Vinayak Suresh

With the advent of 5G and technologies such as cloud computing, Internet-of-Things (IoT), etc, future communication networks will consist of a large number of heterogeneous devices connected together. A critical aspect will be ensuring that communication is not only fast and reliable, but also resilient to malicious attack. As networks increasingly adopt zero-trust principles in their security frameworks, it is important to consider such attacks from a zero-trust perspective. Motivated by this, we consider the problem of communicating a message reliably across a binary erasure channel (BEC( $q$ )) or a binary symmetric channel (BSC( $q$ )) against an adversary actively injecting additional erasures or flips at the channel’s input. The adversary can corrupt up to a fraction $p$ of the transmitted bits and knows the transmission scheme agreed upon by the communicating terminals. Further, he has the capability to causally snoop in on both the transmitter and the receiver in real time, i.e., if ${\mathbf {x}}=(x_{1},x_{2},\ldots, x_{n})$ and ${\mathbf {y}}=(y_{1},y_{2},\ldots, y_{n})$ denote the transmitted and the received codewords respectively, at each time $k$ , he knows $(x_{1},x_{2},\ldots, x_{k})$ and $(y_{1},y_{2},\ldots, y_{k-1})$ . Using his side-information, the adversary is free to employ any attack that respects his constraint on the number of corruptible bits. We prove an information-theoretic tight capacity characterization as a function of $p$ and $q$ for (i) the erasure adversary with a BEC( $q$ ) and (ii) the bit-flip adversary with a BSC( $q$ ). A unique feature of our models is the compounding of stochastic and adversarial noise sources. Our analysis reveals the worst-case adversarial attacks for both models and proves the existence of coding schemes that achieve rates equal to the capacity for any adversarial attack. In the case of bit-flips, we show that, interestingly, when $p$ is below a certain threshold (that depends on $q$ ), the adversary is no worse than an i.i.d. memory-less noise source.

Millimeter wave technology is an essential component of most solutions that address the coverage and throughput demands of next-generation cellular networks. To overcome the high propagation losses however, it is necessary to deploy large antenna arrays for spatial localization of energy by beamforming. This imposes a significant communication overhead, especially when channel reciprocity does not hold. In this work, we study the problem of successive one-bit feedback-assisted beam alignment. We exploit the sparse nature of the millimeter wave channel to pose the beamforming problem as a questioning strategy. We consider both adaptive (closed-loop) and non-adaptive (open-loop) channel sounding techniques which are robust to erroneous feedback signals caused by noisy quantization. In the adaptive case, we formulate new sounding signals by drawing a parallel with the well known Ulam's problem. In the non-adaptive case, the beams are designed in accordance to an open-loop code. We demonstrate that multiple paths can also be resolved by using ideas from group testing. Finally, we show the efficacy of our proposed techniques via simulations.

We propose a novel beam alignment and tracking algorithm for time-varying millimeter wave channels with a dynamic channel support. Millimeter wave beam alignment is challenging due to the expected large number of antennas. A multi-armed bandit training beam selection policy is used to balance exploration of the set of feasible beams. We track the channel using a synthesis of sparse Bayesian learning and Kalman filtering and smoothing. Results show our algorithm has a more rapid rate of initial beam alignment compared to other beam selection policies and, for dynamic channel support, long-term beamforming gain commensurate to omni-directional training.