Learning Algorithms for Minimizing Queue Length Regret

We consider a system consisting of a single transmitter/receiver pair and $N$ channels over which they may communicate. Packets randomly arrive to the transmitter's queue and wait to be successfully sent to the receiver. The transmitter may attempt a frame transmission on one channel at a time, where each frame includes a packet if one is in the queue. For each channel, an attempted transmission is successful with an unknown probability. The transmitter's objective is to quickly identify the best channel to minimize the number of packets in the queue over $T$ time slots. To analyze system performance, we introduce queue length regret, which is the expected difference between the total queue length of a learning policy and a controller that knows the rates, a priori. One approach to designing a transmission policy would be to apply algorithms from the literature that solve the closely-related stochastic multi-armed bandit problem. These policies would focus on maximizing the number of successful frame transmissions over time. However, we show that these methods have $\Omega(\log{T})$ queue length regret. On the other hand, we show that there exists a set of queue-length based policies that can obtain order optimal $O(1)$ queue length regret. We use our theoretical analysis to devise heuristic methods that are shown to perform well in simulation.Comment: 28 Pages, 11 figure

Topology control for wireless networks with highly-directional antennas

In order to steer antenna beams towards one another for communication, wireless nodes with highly-directional antennas must track the channel state of their neighbors. To keep this overhead manageable, each node must limit the number of neighbors that it tracks. The subset of neighbors that each node chooses to track constitutes a network topology over which traffic can be routed. We consider this topology design problem, taking into account channel modeling, transmission scheduling, and traffic demand. We formulate the optimal topology design problem, with the objective of maximizing the scaling of traffic demand, and propose a distributed method, where each node rapidly builds a segment of the topology around itself by forming connections with its nearest neighbors in discretized angular regions. The method has low complexity and message passing overhead. The resulting topologies are shown to have desirable structural properties and approach the optimal solution in high path loss environments.National Science Foundation (U.S.) (Grant CNS-1524317)National Science Foundation (U.S.) (Grant CNS-1116209)National Science Foundation (U.S.) (Grant AST-1547331)United States. Air Force (Contract FA8721-05-C-0002

CRYPTO-MINE: Cryptanalysis via Mutual Information Neural Estimation

The use of Mutual Information (MI) as a measure to evaluate the efficiency of cryptosystems has an extensive history. However, estimating MI between unknown random variables in a high-dimensional space is challenging. Recent advances in machine learning have enabled progress in estimating MI using neural networks. This work presents a novel application of MI estimation in the field of cryptography. We propose applying this methodology directly to estimate the MI between plaintext and ciphertext in a chosen plaintext attack. The leaked information, if any, from the encryption could potentially be exploited by adversaries to compromise the computational security of the cryptosystem. We evaluate the efficiency of our approach by empirically analyzing multiple encryption schemes and baseline approaches. Furthermore, we extend the analysis to novel network coding-based cryptosystems that provide individual secrecy and study the relationship between information leakage and input distribution

Control of wireless networks under uncertain state information

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 157-162).In shared spectrum, wireless communication systems experience interference that can cause packet transmission failures. The channel conditions that determine these losses are driven by an underlying time-evolving state, which is usually hidden from the wireless network and can only be partially observed through interaction with the channel. This introduces a trade-off between exploration and exploitation: the nodes of the network must schedule their transmissions to both observe the channels and achieve high throughput. The optimal balance between these objectives is determined by the network's stochastic traffic demand. Solving this joint learning and scheduling problem is complex. In this thesis, we devise queue-length-based scheduling policies that can adapt to the network's traffic, while simultaneously exploring the channel conditions. We begin by considering controller policies for a transmitter that has multiple available channels. Packets stochastically arrive to the transmitter's queue, and at each time slot, the transmitter can attempt transmission on one of the channels. For each channel, transmission attempts fail according to a random process with unknown mean. The objective of the transmitter is to learn the channel's rates while simultaneously minimizing its queue backlog. We proceed to formulate transmission policies that are asymptotically order optimal. Next, we consider transmission scheduling when the network under our control is sharing its channels with an uncooperative network. Transmission collisions cause the uncooperative network to reattempt transmission. Therefore, the experienced interference is correlated over time through the uncooperative network's queueing dynamics, which are hidden from our network and must be estimated through observation. We derive upper and lower bounds on the maximum attainable rate of successful transmissions in a two user network and use these bounds to characterize the performance of larger networks. These results lead to a queue-length-based method for stabilizing the networks. Finally, we extend our results to networks that have complex constraints on simultaneous transmissions. The network must learn its channel rates while also supporting its stochastic traffic demand. We devise a frame-based max-weight algorithm that learns the channel rates over the duration of a frame to stabilize the network.Supported by the United States Air Force Air Force Contract No. FA8702-15-D-0001 Sponsored by by NSF AST-1547331 Sponsored by by NSF CNS-1701964 Sponsored by by NSF CNS-1524317 Sponsored by by NSF CNS-1217048 Sponsored by Army Research Office (ARO) grant number W911NF-17-1-0508by Thomas Benjamin Stahlbuhk.Ph. D

Learning Algorithms for Minimizing Queue Length Regret

© 1963-2012 IEEE. We consider a system consisting of a single transmitter/receiver pair and N channels over which they may communicate. Packets randomly arrive to the transmitter's queue and wait to be successfully sent to the receiver. The transmitter may attempt a frame transmission on one channel at a time, where each frame includes a packet if one is in the queue. For each channel, an attempted transmission is successful with an unknown probability. The transmitter's objective is to quickly identify the best channel to minimize the number of packets in the queue over T time slots. To analyze system performance, we introduce queue length regret, which is the expected difference between the total queue length of a learning policy and a controller that knows the rates, a priori. One approach to designing a transmission policy would be to apply algorithms from the literature that solve the closely-related stochastic multi-armed bandit problem. These policies would focus on maximizing the number of successful frame transmissions over time. However, we show that these methods have Omega (log {{T}}) queue length regret. On the other hand, we show that there exists a set of queue-length based policies that can obtain order optimal {O}(1) queue length regret. We use our theoretical analysis to devise heuristic methods that are shown to perform well in simulation

Throughput Maximization in Uncooperative Spectrum Sharing Networks

Throughput-optimal transmission scheduling in wireless networks has been a well considered problem in the literature, and the method for achieving optimality, MaxWeight scheduling, has been known for several decades. This algorithm achieves optimality by adaptively scheduling transmissions relative to each user's stochastic traffic demands. To implement the method, users must report their queue backlogs to the network controller and must rapidly respond to the resulting resource allocations. However, many currently-deployed wireless systems are not able to perform these tasks and instead expect to occupy a fixed assignment of resources. To accommodate these limitations, adaptive scheduling algorithms need to interactively estimate these uncooperative users' queue backlogs and make scheduling decisions to account for their predicted behavior. In this work, we address the problem of scheduling with uncooperative legacy systems by developing algorithms to accomplish these tasks. We begin by formulating the problem of inferring the uncooperative systems' queue backlogs as a partially observable Markov decision process and proceed to show how our resulting learning algorithms can be successfully used in a queue-length-based scheduling policy. Our theoretical analysis characterizes the throughput-stability region of the network and is verified using simulation results.NSF (Grants CNS-1524317 and AST-1547331)Department of the Air Force (Contract FA8721-05-C- 0002