Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Allocation for Device-to-Multi-Device Enabled HetNets: A Deep Reinforcement Learning Approach

Device-to-device ($D$ 2D) communication exploits the geographical proximity by allowing neighboring devices to di-rectly communicate with each other, which becomes one of the most promising technologies to improve the spectral and energy efficiency for 5G and beyond communication systems. To further improve the spectral efficiency and generalize ap-plication scenarios, the emerging device-to-multi-device (D2M $D$) communication enables the D2D transmitter to communicate with multiple receivers simultaneously. In this paper, we consider a heterogeneous network (HetNet) where multiple D2M $D$ clusters coexist with the base station (BS) and cellular users (CUs). All D2MD clusters share the same downlink channel as the cellular network, which potentially leads to severe co-channel interfer-ence. To solve this problem, we leverage the deep reinforcement learning (DRL) and propose the deep reinforcement power allocation (DRPA) algorithm to dynamically allocate power for D2MD communication in HetNets. In addition, we apply the centralized training distributed execution (CTDE) technique to accelerate the training process and improve the robustness of DRPA. Simulation results demonstrate that the DRPA algorithm outperforms baseline methods in terms of maximizing the average sum-rate. In addition, the DRPA algorithm is robust to the changes of network environment while achieving near-optimal performance.
RevOPT: An LSTM-based Efficient Caching Strategy for CDN

In order to face the rise in data consumption and network congestion, caching structures like Content Delivery Networks (CDNs) are being more and more used and integrated into the network infrastructure. Knowing that the capacities of caching resources are most often limited due to their large operational cost, it has become very important that these entities are managed efficiently. Especially, at the caching operations level, the question that arises is what content should be cached or evicted from the cache when it becomes full. Having these in mind, we introduce a lightweight Artificial Intelligence-based caching scheme called Reversed OPT (RevOPT). In our proposal, we use a Long Short-Term Memory (LSTM) encoder-decoder model to learn future requests patterns from the past and exploit its outcome with a Counting Bloom Filter (CBF) structure to manage efficiently the caching decisions and to keep in the cache only contents expected to be reused in the near future. The conducted simulations show promising results of RevOPT in terms of the cache hit ratio compared to existing caching algorithms.
A One-shot Convex Optimization Approach to Risk-Averse Q-Learning

This paper presents a model-free Q-learning algorithm for solving the risk-averse optimal control (RAOC) problem. The entropic risk measure is used in the RAOC to account for the variance of the objective function. A one-shot Q-based convex optimization problem is then formed for which the decision variables are the Q-function parameters and the constraints are formed by sampling from an exponential utility-based entropic Bellman inequality. Samples are constructed using only a batch of data collected from a variety of control policies in a fully off-policy manner, which turns a dataset into a Q-learning based risk-averse optimal policy engine. Convergence of the exact optimization problem, which is infinite- dimensional in decision variables and constraints, to the optimal risk-averse Q-function is shown. For the standard convex optimization problem for which function approximation for Q-value estimations as well as constraint sampling are leveraged, the performance of the approximated solutions is verified through a weighted-norm bound and the Lyapunov bound. A simulation example is provided to verify the effectiveness of the presented approach.
A Distributed Approach to Energy Efficiency in Seamless IoT Communications

The rapid growth of Internet-of-things (IoT) devices modernizes our society and lives. Such a growth leads to the pro-liferation of the wireless technologies, most of which occupy the unlicensed/open industrial, scientific and medical (ISM) bands. Since each of these wireless technologies adheres to different standards, they give rise to challenges like cross technology interference, incompatible security protocols and high energy consumption. In this work, we envision to achieve an energy-efficient communication on a seamless IoT platform. In specific, a game theoretical approach is proposed to formulate a multi-device IoT communication system. The existence and uniqueness of the Nash equilibrium is proven to achieve the optimal transmit power for the IoT devices. Moreover, a distributed scheme is developed for practical implementations depending on receiving signal strength indicators. Simulation results demonstrate that optimal transmit power can be achieved through the proposed scheme. We further implement and demonstrate the distributed approach on a universal software radio peripheral (USRP) testbed.
Reliable Target Positioning in Complicated Environments Using Multiple Radar Observations

