arXiv subjects

I-Hsiang Wang

Publications and source records attributed to I-Hsiang Wang.

At least 19 recordsLinked to original sources

arXiv2026

On the Information Velocity over a Tandem of Erasure Channels

Information velocity (IV) is a recently proposed notion to capture the speed of reliable information dissemination over a large-scale network. It is the speed at which reliable end-to-end communication over $k$ hops can be achieved within $t$ time instances, and is defined formally as the asymptotic ratio $k/t$ as $k$ tends to infinity subject to vanishing error probability. To date, even for a tandem of binary erasure channels without feedback, the optimal IV for disseminating multiple (say $m$) bits remains unknown. We make progress on this open problem by characterizing the optimal IV for the regime where the message size $m = o(k^{1/2})$. The main contribution lies in achievability, where we propose a simple bit-separation scheme that pipelines message bits in an orderly fashion with carefully designed temporal spacing so that the flows of different bits do not collide with one another with high probability. This is in sharp contrast to previous attempts in the literature where schemes involve coding over time and across nodes. To go beyond the regime $m = o(k^{1/2})$, we further investigate the setting where every node in the network has strictly causal access to the state information of the BEC links in the entire network. For such a setting with global state information (GSI), we develop an enhanced scheme and characterize the optimal IV for the regime where the message size $m = o(k)$. Interestingly, for the regime $m = o(k^{1/2})$, GSI does not improve the information velocity.

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arXiv2025

A Multi-stage Error Diagnosis for APB Transaction

Functional verification and debugging are critical bottlenecks in modern System-on-Chip (SoC) design, with manual detection of Advanced Peripheral Bus (APB) transaction errors in large Value Change Dump (VCD) files being inefficient and error-prone. Addressing the 2025 ICCAD Contest Problem D, this study proposes an automated error diagnosis framework using a hierarchical Random Forest-based architecture. The multi-stage error diagnosis employs four pre-trained binary classifiers to sequentially detect Out-of-Range Access, Address Corruption, and Data Corruption errors, prioritizing high-certainty address-related faults before tackling complex data errors to enhance efficiency. Experimental results show an overall accuracy of 91.36%, with near-perfect precision and recall for address errors and robust performance for data errors. Although the final results of the ICCAD 2025 CAD Contest are yet to be announced as of the submission date, our team achieved first place in the beta stage, highlighting the method's competitive strength. This research validates the potential of hierarchical machine learning as a powerful automated tool for hardware debugging in Electronic Design Automation (EDA).

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arXiv2025

Tradeoffs among Action Taking Policies Matter in Active Sequential Multi-Hypothesis Testing: the Optimal Error Exponent Region

Reliability of sequential hypothesis testing can be greatly improved when the decision maker is given the freedom to adaptively take an action that determines the distribution of the current collected sample. Such advantage of sampling adaptivity has been realized since Chernoff's seminal paper in 1959 [1]. While a large body of works have explored and investigated the gain of adaptivity, in the general multiple-hypothesis setting, the fundamental limits of individual error probabilities have not been fully understood. In particular, in the asymptotic regime as the expected stopping time tends to infinity, the error exponents are only characterized in specific cases, such as that of the total error probability. In this paper, we consider a general setup of active sequential multiple-hypothesis testing where at each time slot, a temporally varying subset of data sources (out of a known set) emerges from which the decision maker can select to collect samples, subject to a family of expected selection budget constraints. The selection of sources, understood as the ``action'' at each time slot, is constrained in a predefined action space. At the end of each time slot, the decision maker either decides to make the inference on the $M$ hypotheses, or continues to observe the data sources for the next time slot. The optimal tradeoffs among $M(M-1)$ types of error exponents are characterized. A companion asymptotically optimal test that strikes the balance between exploration and exploitation is proposed to achieve any target error exponents within the region. To the best of our knowledge, this is the first time in the literature to identify such tradeoffs among error exponents in active sequential hypothesis testing, and it uncovers the tension among different action taking policies even in the basic setting of Chernoff [1].

