313 research outputs found
Channel, Phase Noise, and Frequency Offset in OFDM Systems: Joint Estimation, Data Detection, and Hybrid Cramer-Rao Lower Bound
Oscillator phase noise (PHN) and carrier frequency offset (CFO) can adversely
impact the performance of orthogonal frequency division multiplexing (OFDM)
systems, since they can result in inter carrier interference and rotation of
the signal constellation. In this paper, we propose an expectation conditional
maximization (ECM) based algorithm for joint estimation of channel, PHN, and
CFO in OFDM systems. We present the signal model for the estimation problem and
derive the hybrid Cramer-Rao lower bound (HCRB) for the joint estimation
problem. Next, we propose an iterative receiver based on an extended Kalman
filter for joint data detection and PHN tracking. Numerical results show that,
compared to existing algorithms, the performance of the proposed ECM-based
estimator is closer to the derived HCRB and outperforms the existing estimation
algorithms at moderate-to-high signal-to-noise ratio (SNR). In addition, the
combined estimation algorithm and iterative receiver are more computationally
efficient than existing algorithms and result in improved average uncoded and
coded bit error rate (BER) performance
Iterative channel estimation techniques for multiple input multiple output orthogonal frequency division multiplexing systems
Thesis (Master)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2007Includes bibliographical references (leaves: 77-78)Text in English; Abstract: Turkish and Englishxii, 78 leavesOrthogonal frequency division multiplexing (OFDM) is well-known for its efficient high speed transmission and robustness to frequency-selective fading channels. On the other hand, multiple-input multiple-output (MIMO) antenna systems have the ability to increase capacity and reliability of a wireless communication system compared to single-input single-output (SISO) systems. Hence, the integration of the two technologies has the potential to meet the ever growing demands of future communication systems. In these systems, channel estimation is very crucial to demodulate the data coherently. For a good channel estimation, spectral efficiency and lower computational complexity are two important points to be considered. In this thesis, we explore different channel estimation techniques in order to improve estimation performance by increasing the bandwidth efficiency and reducing the computational complexity for both SISO-OFDM and MIMO-OFDM systems. We first investigate pilot and Expectation-Maximization (EM)-based channel estimation techniques and compare their performances. Next, we explore different pilot arrangements by reducing the number of pilot symbols in one OFDM frame to improve bandwidth efficiency. We obtain the bit error rate and the channel estimation performance for these pilot arrangements. Then, in order to decrase the computational complexity, we propose an iterative channel estimation technique, which establishes a link between the decision block and channel estimation block using virtual subcarriers. We compare this proposed technique with EM-based channel estimation in terms of performance and complexity. These channel estimation techniques are also applied to STBC-OFDM and V-BLAST structured MIMO-OFDM systems. Finally, we investigate a joint EM-based channel estimation and signal detection technique for V-BLAST OFDM system
EM-based Enhancement of the Wiener Pilot-aided Channel Estimation in MIMO-OFDM Systems
Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200
Improving the Performance of OTDOA based Positioning in NB-IoT Systems
In this paper, we consider positioning with
observed-time-difference-of-arrival (OTDOA) for a device deployed in
long-term-evolution (LTE) based narrow-band Internet-of-things (NB-IoT)
systems. We propose an iterative expectation-maximization based successive
interference cancellation (EM-SIC) algorithm to jointly consider estimations of
residual frequency-offset (FO), fading-channel taps and time-of-arrival (ToA)
of the first arrival-path for each of the detected cells. In order to design a
low complexity ToA detector and also due to the limits of low-cost analog
circuits, we assume an NB-IoT device working at a low-sampling rate such as
1.92 MHz or lower. The proposed EM-SIC algorithm comprises two stages to detect
ToA, based on which OTDOA can be calculated. In a first stage, after running
the EM-SIC block a predefined number of iterations, a coarse ToA is estimated
for each of the detected cells. Then in a second stage, to improve the ToA
resolution, a low-pass filter is utilized to interpolate the correlations of
time-domain PRS signal evaluated at a low sampling-rate to a high sampling-rate
such as 30.72 MHz. To keep low-complexity, only the correlations inside a small
search window centered at the coarse ToA estimates are upsampled. Then, the
refined ToAs are estimated based on upsampled correlations. If at least three
cells are detected, with OTDOA and the locations of detected cell sites, the
position of the NB-IoT device can be estimated. We show through numerical
simulations that, the proposed EM-SIC based ToA detector is robust against
impairments introduced by inter-cell interference, fading-channel and residual
FO. Thus significant signal-to-noise (SNR) gains are obtained over traditional
ToA detectors that do not consider these impairments when positioning a device.Comment: Accepted in GlobeCom 2017, 7 pages, 11 figure
Robust massive MIMO Equilization for mmWave systems with low resolution ADCs
Leveraging the available millimeter wave spectrum will be important for 5G.
In this work, we investigate the performance of digital beamforming with low
resolution ADCs based on link level simulations including channel estimation,
MIMO equalization and channel decoding. We consider the recently agreed 3GPP NR
type 1 OFDM reference signals. The comparison shows sequential DCD outperforms
MMSE-based MIMO equalization both in terms of detection performance and
complexity. We also show that the DCD based algorithm is more robust to channel
estimation errors. In contrast to the common believe we also show that the
complexity of MMSE equalization for a massive MIMO system is not dominated by
the matrix inversion but by the computation of the Gram matrix.Comment: submitted to WCNC 2018 Workshop
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Low-Complexity Blind Parameter Estimation in Wireless Systems with Noisy Sparse Signals
Baseband processing algorithms often require knowledge of the noise power,
signal power, or signal-to-noise ratio (SNR). In practice, these parameters are
typically unknown and must be estimated. Furthermore, the mean-square error
(MSE) is a desirable metric to be minimized in a variety of estimation and
signal recovery algorithms. However, the MSE cannot directly be used as it
depends on the true signal that is generally unknown to the estimator. In this
paper, we propose novel blind estimators for the average noise power, average
receive signal power, SNR, and MSE. The proposed estimators can be computed at
low complexity and solely rely on the large-dimensional and sparse nature of
the processed data. Our estimators can be used (i) to quickly track some of the
key system parameters while avoiding additional pilot overhead, (ii) to design
low-complexity nonparametric algorithms that require such quantities, and (iii)
to accelerate more sophisticated estimation or recovery algorithms. We conduct
a theoretical analysis of the proposed estimators for a Bernoulli complex
Gaussian (BCG) prior, and we demonstrate their efficacy via synthetic
experiments. We also provide three application examples that deviate from the
BCG prior in millimeter-wave multi-antenna and cell-free wireless systems for
which we develop nonparametric denoising algorithms that improve
channel-estimation accuracy with a performance comparable to denoisers that
assume perfect knowledge of the system parameters
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