335 research outputs found
On the Performance Limits of Pilot-Based Estimation of Bandlimited Frequency-Selective Communication Channels
In this paper the problem of assessing bounds on the accuracy of pilot-based
estimation of a bandlimited frequency selective communication channel is
tackled. Mean square error is taken as a figure of merit in channel estimation
and a tapped-delay line model is adopted to represent a continuous time channel
via a finite number of unknown parameters. This allows to derive some
properties of optimal waveforms for channel sounding and closed form Cramer-Rao
bounds
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
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
Orthogonal or superimposed pilots? A rate-efficient channel estimation strategy for stationary MIMO fading channels
©IEEE, 2017This paper considers channel estimation for multiple-input multiple-output (MIMO) channels and revisits two competing concepts of including training data into the transmit signal, namely orthogonal pilot (OP) that periodically transmits alternating pilot-data symbols, and superimposed pilot (SP) that overlays pilot-data symbols over time. We investigate rates achievable by both schemes when the channel undergoes time-selective bandlimited fading and analyze their behaviors with respect to the MIMO dimension and fading speed. By incorporating the multiple-antenna factors, we demonstrate that the widely-known trend, in which the OP is superior to the SP in the regimes of high signal-to-noise ratio (SNR) and slow-fading, and vice-versa, does not hold in general. As the number of transmit antennas (nt) increases, the range of operable fading speeds for the OP is significantly narrowed due to limited time resources for channel estimation and insufficient fading samples, which results in the SP being competitive in wider speed and SNR ranges. For a sufficiently small nt, we demonstrate thatas the fading variation becomes slower, the estimation quality for the SP can be superior to that for the OP. In this case, the SP outperforms the OP in the slow-fading regime due to full utilization of time for data transmission
Channel Prediction for Mobile MIMO Wireless Communication Systems
Temporal variation and frequency selectivity of wireless channels constitute
a major drawback to the attainment of high gains in capacity
and reliability offered by multiple antennas at the transmitter and receiver
of a mobile communication system. Limited feedback and adaptive transmission
schemes such as adaptive modulation and coding, antenna selection,
power allocation and scheduling have the potential to provide the platform
of attaining the high transmission rate, capacity and QoS requirements in
current and future wireless communication systems. Theses schemes require
both the transmitter and receiver to have accurate knowledge of Channel
State Information (CSI). In Time Division Duplex (TDD) systems, CSI at
the transmitter can be obtained using channel reciprocity. In Frequency Division
Duplex (FDD) systems, however, CSI is typically estimated at the
receiver and fed back to the transmitter via a low-rate feedback link. Due to
the inherent time delays in estimation, processing and feedback, the CSI obtained
from the receiver may become outdated before its actual usage at the
transmitter. This results in significant performance loss, especially in high
mobility environments. There is therefore a need to extrapolate the varying
channel into the future, far enough to account for the delay and mitigate the
performance degradation.
The research in this thesis investigates parametric modeling and prediction
of mobile MIMO channels for both narrowband and wideband systems.
The focus is on schemes that utilize the additional spatial information offered
by multiple sampling of the wave-field in multi-antenna systems to
aid channel prediction. The research has led to the development of several
algorithms which can be used for long range extrapolation of time-varyingchannels. Based on spatial channel modeling approaches, simple and efficient
methods for the extrapolation of narrowband MIMO channels are proposed.
Various extensions were also developed. These include methods for wideband
channels, transmission using polarized antenna arrays, and mobile-to-mobile
systems.
Performance bounds on the estimation and prediction error are vital when
evaluating channel estimation and prediction schemes. For this purpose, analytical
expressions for bound on the estimation and prediction of polarized
and non-polarized MIMO channels are derived. Using the vector formulation
of the Cramer Rao bound for function of parameters, readily interpretable
closed-form expressions for the prediction error bounds were found for cases
with Uniform Linear Array (ULA) and Uniform Planar Array (UPA). The
derived performance bounds are very simple and so provide insight into system
design.
The performance of the proposed algorithms was evaluated using standardized
channel models. The effects of the temporal variation of multipath
parameters on prediction is studied and methods for jointly tracking the
channel parameters are developed. The algorithms presented can be utilized
to enhance the performance of limited feedback and adaptive MIMO
transmission schemes
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