1,091 research outputs found
Zero-Energy RIS-Assisted Communications With Noise Modulation and Interference-Based Energy Harvesting
Full text linkTo advance towards carbon-neutrality and improve the limited performance of conventional passive wireless communications, in this paper, we investigate the integration of noise modulation with zero-energy reconfigurable intelligent surfaces (RISs). In particular, the RIS reconfigurable elements (REs) are divided into two groups: one for beamforming the desired signals in reflection mode and another for harvesting energy from interference signals in an absorption mode, providing the power required for RIS operation. Since the harvested energy is a random variable, a random number of REs can beamform the signals, while the remainder blindly reflects them. We present a closed-form solution and a search algorithm for REs allocation, jointly optimizing both the energy harvesting (EH) and communication performance. Considering the repetition coding technique and discrete phase shifts, we derive analytical expressions for the energy constrained success rate, bit error rate, optimal threshold, mutual information, and energy efficiency. Numerical and simulation results confirm the effectiveness of the algorithm and expressions, demonstrating the superiority of the proposed integration over conventional noise-modulation systems. It is shown that by properly allocating the REs, both the EH and communication performance can be improved in low to moderate interference scenarios, while the latter is restricted in the high-interference regime
Video-based facial expression analysis
No full textRecognizing facial expressions from facial video sequences is an important and unsolved problem. Among many factors that contribute to the challenges of this task are: non-frontal facial poses, poorly aligned face images, large variations in the temporal scale of facial expressions, and the subtle differences between different subjects for the same facial expression etc. A successful video-based facial expression analysis system should be able to handle at least the following problems: robust face tracking, or spatial alignment of the faces, video segmentation, effective feature representation and selection schemes which are robust to face mis-alignment and temporal normalization by sequential classifier. In this work we report several advances we made in building various components of a system for classifying facial expressions from video inputs. Particularly, my work focus on robust face tracking, facial feature representation and selection under different face alignment conditions, sequential modeling for facial expression recognition. We performed extensive experiments using the proposed algorithms on publicly available dataset and achieved state of the art performances.Ph.D.Includes bibliographical referencesIncludes vitaby Zhiguo L
Adaptive UAV-trajectory optimization under quality of service constraints: a model-free solution
No full textUnmanned aerial vehicles (UAVs) with the potential of providing reliable high-rate connectivity, are becoming a promising component of future wireless networks. A UAV collects data from a set of randomly distributed sensors, where both the locations of these sensors and their data volume to be transmitted are unknown to the UAV. In order to assist the UAV in finding the optimal motion trajectory in the face of the uncertainty without the above knowledge whilst aiming for maximizing the cumulative collected data, we formulate a reinforcement learning problem by modelling the motion-trajectory as a Markov decision process with the UAV acting as the learning agent.Then, we propose a pair of novel trajectory optimization algorithms based on stochastic modelling and reinforcement learning, which allows the UAV to optimize its flight trajectory without the need for system identification. More specifically, by dividing the considered region into small tiles, we conceive state-action-reward-state-action (Sarsa) and -learning based UAV-trajectory optimization algorithms (i.e., SUTOA and QUTOA) aiming to maximize the cumulative data collected during the finite flight-time. Our simulation results demonstrate that both of the proposed approaches are capable of finding an optimal trajectory under the flight-time constraint. The preference for QUTOA vs. SUTOA depends on the relative position of the start and the end points of the UAVs
MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems
No full textNowadays, automatic modulation classification (AMC) has become a key component of
next-generation drone communication systems, which are crucial for improving communication
efficiency in non-cooperative environments. The contradiction between the accuracy and efficiency
of current methods hinders the practical application of AMC in drone communication systems. In
this paper, we propose a real-time AMC method based on the lightweight mobile radio transformer
(MobileRaT). The constructed radio transformer is trained iteratively, accompanied by pruning
redundant weights based on information entropy, so it can learn robust modulation knowledge from
multimodal signal representations for the AMC task. To the best of our knowledge, this is the first
attempt in which the pruning technique and a lightweight transformer model are integrated and
applied to processing temporal signals, ensuring AMC accuracy while also improving its inference
efficiency. Finally, the experimental results—by comparing MobileRaT with a series of state-of-the-art
methods based on two public datasets—have verified its superiority. Two models, MobileRaT-A
and MobileRaT-B, were used to process RadioML 2018.01A and RadioML 2016.10A to achieve
average AMC accuracies of 65.9% and 62.3% and the highest AMC accuracies of 98.4% and 99.2% at
