568 research outputs found
Novel Complex Adaptive Signal Processing Techniques Employing Optimally Derived Time-varying Convergence Factors With Applicatio
In digital signal processing in general, and wireless communications in particular, the increased usage of complex signal representations, and spectrally efficient complex modulation schemes such as QPSK and QAM has necessitated the need for efficient and fast-converging complex digital signal processing techniques. In this research, novel complex adaptive digital signal processing techniques are presented, which derive optimal convergence factors or step sizes for adjusting the adaptive system coefficients at each iteration. In addition, the real and imaginary components of the complex signal and complex adaptive filter coefficients are treated as separate entities, and are independently updated. As a result, the developed methods efficiently utilize the degrees of freedom of the adaptive system, thereby exhibiting improved convergence characteristics, even in dynamic environments. In wireless communications, acceptable co-channel, adjacent channel, and image interference rejection is often one of the most critical requirements for a receiver. In this regard, the fixed-point complex Independent Component Analysis (ICA) algorithm, called Complex FastICA, has been previously applied to realize digital blind interference suppression in stationary or slow fading environments. However, under dynamic flat fading channel conditions frequently encountered in practice, the performance of the Complex FastICA is significantly degraded. In this dissertation, novel complex block adaptive ICA algorithms employing optimal convergence factors are presented, which exhibit superior convergence speed and accuracy in time-varying flat fading channels, as compared to the Complex FastICA algorithm. The proposed algorithms are called Complex IA-ICA, Complex OBA-ICA, and Complex CBC-ICA. For adaptive filtering applications, the Complex Least Mean Square algorithm (Complex LMS) has been widely used in both block and sequential form, due to its computational simplicity. However, the main drawback of the Complex LMS algorithm is its slow convergence and dependence on the choice of the convergence factor. In this research, novel block and sequential based algorithms for complex adaptive digital filtering are presented, which overcome the inherent limitations of the existing Complex LMS. The block adaptive algorithms are called Complex OBA-LMS and Complex OBAI-LMS, and their sequential versions are named Complex HA-LMS and Complex IA-LMS, respectively. The performance of the developed techniques is tested in various adaptive filtering applications, such as channel estimation, and adaptive beamforming. The combination of Orthogonal Frequency Division Multiplexing (OFDM) and the Multiple-Input-Multiple-Output (MIMO) technique is being increasingly employed for broadband wireless systems operating in frequency selective channels. However, MIMO-OFDM systems are extremely sensitive to Intercarrier Interference (ICI), caused by Carrier Frequency Offset (CFO) between local oscillators in the transmitter and the receiver. This results in crosstalk between the various OFDM subcarriers resulting in severe deterioration in performance. In order to mitigate this problem, the previously proposed Complex OBA-ICA algorithm is employed to recover user signals in the presence of ICI and channel induced mixing. The effectiveness of the Complex OBA-ICA method in performing ICI mitigation and signal separation is tested for various values of CFO, rate of channel variation, and Signal to Noise Ratio (SNR)
MEDUSA: Scalable Biometric Sensing in the Wild through Distributed MIMO Radars
Radar-based techniques for detecting vital signs have shown promise for
continuous contactless vital sign sensing and healthcare applications. However,
real-world indoor environments face significant challenges for existing vital
sign monitoring systems. These include signal blockage in non-line-of-sight
(NLOS) situations, movement of human subjects, and alterations in location and
orientation. Additionally, these existing systems failed to address the
challenge of tracking multiple targets simultaneously. To overcome these
challenges, we present MEDUSA, a novel coherent ultra-wideband (UWB) based
distributed multiple-input multiple-output (MIMO) radar system, especially it
allows users to customize and disperse the into sub-arrays.
MEDUSA takes advantage of the diversity benefits of distributed yet wirelessly
synchronized MIMO arrays to enable robust vital sign monitoring in real-world
and daily living environments where human targets are moving and surrounded by
obstacles. We've developed a scalable, self-supervised contrastive learning
model which integrates seamlessly with our hardware platform. Each attention
weight within the model corresponds to a specific antenna pair of Tx and Rx.
