arXiv subjects

Zihang Song

Publications and source records attributed to Zihang Song.

At least 19 recordsLinked to original sources

arXiv2026

Conformal Decode-or-Erase: Certified Spiking Decoding for Short-Packet URLLC

Ultra-reliable low-latency communication (URLLC) must deliver short packets within a hard deadline at low error probability. A conventional receiver waits for the full packet before deciding, spending the full latency and energy even though many packets are resolvable well before the deadline. Committing early without a reliability guarantee, however, risks a silent wrong delivery, so the receiver is left choosing between wasted resources and uncontrolled errors. We propose Conformal Decode-or-Erase (CoDE), a spiking neural network (SNN) receiver that resolves this tension. The SNN reads one symbol per channel use and forms, at predetermined checkpoints, a set of candidate messages that provably contains the true one with a prescribed probability. CoDE commits once the set narrows to a singleton and otherwise declares an erasure that triggers hybrid automatic repeat request (HARQ) retransmission. A wrong commit means the true message fell outside that singleton. Hence, the prediction set provides an upper bound on the undetected error rate in a distribution-free manner and for any pretrained SNN and any calibration size. Simulations confirm reliability at roughly half a fixed-length decoder's latency and compute.

eess.SP↗

arXiv2026

Neuromorphic Non-Orthogonal Multiple Access for Parallel Remote Inference via Vector Symbolic Architecture

Emerging edge intelligence systems increasingly rely on dense deployments of always-on sensors that must convey task-relevant information to a remote model under tight energy and spectral budgets. The deployment of event-driven neuromorphic sensing paired with spiking neural networks (SNNs) is attractive in this regime because it produces dynamically sparse representations, so that energy is spent on communication and computation only when informative events occur. Prior multiple-access protocols for remote inference using neuromorphic sensing and computing targeted collaborative settings, in which the server fuses information from all devices into a single decision. This paper instead addresses parallel remote inference, in which each device observes a distinct input, and requires its own classification decision. We propose NOMA-NC, a non-orthogonal multiple-access (NOMA) neuromorphic communication (NC) protocol built on the vector symbolic architecture (VSA) framework. In NOMA-NC, each device binds its sparse spike feature map with a device-specific permutation key, and all devices in a group transmit concurrently so that the over-the-air superposition directly realizes the VSA bundling operation. A shared decoding SNN, together with lightweight per-device learned unbinding, recovers all decisions in a single inference pass. Experiments on the N-MNIST and DVS128 Gesture datasets show that NOMA-NC yields goodput gains and savings in terms of receiver computing energy that are sub-proportional to the number of simultaneously active devices, without increasing the per-device transmission energy.

eess.SP↗

arXiv2026

Sizable Ligand-Mediated Bond-Dependent Interactions in a Spin-1 Triangular Antiferromagnet NiI$_2$

The bond-dependent anisotropic Kitaev interactions are the key for the Kitaev model, which has attracted intense interest for its potential to host quantum-spin-liquid states and fractional excitations. However, experimental realizations of such interactions remain scarce. Here, we investigate the magnetic excitations of NiI$_2$, a van der Waals magnet with spin $S=1$. By combining inelastic neutron scattering, magnetization measurements, magnetic structure analysis, first-principles calculations, and linear-spin-wave simulations, we identify a minimal model that features substantial Kitaev and off-diagonal $\Gamma$ interactions, which together stabilize the canted magnetic ground state and open a gap in the spin-wave spectrum. Notably, these interactions arise from strong spin-orbit coupling on the ligand ions, despite the quenched orbital moment of the magnetic Ni$^{2+}$ ions. Our results provide compelling experimental evidence for the ligand-driven Kitaev mechanism. This demonstrates a concrete pathway to generating strong bond-dependent anisotropy in systems where the magnetic ions themselves have weak spin-orbit coupling, thereby substantially broadening the range of potential Kitaev materials.

cond-mat.str-el↗

arXiv2026

Magnetic Signature of Chiral Phonons Revealed by Neutron Spectroscopy in Ferrimagnetic Fe$_{1.75}$Zn$_{0.25}$Mo$_3$O$_8$

