Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy and Delay Aware Physical Collision Avoidance in Unmanned Aerial Vehicles

Several solutions have been proposed in the literature to address the Unmanned Aerial Vehicles (UAVs) collision avoidance problem. Most of these solutions consider that the ground controller system (GCS) determines the path of a UAV before starting a particular mission at hand. Furthermore, these solutions expect the occurrence of collisions based only on the GPS localization of UAVs as well as via object-detecting sensors placed on board UAVs. The sensors' sensitivity to environmental disturbances and the UAVs' influence on their accuracy impact negatively the efficiency of these solutions. In this vein, this paper proposes a new energy- and delay-aware physical collision avoidance solution for UAVs. The solution is dubbed EDCUAV. The primary goal of EDC-UAV is to build in-flight safe UAVs trajectories while minimizing the energy consumption and response time. We assume that each UAV is equipped with a global positioning system (GPS) sensor to identify its position. Moreover, we take into account the margin error of the GPS to provide the position of a given UAV. The location of each UAV is gathered by a cluster head, which is the UAV that has either the highest autonomy or the greatest computational capacity. The cluster head runs the EDC-UAV algorithm to control the rest of the UAVs, thus guaranteeing a collision free mission and minimizing the energy consumption to achieve different purposes. The proper operation of our solution is validated through simulations. The obtained results demonstrate the efficiency of EDC-UAV in achieving its design goals.
Towards a Benefit-based Optimizer for Interactive Data Analysis

This vision paper introduces several ideas around the optimization of Interactive Data Analysis (IDA) tasks. With an eye on traditional query optimization (QO) in Relational DataBase Management Systems (RDBMS), we suggest that IDA tasks should be specified through high-level statements and optimized globally, particularly by maximizing the number and significance of insights that can be automatically collected for the task. We envision an architecture for IDA and propose in the context of IDA what corresponds to statistics, cost model and execution plan pruning strategy in relational systems. We give elements pertaining to the feasibility of the vision and draw perspectives.
Delay Constrained Hybrid CRAN: A Functional Split Optimization Framework

Hybrid cloud radio access network (CRAN) architecture supports the realization of splitting/virtualization of the baseband processing unit (BBU) functions processing between the central cloud, central office that has large processing capacity and efficiency, and the edge cloud, an aggregation node which is closer to the user, but usually has less processing efficiency. In our previous work, we studied the impact of different split points (which decide the amount of communication processing at center and edge clouds) on the system's energy and midhaul link bandwidth consumptions. Midhaul is defined as the part of the network connecting center cloud to edge cloud. In this study, we propose an optimization framework to incorporate the end-to-end delay, from the central cloud to the end user, under different/flexible function split points. For all services requests, all users have the same services' size (in Mbits) that are associated with different delay requirements based on the service. Different service/delay requirements enforce different function splits. Our proposed optimization framework minimizes both system's power and bandwidth consumption with guaranteed end-to-end latency performance (control plane is not considered). The required split decision is significantly dependent on the processing power efficiency ratio between processing units at edge and central clouds. As shown in our previous results, Hybrid CRAN achieves about 55 percentage power saving compared fully distributed system, at the expense of midhaul bandwidth consumption. Simulation results showed that as the delay requirements decrease, average allowable latency reduces from 65msec to 45msec, this power consumption (achieved by relaxed delay case) increases by average of 33 percentage, at 20 resource blocks per user.
Quantification of Resting-State fMRI Networks Driven by Hemodynamically Informed Spatiotemporal Regularization

The brain’s spontaneous fluctuations measured by functional magnetic resonance imaging during rest cluster into recurrent activity patterns known as resting-state networks (RSNs). The spatial organization of RSNs in health and disease has been immensely investigated by conventional correlational analyses of fMRI time series. Recent findings of time-resolved analyses have provided evidence of reoccuring activation patterns that are accessible at instantaneous time points enabling the dynamic characterization of RSNs. We have proposed a method to recover spatially and temporally overlapping RSNs, which we named innovation-driven co-activation patterns (iCAPs), to study the dynamic engagement of RSNs unconstrained by the slow hemodynamic response. The iCAPs are extracted by temporal clustering of sparse innovation signals recovered from Total Activation (TA) framework, which is cast as a variational problem with sparsity-promoting spatial and temporal priors for fMRI data deconvolution. The temporal prior uses the inverse of the hemodynamic response function as a general differential operator and exploits sparsity of the innovation signals. In this work, we perform a quantitative analysis to assess the stability of iCAPs recovered from a group of patients with mood disorders and healthy volunteers.
Transmission and Latency-Aware Load Balancing for Fog Radio Access Networks

