Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Reinforcement Learning for Modulation and Coding Scheme Selection in Cognitive HetNets

We study a cognitive heterogeneous network (Het-Net), in which multiple pairs of secondary users coexist with a pair of primary users on a certain spectrum band. To protect primary transmissions, secondary transmitters (STs) adopt a sensing-based approach to access the spectrum band. Nevertheless, STs may cause uncertain interference to the primary receiver (PR) due to imperfect spectrum sensing, which is particularly significant when the wireless links between the primary transmitter (PT) and STs are extremely weak and the wireless links between STs and the PR are non-ignorable. This makes it difficult for the PR to select a proper modulation and/or coding scheme (MCS). To deal with the issue, we propose an intelligent deep reinforcement learning (DRL) based MCS selection algorithm for the primary transmission. With the proposed algorithm, the DRL agent at the PR is able to learn the pattern of the interference from the STs and predict the interference in the future. Simulation results show that the transmission rate of the proposed algorithm can converge to 90 ∼ 100 transmission rate of the optimal MCS selection algorithm, which assumes that the interference from the STs is perfectly known at the PR as prior information. Meanwhile, the transmission rate of the proposed algorithm is around 100 higher than the transmission rate of the benchmark algorithm, which selects the MCS without the information about interference.
Low Complexity Iterative LMMSE-PIC Equalizer for OTFS

Orthogonal time frequency space modulation (OTFS) which is a novel modulation scheme, aims at offering reliable communications to users under high mobility conditions. Since each symbol in OTFS experiences the same fading over the doubly selective channel, OTFS is able to exploit full time-frequency (TF) diversity when coupled with a proper equalizer. In this paper, we study the low complexity iterative equalizer design for the coded OTFS system. In order to jointly combat the two-dimensional interference, we adopt linear minimum mean squared error based parallel interference cancellation (LMMSE-PIC) as the equalizer for OTFS. The first order Neumann series is used to approximate the matrix inversion involved in LMMSE-PIC for OTFS, which reduces the equalizer complexity to quasi-linear to the total number of transmitted symbols. Extrinsic information transfer (EXIT) charts and coded bit error rate (BER) simulation results show that our proposed LMMSE-PIC equalizer outperforms the recently proposed PIC approach for OTFS and also achieves considerable gain compared to orthogonal frequency division multiplexing (OFDM).
An academic nomad? : Identity formation of iSchool students

Students come to an iSchool program with a passion for exciting and novel research which they perceive as being rare in traditional disciplinary areas. However, the range of freedom allowed in conducting research at iSchools sometimes poses unexpected difficulties for students. This study aims to identify the unique problems that graduate students in an interdisciplinary iSchool program encounter when designing a research project. A qualitative content analysis method is used to analyze students’ responses regarding the difficulties they have experienced. The findings indicate that the difficulties, which range from a paradox of choice in determining a research topic to communicating the implications of their research outcomes, generally point to a problem of identity formation. This study argues that students’ identity problems have been overshadowed by the hype of interdisciplinary studies and that helping students establish a solid identity within iSchool programs is necessary to produce healthy and confident researchers.
Wireless Robotic Communication for Collaborative Multi-Agent Systems

Collaborative robots as a multi-agent system to complete a common mission without public reference but operate individual decision and learning mechanism represent a wide range of applications in artificial intelligence. With an illustrative example, reinforcement learning with localization and planning capabilities is developed to represent each robot's operation. It is shown that wireless robotic communication can significantly enhance overall performance of collaborative MAS in the distributed operating manner. After identify useful information (i.e. reward map and private reference) to exchange, according to properties of content, p-persistent real-time ALOHA is suggested to serve as the multiple access protocol of the ad-hoc style networking toward ultra-reliability and ultra-low latency, resulting in satisfactory overall performance close to ideal communication. Wireless robotic communication therefore reveals new technological opportunities for robotics, multi-agent systems, artificial intelligence, and communications.
A Reinforcement Learning Approach to Dynamic Spectrum Access in Internet-of-Things Networks

