Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement learning-based waveform optimization for MIMO multi-target detection

A cognitive beamforming algorithm for colocated MIMO radars, based on Reinforcement Learning (RL) framework, is proposed. We analyse an RL-based optimization protocol that allows the MIMO radar, i.e. the agent, to iteratively sense the unknown environment, i.e. the radar scene involving an unknown number of targets at unknown angular positions, and consequently, to synthesize a set of transmitted waveforms whose related beam patter is tailored on the acquired knowledge. The performance of the proposed RL-based beamforming algorithm is assessed through numerical simulations in terms of Probability of Detection $(P_{D})$.
A Hybrid Interference Cancellation Scheme for Massive MIMO Heterogeneous Networks

For co-channel deployed massive multiple input multiple output (mMIMO) heterogeneous networks (HetNet), serious performance degradation will be caused by various types of interferences, such as the inter-tier and intra-tier interferences. Tremendous efforts have focused on the interference management (IM) for HetNet. Nevertheless, for different network configurations' e.g., different deployments of small cell (SC) base stations (BSs) and users, different number of SC BSs and users, etc., the performances of different IM schemes may vary. The current fixed only one or two types of IM schemes can not guarantee the optimal performance for different scenarios of mMIMO HetNet. Therefore, a hybrid IM schemes is proposed here. We formulate the determination of IM schemes as an optimization problem for maximizing the overall data rate, which is solved by the D.C. (difference of two convex functions) program. Numerical results show the significant performance enhancement compared with the fixed IM schemes. Moreover, it is found that, for large coverage of SC case, nearly maximum system performance in practical scenario can be achieved with not so large number of antennas at macro-cell BS.
Beam Design with Quantized Phase Shifters for Millimeter Wave Massive MIMO

In this paper, beam design for millimeter wave (mmWave) massive MIMO systems is studied regarding quantized phase shifters and different number of RF chains. Given the objective beam, the beam design problem is formulated as a hybrid optimization problem involving continuous variables as well as discrete variables. To reduce the difficulty in solving this problem, it is converted into several discrete optimization subproblems. Then beam design methods are proposed for the system with only one RF chain and two RF chains, respectively. For the general system with more than two RF chains, the parameter estimation is incorporated into the random search. Based on these findings, a partial random search (PRS) based beam design algorithm is proposed. To further improve the convergence speed, a fast search (FS) based beam design algorithm is proposed. Simulation results verify the effectiveness of the proposed algorithms and show that the beam pattern using the proposed PRS and FS based algorithm can well approach the objective beam with much less RF chains than OMP.
WiFi and Multiple Interfaces: Adequate for Virtual Reality?

In this paper, we investigate whether IEEE 802.11ac WiFi can support VR applications. To this end we conduct a controlled study of WiFi performance in an indoor setting. Our measurements reveal that WiFi transmissions suffer from high latency and jitter, which makes WiFi systems inadequate for VR applications. For example, the round-trip delay can be as high as 228ms, and more than 24.2% packets experience jitter higher than 1ms. To locate the root cause of the high latency and jitter, we dissect the network stack layer by layer and find that the main culprit is the wireless channel transmission time. To reduce the channel transmission time, we propose using multiple network interfaces running on non-overlapping channels. By using only two interfaces, we (1) reduce the median round-trip delay by 28.6% and jitters of higher than 1ms by 11.5% compared to the best single interface in UDP transmissions, and (2) reduce the median round-trip delay by 38.9% in TCP transmissions. We believe that this paper sheds some light on whether we can make today's WiFi systems VR-ready by using multiple interfaces.
Dynamically Connectable UAV Base Stations with Cooperative Energy Sharing

