11 citations · 23 across the 7 of their papers we have counts for
9 papers
Energy-Efficient Timely Relevant IoT Data Retrieval with Wireless Memory Approximation
Junya Shiraishi, Zihang Song, Morten Andersen +1
Collecting relevant data from Internet of Things devices under freshness and energy constraints is a key challenge in 6G edge networks. A lightweight neural network on the device f…
Conformal Decode-or-Erase: Certified Spiking Decoding for Short-Packet URLLC
Zihang Song, Kai Yu, Anders E. Kalør +1
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 befor…
Neuromorphic Non-Orthogonal Multiple Access for Parallel Remote Inference via Vector Symbolic Architecture
Jiechen Chen, Zihang Song, Dengyu Wu +2
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 s…
CSI-Free Symbol Detection for Atomic MIMO Receivers via In-Context Learning
Zihang Song, Qihao Peng, Pei Xiao +2
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 config…
In-Context Learning for MIMO Turbo Receivers
Zihang Song, Matteo Zecchin, Bipin Rajendran +2
Multiple-input multiple-output (MIMO) turbo receivers iteratively exchange information between a soft-input soft-output detector and a decoder. Traditional implementations rely on…
In-Context Learned Equalization in Cell-Free Massive MIMO via State-Space Models
Zihang Song, Matteo Zecchin, Bipin Rajendran +1
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…
Xpikeformer: Hybrid Analog-Digital Hardware Acceleration for Spiking Transformers
Zihang Song, Prabodh Katti, Osvaldo Simeone +1
The integration of neuromorphic computing and transformers through spiking neural networks (SNNs) offers a promising path to energy-efficient sequence modeling, with the potential…
Neuromorphic In-Context Learning for Energy-Efficient MIMO Symbol Detection
Zihang Song, Osvaldo Simeone, Bipin Rajendran
In-context learning (ICL), a property demonstrated by transformer-based sequence models, refers to the automatic inference of an input-output mapping based on examples of the mappi…
Stochastic Spiking Attention: Accelerating Attention with Stochastic Computing in Spiking Networks
Zihang Song, Prabodh Katti, Osvaldo Simeone +1
Spiking Neural Networks (SNNs) have been recently integrated into Transformer architectures due to their potential to reduce computational demands and to improve power efficiency.…