Publications (31)
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration
Fahri Wisnu Murti, Samad Ali, Matti Latva-aho
Multi-access Edge Computing (MEC) can be implemented together with Open Radio Access Network (O-RAN) over commodity platforms to offer low-cost deployment and bring the services cl…
Contextual Bandit Learning for Machine Type Communications in the Null Space of Multi-Antenna Systems
Samad Ali, Hossein Asgharimoghaddam, Nandana Rajatheva +2
In this paper, a novel approach based on the concept of opportunistic spatial orthogonalization (OSO) is proposed for interference management between machine type communications (M…
Probabilistic Constellation Shaping With Denoising Diffusion Probabilistic Models: A Novel Approach
Mehdi Letafati, Samad Ali, Matti Latva-aho
With the incredible results achieved from generative pre-trained transformers (GPT) and diffusion models, generative AI (GenAI) is envisioned to yield remarkable breakthroughs in v…
Deep Reinforcement Learning for Orchestrating Cost-Aware Reconfigurations of vRANs
Fahri Wisnu Murti, Samad Ali, George Iosifidis +1
Virtualized Radio Access Networks (vRANs) are fully configurable and can be implemented at a low cost over commodity platforms to enable network management flexibility. In this pap…
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…
Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models
Mehdi Letafati, Samad Ali, Matti Latva-aho
Diffusion models are at the vanguard of generative AI research with renowned solutions such as ImageGen by Google Brain and DALL.E 3 by OpenAI. Nevertheless, the potential merits o…
Denoising Diffusion Probabilistic Models for Hardware-Impaired Communications
Mehdi Letafati, Samad Ali, Matti Latva-aho
Generative AI has received significant attention among a spectrum of diverse industrial and academic domains, thanks to the magnificent results achieved from deep generative models…
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…
Fast Uplink Grant for Machine Type Communications: Challenges and Opportunities
Samad Ali, Nandana Rajatheva, Walid Saad
The notion of a fast uplink grant is emerging as a promising solution for enabling massive machine type communications (MTCs) in the Internet of Things over cellular networks. By u…
Deep Contextual Bandits for Fast Neighbor-Aided Initial Access in mmWave Cell-Free Networks
Insaf Ismath, Samad Ali, Nandana Rajatheva +1
Access points (APs) in millimeter-wave (mmWave) and sub-THz-based user-centric (UC) networks will have sleep mode functionality. As a result of this, it becomes challenging to solv…
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…
Learning-Based Orchestration for Dynamic Functional Split and Resource Allocation in vRANs
Fahri Wisnu Murti, Samad Ali, George Iosifidis +1
One of the key benefits of virtualized radio access networks (vRANs) is network management flexibility. However, this versatility raises previously-unseen network management challe…
Average Rate and Error Probability Analysis in Short Packet Communications over RIS-aided URLLC Systems
Ramin Hashemi, Samad Ali, Nurul Huda Mahmood +1
In this paper, the average achievable rate and error probability of a reconfigurable intelligent surface (RIS) aided systems is investigated for the finite blocklength (FBL) regime…
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…
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…
A Directed Information Learning Framework for Event-Driven M2M Traffic Prediction
Samad Ali, Walid Saad, Nandana Rajatheva
Burst of transmissions stemming from event-driven traffic in machine type communication (MTC) can lead to congestion of random access resources, packet collisions, and long delays.…
Constrained Deep Reinforcement Based Functional Split Optimization in Virtualized RANs
Fahri Wisnu Murti, Samad Ali, Matti Latva-aho
In virtualized radio access network (vRAN), the base station (BS) functions are decomposed into virtualized components that can be hosted at the centralized unit or distributed uni…
Opportunistic Scheduling of Machine Type Communications as Underlay to Cellular Networks
Samad Ali, Nandana Rajatheva
In this paper we present a simple method to exploit the diversity of interference in heterogenous wireless communication systems with large number of machine-type-devices (MTD). We…
Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications
Mehdi Letafati, Samad Ali, Matti Latva-aho
In this paper, conditional denoising diffusion probabilistic models (DDPMs) are proposed to enhance the data transmission and reconstruction over wireless channels. The underlying…
Cyber-Physical Security and Safety of Autonomous Connected Vehicles: Optimal Control Meets Multi-Armed Bandit Learning
Aidin Ferdowsi, Samad Ali, Walid Saad +1
Autonomous connected vehicles (ACVs) rely on intra-vehicle sensors such as camera and radar as well as inter-vehicle communication to operate effectively. This reliance on cyber co…
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…
Conditional Denoising Diffusion Autoencoders for Wireless Semantic Communications
Mehdi Letafati, Samad Ali, Matti Latva-aho
Semantic communication (SemCom) systems aim to learn the mapping from low-dimensional semantics to high-dimensional ground-truth. While this is more akin to a "domain translation"…
Sleeping Multi-Armed Bandit Learning for Fast Uplink Grant Allocation in Machine Type Communications
Samad Ali, Aidin Ferdowsi, Walid Saad +2
Scheduling fast uplink grant transmissions for machine type communications (MTCs) is one of the main challenges of future wireless systems. In this paper, a novel fast uplink grant…
Deep Reinforcement Based Optimization of Function Splitting in Virtualized Radio Access Networks
Fahri Wisnu Murti, Samad Ali, Matti Latva-aho
Virtualized Radio Access Network (vRAN) is one of the key enablers of future wireless networks as it brings the agility to the radio access network (RAN) architecture and offers de…
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…
Average Rate Analysis of RIS-aided Short Packet Communication in URLLC Systems
Ramin Hashemi, Samad Ali, Nurul Huda Mahmood +1
In this paper, the average achievable rate of a re-configurable intelligent surface (RIS) aided factory automation is investigated in finite blocklength (FBL) regime. First, the co…
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…
Diffusion Models for Wireless Communications
Mehdi Letafati, Samad Ali, Matti Latva-aho
A comprehensive study on the applications of denoising diffusion models for wireless systems is provided. The article highlights the capabilities of diffusion models in learning co…
Deep Reinforcement Learning for Practical Phase Shift Optimization in RIS-aided MISO URLLC Systems
Ramin Hashemi, Samad Ali, Nurul Huda Mahmood +1
We study the joint active/passive beamforming and channel blocklength (CBL) allocation in a non-ideal reconfigurable intelligent surface (RIS)-aided ultra-reliable and low-latency…
Joint Sum Rate and Blocklength Optimization in RIS-aided Short Packet URLLC Systems
Ramin Hashemi, Samad Ali, Nurul Huda Mahmood +1
In this paper, a multi-objective optimization problem (MOOP) is proposed for maximizing the achievable finite blocklength (FBL) rate while minimizing the utilized channel blockleng…
Deep-Unfolded Wideband ISAC Beamforming for DMA Under Frequency-Selective Lorentzian Model
Abdolrasoul Sakhaei Gharagezlou, Pouya Mobaraki, Mehdi Monemi +4
Integrated sensing and communications (ISAC), empowered by dynamic metasurface antennas (DMAs), has emerged as a promising paradigm for next-generation wireless networks. However,…