Miracle-dz/UniFAR-FTUs huggingface dataset:
Community engaged breast cancer screening program in Kannur District, Kerala, India: A ray of hope for early diagnosis and treatment

INTRODUCTION: Community based programs can assist in early detection and improved survival of breast cancer. AIMS: To assess the feasibility and explore challenges of a district-wide door-to-door breast cancer screening program “ASWAS” conducted in Kannur district, Kerala, India from 2011 to 2014. METHODS: Aggregate data from survey records were collected in terms of the population screened, referred, diagnosed, and treated. Case records of breast cancer patients who were identified were reviewed and updated. In-depth interviews were conducted with program stakeholders. The contents of the interview were organized into a strength, weakness, opportunity and threat (SWOT) matrix to describe the screening program. RESULTS: A total of 1,049,410 eligible women above 30 years residing in 81 panchayats were visited door-to-door by 8,200 community volunteers; of them, 93% were screened using a symptom-risk factor checklist. Of those referred with symptoms (n = 5353), 81% attended the cancer camp. In total, 23 breast cancer cases were confirmed. 14 (61%) were in early stages, treated, and are disease free at 3-year follow-up. Those in the advanced stage and old age had poor outcomes. SWOT analysis identified political support, female volunteers, community engagement, dedicated fund for treatment, and teamwork as strengths. Weaknesses included poor healthcare access, maintaining volunteer motivation, and issues around sustainability. CONCLUSION: Community participation with the engagement of the health system and local self-government are required for implementing a comprehensive cancer screening strategy. Breast-cancer screening program using local volunteers for early detection is feasible in low-income settings, thereby improving survival.
Towards Faster-Than-Nyquist Transmission for Beyond 5G Wireless Communications

Faster-Than-Nyquist (FTN) is a technique that can improve the spectral efficiency of communication systems by making better use of available spectrum resources at the cost of inter-symbol interference (ISI) and inter-carrier interference (ICI). In this paper, we propose a hybrid signaling scheme for a practical application of FTN for MIMO transmission. We propose a new slot structure optimized for the hybrid signaling supporting both FTN signaling and orthogonal frequency division multiplexing (OFDM) signaling. Specifically, in the proposed slot structure, data transmission is based on the FTN signaling and the pilot transmission is based on the OFDM signaling. Numerical results confirm that the proposed signaling scheme has clear benefit over the systems employing only OFDM or FTN signaling.
Hierarchical Bandwidth Modulation for Ultra-Broadband Terahertz Communications

Terahertz (THz)-band (0.1–10 THz) communication has been envisioned as a key technology to enable wireless Terabit-per-second (Tbps) links. At THz frequencies, the path-loss is governed by the spreading loss and the molecular absorption loss. The latter also determines the available transmission bandwidth, which drastically shrinks with distance. New physical layer solutions that capture this behavior are needed to maximally utilize the THz band. In this paper, the concept of hierarchical bandwidth modulation is introduced for single-transmitter multiple-receiver communication in the THz band. In the proposed modulation scheme, multiple flows of information aimed at users at different distances are transmitted at the same time and over the same frequency, by simultaneously adapting both the modulation order and, more importantly, the symbol time. Details for the modulator and the demodulator implementations are provided. The performance of the proposed modulation is analytically derived, both in terms of the achievable data rate as well as symbol error rate. Extensive numerical results based on analytical channel models that capture the peculiarities of the THz band are provided to illustrate the performance of the proposed scheme. The results show that the proposed modulation scheme can maximize the utilization of the distance-dependent bandwidth of the THz channel, turning molecular absorption into an advantage.
On the Performance Analysis of Cooperative Detection in Mobile Multi-Agent Networks

Cooperative detection in multi-agent networks has raised increasing concern for a variety of collaborative applications. Thorough research has been developed assuming exact knowledge about the space or space-time signature vector embedded at the received signal in fixed networks, but most of them fail to apply to a mobile scenario with inaccurate agent positions. In this paper, we propose a direct generalized likelihood ratio test (DGLRT) not only taking the position uncertainty of agents into consideration, but also obtaining the optimal steering vector by directly estimating target and agent positions instead of implementing testing under each possible intermediate parameter combination of range and angle-of-arrival (AOA). Furthermore, we conduct performance analysis on the proposed DGLRT and introduce the notation of effective signal-to-noise ratio (SNR) to measure its detection capability, from which the detection performance loss induced by position uncertainty is revealed. Comparison to the conventional detector demonstrates the superior detection performance of the DGLRT based on direct estimation of target and agent positions.
Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks

