Miracle-dz/UniFAR-FTUs huggingface dataset:
Global Energy Efficiency in Secure MISO SWIPT Systems With Non-Linear Power-Splitting EH Model

This paper considers an MISO simultaneous wireless information and power transfer (SWIPT) system, where one transmitter serves multiple authorized receivers in the presence of several potential eavesdroppers (idle receivers). To prevent the information interception by eavesdroppers, artificial noise (AN) is embedded into the transmit signals. The non-linear energy harvesting (EH) model is adopted and a novel power-splitting (PS) EH receiver architecture is proposed. Stochastic uncertainty channel model (SUM) is considered for the idle receivers due to outdated channel feedback. A global energy efficiency (GEE) maximization problem is formulated by jointly optimizing the transmit beamforming vectors, the AN covariance matrix, and the PS ratios, under the minimal rate and secure transmission constraints of authorized receivers, the EH requirement constraints of idle receivers, and the total available power constraint at the transmitter. Since the problem is non-convex with no known solution, it is solved based on the following solution framework. Firstly, the PS ratios are optimized by using the bisection method and successive convex approximation (SCA), and then, the transmit beamforming vectors and the AN covariance matrix are jointly optimized by using a Dinkelbach’s Algorithm based method, where SCA is applied to solve its inner subproblem. It is theoretically proved that by involving AN, the system GEE can be improved. Numerous results show that system GEE first increases and then keeps unchanged with the increment of the total available power, but it first keeps unchanged and then decreases with the increment of the minimal rate requirement. It is also observed that compared with traditional EH receiver architecture and linear EH model, our proposed PS EH receiver architecture is able to achieve higher GEE and avoid false output power at idle receivers.
A Game Theoretic Model for the Formation of Navigable Small-World Networks—The Tradeoff between Distance and Reciprocity

Kleinberg proposed a family of small-world networks to explain the navigability of large-scale real-world social networks. However, the underlying mechanism that drives real networks to be navigable is not yet well understood. In this article, we present a game theoretic model for the formation of navigable small-world networks. We model the network formation as a game called the Distance-Reciprocity Balanced (DRB) game in which people seek for both high reciprocity and long-distance relationships. We show that the game has only two Nash equilibria: One is the navigable small-world network, and the other is the random network in which each node connects with each other node with equal probability, and any other network state can reach the navigable small world via a sequence of best-response moves of nodes. We further show that the navigable small-world equilibrium is very stable—(a) no collusion of any size would benefit from deviating from it; and (b) after an arbitrary deviations of a large random set of nodes, the network would return to the navigable small world as soon as every node takes one best-response step. In contrast, for the random network, a small group collusion or random perturbations is guaranteed to bring the network out of the random-network equilibrium and move to the navigable network as soon as every node takes one best-response step. Moreover, we show that navigable small-world equilibrium has much better social welfare than the random network, and we provide the price-of-anarchy and price-of-stability results of the game. Our empirical evaluation further demonstrates that the system always converges to the navigable network even when limited or no information about other players’ strategies is available, and the DRB game simulated on real-world networks leads to navigability characteristic that is very close to that of the real networks, even though the real-world networks have non-uniform population distributions different from Kleinberg’s small-world model. Our theoretical and empirical analyses provide important new insight on the connection between distance, reciprocity, and navigability in social networks.
Learning for Computation Offloading in Mobile Edge Computing

Mobile edge computing (MEC) is expected to provide cloud-like capacities for mobile users (MUs) at the edge of wireless networks. However, deploying MEC systems faces many challenges, one of which is to achieve an efficient distributed offloading mechanism for multiple users in time-varying wireless environments. In this paper, we study a multi-user multi-edge-node computation offloading problem. Since edge nodes’ communication and computing capacities are limited which leads resource contention when many MUs offload to the same edge node at the same time, we formulate this problem as a non-cooperative exact potential game (EPG), where each MU, in each time slot, selfishly maximizes its number of processed central processor unit (CPU) cycles and reduces its energy consumption. Assuming that channel information is static and available to MUs, we show that MUs could achieve a Nash equilibrium via a best response-based offloading mechanism. Next, we extend the problem to a practical scenario, where the number of processed CPU cycles is time-varying and unknown to MUs because of the uncertain channel information. In this case, we adopt an unknown payoff game framework and prove that the EPG properties still hold. Then, we propose a model-free reinforcement learning offloading mechanism which helps MUs learn their long-term offloading strategies to maximize their long-term utilities. Numerical results illustrate that our proposed algorithm for unknown CSI outperforms other schemes, such as local processing and random assignment, and achieves up to 87.87% average long-term payoffs compared to the perfect CSI case.
Joint Admission Control and Resource Allocation for Multi-Carrier Uplink NOMA Networks

