A causal effect can be defined as a comparison of outcomes that result from two or more alternative actions, with only one of the action-outcome pairs actually being observed. In healthcare, the gold standard for causal effect measurements is randomized controlled trials (RCTs), in which a target population is explicitly defined and each study sample is randomly assigned to either the treatment or control cohorts. The great potential to derive actionable insights from causal relationships has led to a growing body of machine-learning research applying causal effect estimators to observational data in the fields of healthcare, education, and economics. The primary difference between causal effect studies utilizing observational data and RCTs is that for observational data, the study occurs after the treatment, and therefore we do not have control over the treatment assignment mechanism. This can lead to massive differences in covariate distributions between control and treatment samples, making a comparison of causal effects confounded and unreliable. Classical approaches have sought to solve this problem piecemeal, first by predicting treatment assignment and then treatment effect separately. Recent work extended part of these approaches to a new family of representation-learning algorithms, showing that the upper bound of the expected treatment effect estimation error is determined by two factors: the outcome generalization-error of the representation and the distance between the treated and control distributions induced by the representation. To achieve minimal dissimilarity in learning such distributions, in this work we propose a specific auto-balancing, self-supervised objective. Experiments on real and benchmark datasets revealed that our approach consistently produced less biased estimates than previously published state-of-the-art methods. We demonstrate that the reduction in error can be directly attributed to the ability to learn representations that explicitly reduce such dissimilarity; further, in case of violations of the positivity assumption (frequent in observational data), we show our approach performs significantly better than the previous state of the art. Thus, by learning representations that induce similar distributions of the treated and control cohorts, we present evidence to support the error bound dissimilarity hypothesis as well as providing a new state-of-the-art model for causal effect estimation.

A class of carrier sense multiple access (CSMA) protocols used in a broad range of wireless applications uses synchronized contention where nodes periodically contend at intervals of fixed duration. While several models exist for asynchronous CSMA contention used in protocols like IEEE 802.11 MAC, no model exists for synchronized CSMA contention that also incorporates realistic factors like clock drifts. In this paper, we introduce a model that quantifies the interplay of clock drifts with contention window size, control packet size, and carrier sense regulated by usage of guard time. Using a field programmable gate array (FPGA)-based MAC protocol implementation and controlled experiments on a wireless testbed, we evaluate the model predictions on the isolated and combined impact of these key performance factors to per-flow throughput and fairness properties in both single-hop and multihop networks. Our model and experimental evaluation reveal conditions on protocol parameters under which the throughput of certain flows can exponentially decrease; while at the same time, it enables solutions that can offset such problems in a predictable manner.

Significant progress has been made in understanding the behavior of TCP and congestion-controlled traffic over multi- hop wireless networks. Despite these advances, however, no prior work identified severe throughput imbalances in the basic scenario of mesh networks, in which one-hop flows contend with two-hop flows for gateway access. In this paper, we demonstrate via real network measurements, test-bed experiments, and an analytical model that starvation exists in such a scenario, i.e., the one-hop flow receives most of the bandwidth while the two- hop flow starves. Our analytical model yields a solution consisting of a simple contention window policy that can be implemented via mechanisms in IEEE 802.11e. Despite its simplicity, we demonstrate through analysis, experiments, and simulations, that the policy has a powerful effect on network-wide behavior, shifting the network's queuing points, mitigating problematic MAC behavior, and ensuring that TCP flows obtain a fair share of the gateway bandwidth, irrespective of their spatial locations.

A major barrier for the adoption of wireless mesh networks is severe limits on throughput. In this paper, we apply superposition coding to substantially improve network capacity of large, dense wireless mesh networks. Superposition coding is a physical layer technique that allows a transmitter to simultaneously send independent packets to multiple receivers. While superposition coding has been studied extensively by the physical layer community, we present the first design of practical and effective MAC protocols to take advantage of superposition coding in wireless mesh networks. Extensive evaluations show that superposition coding can be a practical method to increase the throughput of large, dense wireless mesh networks. Specifically, in a mesh network with 2 to 64 active receivers and one gateway, we show that our system can increase throughput up to 154%, with average gain ranging from 10% to 21%. When there are multiple gateways forming a mesh network, our system gains up to 98%, with average gain ranging from 24% to 46%. These results clearly demonstrate the potential benefits of our system. We also present results from an implementation of superposition coding using GNU Radio.

Existing multi-channel protocols have been demonstrated to significantly increase aggregate throughput compared to single-channel protocols. However, we show that despite such improvements in aggregate throughput, existing protocols can lead to flow starvation in a multi-hop network, a phenomenon that also occurs with single-channel protocols. In this paper, we devise Asynchronous Multi-channel Coordination Protocol (AMCP), a distributed medium access protocol that not only increases aggregate throughput, but more importantly, addresses the fundamental coordination problems that lead to starvation. Based on AMCP's counter-starvation mechanisms, we analytically derive and experimentally validate an ap-proximate lower bound on the throughput of any flow in an arbitrary topology. We also demonstrate that AMCP can deliver significantly higher per-flow throughput than both IEEE 802.11 and existing multi-channel solutions. In addition to its performance properties, AMCP is both simple in that it operates using the primitives of IEEE 802.11 DCF, and cost-effective in that it requires only a single half-duplex transceiver and no infrastructure support.

In this paper, we decompose a large- or small-scale multi-hop wireless network into embedded subgraphs, each consisting of four nodes and two flow pairs. We systematically study all twelve possible topologies that arise according to whether the different nodes are in radio range of each other. We show that under both a random spatial distribution of nodes and random waypoint mobility with shortest-path routing, a critical and highly probable scenario is a class in which the channel state shared by the two flows is not only incomplete (i.e., the graph is not fully connected), but there is also asymmetry in the state between the two flows. We develop an accurate analytical model validated by simulations to characterize the long-term unfairness that naturally arises when CSMA with two- or four-way handshake is employed as a random access protocol. Moreover, we show that another key class of topologies consists of incomplete but symmetric shared state. We show via modeling and simulations that in this case, the system achieves long-term fairness, yet endures significant durations in which one flow dominates channel access with many repeated transmissions before relinquishing the channel. The model predicts the time-scales of this unfairness as a function of system parameters such as the maximum retransmission limit.

A model called direction-basis-function (DBF) neural networks and its relevant algorithm for pattern classification are proposed. Also, one implementation of the model based on the architecture of priority ordered neural networks (PONN), that is PODBFN, is discussed. When adapted as pattern classifier, the neurons with linear output are used in the course of learning, while hard limited step activation function is for pattern classification. The computer simulations show that not only the convergence speed is much faster than the improved BP algorithm, but also the performance of the network is better. The DBF pattern classifier was implemented on the micro-neural computer CASSANDRA-I, and the experimental results obtained are presented.