Lattice vibrations can carry angular momentum and magnetic moments under broken inversion or time-reversal symmetry, forming so-called chiral phonons. While such excitations have been explored in nonmagnetic systems via optical probes, their direct detection in magnetic materials and coupling to spin excitations remain largely unexplored. Here, using neutron spectroscopy, sensitive to both nuclear and magnetic scattering, we reveal the magnetic signature of chiral phonons in ferrimagnetic Fe$_{1.75}$Zn$_{0.25}$Mo$_3$O$_8$ with Curie temperature $T_{\rm C}\sim49$ K. Below $T_{\rm C}$, we observe enhanced magnetic scattering of phonons at small momenta, arising from strong magnon-phonon coupling. In addition, out-of-plane intensity modulation, phonon mode splitting, and field-induced Zeeman shifts are observed, all closely associated with the ferrimagnetic order. These features vanish above $T_{\rm C}$, where phonon spectra are dominated by nuclear scattering. These observations demonstrate the existence of chiral phonons carrying substantial magnetic moments that directly contribute to magnetic scattering, and establish neutron spectroscopy as a powerful, momentum-resolved probe of their magnetic character.

cond-mat.str-el↗

arXiv2025

Enhanced Ground-Satellite Direct Access via Onboard Rydberg Atomic Quantum Receivers

Ground-satellite links for 6G networks face critical challenges, including severe path loss, tight size-weight-power limits, and congested spectrum, all of which significantly hinder the performance of traditional radio frequency (RF) front ends. This article introduces the Rydberg Atomic Quantum Receiver (RAQR) for onboard satellite systems, a millimeter-scale front end that converts radio fields to optical signals through atomic electromagnetically induced transparency. RAQR's high sensitivity and high frequency selectivity address link budget, payload, and interference challenges while fitting within space constraints. A hybrid atomic-electronic design and supporting signal model demonstrate enhanced data rate, coverage, and sensing accuracy relative to conventional RF receivers. The article concludes with integration strategies, distributed-satellite concepts, and open research problems for bringing RAQR-enabled satellite payloads into service.

eess.SY↗

arXiv2025

Rydberg Atomic Quantum Satellites for Enhanced Ground-to-Space Direct Uplink Access

This paper investigates the performance advantages of Rydberg atomic quantum (RAQ)-based multiple-input multiple-output (MIMO) satellites for enhancing direct ground-to-space uplink access.We analytically evaluate the impact of Rydberg atoms on channel estimation by deriving closed-form expressions for the mean-square error (MSE) and normalized mean-square error (NMSE). Based on the estimated channels, we further derive lower bounds on the achievable data rates for maximum ratio combining (MRC) and zero-forcing (ZF) detection schemes. Rigorous analysis demonstrates that RAQ-MIMO outperforms conventional radio-frequency (RF) MIMO under both Rayleigh and satellite channel conditions. Specifically, compared with conventional MIMO, RAQR achieves a ``squaring" gain under Rayleigh fading, especially in long-distance transmission scenarios with stringent power constraints. In contrast, under line-of-sight (LoS)-dominated satellite channels, this gain saturates as channel-estimation benefits diminish, with the remaining improvement primarily arising from the normalized noise background. Monte Carlo simulations validate the analytical results and show that the performance gains of RAQ-MIMO satellites translate into smaller antenna apertures, lower transmit power, and longer communication ranges, thereby paving the way for next-generation satellite networks.

eess.SP↗

arXiv2025

Disorder-induced spin excitation continuum and spin-glass ground state in the inverse spinel CuGa$_2$O$_4$

Spinel-structured compounds serve as prototypical examples of highly frustrated systems, and are promising candidates for realizing the long-sought quantum spin liquid (QSL) state. However, structural disorder is inevitable in many real QSL candidates and its impact remains a topic of intense debate. In this work, we conduct comprehensive investigations on CuGa$_2$O$_4$, a spinel compound with significant structural disorder, focusing on its thermodynamic properties and spectroscopic behaviors. No long-range magnetic order is observed down to $\sim$80 mK, as evidenced by magnetic susceptibility, specific heat and elastic neutron scattering measurements. More intriguingly, inelastic neutron scattering experiments reveal a broad gapless continuum of magnetic excitations around the Brillouin zone boundary, resembling the magnetic excitation spectra expected for a QSL. Nevertheless, a spin-freezing transition at $T_{\rm{f}} \approx $ 0.88 K is identified from the cusp in the dc susceptibility curves, where a bifurcation between zero-field-cooling and field-cooling curves occurs. Furthermore, ac susceptibility measurements show a peak close to $T_{\rm{f}}$ at low frequency, which shifts to higher temperature with increasing frequency. These results are evident that CuGa$_2$O$_4$ has spin-glass ground state, consistent with the establishment of short-range order inferred from the specific heat measurements. Collectively, these results illustrate the crucial role of disorder in defining the excitation spectrum out of the disordered ground state. Our findings shed light onto the broader class of AB$_2$O$_4$ spinels and advance our understanding of the spin dynamics in magnetically disordered systems.

