1,722,127 research outputs found
Intelligent Reflecting Surfaces Assisted Millimeter Wave MIMO Full Duplex Systems
Full text linkpeer reviewedIn this paper, we propose to remove the analog stage of hybrid beamforming (HYBF) in the millimeter wave (mmWave) full-duplex (FD) systems. Such a solution is highly desirable as the analog stage suffers from high insertion loss and high power consumption. Consequently, the mmWave FD nodes can operate with a fewer number of antennas, instead of relying on a massive number of antennas, and to tackle the propagation challenges of the mmWave band we propose to use near-field intelligent reflecting surfaces (NF-IRSs). The objective of the NF-IRSs is to simultaneously and smartly control the uplink (UL) and downlink (DL) channels while assisting in shaping the SI channel: this to obtain very strong passive SI cancellation. A novel joint active and passive beamforming design for the weighted sum-rate (WSR) maximization for the NF-IRSs-assisted mmWave point-to-point FD system is presented. Results show that the proposed solution fully reaps the benefits of the IRSs, only when they operate in the NF, which leads to considerably higher gains compared to the conventional massive MIMO (mMIMO) mmWave FD and half duplex (HD) systems
Parallel and distributed hybrid beamforming for multicell millimeter wave MIMO full duplex
No full textImproved differential evolution for enhancing the aggregated channel estimation of RIS-aided cell-free massive MIMO
No full textCell-Free Massive multiple-input multiple-output(MIMO) systems are investigated with the support of a reconfigurable intelligent surface (RIS). The RIS phase shifts are designed for improved channel estimation in the presence of spatial correlation. Specifically, we formulate the channel estimate and estimation error expressions using linear minimum mean square error (LMMSE) estimation for the aggregated channels. An optimization problem is then formulated to minimize the average normalized mean square error (NMSE) subject to practical phase shift constraints. To circumvent the problem of inherent nonconvexity, we then conceive an enhanced version of the differential evolution algorithm that is capable of avoiding local minima by introducing an augmentation operator applied to some high-performing Diffential Evolution (DE) individuals. Numerical results indicate that our proposed algorithm can significantly improve the channel estimation quality of the state-of-the-art benchmarks.<br/
Space-Terrestrial Cooperation Over Spatially Correlated Channels Relying on Imperfect Channel Estimates: Uplink Performance Analysis and Optimization
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Detection of spoofing attacks in aeronautical ad-hoc networks using deep autoencoders
No full textWe consider an aeronautical ad-hoc network relying on aeroplanes operating in the presence of a spoofer. The aggregated signal received by the terrestrial base station is considered as “clean” or “normal”, if the legitimate aeroplanes transmit their signals and there is no spoofing attack. By contrast, the received signal is considered as “spurious” or “abnormal” in the face of a spoofing signal. An autoencoder (AE) is trained to learn the characteristics/features from a training dataset, which contains only normal samples associated with no spoofing attacks. The AE takes original samples as its input samples and reconstructs them at its output. Based on the trained AE, we define the detection thresholds of our spoofing discovery algorithm. To be more specific, contrasting the output of the AE against its input will provide us with a measure of geometric waveform similarity/dissimilarity in terms of the peaks of curves. To quantify the similarity between unknown testing samples and the given training samples (including normal samples), we first propose a so-called deviation-based algorithm . Furthermore, we estimate the angle of arrival (AoA) from each legitimate aeroplane and propose a so-called AoA-based algorithm . Then based on a sophisticated amalgamation of these two algorithms, we form our final detection algorithm for distinguishing the spurious abnormal samples from normal samples under a strict testing condition. In conclusion, our numerical results show that the AE improves the trade-off between the correct spoofing detection rate and the false alarm rate as long as the detection thresholds are carefully selected
Spectrum Monitoring Algorithms for Wireless and Satellite Communications
Full text linkNowadays, there is an increasing demand for more efficient utilization of the radio frequency
spectrum as new terrestrial and space services are deployed resulting in the
congestion of the already crowded frequency bands. In this context, spectrum monitoring
is a necessity. Spectrum monitoring techniques can be applied in a cognitive radio
network, exploiting the spectrum holes and allowing the secondary users to have access
in an unlicensed frequency band for them, when it is not occupied by the primary user.
Furthermore, spectrum monitoring techniques can be used for interference detection in
wireless and satellite communications. These two topics are addressed in this thesis.
