This work addresses control information loss in manned-unmanned teaming networks. We consider methods for mitigating smart jammers that target control channels to disrupt transmission scheduling and degrade communication. In this context, the well-studied Max-Weight algorithm achieves throughput optimality without prior knowledge of arrival rates, but its performance in the presence of control information jamming is under-explored. We propose two novel scheduling policies, VFMW-OS and VFMW-HS, to improve network resilience. VFMW-OS dynamically adjusts the control-information coding rate to overcome jamming of the control channel. Likewise, VFMW-HS opportunistically switches between centralized and decentralized scheduling based on the network's queue lengths to further adjust the rate at which control information is exchanged so as to minimize delay. Simulations show both policies enhance robustness and reduce delay under adversarial conditions.

We present CHOKE, a novel code-based hybrid key-encapsulation mechanism (KEM) designed to securely and efficiently transmit multiple session keys simultaneously. By encoding n independent session keys with an individually secure linear code and encapsulating each resulting coded symbol using a separate KEM, CHOKE achieves computational individual security–each key remains secure as long as at least one underlying KEM remains unbroken. Compared to traditional serial or combiner-based hybrid schemes, CHOKE reduces computational and communication costs by an n-fold factor. Furthermore, we show that the communication cost of our construction is optimal under the requirement that each KEM must be used at least once.

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.

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 Ω(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-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.

We consider a system consisting of a single transmitter and N channels. Packets randomly arrive to the transmitter's queue, and at each time slot a controller can schedule one of the N channels for transmission. The channel's rates are time-varying with unknown statistics and must be learned through observation. Our objective is to minimize the number of packets in the system's queue over T time slots. We define the regret of the system to be the expected difference between the total queue length of a controller that must learn the channels' average rates and a controller that knows the rates, a priori. One approach to solving this problem would be to apply algorithms from the literature that were developed to solve the closely-related stochastic multi-armed bandit problem. However, we show that these methods have Ω(log(T)) queue length regret. On the other hand, we show that there exists a set of queue-length based policies that are able to obtain order optimal, O(1), regret.

We study the problem of learning channel statistics in order to efficiently schedule transmissions in wireless networks subject to interference constraints. In particular, we focus on the primary interference model which requires that at any time the set of activated links be a matching in the corresponding graph. We propose a distributable algorithm that forms greedy matchings in the graph in order to learn the channels' transmission rates, while simultaneously exploiting previous observations to obtain high throughput. Comparison to the offline solution shows our algorithm to have good performance that scales well with the number of links in the network. We then turn our attention to the stochastic setting where packets randomly arrive to the network and await transmission in queues at the nodes. We develop a queue-length-based scheduling policy that uses the channel learning algorithm as a component. We analyze our method in time varying environments and show that it achieves the same stability region as that of a greedy matching policy with full channel knowledge (i.e., half of the full stability region).

We consider an opportunistic communication system in which a secondary transmitter communicates over the unused time slots of a primary user. In particular, we consider a system in which the primary user is uncooperative and transmits whenever its buffer is nonempty, and the secondary user relies on feedback from its receiver in order to decide when to transmit. The objective of the secondary user is to maximize its own throughput without degrading the throughput of the primary user. We analyze the maximum achievable throughput of the secondary user by formulating the problem as a partially observable Markov decision process. We derive bounds on the optimal solution and find a channel access policy for the secondary user that is near-optimal when the primary user's exogenous arrival rate is low. These results are then used to characterize the set of arrival rates to the primary and secondary users that may be stably supported by the system.

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.