Fog computing-based radio access networks (F-RANs) aim to extend the computing and storage facilities of the centralized cloud radio access networks (C-RANs) to the network edge. Compared with the centralized baseband unit pool in the C-RAN, F-RAN reduces the burden on fronthaul. Thus, the F-RAN is foreseen as a viable solution towards ultra-low latency service provisioning. However, due to limited computing and storage facilities in fog computing-enabled access points (F-APs), some tasks that cannot be executed on the primary F-APs are transferred to other F-APs. In worst-case, the tasks are sent to the resource-enriched centralized cloud for processing. The transmission latency between F-APs, F-AP-to-end- user, and fronthaul latency strongly depends on interference power from the undesired network element as well as end-users. At the same time, the computational latency increases with the queuing delay. In this paper, we propose a load balancing scheme to address the tradeoff between transmission and computing latencies in F- RANs. Finally, the extensive simulation results show that the proposed scheme outperforms the greedy approach to meet the critical requirements, such as low-latency and minimal task offloading to the cloud in the F-RAN for the low-latency communications.
Speeding-Up DPI Traffic Classification with Chaining

The importance of network traffic classification has grown over the last two decades in line with the increasing diver- sity of networked applications. Nowadays traditional approaches to traffic classification, relying on port numbers and on Deep Packet Inspection (DPI), are not very effective in real scenarios respectively due to the usage of random or non-standard port numbers and to the wide usage of end-to-end encryption. Despite their limitations, port- based and DPI approaches are still widely used in operational networks for a number of network monitoring and management tasks. This paper proposes a practical approach for improving the efficiency of traditional traffic classification techniques by chain- ing fast classification stages (port-based and machine-learning- based), combined to lower their false-positive rate, and a more precise - but time- and resource-demanding - stage based on DPI. Experimental results demonstrate that Chain obtains results in line with DPI approaches in term of Precision, Recall, Accuracy and Area Under the Curve (AUC), while it is 45% faster when compared to nDPIng, a well- known DPI implementation. The appealing of the proposed approach in Network Function Virtualization (NFV) contexts is also discussed.
Towards a Distributed Routing Protocol for In-Band Control Channel with Elastic Controller Clusters

In software-defined networks (SDN), network control logic is implemented in a (logically-) centralized SDN controller that communicates with SDN switches via an SDN control channel. As the ability of the controller to communicate with switches is critical for the operationability of the network, reliable connectivity of the control channel must be provided. This question is especially important for in-band control channels, where no separate management network is available and most switches are not directly connected to the controller(s). In our previous work [1], we argued for providing reliable connectivity for the in-band control channel using a distributed routing protocol. We presented the protocol Izzy which is optimized for robust connectivity between a single SDN controller and the switches under its control. Realistic deployments, however, use logically-centralized controller clusters for reliability and scalability reasons. In this paper, we describe how to extend Izzy to support such logically-centralized clusters. We propose to divide controllers into groups based on distance between them. Each such group runs a separate instance of Izzy. This paper presents the distributed algorithm Seedling that accomplishes the former. We describe how Seedling works in static scenarios followed by an outline of necessary steps to extend it to dynamic settings including changes to both the number and location of controllers as well as to network topology, especially link failures.
Impact of Device Population on Beam Alignment Performance of 802.11ad

Millimeter Wave (MmWave) based communication standards including IEEE 802.11ad WiFi show great promise in supporting next generation service demands such as real-time streaming of ultra-high-definition video. However, MmWave signals suffers from high path loss and often required to have unobstructed line-of-sight. Narrow beamwidth antenna arrays along with adaptive beamforming is currently deployed to mitigate those issues. Nevertheless, the impact of device population on beam alignment has not yet been well studied in the literature. In this paper, we quantify the loss of antenna gain when there are multiple devices in the communication range completing for channel access to perform beamforming. We further study the link outage probability for devices with angular displacement similar to change in orientation of hand-held or head-worn devices in home wireless networks. The simulation results reveal that loss of antenna gain can be as high as 7dB and link outage probability increases up to 90% for 16 antenna sectors and 10 competing devices.
Signal Restoration Based on Temporal Structure and Multi-Layer Architecture

Signal restoration involves the removal or minimization of degradation such as attenuation, interference, and noise. Blind signal restoration is the process of estimating either the original signals or mixture functions from the degraded signals, without any prior information about original sources. In this paper, we present a novel approach to tackle the ill-posedness of the nonlinear blind source separation problem. The derivation of our algorithm is inspired by the idea of an efficient layer-by-layer representation to approximate the nonlinearity. Once such representations are built, a final output layer is constructed by solving a convex optimization problem. Thus, the projected data can break a nonlinear problem down into the version of generalized joint diagonalization problem in the feature space. Importantly, the parameters and forms of polynomials depend solely on the input data, which guarantee the robustness of the structure. We thus address the general problem without being restricted to any specific mixture or parametric model. Experimental results show that the proposed algorithm is able to recover the nonlinear mixture with higher separation accuracy on audio datasets from the real world.
Wireless Mediation for Multi-Hop Networks in Time Critical Industrial Applications