This work examines joint beamforming and power allocation schemes as well as trajectory learning and clustering mechanisms for dynamically connectable unmanned aerial vehicle (UAV) base- stations (BSs). Here, UAV-BSs are allowed to join together dynamically and form physically-connected collocated antenna arrays that enable joint transmission and cooperative energy sharing among the connected UAVs. A joint design of the transmit powers and beamformers in each time slot is first proposed based on the maximization of the sum signal-to-leakage-plus-noise-ratio (SLNR) of all users. The solution is obtained via alternating optimization where the transmit powers and beamformers are optimized in turn until convergence. Then, based on the per-time-slot design, a dynamic UAV moving and clustering policy is proposed where the expected sum rate of the system is maximized while adapting to changes in the users' locations and environment. Here, UAV locations are adjusted gradually in each time slot according to the stochastic gradient of the expected system sum rate. The UAV clusters are updated every T time slots by combining the two clusters that yield the maximum increase in the expected system sum rate. Simulations are provided to demonstrate the effectiveness of the proposed schemes.
Dynamic Distributed Antenna Systems: A Transitional Solution for CRAN Implementation

Cloud radio access network (CRAN) architectures promise improved scalability of future mobile networks, both in terms of network capacity as well as capital expenditure for mobile network operators. Yet, the transition towards CRAN with fully virtualized base band units (BBUs) implemented in the cloud is no minor step for network operators, since most of the existing base band processing hardware in already deployed macro base stations (BSs) becomes outdated. A gradual transition towards CRAN can be facilitated by initially reusing existing BBUs in macro BSs and augmenting them with additional remote radio units (RRUs), which are spatially distributed in the network coverage area and can be freely associated to any macro BS over a reconfigurable front-haul network. In this paper, we consider optimal allocation of RRUs amongst BBUs, which are implemented in existing macro BSs. We assume that the network of macro BSs provides a basic coverage layer by implementing the control-plane of the mobile network (e.g. at low GHz), whereas RRUs provide on-demand network capacity through the user-plane (e.g. in the millimeter wave band).
Probabilistic Cooperative Caching in VANETs for Social Networking

Social media traffic constitutes the highest percentage of Internet traffic, which is mostly facilitated by mobile devices. This leads to high cellular costs incurred by mobile users. To reduce these costs, we strive to enable social media users to rely more on vehicular rather than cellular networks for content access. However, this can be hindered by the high delay and low packet delivery ratio often associated with accessing data from distant content providers in vehicular networks. Thus, to bring the data closer to the requester, we propose the Probabilistic Cooperative Caching at Moving and Parked Vehicles (PCCMPV) scheme. In PCCMPV, we exploit the static and mobile nature of parked and moving vehicles, respectively, to dynamically populate valuable road segments with diverse cached data. To do so, we dynamically assign a probability of caching to nodes along the data delivery path to assess their importance as caching nodes. For parked vehicles, such a probability relies primarily on the traffic density of the corresponding road segment, as well as its closeness centrality, and remoteness from the nearest data holder. PCCMPV provides an implicit form of off-path caching by assessing the trajectory of moving vehicles encountered along the data delivery path to calculate their probability of caching. Performance evaluation of PCCMPV demonstrates significant improvements in terms of delay, packet delivery ratio, and cache hit ratio compared to other caching schemes in vehicular networks.
Reputation-Based Byzantine Fault-Tolerance for Consortium Blockchain

The Practical Byzantine Fault Tolerance algorithm (PBFT)has been highly applied in consortium blockchain systems, however, this kind of consensus algorithm can hardly identify and remove faulty nodes in time, and also vulnerable to many attacks against the primary node of PBFT. The equality of consortium members' discourse rights is inapplicable to some real scenarios where dominating members are likely to have a larger discourse rights in the voting process. To address these problems, this paper presents Reputation-based Byzantine Fault Tolerance (RBFT)algorithm that incorporates a reputation model to evaluate the operations of each node in the consensus process. The faulty nodes will get lower discourse rights in the voting process if any malicious behavior is detected, with their reputation decreased. Furthermore, this paper presents an innovative reputation-based primary change scheme. The node with higher reputation obtains greater opportunities to be a primary to generate new valid blocks, which reduces the security risk of the primary. The experimental results demonstrate that RBFT gains better performance and ensures system security and reliability. Compared with PBFT, it increases the average throughput by 15% and reduces delay by 10%, and the faulty node rate of the system can continue to decrease over time.
Decentralized clustering for node-variant graph filtering with graph diffusion LMS