In this letter, we study the maximum access problem (MAP) for uplink networks with non-orthogonal multiple access technique. Specifically, the MAP is formulated as a mixed integer programming problem with admission control, user clustering, channel assignment, and power control taken into account. To deal with this challenging problem, we first recast it as the maximum independent set (MIS) problem in graph theory. Then, we propose an efficient algorithm to solve this MIS problem in a low complexity manner. Finally, simulation results indicate that our algorithm can greatly improve the number of supported users compared to other schemes. The performance of our algorithm under different channel conditions and non-uniform distributions of users is also investigated.
From Signal to Social: Steps Towards Pervasive Social Context

The widespread adoption of smartphones with advanced sensing, computing and data transfer capabilities has made scientific studies of human social behavior possible at a previously unprecedented scale. It has also allowed context-awareness to become a natural feature in many applications using features such as activity recognition and location information. However, one of the most important aspects of context remains largely untapped at scale, i.e. social interactions and social context. Social interaction sensing has been explored using smartphones and specialized hardware for research purposes within computational social science and ubiquitous computing, but several obstacles remain to make it usable in practice by applications at industrial scale. In this thesis, I explore methods of physical proximity sensing and extraction of social context information from user-generated data for the purpose of context-aware applications. Furthermore, I explore the application space made possible through these methods, especially in the class of use cases that are characterized by embodied social agency, through field studies and a case study.A major concern when collecting context information is the impact on user privacy. I have performed a user study in which I have surveyed the user attitudes towards the privacy implications of proximity sensing. Finally, I present results from quantitatively estimating the sensitivity of a simple type of context information, i.e. application usage, in terms of risk of user re-identification. (Less)
Adaptive Ambient Backscatter Communication Systems With MRC

Ambient backscatter (AmBack) communication, also called “modulation in the air,” has drawn growing interest by both academia and industry recently. In this paper, we investigate and analyze an AmBack system, where the user is equipped with an AmBack circuit at the transmitter and maximum ratio combing at the receiver. By harvesting power from the surrounding radio frequency source, the circuit operation and the signal backscattering can be supported. However, if there is not enough harvested power for the circuit operation, the signal backscattering will be suspended. Different from previous works on AmBack, an adaptive scheme is proposed to opportunistically exploit the residual or full user battery power for insufficient harvested power so that the signal backscattering is always available. However, there is no exact closed-form expression for the outage probability, and its approximation is obtained to facilitate our analysis. For comparison, the traditional non-adaptive scheme is also analyzed, and a closed-form expression for the outage probability is derived. In order to get more insight into both schemes, asymptotic outage performance is also derived when the number of receiver antennas/the noise power is sufficiently large/low. Simulation results demonstrate the tightness and the correctness of the derived outage probabilities, and show that the proposed adaptive scheme can significantly outperform the traditional non-adaptive scheme.
Slicing the Edge: Resource Allocation for RAN Network Slicing

Network slicing is considered to be a crucial feature of fifth generation cellular systems. By dividing the network infrastructure into multiple logical segments, network slicing can support parallel services with different requirements. While technology developments focus on slicing the core networks, there are limited studies in network slicing for radio access networks (RAN). Hence, in this letter, we study the RAN slicing and slice coordination, which is formulated as a bi-convex problem. Even though there are complicated couplings between the RAN resource allocation for each slice and the coordination of the slices that share the same resources, we design two algorithms addressing these couplings of this bi-convex problem. Simulation results validate the efficacy of our proposed algorithms.
Compressed Sensing Beyond the IID and Static Domains: Theory, Algorithms and Applications

