46 research outputs found
Non-Coherent OFDM Transmission via Off-the-Grid Joint Channel and Data Estimation
Pilot-aided channel estimation techniques are known to waste the spectral bandwidth. An off-the-grid blind estimator for time-variant orthogonal frequency division multiplexing (OFDM) systems is studied in this letter. In this regard, we propose a blind estimator based on atomic norm minimization (ANM) for OFDM systems. To do so, at the first transmission block, using a lifted ANM (LANM) and simple constraint on ℓ2 norm of data, we simultaneously estimate the channel and data. For the subsequent blocks, we use a penalized ANM (PANM) to simultaneously track the channel’s parameters and detect transmit signals. The proposed problems require an infinitedimensional search, hence are NP-hard. Therefore, we propose two semidefinite programs (SDPs) to implement them. We then derive the total computational complexity of the proposed estimator. The simulation results show the superiority of the proposed estimator to the state-of-the-arts
An Iterative Receiver for OFDM With Sparsity-Based Parametric Channel Estimation
In this work we design a receiver that iteratively passes soft information
between the channel estimation and data decoding stages. The receiver
incorporates sparsity-based parametric channel estimation. State-of-the-art
sparsity-based iterative receivers simplify the channel estimation problem by
restricting the multipath delays to a grid. Our receiver does not impose such a
restriction. As a result it does not suffer from the leakage effect, which
destroys sparsity. Communication at near capacity rates in high SNR requires a
large modulation order. Due to the close proximity of modulation symbols in
such systems, the grid-based approximation is of insufficient accuracy. We show
numerically that a state-of-the-art iterative receiver with grid-based sparse
channel estimation exhibits a bit-error-rate floor in the high SNR regime. On
the contrary, our receiver performs very close to the perfect channel state
information bound for all SNR values. We also demonstrate both theoretically
and numerically that parametric channel estimation works well in dense
channels, i.e., when the number of multipath components is large and each
individual component cannot be resolved.Comment: Major revision, accepted for IEEE Transactions on Signal Processin
Demixing Sines and Spikes Using Multiple Measurement Vectors
In this paper, we address the line spectral estimation problem with multiple
measurement corrupted vectors. Such scenarios appear in many practical
applications such as radar, optics, and seismic imaging in which the signal of
interest can be modeled as the sum of a spectrally sparse and a blocksparse
signal known as outlier. Our aim is to demix the two components and for that,
we design a convex problem whose objective function promotes both of the
structures. Using positive trigonometric polynomials (PTP) theory, we
reformulate the dual problem as a semi-definite program (SDP). Our theoretical
results states that for a fixed number of measurements N and constant number of
outliers, up to O(N) spectral lines can be recovered using our SDP problem as
long as a minimum frequency separation condition is satisfied. Our simulation
results also show that increasing the number of samples per measurement
vectors, reduces the minimum required frequency separation for successful
recovery.Comment: 9 pages, 3 figure
RIS-Position and Orientation Estimation in MIMO-OFDM Systems with Practical Scatterers
In this paper, we investigate the problem of estimating the position and the
angle of rotation of a mobile station (MS) in a millimeter wave (mmWave)
multiple-input-multiple-output (MIMO) system aided by a reconfigurable
intelligent surface (RIS). The virtual line-of-sight (VLoS) link created by the
RIS and the non-line-of-sight (NLoS) links that originate from scatterers in
the considered environment are utilized to facilitate the estimation. A
two-step positioning scheme is exploited, where the channel parameters are
first acquired, and the position-related parameters are then estimated. The
channel parameters are obtained through a coarser and a subsequent finer
estimation processes. As for the coarse estimation, the distributed compressed
sensing orthogonal simultaneous matching pursuit (DCS-SOMP) algorithm, the
maximum likelihood (ML) algorithm, and the discrete Fourier transform (DFT) are
utilized to separately estimate the channel parameters. The obtained channel
parameters are then jointly refined by using the space-alternating generalized
expectation maximization (SAGE) algorithm, which circumvents the
high-dimensional optimization issue of ML estimation. Departing from the
estimated channel parameters, the positioning-related parameters are estimated.
The performance of estimating the channel-related and position-related
parameters is theoretically quantified by using the Cramer-Rao lower bound
(CRLB). Simulation results demonstrate the superior performance of the proposed
positioning algorithms.Comment: This work has been submitted to the IEEE for possible publication.
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Advanced Techniques for High-Throughput Cellular Communications
The next generation wireless communication systems require ubiquitous high-throughput mobile connectivity under a range of challenging network settings (urban versus rural, high device density, mobility, etc). To improve the performance of the system, the physical layer design is of great importance. The previous research on improving the physical layer properties includes: a) highly directional transmissions that can enhance the throughput and spatial reuse; b) enhanced MIMO that can eliminate
contention, enabling linear increase of capacity with number of antennas; c) mmWave technologies which operate on GHz bandwidth to over substantially higher throughput; d) better cooperative spectrum sharing with cognitive radios; e) better multiple access method which can mitigate multiuser interference and allow more multi-users.
This dissertation addresses several techniques in the physical layer design of the next generation wireless communication systems. In chapter two, an orthogonal frequency division with code division multiple access (OFDM-CDMA) systems is proposed and a polyphase code is used to improve multiple access performance and make the OFDM signal satisfy the peak to average ratio (PAPR) constraint. Chapter three studies the I/Q imbalance for direct down converter. For wideband transmitter and receiver that use direct conversion for I/Q sampling, the I/Q imbalance becomes a critical issue. With higher I/Q imbalance, there will be higher degradation in quadrature amplitude modulation, which degrades the throughput tremendously. Chapter four investigate a problem of spectrum sharing for cognitive wideband communication. An energy-efficient sub-Nyquist sampling algorithm is developed for optimal sampling and spectrum sensing. In chapter ve, we study the channel estimation of millimeter wave full-dimensional MIMO communication. The problem is formulated as an atomic-norm minimization problem and algorithms are derived for the channel estimation in different situations.
In this thesis, mathematical optimization is applied as the main approach to analyze and solve the problems in the physical layer of wireless communication so that the high-throughput is achieved. The algorithms are derived along with the theoretical analysis, which are validated with numerical results