In this work, we consider the problem of estimating the coefficients of linear shift-invariant FIR graph filters. We assume hybrid node-varying graph filters where the network is decomposed into clusters of nodes and, within each cluster, all nodes have the same filter coefficients to estimate. We assume that there is no prior information on the clusters composition and that the nodes do not know which other nodes share the same estimation task. We are interested in distributed, adaptive, and collaborative solutions. In order to limit the cooperation between clustered agents sharing the same estimation task, we propose an extended diffusion preconditioned LMS strategy allowing the nodes to perform automatic network clustering. Simulation results illustrate the effectiveness of the proposed unsupervised method for clustering nodes into clusters and collaborative estimation.
User Access and Resource Allocation in Full-Duplex User-Centric Ultra-Dense Heterogeneous Networks

User-centric ultra-dense network (UUDN) has been envisioned as a promising network paradigm due to its advantages over traditional cell-centric structure. This paper introduces full duplex (FD) technology into UUDN and presents a joint scheme of user access, subchannel allocation and power control for FD UUDN. The scheme firstly maximizes the total network capacity under given rate requirements and transmit power constraints. Then if there are no feasible solutions, the system finds the minimal total power of the network to satisfy the rate requirements. The capacity maximization problem is solved by MAPEL algorithm and the power minimization problem is solved by a proposed heuristic algorithm. Simulation demonstrates the spectrum efficiency enhancement and the consumed power reduction of UUDN compared with cell-centric UDN.
DoS Mitigation Mechanism Based on Non-Cooperative Repeated Game for SDN

Software defined network (SDN)can manage the whole network flexibly because of its programmability and logically centralized architecture. However, the centralized architecture of SDN makes it more vulnerable to Denial of Service (DoS)attack which is launched by sending a large number of malicious packet_in packets to consume the resources of the controller and data planes. In order to protect the normal operation of the network from DoS, we propose an effective DoS mitigation framework based on non-cooperative repeated game called PrioGuard. DoS can be detected based on the information entropy, packet_in rate and packet_in response rate. Furthermore, the penalty-incentive mechanism of repeated game is adopted to punish these attackers by lowering their priority in order to postpone their requests. The requests from attackers will be migrated to data plane cache, which can mitigate the interface cache of control plane and make the controller process the normal requests effectively. We have implemented a prototype system of PrioGuard. Simulation evaluations demonstrate that our scheme is very effective with less response time, less packet loss rate and lower controller load.
Server Consolidation in Cloud Computing

Minimizing service-level agreement (SLA) violations and energy consumption through server consolidation is of paramount importance for the sustainability of cloud environments. In this paper, we propose an online method to reduce cloud SLA violations by taking into account: (a) the energy consumption of migrating virtual machines and (b) server consolidation techniques to minimize the energy consumption within the system. Rigorous mathematical competitive analysis shows that the proposed method achieves a 2.4 competitive ratio against a cognitive adversary. Our solution is superior compared to the current state of the art algorithms, such as UP-VMC, KMI, MBFD, both theoretically (competitive ratios of other alternatives are unbounded) and empirically through simulations using CloudSim. More specifically, the competitive ratios of state of the art algorithms are unbounded and our proposed methodology reduces the VM migrations and energy consumption by 85% and 45%, respectively. The aforementioned improvement comes at an expense of a small increase in terms of SLA violations. The above results are achieved without the a priori knowledge of VM utilization patterns.
Dual-Kernel Online Reconstruction of Power Maps

