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Found 24 papers, 6 papers with code
Process-and-Forward: Deep Joint Source-Channel Coding Over Cooperative Relay Networks
In the proposed scheme, the source transmits information in blocks, and the relay updates its knowledge about the input signal after each block and generates its own signal to be conveyed to the destination.
DEEP-IoT: Downlink-Enhanced Efficient-Power Internet of Things
At the heart of the Internet of Things (IoT) -- a domain witnessing explosive growth -- the imperative for energy efficiency and the extension of device lifespans has never been more pressing.
Point Cloud in the Air
Acquisition and processing of point clouds (PCs) is a crucial enabler for many emerging applications reliant on 3D spatial data, such as robot navigation, autonomous vehicles, and augmented reality.
LLMind: Orchestrating AI and IoT with LLM for Complex Task Execution
The exploration of large language models (LLMs) for task planning and IoT automation has recently gained significant attention.
A Hybrid Joint Source-Channel Coding Scheme for Mobile Multi-hop Networks
To this end, we propose a hybrid solution, where DeepJSCC is adopted for the first hop, while the received signal at the first relay is digitally compressed and forwarded through the mobile core network.
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.
Wireless Point Cloud Transmission
3D point cloud is a three-dimensional data format generated by LiDARs and depth sensors, and is being increasingly used in a large variety of applications.
DeepJSCC-l++: Robust and Bandwidth-Adaptive Wireless Image Transmission
This paper presents a novel vision transformer (ViT) based deep joint source channel coding (DeepJSCC) scheme, dubbed DeepJSCC-l++, which can be adaptive to multiple target bandwidth ratios as well as different channel signal-to-noise ratios (SNRs) using a single model.
Feedback is Good, Active Feedback is Better: Block Attention Active Feedback Codes
Deep neural network (DNN)-assisted channel coding designs, such as low-complexity neural decoders for existing codes, or end-to-end neural-network-based auto-encoder designs are gaining interest recently due to their improved performance and flexibility; particularly for communication scenarios in which high-performing structured code designs do not exist.
Space-time design for deep joint source channel coding of images Over MIMO channels
We propose novel deep joint source-channel coding (DeepJSCC) algorithms for wireless image transmission over multi-input multi-output (MIMO) Rayleigh fading channels, when channel state information (CSI) is available only at the receiver.
Decentralized Channel Management in WLANs with Graph Neural Networks
Wireless local area networks (WLANs) manage multiple access points (APs) and assign scarce radio frequency resources to APs for satisfying traffic demands of associated user devices.
Semantic Communications with Discrete-time Analog Transmission: A PAPR Perspective
Recent progress in deep learning (DL)-based joint source-channel coding (DeepJSCC) has led to a new paradigm of semantic communications.
Learning-based Autonomous Channel Access in the Presence of Hidden Terminals
We consider the problem of autonomous channel access (AutoCA), where a group of terminals tries to discover a communication strategy with an access point (AP) via a common wireless channel in a distributed fashion.
All you need is feedback: Communication with block attention feedback codes
Deep learning based channel code designs have recently gained interest as an alternative to conventional coding algorithms, particularly for channels for which existing codes do not provide effective solutions.
AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications
The training methods can potentially be generalized to other wireless communication applications with machine learning.
Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs
As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency.
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.
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.
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.
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.
