arXiv subjects

Jizhao Wang

Publications and source records attributed to Jizhao Wang.

2 recordsLinked to original sources

arXiv2026

Digital Self-Interference Cancellation in Full-Duplex Radios: A Fundamental Limit Perspective

D-SIC is of crucial importance for the implementation of IBFD radios. Unfortunately, the achievable performance limit remains underexplored. To fill this gap, in this paper we aim to explore the performance limit, i.e., the minimum residual self-interference (RSI) of the most commonly used PH canceller, and provide the achievable pilot design accordingly. To this end, we first conduct a systematic analysis of the RSI power for the PH canceller, which takes into account both the truncation-induced error and the noise-induced error, whereas the former is usually ignored in the existing works. To simplify the performance analysis of RSI power, we employ the generalized Laguerre polynomial (GLP)-based PH canceller instead of the conventional monomial-based one, due to the appealing orthogonality property of the GLP for Gaussian inputs. With the GLP representation of the PH canceller, we further prove that the least-squares channel estimator is asymptotically unbiased, thus demonstrating the asymptotic optimality of Gaussian pilot sequences. Moreover, for the pilot sequence with a finite length, a succinct criterion for minimizing the RSI, namely, the condition-number-to-minimum eigenvalue ratio (CMER) criterion, which essentially balances the truncation-induced and noise-induced error, is presented. By contrast, the existing works normally consider the latter only. Interestingly, it is revealed that an appropriate PAPR of the pilot sequence is of critical importance to achieve the above balance. Simulation results demonstrate that the pilot sequence optimized according to our proposed CMER criterion can achieve an RSI as low as -87.3 dBm, which is over 14 dB lower than that of HE-LTF and over 6 dB lower than that of the state-of-the-art pilot sequence proposed in [1], provided that the order of the PH canceller is no higher than 9 because of the complexity constraint.

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arXiv2024

HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysis

In pathological research, education, and clinical practice, the decision-making process based on pathological images is critically important. This significance extends to digital pathology image analysis: its adequacy is demonstrated by the extensive information contained within tissue structures, which is essential for accurate cancer classification and grading. Additionally, its necessity is highlighted by the inherent requirement for interpretability in the conclusions generated by algorithms. For humans, determining tumor type and grade typically involves multi-scale analysis, which presents a significant challenge for AI algorithms. Traditional patch-based methods are inadequate for modeling such complex structures, as they fail to capture the intricate, multi-scale information inherent in whole slide images. Consequently, there is a pressing need for advanced AI techniques capable of efficiently and accurately replicating this complex analytical process. To address this issue, we introduce HistoGym, an open-source reinforcement learning environment for histopathological image analysis. Following OpenAI Gym APIs, HistoGym aims to foster whole slide image diagnosis by mimicking the real-life processes of doctors. Leveraging the pyramid feature of WSIs and the OpenSlide API, HistoGym provides a unified framework for various clinical tasks, including tumor detection and classification. We detail the observation, action, and reward specifications tailored for the histopathological image analysis domain and provide an open-source Python-based interface for both clinicians and researchers. To accommodate different clinical demands, we offer various scenarios for different organs and cancers, including both WSI-based and selected region-based scenarios, showcasing several noteworthy results.

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