
random rotational effects of phase noise. At the receiver, a two-stage detector
is implemented, which first detects the amplitude of the transmitted symbols
from a nonlinear combination of the received signal amplitudes. Then in the
second stage, the detector performs phase detection. The studied signaling
method does not require transmission of any known symbols that act as pilots.
Furthermore, no phase noise estimator (or a tracker) is needed at the receiver
to compensate the effect of phase noise. This considerably reduces the
complexity of the receiver structure. Moreover, it is observed that the studied
algorithm can be used for the setups where a common local oscillator or
separate independent oscillators drive the radio-frequency circuitries
connected to each antenna. Due to the differential encoding/decoding of the
phase, weighted averaging can be employed at a multi-antenna receiver, allowing
for phase noise suppression to leverage the large number of antennas. Hence, we
observe that the performance improves by increasing the number of antennas,
especially in the separate oscillator case. Further increasing the number of
receive antennas results in a performance error floor, which is a function of
the quality of the oscillator at the transmitter.Comment: IEEE GLOBECOM 201
Array signal processing for maximum likelihood direction-of-arrival estimation
Emitter Direction-of-Arrival (DOA) estimation is a fundamental problem in a variety of applications including radar, sonar, and wireless communications. The research has received considerable attention in literature and numerous methods have been proposed. Maximum Likelihood (ML) is a nearly optimal technique producing superior estimates compared to other methods especially in unfavourable conditions, and thus is of significant practical interest. This paper discusses in details the techniques for ML DOA estimation in either white Gaussian noise or unknown noise environment. Their performances are analysed and compared, and evaluated against the theoretical lower bounds
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