cond-mat.str-el↗

arXiv2025

Neuromorphic Split Computing via Optical Inter-Satellite Links

We present a neuromorphic split-computing framework for energy-efficient low-latency inference over optical inter-satellite links. The system partitions a spiking neural network (SNN) between edge and core nodes. To transmit sparse spiking features efficiently, we introduce a lossless channel-block-sparse event representation that exploits inter- and intra-channel sparsity. We employ hierarchical error protection using multi-level forward error correction and cyclic redundancy checks to ensure reliable communication without retransmission. The framework uses end-to-end training with sparsity and clustering regularizers, combined with channel-aware stochastic masking to optimize feature compression and channel robustness jointly. In a proof-of-concept implementation on remote sensing imagery, the framework achieves over $10 \times$ reduction in both computational energy and transmission load compared to conventional dense split systems, with less than 1% accuracy loss. The proposed approach also outperforms address-event-based split SNNs by $3.7 \times$ in transmission efficiency and shows superior resilience to optical pointing jitter.

eess.IV↗

arXiv2025

CSI-Free Symbol Detection for Atomic MIMO Receivers via In-Context Learning

Atomic receivers based on Rydberg vapor cells as sensors of electromagnetic fields offer a promising alternative to conventional radio frequency front-ends. In multi-antenna configurations, the magnitude-only, phase-insensitive measurements produced by atomic receivers pose challenges for traditional detection methods. Existing solutions rely on two-step iterative optimization processes, which suffer from cascaded channel estimation errors and high computational complexity. We propose a channel state information (CSI)-free symbol detection method based on in-context learning (ICL), which directly maps pilot-response pairs to data symbol predictions without explicit channel estimation. Simulation results show that ICL achieves competitive accuracy with {higher computational efficiency} compared to existing solutions.

eess.SP↗

arXiv2025

Turbo-ICL: In-Context Learning-Based Turbo Equalization

This paper introduces a novel in-context learning (ICL) framework, inspired by large language models (LLMs), for soft-input soft-output channel equalization in coded multiple-input multiple-output (MIMO) systems. The proposed approach learns to infer posterior symbol distributions directly from a prompt of pilot signals and decoder feedback. A key innovation is the use of prompt augmentation to incorporate extrinsic information from the decoder output as additional context, enabling the ICL model to refine its symbol estimates iteratively across turbo decoding iterations. Two model variants, based on Transformer and state-space architectures, are developed and evaluated. Extensive simulations demonstrate that, when traditional linear assumptions break down, e.g., in the presence of low-resolution quantization, ICL equalizers consistently outperform conventional model-based baselines, even when the latter are provided with perfect channel state information. Results also highlight the advantage of Transformer-based models under limited training diversity, as well as the efficiency of state-space models in resource-constrained scenarios.

eess.SP↗

arXiv2025

Significant Chiral Magnetotransport Magnified by Multiple Weyl Nodes

The intertwining of magnetism with topology is known to give rise to exotic quantum phenomena. Here, we explore the magnetotransport properties of NdAlSi, a magnetic Weyl semimetal that spontaneously breaks inversion and time-reversal symmetries and hosts a large number of Weyl nodes. We observe a significant negative magnetoresistance, which we attribute to the chiral anomaly associated with multiple Weyl nodes. Remarkably, the extracted chiral coefficient reaches approximately $52~\mathrm{m\Omega}^{-1}~\mathrm{m}^{-1}~\mathrm{T}^{-2}$, larger than many other topological materials. Additionally, we observe an exotic anomalous Hall effect with an out-of-sync behavior, where the anomalous Hall resistance does not exactly follow the field dependence of the magnetization, in contrast to that in conventional ferromagnets. These rich quantum transport phenomena, driven by the interplay between magnetism and Weyl nodes, establish NdAlSi as a prime platform for exploring the intricate topological behaviors of magnetic Weyl semimetals.

