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Found 26 papers, 0 papers with code
A CNN-based End-to-End Learning for RIS-assisted Communication System
Numerically we have shown that the bit error rate (BER) performance of the CNN-based autoencoder system is better than the theoretical BER performance of the RIS-assisted communication systems.
Comprehensive Review of Deep Unfolding Techniques for Next-Generation Wireless Communication Systems
The application of machine learning in wireless communications has been extensively explored, with deep unfolding emerging as a powerful model-based technique.
Efficient Channel Prediction for Beyond Diagonal RIS-Assisted MIMO Systems with Channel Aging
Moreover, our scheme can deliver a high average downlink sum rate, outperforming other state-of-the-art channel estimation methods.
Waveform Learning under Phase Noise Impairment for Sub-THz Communications
The large untapped spectrum in sub-THz allows for ultra-high throughput communication to realize many seemingly impossible applications in 6G.
Machine Learning-Based Channel Prediction for RIS-assisted MIMO Systems With Channel Aging
Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology to enhance the performance of sixth-generation (6G) and beyond communication systems.
5G-Advanced AI/ML Beam Management: Performance Evaluation with Integrated ML Models
In this study, we discuss different sub-use cases of SBP and TBP and evaluate the beam prediction accuracy of AI/ML models designed for each sub-use case along with AI/ML model generalization aspects.
GDOP Based BS Selection for Positioning in mmWave 5G NR Networks
We propose a BS selection algorithm for UE positioning based on the GDOP of the BSs participating in the positioning process.
Decomposition Based Interference Management Framework for Local 6G Networks
Managing inter-cell interference is among the major challenges in a wireless network, more so when strict quality of service needs to be guaranteed such as in ultra-reliable low latency communications (URLLC) applications.
Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks with Asymmetric Loss
In this paper, we consider the downlink transmission of an MU-MIMO network where TAM is formulated to minimize the number of active antennas in the BS while guaranteeing the per-user throughput requirements.
Predictive Resource Allocation for URLLC using Empirical Mode Decomposition
It is found that such a decomposition-based prediction method reduces the root mean squared error of the prediction by $20 - 25\%$.
Wireless End-to-End Image Transmission System using Semantic Communications
Semantic communication is considered the future of mobile communication, which aims to transmit data beyond Shannon's theorem of communications by transmitting the semantic meaning of the data rather than the bit-by-bit reconstruction of the data at the receiver's end.
Supervised Learning based Sparse Channel Estimation for RIS aided Communications
In the on-grid case, we propose an algorithm to estimate the direct and RIS channels.
LiDAR Aided Human Blockage Prediction for 6G
Leveraging higher frequencies up to THz band paves the way towards a faster network in the next generation of wireless communications.
A Low Complexity Learning-based Channel Estimation for OFDM Systems with Online Training
Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission.
Untrained DNN for Channel Estimation of RIS-Assisted Multi-User OFDM System with Hardware Impairments
Further, we have shown that the proposed estimator is robust to interference caused by the hardware impairments at the transceiver and RIS.
Deep Learning-Based Active User Detection for Grant-free SCMA Systems
Grant-free random access and uplink non-orthogonal multiple access (NOMA) have been introduced to reduce transmission latency and signaling overhead in massive machine-type communication (mMTC).
Deep Contextual Bandits for Fast Neighbor-Aided Initial Access in mmWave Cell-Free Networks
Access points (APs) in millimeter-wave (mmWave) and sub-THz-based user-centric (UC) networks will have sleep mode functionality.
Elevated LiDAR based Sensing for 6G -- 3D Maps with cm Level Accuracy
However, LiDARs are power hungry devices that generate a lot of data, and these characteristics limit their use as on-board sensors in robots.
Deep Learning-based Power Control for Cell-Free Massive MIMO Networks
Specifically, we model a deep neural network (DNN) and train it in an unsupervised manner to learn the optimum user power allocations which maximize the minimum user rate.
Event-Driven Source Traffic Prediction in Machine-Type Communications Using LSTM Networks
This is done by restructuring the transmission data in a way that the LSTM network can identify the causal relationship between the devices.
Intelligent Reflecting Surface Aided Vehicular Communications
An intelligent reflecting surface consists of passive elements, which can reflect the incoming signals with adjustable phase shifts.
Deep Contextual Bandits for Fast Initial Access in mmWave Based User-Centric Ultra-Dense Networks
In this paper, we propose a novel deep contextual bandit (DCB) based approach to perform fast and efficient IA in mmWave based UC UD networks.
Scoring the Terabit/s Goal:Broadband Connectivity in 6G
Several categories of enablers at the infrastructure, spectrum, and protocol/algorithmic levels are required to realize the intended broadband connectivity goals in 6G.
Low Complexity Autoencoder based End-to-End Learning of Coded Communications Systems
The newly proposed low complexity autoencoder was capable of achieving a better BER performance than half-rate 16-QAM with hard decision decoding over the full 0-10 dB $E_{b}/N_{0}$ range and a better BER performance than the soft decision decoding in 0-4 dB $E_{b}/N_{0}$ range.
Performance Analysis on Machine Learning-Based Channel Estimation
In this situation, our analysis results can be applied to assess the performance and support the design of machine learning-based channel estimation.
Autonomous Driving without a Burden: View from Outside with Elevated LiDAR
If we are to reduce the effort for the processing units inside the car, we need to uplink the data to edge or an appropriately placed cloud.
