17 research outputs found
Joint RIS Phase Profile Design and Power Allocation for Parameter Estimation in Presence of Eavesdropping
We consider secure transmission of a deterministic complex-valued parameter
vector from a transmitter to an intended receiver in the presence of an
eavesdropper in a reconfigurable intelligent surface (RIS)-integrated
environment. We aim to jointly optimize the RIS phase profile and the power
allocation matrix at the transmitter to enhance the estimation accuracy at the
intended receiver while limiting that at the eavesdropper. We utilize the trace
of the Fisher information matrix (FIM), equivalently, the average Fisher
information, as the estimation accuracy metric, and obtain its closed form
expression for the intended receiver and the eavesdropper. Accordingly, the
joint RIS phase profile and power allocation problem is formulated, and it is
solved via alternating optimization. When the power allocation matrix is fixed
during alternating optimization, the optimal RIS phase profile design problem
is formulated as a non-convex problem and it is solved via semidefinite
relaxation and rank reduction. When the RIS phase profile is fixed, a linear
programming formulation is obtained for optimal power allocation. Via
simulations, the effects of RIS phase design and power allocation are
illustrated individually and jointly. Moreover, extensions are provided by
considering the presence of line of sight paths in the environment and the
availability of RIS elements with adjustable magnitudes
Recommended from our members
Performance Analysis for User-centric Cell-free Massive MIMO Systems with Hardware Impairments and Multi-antenna Users
This work is supported in part by National Natural Science Foundation of China (62031017, 61971220, 61971221) and Open Research Fund of State Key Laboratory of Millimeter Waves of Southeast University (K202215)
Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography
Radio maps characterize quantities of interest in radio communication
environments, such as the received signal strength and channel attenuation, at
every point of a geographical region. Radio map estimation typically entails
interpolative inference based on spatially distributed measurements. In this
tutorial article, after presenting some representative applications of radio
maps, the most prominent radio map estimation methods are discussed. Starting
from simple regression, the exposition gradually delves into more sophisticated
algorithms, eventually touching upon state-of-the-art techniques. To gain
insight into this versatile toolkit, illustrative toy examples will also be
presented
Channel estimation and beam training with machine learning applications for millimetre-wave communication systems
The fifth generation (5G) wireless system will extend the capabilities of the fourth generation
(4G) standards to serve more users and provide timely communication. To this end, the carriers
of 5G systems will be able to operate at higher frequency bands, such as the millimetre-wave
(mmWave) bands that span from 30 GHz to 300 GHz, to obtain greater bandwidths and higher
data rates. As a result, the deployment of 5G networks is required to accommodate more antennas
and offer pervasive coverage with controlled power consumption. The complexity of 5G
systems introduces new challenges to traditional signal processing techniques. To address these
challenges, a major step is to integrate machine learning (ML) algorithms into wireless communication
systems. ML can learn patterns from datasets to achieve control and optimisation of
complex radio frequency (RF) networks. This PhD thesis focuses on developing efficient channel
estimation methods and beam training strategies with the application of ML algorithms for
mmWave wireless systems.
Firstly, the channel estimation and signal detection problem is investigated for orthogonal
frequency-division multiplexing (OFDM) systems that operate at mmWave bands. A deep
neural network (DNN)-based joint channel estimation and signal detection approach is proposed
to achieve multi-user detection in a one-shot process for non-orthogonal multiple access
(NOMA) systems. The DNN acts as the receiver, which can recover the transmitted data by
learning the channel implicitly from suitable training. The proposed approach can be adapted to
work for both single-input and single-output (SISO) systems and multiple-output and multipleoutput
(MIMO) systems. This DNN-based approach is shown to provide good performance for
OFDM systems that suffer from severe inter-symbol interference or where small numbers of
pilot symbols are used.
Secondly, the beam training and tracking problem is studied for mmWave channels with receiver
mobility. To reduce the signalling overhead caused by frequent beam training, a lowcomplexity
beam training strategy is proposed for mobile mmWave channels, which searches
a set of selected beams obtained based on the recent beam search results. By searching only
the adjacent beams to the one recently used, the proposed beam training strategy can reduce
the beam training delay significantly while maintaining high transmission rates. The proposed
strategy works effectively for channel datasets generated using either the stochastic or the raytracing
channel model. This strategy is shown to approach the performance for an exhaustive
beam search while saving up to 92% on the required beam training overhead.