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arXiv2025

On the Price of Decentralization in Decentralized Detection

Fundamental limits on the error probabilities of a family of decentralized detection algorithms (eg., the social learning rule proposed by Lalitha et al. over directed graphs are investigated. In decentralized detection, a network of nodes locally exchanging information about the samples they observe with their neighbors to collectively infer the underlying unknown hypothesis. Each node in the network weighs the messages received from its neighbors to form its private belief and only requires knowledge of the data generating distribution of its observation. In this work, it is first shown that while the original social learning rule of Lalitha et al. achieves asymptotically vanishing error probabilities as the number of samples tends to infinity, it suffers a gap in the achievable error exponent compared to the centralized case. The gap is due to the network imbalance caused by the local weights that each node chooses to weigh the messages received from its neighbors. To close this gap, a modified learning rule is proposed and shown to achieve error exponents as large as those in the centralized setup. This implies that there is essentially no first-order penalty caused by decentralization in the exponentially decaying rate of error probabilities.

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arXiv2025

A Unified Study on Sequentiality in Universal Classification with Empirically Observed Statistics

In the binary hypothesis testing problem, it is well known that sequentiality in taking samples eradicates the trade-off between two error exponents, yet implementing the optimal test requires the knowledge of the underlying distributions, say $P_0$ and $P_1$. In the scenario where the knowledge of distributions is replaced by empirically observed statistics from the respective distributions, the gain of sequentiality is less understood when subject to universality constraints over all possible $P_0,P_1$. In this work, the gap is mended by a unified study on sequentiality in the universal binary classification problem, where the universality constraints are set on the expected stopping time as well as the type-I error exponent. The type-I error exponent is required to achieve a pre-set distribution-dependent constraint $λ(P_0,P_1)$ for all $P_0,P_1$. Under the proposed framework, different sequential setups are investigated so that fair comparisons can be made with the fixed-length counterpart. By viewing these sequential classification problems as special cases of a general sequential composite hypothesis testing problem, the optimal type-II error exponents are characterized. Specifically, in the general sequential composite hypothesis testing problem subject to universality constraints, upper and lower bounds on the type-II error exponent are proved, and a sufficient condition for which the bounds coincide is given. The results for sequential classification problems are then obtained accordingly. With the characterization of the optimal error exponents, the benefit of sequentiality is shown both analytically and numerically by comparing the sequential and the fixed-length cases in representative examples of type-I exponent constraint $λ$.

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arXiv2024

Robust Privatization with Multiple Tasks and the Optimal Privacy-Utility Tradeoff

In this work, fundamental limits and optimal mechanisms of privacy-preserving data release that aims to minimize the privacy leakage under utility constraints of a set of multiple tasks are investigated. While the private feature to be protected is typically determined and known by the sanitizer, the target task is usually unknown. To address the lack of information on the specific task, utility constraints laid on a set of multiple possible tasks are considered. The mechanism protects the specific privacy feature of the to-be-released data while satisfying utility constraints of all possible tasks in the set. First, the single-letter characterization of the rate-leakage-distortion region is derived, where the utility of each task is measured by a distortion function. It turns out that the minimum privacy leakage problem with log-loss distortion constraints and the unconstrained released rate is a non-convex optimization problem. Second, focusing on the case where the raw data consists of multiple independent components, we show that the above non-convex optimization problem can be decomposed into multiple parallel privacy funnel (PF) problems with different weightings. We explicitly derive the optimal solution to each PF problem when the private feature is a component-wise deterministic function of a data vector. The solution is characterized by a leakage-free threshold: when the utility constraint is below the threshold, the minimum leakage is zero; once the required utility level is above the threshold, the privacy leakage increases linearly. Finally, we show that the optimal weighting of each privacy funnel problem can be found by solving a linear program (LP). A sufficient released rate to achieve the minimum leakage is also derived. Numerical results are shown to illustrate the robustness of our approach against the task non-specificity.

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arXiv2022

Non-adaptive Combinatorial Quantitative Group Testing with Adversarially Perturbed Measurements

In this paper, combinatorial quantitative group testing (QGT) with noisy measurements is studied. The goal of QGT is to detect defective items from a data set of size $n$ with counting measurements, each of which counts the number of defects in a selected pool of items. While most literatures consider either probabilistic QGT with random noise or combinatorial QGT with noiseless measurements, our focus is on the combinatorial QGT with noisy measurements that might be adversarially perturbed by additive bounded noises. Since perfect detection is impossible, a partial detection criterion is adopted. With the adversarial noise being bounded by $d_n = Θ(n^δ)$ and the detection criterion being to ensure no more than $k_n = Θ(n^κ)$ errors can be made, our goal is to characterize the fundamental limit on the number of measurement, termed \emph{pooling complexity}, as well as provide explicit construction of measurement plans with optimal pooling complexity and efficient decoding algorithms. We first show that the fundamental limit is $\frac{1}{1-2δ}\frac{n}{\log n}$ to within a constant factor not depending on $(n,κ,δ)$ for the non-adaptive setting when $0<2δ\leq κ<1$, sharpening the previous result by Chen and Wang [2]. We also provide an explicit construction of a non-adaptive deterministic measurement plan with $\frac{1}{1-2δ}\frac{n}{\log_{2} n}$ pooling complexity up to a constant factor, matching the fundamental limit, with decoding complexity being $o(n^{1+ρ})$ for all $ρ> 0$, nearly linear in $n$, the size of the data set.