In the beginning, a detailed survey of the existing spectrum monitoring techniques according
to the way that cognitive radio users 1) can detect the presence or absence of
the primary user; and 2) can access the licensed spectrum is provided. Subsequently, an
overview of the problem of satellite interference and existing methods for its detection
are discussed, while the contributions of this thesis are presented as well.
Moreover, this thesis discusses some issues in a cognitive radio system such as the reduction
of the secondary user's throughput of the conventional \listen before talk" access
method in the spectrum. Then, the idea of simultaneous spectrum sensing and data
transmission through the collaboration of the secondary transmitter with receiver is
proposed to address these concerns. First, the secondary receiver decodes the signal
from the secondary transmitter, then, removes it from the total received signal and finally, applies spectrum sensing in the remaining signal in order to decide if the primary
user is active or idle. The effects of the imperfect signal cancellation due to decoding
errors, which are ignored in the existing literature, are considered in our analysis. The
analytical expressions for the probabilities of false alarm and detection are derived and
numerical results through simulations are also presented to validate the proposed study.
Furthermore, the threat of interference for the satellite communications services is studied
in this thesis. It proposes the detection of interference on-board the satellite by
introducing a spectrum monitoring unit within the satellite transponder. This development
will bring several benefits such as faster reaction time and simplification of the
ground stations in multi-beam satellite systems. Then, two algorithms for the detection
of interference are provided. The first detection scheme is based on energy detector with
signal cancellation exploiting the pilot symbols. The second detection scheme considers
a two-stage detector, where first, the energy detector with signal cancellation in the pilot
domain is performed, and if required, an energy detector with signal cancellation in the
data domain is carried out in the second stage. Moreover, the analytical expressions for the probabilities of false alarm and detection are derived and numerical results through
simulations are provided to verify the accuracy of the proposed analysis.
Finally, this thesis goes one step further and the developed algorithms are evaluated
experimentally using software defined radios, particularly universal software radio peripherals
(USRPs), while it concludes discussing some open research topics
Machine Learning-Based Efficient Resource Scheduling for Future Wireless Communication Networks
Full text linkThe next-generation mobile communication system, e.g., 6G communication system, is envisioned to support unprecedented performance requirements such as exponentially increasing data requests, heterogeneous service demands, and massive connectivity. When these challenging tasks meet the scarcity of wireless resources, efficient resource management becomes crucial. Conventionally, optimization algorithms, either optimal or suboptimal, are the main approaches for solving resource allocation problems. However, the efficiency of these iterative optimization algorithms can significantly degrade when the problems become large or difficult, e.g., non-convex or combinatorial optimization problems. Over the past few years, machine learning (ML), as an emerging approach in the toolbox, is widely investigated to accelerate the decision-making process. Since applying ML-based approaches to solve complex resource management problems is in its early-stage study, many open issues and challenges need to be solved towards the maturity and practical applications. The motivation and objective of this dissertation lie at investigating and providing answers to the following research questions: 1) How to overcome the shortcomings of extensively adopted end-to-end learning in addressing resource management problems, and which types of features are suited to be learned if supervised learning is applied? 2) What are the limitations and benefits when widely-used deep reinforcement learning (DRL) approaches are used to address constrained and combinatorial optimization problems in wireless networks, and are there tailored solutions to overcome the inherent drawbacks? 3) How to enable ML-based approaches to timely adapt to dynamic and complex wireless environments? 4) How to enlarge the performance gains when the paradigm shifts from centralized learning to distributed learning? The main contributions are organized by the following four research works.
Firstly, from a supervised-learning perspective, we address common issues, e.g., unsatisfactory pre- diction performance and resultant infeasible solutions, when end-to-end learning approaches are applied to resource scheduling problems. Based on the analysis of optimal results, we design suited-to-learn features for a class of resource scheduling problems, and develop combined learning-and-optimization approaches to enable time-efficient and energy-efficient resource scheduling in multi-antenna systems. The original optimization problems are mixed-integer programming problems with high-dimensional decision vectors. The optimal solution requires exponential complexity due to the inherent difficulties of the problems. Towards an efficient and competitive solution, we apply fully-connected deep neural network (DNN) and convolutional neural network (CNN) to learn the designed features. The predicted information can effectively reduce the large search space and accelerate the optimization process. Compared to the conventional optimization and pure ML algorithms, the proposed method achieves a good trade-off between quality and complexity.