Thirdly, the proposed low-complexity beam training strategy is enhanced with the use of deep
reinforcement learning (DRL) for mobile mmWave channels. A DRL-based beam training algorithm
is proposed, which can intelligently switch between different beam training methods
such that the average beam training overhead is minimised while achieving good spectral efficiency
or energy efficiency performance. Given the desired performance requirement in the
reward function for the DRL model, the spectral efficiency or energy efficiency can be maximised
for the current channel condition by controlling the number of activated RF chains. The
DRL-based approach can adjust the amount of beam training overhead required according to
the dynamics of the environment. This approach can provide a good overhead-performance
trade-off and achieve higher data rates in channels with significant levels of signal blockage
Modelling, Simulation and Data Analysis in Acoustical Problems
Modelling and simulation in acoustics is currently gaining importance. In fact, with the development and improvement of innovative computational techniques and with the growing need for predictive models, an impressive boost has been observed in several research and application areas, such as noise control, indoor acoustics, and industrial applications. This led us to the proposal of a special issue about âModelling, Simulation and Data Analysis in Acoustical Problemsâ, as we believe in the importance of these topics in modern acousticsâ studies. In total, 81 papers were submitted and 33 of them were published, with an acceptance rate of 37.5%. According to the number of papers submitted, it can be affirmed that this is a trending topic in the scientific and academic community and this special issue will try to provide a future reference for the research that will be developed in coming years
Sensor Signal and Information Processing II
In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing
Low-complexity symbol detection and interference cancellation for OTFS system
Orthogonal time frequency space (OTFS) is a two-dimensional modulation scheme realized in the delay-Doppler domain, which targets the robust wireless transmissions in high-mobility environments. In such scenarios, OTFS signal suffers from multipath channel with continuous Doppler spread, which results in significant inter-symbol interference and inter-Doppler interference (IDI). In this paper, we analyze the interference generation mechanism, and compare statistical distributions of the IDI in two typical cases, i.e., limited-Doppler-shift channel and continuous-Doppler-spread channel (CoDSC). Focusing on the OTFS signal transmission over the CoDSC, our study firstly indicates that the widespread IDI incurs a computational burden for the element-wise detector like the message passing in the state-of-the-art works. Addressing this challenge, we propose a block-wise OTFS receiver by exploiting the structure and characteristics of the OTFS transmission matrix. In the receiver, we deliberately design an iteration strategy among the least squares minimum residual based channel equalizer, reliability-based symbol detector and interference eliminator, which can realize fast convergence by leveraging the sparsity of channel matrix. The simulations demonstrate that, in the CoDSC, the proposed scheme achieves much less detection error, and meanwhile reduces the computational complexity by an order of magnitude, compared with the state-of-the-art OTFS receivers
High mobility in OFDM based wireless communication systems
Orthogonal Frequency Division Multiplexing (OFDM) has been adopted as the transmission scheme in most of the wireless systems we use on a daily basis. It brings with it several inherent advantages that make it an ideal waveform candidate in the physical layer. However, OFDM based wireless systems are severely affected in High Mobility scenarios. In this thesis, we investigate the effects of mobility on OFDM based wireless systems and develop novel techniques to estimate the channel and compensate its effects at the receiver. Compressed Sensing (CS) based channel estimation techniques like the Rake Matching Pursuit (RMP) and the Gradient Rake Matching Pursuit (GRMP) are developed to estimate the channel in a precise, robust and computationally efficient manner. In addition to this, a Cognitive Framework that can detect the mobility in the channel and configure an optimal estimation scheme is also developed and tested. The Cognitive Framework ensures a computationally optimal channel estimation scheme in all channel conditions. We also demonstrate that the proposed schemes can be adapted to other wireless standards easily. Accordingly, evaluation is done for three current broadcast, broadband and cellular standards. The results show the clear benefit of the proposed schemes in enabling high mobility in OFDM based wireless communication systems.Orthogonal Frequency Division Multiplexing (OFDM) wurde als Ăbertragungsschema in die meisten drahtlosen Systemen, die wir tĂ€glich verwenden, ĂŒbernommen. Es bringt mehrere inhĂ€rente Vorteile mit sich, die es zu einem idealen Waveform-Kandidaten in der BitĂŒbertragungsschicht (Physical Layer) machen. Allerdings sind OFDM-basierte drahtlose Systeme in Szenarien mit hoher MobilitĂ€t stark beeintrĂ€chtigt. In dieser Arbeit untersuchen wir die Auswirkungen der MobilitĂ€t auf OFDM-basierte