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Found 11 papers, 1 papers with code
Random Aggregate Beamforming for Over-the-Air Federated Learning in Large-Scale Networks
Extensive simulation results are presented to demonstrate the effectiveness of the proposed random aggregate beamforming-based scheme as well as the refined method.
In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks
Owing to the ubiquity of cellular communication signals, positioning with the fifth generation (5G) signal has emerged as a promising solution in global navigation satellite system-denied areas.
Fast Direct Localization for Millimeter Wave MIMO Systems via Deep ADMM Unfolding
Next, we develop a deep ADMM unfolding network (DAUN) to learn the ADMM parameter settings from the training data and a position refinement algorithm is proposed for DAUN.
Link-level simulator for 5G localization
Channel-state-information-based localization in 5G networks has been a promising way to obtain highly accurate positions compared to previous communication networks.
Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks
In this article, we propose an unsupervised and privacy-preserving popularity prediction framework for MEC-enabled IIoT.
Efficient Joint DOA and TOA Estimation for Indoor Positioning with 5G Picocell Base Stations
Secondly, based on the deployment reality that 5G picocell gNBs only have a small-scale antenna array but have a large signal bandwidth, the proposed scheme decouples the estimation of time-of-arrival (TOA) and direction-of-arrival (DOA) to reduce the huge complexity induced by two-dimensional joint processing.
Joint DoA-Range Estimation Using Space-Frequency Virtual Difference Coarray
In this paper, we address the problem of joint direction-of-arrival (DoA) and range estimation using frequency diverse coprime array (FDCA).
Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching
In this paper, a privacy-preserving distributed deep deterministic policy gradient (P2D3PG) algorithm is proposed to maximize the cache hit rates of devices in the MEC networks.
Hybrid Policy Learning for Energy-Latency Tradeoff in MEC-Assisted VR Video Service
Virtual reality (VR) is promising to fundamentally transform a broad spectrum of industry sectors and the way humans interact with virtual content.
Rank Minimization-based Toeplitz Reconstruction for DoA Estimation Using Coprime Array
The atomic-norm representation of the measurements from the interpolated virtual array is considered, and the equivalent dual-variable rank minimization problem is formulated and solved using a cyclic optimization approach.
True-data Testbed for 5G/B5G Intelligent Network
Future beyond fifth-generation (B5G) and sixth-generation (6G) mobile communications will shift from facilitating interpersonal communications to supporting Internet of Everything (IoE), where intelligent communications with full integration of big data and artificial intelligence (AI) will play an important role in improving network efficiency and providing high-quality service.