To support wireless communication traffic of Internet-of-Things (IoT) systems in terms of massive connectivity, dynamic spectrum access (DSA) is important issue. This paper proposes spectrum sensor-aided DSA system based on a reinforcement learning (RL) algorithm that aims at efficient spectrum usage for IoT network over the incumbent network. Due to small-form-factor of IoT devices, they do not have spectrum sensing capability. To support DSA of IoT devices, we introduce sensor-aided DSA system that enhances spatial spectrum reusability by means of RL algorithm. With the RL algorithm, proposed DSA system provides self-organizing feature for massive number of IoT devices. We show that the performance of proposed RL based DSA system in various densities of IoT devices utilizing slotted ALOHA protocol that has spectrum access probability learned by proposed DSA system. We also present the performance of proposed RL based DSA system surpass that of distributed Carrier Sensing Multiple Access with Collision Avoidance (CSMA/CA) protocol for channel access coordination. We also present the consistent performance of incumbent user when the IoT devices access to the spectrum band with learned spectrum access probability.
A Truthful Location-Protected Mobile Crowdsensing Framework with User Mobility

Mobile Crowdsensing (MCS) is a promising paradigm for the large-scale sensing. In this paper, we aim to build a truthful framework of MCS taking user mobility and incentive into account. The proposed framework is composed of two challenging problems, path planning and incentive mechanism design. In path planning, every user as a worker independently plans a tour to carry out tasks based on its own strategy. In incentive mechanism design, the platform leverages a mechanism to select the winners and determine the payments. To solve these two problems, a heuristic bidirectional searching algorithm is proposed for path planning and an incentive mechanism is designed. The proposed mechanism is proved to be computationally efficient, individually rational, and truthful. Finally, the simulation results show that our proposed heuristic algorithm outperforms the baseline algorithms and approaches the optimal solution in path planning. Our proposed mechanism has a total payment smaller than that of a Vickrey-Clarke-Groves (VCG) mechanism.
SoftGuard: Defend Against the Low-Rate TCP Attack in SDN

The low-rate TCP attack is essentially a great threat to the Internet. It causes significant throughput degradation of TCP flows by generating periodical pulsing flows. Due to its low rate, the attack is difficult to be detected and throttled. Recently, Software-Defined Networking (SDN) has emerged as a promising network paradigm. Several SDN-based defense systems have been proposed to deal with various Denial of Service (DoS) attacks. However, they fail to consider the low-rate TCP attack. In this paper, we propose SoftGuard, which is an SDN-based defense that effectively detects and mitigates the low-rate TCP attack. SoftGuard detects the attack by installing crafted flow rules to monitor the degradation of aggregated TCP throughput in ports of switches. It confirms the attack by judging whether there is periodicity for aggregated TCP throughput with adaptive Fast Fourier Transform, and accurately identifies attack flows with Mean Euclidean Distance. Identified attack flows will be effectively throttled by installing mitigation rules in ingress switches. We implement SoftGuard in the Floodlight controller. Experiments in a real SDN testbed demonstrate its effectiveness on defending against the low-rate TCP attack.
High-Accuracy Joint Position and Orientation Estimation in Sparse 5G mmWave Channel

With the emergence of new 5G radio networks, high-accuracy positioning solutions are becoming extensively more important for numerous 5G-enabled applications and radio resource management tasks. In this paper, we focus on 5G mm-wave systems, and propose a method for high-accuracy estimation of the User Equipment (UE) position and antenna orientation. Based on the sparsity of the mm-wave channel, we utilize a compressive sensing approach for estimating the departure and arrival angles as well as the time-of-arrival for each observed radio propagation path. After this, in order to obtain statistical descriptions of the unknown parameters, we analytically derive a set of sampling distributions, which enable utilization of an iterative Gibbs sampling method. As shown by the obtained simulation results, the proposed method is able to achieve centimeter-level positioning accuracy with degree-level orientation accuracy, even in the absence of a line-of-sight path.
Traffic Reduction in Video Call and Chat using DNN-Based Image Reconstruction

