Related papers: AI-Based Channel Prediction in D2D Links: An Empir…

Channel estimation is a critical task in digital communications that greatly impacts end-to-end system performance. In this work, we introduce a novel approach for multiple-input multiple-output (MIMO) channel estimation using score-based…

This paper studies energy efficiency maximization in device-to-device (D2D) communications underlaying cellular networks in millimeter-wave (mm-wave) band. A stochastic geometry framework has been used to extract the results. First,…

Deep Neural Networks are allowing mobile devices to incorporate a wide range of features into user applications. However, the computational complexity of these models makes it difficult to run them effectively on resource-constrained mobile…

With the phenomenal success of diffusion models and ChatGPT, deep generation models (DGMs) have been experiencing explosive growth from 2022. Not limited to content generation, DGMs are also widely adopted in Internet of Things, Metaverse,…

This paper analyses the performance of reconfigurable intelligent surface (RIS)-assisted device-to-device (D2D) communication systems, focusing on addressing co-channel interference, a prevalent issue due to the frequency reuse of sidelink…

In cellular-D2D networks, users can select the communication mode either direct and form D2D links or indirect and communicate with BS. In former case, users should perform pairing selection and choose their pairs. The main focus in this…

We present DeepIA, a deep neural network (DNN) framework for enabling fast and reliable initial access for AI-driven beyond 5G and 6G millimeter (mmWave) networks. DeepIA reduces the beam sweep time compared to a conventional exhaustive…

In this paper, we provide an analytical framework to analyze the uplink performance of device-to-device (D2D)-enabled millimeter wave (mmWave) cellular networks. Signal-to- interference-plus-noise ratio (SINR) outage probabilities are…

Time series classification underpins applications such as human activity recognition, healthcare monitoring, and gesture detection in the IoT domain. Tiny Machine Learning enables models to run directly on low-power microcontroller units,…

Massive multiple-input multiple-output (MIMO) systems rely on channel state information (CSI) feedback to perform precoding and achieve performance gain in frequency division duplex (FDD) networks. However, the huge number of antennas poses…

Achieving reliable multidimensional Vehicle-to-Vehicle (V2V) channel state information (CSI) prediction is both challenging and crucial for optimizing downstream tasks that depend on instantaneous CSI. This work extends traditional…

In the future, with the advent of Internet of Things (IoT), wireless sensors, and multiple 5G killer applications, an indoor room might be filled with $1000$s of devices demanding low data rates. Such high-level densification and mobility…

In this report, we consider how to dynamically select transmission bands and multi-hop routes for device-to-device (D2D) communications in co-existence with a cellular network overlay. Firstly, we consider different wireless routing…

Spectrum prediction is considered to be a promising technology that enhances spectrum efficiency by assisting dynamic spectrum access (DSA) in cognitive radio networks (CRN). Nonetheless, the highly nonlinear nature of spectrum data across…

This paper proposes a communication strategy for decentralized learning on wireless systems. Our discussion is based on the decentralized parallel stochastic gradient descent (D-PSGD), which is one of the state-of-the-art algorithms for…

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial intelligence (AI), including computer vision, natural language processing and speech recognition. However, their superior performance comes at the…

The performance of downlink massive multiple-input-multiple-output networks with co-channel device-to-device communications is investigated in this paper. Specifically, we consider a cellular network with sufficient number of antennas at…

Proximity based applications are becoming fast growing markets suggesting that Device-to-Device (D2D) communications is becoming an essential part of future mobile data networks. We propose scalable admission and power control methods for…

Vehicle acceleration and deceleration maneuvers at traffic signals results in significant fuel and energy consumption levels. Green light optimal speed advisory systems require reliable estimates of signal switching times to improve vehicle…

This work centers on the communication aspects of decentralized learning over wireless networks, using consensus-based decentralized stochastic gradient descent (D-SGD). Considering the actual communication cost or delay caused by…