activity
20132025
most cited6G White Paper on Machine Learning in Wireless Communication Networks

94 citations · 125 across the 34 of their papers we have counts for

collaborators

41 papers

eess.SP2025

Max-Min Fairness for Stacked Intelligent Metasurface-Assisted Multi-User MISO Systems

Nipuni Ginige, Prathapasinghe Dharmawansa, Arthur Sousa de Sena +3

Stacked intelligent metasurface (SIM) is an emerging technology that uses multiple reconfigurable surface layers to enable flexible wave-based beamforming. In this paper, we focus…

cs.NI2025

Towards Specialized Wireless Networks Using an ML-Driven Radio Interface

Kamil Szczech, Maksymilian Wojnar, Katarzyna Kosek-Szott +8

Future wireless networks will need to support diverse applications (such as extended reality), scenarios (such as fully automated industries), and technological advances (such as t…

eess.SP2025

Comprehensive Review of Deep Unfolding Techniques for Next-Generation Wireless Communication Systems

Sukanya Deka, Kuntal Deka, Nhan Thanh Nguyen +3

The application of machine learning in wireless communications has been extensively explored, with deep unfolding emerging as a powerful model-based technique. Deep unfolding enhan…

cs.IT2023★ 2 cited

Unrolled Architectures for High-Throughput Encoding of Multi-Kernel Polar Codes

Hossein Rezaei, Elham Abbasi, Nandana Rajatheva +1

Over the past decade, polar codes have received significant traction and have been selected as the coding method for the control channel in fifth-generation (5G) wireless communica…

cs.IT2023★ 1 cited

High-Throughput Rate-Flexible Combinational Decoders for Multi-Kernel Polar Codes

Hossein Rezaei, Nandana Rajatheva, Matti Latva-aho

Polar codes have received growing attention in the past decade and have been selected as the coding scheme for the control channel in the fifth generation (5G) wireless communicati…

cs.IT2022

Graph Representation Learning for Wireless Communications

Maryam Mohsenivatani, Samad Ali, Vismika Ranasinghe +2

Wireless networks are inherently graph-structured, which can be utilized in graph representation learning to solve complex wireless network optimization problems. In graph represen…

cs.IT2022★ 4 cited

A Combinational Multi-Kernel Decoder for Polar Codes

Hossein Rezaei, Nandana Rajatheva, Matti Latva-aho

Polar codes have been selected as the channel coding scheme for control channel in the fifth generation (5G) communication system thanks to their capacity achieving characteristics…

cs.IT2022★ 3 cited

Deep Learning-Based Blind Multiple User Detection for Grant-free SCMA and MUSA Systems

Thushan Sivalingam, Samad Ali, Nurul Huda Mahmood +2

Massive machine-type communications (mMTC) in 6G requires supporting a massive number of devices with limited resources, posing challenges in efficient random access. Grant-free ra…

eess.SP2022

Supervised Learning based Sparse Channel Estimation for RIS aided Communications

Dilin Dampahalage, K. B. Shashika Manosha, Nandana Rajatheva +1

An reconfigurable intelligent surface (RIS) can be used to establish line-of-sight (LoS) communication when the direct path is compromised, which is a common occurrence in a millim…

cs.IT2021★ 4 cited

Permutation Matrix Modulation

Rahmat Faddli Siregar, Nandana Rajatheva, Matti Latva-Aho

We propose a novel scheme that allows MIMO system to modulate a set of permutation matrices to send more information bits, extending our initial work on the topic. This system is c…

eess.SP2021

LiDAR Aided Human Blockage Prediction for 6G

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. However, such shorter wavelengths are suscept…

eess.SP2021

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

Nipuni Ginige, K. B. Shashika Manosha, Nandana Rajatheva +1

Reconfigurable intelligent surface (RIS) is an emerging technology for improving performance in fifth-generation (5G) and beyond networks. Practically channel estimation of RIS-ass…

cs.IT2021

Graph Neural Network Based Access Point Selection for Cell-Free Massive MIMO Systems

Vismika Ranasinghe, Nandana Rajatheva, Matti Latva-aho

A graph neural network (GNN) based access point (AP) selection algorithm for cell-free massive multiple-input multiple-output (MIMO) systems is proposed. Two graphs, a homogeneous…

cs.IT2021★ 2 cited

Deep Neural Network-Based Blind Multiple User Detection for Grant-free Multi-User Shared Access

Thushan Sivalingam, Samad Ali, Nurul Huda Mahmood +2

Multi-user shared access (MUSA) is introduced as advanced code domain non-orthogonal complex spreading sequences to support a massive number of machine-type communications (MTC) de…

cs.IT2021★ 1 cited

Deep Learning-Based Active User Detection for Grant-free SCMA Systems

Thushan Sivalingam, Samad Ali, Nurul Huda Mahmood +2

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 communi…

eess.SP2021

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

Madhushanka Padmal, Dileepa Marasinghe, Vijitha Isuru +3

One key vertical application that will be enabled by 6G is the automation of the processes with the increased use of robots. As a result, sensing and localization of the surroundin…

eess.SP2021

Deep Learning-based Power Control for Cell-Free Massive MIMO Networks

Nuwanthika Rajapaksha, K. B. Shashika Manosha, Nandana Rajatheva +1

A deep learning (DL)-based power control algorithm that solves the max-min user fairness problem in a cell-free massive multiple-input multiple-output (MIMO) system is proposed. Ma…

cs.IT2021

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

Thulitha Senevirathna, Bathiya Thennakoon, Tharindu Sankalpa +3

Source traffic prediction is one of the main challenges of enabling predictive resource allocation in machine type communications (MTC). In this paper, a Long Short-Term Memory (LS…

eess.SP2020★ 6 cited

Intelligent Reflecting Surface Aided Vehicular Communications

Dilin Dampahalage, K. B. Shashika Manosha, Nandana Rajatheva +1

We investigate the use of an intelligent reflecting surface (IRS) in a millimeter-wave (mmWave) vehicular communication network. An intelligent reflecting surface consists of passi…

eess.SP2020

Deep Contextual Bandits for Fast Initial Access in mmWave Based User-Centric Ultra-Dense Networks

Insaf Ismath, K. B. Shashika Manosha, Samad Ali +2

Millimeter wave (mmWave) based multiple-input multiple-output (MIMO) capable user-centric (UC) ultra-dense (UD) networks are suggested to facilitate high throughput requirements of…

cs.IT2020★ 94 cited

6G White Paper on Machine Learning in Wireless Communication Networks

Samad Ali, Walid Saad, Nandana Rajatheva +24

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of socie…

cs.IT2020

Factory Automation: Resource Allocation of an Elevated LiDAR System with URLLC Requirements

Nalin Jayaweera, Dileepa Marasinghe, Nandana Rajatheva +1

Ultra-reliable and low-latency communications (URLLC) play a vital role in factory automation. To share the situational awareness data collected from the infrastructure as raw or p…

eess.SP2020

Hierarchical User Clustering for mmWave-NOMA Systems

Dileepa Marasinghe, Nalin Jayaweera, Nandana Rajatheva +1

Non-orthogonal multiple access (NOMA) and mmWave are two complementary technologies that can support the capacity demand that arises in 5G and beyond networks. The increasing numbe…

cs.IT2019

Low Latency Decoder for Short Blocklength Polar Codes

Heshani Gamage, Vismika Ranasinghe, Nandana Rajatheva +1

Polar codes have been gaining a lot of interest due to it being the first coding scheme to provably achieve the symmetric capacity of a binary memoryless channel with an explicit c…