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Found 18 papers, 4 papers with code
Rephrase and Contrast: Fine-Tuning Language Models for Enhanced Understanding of Communication and Computer Networks
Large language models (LLMs) are being widely researched across various disciplines, with significant recent efforts focusing on adapting LLMs for understanding of how communication networks operate.
GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits
GINO-Q mitigates the curse of dimensionality by decomposing the RMAB into a series of subproblems, each with the same dimension as a single arm, ensuring that complexity increases linearly with the number of arms.
Addressing Out-of-Distribution Challenges in Image Semantic Communication Systems with Multi-modal Large Language Models
Semantic communication is a promising technology for next-generation wireless networks.
LLMind: Orchestrating AI and IoT with LLM for Complex Task Execution
Task-oriented communications are an important element in future intelligent IoT systems.
The Power of Large Language Models for Wireless Communication System Development: A Case Study on FPGA Platforms
We begin by exploring LLM-assisted code refactoring, reuse, and validation, using an open-source software-defined radio (SDR) project as a case study.
Efficient FFT Computation in IFDMA Transceivers
Our experimental results indicate that when the number of hardware processors is a power of two: 1) MPS-FFT has near-optimal computation time; 2) MPS-FFT incurs less than 44. 13\% of the computation time of the conventional pipelined FFT.
Bayesian Over-The-Air Computation
In the low signal-to-noise ratio (SNR) regime, the LMMSE estimator reduces the mean squared error (MSE) by at least 6 dB; in the high SNR regime, the LMMSE estimator lowers the error floor of MSE by 86. 4%; 2) For the asynchronous OAC, our LMMSE and sum-product maximum a posteriori (SP-MAP) estimators are on an equal footing in terms of the MSE performance, and are significantly better than the ML estimator.
Denoising Noisy Neural Networks: A Bayesian Approach with Compensation
Deep neural networks (DNNs) with noisy weights, which we refer to as noisy neural networks (NoisyNNs), arise from the training and inference of DNNs in the presence of noise.
Federated Edge Learning with Misaligned Over-The-Air Computation
Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning.
Flow Sampling: Network Monitoring in Large-Scale Software-Defined IoT Networks
Software-defined Internet-of-Things networking (SDIoT) greatly simplifies the network monitoring in large-scale IoT networks by per-flow sampling, wherein the controller keeps track of all the active flows in the network and samples the IoT devices on each flow path to collect real-time flow statistics.
Multi-Agent Deep Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels
This paper aims to design a distributed deep reinforcement learning (DRL) based MAC protocol for a particular network, and the objective of this network is to achieve a global $\alpha$-fairness objective.
Networking and Internet Architecture
Sporadic Ultra-Time-Critical Crowd Messaging in V2X
To circumvent potential inefficiency arising from sporadicity, we propose an override network architecture whereby warning messages are delivered on the spectrum of the ordinary vehicular messages.
When Blockchain Meets AI: Optimal Mining Strategy Achieved By Machine Learning
Experimental results indicate that, without knowing the parameter values of the mining MDP model, our multi-dimensional RL mining algorithm can still achieve the optimal performance over time-varying blockchain networks.
Cryptography and Security
New Transceiver Designs for Interleaved Frequency Division Multiple Access
For flexible resource allocation, this paper puts forth a new IFDMA resource allocation framework called Multi-IFDMA, in which a user can be allocated multiple IFDMA streams.
Carrier-Sense Multiple Access for Heterogeneous Wireless Networks Using Deep Reinforcement Learning
In particular, in a heterogeneous environment with nodes adopting different MAC protocols (e. g., CS-DLMA, TDMA, and ALOHA), a CS-DLMA node can learn to maximize the sum throughput of all nodes.
Networking and Internet Architecture
AlphaSeq: Sequence Discovery with Deep Reinforcement Learning
We demonstrate the searching capabilities of AlphaSeq in two applications: 1) AlphaSeq successfully rediscovers a set of ideal complementary codes that can zero-force all potential interferences in multi-carrier CDMA systems.
Sparsity Learning Based Multiuser Detection in Grant-Free Massive-Device Multiple Access
In this work, we study the multiuser detection (MUD) problem for a grant-free massive-device multiple access (MaDMA) system, where a large number of single-antenna user devices transmit sporadic data to a multi-antenna base station (BS).
Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks
In particular, the use of neural networks in DRL (as opposed to traditional reinforcement learning) allows for fast convergence to optimal solutions and robustness against perturbation in hyper-parameter settings, two essential properties for practical deployment of DLMA in real wireless networks.
Networking and Internet Architecture
