Related papers: NetMind: Adaptive RAN Baseband Function Placement …

Artificial intelligence approaches for base-band processing for radio receivers have demonstrated significant performance gains. Most of the proposed methods are characterized by high compute and memory requirements, hindering their…

Softwarization and virtualization are key concepts for emerging industries that require ultra-low latency. This is only possible if computing resources, traditionally centralized at the core of communication networks, are moved closer to…

Deep Reinforcement Learning (DRL) is emerging as a promising approach to generate adaptive behaviors for robotic platforms. However, a major drawback of using DRL is the data-hungry training regime that requires millions of trial and error…

Despite the superior performance of deep learning in many applications, challenges remain in the area of regression on function spaces. In particular, neural networks are unable to encode function inputs compactly as each node encodes just…

In this paper, we leverage reinforcement learning (RL) and cross-layer network coding (CLNC) for efficiently pre-fetching users' contents to the local caches and delivering these contents to users in a downlink fog-radio access network…

The quantum cloud computing paradigm presents unique challenges in task placement due to the dynamic and heterogeneous nature of quantum computation resources. Traditional heuristic approaches fall short in adapting to the rapidly evolving…

Multi-agent deep learning (MADL), including multi-agent deep reinforcement learning (MADRL), distributed/federated training, and graph-structured neural networks, is becoming a unifying framework for decision-making and inference in…

The recent breakthroughs of deep reinforcement learning (DRL) technique in Alpha Go and playing Atari have set a good example in handling large state and actions spaces of complicated control problems. The DRL technique is comprised of (i)…

With the widespread adoption of electric vehicles (EVs), navigating for EV drivers to select a cost-effective charging station has become an important yet challenging issue due to dynamic traffic conditions, fluctuating electricity prices,…

Network slicing envisions the 5th generation (5G) mobile network resource allocation to be based on different requirements for different services, such as Ultra-Reliable Low Latency Communication (URLLC) and Enhanced Mobile Broadband…

Renewable energy resources (RERs) have been increasingly integrated into distribution networks (DNs) for decarbonization. However, the variable nature of RERs introduces uncertainties to DNs, frequently resulting in voltage fluctuations…

AI heralds a step-change in the performance and capability of wireless networks and other critical infrastructures. However, it may also cause irreversible environmental damage due to their high energy consumption. Here, we address this…

As emerging networks such as Open Radio Access Networks (O-RAN) and 5G continue to grow, the demand for various services with different requirements is increasing. Network slicing has emerged as a potential solution to address the different…

In the 5G era and beyond, it is favorable to deploy latency-sensitive and reliability-aware services on edge computing networks in which the computing and network resources are more limited compared to cloud and core networks but can…

This paper addresses a multi-echelon inventory management problem with a complex network topology where deriving optimal ordering decisions is difficult. Deep reinforcement learning (DRL) has recently shown potential in solving such…

The combination of cloud computing capabilities at the network edge and artificial intelligence promise to turn future mobile networks into service- and radio-aware entities, able to address the requirements of upcoming latency-sensitive…

This paper presents a distributed beamforming framework for a constellation of airborne platform stations (APSs) in a massive Multiple-Input and Multiple-Output (MIMO) non-terrestrial network (NTN) that targets the downlink sum-rate…

Resource allocation in integrated sensing and communication (ISAC) systems needs to be optimized to balance the requirements of the communication and sensing modules considering complicated cross-layer data traffic and queue status in…

5G radio access network (RAN) slicing aims to logically split an infrastructure into a set of self-contained programmable RAN slices, with each slice built on top of the underlying physical RAN (substrate) is a separate logical mobile…

Network representation learning (NRL) plays a vital role in a variety of tasks such as node classification and link prediction. It aims to learn low-dimensional vector representations for nodes based on network structures or node…