Sparsity is a ubiquitous feature of many real world signals such as natural images and neural spiking activities. Conventional compressed sensing utilizes sparsity to recover low dimensional signal structures in high ambient dimensions using few measurements, where i.i. d measurements are at disposal. However real world scenarios typically exhibit non i.i. d and dynamic structures and are confined by physical constraints, preventing applicability of the theoretical guarantees of compressed sensing and limiting its applications. In this thesis we develop new theory, algorithms and applications for non i.i. d and dynamic compressed sensing by considering such constraints. In the first part of this thesis we derive new optimal sampling-complexity tradeoffs for two commonly used processes used to model dependent temporal structures: the autoregressive processes and self-exciting generalized linear models. Our theoretical results successfully recovered the temporal dependencies in neural activities, financial data and traffic data. Next, we develop a new framework for studying temporal dynamics by introducing compressible state-space models, which simultaneously utilize spatial and temporal sparsity. We develop a fast algorithm for optimal inference on such models and prove its optimal recovery guarantees. Our algorithm shows significant improvement in detecting sparse events in biological applications such as spindle detection and calcium deconvolution. Finally, we develop a sparse Poisson image reconstruction technique and the first compressive two-photon microscope which uses lines of excitation across the sample at multiple angles. We recovered diffraction-limited images from relatively few incoherently multiplexed measurements, at a rate of 1.5 billion voxels per second.
A Distributed Computation Offloading Strategy in Small-Cell Networks Integrated With Mobile Edge Computing

Mobile edge computing is conceived as an appealing technology to enhance cloud computing capability of mobile devices (MDs) at the edge of the networks. Although some researchers use the technology to address the intensive tasks’ high computation needs of MDs in small-cell networks (SCNs), most of them ignore considering the interests interaction between small cells and MDs. In this paper, we study a distributed computation offloading strategy for a multi-device and multi-server system based on orthogonal frequency-division multiple access in SCNs. First, to satisfy the interest requirements of different MDs and analyze the interactions among multiple small cells, we formulate a distributed overhead minimization problem, aiming at jointly optimizing energy consumption and latency of each MD. Second, to ensure the individuals of different MDs, we formulate the proposed overhead minimization problem as a strategy game. Then, we prove the strategy game is a potential game by the feat of potential game theory. Moreover, the potential game-based offloading algorithm is proposed to reach a Nash equilibrium. In addition, to guarantee the performance of the designed algorithm, we consider the lower bound of iteration times to derive the worst case performance guarantee. Finally, the simulation results corroborate that the proposed algorithm can effectively minimize the overhead of each MD compared with different other existing algorithms.
Unsupervised Similarity Learning through Rank Correlation and kNN Sets

The increasing amount of multimedia data collections available today evinces the pressing need for methods capable of indexing and retrieving this content. Despite the continuous advances in multimedia features and representation models, to establish an effective measure for comparing different multimedia objects still remains a challenging task. While supervised and semi-supervised techniques made relevant advances on similarity learning tasks, scenarios where labeled data are non-existent require different strategies. In such situations, unsupervised learning has been established as a promising solution, capable of considering the contextual information and the dataset structure for computing new similarity/dissimilarity measures. This article extends a recent unsupervised learning algorithm that uses an iterative re-ranking strategy to take advantage of different k-Nearest Neighbors (kNN) sets and rank correlation measures. Two novel approaches are proposed for computing the kNN sets and their corresponding top-k lists. The proposed approaches were validated in conjunction with various rank correlation measures, yielding superior effectiveness results in comparison with previous works. In addition, we also evaluate the ability of the method in considering different multimedia objects, conducting an extensive experimental evaluation on various image and video datasets.
Interference Analysis for Millimeter-Wave Networks With Geometry-Dependent First-Order Reflections

Recent experimental studies reveal that urban millimeter wave (mmWave) channels are often dominated by both the line-of-sight (LoS) and geometry-dependent first-order reflection paths. In such environments, it is possible that any LoS and/or reflection components from surrounding interferers can critically deteriorate the link quality. Analyzing such impacts is, however, challenging in the absence of appropriate mmWave network interference models. In this correspondence paper, we address this challenge by providing an analytical stochastic geometry model that quantifies the total amount of network interference. Our study reveals that dense mmWave networks are often interference limited. The geometry-dependent first-order reflection interference is a dispensable component and must be encountered when analyzing and designing mmWave networks.
BWManager: Mitigating Denial of Service Attacks in Software-Defined Networks Through Bandwidth Prediction

Software-defined networking (SDN) has emerged as a new networking paradigm that can provide fine-grained network management service. Since the SDN controller makes control decision for the network, it becomes the main target of denial of service (DoS) attacks. In this paper, we propose BWManager to mitigate. which mainly consists mitigate the DoS attacks on the SDN controller with BWManager that mainly consists of four key components: 1) simplified DoS detection module; 2) forecasting engine; 3) priority manager; and 4) scheduler. The simplified DoS detection module calculates a comprehensive judgment score for each switch, which indicates the attacking severity of each switch and is used to decide time slice allocation of the controller. The forecasting engine is the basis of the controller scheduling method and forecasts the bandwidth consumption of users to determine the users’ trust values. The trust values are used by the priority manager to manage multiple buffer queues with different priorities for the users. The scheduler protects the controller and the normal users under DoS attacks by running a weighted Round-Robin algorithm to process flow requests in different priority queues. We evaluate the performance and overhead of BWManager in both hardware and software OpenFlow environments. The results demonstrate that BWManager is effective with a limited overhead.
Fair Resource Allocation in Systems With Complete Information Sharing

