854 research outputs found
Efficient Robust Adaptive Beamforming Algorithms for Sensor Arrays
Sensor array processing techniques have been an important research area in recent years.
By using a sensor array of a certain configuration, we can improve the parameter estimation
accuracy from the observation data in the presence of interference and noise. In this
thesis, we focus on sensor array processing techniques that use antenna arrays for beamforming,
which is the key task in wireless communications, radar and sonar systems.
Firstly, we propose a low-complexity robust adaptive beamforming (RAB) technique
which estimates the steering vector using a Low-Complexity Shrinkage-Based Mismatch
Estimation (LOCSME) algorithm. The proposed LOCSME algorithm estimates the covariance
matrix of the input data and the interference-plus-noise covariance (INC) matrix
by using the Oracle Approximating Shrinkage (OAS) method. Secondly, we present
cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms.
The proposed algorithms are based on the exploitation of the cross-correlation
between the array observation data and the output of the beamformer. Thirdly, we propose
distributed beamforming techniques that are based on wireless relay systems. Algorithms
that combine relay selections and SINR maximization or Minimum Mean-Square-
Error (MMSE) consensus are developed, assuming the relay systems are under total relay
transmit power constraint. Lastly, we look into the research area of robust distributed
beamforming (RDB) and develop a novel RDB approach based on the exploitation of
the cross-correlation between the received data at the relays and the destination and a
subspace projection method to estimate the channel errors, namely, the cross-correlation
and subspace projection (CCSP) RDB technique, which efficiently maximizes the output
SINR and minimizes the channel errors. Simulation results show that the proposed
techniques outperform existing techniques in various performance metrics
Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding
In this work, we propose a subspace-based algorithm for DOA estimation which
iteratively reduces the disturbance factors of the estimated data covariance
matrix and incorporates prior knowledge which is gradually obtained on line. An
analysis of the MSE of the reshaped data covariance matrix is carried out along
with comparisons between computational complexities of the proposed and
existing algorithms. Simulations focusing on closely-spaced sources, where they
are uncorrelated and correlated, illustrate the improvements achieved.Comment: 7 figures. arXiv admin note: text overlap with arXiv:1703.1052
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels
In this paper, we establish a general framework on the reduced dimensional
channel state information (CSI) estimation and pre-beamformer design for
frequency-selective massive multiple-input multiple-output MIMO systems
employing single-carrier (SC) modulation in time division duplex (TDD) mode by
exploiting the joint angle-delay domain channel sparsity in millimeter (mm)
wave frequencies. First, based on a generic subspace projection taking the
joint angle-delay power profile and user-grouping into account, the reduced
rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived
for spatially correlated wideband MIMO channels. Second, the statistical
pre-beamformer design is considered for frequency-selective SC massive MIMO
channels. We examine the dimension reduction problem and subspace (beamspace)
construction on which the RR-MMSE estimation can be realized as accurately as
possible. Finally, a spatio-temporal domain correlator type reduced rank
channel estimator, as an approximation of the RR-MMSE estimate, is obtained by
carrying out least square (LS) estimation in a proper reduced dimensional
beamspace. It is observed that the proposed techniques show remarkable
robustness to the pilot interference (or contamination) with a significant
reduction in pilot overhead
Two-Stage Subspace Constrained Precoding in Massive MIMO Cellular Systems
We propose a subspace constrained precoding scheme that exploits the spatial
channel correlation structure in massive MIMO cellular systems to fully unleash
the tremendous gain provided by massive antenna array with reduced channel
state information (CSI) signaling overhead. The MIMO precoder at each base
station (BS) is partitioned into an inner precoder and a Transmit (Tx) subspace
control matrix. The inner precoder is adaptive to the local CSI at each BS for
spatial multiplexing gain. The Tx subspace control is adaptive to the channel
statistics for inter-cell interference mitigation and Quality of Service (QoS)
optimization. Specifically, the Tx subspace control is formulated as a QoS
optimization problem which involves an SINR chance constraint where the
probability of each user's SINR not satisfying a service requirement must not
exceed a given outage probability. Such chance constraint cannot be handled by
the existing methods due to the two stage precoding structure. To tackle this,
we propose a bi-convex approximation approach, which consists of three key
ingredients: random matrix theory, chance constrained optimization and
semidefinite relaxation. Then we propose an efficient algorithm to find the
optimal solution of the resulting bi-convex approximation problem. Simulations
show that the proposed design has significant gain over various baselines.Comment: 13 pages, accepted by IEEE Transactions on Wireless Communication
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