drahtlose Systeme und entwickeln neuartige Techniken, um das Verhalten des Kanals abzuschĂ€tzen und seine Auswirkungen am EmpfĂ€nger zu kompensieren. Auf Compressed Sensing (CS) basierende KanalschĂ€tzverfahren wie das Rake Matching Pursuit (RMP) und das Gradient Rake Matching Pursuit (GRMP) werden entwickelt, um den Kanal prĂ€zise, robust und rechnerisch effizient abzuschĂ€tzen. DarĂŒber hinaus wird ein Cognitive Framework entwickelt und getestet, das die MobilitĂ€t im Kanal erkennt und ein optimales SchĂ€tzungsschema konfiguriert. Das Cognitive Framework gewĂ€hrleistet ein rechnerisch optimales KanalschĂ€tzungsschema fĂŒr alle möglichen Kanalbedingungen. Wir zeigen auĂerdem, dass die vorgeschlagenen Schemata auch leicht an andere Funkstandards angepasst werden können. Dementsprechend wird eine Evaluierung fĂŒr drei aktuelle Rundfunk-, Breitband- und Mobilfunkstandards durchgefĂŒhrt. Die Ergebnisse zeigen den klaren Vorteil der vorgeschlagenen Schemata bei der Ermöglichung hoher MobilitĂ€t in OFDM-basierten drahtlosen Kommunikationssystemen
Mobile node-aided localization and tracking in terrestrial and underwater networks
In large-scale wireless sensor networks (WSNs), the position information of individual
sensors is very important for many applications. Generally, there are a small number
of position-aware nodes, referred to as the anchors. Every other node can estimate its
distances to the surrounding anchors, and then employ trilateration or triangulation for
self-localization. Such a system is easy to implement, and thus popular for both terrestrial
and underwater applications, but it suffers from some major drawbacks. First, the density
of the anchors is generally very low due to economical considerations, leading to poor
localization accuracy. Secondly, the energy and bandwidth consumptions of such systems
are quite significant. Last but not the least, the scalability of a network based on fixed
anchors is not good. Therefore, whenever the network expands, more anchors should be
deployed to guarantee the required performance. Apart from these general challenges,
both terrestrial and underwater networks have their own specific ones. For example, realtime
channel parameters are generally required for localization in terrestrial WSNs. For
underwater networks, the clock skew between the target sensor and the anchors must
be considered. That is to say, time synchronization should be performed together with
localization, which makes the problem complicated.
An alternative approach is to employ mobile anchors to replace the fixed ones. For
terrestrial networks, commercial drones and unmanned aerial vehicles (UAVs) are very
good choices, while autonomous underwater vehicles (AUVs) can be used for underwater
applications. Mobile anchors can move along a predefined trajectory and broadcast beacon
signals. By listening to the messages, the other nodes in the network can localize themselves
passively. This architecture has three major advantages: first, energy and bandwidth consumptions can be significantly reduced; secondly, the localization accuracy can be much
improved with the increased number of virtual anchors, which can be boosted at negligible
cost; thirdly, the coverage can be easily extended, which makes the solution and the network
highly scalable.
Motivated by this idea, this thesis investigates the mobile node-aided localization and
tracking in large-scale WSNs. For both terrestrial and underwater WSNs, the system
design, modeling, and performance analyses will be presented for various applications,
including: (1) the drone-assisted localization in terrestrial networks; (2) the ToA-based
underwater localization and time synchronization; (3) the Doppler-based underwater localization;
(4) the underwater target detection and tracking based on the convolutional
neural network and the fractional Fourier transform. In these applications, different challenges
will present, and we will see how these challenges can be addressed by replacing
the fixed anchors with mobile ones. Detailed mathematical models will be presented, and
extensive simulation and experimental results will be provided to verify the theoretical
results. Also, we will investigate the channel estimation for the fifth generation (5G) wireless
communications. A pilot decontamination method will be presented for the massive
multiple-input-multiple-output communications, and the data-aided channel tracking will
be discussed for millimeter wave communications. We will see that the localization problem
is highly coupled with the channel estimation in wireless communications
An Experimental Investigation of Enhanced SM-OFDM Over Indoor Rician Multipath Channels
In this Correspondence, we investigate enhanced spatial modulation (ESM) aided orthogonal frequency division multiplexing (OFDM) systems. ESM extends the conventional SM by introducing a series of signal constellations. Existing research shows that ESM displays performance superiority over SM in flat-fading channels. For frequency-selective fading channels, we design a practical implementation of an ESM&SM aided OFDM hybrid system to characterize the bit error rate (BER) performance of ESM and conventional SM with an experimental wireless testbed. From the experimental results, it is found that ESM achieves better BER performance than SM on frequency selective channels, which are consistent with the Monte Carlo simulation results