arXiv subjects

Grace Villacrés

Publications and source records attributed to Grace Villacrés.

4 recordsLinked to original sources

arXiv2025

Fundamental Limits of Noncoherent Massive Random Access Networks

This paper studies the capacity of massive random-access cellular networks, modeled as a MIMO fading channel with an infinite number of interfering cells. To characterize the symmetric sum rate of the network, a random-coding argument is invoked together with the assumption that in all cells users draw their codebooks according to the same distribution. This can be viewed as a generalization of the assumption of Gaussian codebooks, often encountered in the literature. The network is further assumed to be noncoherent: the transmitters and receivers are cognizant of the statistics of the fading coefficients, but are ignorant of their realizations. Finally, it is assumed that the users access the network at random. For the considered channel model, rigorous bounds on the capacity are derived. The behavior of these bounds depends critically on the path loss from signals transmitted in interfering cells to the intended cell. In particular, if the fading coefficients of the interferers (ordered according to their distance to the receiver) decay exponentially or more slowly, then the capacity is bounded in the transmit power. This confirms that the saturation regime in interference-limited networks -- observed by Lozano, Heath, and Andrews ("Fundamental limits of cooperation", IEEE Trans. Inf. Theory, Sept. 2013) -- cannot be avoided by random user activity or by using channel inputs beyond the scale family. In contrast, if the fading coefficients decay faster than double-exponentially, then the capacity is unbounded in the transmit power. Proving an unbounded capacity is nontrivial even if the number of interfering cells is finite, since the condition that the users' codebooks follow the same distribution prevents interference-avoiding strategies such as time- or frequency-division multiple access. We obtain this result by using bursty signaling together with treating interference as noise.

cs.IT↗

arXiv2025

Improved Variational Inference in Discrete VAEs using Error Correcting Codes

Despite advances in deep probabilistic models, learning discrete latent representations remains challenging. This work introduces a novel method to improve inference in discrete Variational Autoencoders by reframing the inference problem through a generative perspective. We conceptualize the model as a communication system, and propose to leverage Error-Correcting Codes (ECCs) to introduce redundancy in latent representations, allowing the variational posterior to produce more accurate estimates and reduce the variational gap. We present a proof-of-concept using a Discrete Variational Autoencoder with binary latent variables and low-complexity repetition codes, extending it to a hierarchical structure for disentangling global and local data features. Our approach significantly improves generation quality, data reconstruction, and uncertainty calibration, outperforming the uncoded models even when trained with tighter bounds such as the Importance Weighted Autoencoder objective. We also outline the properties that ECCs should possess to be effectively utilized for improved discrete variational inference.

cs.LG↗

arXiv2018

Robust Signaling for Bursty Interference

This paper studies a bursty interference channel, where the presence/absence of interference is modeled by a block-i.i.d.\ Bernoulli process that stays constant for a duration of $T$ symbols (referred to as coherence block) and then changes independently to a new state. We consider both a quasi-static setup, where the interference state remains constant during the whole transmission of the codeword, and an ergodic setup, where a codeword spans several coherence blocks. For the quasi-static setup, we study the largest rate of a coding strategy that provides reliable communication at a basic rate and allows an increased (opportunistic) rate when there is no interference. For the ergodic setup, we study the largest achievable rate. We study how non-causal knowledge of the interference state, referred to as channel-state information (CSI), affects the achievable rates. We derive converse and achievability bounds for (i) local CSI at the receiver-side only; (ii) local CSI at the transmitter- and receiver-side, and (iii) global CSI at all nodes. Our bounds allow us to identify when interference burstiness is beneficial and in which scenarios global CSI outperforms local CSI. The joint treatment of the quasi-static and ergodic setup further allows for a thorough comparison of these two setups.

cs.IT↗

arXiv2015

Wireless networks of bounded capacity

We consider a noncoherent wireless network, where the transmitters and receivers are cognizant of the statistics of the fading coefficients, but are ignorant of their realizations. We demonstrate that if the nodes do not cooperate, if they transmit symbols that all follow the same distribution, and if the variances of the fading coefficients decay exponentially or more slowly, then the channel capacity is bounded in the SNR. This confirms a similar result by Lozano, Heath, and Andrews.

cs.IT↗