In networking and computing, resource allocation is typically addressed using classical resource allocation protocols as the proportional rule, the max–min fair allocation, or solutions inspired by cooperative game theory. In this paper, we argue that, under awareness about the available resource and other users demands, a cooperative setting has to be considered in order to revisit and adapt the concept of fairness. Such a complete information sharing setting is expected to happen in 5G environments, where resource sharing among tenants (slices) need to be made acceptable by users and applications, which therefore need to be better informed about the system status via ad-hoc (northbound) interfaces than in legacy environments. We identify in the individual satisfaction rates the key aspect of the challenge of defining a new notion of fairness in systems with complete information sharing, consequently, a more appropriate resource allocation algorithm. We generalize the concept of user satisfaction considering the set of admissible solutions for bankruptcy games and we adapt to it the fairness indices. Accordingly, we propose a new allocation rule we call mood value: for each user, it equalizes our novel game-theoretic definition of user satisfaction with respect to a distribution of the resource. We test the mood value and a new fairness index through extensive simulations about the cellular frequency scheduling use-case, showing how they better support the fairness analysis. We complete the paper with further analysis on the behavior of the mood value in the presence of multiple competing providers and with cheating users.
OFDM With Subcarrier Number Modulation

A new modulation technique, named orthogonal frequency division multiplexing (OFDM) with subcarrier number modulation, is proposed for efficient data transmission. In this scheme, the information bits are conveyed by changing the number of active subcarriers in each OFDM subblock. The idea behind this scheme is inspired from the integration of OFDM with pulse width modulation, where the width of the pulse represents the number of active subcarriers corresponding to specific information bits. This is different from OFDM with index modulation (OFDM-IM), where the information bits are sent by the indices of the subcarriers instead of their number. The scheme is shown to provide better spectral efficiency than that of OFDM-IM at comparable bit error rate performances. Another key merit of the proposed scheme over OFDM-IM is that the active subcarriers can be located in any position within the subblock, thus enabling channel-dependent optimal subcarrier selection that can further enhance the system performance.
5-HTTLPR and MTHFR 677C>T polymorphisms and response to yoga-based lifestyle intervention in major depressive disorder: A randomized active-controlled trial

Background: There is growing evidence suggesting that both genetic and environmental factors modulate treatment outcome in, a highly heterogeneous, major depressive disorder (MDD). 5-HTTLPR variant of the serotonin transporter gene (SLC6A4) and MTHFR 677C>T polymorphisms have been linked to the pathogenesis of MDD, and antidepressant treatment response. The evidence is lacking on the clinical utility of yoga in patients with MDD who have 5-HTTLPR and MTHFR 677C>T polymorphisms and less likely to respond to medications (SSRIs). Aims: We aimed to examine the impact of YBLI in those who have susceptible 5-HTTLPR and MTHFR 677C>T polymorphisms and are less likely to drug therapy with SSRIs. Settings and Design: In a 12 week randomized active-controlled trial, MDD patients (n = 178) were randomized to receive YBLI or drug therapy. Methods: Genotyping was conducted using PCR-based methods. The clinical remission was defined as BDI-II score ≤ 9. Statistical Analysis Used: An intent-to-treat analysis was performed, and the association of genotype with treatment remission consisted of the logistic regression model. A P value of <0.05 was considered statistically significant. Results: Multivariate logistic regression models for remission including either 5-HTTLPR or MTHFR 677C>T genotypes showed statistically significant odds of remission in YOGA arm vs. DRUG arm. Neither 5-HTTLPR nor MTHFR 677C>T genotype showed any influence on remission to YBLI (P = 0.73 and P = 0.64, respectively). Further analysis showed childhood adversity interact with 5-HTTLPR and MTHFR 677C>T polymorphisms to decrease treatment response in DRUG treatment arm, but not in YOGA arm. Conclusions: YBLI provides MDD remission in those who have susceptible 5-HTTLPR and MTHFR 677C>T polymorphisms and are resistant to SSRIs treatment. YBLI may be therapeutic for MDD independent of heterogeneity in its etiopathogenesis.