343 research outputs found
Adaptive Decision Feedback Reduced-Rank Equalization Based on Joint Iterative Optimization of Adaptive Estimation Algorithms for Multi-Antenna Systems
This paper presents a novel adaptive reduced-rank multi-input-multi-output
(MIMO) decision feedback equalization structure based on joint iterative
optimization of adaptive estimators. The novel reduced-rank equalization
structure consists of a joint iterative optimization of two equalization
stages, namely, a projection matrix that performs dimensionality reduction and
a reduced-rank estimator that retrieves the desired transmitted symbol. The
proposed reduced-rank structure is followed by a decision feedback scheme that
is responsible for cancelling the inter-antenna interference caused by the
associated data streams. We describe least squares (LS) expressions for the
design of the projection matrix and the reduced-rank estimator along with
computationally efficient recursive least squares (RLS) adaptive estimation
algorithms. Simulations for a MIMO equalization application show that the
proposed scheme outperforms the state-of-the-art reduced-rank and the
conventional estimation algorithms at about the same complexity.Comment: 6 figures. arXiv admin note: substantial text overlap with
arXiv:1301.269
Coordinate Tomlinson-Harashima Precoding Design for Overloaded Multi-user MIMO Systems
Tomlinson-Harashima precoding (THP) is a nonlinear processing technique
employed at the transmit side to implement the concept of dirty paper coding
(DPC). The perform of THP, however, is restricted by the dimensionality
constraint that the number of transmit antennas has to be greater or equal to
the total number of receive antennas. In this paper, we propose an iterative
coordinate THP algorithm for the scenarios in which the total number of receive
antennas is larger than the number of transmit antennas. The proposed algorithm
is implemented on two types of THP structures, the decentralized THP (dTHP)
with diagonal weighted filters at the receivers of the users, and the
centralized THP (cTHP) with diagonal weighted filter at the transmitter.
Simulation results show that a much better bit error rate (BER) and sum-rate
performances can be achieved by the proposed iterative coordinate THP compared
to the previous linear art.Comment: 3 figures, 6 pages, ISWCS 2014. arXiv admin note: text overlap with
arXiv:1401.475
Resource Allocation and Interference Mitigation Techniques for Cooperative Multi-Antenna and Spread Spectrum Wireless Networks
This chapter presents joint interference suppression and power allocation
algorithms for DS-CDMA and MIMO networks with multiple hops and
amplify-and-forward and decode-and-forward (DF) protocols. A scheme for joint
allocation of power levels across the relays and linear interference
suppression is proposed. We also consider another strategy for joint
interference suppression and relay selection that maximizes the diversity
available in the system. Simulations show that the proposed cross-layer
optimization algorithms obtain significant gains in capacity and performance
over existing schemes.Comment: 10 figures. arXiv admin note: substantial text overlap with
arXiv:1301.009
Detection and Estimation Algorithms in Massive MIMO Systems
This book chapter reviews signal detection and parameter estimation
techniques for multiuser multiple-antenna wireless systems with a very large
number of antennas, known as massive multi-input multi-output (MIMO) systems.
We consider both centralized antenna systems (CAS) and distributed antenna
systems (DAS) architectures in which a large number of antenna elements are
employed and focus on the uplink of a mobile cellular system. In particular, we
focus on receive processing techniques that include signal detection and
parameter estimation problems and discuss the specific needs of massive MIMO
systems. Simulation results illustrate the performance of detection and
estimation algorithms under several scenarios of interest. Key problems are
discussed and future trends in massive MIMO systems are pointed out.Comment: 7 figures, 14 pages. arXiv admin note: substantial text overlap with
arXiv:1310.728
Low-complexity implementation of convex optimization-based phase retrieval
Phase retrieval has important applications in optical imaging, communications
and sensing. Lifting the dimensionality of the problem allows phase retrieval
to be approximated as a convex optimization problem in a higher-dimensional
space. Convex optimization-based phase retrieval has been shown to yield high
accuracy, yet its low-complexity implementation has not been explored. In this
paper, we study three fundamental approaches for its low-complexity
implementation: the projected gradient method, the Nesterov accelerated
gradient method, and the alternating direction method of multipliers (ADMM)
method. We derive the corresponding estimation algorithms and evaluate their
complexities. We compare their performance in the application area of
direct-detection mode-division multiplexing. We demonstrate that they yield
negligible estimation penalties (less than 0.2 dB for transmitter processing
and less than 0.6 dB for receiver equalization) while yielding low
computational cost, as their implementation complexities all scale