The downlink communications are vulnerable to intelligent unmanned aerial vehicle (UAV) jamming attack which can learn the optimal attack strategy in complex communication environments. In this paper, we propose an anti-intelligent UAV jamming strategy, in which the mobile users can learn the optimal defense strategy to prevent jamming. Specifically, the UAV jammer acts as a leader and the users act as followers. The problem is formulated as a stackelberg dynamic game, which includes the leader sub-game and the followers sub-game. As the UAV jammer is only aware of the incomplete channel state information (CSI) of the users, we model the leader sub-game as a partially observable Markov decision process (POMDP). The optimal jamming trajectory is obtained via deep recurrent Q-networks (DRQN) in the three-dimension space. For the followers sub-game, we use the Markov decision process (MDP) to model it. Then the optimal communication trajectory can be learned via deep Q-networks (DQN) in the two-dimension space. We prove the existence of the stackelberg equilibrium. The simulations show that the proposed strategy outperforms the benchmark strategies.
Sliced-RAN: Joint Slicing and Functional Split in Future 5G Radio Access Networks

The unprecedented surge in mobile data traffic, along with the wide range of services and the corresponding different performance requirements, has raised the need for a new mobile network architecture. In that sense, 5G has been conceived as a software defined network able to provide service-tailored connectivity. Network slicing is a key mechanism to serve efficiently the diversified service requirements. In this paper, we formulate the joint RAN slicing and functional split with optimization of centralization degree (CD) and throughput. Our results show that even though in terms of CD we have more costs, we can better meet the service requirements and also have more throughput in the network.
Improving Spectral Efficiency in URLLC via NOMA-Based Retransmissions

The requirement to accommodate ultra-reliable low latency communication (URLLC) is one of the most attractive, yet challenging, new features of upcoming 5G systems. A common way to achieve reliability is retransmission; however, the applicability of this mechanism is hindered by the strict latency requirements. Furthermore, the bandwidth is often limited and shared by multiple connections, which may put the packet into a retransmission queue, leading to even larger latency. We address this problem in an uplink setting by introducing the concept of non-orthogonal multiple access hybrid automatic repeat request (NOMA-HARQ). In essence, NOMA-HARQ allows newly incoming packets to share non-orthogonally the same resource with retransmitted packets. The reliability guarantees are preserved by designing a power optimization procedure that takes into account past transmission attempts as well as the time remaining until the deadline.
Deep CNN for Spectrum Sensing in Cognitive Radio

The existing spectrum sensing methods mostly make decisions using model-driven test statistics, such as energy and eigenvalues. A weakness of these model-driven methods is the difficulty in accurately modeling for practical environment. In contrast to the model-driven approach, in this paper, we use a deep neural network to automatically learn features from data itself, and develop a data-driven detection approach. Inspired by the powerful capability of convolutional neural network (CNN) in extracting features of matrix-shaped data, we use the sample covariance matrix as the input of CNN, proposing a novel covariance matrix-aware CNN-based detection scheme, which consists of offline training and online detection. Different from the existing deep learning-based detection methods which replace the whole detection system by an end-to-end neural network, in this work, we use CNN for offline test statistic design and develop a practical threshold-based online detection mechanism. Specially, according to the maximum a posteriori probability (MAP) criterion, we derive the cost function for offline training in the spectrum sensing model, which guarantees the optimality of the designed test statistic. Simulation results have shown that whether the PU signals are independent or correlated, the detection performance of the proposed method is close to the optimal bound of estimator-correlator detector. Particularly, when the PU signals are correlated with a correlation coefficient 0.7, the probability of detection of the proposed method outperforms the conventional maximum eigenvalue detection method by nearly 7.5 times at SNR = −14dB.
Machine Learning-Based Hybrid Precoding with Robust Error for UAV mmWave Massive MIMO

Unmanned aerial vehicles (UAVs) can now be considered as aerial base stations (BSs) to support ultra-reliable and low-latency communications by establishing line-of-sight (LoS) connections to ground users. Moreover, combining UAVs with millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) will be a promissing solution. It can provide potentially high capacity wireless services due to their aerial positions and their ability to deploy on demand at specific locations. In this paper, we propose a low-cost and energy-efficient hybrid precoding architecture for UAVs, where the antenna part is realized by lens array. We investigate an efficient and energy-saving hybrid precoding scheme with robustness, which is inspired by the cross-entropy (CE) optimization in machine learning and the relative error estimation optimization. As for each selection of the hybrid precoders for obtaining the optimized precoder, we regarded it as a training process in machine learning, in which the training target is the CE-loss function between the predicted precoders and the target precoders. It aims to minimize the relative error between the predicted and actual values for optimizing the probability distributions of the elements in the analog hybrid precoder. Simulation results show that our proposed scheme can achieve higher sum rate and energy efficiency.
Probabilistic Event Dropping for Intermittently Connected Subscribers Over Pub/Sub Systems

