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Found 10 papers, 2 papers with code
Distributed Deep Joint Source-Channel Coding with Decoder-Only Side Information
We consider low-latency image transmission over a noisy wireless channel when correlated side information is present only at the receiver side (the Wyner-Ziv scenario).
Precoding-oriented Massive MIMO CSI Feedback Design
Downlink massive multiple-input multiple-output (MIMO) precoding algorithms in frequency division duplexing (FDD) systems rely on accurate channel state information (CSI) feedback from users.
Feature Compression for Rate Constrained Object Detection on the Edge
In this work, we consider a "split computation" system to offload a part of the computation of the YOLO object detection model.
Understanding Energy Efficiency and Interference Tolerance in Millimeter Wave Receivers
Power consumption is a key challenge in millimeter wave (mmWave) receiver front-ends, due to the need to support high dimensional antenna arrays at wide bandwidths.
Access Delay Constrained Activity Detection in Massive Random Access
Comparison with 3GPP standardized random access protocol for NB-IoT indicates the superiority of our proposed strategy in terms of access delay and overall resource utilization.
Sparse Activity Discovery in Energy Constrained Multi-Cluster IoT Networks Using Group Testing
Current IoT networks are characterized by an ultra-high density of devices with different energy budget constraints, typically having sparse and sporadic activity patterns.
On Single-User Interactive Beam Alignment in Next Generation Systems: A Deep Learning Viewpoint
Since the BS measurements are noisy, it is not possible to find a narrow beam that includes the angle of arrival (AoA) of the user with probability one.
Interference Reduction in Virtual Cell Optimization
Our numerical results show that our scheme decreases the number of users in the system whose rate falls below the guaranteed rate, set to $128$kbps, $256$kbps or $512$kbps, when compared with our previously proposed optimization methods.
Rényi Entropy Bounds on the Active Learning Cost-Performance Tradeoff
Semi-supervised classification, one of the most prominent fields in machine learning, studies how to combine the statistical knowledge of the often abundant unlabeled data with the often limited labeled data in order to maximize overall classification accuracy.
SafePredict: A Meta-Algorithm for Machine Learning That Uses Refusals to Guarantee Correctness
Allowing refusals means that the meta-algorithm may refuse to emit a prediction produced by the base algorithm on occasion so that the error rate on non-refused predictions does not exceed $\epsilon$.