In this paper, a traffic reduction scheme for videobased call and chat applications that uses deep neural network (DNN) based super resolution is proposed. Specifically, a sender transmits low-quality, low-resolution video frames containing face information in order to reduce the amount of video traffic. The receiver uses DNN-based super resolution to reconstruct high-quality, high-resolution video frames from the low-quality video frames. The proposed scheme makes two contributions. First, face features are adopted for parameter optimization of DNN-based super resolution for high-quality image reconstruction, and second, the scheme includes a newly designed loss function that considers face features that allow high-quality face-containing video frames to be reconstructed at the receiver. According to our evaluation results using real video frames of video calls, the proposed scheme reduces the amount of video traffic by more than 90% as compared with conventional schemes that implement the standard video encoder. In this case, the proposed scheme achieves a reconstructed image quality up to 0.85 in terms of structural similarity (SSIM).
A Self-Organized Approach for Neighboring Message Interaction in UAV Swarms

Message interaction among neighborhood is necessary for unmanned aerial vehicles (UAVs) and Time Division Multiple Access is a feasible implementation by which each UAV broadcasts in one dedicated slot. However, the dynamic character requires the access process fast and the constrained energy limits the feedbacks during the process uninformative. Therefore, this article focuses on the slot access problem in UAV swarms. To be energy saving, a collision discovery method which is independent of valid information is designed to play the role of feedback. Furthermore, considering the decentralized structure, the slot access problem is formulated as a game model. To reach the Nash Equilibrium of the game fast, a synchronous and uncoupled learning algorithm is proposed to, in which all UAVs update simultaneously based on the information they obtain. The algorithm releases the requirement for a scheduler or a common control channel and is therefore applicable in UAV swarms. Simulation results verify the effectiveness of the proposed method and some investigations about the requirement for the number of slots are made.
Joint Power and Trajectory Optimization in UAV Anti-Jamming Communication Networks

This paper mainly investigates the unmanned aerial vehicle (UAV) communication networks under the threat of a static malicious jammer. While taking the flying process of the user (UAV transmitter-receiver pair) into consideration, we propose a joint power and trajectory optimization method. Moreover, a Stackelberg framework is formulated to solve the proposed optimization problem. In addition, a joint power and trajectory optimization algorithm (JPTOA) based on best-response (BR) is designed to obtain the user's strategy as well as Stackelberg equilibrium (SE) in each time stage. Furthermore, as an extension of single stage optimization, the I-step exploration and one-step decision (IEOD) scheme is designed to enhance the user's cumulative utility. Finally, simulation results are presented to show the performance of the proposed JPTOA scheme.
Multi-Task Deep Learning Based Dynamic Service Function Chains Routing in SDN/NFV-Enabled Networks

With the development of Software-Defined Networks (SDNs) and Network Function Virtualization (NFV), Service Function Chains (SFCs), that steer the traffic through a series of specified Virtual Network Functions (VNFs) with predefined orders, has become a popular network service paradigm. Compared with the rule-based routing algorithms, deep-learning technology has great potential to achieve efficient path computation for SFC Requests (SFCRs) in an intelligent way. However, traditional intelligent models have trouble in the speed of convergence, which incurs long training time and high computation consumptions. In this paper, we propose a novel Multi-Task Deep Learning (MTDL) based architecture, which improve generalization by sharing related information of tasks, to assure fast convergence in training process, and an MTDL-based Routing Algorithm (MTDL-RA) to efficiently compute routing paths with the minimum end-to-end delay for SFCRs. Performance evaluation results demonstrate that our proposed MTDL-based architechture and routing algorithm can achieve significantly reduction in the training time and obtain high performance in terms of SFCR acceptance rate and the delay of paths, respectively.
Model-Free Online TDD Configuration for Mobile Edge Computing