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arXiv2021

Content Delivery over Broadcast Erasure Channels with Distributed Random Cache

We study the content delivery problem between a transmitter and two receivers through erasure links, when each receiver has access to some random side-information about the files requested by the other user. The random side-information is cached at the receiver via the decentralized content placement. The distributed nature of receiving terminals may also make the erasure state of two links and indexes of the cached bits not perfectly known at the transmitter. We thus investigate the capacity gain due to various levels of availability of channel state and cache index information at the transmitter. More precisely, we cover a wide range of settings from global delayed channel state knowledge and a non-blind transmitter (i.e. one that knows the exact cache index information at each receiver) all the way to no channel state information and a blind transmitter (i.e. one that only statistically knows cache index information at the receivers). We derive new inner and outer bounds for the problem under various settings and provide the conditions under which the two match and the capacity region is characterized. Surprisingly, for some interesting cases the capacity regions are the same even with single-user channel state or single-user cache index information at the transmitter.

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arXiv2021

Erasure Broadcast Channels with Intermittent Feedback

Achievable data rates in wireless systems rely heavily on the available channel state information (CSI) throughout the network. However, feedback links, which provide this information, are scarce, unreliable, and subject to security threats. In this work, we study the impact of having intermittent feedback links on the capacity region of the canonical two-user erasure broadcast channels. In our model, at any time instant, each receiver broadcasts its CSI, and at any other node, this information either becomes available with unit delay or gets erased. For this setting, we develop a new set of outer bounds to capture the intermittent nature of the feedback links. These outer bounds depend on the probability that the CSI from both receivers are erased at the transmitter. In particular, if at any time, the CSI from at least one of the two receivers is available at the other two nodes, then the outer-bounds match the capacity with global delayed CSI. We also provide capacity-achieving transmission strategies under certain scenarios, and we establish a connection between this problem and Blind Index Coding with feedback.

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arXiv2020

Capacity Region of Erasure Broadcast Channels with Common Message and Feedback

Jolfaei et al. used feedback to create transmit signals that are simultaneously useful for multiple users in a broadcast channel. Later, Georgiadis and Tassiulas studied erasure broadcast channels with feedback, and presented the capacity region under certain assumptions. These results provided the fundamental ideas used in communication protocols for networks with delayed channel state information. However, to the best of our knowledge, the capacity region of erasure broadcast channels with feedback and with a common message for both receivers has never been presented. This latter problem shows up as a sub-problem in many multi-terminal communication networks such as the X-Channel, and the two-unicast problem. In this work, we present the capacity region of the two-user erasure broadcast channels with delayed feedback, private messages, and a common message. We consider arbitrary and possibly correlated erasure distributions. We develop new outer-bounds that capture feedback and quantify the impact of delivering a common message on the capacity region. We also propose a transmission strategy that achieves the outer-bounds. Our transmission strategy differs from prior results in that to achieve the capacity, it creates side-information at the weaker user such that the decodability is ensured even if we multicast the common message with a rate higher than its link capacity.

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arXiv2018

Anonymous Heterogeneous Distributed Detection: Optimal Decision Rules, Error Exponents, and the Price of Anonymity