quadratically in the number of unknown parameters. Among the three methods,
ADMM achieves convergence after the smallest number of iterations
Low-Complexity Robust Adaptive Beamforming Algorithms Based on Shrinkage for Mismatch Estimation
In this paper, we propose low-complexity robust adaptive beamforming (RAB)
techniques that based on shrinkage methods. The only prior knowledge required
by the proposed algorithms are the angular sector in which the actual steering
vector is located and the antenna array geometry. We firstly present a
Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) algorithm to
estimate the desired signal steering vector mismatch, in which the
interference-plus-noise covariance (INC) matrix is estimated with Oracle
Approximating Shrinkage (OAS) method and the weights are computed with matrix
inversions. We then develop low-cost stochastic gradient (SG) recursions to
estimate the INC matrix and update the beamforming weights, resulting in the
proposed LOCSME-SG algorithm. Simulation results show that both LOCSME and
LOCSME-SG achieve very good output signal-to-interference-plus-noise ratio
(SINR) compared to previously reported adaptive RAB algorithms.Comment: 8 pages, 2 figures, WSA. arXiv admin note: text overlap with
arXiv:1311.233
Adaptive Minimum BER Reduced-Rank Linear Detection for Massive MIMO Systems
In this paper, we propose a novel adaptive reduced-rank strategy for very
large multiuser multi-input multi-output (MIMO) systems. The proposed
reduced-rank scheme is based on the concept of joint iterative optimization
(JIO) of filters according to the minimization of the bit error rate (BER) cost
function. The proposed optimization technique adjusts the weights of a
projection matrix and a reduced-rank filter jointly. We develop stochastic
gradient (SG) algorithms for their adaptive implementation and introduce a
novel automatic rank selection method based on the BER criterion. Simulation
results for multiuser MIMO systems show that the proposed adaptive algorithms
significantly outperform existing schemes.Comment: 6 figures. arXiv admin note: substantial text overlap with
arXiv:1302.413
Study of Unique-Word Based GFDM Transmission Systems
In this paper, we propose the use of a deterministic sequence, known as
unique word (UW), instead of the cyclic prefix (CP) in generalized frequency
division multiplexing (GFDM) systems. The UW consists of known sequences that,
if not null, can be used advantageously for synchronization and channel
estimation purposes. In addition, UW allows the application of a highly
efficient linear minimum mean squared error (LMMSE) smoother for noise
reduction at the receiver. To avoid the conditions of non-orthogonality caused
by the insertion of the UW and performance degradation in time varying
frequency-selective channels, we use frequency-shift offset quadrature
amplitude modulation (FS-OQAM). We present a signal model of a UW-GFDM system
considering a single and multiple UWs. We then develop an LMMSE receive filter
for signal reception of the proposed UW-GFDM system. Simulations show that the
proposed UW-GFDM system outperforms prior work.Comment: 5 pages, 4 figure
Study of Set-Membership Kernel Adaptive Algorithms and Applications
Adaptive algorithms based on kernel structures have been a topic of
significant research over the past few years. The main advantage is that they
form a family of universal approximators, offering an elegant solution to
problems with nonlinearities. Nevertheless these methods deal with kernel
expansions, creating a growing structure also known as dictionary, whose size
depends on the number of new inputs. In this paper we derive the set-membership
kernel-based normalized least-mean square (SM-NKLMS) algorithm, which is
capable of limiting the size of the dictionary created in stationary
environments. We also derive as an extension the set-membership kernelized
affine projection (SM-KAP) algorithm. Finally several experiments are presented
to compare the proposed SM-NKLMS and SM-KAP algorithms to the existing methods.Comment: 4 figures, 6 page
Study of BEM-Type Channel Estimation Techniques for 5G Multicarrier Systems
In this paper, we investigate channel estimation techniques for 5G
multicarrier systems. Due to the characteristics of the 5G application
scenarios, channel estimation techniques have been tested in Orthogonal
Frequency Division Multiplexing (OFDM) and Generalized Frequency Division
Multiplexing (GFDM) systems. The orthogonality between subcarriers in OFDM
systems permits inserting and extracting pilots without interference. However,
due to pulse shaping, subcarriers in GFDM are no longer orthogonal and
interfere with each other. Due to such interference, the channel estimation for
GFDM is not trivial. A robust and low-complexity channel estimator can be
obtained by combining a minimum mean-square error (MMSE) regularization and the
basis expansion model (BEM) approach. In this work, we develop a BEM-type
channel estimator along with a strategy to obtain the covariance matrix of the
BEM coefficients. Simulations show that the BEM-type channel estimation shows
performance close to that of the linear MMSE (LMMSE), even though there is no
need to know the channel power delay profile, and its complexity is low.Comment: 2 figures, 7 page
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