Jamming-Resistant Learning in Wireless Networks

We consider capacity maximization in wireless networks under adversarial interference conditions. There are n links, each consisting of a sender and a receiver, which repeatedly try to perform a successful transmission. In each time step, the success of attempted transmissions depends on interference conditions, which are captured by an interference model (e.g. the SINR model). Additionally, an adversarial jammer can render a (1-delta)-fraction of time steps unsuccessful. For this scenario, we analyze a framework for distributed learning algorithms to maximize the number of successful transmissions. Our main result is an algorithm based on no-regret learning converging to an O(1/delta)-approximation. It provides even a constant-factor approximation when the jammer exactly blocks a (1-delta)-fraction of time steps. In addition, we consider a stochastic jammer, for which we obtain a constant-factor approximation after a polynomial number of time steps. We also consider more general settings, in which links arrive and depart dynamically, and where each sender tries to reach multiple receivers. Our algorithms perform favorably in simulations.Comment: 22 pages, 2 figures, typos remove

Study of the 14C-contamination potential of C-impurities in CuO and Fe

The carbon concentration in CuO and iron was determined by isolating C. The values were in agreement with results reported in other studies. Contaminating carbon from CuO and Fe was transformed to AMS targets and measured for C-14. C-traces in CuO were shown to be the major contribution to the C-14 Sample processing blank. In addition, there is a significant variability in the C-14 content of CuO observed between different production batches. The combined contamination potential of CuO and Fe was found to be 4.47-8.92 mu g recent carbon, whereas the more realistic estimate for AMS-target preparation conditions ranged between 1.63 and 3.24 mu g recent carbon, depending on the C-14 level in CuO

The Achilles tendon Total Rupture Score is a responsive primary outcome measure:an evaluation of the Dutch version including minimally important change

PURPOSE: Aim of this study was to evaluate the responsiveness of the Dutch version of the Achilles tendon Total Rupture Score (ATRS-NL). METHODS: Patients (N = 47) completed the ATRS-NL at 3 and 6 months after Achilles tendon rupture (ATR). Additionally, they filled out the Euroqol-5D-5L (EQ-5D-5L) and Global Rating of Change Score (GRoC). Effect sizes (ES) and standardized response means (SRM) were calculated. The anchor-based method for determining the minimally important change (MIC) was used. GRoC and improvement on the items mobility and usual activities on the EQ-5D-5L served as external criteria. The scores on these anchors were used to categorize patients' physical functioning as improved or unchanged between 3 and 6 months after ATR. Receiver operating curve (ROC) analysis was performed, with the calculation of the area under the ROC curve (AUC) and the estimation of MIC values using the optimal cut-off points. RESULTS: There was a large change (ES: 1.58) and good responsiveness (SRM: 1.19) of the ATRS-NL between 3 and 6 months after ATR. Using ROC analysis, the MIC values ranged from 13.5 to 28.5 for reporting improvement on EQ-5D-5L mobility and GRoC, respectively. The AUC of improvement on mobility and improvement on GRoC were > 0.70. CONCLUSION: The ATRS-NL showed good responsiveness in ATR patients between 3 and 6 months after injury. Use of this questionnaire is recommended in clinical follow-up and longitudinal research of ATR patients. MIC values of 13.5 and 28.5 are recommended to consider ATR patients as improved and greatly improved between 3 and 6 months after ATR. LEVEL OF EVIDENCE: II

Psychological Factors Change during the Rehabilitation of an Achilles Tendon Rupture:A Multicenter Prospective Cohort Study