We explore the fundamental limits of heterogeneous distributed detection in an anonymous sensor network with n sensors and a single fusion center. The fusion center collects the single observation from each of the n sensors to detect a binary parameter. The sensors are clustered into multiple groups, and different groups follow different distributions under a given hypothesis. The key challenge for the fusion center is the anonymity of sensors - although it knows the exact number of sensors and the distribution of observations in each group, it does not know which group each sensor belongs to. It is hence natural to consider it as a composite hypothesis testing problem. First, we propose an optimal test called mixture likelihood ratio test based on the ratio of the uniform mixture of all distributions under one hypothesis to that under the other. Optimality is shown by first arguing that symmetric tests are optimal, which do not depend on the order of observations across the sensors, and then proving that the mixture likelihood ratio test is optimal among all symmetric tests. Second, we focus on the Neyman-Pearson setting and characterize the error exponent of the worst-case type-II error probability as n tends to infinity, assuming the number of sensors in each group is proportional to n. Finally, we generalize our result to find the collection of all achievable type-I and type-II error exponents, where the boundary of the region can be obtained by solving a convex program. Our results elucidate the price of anonymity in heterogeneous distributed detection, and can be extended to M-ary hypothesis testing with heterogeneous observations generated according to hidden latent variables. The results are also applied to distributed detection under Byzantine attacks, which hints that the conventional approach based on simple hypothesis testing might be too pessimistic.

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arXiv2018

Degrees of Freedom of the Bursty MIMO X Channel with Instantaneous Topological Information

We study the effects of instantaneous feedback of channel topology on the degrees of freedom (DoF) of the bursty MIMO X channel, where the four transmitter-receiver links are intermittently on-and-off, governed by four independent Bernoulli $(p)$ random sequences, and each transmitter and receiver are equipped with $M$ and $N$ antennas, respectively. We partially characterize this channel: The sum DoF is characterized when $p\le \frac{1}{2}$ or when $\frac{\min(M,N)}{\max(M,N)} \le \frac{2}{3}$. In the remaining regime, the lower bound is within $5.2\%$ of the upper bound. Strictly higher DoF is achieved by coding across channel topologies. In particular, codes over as many as $5$ topologies are proposed to achieve the sum DoF of the channel when $p\le \frac{1}{2}$. A transfer function view of the network is employed to simplify the code design and to elucidate the fact that these are space-time codes, obtained by interference alignment over space and time.

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arXiv2018

On the Minimax Misclassification Ratio of Hypergraph Community Detection

Community detection in hypergraphs is explored. Under a generative hypergraph model called "d-wise hypergraph stochastic block model" (d-hSBM) which naturally extends the Stochastic Block Model from graphs to d-uniform hypergraphs, the asymptotic minimax mismatch ratio is characterized. For proving the achievability, we propose a two-step polynomial time algorithm that achieves the fundamental limit. The first step of the algorithm is a hypergraph spectral clustering method which achieves partial recovery to a certain precision level. The second step is a local refinement method which leverages the underlying probabilistic model along with parameter estimation from the outcome of the first step. To characterize the asymptotic performance of the proposed algorithm, we first derive a sufficient condition for attaining weak consistency in the hypergraph spectral clustering step. Then, under the guarantee of weak consistency in the first step, we upper bound the worst-case risk attained in the local refinement step by an exponentially decaying function of the size of the hypergraph and characterize the decaying rate. For proving the converse, the lower bound of the minimax mismatch ratio is set by finding a smaller parameter space which contains the most dominant error events, inspired by the analysis in the achievability part. It turns out that the minimax mismatch ratio decays exponentially fast to zero as the number of nodes tends to infinity, and the rate function is a weighted combination of several divergence terms, each of which is the Renyi divergence of order 1/2 between two Bernoulli's. The Bernoulli's involved in the characterization of the rate function are those governing the random instantiation of hyperedges in d-hSBM. Experimental results on synthetic data validate our theoretical finding that the refinement step is critical in achieving the optimal statistical limit.

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arXiv2018

Degrees of Freedom of the Bursty MIMO X Channel without Feedback

We study the sum degrees of freedom (DoF) of the bursty MIMO X channel without feedback, where the four transmitter-receiver links are intermittently on-and-off, controlled by four Bernoulli random sequences which may be arbitrarily correlated, subject to a symmetry assumption: The two direct-links have the same level of burstiness, modeled by $\mathrm{Ber}(p_d)$, and so do the cross-links, modeled by $\mathrm{Ber}(p_c)$. The sum DoF is fully characterized in the regime where $\frac{p_c}{p_d}$ is small, i.e. below a certain threshold, and is partially characterized in the other regime where $\frac{p_c}{p_d}$ is above the threshold. The achievability is proved with a combination of Han-Kobayashi strategy and interference alignment, which can achieve strictly higher DoF than interference alignment alone. The converse proof employs a channel-state-sequence pairing technique. We highlight that burstiness of the channel disrupts the network topology, turning the MIMO X channel into a network with time-varying topology. This fundamental difference has striking ramifications. In particular, various interference alignment schemes that achieve the DoF of non-bursty X channels are found to be suboptimal when the channels become bursty. The reciprocity between the forward and the reverse links is lost, and the sum DoF does not saturate when the ratio of the transmitter and the receiver antennas exceeds $\frac{2}{3}$.

