38 research outputs found
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from the receiver may become outdated before its actual usage at the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
Nested Arrays: A Novel Approach to Array Processing With Enhanced Degrees of Freedom
A new array geometry, which is capable of significantly
increasing the degrees of freedom of linear arrays, is
proposed. This structure is obtained by systematically nesting two
or more uniform linear arrays and can provide O(N^2) degrees
of freedom using only physical sensors when the second-order
statistics of the received data is used. The concept of nesting is
shown to be easily extensible to multiple stages and the structure
of the optimally nested array is found analytically. It is possible to
provide closed form expressions for the sensor locations and the
exact degrees of freedom obtainable from the proposed array as a
function of the total number of sensors. This cannot be done for
existing classes of arrays like minimum redundancy arrays which
have been used earlier for detecting more sources than the number
of physical sensors. In minimum-input–minimum-output (MIMO)
radar, the degrees of freedom are increased by constructing a
longer virtual array through active sensing. The method proposed
here, however, does not require active sensing and is capable of
providing increased degrees of freedom in a completely passive
setting. To utilize the degrees of freedom of the nested co-array, a
novel spatial smoothing based approach to DOA estimation is also
proposed, which does not require the inherent assumptions of the
traditional techniques based on fourth-order cumulants or quasi
stationary signals. As another potential application of the nested
array, a new approach to beamforming based on a nonlinear
preprocessing is also introduced, which can effectively utilize the
degrees of freedom offered by the nested arrays. The usefulness of
all the proposed methods is verified through extensive computer
simulations