+18 dB and +14 dB, respectively. Ablation studies were conducted to demonstrate the robustness of
MobileRaT to hyper-parameters and signal representations. All the experimental results indicate
the adaptability of MobileRaT to communication conditions and that MobileRaT can be deployed
on the receivers of drones to achieve air-to-air and air-to-ground cognitive communication in less
demanding communication scenarios
A survey of non-orthogonal multiple access for 5G
No full textIn the 5th generation (5G) of wireless communication systems, hitherto unprecedented requirements are expected to be satisfied. As one of the promising techniques of addressing these challenges, non-orthogonal multiple access (NOMA) has been actively investigated in recent years. In contrast to the family of conventional orthogonal multiple access (OMA) schemes, the key distinguishing feature of NOMA is to support a higher number of users than the number of orthogonal resource slots with the aid of non-orthogonal resource allocation. This may be realized by the sophisticated inter-user interference cancellation at the cost of an increased receiver complexity. In this article, we provide a comprehensive survey of the original birth, the most recent development, and the future research directions of NOMA. Specifically, the basic principle of NOMA will be introduced at first, with the comparison between NOMA and OMA especially from the perspective of information theory. Then, the prominent NOMA schemes are discussed by dividing them into two categories, namely, power-domain and code-domain NOMA. Their design principles and key features will be discussed in detail, and a systematic comparison of these NOMA schemes will be summarized in terms of their spectral efficiency, system performance, receiver complexity, etc. Finally, we will highlight a range of challenging open problems that should be solved for NOMA, along with corresponding opportunities and future research trends to address these challenges
Non-orthogonal multiple access for 5G and beyond
Full text linkDriven by the rapid escalation of the wireless capacity requirements imposed by advanced multimedia applications (e.g., ultrahigh-definition video, virtual reality, etc.), as well as the dramatically increasing demand for user access required for the Internet of Things (IoT), the fifth-generation (5G) networks face challenges in terms of supporting large-scale heterogeneous data traffic. Nonorthogonal multiple access (NOMA), which has been recently proposed for the third-generation partnership projects long-term evolution advanced (3GPP-LTE-A), constitutes a promising technology of addressing the aforementioned challenges in 5G networks by accommodating several users within the same orthogonal resource block. By doing so, significant bandwidth efficiency enhancement can be attained over conventional orthogonal multiple-access (OMA) techniques. This motivated numerous researchers to dedicate substantial research contributions to this field. In this context, we provide a comprehensive overview of the state of the art in power-domain multiplexing-aided NOMA, with a focus on the theoretical NOMA principles, multiple-antenna-aided NOMA design, on the interplay between NOMA and cooperative transmission, on the resource control of NOMA, on the coexistence of NOMA with other emerging potential 5G techniques and on the comparison with other NOMA variants. We highlight the main advantages of power-domain multiplexing NOMA compared to other existing NOMA techniques. We summarize the challenges of existing research contributions of NOMA and provide potential solutions. Finally, we offer some design guidelines for NOMA systems and identify promising research opportunities for the future
Next-generation mm-wave small-cell networks: multiple access, caching and resource management
No full textMillimeter wave (mmWave) small cells have been considered as an effective technique of significantly improving the data rates of future networks. More particularly, this article investigates the potential benefits of mmWave small cell networks from the perspective of non-orthogonal multiple access (NOMA) and wireless caching. We highlight a range of innovative resource management solutions conceived for mmWave small cell networks by invoking adaptive learning. Finally, several promising future research directions of mmWave small cell networks are identified
NOMA for Next-generation Massive IoT: Performance Potential and Technology Directions
No full textBroader applications of the Internet of Things (IoT) are expected in the forthcoming 6G system, although massive IoT is already a key scenario in 5G, predominantly relying on physical layer solutions inherited from 4G LTE and primarily using orthogonal multiple access (OMA). In 6G IoT, supporting a massive number of connections will be required for diverse services of the vertical sectors, prompting fundamental studies on how to improve the spectral efficiency of the system. One of the key enabling technologies is non-orthogonal multiple access (NOMA). This paper consists of two parts. In the first part, finite block length theory and the diversity order of multi-user systems will be used to show the significant potential of NOMA compared to traditional OMA. The supremacy of NOMA over OMA is particularly pronounced for asynchronous contention-based systems relying on imperfect link adaptation, which are commonly assumed for massive IoT systems. To approach these performance bounds, in the second part of the paper, several promising technology directions are proposed for 6G massive IoT, including linear spreading, joint spreading & modulation, multi-user channel coding in the context of various techniques for practical uncoordinated transmissions, cell-free operations, etc., from the perspective of NOMA
