arXiv subjects

Qipeng Wang

Publications and source records attributed to Qipeng Wang.

22 records · Page 2Linked to original sources

arXiv2022

Exploiting Temporal Side Information in Massive IoT Connectivity

This paper considers the joint device activity detection and channel estimation problem in a massive Internet of Things (IoT) connectivity system, where a large number of IoT devices exist but merely a random subset of them become active for short-packet transmission in each coherence block. In particular, we propose to leverage the temporal correlation in device activity, e.g., a device active in the previous coherence block is more likely to be still active in the current coherence block, to improve the detection and estimation performance. However, it is challenging to utilize this temporal correlation as side information (SI), which relies on the knowledge about the exact statistical relation between the estimated activity pattern for the previous coherence block (which may be imperfect with unknown error) and the true activity pattern in the current coherence block. To tackle this challenge, we establish a novel SI-aided multiple measurement vector approximate message passing (MMV-AMP) framework. Specifically, thanks to the state evolution of the MMV-AMP algorithm, the correlation between the activity pattern estimated by the MMV-AMP algorithm in the previous coherence block and the real activity pattern in the current coherence block is quantified explicitly. Based on the well-defined temporal correlation, we further manage to embed this useful SI into the denoiser design under the MMV-AMP framework. Specifically, the SI-based soft-thresholding denoisers with binary thresholds and the SI-based minimum mean-squared error (MMSE) denoisers are characterized for the cases without and with the knowledge of the channel distribution, respectively. Numerical results are given to show the significant gain in device activity detection and channel estimation performance brought by our proposed SI-aided MMV-AMP framework.

eess.SP↗

arXiv2021

On Massive IoT Connectivity with Temporally-Correlated User Activity

This paper considers joint device activity detection and channel estimation in Internet of Things (IoT) networks, where a large number of IoT devices exist but merely a random subset of them become active for short-packet transmission at each time slot. In particular, we propose to leverage the temporal correlation in user activity, i.e., a device active at the previous time slot is more likely to be still active at the current moment, to improve the detection performance. Despite the temporally-correlated user activity in consecutive time slots, it is challenging to unveil the connection between the activity pattern estimated previously, which is imperfect but the only available side information (SI), and the true activity pattern at the current moment due to the unknown estimation error. In this work, we manage to tackle this challenge under the framework of approximate message passing (AMP). Specifically, thanks to the state evolution, the correlation between the activity pattern estimated by AMP at the previous time slot and the real activity pattern at the previous and current moment is quantified explicitly. Based on the well-defined temporal correlation, we further manage to embed this useful SI into the design of the minimum mean-squared error (MMSE) denoisers and log-likelihood ratio (LLR) test based activity detectors under the AMP framework. Theoretical comparison between the SI-aided AMP algorithm and its counterpart without utilizing temporal correlation is provided. Moreover, numerical results are given to show the significant gain in activity detection accuracy brought by the SI-aided algorithm.

cs.IT↗

arXiv2021

Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data

Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm, drawing tremendous attention in both academia and industry. A unique characteristic of FL is heterogeneity, which resides in the various hardware specifications and dynamic states across the participating devices. Theoretically, heterogeneity can exert a huge influence on the FL training process, e.g., causing a device unavailable for training or unable to upload its model updates. Unfortunately, these impacts have never been systematically studied and quantified in existing FL literature. In this paper, we carry out the first empirical study to characterize the impacts of heterogeneity in FL. We collect large-scale data from 136k smartphones that can faithfully reflect heterogeneity in real-world settings. We also build a heterogeneity-aware FL platform that complies with the standard FL protocol but with heterogeneity in consideration. Based on the data and the platform, we conduct extensive experiments to compare the performance of state-of-the-art FL algorithms under heterogeneity-aware and heterogeneity-unaware settings. Results show that heterogeneity causes non-trivial performance degradation in FL, including up to 9.2% accuracy drop, 2.32x lengthened training time, and undermined fairness. Furthermore, we analyze potential impact factors and find that device failure and participant bias are two potential factors for performance degradation. Our study provides insightful implications for FL practitioners. On the one hand, our findings suggest that FL algorithm designers consider necessary heterogeneity during the evaluation. On the other hand, our findings urge system providers to design specific mechanisms to mitigate the impacts of heterogeneity.

cs.LG↗

arXiv2020

Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints

We study the combinatorial sleeping multi-armed semi-bandit problem with long-term fairness constraints~(CSMAB-F). To address the problem, we adopt Thompson Sampling~(TS) to maximize the total rewards and use virtual queue techniques to handle the fairness constraints, and design an algorithm called \emph{TS with beta priors and Bernoulli likelihoods for CSMAB-F~(TSCSF-B)}. Further, we prove TSCSF-B can satisfy the fairness constraints, and the time-averaged regret is upper bounded by $\frac{N}{2η} + O\left(\frac{\sqrt{mNT\ln T}}{T}\right)$, where $N$ is the total number of arms, $m$ is the maximum number of arms that can be pulled simultaneously in each round~(the cardinality constraint) and $η$ is the parameter trading off fairness for rewards. By relaxing the fairness constraints (i.e., let $η\rightarrow \infty$), the bound boils down to the first problem-independent bound of TS algorithms for combinatorial sleeping multi-armed semi-bandit problems. Finally, we perform numerical experiments and use a high-rating movie recommendation application to show the effectiveness and efficiency of the proposed algorithm.

cs.LG↗