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arXiv2015

Gaussian Interference Channel with Intermittent Feedback

We investigate how to exploit intermittent feedback for interference management by studying the two-user Gaussian interference channel (IC). We approximately characterize (within a universal constant) the capacity region for the Gaussian IC with intermittent feedback. We exactly characterize the the capacity region of the linear deterministic version of the problem, which gives us insight into the Gaussian problem. We find that the characterization only depends on the forward channel parameters and the marginal probability distribution of each feedback link. The result shows that passive and unreliable feedback can be harnessed to provide multiplicative capacity gain in Gaussian interference channels. We find that when the feedback links are active with sufficiently large probabilities, the perfect feedback sum-capacity is achieved to within a constant gap. In contrast to other schemes developed for interference channel with feedback, our achievable scheme makes use of quantize-map-and-forward to relay the information obtained through feedback, performs forward decoding, and does not use structured codes. We also develop new outer bounds enabling us to obtain the (approximate) characterization of the capacity region.

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arXiv2015

A Relay Can Increase Degrees of Freedom in Bursty Interference Networks

We investigate the benefits of relays in multi-user wireless networks with bursty user traffic, where intermittent data traffic restricts the users to bursty transmissions. To this end, we study a two-user bursty MIMO Gaussian interference channel with a relay, where two Bernoulli random states govern the bursty user traffic. We show that an in-band relay can provide a degrees of freedom (DoF) gain in this bursty channel. This beneficial role of in-band relays in the bursty channel is in direct contrast to their role in the non-bursty channel which is not as significant to provide a DoF gain. More importantly, we demonstrate that for certain antenna configurations, an in-band relay can help achieve interference-free performances with increased DoF. We find the benefits particularly substantial with low data traffic, as the DoF gain can grow linearly with the number of antennas at the relay. In this work, we first derive an outer bound from which we obtain a necessary condition for interference-free DoF performances. Then, we develop a novel scheme that exploits information of the bursty traffic states to achieve them.

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arXiv2015

On Two-Pair Two-Way Relay Channel with an Intermittently Available Relay

When multiple users share the same resource for physical layer cooperation such as relay terminals in their vicinities, this shared resource may not be always available for every user, and it is critical for transmitting terminals to know whether other users have access to that common resource in order to better utilize it. Failing to learn this critical piece of information may cause severe issues in the design of such cooperative systems. In this paper, we address this problem by investigating a two-pair two-way relay channel with an intermittently available relay. In the model, each pair of users need to exchange their messages within their own pair via the shared relay. The shared relay, however, is only intermittently available for the users to access. The accessing activities of different pairs of users are governed by independent Bernoulli random processes. Our main contribution is the characterization of the capacity region to within a bounded gap in a symmetric setting, for both delayed and instantaneous state information at transmitters. An interesting observation is that the bottleneck for information flow is the quality of state information (delayed or instantaneous) available at the relay, not those at the end users. To the best of our knowledge, our work is the first result regarding how the shared intermittent relay should cooperate with multiple pairs of users in such a two-way cooperative network.

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arXiv2014

Harnessing Bursty Interference in Multicarrier Systems with Feedback

We study parallel symmetric 2-user interference channels when the interference is bursty and feedback is available from the respective receivers. Presence of interference in each subcarrier is modeled as a memoryless Bernoulli random state. The states across subcarriers are drawn from an arbitrary joint distribution with the same marginal probability for each subcarrier and instantiated i.i.d. over time. For the linear deterministic setup, we give a complete characterization of the capacity region. For the setup with Gaussian noise, we give outer bounds and a tight generalized degrees of freedom characterization. We propose a novel helping mechanism which enables subcarriers in very strong interference regime to help in recovering interfered signals for subcarriers in strong and weak interference regimes. Depending on the interference and burstiness regime, the inner bounds either employ the proposed helping mechanism to code across subcarriers or treat the subcarriers separately. The outer bounds demonstrate a connection to a subset entropy inequality by Madiman and Tetali.

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