OBJECTIVE: The authors sought to gain insight into the changes in psychological factors during rehabilitation after Achilles tendon rupture (ATR) and to explore the association between psychological factors during rehabilitation and functional outcome 12 months after ATR. METHODS: Fifty patients clinically diagnosed with ATR were invited to visit the hospital 3, 6, and 12 months after injury for data collection. They completed questionnaires assessing psychological factors: psychological readiness to return to sport (Injury Psychological Readiness to Return to Sport Questionnaire); kinesiophobia (Tampa Scale for Kinesiophobia); expectations, motivation, and outcome measures related to symptoms and physical activity (Achilles Tendon Total Rupture Score); and sports participation and performance (Oslo Sports Trauma Research Centre Overuse Injury Questionnaire). To determine whether psychological factors changed over time, generalized estimating equation analyses were performed. Multivariate regression analyses were used to study the association between psychological factors at 3, 6, and 12 months and outcome measures at 12 months after ATR. RESULTS: Psychological readiness to return to sport improved, and kinesiophobia decreased significantly during rehabilitation. Psychological readiness at 6 and 12 months showed significant associations with sports participation and performance. Kinesiophobia at 6 months was significantly associated with symptoms and physical activity. Motivation remained high during rehabilitation and was highly associated with symptoms and physical activity, sports participation, and performance. CONCLUSION: Psychological factors change during rehabilitation after ATR. Patients with lower motivation levels during rehabilitation, low psychological readiness to return to sports, and/or high levels of kinesiophobia at 6 months after ATR need to be identified. IMPACT: According to these results, psychological factors can affect the rehabilitation of patients with ATR. Physical therapists can play an important role in recognizing patients with low motivation levels and low psychological readiness for return to sport and patients with high levels of kinesiophobia at 6 months post-ATR. Physical therapist interventions to enhance motivation and psychological readiness to return to sport and to reduce kinesiophobia need to be developed and studied in the post-ATR population. LAY SUMMARY: With Achilles tendon rupture, level of motivation, psychological readiness for return to sport, and fear of movement can affect rehabilitation outcome. A physical therapist can help recognize these factors

Computing Nash Equilibrium in Wireless Ad Hoc Networks: A Simulation-Based Approach

This paper studies the problem of computing Nash equilibrium in wireless networks modeled by Weighted Timed Automata. Such formalism comes together with a logic that can be used to describe complex features such as timed energy constraints. Our contribution is a method for solving this problem using Statistical Model Checking. The method has been implemented in UPPAAL model checker and has been applied to the analysis of Aloha CSMA/CD and IEEE 802.15.4 CSMA/CA protocols.Comment: In Proceedings IWIGP 2012, arXiv:1202.422

The recovery after Achilles tendon rupture:a protocol for a multicenter prospective cohort study

BackgroundAchilles tendon rupture (ATR) is a common sports injury, with a rising incidence and significant impairments. Due to the lack of treatment guidelines, there is no consensus about diagnostic methods, primary treatment (non-surgical or surgical) and rehabilitation. It is hypothesized that this lack of consensus and guidelines leads to sub-optimal recovery and higher societal costs.The primary aim of this study is to give a broad insight into the recovery after ATR. Secondarily this study aims to explore factors contributing to recovery and gain insight into the cost-effectiveness of ATR management.MethodsThis multicenter prospective cohort study will include all adult ( 18years) patients with an ATR treated at the three main hospitals in the Northern Netherlands: University Medical Center Groningen, Martini Hospital Groningen and Medical Center Leeuwarden. All subjects will be invited for three visits at 3, 6 and 12months post-injury. The following data will be collected: patient-reported outcome measures (PROMs), physical tests, imaging and economic questionnaires. At 3months post-injury personal, injury, and treatment data will be collected through a baseline questionnaire and assessment of the medical file. The PROMs concern the Dutch version of the Achilles Tendon Total Rupture Score, EQ-5D-5L, Oslo Sport Trauma Research Center Overuse Injury Questionnaire, Injury Psychological Readiness Return to Sport Scale, Tampa Scale of Kinesiophobia, Expectations, Motivation and Satisfaction questionnaire and a ranking of reasons for not returning to sport. The administered physical tests are the heel-rise test, standing dorsiflexion range of motion, resting tendon length and single leg hop for distance. Ultrasound Tissue Characterization will be used for imaging. Finally, economic data will be collected using the Productivity Cost Questionnaire and Medical Consumption Questionnaire.DiscussionThis prospective cohort study will contribute to optimal decision making in the primary treatment and rehabilitation of ATRs by providing insight into (1) ATR recovery (2) novel imaging for monitoring recovery (3) (barriers to) return to sport and (4) cost-effectiveness of management. The analysis of these data strives to give a broad insight into the recovery after ATR as well as provide data on novel imaging and costs, contributing to individualized ATR management.Trial registrationTrialregister.nl. NTR6484. 20/06/2017. 20/07/2017