Accurate and reliable multi-target detection and localization in complicated environments are challenging due to the existence of multiple reflectors that may distract the observations. In this paper, we try to adopt the linear frequency modulated continuous wave (LFMCW) signals, in addition to the antenna array, to perform target detection and positioning. Aiming at the ghost observations which may be generated during the measurement and data fusion phases, we try to properly combine the observations from multiple radar nodes using a probabilistic data fusion framework. The positions of targets could be obtained straightforwardly using the minimum mean squared error (MMSE) estimator. The fundamental limits of target positioning are also provided in terms of the Cramer Rao Lower Bound (CRLB). Furthermore, we try to accurately identify the target of interest by characterizing its micro-Doppler effects. Both simulations and experiments are carried out to show that the ghosts could be effectively eliminated by the proposed multi-radar fusion framework. Meanwhile, the target positioning accuracy can also be obviously improved.
Control Strategy for Fault-tolerant Three-phase Four-switch Inverters

The three-phase four-switch inverter is being attached much importance as a fault-tolerant topology for traditional six-switch scheme, but its performance is lower than the latter. Based on analysis of the operation principle of four-switch inverters, space vector pulse width modulation (SVPWM) was studied and its essence was posted, that is a sinusoidal pulse width modulation (SPWM) using a triangle wave and two implicit sine waves whose phases are separated by 60° electrical angle. To reduce the number of power device switching, the implementation method of four-switch SVPWM with seven-segment scheme was presented. The maximal linear modulation ratio of four-switch SVPWM is half of the one in six-switch scheme, which is indicated as a conclusion. A compensatory voltage vector based overmodulation method for four-switch SVPWM was presented for improving the utilization ratio of the DC voltage. The simulation results as well as experimental ones illustrate the validity and efficiency of the proposed algorithm.
Coping with smartly malicious leaders: PBFT with arbitration for blockchain-based IoT applications

Practical Byzantine Fault Tolerance (PBFT) is a widely used consensus protocol which is sensitive to malicious behavior of the consensus leader, esp. when that behavior occurs only sporadically which makes it harder to detect. We propose an arbitration scheme suitable for Internet of Things (IoT) applications in which a replicated blockchain ledger is used for storage of IoT data. The scheme removes most of the vulnerabilities at the expense of slight performance degradation. We further describe a modification which adds redundancy to remove the possibility of additional attacks. Experimental results confirm the validity and efficacy of the proposed scheme.
Quality Optimization of Live Streaming Services over HTTP with Reinforcement Learning

Recent years have seen tremendous growth in HTTP adaptive live video traffic over the Internet. In the presence of highly dynamic network conditions and diverse request patterns, existing yet simple hand-crafted heuristic approaches for serving client requests at the network edge might incur a large overhead and significant increase in time complexity. Therefore, these approaches might fail in delivering acceptable Quality of Experience (QoE) to end users. To bridge this gap, we propose ROPL, a learning-based client request management solution at the edge that leverages the power of the recent breakthroughs in deep reinforcement learning, to serve requests of concurrent users joining various HTTP-based live video channels. ROPL is able to react quickly to any changes in the environment, performing accurate decisions to serve clients requests, which results in achieving satisfactory user QoE. We validate the efficiency of ROPL through trace-driven simulations and a real-world setup. Experimental results from real-world scenarios confirm that ROPL outperforms existing heuristic-based approaches in terms of QoE, with a factor up to $3.7\times$.
Dual-Net for Joint Channel Estimation and Data Recovery in Grant-free Massive Access

In massive machine-type communications (mMTC), the conflict between millions of potential access devices and limited channel freedom leads to a sharp decrease in spectral efficiency. The sparse nature of mMTC provides a solution by using compressive sensing (CS) to perform multiuser detection (MUD) but suffers conflict between the high computation complexity and low latency requirements. In this paper, we propose a novel Dual-network for joint channel estimation and data recovery. The proposed Dual-Net utilizes the sparse consistency between the channel vector and data matrix of all users. Experimental results show that the proposed Dual-Net outperforms existing CS algorithms and general neural networks in computation complexity and accuracy, which means reduced access delay and more supported devices.
Activity Detection using 2D LIDAR for Healthcare and Monitoring

Monitoring elderly people living alone is of the utmost importance given the amount of risk they are exposed to. Being aware of the activities of the elderly person in real time could help prevent/detect dangerous event that might occur such as falling. In this paper, we propose a method for activity detection using a 2D LIght Detection and Ranging (LIDAR) and deep learning. Unlike conventional work, where an activity refers to moving from one position to another, we use the term “activity” to refer to a set of movements including walking, standing, falling and sitting. Not only does our approach detect these activities, but it also identifies a given person from his gait, and identifies unsteady gait (i.e., when he is about to fall or feeling dizzy). Throughout our experiments, we show that the proposed approach could reach an accuracy equal to 92.3% and 91.3% in activity and unsteady gait detection, respectively. It is also capable of identifying up to 3 people's gait with an accuracy equal to 92.4% using 10 seconds of walking data.
Applications of Gaussian Process Regression in the Aero-Thermo-Servo-Elastic Analysis Towards Integrated Hypersonic Flight Dynamic Analysis