cond-mat.mes-hall↗

arXiv2025

Magnetic Interactions in the Polar Ferrimagnet with a Bipartite Structure

The polar magnets A$_2$Mo$_3$O$_8$ (A=Fe, Mn, Co, and Ni) feature a bipartite structure, where the magnetic A$^{2+}$ ions occupy two different sites with octahedral and tetrahedral oxygen coordinations. This bipartite structure provides a platform for the emergence of nontrivial magnetoelectric (ME) effects and intriguing excitation behaviors, and thus creates significant research interest. In this study, we conduct inelastic neutron scattering measurements on single crystals of Mn$_2$Mo$_3$O$_8$, an L-type ferrimagnet in the A$_2$Mo$_3$O$_8$ family, to investigate its spin dynamics. The obtained magnetic excitation spectra reveal two distinct magnon dispersions corresponding to the octahedral and tetrahedral spins in Mn$_2$Mo$_3$O$_8$. These magnon bands can be well described by a spin Hamiltonian including Heisenberg and single-ion anisotropy terms. Employing our effective spin model, we successfully reproduce the unusual temperature dependence of the L-type ferrimagnetic susceptibility through self-consistent mean-field theory. This research reveals the significance of the bipartite structure in determining the excitation properties of the polar magnets $\rm{A_{2}Mo_{3}O_{8}}$ and provides valuable insights into the spin dynamics of L-type ferrimagnets.

cond-mat.str-el↗

arXiv2024

Compressive Spectrum Sensing with 1-bit ADCs

Efficient wideband spectrum sensing (WSS) is essential for managing spectrum scarcity in wireless communications. However, existing compressed sensing (CS)-based WSS methods require high sampling rates and power consumption, particularly with high-precision analog-to-digital converters (ADCs). Although 1-bit CS with low-precision ADCs can mitigate these demands, most approaches still depend on multi-user cooperation and prior sparsity information, which are often unavailable in WSS scenarios. This paper introduces a non-cooperative WSS method using multicoset sampling with 1-bit ADCs to achieve sub-Nyquist sampling without requiring sparsity knowledge. We analyze the impact of 1-bit quantization on multiband signals, then apply eigenvalue decomposition to isolate the signal subspace from noise, enabling spectrum support estimation without signal reconstruction. This approach provides a power-efficient solution for WSS that eliminates the need for cooperation and prior information.

eess.SP↗

arXiv2024

In-Context Learned Equalization in Cell-Free Massive MIMO via State-Space Models

Sequence models have demonstrated the ability to perform tasks like channel equalization and symbol detection by automatically adapting to current channel conditions. This is done without requiring any explicit optimization and by leveraging not only short pilot sequences but also contextual information such as long-term channel statistics. The operating principle underlying automatic adaptation is in-context learning (ICL), an emerging property of sequence models. Prior art adopted transformer-based sequence models, which, however, have a computational complexity scaling quadratically with the context length due to batch processing. Recently, state-space models (SSMs) have emerged as a more efficient alternative, affording a linear inference complexity in the context size. This work explores the potential of SSMs for ICL-based equalization in cell-free massive MIMO systems. Results show that selective SSMs achieve comparable performance to transformer-based models while requiring approximately eight times fewer parameters and five times fewer floating-point operations.

eess.SP↗

arXiv2024

GBSense: A GHz-Bandwidth Compressed Spectrum Sensing System

This paper presents GBSense, an innovative compressed spectrum sensing system designed for GHz-bandwidth signals in dynamic spectrum access (DSA) applications. GBSense introduces an efficient approach to periodic nonuniform sampling, capturing wideband signals using significantly lower sampling rates compared to traditional Nyquist sampling. By integrating time-interleaved analog-to-digital conversion, GBSense overcomes the hardware complexity typically associated with traditional multicoset sampling, providing precise, real-time adjustable sampling patterns without the need for analog delay circuits. The system's ability to process signals with a 2 GHz radio frequency bandwidth using only a 400 MHz average sampling rate enables more efficient spectrum monitoring and access in wideband cognitive radios. Lab tests demonstrate 100\% accurate spectrum detection when the spectrum occupancy is below 100 MHz and over 80\% accuracy for occupancy up to 200 MHz. Additionally, an integrated system utilizing a low-power Raspberry Pi processor achieves a low processing latency of around 30 ms per frame, demonstrating the system's potential for real-time applications in cognitive radio networks, 5G, and future 6G infrastructures.