The model proficiently recovers accurate vital sign waveforms by decomposing
and correlating the mixed received signals, including comprising human motion,
mobility, noise, and vital signs. Through extensive evaluations involving 21
participants and over 200 hours of collected data (3.75 TB in total, with 1.89
TB for static subjects and 1.86 TB for moving subjects), MEDUSA's performance
has been validated, showing an average gain of 20% compared to existing systems
employing COTS radar sensors. This demonstrates MEDUSA's spatial diversity gain
for real-world vital sign monitoring, encompassing target and environmental
dynamics in familiar and unfamiliar indoor environments.Comment: Preprint. Under Revie
Vector sensors for underwater : acoustic communications
Acoustic vector sensors measure acoustic pressure and directional components separately.
A claimed advantage of vector sensors over pressure-only arrays is the directional information
in a collocated device, making it an attractive option for size-restricted applications.
The employment of vector sensors as a receiver for underwater communications is relatively
new, where the inherent directionality, usually related to particle velocity, is used
for signal-to-noise gain and intersymbol interference mitigation. The fundamental question
is how to use vector sensor directional components to bene t communications, which
this work seeks to answer and to which it contributes by performing: analysis of acoustic
pressure and particle velocity components; comparison of vector sensor receiver structures
exploring beamforming and diversity; quanti cation of adapted receiver structures in distinct
acoustic scenarios and using di erent types of vector sensors. Analytic expressions
are shown for pressure and particle velocity channels, revealing extreme cases of correlation
between vector sensors' components. Based on the correlation hypothesis, receiver
structures are tested with simulated and experimental data. In a rst approach, called
vector sensor passive time-reversal, we take advantage of the channel diversity provided
by the inherent directivity of vector sensors' components. In a second approach named
vector sensor beam steering, pressure and particle velocity components are combined, resulting
in a steered beam for a speci c direction. At last, a joint beam steering and
passive time-reversal is proposed, adapted for vector sensors. Tested with two distinct
experimental datasets, where vector sensors are either positioned on the bottom or tied
to a vessel, a broad performance comparison shows the potential of each receiver structure.
Analysis of results suggests that the beam steering structure is preferable for shorter
source-receiver ranges, whereas the passive time-reversal is preferable for longer ranges.
Results show that the joint beam steering and passive time-reversal is the best option to
reduce communication error with robustness along the range.Sensores vetoriais acústicos (em inglês, acoustic vector sensors) são dispositivos que
medem, alem da pressão acústica, a velocidade de partícula. Esta ultima, é uma medida que
se refere a um eixo, portando, esta associada a uma direção. Ao combinar pressão acústica
com componentes de velocidade de partícula pode-se estimar a direção de uma fonte sonora
utilizando apenas um sensor vetorial. Na realidade, \um" sensor vetorial é composto de um
sensor de pressão (hidrofone) e um ou mais sensores que medem componentes da velocidade
de partícula. Como podemos notar, o aspecto inovador está na medição da velocidade de
partícula, dado que os hidrofones já são conhecidos.(...)This PhD thesis was supported by the Brazilian Navy Postgraduate Study Abroad
Program Port. 227/MB-14/08/2019
Direction of Arrival Estimation for Radio Positioning: a Hardware Implementation Perspective
Nowadays multiple antenna wireless systems have gained considerable attention due to their
capability to increase performance. Advances in theory have introduced several new schemes
that rely on multiple antennas and aim to increase data rate, diversity gain, or to provide
multiuser capabilities, beamforming and direction finding (DF) features. In this respect, it
has been shown that a multiple antenna receiver can be potentially used to perform radio
localization by using the direction of arrival (DoA) estimation technique.
In this field, the literature is extensive and gathers the results of almost four decades
of research activities. Among the most cited techniques that have been developed, we find
the so called high-resolution algorithms, such as multiple signal classification (MUSIC), or
estimation of signal parameters via rotational invariance (ESPRIT). Theoretical analysis
as well as simulation results have demonstrated their excellent performance to the point
that they are usually considered as reference for the comparison with other algorithms.
