105 research outputs found
Space time transceiver design over multipath fading channels
Imperial Users onl
Signal Processing in Arrayed MIMO Systems
Multiple-Input Multiple-Output (MIMO) systems, using antenna arrays at both
receiver and transmitter, have shown great potential to provide high bandwidth
utilization efficiency. Unlike other reported research on MIMO systems which
often assumes independent antennas, in this thesis an arrayed MIMO system
framework is proposed, which provides a richer description of the channel charac-
teristics and additional degrees of freedom in designing communication systems.
Firstly, the spatial correlated MIMO system is studied as an array-to-array
system with each array (Tx or Rx) having predefined constrained aperture. The
MIMO system is completely characterized by its transmit and receive array man-
ifolds and a new spatial correlation model other than Kronecker-based model is
proposed. As this model is based on array manifolds, it enables the study of the
effect of array geometry on the capacity of correlated MIMO channels.
Secondly, to generalize the proposed arrayed MIMO model to a frequency
selective fading scenario, the framework of uplink MIMO DS-CDMA (Direct-
Sequence Code Division Multiple Access) systems is developed. DOD estimation
is developed based on transmit beamrotation. A subspace-based joint DOA/TOA
estimation scheme as well as various spatial temporal reception algorithms is also
proposed.
Finally, the downlink MIMO-CDMA systems in multiple-access multipath fading channels are investigated. Linear precoder and decoder optimization problems
are studied under different criterions. Optimization approaches with different
power allocation schemes are investigated. Sub-optimization approaches with
close-form solution and thus less computation complexity are also proposed
Cyclic Prefix-Free MC-CDMA Arrayed MIMO Communication Systems
The objective of this thesis is to investigate MC-CDMA MIMO systems where
the antenna array geometry is taken into consideration. In most MC-CDMA
systems, cyclic pre xes, which reduce the spectral e¢ ciency, are used. In order
to improve the spectral efficiency, this research study is focused on cyclic pre x-
free MC-CDMA MIMO architectures.
Initially, space-time wireless channel models are developed by considering the
spatio-temporal mechanisms of the radio channel, such as multipath propaga-
tion. The spatio-temporal channel models are based on the concept of the array
manifold vector, which enables the parametric modelling of the channel.
The array manifold vector is extended to the multi-carrier space-time array
(MC-STAR) manifold matrix which enables the use of spatio-temporal signal
processing techniques. Based on the modelling, a new cyclic pre x-free MC-
CDMA arrayed MIMO communication system is proposed and its performance
is compared with a representative existing system. Furthermore, a MUSIC-type
algorithm is then developed for the estimation of the channel parameters of the
received signal.
This proposed cyclic pre x-free MC-CDMA arrayed MIMO system is then
extended to consider the effects of spatial diffusion in the wireless channel. Spatial
diffusion is an important channel impairment which is often ignored and the
failure to consider such effects leads to less than satisfactory performance. A
subspace-based approach is proposed for the estimation of the channel parameters
and spatial spread and reception of the desired signal.
Finally, the problem of joint optimization of the transmit and receive beam-
forming weights in the downlink of a cyclic pre x-free MC-CDMA arrayed MIMO
communication system is investigated. A subcarrier-cooperative approach is used
for the transmit beamforming so that there is greater flexibility in the allocation
of channel symbols. The resulting optimization problem, with a per-antenna
transmit power constraint, is solved by the Lagrange multiplier method and an
iterative algorithm is proposed
Spatiotemporal-MIMO channel estimator and beamformer for 5G
With requirements of spiraling data rates and limited spectrum availability, there is an increased interest in mm-wave beamformer-based communications for 5G. For upcoming cellular networks, the critical point is to exploit the increased number of employable antennas at both Tx and Rx to: 1) combat increased path loss; 2) tackle higher interference due to higher user density; and 3) handle multipath effects in frequency selective channels. Toward this, a multi-beam spatiotemporal superresolution beamforming framework is proposed in this paper as a promising candidate to design beampatterns that mitigate/suppress co-channel interference and deliver massive gain in the desired directions. Initially, channel and signal models suitable for the mm-wave MIMO system are presented using the manifold vectors of both Tx and Rx antenna arrays. Based on these models, a novel subspace-based channel estimator is employed, which estimates delays, directions, velocities, and fading coefficients of the desired signal paths. This information is then exploited by the proposed spatiotemporal beamformer to provide a massive array gain that combats path loss without increasing the number of antenna array elements and to be tolerant to the near-far problem in a high interference environment. The performance of the proposed channel estimator and beamformer is examined using computer simulation studies
Sensor array signal processing : two decades later
Caption title.Includes bibliographical references (p. 55-65).Supported by Army Research Office. DAAL03-92-G-115 Supported by the Air Force Office of Scientific Research. F49620-92-J-2002 Supported by the National Science Foundation. MIP-9015281 Supported by the ONR. N00014-91-J-1967 Supported by the AFOSR. F49620-93-1-0102Hamid Krim, Mats Viberg
Antenna array calibration in wireless communications
Imperial Users onl
Blind adaptive constrained reduced-rank parameter estimation based on constant modulus design for CDMA interference suppression
This paper proposes a multistage decomposition for blind adaptive parameter estimation in the Krylov subspace with the code-constrained constant modulus (CCM) design criterion. Based on constrained optimization of the constant modulus cost function and utilizing the Lanczos algorithm and Arnoldi-like iterations, a multistage decomposition is developed for blind parameter estimation. A family of computationally efficient blind adaptive reduced-rank stochastic gradient (SG) and recursive least squares (RLS) type algorithms along with an automatic rank selection procedure are also devised and evaluated against existing methods. An analysis of the convergence properties of the method is carried out and convergence conditions for the reduced-rank adaptive algorithms are established. Simulation results consider the application of the proposed techniques to the suppression of multiaccess and intersymbol interference in DS-CDMA systems
Multiple and single snapshot compressive beamforming
For a sound field observed on a sensor array, compressive sensing (CS)
reconstructs the direction-of-arrival (DOA) of multiple sources using a
sparsity constraint. The DOA estimation is posed as an underdetermined problem
by expressing the acoustic pressure at each sensor as a phase-lagged
superposition of source amplitudes at all hypothetical DOAs. Regularizing with
an -norm constraint renders the problem solvable with convex
optimization, and promoting sparsity gives high-resolution DOA maps. Here, the
sparse source distribution is derived using maximum a posteriori (MAP)
estimates for both single and multiple snapshots. CS does not require inversion
of the data covariance matrix and thus works well even for a single snapshot
where it gives higher resolution than conventional beamforming. For multiple
snapshots, CS outperforms conventional high-resolution methods, even with
coherent arrivals and at low signal-to-noise ratio. The superior resolution of
CS is demonstrated with vertical array data from the SWellEx96 experiment for
coherent multi-paths.Comment: In press Journal of Acoustical Society of Americ
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