We present a measurement-driven algorithm to map the large-scale channel losses observed between a cellular base station and any point in its coverage area. The algorithm is on-line, meaning that it operates on continuously arriving measurements. Its distinguishing features are the use of two kernel functions, suitably chosen for the problem at hand, and a simple technique to sparsify the dictionary of measurements retained in memory. Evaluations in campus and urban settings indicate that the proposed algorithm reduces, roughly in half, the prediction error of existing single- kernel and multikernel algorithms.
Artificial Intelligence Driven Optimization of Channel and Location in Wireless Networks

In this work, we develop a framework that jointly decides on the optimal location of wireless extenders11Wireless extenders are wireless devices used to extend the coverage area of a main access point; they are also called mesh points, since the main access point refers to a mesh gateway in a WMN. and the channel configuration of wireless extenders and access points (APs) in a Wireless Mesh Network (WMN). Artificial Intelligence (AI) is adopted to support network autonomy and to capture insights on system and environment evolution. We propose a Self-X (self-optimizing and self-learning) framework that encapsulates both environment and intelligent agent to reach optimal operation through sensing, perception, reasoning and learning, in a truly autonomous fashion. The agent derives adequate knowledge from previous actions, improving the quality of future decisions. Extensive simulations are run to validate its fast convergence, improved throughput and resilience to dynamic interference conditions.
Cooperative Game Aided Spectrum Sharing in Underlay Cognitive Radio Networks Employing NOMA Schemes

In this paper, we investigate the spectrum sharing in an underlay cognitive radio network conceived, in which non-orthogonal multiple access (NOMA) scheme is applied. One of the famous cooperative games, namely non-transferable utility (NTU) coalition formation game, is leveraged for the implementation of spectrum sharing between the cognitive users (CUs) and the primary user (PU) in the underlay CR network. In particular, the pragmatic coalition formation algorithm is proposed to find the stable coalitions of CUs. Then, upon choosing the best CU coalition for accessing the PU's spectrum, we carry out the appropriate power allocation for the CUs. The simulation results show that, the proposed algorithm can achieve an enhance spectrum efficiency for the underlay CR network. Furthermore, the results reveal the superior performance of the NOMA scheme over traditional orthogonal multiple access (OMA) scheme in the context of our underlay CR network.
Co2-Robot: A Collaborative Communication Protocol for Swarm Robots

Modern high-end scientific and technological robots have been utilized in various fields. With regard to distribution, reliability, flexibility, and economy, swarm robots can accomplish more arduous tasks in parallel and demonstrate a superior performance compared to single robot. However, information interaction, which causes extra time overhead, is essential to realize synchronization or coordination in multi robot system. This paper focuses on improving the edge coverage ability of base stations especially when swarm robots cover a wide range. It proposes a novel collaborative communication protocol named Co2-Robot for these robots in order to carry out distributed beamforming, thus enhancing the coverage range. In particular, efficient strategies are designed to cut down the time overhead among swarm robots. Subsequently, we develop a novel cluster head selection scheme to be responsible for the parallel operations. Then, distributed beamforming is performed to improve the uplink coverage when no signal from anyone of swarm robots can reach the base station. In addition, several practical user-cases are considered and analyzed in distributed beamforming, in order to enhance the robustness of this parallel system. Finally, extensive simulations show that Co2-Robot will just result in a small amount of time overhead, which is acceptable in most scenarios. Furthermore, it can substantially extend the coverage range up to nearly 1000m while single robot can just cover a coverage range of more than 100m.
Improved Session Continuity in 5G NR with Joint Use of Multi-Connectivity and Guard Bandwidth