Industrial Internet-of-things (IIoT) networks have recently gained enormous attention because of the huge advantages they offer. A typical IIoT network consists of a large number of sensor and actuator devices distributed randomly in an industrial area to automate various processes, where a major goal is to collect data from all these devices and to process it centrally at an aggregator. However, for an efficient system operation, a proficient scheduling mechanism is required due to its direct association with performance parameters. Many existing techniques such as time division multiple access (TDMA), do not perform well in industrial environments due to their stringent timeliness requirements. In this paper, we propose a medium access control (MAC) layer protocol for node scheduling in a scenario where some devices may not be in one-hop range of the aggregator and thus renders a multi-hop mechanism inevitable. A discrete time Markov chain (DTMC) model is proposed to characterize the transmission of multi-tier nodes and the analytical expressions of throughput and latency are derived. It has been oberved that the delay scales linearly with the number of nodes which are away not in one-hop distance of the aggregator. Numerical simulations have been performed to validate the theoretical results.
Ordered Sequence Detection and Robust Design for Pulse Interval Modulation

This paper proposes an ordered sequence detection (OSD) for digital pulse interval modulation (DPIM) applied in optical wireless communications (OWC). To detect a packet consisting of $L$-chips, the computational complexity of OSD is of the order $\mathcal{O}(L\log_{2}L)$. Moreover, this paper also proposes a robust pulse interval modulation (RPIM) scheme based on OSD. In RPIM, the last of every $K$ symbols is with more power to transmit information and simultaneously to provide a built-in barrier signal. In this way, error propagation is bounded in a slot of $K$ symbols. Together with interleaver and forward error correction (FEC) codes, the bit error rate (BER) can be greatly reduced. We derive the approximate uncoded BER performance of conventional DPIM with OSD and the newly proposed RPIM with OSD based on order statistic theory. Simulations are conducted to collaborate on theoretical analysis and show that RPIM with OSD considerably outperforms existing DPIM with optimal threshold detection in either uncoded or coded systems over various channels.
Millimeter Wave Channel Estimation using Data-Aided DoA Estimation

In this paper, we study channel estimation for multiuser millimeter wave (mmWave) systems. Since most conventional channel estimation schemes rely entirely on pilot signals, such methods cannot accurately estimate mmWave channels with low signal-to-noise ratio (SNR). To address this challenge, a direction of arrival (DoA)-assisted channel estimation scheme exploiting the sparsity of mmWave channels is developed. The proposed scheme utilizes data symbols as well as the pilot signals to estimate DoA of effective paths, and the estimated DoA information is used to suppress the noise given that the number of effective path directions is much smaller than the number of antennas. Furthermore, it is shown that sparse Bayesian learning (SBL) approach is suitable for DoA estimation in mmWave communications scenarios where signals from different angles are highly correlated as a result of multipath propagation, and a low complexity SBL algorithm for this setup is derived. By utilizing data symbols and adopting the SBL approach, the proposed scheme can identify the directions of effective path even in low SNR regime, enabling accurate estimation of mmWave channels.
HBF-PDVG: Hybrid Beamforming and User Selection for UL MU-MIMO mmWave Systems

In this paper, a novel hybrid beamforming (HBF) design utilizing a pre-defined virtual grouping (PDVG) algorithm is proposed for the uplink (UL) multi-user (MU) multiple-input multiple-output (MIMO) millimeter wave (mmWave) systems. The computational complexity of achieving traditional MU-MIMO beamforming (BF) methods in the mmWave systems becomes significant due to the hardware constraints. Thus, the proposed HBF-PDVG algorithm aims to reduce the feedback overhead by employing a three-phase algorithm. The essential idea is to perform the user selection independently from the HBF design by utilizing the PDVG algorithm after completing the analog BF training and then mitigating the remaining MU interference among the selected users using digital BF. Simulation results demonstrate the significant sum-rate benefits of the HBF-PDVG algorithm compared with zero-forcing (ZF) and random selection algorithm methods.
Outage Probability of NOMA with Partial HARQ Over Time-Correlated Fading Channels

This paper investigates the outage performance of downlink non-orthogonal multiple access (NOMA) system over time-correlated Rayleigh fading channels, where the users have heterogeneous quality of service requirements, e.g., a latency-critical user with a low target rate and a delay-tolerant user with a large target rate. In order to meet the different requirements of the users, a partial hybrid automatic repeat request (HARQ) scheme is proposed. The closed-form expressions of outage probabilities for NOMA without and with re-transmission are derived. With the developed outage probabilities, a condition on the superiority of NOMA to orthogonal multiple access (OMA) is obtained. Simulation results demonstrate the accuracy of developed analytical results. It is shown that the performance of NOMA is superior to orthogonal multiple access if the derived condition is satisfied and HARQ-CC can enhance the performance of NOMA over time-correlated fading channels.
On Wireless Energy Harvesting Based Relaying for Interference-Limited User: A DMT Perspective

