Person:

Comiter, Marcus

We introduce State-Informed Link-Layer Queuing (SILQ), a system that models, predicts, and avoids packet delivery failures due to temporary wireless outages in everyday scenarios. By stabilizing connections in adverse link conditions, SILQ boosts throughput and reduces performance variation for network applications, for example by preventing unnecessary TCP timeouts caused by dead zones, elevators, and subway tunnels. SILQ makes predictions in real-time by actively probing links, matching measurements to an overcomplete dictionary of patterns learned offline, and classifying the resulting sparse feature vectors to identify those that precede outages. We use a clustering method called sparse coding to build our data-driven link model, and show that it produces more variation-tolerant predictions than traditional loss-rate, location-based, or Markov chain techniques. We present extensive data collection and field-validation of SILQ in airborne, indoor, and urban scenarios of practical interest. We show how offline unsupervised learning discovers link-state patterns that are stable across diverse networks and signal-propagation environments. Using these canonical primitives, we train outage predictors for 802.11 (Wi-Fi) and 3G cellular networks to demonstrate TCP throughput gains of 4x with off-the-shelf mobile devices. SILQ addresses delivery failures solely at the link layer, requires no new hardware, and upholds the end-to-end design principle, enabling easy integration across applications, devices, and networks.

We present novel applications and analysis of the use of sparse coding within the con- text of machine learning. We first present Sparse Coding Trees (SC-trees), a sparse coding-based framework for resolving classification conflicts, which occur when different classes are mapped to similar feature representations. More specifically, SC-trees are novel supervised hierarchical clustering trees that use node specific dictionary and classifier training to direct input images based on classification results in the feature space at each node. We validate SC-trees on image-based emotion classification, combining it with Mirrored Nonnegative Sparse Coding (MNNSC), a novel sparse coding algorithm leveraging a nonnegativity constraint and the inherent symmetry of the domain, to achieve results exceeding or competitive with the state-of-the-art. We next present SILQ, a sparse coding-based link state model that can predictively buffer packets during wireless link outages to avoid disruption to higher layer protocols such as TCP. We demonstrate empirically that SILQ increases TCP throughput by a factor of 2-4x in varied scenarios.

Next-generation wireless networks, such as 5G networks, will use millimeter waves (mmWaves) operating at 28 GHz, 38 GHz, 60 GHz, or higher frequencies to deliver unprecedentedly high data rates, e.g., 10 gigabits per second. Due to high attenuation at this higher frequency, use of directional antennas is commonly suggested for mmWave communication. It is therefore important to study how different antenna configurations at the transmitter and receiver effect received power and data throughput. In this paper, we describe field experiments with mmWave antennas for indoor multipath environments and report measurement results on a multitude of antenna configurations. Specifically, we examine four different mmWave systems, operating at two different frequencies (38 and 60 GHz), using a number of different antennas (horn antennas, omnidirectional antennas, and phase arrays). For each system, we systematically collect performance measurements (e.g., received power), and use these to examine the effects of beam misalignment on signal quality, the presence of multipath effects, and susceptibility to blockage. We capture interesting phenomena, including a multipath scenario in which a single receiver antenna can receive two copies of signals transmitted from the same transmitter antenna over multiple paths. From these field experiments, we discuss lessons learned and draw several conclusions, and their applicability to the design of future mmWave networks.

Compressive sensing, which enables signal recovery from fewer samples than traditional sampling theory dictates, assumes that the sampling process is linear. However, this linearity assumption may not hold in the analog domain without significant trade-offs, such as power amplifiers sacrificing substantial power efficiency in exchange for producing linear outputs. Since compressive sensing is most impactful when implemented in the analog domain, it is of interest to integrate the nonlinearity in compressive measurements into the signal recovery process such that nonlinear effects can be mitigated. As such, in this paper, we describe a nonlinear compressive sensing formulation and associated signal recovery algorithms, providing both compression and improved efficiency of a power amplifier simultaneously with one procedure. We present evaluations of the proposed framework using both measurements from real power amplifiers and simulations.

Recent advances in clustering have shown that ensuring a minimum separation between cluster centroids leads to higher quality clusters compared to those found by methods that explicitly set the number of clusters to be found, such as k-means. One such algorithm is DP-means, which sets a distance parameter λ for the minimum separation. However, without knowing either the true number of clusters or the underlying true distribution, setting λ itself can be difficult, and poor choices in setting λ will negatively impact cluster quality. As a general solution for finding λ, in this paper we present λ-means, a clustering algorithm capable of deriving an optimal value for λ automatically. We contribute both a theoretically-motivated cluster-based version of λ-means, as well as a faster conflict-based version of λ-means. We demonstrate that λ-means discovers the true underlying value of λ asymptotically when run on datasets generated by a Dirichlet Process, and achieves competitive performance on a real world test dataset. Further, we demonstrate that when run on both parallel multicore computers and distributed cluster computers in the cloud, cluster-based λ-means achieves near perfect speedup, and while being a more efficient algorithm, conflict-based λmeans achieves speedups only a factor of two away from the maximum-possible.

Next-generation wireless networks such as 5G and 802.11ad networks will use millimeter waves operating at 28GHz, 38GHz, or higher frequencies to deliver unprecedentedly high data rates, e.g., 10 gigabits per second. However, millimeter waves must be used directionally with narrow beams in order to overcome the large attenuation due to their higher frequency. To achieve high data rates in a mobile setting, communicating nodes need to align their beams dynamically, quickly, and in high resolution. We propose a data-driven, deep neural network (DNN) approach to provide robust localization for beam alignment, using a lower frequency spectrum (e.g., 2.4 GHz). The proposed DNN-based localization methods use the angle of arrival derived from phase differences in the signal received at multiple antenna arrays to infer the location of a mobile node. Our methods differ from others that use DNNs as a black box in that the structure of our neural network model is tailored to address difficulties associated with the domain, such as collinearity of the mobile node with antenna arrays, fading and multipath. We show that training our models requires a small number of sample locations, such as 30 or fewer, making the proposed methods practical. Our specific contributions are: (1) a structured DNN approach where the neural network topology reflects the placement of antenna arrays, (2) a simulation platform for generating training and evaluation data sets under multiple noise models, and (3) demonstration that our structured DNN approach improves localization under noise by up to 25% over traditional off-the-shelf DNNs, and can achieve sub-meter accuracy in a real-world experiment.