With tremendous computing power in the radio access network, mobile edge computing (MEC) that can support localized context awareness creates a new technological frontier for 5G and beyond. To efficiently exploit the networking and computing functionalities, Time Division Duplex Orthogonal Frequency Division Multiple Access (TDD-OFDMA) type has been considered in this paper. To take advantage of dynamic features in TDD, a model-free online TDD configuration scheme is proposed based on context analysis and Multi-Armed Bandit (MAB) optimization. The TDD configuration problem is therefore novelly modeled as a contextual MAB problem, and is solved by the contextual Upper-Confidence-Bound (C-UCB), which dynamically adjusts TDD configuration to network traffic since that the system cost can be reduced. To further reduce the energy consumption and makespan of mobile devices (MDs), a greedy resource allocation (GRA) embedded in the TDD configuration is developed to select MDs and allocate resources. The simulations demonstrate that proper TDD configuration successfully reduces the system cost, and C-UCB technique approaches the ideal TDD configuration, with significant performance gain when the GRA effectively select and allocate, to strike simultaneous efficiency for mobile networking and MEC.
A Contactless and Fine-Grained Sleep Monitoring System Leveraging WiFi Channel Response

How can we effectively log a fine-grained sleep record consisting of still postures and in-place motions for the sleep disorder diagnosis without any specialized hardware? Existing sensor-based or vision-based solutions are either obstructive to use or rely on particular devices. This paper introduces SleepGuardian, a Radio Frequency (RF) based sleep monitoring system leveraging only omnipresent WiFi signals to provide a silent (unobtrusive and free of privacy concerns) yet loyal (finegrained and reliable) logging service. The key to SleepGuardian is to model the energy feature of wireless channel as a Gaussian Mixture Model (GMM) to adaptively recognize motions happened during sleep. We prototype SleepGuardian with off-the-shelf WiFi devices and evaluate it in an office. Experimental results over 11 subjects with several artificial and real periods of sleep demonstrate that SleepGuardian is effective since it achieves 100% overall accuracy (ACC), 0% false negative rate (FNR) and 0.64 s mean absolute error (MAE) on average. Considering that SleepGuardian is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for sleep monitoring.
Classification of Indoor Point Clouds Using Multiviews

We present an approach for classifying 3D point clouds of indoor environments using Convolutional Neural Network (CNN)-based image analysis for entropy-selected 3D views. Prior to classification, the 3D point clouds are clustered using either Density-based Spatial Clustering of Applications with Noise (DBSCAN) or k-means algorithms. We then use randomly sampled 3D point normal vectors as an entropy descriptor to calculate the direction and position of the virtual camera, which is placed around these clusters. It synthesizes 2D images of a given cluster from multiple views with positions and directions that have highest visual entropy. We then proceed to classify the images using a retrained CNN. We test our approach for classifying common office furniture items using both synthetic and actual 3D point clouds of typical indoor office spaces. The empirical results demonstrate that our approach is suited towards classifying specific indoor furniture objects. We also describe how our approach can be implemented as a lightweight component within a service-oriented system that is used for visualization and classification of 3D point clouds using a given Web3D tool. The resulting semantically-enriched 3D point clouds can further be used for digital indoor environment representations, with further use as base data for Building Information Modeling (BIM), Facility Management (FM), and Digital Twin (DT) applications.
Angle Diversity Receiver in LiFi Cellular Networks

As an emerging technology for wireless communications based on the visible light communication (VLC), Light-fidelity (LiFi) is considered as a complementary building block for fifth generation (5G) mobile networks. In this paper, we investigate the optimum field of view (FOV) for the angle diversity receiver (ADR) in LiFi cellular networks. The probability of visibility and the visible area of the ADR is defined. There is a trade off between the channel gain and visibility. An optimisation problem is formulated to maximise the channel gain, and the lower bound of the FOV of photodiodes (PDs) mounted on an ADR is derived. The simulation results show that the data rate is maximised when the FOV is minimised and the structure for the pyramid receiver (PR) achieving highest data rate is the one consisting of 6 PDs each with a FOV of 30 degree.
Maximum-Eigenvalue Detector for Multi-Antenna Ambient Backscatter Communication Systems