However, such a performance is not necessarily obtained in a real system due to the presence
of non idealities. These can be divided into two categories: the impairments due to the
antenna array, and the impairments due to the multiple radio frequency (RF) and acquisition
front-ends (FEs). The former are strongly influenced by the manufacturing accuracy and,
depending on the required DoA resolution, have to be taken into account. Several works
address these issues in the literature. The multiple FE non idealities, instead, are usually
not considered in the DoA estimation literature, even if they can have a detrimental effect
on the performance. This has motivated the research work in this thesis that addresses the
problem of DoA estimation from a practical implementation perspective, emphasizing the
impact of the hardware impairments on the final performance. This work is substantiated
by measurements done on a state-of-the-art hardware platform that have pointed out the
presence of non idealities such as DC offsets, phase noise (PN), carrier frequency offsets
(CFOs), and phase offsets (POs) among receivers. Particularly, the hardware platform will
be herein described and examined to understand what non idealities can affect the DoA
estimation performance. This analysis will bring to identify which features a DF system
should have to reach certain performance.
Another important issue is the number of antenna elements. In fact, it is usually limited by practical considerations, such as size, costs, and also complexity. However, the most
cited DoA estimation algorithms need a high number of antenna elements, and this does not
yield them suitable to be implemented in a real system. Motivated by this consideration,
the final part of this work will describe a novel DoA estimation algorithm that can be
used when multipath propagation occurs. This algorithm does not need a high number
of antenna elements to be implemented, and it shows good performance despite its low
implementation/computational complexity
Semi-blind CFO estimation and ICA based equalization for wireless communication systems
In this thesis, a number of semi-blind structures are proposed for Orthogonal Frequency Division Multiplexing (OFDM) based wireless communication systems, with Carrier Frequency Offset (CFO) estimation and Independent Component Analysis (ICA) based equalization. In the first contribution, a semi-blind non-redundant single-user Multiple-Input Multiple-Output (MIMO) OFDM system is proposed, with a precoding aided CFO estimation approach and an ICA based equalization structure. A number of reference data sequences are carefully designed and selected from a pool of orthogonal sequences, killing two birds with one stone. On the one hand, the precoding based CFO estimation is performed by minimizing the sum cross-correlations between the CFO compensated signals and the rest of the orthogonal sequences in the pool. On the other hand, the same reference data sequences enable the elimination of permutation and quadrant ambiguities in the ICA equalized signals. Simulation results show that the proposed semi-blind MIMO OFDM system can achieve a Bit Error Rate (BER) performance close to the ideal case with perfect Channel State Information (CSI) and no CFO. In the second contribution, a low-complexity semi-blind structure, with a multi-CFO estimation method and an ICA based equalization scheme, is proposed for multiuser Coordinated Multi-Point (CoMP) OFDM systems. A short pilot is carefully designed offline for each user and has a two-fold advantage. On the one hand, using the pilot structure, a complex multi-dimensional search for multiple CFOs is divided into a number of low-complexity mono-dimensional searches. On the other hand, the cross-correlation between the transmitted and received pilots is explored to allow the simultaneous elimination of permutation and quadrant ambiguities in the ICA equalized signals. Simulation results show that the proposed semi-blind CoMP OFDM system can provide a BER performance close to the ideal case with perfect CSI and no CFO. In the third contribution, a semi-blind structure is proposed for Carrier Aggregation (CA) based CoMP Orthogonal Frequency Division Multiple Access (OFDMA) systems, with an ICA based joint Inter-Carrier Interference (ICI) mitigation and equalization scheme. The CFO-induced ICI is mitigated implicitly via ICA based equalization, without introducing feedback overhead for CFO correction. The permutation and quadrant ambiguities in the ICA equalized signals can be eliminated by a small number of pilots. Simulation results show that with a low training overhead, the proposed semi-blind equalization scheme can provide a BER performance close to the ideal case with perfect CSI and no CFO
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