Search Results for author: Nandana Rajatheva

Found 22 papers, 0 papers with code

Machine Learning-Based Channel Prediction for RIS-assisted MIMO Systems With Channel Aging

no code implementations9 May 2024 Nipuni Ginige, Arthur Sousa de Sena, Nurul Huda Mahmood, Nandana Rajatheva, Matti Latva-aho

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

no code implementations6 Apr 2024 Nalin Jayaweera, Andrea Bonfante, Mark Schamberger, Amir Mehdi Ahmadian Tehrani, Tachporn Sanguanpuak, Preetish Tilak, Keeth Jayasinghe, Frederick W. Vook, Nandana Rajatheva

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.

Management

GDOP Based BS Selection for Positioning in mmWave 5G NR Networks

no code implementations23 Oct 2023 A. Indika Perera, K. B. Shashika Manosha, Nandana Rajatheva, Matti Latva-aho

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

no code implementations9 Oct 2023 Samitha Gunarathne, Thushan Sivalingam, Nurul Huda Mahmood, Nandana Rajatheva, Matti Latva-aho

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.

Management

Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks with Asymmetric Loss

no code implementations8 Jun 2023 Nuwanthika Rajapaksha, Jafar Mohammadi, Stefan Wesemann, Thorsten Wild, Nandana Rajatheva

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.

Classification Combinatorial Optimization

Predictive Resource Allocation for URLLC using Empirical Mode Decomposition

no code implementations4 Apr 2023 Chandu Jayawardhana, Thushan Sivalingam, Nurul Huda Mahmood, Nandana Rajatheva, Matti Latva-aho

It is found that such a decomposition-based prediction method reduces the root mean squared error of the prediction by $20 - 25\%$.

Management

Wireless End-to-End Image Transmission System using Semantic Communications

no code implementations27 Feb 2023 Maheshi Lokumarambage, Vishnu Gowrisetty, Hossein Rezaei, Thushan Sivalingam, Nandana Rajatheva, Anil Fernando

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.

Decoder Quantization +2

LiDAR Aided Human Blockage Prediction for 6G

no code implementations1 Oct 2021 Dileepa Marasinghe, Nandana Rajatheva, Matti Latva-aho

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

no code implementations14 Jul 2021 Kai Mei, Jun Liu, Xiaoying Zhang, Kuo Cao, Nandana Rajatheva, Jibo Wei

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.

BIG-bench Machine Learning

Untrained DNN for Channel Estimation of RIS-Assisted Multi-User OFDM System with Hardware Impairments

no code implementations13 Jul 2021 Nipuni Ginige, K. B. Shashika Manosha, Nandana Rajatheva, Matti Latva-aho

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

no code implementations21 Jun 2021 Thushan Sivalingam, Samad Ali, Nurul Huda Mahmood, Nandana Rajatheva, Matti Latva-aho

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

no code implementations17 Mar 2021 Insaf Ismath, Samad Ali, Nandana Rajatheva, Matti Latva-aho

Access points (APs) in millimeter-wave (mmWave) and sub-THz-based user-centric (UC) networks will have sleep mode functionality.

Multi-Armed Bandits

Elevated LiDAR based Sensing for 6G -- 3D Maps with cm Level Accuracy

no code implementations22 Feb 2021 Madhushanka Padmal, Dileepa Marasinghe, Vijitha Isuru, Nalin Jayaweera, Samad Ali, Nandana Rajatheva

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

no code implementations20 Feb 2021 Nuwanthika Rajapaksha, K. B. Shashika Manosha, Nandana Rajatheva, Matti Latva-aho

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.

Fairness

Event-Driven Source Traffic Prediction in Machine-Type Communications Using LSTM Networks

no code implementations12 Jan 2021 Thulitha Senevirathna, Bathiya Thennakoon, Tharindu Sankalpa, Chatura Seneviratne, Samad Ali, Nandana Rajatheva

This is done by restructuring the transmission data in a way that the LSTM network can identify the causal relationship between the devices.

Traffic Prediction

Intelligent Reflecting Surface Aided Vehicular Communications

no code implementations5 Nov 2020 Dilin Dampahalage, K. B. Shashika Manosha, Nandana Rajatheva, MattiLatva-aho

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

no code implementations15 Sep 2020 Insaf Ismath, K. B. Shashika Manosha, Samad Ali, Nandana Rajatheva, Matti Latva-aho

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.

Management Multi-Armed Bandits

Low Complexity Autoencoder based End-to-End Learning of Coded Communications Systems

no code implementations19 Nov 2019 Nuwanthika Rajapaksha, Nandana Rajatheva, Matti Latva-aho

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

no code implementations10 Nov 2019 Kai Mei, Jun Liu, Xiaochen Zhang, Nandana Rajatheva, Jibo Wei

In this situation, our analysis results can be applied to assess the performance and support the design of machine learning-based channel estimation.

BIG-bench Machine Learning

Autonomous Driving without a Burden: View from Outside with Elevated LiDAR

no code implementations26 Aug 2018 Nalin Jayaweera, Nandana Rajatheva, Matti Latva-aho

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.

Autonomous Driving Decision Making

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