The intermittent millimeter-wave radio links as a result of human-body blockage are an inherent feature of the 5G New Radio (NR) technology by 3GPP. To improve session continuity in these emerging systems, two mechanisms have recently been proposed, namely, multi-connectivity and guard bandwidth. The former allows to establish multiple spatially-diverse connections and switch between them dynamically, while the latter reserves a fraction of system bandwidth for sessions changing their state from non-blocked to blocked, which ensures that the ongoing sessions have priority over the new ones. In this paper, we assess the joint performance of these two schemes for the user- and system-centric metrics of interest. Our numerical results reveal that the multi-connectivity operation alone may not suffice to increase the ongoing session drop probability considerably. On the other hand, the use of guard bandwidth significantly improves session continuity by somewhat compromising new session drop probability and system resource utilization. Surprisingly, the 5G NR system implementing both these techniques inherits their drawbacks. However, complementing it with an initial AP selection procedure effectively alleviates these limitations by maximizing the system resource utilization, while still providing sufficient flexibility to enable the desired trade-off between new and ongoing session drop probabilities.
On Interplay Between Network Topology and Alternating CSIT for Multi-Receiver Wiretap Channel

We study the problem of secure transmission over a Gaussian multi-input single-output (MISO) two receiver channel with an external eavesdropper under the assumption that links connecting the transmitter to three receivers may have unequal strength statistically. In addition to this, the state of the channel to each receiver is conveyed either perfectly (${P}$) or with delay (${D}$) to the transmitter. Let $S_{1}, S_{2}$, and ${S}3$bethe channel state information at the transmitter (CSIT) of user 1, user 2, and eavesdropper, respectively. The overall CSIT can then alternate between eight possible states, i.e., $(S_{1},S_{2},S3) \in \{P,D\}^{3}$. We denote by $\lambda _{\mathrm {S}_{1}\mathrm {S}_{2}\mathrm {S}_{3}}$ the fraction of time during which the state $S_{1}S_{2}S_{3}$ occurs; and, focus on a two state topological setting of strong v.s. weak links with symmetric alternating CSIT, $\lambda _{PDD}=\lambda _{DPD}$. For this setting, we establish an inner bound on the Generalized Secure Degrees of Freedom (GSDoF) region with different topology states. The encoding scheme sheds light on the usage of both resources, i.e., alternating - topology and CSIT; and, shows that as opposed to coding independently over different states, joint encoding across the CSIT and topological states, enables strictly better secure rates.
A Sentiment Analysis Approach for supporting Blended Learning Process

E-Learning is one of the most widely used training approaches in recent years. Numerous universities and training institutions adopt this approach to deliver courses or support the students in their training process. In particular, the blended ELearning is a useful approach for supporting students and better understanding their learning issues. The possibility of using collaborative tools and interacting with other students allows the student to share doubts on certain topics. The teacher often remains outside of this dynamic and does not understand the learning problems that characterize the class. A possible solution, which ensures the privacy of communication between students, is the Sentiment Analysis. The computational study of opinions, feelings and emotions expressed in a text often relates to the identification of agreement or disagreement with statements, contained in comments that convey positive or negative feelings. In this paper, we investigate the adoption of a probabilistic approach based on the Latent Dirichlet Allocation (LDA) as Sentiment Grabber. Through this approach, for a set of documents belonging to a same knowledge domain, a graph, the Mixed Graph of Terms, can be automatically extracted. The paper shows how this graph contains a set of weighted word pairs, which are discriminative for sentiment classification. In this way, the system can detect the feeling of students on some topics and teacher can better tune his/her teaching approach. In fact, the proposed method has been tested in real cases with effective and satisfactory results. Keywords-Sentiment Analysis; e-Learning; Collaborative Learning Approach; Blended Approach.
Implementation and Deployment of an Outdoor IoT-Based Air Quality Monitoring Testbed

This paper presents an outdoor IoT-based air quality monitoring testbed deployed in the city of Uppsala, Sweden. Our IoT sensing unit is designed and developed using low-cost hardware components and open source software, which makes it easy to replicate. We demonstrate that it can serve as an affordable solution for real-time measurements and has potential to complement traditional monitoring to cover larger areas. We use low-power communication based on IEEE 802.15. 4, RPL, and MQTT, and achieve high end-to-end delivery ratio (>98%) in an outdoor setting. Moreover, we carry out network analysis of our testbed and provide detailed insights into its characteristics.