A Real-Time Vehicle Speed Prediction Method Based on a Lightweight Informer Driven by Big Temporal Data
Full text linkAt present, the design of modern vehicles requires improving driving performance while
meeting emission standards, leading to increasingly complex power systems. In autonomous driving
systems, accurate, real-time vehicle speed prediction is one of the key factors in achieving automated
driving. Accurate prediction and optimal control based on future vehicle speeds are key strategies
for dealing with ever-changing and complex actual driving environments. However, predicting
driver behavior is uncertain and may be influenced by the surrounding driving environment, such as
weather and road conditions. To overcome these limitations, we propose a real-time vehicle speed
prediction method based on a lightweight deep learning model driven by big temporal data. Firstly,
the temporal data collected by automotive sensors are decomposed into a feature matrix through
empirical mode decomposition (EMD). Then, an informer model based on the attention mechanism is
designed to extract key information for learning and prediction. During the iterative training process
of the informer, redundant parameters are removed through importance measurement criteria to
achieve real-time inference. Finally, experimental results demonstrate that the proposed method
achieves superior speed prediction performance through comparing it with state-of-the-art statistical
modelling methods and deep learning models. Tests on edge computing devices also confirmed that
the designed model can meet the requirements of actual tasks
Advanced Resource Allocation for 5G Wireless Communication Systems and Beyond
Full text linkThe future fifth generation (5G) and beyond wireless communication systems are expected to provide ubiquitous high data-rate communication services and support a large number of devices while maintaining fairness. Full-duplex (FD), non-orthogonal multiple access (NOMA), and unmanned aerial vehicle (UAV) based communication are key concepts for meeting these requirements. However, since the future wireless networks suffer from interference, potential eavesdropping, imperfect channel estimation, and limited energy, realizing the performance gains promised by FD, NOMA, and UAV based communication is challenging. This thesis studies advanced resource allocation design for FD, NOMA, and UAV based communication with the objective to improve the spectral efficiency, communication security, and service fairness of future wireless networks.
First, we study multiuser FD systems and propose a novel resource allocation algorithm for guaranteeing concurrent secure downlink and uplink transmission. A multi-objective optimization framework is proposed to unveil the tradeoff between total downlink and total uplink transmit power minimization. The proposed algorithm takes into account artificial noise generation to combat potential eavesdroppers and the imperfect knowledge of the channel state information (CSI) of the eavesdropping channels.
Next, we investigate the resource allocation algorithm design for FD multicarrier single-input single-output (SISO) NOMA systems to further improve the spectral efficiency and fairness. The proposed algorithms are obtained as the solution of a non-convex optimization problem for maximization of the weighted sum system throughput. We apply monotonic optimization to develop an optimal joint power and subcarrier allocation policy. Furthermore, a suboptimal iterative scheme is proposed to strike a balance between computational complexity and optimality.
Subsequently, we extend our consideration from FD SISO NOMA systems to FD multiple-input single-output (MISO) NOMA systems. The resource allocation is optimized for maximization of the weighted sum system throughput while the information leakage is constrained and artificial noise is injected to guarantee secure communication in the presence of potential eavesdroppers. The imperfect CSI knowledge of the eavesdropping channels and the quality-of-service (QoS) requirements of the legitimate users are taken into account for robust resource allocation algorithm design.
Finally, we propose a novel solar-powered UAV communication system for providing ubiquitous and sustainable wireless services. We study the joint design of the three dimensional aerial trajectory and the wireless resource allocation for maximization of the system sum throughput. The proposed resource allocation algorithm design takes into account the aerodynamic power consumption, the solar energy harvesting, the finite on-board energy storage, and the minimum QoS requirements of the users. Simulation results show that to maximize the system sum throughput, the solar-powered UAV first climbs up to a high altitude to harvest a sufficient amount of solar energy and then descents to a lower altitude to shorten the communication distance to the users.
FD, NOMA, and solar-powered UAV based communication systems employing the proposed resource allocation algorithms provide a substantial performance improvement over traditional half-duplex (HD), orthogonal multiple access (OMA), and battery-powered UAV systems, respectively. In particular, the proposed FD schemes not only achieve a significant system throughput improvement over HD but simultaneously ensure downlink and uplink communication security. Besides, the proposed NOMA schemes provide a substantial system throughput improvement compared with OMA while also maintaining fairness among users. Furthermore, the proposed solar-powered UAV communication schemes not only facilitate flexible deployment of communication infrastructure but also provide sustainable wireless service
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