681 research outputs found
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
Uplink Performance of Time-Reversal MRC in Massive MIMO Systems Subject to Phase Noise
Multi-user multiple-input multiple-output (MU-MIMO) cellular systems with an
excess of base station (BS) antennas (Massive MIMO) offer unprecedented
multiplexing gains and radiated energy efficiency. Oscillator phase noise is
introduced in the transmitter and receiver radio frequency chains and severely
degrades the performance of communication systems. We study the effect of
oscillator phase noise in frequency-selective Massive MIMO systems with
imperfect channel state information (CSI). In particular, we consider two
distinct operation modes, namely when the phase noise processes at the BS
antennas are identical (synchronous operation) and when they are independent
(non-synchronous operation). We analyze a linear and low-complexity
time-reversal maximum-ratio combining (TR-MRC) reception strategy. For both
operation modes we derive a lower bound on the sum-capacity and we compare
their performance. Based on the derived achievable sum-rates, we show that with
the proposed receive processing an array gain is achievable. Due
to the phase noise drift the estimated effective channel becomes progressively
outdated. Therefore, phase noise effectively limits the length of the interval
used for data transmission and the number of scheduled users. The derived
achievable rates provide insights into the optimum choice of the data interval
length and the number of scheduled users.Comment: 13 pages, 6 figures, 2 tables, IEEE Transactions on Wireless
Communications (accepted
Effect of Synchronizing Coordinated Base Stations on Phase Noise Estimation
In this paper, we study the problem of oscillator phase noise (PN) estimation
in coordinated multi-point (CoMP) transmission systems. Specifically, we
investigate the effect of phase synchronization between coordinated base
stations (BSs) on PN estimation at the user receiver (downlink channel). In
this respect, the Bayesian Cram\'er-Rao bound for PN estimation is derived
which is a function of the level of phase synchronization between the
coordinated BSs. Results show that quality of BS synchronization has a
significant effect on the PN estimation
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
On the Capacity of the Wiener Phase-Noise Channel: Bounds and Capacity Achieving Distributions
In this paper, the capacity of the additive white Gaussian noise (AWGN)
channel, affected by time-varying Wiener phase noise is investigated. Tight
upper and lower bounds on the capacity of this channel are developed. The upper
bound is obtained by using the duality approach, and considering a specific
distribution over the output of the channel. In order to lower-bound the
capacity, first a family of capacity-achieving input distributions is found by
solving a functional optimization of the channel mutual information. Then,
lower bounds on the capacity are obtained by drawing samples from the proposed
distributions through Monte-Carlo simulations. The proposed capacity-achieving
input distributions are circularly symmetric, non-Gaussian, and the input
amplitudes are correlated over time. The evaluated capacity bounds are tight
for a wide range of signal-to-noise-ratio (SNR) values, and thus they can be
used to quantify the capacity. Specifically, the bounds follow the well-known
AWGN capacity curve at low SNR, while at high SNR, they coincide with the
high-SNR capacity result available in the literature for the phase-noise
channel.Comment: IEEE Transactions on Communications, 201
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