Ambient backscatter communication is a newly emerged ultra-low-power technology for the internet of things network. In this paper, we study the symbol detection of multi-antenna ambient backscatter communication system. In particular, the maximum-eigenvalue detector is derived from general likelihood ratio test, and the approximative BER expressions are characterized. The analytical results show that the BER of the proposed detector decreases with sampling rate N, signal-to-noise ratio γ and number of receiving antenna M, however, settles in high γ or M regime. In addition, we find the proposed detector eliminates the knowledge of noise power, which is of uncertainty and difficult to estimate. Then the simulation results validate the correctness of the theoretical analysis, and show that the proposed detector outperforms the existing energy detector in terms of BER performance.
Simultaneous Wireless Information and Power Transfer using Rectangular Pulse and CP-OFDM

Simultaneous wireless information and power transfer (SWIPT) has recently attracted researchers and may help satisfy future technology demands. Its principle is to let wireless power transfer (WPT) and wireless information transfer (WIT) coexist based on shared resources (or sometimes even on the same signal). SWIPT can be implemented in many different ways and the literature on the topic is very wide, each solution having different obstacles. Recent studies have shown that, due to the non linearity of the rectifiers, high-PAPR waveforms provide better performance in terms of energy harvesting; hence the design of the power signal is an important issue. In addition, the presence of this power signal should consume the smallest possible amount of resources for the information transfer. In this paper, a new waveform design is proposed where the information and power signals are superposed utilizing the same frequencies at the same time. The power signal is composed of a high peak modulated rectangular wave sent during the cyclic prefix of the CP-OFDM system such that it does not interfere with the useful OFDM data symbol duration. Interference evaluation and measurements show that a good choice of signal parameters can minimize interference on the information decoder and at the same time provide good performance in terms of energy harvesting which validates the possibility of the proposed design.
The Meta Distribution of SINR for Small Cell Networks with Temporal Traffic

In this paper, we develop a mathematical framework for the analysis of SINR in small cell networks with temporal traffic variation. Our model captures the interplay between the spatial location of small access points, which determines the magnitude of mutual interference, and their temporal traffic dynamic. We derive a tractable expression for the SINR meta distribution, and verify its accuracy via simulations. Based on our analysis, we find that i) under the same configuration, when traffic condition changes from light to heavy, the corresponding SINR requirement can differ by more than 10 dB for the network to maintain coverage, ii) the SINR coverage probability varies largely with traffic fluctuation in the sub-medium load regime, and iii) spatial densification can boost up the cell-edge rate almost linearly in the dense regime, thus demonstrates the effectiveness from cell densification.
FRESH: FReshness-Aware Energy-Efficient ScHeduler for Cellular IoT Systems

In cellular Internet of things (IoT) systems, massive low-power terminals update information status to cellular base stations to support diverse IoT applications. In this circumstance, information freshness and energy efficiency become two fundamental concerns. Except data transmissions, information updates consume additional energy for radio activation. To improve the energy efficiency, it is reasonable to aggregate the dynamically generated data. However, the reduced updates will severely deteriorate the information freshness, especially for time-critical IoT applications. To address this issue, we propose an upload scheduling scheme in this paper. Considering dynamic packet arrivals and channel conditions, the upload scheduling problem is formulated from a long-term perspective. To solve this problem, a practical online upload scheduler, named as FReshness-aware Energy efficient ScHeduler (FRESH), is proposed to minimize the update energy consumption subject to information freshness constraints. We theoretically show that FRESH can make the energy saving arbitrarily close to that of the optimal scheduling decision. Simulation results demonstrate the necessity and effectiveness of implementing FRESH for cellular IoT systems.