In this paper, the diversity-multiplexing tradeoff (DMT) is investigated for an interference-limited relay-aided communication system in the moderate interference regime. Further, in the proposed system, the relay node is powered by wireless energy harvesting (WEH). In particular, this work studies the impact of energy harvest duration at the relay node on the achievable DMT of an interference-limited user using a variant of dynamic decode-and-forward based protocol. Our results show that the DMT of interference-limited user degrades with the proportional increase in energy harvest duration at low multiplexing gains. On the other hand, it is observed that the DMT of WEH-based interference-limited user approaches that of non-EH-based counterpart for higher multiplexing gains.
Optimal and Near-Optimal Policies for Wireless Power Transfer Considering Fairness

Radio frequency wireless power transfer (RF- WPT) is an emerging technology that enables transferring energy from a power source to wireless devices over the air. In this paper, we aim at finding {the optimal policy for wireless power transfer (WPT) from an energy access point} (E-AP) to multiple energy receivers (E-Rs) that maximizes the wireless power transfer efficiency. For this purpose, in the first part of the paper, we formulate the problem of maximizing the total average received power of the E-Rs subject to the average and peak power constraints of the E-AP. The formulated problem is a non-convex stochastic optimization problem, and is highly non-trivial. Using some stochastic optimization techniques, we tackle the aforementioned challenges to solve the problem and derive a closed-form expression for the optimal solution, which requires the CSI distribution. In addition, we propose a heuristic algorithm that does not require any explicit information on the CSI distribution. We prove that the proposed algorithm attains a near-optimal solution within a guaranteed gap to the optimal solution. In the second part of the paper, we focus on the problem of considering fairness among the E-Rs and propose two fair policies, namely Max- Min Fair (MMF) policy and Quality-of-service- aware Proportional Fair (QPF) policy. MMF policy maximizes the minimum received power among the E-Rs. Moreover, QPF policy balances the received power levels of different E-Rs as much as possible, while guaranteeing the required minimum QoS for each of them. Various numerical results demonstrate the significant performance of the proposed solutions.
LocHunt: Angle of Arrival Based Location Estimation in Harsh Multipath Environments

The next generation of cellular networks promises to be the platform for ubiquitous, precise, and accurate location-awareness. Although positioning using radio-frequency waves has many advantages in terms of cost and performance, propagation phenomena such as multipath propagation and shadowing can immensely deteriorate this performance leading to incorrect estimation of a user's location. This paper introduces LocHunt, an algorithm that addresses such a problem. Concretely, at each time-instance, angle-of-arrivals (AoAs) of multipath waves from several anchors are estimated and all the candidate locations of the user are derived. Repeating this operation for a span of time, the algorithm exploits the heuristics that those candidate locations obtained from non-line-of-sight (NLoS) paths exhibit large spatial variations, whereas the true location of the user is manifested as a denser cluster. Subsequently, the peak of the underlying empirical probability density function corresponds to an estimate of the user's location, which can be found by the mean-shift algorithm followed by the connected-components algorithm. The proposed data-driven approach is compared to the state-of-the-art, showing improved localization accuracy with up to one order of magnitude reduction in computational complexity. Channel state information (CSI), similar to that exposed by 802.11n MIMO-OFDM systems, is used for testing the algorithms and is generated by a modified Winner 2 (WIM2) channel simulator.
An Energy Efficient Embedding Algorithm for Multi-Domain Virtual Network Embedding

Network virtualization (NV) is one fundamental attribute of the next generation network. Virtual network embedding (VNE) is about the resource allocation in NV topic. Researchers have proposed multiple embedding algorithms since 2008. Preceding algorithms try to accept more virtual networks (VNs) in one underlying substrate network (SN) and maximize embedding revenue. As energy cost accounts for approximately half whole network cost, it is essential for Internet service providers to optimize energy cost. In addition, in real networking environment, it is vital to embed VNs among multiple SNs, geographically distributed in different regions. To solve both issues, we firstly propose the Multi-Domain VNE problem model and formal energy cost model for VNE. Then, we present a two-stage and energy efficient algorithm (EEMD-VNE-T), aiming at optimizing energy cost. Meanwhile, the EEMD-VNE-T is able to embed each VN among multiple SNs in polynomial time. Preliminary results reveal that EEMD-VNE-T significantly reduces the energy cost by roughly 8.7% over two representative energy algorithms.