Internet of Things (IoT) aim to leverage data from multiple sensors, actuators and devices for improving peoples' daily life and safety. Multiple data sources must be integrated, analyzed from the corresponding application and notify interested stakeholders. To support the data exchange between data sources and stakeholders, the publish/subscribe (pub/sub) middleware is often employed. Pub/sub provides additional mechanisms such as reliable messaging, event dropping, prioritization, etc. The event dropping mechanism is often used to satisfy Quality of Service (QoS) requirements and ensure system stability. To enable event dropping, basic approaches apply finite buffers or data validity periods and more sophisticated ones are information-aware. In this paper, we introduce a pub/sub mechanism for probabilistic event dropping by considering the stakeholders' intermittent connectivity and QoS requirements. We model the pub/sub middleware as a network of queues which includes a novel ON/OFF queueing model that enables the definition of join probabilities. We validate our analytical model via simulation and compare our mechanism with existing ones. Experimental results can be used as insights for developing hybrid dropping mechanisms.
Position-Based Beamforming Design for UAV Communications in LTE Networks

Unmanned Aerial Vehicles (UAVs) have demonstrated exceptional capabilities in many real-world applications such as remote sensing, emergency medicine delivery and precision agriculture. LTE networks are regarded as key candidates to offer high performance broadband wireless services to support UAV applications and safe deployment. However, the unique features of aerial UAVs including high-altitude manipulation, three-dimension (3D) mobility and rapid velocity changes, pose challenging issues for optimising LTE wireless communications to support UAVs, especially under the severe inter-cell interference generated by UAVs in the sky. To deal with this issue, we propose a novel position-based robust beamforming algorithm to improve the performance of LTE networks to serve UAVs. For obtaining optimal weight vectors that could tolerate Direction-of-Arrival (DoA) estimation errors, we propose a hybrid method to integrate the channel information and UAV flight information to accurately estimate the DoA angle range. In order to validate the performance of the proposed robust beamforming algorithm, we conduct comprehensive simulation experiments under practical configurations. The results show that the proposed robust beamforming algorithm outperforms benchmark Linearly Constrained Minimum Variance (LCMV) beamforming and GPS-based beamforming algorithms.
Deep Learning-Based Proactive Eavesdropping for Wireless Surveillance

In this work, we investigate a proactive eavesdropping system where a central monitor covertly wiretaps the communications between a pair of suspicious users via multiple intermediate nodes. For successful eavesdropping, it is required that the eavesdropping channel capacity is higher than the data rate of the suspicious users so that the central monitor can reliably decode the intercepted information. Hence, the intermediate nodes operate in two different modes, namely eavesdropping mode and jamming mode, to facilitate eavesdropping. Specifically, the eavesdropping nodes forward the intercepted data from the suspicious users to the central monitor, while the jamming nodes transmit jamming signals to proactively control the data rate of the suspicious users. We propose an efficient deep learning-based approach to identify the optimal mode selection for the intermediate nodes and the optimal transmit power for the jamming nodes. Numerical results confirm the significant performance gain of our proposed method both in terms of performance and time complexity over conventional schemes.
Unimodular Sequences Design with Good Correlation Properties via Consensus-PDMM Algorithm

Unimodular sequences with good correlation properties are desired in wireless communication and radar applications. In this paper, we focus on designing these kinds of sequences and the main content is as follows: first, we formulate the design problem as a quartic polynomial minimization problem with constant modulus constraints. Then, by introducing auxiliary variables, the polynomial minimization problem is equivalent to a nonconvex consensus problem. Second, we develop a low-complexity consensus parallel direction method of multipliers (consensus-PDMM) algorithm, in which all subproblems can be performed in parallel with analytical solutions. Moreover, we prove that consensus-PDMM's output is some stationary point of the original nonconvex problem if it is convergent. Third, two variant PDMM algorithms, based on stochastic block coordinate descent and accelerated gradient descent, are proposed to reduce the computational complexity and speed up the convergence rate. Numerical simulation results show that the proposed algorithm offers better performance than the state-of-the-art approaches.
Effective Worst-Case Crosstalk Estimation for Dynamic Translucent SDM Elastic Optical Networks

We focus on dynamic crosstalk-aware lightpath provisioning in translucent space-division multiplexing (SDM) elastic optical networks, in which spectral super-channels are carried over multi-core fibers (MCFs), distance-adaptive transmission is considered, and back-to-back (B2B) signal regeneration is applied. We aim at establishing lightpaths for connection requests in such a way that the inter-core crosstalk (XT) impairment affecting the quality of lightpaths in the network does not exceed allowable threshold levels. To this end, we propose and analyze a method that is based on per-core static/worst-case XT estimation. This method is implemented in a dynamic XT-aware routing, spatial mode, and spectrum allocation (RSSA) algorithm, which is used to solve the lightpath provisioning problem in an efficient way, such that the bandwidth blocking probability is minimized. The performed extensive simulation experiments in realistic network scenarios show a significant impact of this method on network performance and motivate for its application instead of commonly used per-fiber worst-case XT estimation.
A Game-Theoretical Approach for Energy-Efficient Resource Allocation in MEC Network