Secondly, we address typical issues when DRL is adopted to deal with combinatorial and non-convex scheduling problems. The original problem is to provide energy-saving solutions via resource scheduling in energy-constrained networks. An optimal algorithm and a golden section search suboptimal approach are developed to serve as offline benchmarks. For online operations, we propose an actor-critic-based deep stochastic online scheduling (AC-DSOS) algorithm. Compared to supervised learning, DRL is suitable for dynamic environments and capable of making decisions based on the current state without an offline training phase. However, for the specific constrained scheduling problem, conventional DRL may not be able to handle two major issues of exponentially-increased action space and infeasible actions. The proposed AC-DSOS is developed to overcome these drawbacks. In simulations, AC-DSOS is able to provide feasible solutions and save around more energy compared to the conventional DRL algorithms. Compared to the offline benchmarks, AC-DSOS reduces the computational time from second-level to millisecond-level.
Thirdly, the dissertation pays attention to the performance of the ML-based approaches in highly dynamic and complex environments. Most of the ML models are trained by the collected data or the observed environments. They may not be able to timely respond to the large variations of environments, such as dramatically fluctuating channel states or bursty data demands. In this work, we develop ML-based approaches in a time-varying satellite-terrestrial network and address two practical issues. The first is how to efficiently schedule resources to serve the massive number of connected users, such that more data and users can be delivered/served. The second is how to make the algorithmic solution more resilient in adapting to the time-varying wireless environments. We propose an enhanced meta-critic learning (EMCL) algorithm, combining a DRL model with a meta-learning technique, where the meta-learning can acquire meta-knowledge from different tasks and fast adapt to the new task. The results demonstrate EMCL’s effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods.
Fourthly, the dissertation focuses on reducing the energy consumption for federated learning (FL), in mobile edge computing. The power supply and computation capabilities are typically limited in edge devices, thus energy becomes a critical issue in FL. We propose a joint sparsification and resource optimization scheme (JSRO) to jointly reduce computational and transmission energy. In the first part of JSRO, we introduce sparsity and adopt sparse or binary neural networks (SNN or BNN) as the learning model to complete the local training tasks at the devices. Compared to fully-connected DNN, the computational operations can be significantly reduced, and thus requires less energy consumption and fewer transmitted data to the central node. In the second part, we develop an efficient scheduling scheme to minimize the overall transmission energy by optimizing wireless resources and learning parameters. We develop an enhanced FL algorithm in JSRO, i.e., non-smoothness and constraints - stochastic gradient descent, to handle the non-smoothness and constraints of SNN and BNN, and provide guarantees for convergence.
Finally, we conclude the thesis with the main findings and insights on future research directions
RISK-AWARE INTELLIGENCE FOR LARGE-SCALE PARTIALLY OBSERVABLE COMMUNICATION SYSTEMS
Full text linkThe evolution towards Sixth-Generation (6G) communication systems is characterized by an unprecedented increase in scale, decentralization, and dynamism. This trend is evident by terrestrial networks employing massive Multiple-Input Multiple-Output (MIMO) antenna arrays and non-terrestrial networks composed of ultra-dense Low Earth Orbit (LEO) satellite constellations. In such large-scale environments, obtaining a complete and timely view of the true system state, e.g., complete channel state information (CSI) or global network state, is often infeasible due to prohibitive communication overhead and physical constraints. This gives rise to a fundamental challenge we term partial observability at scale. Traditional control and optimization methods, which typically assume complete system knowledge, fail to provide robust solutions in such environments. Crucially, these methods fail because they do not adequately handle the risks of partial observability. Many frameworks are risk-oblivious, optimizing for average performance while ignoring critical QoS degradation caused by incomplete state knowledge. Even recent constrained approaches are risk-myopic, focusing on average performance constraints while failing to control for high-impact tailend events like severe latency spikes or QoS breaches. This leaves the system vulnerable to unacceptable performance violations. Recognizing this gap, this dissertation argues that there is an urgent need for intelligent and autonomous decision-making frameworks capable of operating reliably under partial observability at scale and managing the associated risks effectively. This leads to the central research problem addressed herein: How can communication agents make robust and risk-aware decisions in large-scale partially observable communication systems? To answer this question, this dissertation moves beyond risk-oblivious and risk-myopic approaches by proposing a unified framework for risk-aware intelligence in large-scale partially observable communication systems. We develop this framework through two complementary paradigms: model-based risk-aware planning and model-free risk-aware reinforcement learning, demonstrated on antenna selection in massive MIMO with partial CSI and asynchronous packet routing in LEO mega-constellations, respectively
Cooperative Hybrid Networks with Active Relays and RISs for B5G: Applications, Challenges, and Research Directions
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