This tutorial paper presents the surrogate modeling based on Gaussian process regression (GPR) and its application in the hypersonic aero-thermo-servo-elastic analysis, a key ingredient in the design of hypersonic vehicles as well as its guidance, navigation and control. First, the basic formulations of GPR and practices of model training are presented. Next, the existing applications of GPR in hypersonic problems are reviewed. Subsequently, a pedagogical example and an applied example of GPR are presented. Finally, the pros and cons of the GPR modeling approach for general and hypersonic-specific applications are summarized.
Effect of Spatial Correlation on the Performance of Non-coherent Massive MIMO based on DMPSK

A rigorous analysis of the effect of spatial correlation for non-coherent (NC) massive multiple-input-multiple-output (MIMO) in Rician channels is important to determine its applicability in these scenarios. We conduct such analysis for a single base station (BS) and a more general case of several BSs, all of them showing correlation among their own antennas but with uncorrelated channels with respect to each other. We first perform an analysis of the distribution of the received symbols, then propose some approximations to give a closed form expression of the symbol error probability (SER) and the signal-to-interference-and-noise-ratio (SINR) as performance measures. Finally, we extract some conclusions from this analysis to show how the combined use of several BSs and larger Rician components can be beneficial in this scenario. Some numerical results are added to confirm the accuracy of the analysis.
SRRM: Ranking-based Route Mutation Scheme for Software-Defined WSNs

In WSNs, packets are delivered through mostly static shortest paths to their destination. However, static packet delivery makes WSNs highly vulnerable to traffic analysis attacks due to open area deployment. Existing defence proposals fail to achieve a balance between the protection level and the resource constraints. In this paper, we present a proactive SDN-based Route Mutation (SRRM) scheme that enables changing the routes of the multiple flows in WSNs simultaneously to defend against passive and stealthy reconnaissance and sniffer attacks while preserving reliable and energy-aware routing. Multiple routes are ranked for packet flow based on node reliability, energy consumption, link cost, and route overlapping. Our extensive simulation results show that these techniques can effectively provide route obfuscation for software-defined WSNs.
An Inverse Nash Mean Field Game-based Strategy for the Decentralized Control of Thermostatic Loads

Thermostatic loads have long been recognized as a useful resource for shaping aggregate load dynamics and for mitigation of intermittency in solar and wind based renewable generation. When considering the residential sector, there can be millions of such loads, each with a very small contribution but can be collectively coordinated. Mean field game (MFG) theory has emerged as a natural tool for mathematically capturing this framework. While most existing works on MFG analysis start from agent cost functions to identify the properties of potential Nash equilibria and the associated agent control laws, this work differs in that an inverse process is followed. We reverse engineer the agent cost functions so that, while remaining comfort sensitive, they are guaranteed to lead via decentralized control laws to a precalculated Nash equilibrium. The latter is desirable from an aggregator’s point of view. The approach is illustrated in the case of a power reduction objective for a collection of thermal loads. Furthermore, to effectively manage the impacts of load control actions in a power distribution network, it is shown how the control efforts can be adjusted on a nodal basis using voltage sensitivities.
On Asymptotic Distribution of Generalized Cross Validation Hyper-parameter Estimator for Regularized System Identification*

Asymptotic theory is one of the core subjects in system identification theory and often used to assess properties of model estimators. In this paper, we focus on the asymptotic theory for the kernel-based regularized system identification and study the convergence in distribution of the generalized cross validation (GCV) based hyper-parameter estimator. It is shown that the difference between the GCV based hyper-parameter estimator and the optimal hyper-parameter estimator that minimizes the mean square error scaled by $1/\sqrt N$ converges in distribution to a zero mean Gaussian distribution, where N is the sample size and an expression of covariance matrix is obtained. In particular, for the ridge regression case, a closed-form expression of the variance is obtained and shows the influence of the limit of the regression matrix on the asymptotic distribution. For illustration, Monte Carlo numerical simulations are run to test our theoretical results.
Learning K-Nearest Neighbour Regression for Noisy Dataset with Application in Indoor Localization

Many indoor location estimation algorithms compare the received signal strengths from WiFi access points to a prerecorded dataset, which may be composed of carefully mea-sured and curated data through a massive calibration campaign, and a large volume of crowd-sourced data captured by casual users. The crowd-sourced data usually contains valuable, but at the same time noisy, measurements where the noise might be in the features and/or the labels of data points. The treatment of such a dataset is challenging, as improper application of the dataset might result in inaccurate location estimation. In this paper, we propose a learning algorithm that makes the K-Nearest Neighbour (KNN) regression robust to noises both in features and labels of the training data. An intuition on why the learning algorithm should work is provided and the effectiveness of the algorithm is shown by experiments in a real environment.
Hybrid Beamforming for Full-Duplex Enabled Cellular System in the Unlicensed mmWave Band