Mobile edge computing (MEC) is a promising technique which enables the user equipment (UE) to leverage the vast computation resources on the clouds (or cloudlets). The redundant design and dynamic nature of traffic raise an energy inefficiency issue in MEC network. In this paper, we aim to minimize the energy consumption and average response time in the MEC network. We jointly consider the cloud selection and routing optimization on both wired and wireless links. Based on the game theory, we propose a low-complex resource allocation algorithm, which can achieve the global optimal solution. Further, to reduce the number of re-routing (routing times), an improved algorithm is proposed, which introduces an approximate factor (i.e., β). The β represents the additional cost during the re-routing, such as session migrations, energy consumption. We demonstrate the convergence of the improved algorithm. The simulations show that the proposed algorithms outperform the other conventional algorithms.
Deep Reinforcement Learning Based Multi-User Anti-Jamming Strategy

The threat of radio frequency jamming attack to cognitive radio network is an issue that has been discussed for a long time. Q-learning is a widely used anti-jamming algorithm due to its model-free characteristic. However, the traditional Q-learning based anti-jamming algorithms suffer from some limitations when dealing with high-dimensional or continuous inputs. The recently proposed double Deep Q-learning Network (DQN) overcomes this weakness by approximating the table based Q function with a deep neural network. In this paper, we apply the double DQN algorithm with frequency hopping strategy against RF jamming attack in a multi-user environment. We test the performances of three types of neural networks which are the fully connected network (FCN), the convolutional neural network (CNN) and the long short term memory (LSTM). The simulation shows the effectiveness of the double DQN algorithm. Meanwhile, the FCN agent gives the best result concerning stability.
Millimeter-Wave Space-Time Propagation Characteristics in Urban Macrocell Scenarios

The deployment of millimeter-wave (mmWave) wireless communication systems in urban macrocell scenarios with large coverage and cost-efficient transmission schemes is investigated with a focus on space-time propagation characteristics. First, channel measurement campaigns are conducted at two 5G main candidate frequency bands of 28 GHz and 39 GHz in a central business district and a residential area, based on our new-designed time domain channel sounder, which can support directional scanning sounding with less time consumption. Next, using a reasonable data preprocessing method, small-scale channel characteristics across line-of-sight (LoS) and non-LoS (NLoS) links are analyzed via power delay angular profiles, root mean square delay spread, and azimuth and elevation angular spread. Measurement and analysis results show that space-time propagation parameters are layout-related and have further implications on the mmWave system design.
An Optimal Peak Hour Content Server Cache Update Scheduling Algorithm for 5G HetNets

Most of the existing caching schemes assume that the pushing of popular contents from the macro base station (MBS) to content servers (CSs) is performed during off-peak hours when the network traffic is low. However, since popular files, such as breaking news, may also be generated during peak hours, performing CS content update during peak hours is necessary. In this paper, we propose an optimal cache content update scheduling algorithm for heterogeneous networks (HetNets). The decision-making module is located in the MBS. The action set includes performing CS content update, letting the CSs simultaneously serve user requests, and using the MBS to directly serve user requests. The MBS aims to maximize the total throughput of the system within the duration of the peak hour under the uncertainty of the arrival of new user requests and the addition of new files. We formulate the peak hour CS cache content update scheduling problem as a Markov decision process and propose an optimal cache content update scheduling algorithm based on dynamic programming. We perform simulations and compare our proposed optimal scheduling algorithm with the periodic update and greedy scheduling heuristics. Simulation results show that our proposed algorithm outperforms those two heuristics under different scenarios.
Protecting Semantic Trajectory Privacy for VANET with Reinforcement Learning

Location-based services in vehicular ad hoc networks (VANETs) have to protect user privacy and address the challenge due to the disclosure of the vehicle movement trajectory. In this paper, we propose an reinforcement learning (RL) based differential privacy mechanism that randomizes the released vehicle locations to protect the semantic trajectory of the vehicle and uses RL to select the obfuscation policy. Based on the semantic location of the vehicle and the attack history, this scheme enables a vehicle to optimize the obfuscation policy in terms of the privacy gain and the quality of service loss without being aware of the current attack model in a dynamic privacy protection game. Simulation results show that this scheme can increase the privacy gain, decrease the quality of service loss, and thus improve the utility of the vehicle in comparison with a benchmark scheme.