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

Digital self-interference cancellation (D-SIC) plays a crucial role in in-band full-duplex radios. Unfortunately, its fundamental limit remains unclear. In this paper, we aim to address this problem by exploring the performance limit of the parallel Hammerstein (PH) canceller for D-SIC, which is most commonly used in practice. First, a comprehensive analysis of the power of the residual self-interference (RSI) after the PH canceller with the least squares (LS) estimator is provided, which takes into account the truncation error, reconstruction error and transmitter noise. Specifically, the analysis is greatly simplified by equivalently expanding the PH canceller via generalized Laguerre polynomials (GLP), which enjoys the desirable property of mutual orthogonality among the basis functions. As a by-product of this orthogonal expansion, we establish that the LS estimator for the weights of the GLP canceller is asymptotically \textit{unbiased}, if the pilot sequence is Gaussian distributed. Second, in order to minimize the reconstruction error of the PH canceller, we propose a succinct criterion for optimizing the pilot sequence, which essentially seeks for small eigenvalue spread and large minimum eigenvalue of the Gram matrix corresponding to the pilot sequence. Specifically, the criterion is to minimize the product of the Shannon rank, an effective rank of a positive semidefinite matrix and the minimum eigenvalue of the Gram matrix. Simulation results demonstrate that with the optimized pilot sequence of a single OFDM symbol, over 10 dB gain can be achieved compared to the conventional pilot sequence (HE-LTF) for the PH canceller, and the corresponding RSI can be as low as -87.6 dBm.

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.