No-regret learning in wireless networks

The defining property of wireless networks is that communications are not done over a wired connection. In contrast, radio spectrum is used which is subject to a variety of influences. Especially, interference from other concurrent transmissions can cause communication attempts to be unsuccessful. Wireless devices have to operate and perform transmissions despite this interference. Algorithmic considerations often assume a centralized coordination of devices. This coordination behavior can typically not be assumed, as wireless networks are inherently distributed. Even if a central authority existed, the necessary coordination would introduce additional traffic and interference. In this thesis, we consider distributed adaptive algorithms that only rely on very little information to maximize the number of successful transmissions. We give provable guarantees for the performance of these adaptive protocols. We model a wireless network to consist of sender-receiver pairs denoted as links whose transmissions interfere based on a conflict graph. This model generalizes previous interference models like unit-disk graphs or ones based on the signal-to-interference-plus-noise ratio (SINR). We consider no-regret-learning approaches known from game theory. These algorithms adapt transmission probabilities based on utilities, which represent feedback on the success of previous transmission attempts. Our approach extends previous considerations of no-regret techniques for capacity maximization in wireless networks. In the first part of this thesis, we consider the capacity-maximization problem. The task is to choose a maximal-cardinality set of links that can simultaneously transmit successfully. We identify key properties and use them to introduce a flexible proof template for the analysis of adaptive no-regret learning algorithms. This proof template is applied to settings with adversarial jammers, multiple receivers, and links being allowed to leave the network almost arbitrarily. Settings with multiple channels and channel availabilities are also considered by applying similar techniques. We extend previous works by introducing concurrent networks or malicious devices, which are modeled as adversarial jammers. These jammers are limited and can render all transmissions in a certain fraction of time steps unsuccessful. We show that no-regret learning algorithms are able to converge with approximation factors depending on parameters of the jammer. Assuming these parameters to be known to the algorithm, they even achieve constant-factor approximations when jammers tightly fulfill their limitations or against a stochastic jammer. The proof template yields the same guarantees when applied to further generalized scenarios. Here, senders do not strive to transmit data to one receiver but to a set of receivers. At the expense of O(log n) in the approximation guarantees, we also omit the standard assumption from related works that links stay in the network indefinitely. As the wireless spectrum becomes more and more utilized, regulatory authorities devise plans to let primary users with the exclusive right to use a certain part of the spectrum open up their spectrum for secondary usage. To consider this scenario, we extend the capacity-maximization setting to multiple channels and stochastic channel availabilities. We utilize different notions of regret to achieve constant-factor approximations if the availabilities are uncorrelated among links. Otherwise the guarantees depend on the degree of correlation. Many wireless devices are actually capable of adjusting their transmission power. Thus, in the second part of this thesis, we consider the power-control problem, where we strive to find a minimal power assignment such that all links can transmit successfully. We analyze a well-known fixed-point approach of Foschini and Miljanic in terms of convergence time and relate this to no-regret learning also converging to the optimal power assignment. For no-regret learning algorithms we can even guarantee a certain fraction of time steps to be successful

