289 research outputs found
Robust Stackelberg Equilibria
This paper provides a systematic study of the robust Stackelberg equilibrium
(RSE), which naturally generalizes the widely adopted solution concept of the
strong Stackelberg equilibrium (SSE). The RSE accounts for any possible
up-to- suboptimal follower responses in Stackelberg games and is
adopted to improve the robustness of the leader's strategy. While a few
variants of robust Stackelberg equilibrium have been considered in previous
literature, the RSE solution concept we consider is importantly different -- in
some sense, it relaxes previously studied robust Stackelberg strategies and is
applicable to much broader sources of uncertainties.
We provide a thorough investigation of several fundamental properties of RSE,
including its utility guarantees, algorithmics, and learnability. We first show
that the RSE we defined always exists and thus is well-defined. Then we
characterize how the leader's utility in RSE changes with the robustness level
considered. On the algorithmic side, we show that, in sharp contrast to the
tractability of computing an SSE, it is NP-hard to obtain a fully polynomial
approximation scheme (FPTAS) for any constant robustness level. Nevertheless,
we develop a quasi-polynomial approximation scheme (QPTAS) for RSE. Finally, we
examine the learnability of the RSE in a natural learning scenario, where both
players' utilities are not known in advance, and provide almost tight sample
complexity results on learning the RSE. As a corollary of this result, we also
obtain an algorithm for learning SSE, which strictly improves a key result of
Bai et al. in terms of both utility guarantee and computational efficiency
Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing
The limited over-the-air (OTA) pilot symbols in
multiple-input-multiple-output orthogonal-frequency-division-multiplexing
(MIMO-OFDM) systems presents a major challenge for detecting transmitted data
symbols at the receiver, especially for machine learning-based approaches.
While it is crucial to explore effective ways to exploit pilots, one can also
take advantage of the data symbols to improve detection performance. Thus, this
paper introduces an online attention-based approach, namely RC-AttStructNet-DF,
that can efficiently utilize pilot symbols and be dynamically updated with the
detected payload data using the decision feedback (DF) mechanism. Reservoir
computing (RC) is employed in the time domain network to facilitate efficient
online training. The frequency domain network adopts the novel 2D multi-head
attention (MHA) module to capture the time and frequency correlations, and the
structural-based StructNet to facilitate the DF mechanism. The attention loss
is designed to learn the frequency domain network. The DF mechanism further
enhances detection performance by dynamically tracking the channel changes
through detected data symbols. The effectiveness of the RC-AttStructNet-DF
approach is demonstrated through extensive experiments in MIMO-OFDM and massive
MIMO-OFDM systems with different modulation orders and under various scenarios.Comment: Accepted to IEEE Transactions on Communication
Deformable Model-Driven Neural Rendering for High-Fidelity 3D Reconstruction of Human Heads Under Low-View Settings
Reconstructing 3D human heads in low-view settings presents technical
challenges, mainly due to the pronounced risk of overfitting with limited views
and high-frequency signals. To address this, we propose geometry decomposition
and adopt a two-stage, coarse-to-fine training strategy, allowing for
progressively capturing high-frequency geometric details. We represent 3D human
heads using the zero level-set of a combined signed distance field, comprising
a smooth template, a non-rigid deformation, and a high-frequency displacement
field. The template captures features that are independent of both identity and
expression and is co-trained with the deformation network across multiple
individuals with sparse and randomly selected views. The displacement field,
capturing individual-specific details, undergoes separate training for each
person. Our network training does not require 3D supervision or object masks.
Experimental results demonstrate the effectiveness and robustness of our
geometry decomposition and two-stage training strategy. Our method outperforms
existing neural rendering approaches in terms of reconstruction accuracy and
novel view synthesis under low-view settings. Moreover, the pre-trained
template serves a good initialization for our model when encountering unseen
individuals.Comment: Accepted by ICCV2023. Visit our project page at
https://github.com/xubaixinxbx/3dhead
Learning to Estimate: A Real-Time Online Learning Framework for MIMO-OFDM Channel Estimation
In this paper we introduce StructNet-CE, a novel real-time online learning
framework for MIMO-OFDM channel estimation, which only utilizes over-the-air
(OTA) pilot symbols for online training and converges within one OFDM subframe.
The design of StructNet-CE leverages the structure information in the MIMO-OFDM
system, including the repetitive structure of modulation constellation and the
invariant property of symbol classification to inter-stream interference. The
embedded structure information enables StructNet-CE to conduct channel
estimation with a binary classification task and accurately learn channel
coefficients with as few as two pilot OFDM symbols. Experiments show that the
channel estimation performance is significantly improved with the incorporation
of structure knowledge. StructNet-CE is compatible and readily applicable to
current and future wireless networks, demonstrating the effectiveness and
importance of combining machine learning techniques with domain knowledge for
wireless communication systems
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