eess.SP↗

arXiv2024

Xpikeformer: Hybrid Analog-Digital Hardware Acceleration for Spiking Transformers

The integration of neuromorphic computing and transformers through spiking neural networks (SNNs) offers a promising path to energy-efficient sequence modeling, with the potential to overcome the energy-intensive nature of the artificial neural network (ANN)-based transformers. However, the algorithmic efficiency of SNN-based transformers cannot be fully exploited on GPUs due to architectural incompatibility. This paper introduces Xpikeformer, a hybrid analog-digital hardware architecture designed to accelerate SNN-based transformer models. The architecture integrates analog in-memory computing (AIMC) for feedforward and fully connected layers, and a stochastic spiking attention (SSA) engine for efficient attention mechanisms. We detail the design, implementation, and evaluation of Xpikeformer, demonstrating significant improvements in energy consumption and computational efficiency. Through image classification tasks and wireless communication symbol detection tasks, we show that Xpikeformer can achieve inference accuracy comparable to the GPU implementation of ANN-based transformers. Evaluations reveal that Xpikeformer achieves $13\times$ reduction in energy consumption at approximately the same throughput as the state-of-the-art (SOTA) digital accelerator for ANN-based transformers. Additionally, Xpikeformer achieves up to $1.9\times$ energy reduction compared to the optimal digital ASIC projection of SOTA SNN-based transformers.

cs.AR↗

arXiv2024

SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensing

Low Earth Orbit satellite Internet has recently been deployed, providing worldwide service with non-terrestrial networks. With the large-scale deployment of both non-terrestrial and terrestrial networks, limited spectrum resources will not be allocated enough. Consequently, dynamic spectrum sharing is crucial for their coexistence in the same spectrum, where accurate spectrum sensing is essential. However, spectrum sensing in space is more challenging than in terrestrial networks due to variable channel conditions, making single-satellite sensing unstable. Therefore, we first attempt to design a collaborative sensing scheme utilizing diverse data from multiple satellites. However, it is non-trivial to achieve this collaboration due to heterogeneous channel quality, considerable raw sampling data, and packet loss. To address the above challenges, we first establish connections between the satellites by modeling their sensing data as a graph and devising a graph neural network-based algorithm to achieve effective spectrum sensing. Meanwhile, we establish a joint sub-Nyquist sampling and autoencoder data compression framework to reduce the amount of transmitted sensing data. Finally, we propose a contrastive learning-based mechanism compensates for missing packets. Extensive experiments demonstrate that our proposed strategy can achieve efficient spectrum sensing performance and outperform the conventional deep learning algorithm in spectrum sensing accuracy.

cs.NI↗

arXiv2024

Sums: Sniffing Unknown Multiband Signals under Low Sampling Rates

Due to sophisticated deployments of all kinds of wireless networks (e.g., 5G, Wi-Fi, Bluetooth, LEO satellite, etc.), multiband signals distribute in a large bandwidth (e.g., from 70 MHz to 8 GHz). Consequently, for network monitoring and spectrum sharing applications, a sniffer for extracting physical layer information, such as structure of packet, with low sampling rate (especially, sub-Nyquist sampling) can significantly improve their cost- and energy-efficiency. However, to achieve a multiband signals sniffer is really a challenge. To this end, we propose Sums, a system that can sniff and analyze multiband signals in a blind manner. Our Sums takes advantage of hardware and algorithm co-design, multi-coset sub-Nyquist sampling hardware, and a multi-task deep learning framework. The hardware component breaks the Nyquist rule to sample GHz bandwidth, but only pays for a 50 MSPS sampling rate. Our multi-task learning framework directly tackles the sampling data to perform spectrum sensing, physical layer protocol recognition, and demodulation for deep inspection from multiband signals. Extensive experiments demonstrate that Sums achieves higher accuracy than the state-of-theart baselines in spectrum sensing, modulation classification, and demodulation. As a result, our Sums can help researchers and end-users to diagnose or troubleshoot their problems of wireless infrastructures deployments in practice.

cs.AR↗