No-regret learning in wireless networks

The defining property of wireless networks is that communications are not done over a wired connection. In contrast, radio spectrum is used which is subject to a variety of influences. Especially, interference from other concurrent transmissions can cause communication attempts to be unsuccessful. Wireless devices have to operate and perform transmissions despite this interference. Algorithmic considerations often assume a centralized coordination of devices. This coordination behavior can typically not be assumed, as wireless networks are inherently distributed. Even if a central authority existed, the necessary coordination would introduce additional traffic and interference. In this thesis, we consider distributed adaptive algorithms that only rely on very little information to maximize the number of successful transmissions. We give provable guarantees for the performance of these adaptive protocols. We model a wireless network to consist of sender-receiver pairs denoted as links whose transmissions interfere based on a conflict graph. This model generalizes previous interference models like unit-disk graphs or ones based on the signal-to-interference-plus-noise ratio (SINR). We consider no-regret-learning approaches known from game theory. These algorithms adapt transmission probabilities based on utilities, which represent feedback on the success of previous transmission attempts. Our approach extends previous considerations of no-regret techniques for capacity maximization in wireless networks. In the first part of this thesis, we consider the capacity-maximization problem. The task is to choose a maximal-cardinality set of links that can simultaneously transmit successfully. We identify key properties and use them to introduce a flexible proof template for the analysis of adaptive no-regret learning algorithms. This proof template is applied to settings with adversarial jammers, multiple receivers, and links being allowed to leave the network almost arbitrarily. Settings with multiple channels and channel availabilities are also considered by applying similar techniques. We extend previous works by introducing concurrent networks or malicious devices, which are modeled as adversarial jammers. These jammers are limited and can render all transmissions in a certain fraction of time steps unsuccessful. We show that no-regret learning algorithms are able to converge with approximation factors depending on parameters of the jammer. Assuming these parameters to be known to the algorithm, they even achieve constant-factor approximations when jammers tightly fulfill their limitations or against a stochastic jammer. The proof template yields the same guarantees when applied to further generalized scenarios. Here, senders do not strive to transmit data to one receiver but to a set of receivers. At the expense of O(log n) in the approximation guarantees, we also omit the standard assumption from related works that links stay in the network indefinitely. As the wireless spectrum becomes more and more utilized, regulatory authorities devise plans to let primary users with the exclusive right to use a certain part of the spectrum open up their spectrum for secondary usage. To consider this scenario, we extend the capacity-maximization setting to multiple channels and stochastic channel availabilities. We utilize different notions of regret to achieve constant-factor approximations if the availabilities are uncorrelated among links. Otherwise the guarantees depend on the degree of correlation. Many wireless devices are actually capable of adjusting their transmission power. Thus, in the second part of this thesis, we consider the power-control problem, where we strive to find a minimal power assignment such that all links can transmit successfully. We analyze a well-known fixed-point approach of Foschini and Miljanic in terms of convergence time and relate this to no-regret learning also converging to the optimal power assignment. For no-regret learning algorithms we can even guarantee a certain fraction of time steps to be successful

Scheduling in Wireless Networks with Rayleigh-Fading Interference

We study algorithms for wireless spectrum access of n communication requests when interference conditions are given by the Rayleigh-fading model. This model extends the recently popular deterministic interference model based on the signal-to-interference-plus-noise ratio (SINR) using stochastic propagation to address fading effects observed in reality. We consider worst-case approximation guarantees for the two standard problems of capacity maximization (maximize the expected number of successful transmissions in a single slot) and latency minimization (minimize the expected number of slots until all transmissions were successful). Our main result is a generic reduction of Rayleigh fading to the deterministic SINR model. It allows to apply existing algorithms for the non-fading model in the Rayleigh-fading scenario while losing only a factor of O(log ∗ n) in the approximation guarantee. This way, we obtain the first approximation guarantees for Rayleigh fading and, more fundamentally, show that non-trivial stochastic fading effects can be successfully handled using existing and future techniques for the non-fading model. Using a more detailed argument, a similar result applies even for distributed and game-theoretic capacity maximization approaches. For example, it allows to show that regret learning yields an O(log ∗ n)-approximation with uniform power assignments. Our analytical treatment is supported by simulations illustrating the performance of regret learning and, more generally, the relationship between both models