In this paper, we consider a full-duplex enabled cellular system operated in the unlicensed mmWave band. In particular, the full-duplex gNB is equipped with multiple antennas and applies hybrid beamforming for downlink transmissions, meanwhile mobile users send uplink traffic to the gNB with an omni-directional antenna. In the unlicensed bands, the cellular networks need to ensure a fair coexistence with other systems, such as WiGig. Therefore, a sum rate maximization problem is formulated to improve the throughput of the cellular system meanwhile suppressing its interference to the WiGig system. The formulated problem is non-convex and of high complexity. In order to efficiently address it, the sum rate maximization problem is first reformulated into a weighted mean square error minimization problem, and then the penalty dual decomposition method is applied to jointly optimize the uplink transmit power and downlink hybrid beamforming. Numerical results show that the performance of the proposed unlicensed full-duplex enabled cellular network degrades by nearly 29% in order to fairly coexist with the WiGig system. However, compared with half-duplex systems, applying full-duplex technology to our proposed network can improve the sum rate by nearly 56%.
Power Usage of Energy Harvesting Sensors with a Drone Sink: A Reinforcement Learning Based Approach

Wireless sensor networks (WSNs) can utilize radio frequency energy harvesting from ambient power sources for continuous operation, while drones acting as sink nodes increase the reliability of transmission and decrease a sensor's energy usage. In this paper, we attempt to optimize the transmit power of a sensor such that the overall outage probability is minimized. The sensors are assumed to have a finite-level battery and a buffer with discrete states. Energy is harvested from ambient energy arising from cellular base stations distributed according to a Poisson point process. The sensor's data is transmitted to a drone whenever its buffer becomes full. We consider two scenarios for the drone: i) hovering, and ii) moving on a fixed trajectory. Moreover, we utilize different path loss and fading models for the sensor-drone links due to their line-of-sight nature. An outage occurs due to both transmission outage and buffer overflow. We formulate the problem as a Markov decision process, and utilize a Q-learning based algorithm to learn the power control policy. Our numerical results show that the proposed policy significantly outperforms both the full and single-step energy usage policies. Moreover, it is robust enough to handle environmental/channel changes without a need for re-training.
Distributed Iterative Learning Control for Networked Dynamical Systems with Guaranteed Individual Energy Cost

High performance formation control problem working repetitively has found important applications in various areas. Recent design uses iterative learning control (ILC) to achieve the high performance requirements, since ILC does not require a highly accurate model required by traditional control methods. This paper considers a previously unexplored problem in formation control problem, which aims at achieving the high performance requirement while guaranteeing individual input energy cost using only local information. We propose two novel ILC algorithms to solve the above problem. The proposed algorithms are suitable for both homogeneous and heterogeneous networks, as well as non-minimum phase systems, which are appealing in practice. Distributed implementations using the alternating direction method of multiplies are provided, allowing the proposed algorithms to be applied to large scale networked dynamical systems. Convergence properties of the algorithms are analysed rigorously and numerical examples are presented to demonstrate their effectiveness.
Pri-PGD: Forging privacy-preserving graph towards spectral-based graph neural network

The development of Graph Neural Network (GNN) enables people to explore the value of graph data better. Additionally, graph data often reveals more private information than data in Euclidean space. Some scholars have proposed that differential privacy can be used to add noise to the graph. However, the availability of the data will decrease with the increase of privacy. Some work utilize graph data through federated learning, which is computationally complex and heavily burdened by communication. Therefore, based on the principle of the spectral-based GNN model (Graph convolutional network, GCN), we proposed a method (Pri-PGD) to forge a privacy-preserving graph by disturbing. We set a user-selectable parameter $m$ to convert the first m-order neighbor nodes to the first-order neighbor for each node. Pri-PGD achieves two goals: preserving convolution basis space and preserving first-order neighbor information of the real graph. This allows Pri-Pgdto guarantee the privacy of the graph data while keeping the availability of it. We evaluated the effectiveness of our algorithm on three classical graph datasets (Cora, Citeseer, Pubmed). The accuracy of our method on the GCN model for the three datasets decreases only by about 1% when the parameter $m$ is 2. This verifies the validity of the forged graph obtained by Pri-PGD. As the experiment shows, our method is also valid for the Graph attention network model that is sensitive to first-order neighbor nodes.