Related papers: 5G MIMO Data for Machine Learning: Application to …
Deep learning methods can be found in many medical imaging applications. Recently, those methods were applied directly to the RF ultrasound multi-channel data to enhance the quality of the reconstructed images. In this paper, we apply a…
Millimeter-wave (mmWave) and terahertz (THz) communication systems require large antenna arrays and use narrow directive beams to ensure sufficient receive signal power. However, selecting the optimal beams for these large antenna arrays…
Supporting high mobility in millimeter wave (mmWave) systems enables a wide range of important applications such as vehicular communications and wireless virtual/augmented reality. Realizing this in practice, though, requires overcoming…
Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very…
Leveraging perception from radar data can assist multiple communication tasks, especially in highly-mobile and large-scale MIMO systems. One particular challenge, however, is how to distinguish the communication user (object) from the other…
The convergence of 5G architecture and deep learning has gained a lot of research interests in both the fields of wireless communication and artificial intelligence. This is because deep learning technologies have been identified to be the…
Beam management (BM) protocols are critical for establishing and maintaining connectivity between network radio nodes and User Equipments (UEs). In Distributed Multiple Input Multiple Output systems (D-MIMO), a number of access points…
Cooperative relays improve reliability and coverage in wireless networks by providing multiple paths for data transmission. Relaying will play an essential role in vehicular networks at higher frequency bands, where mobility and frequent…
Machine learning tools are finding interesting applications in millimeter wave (mmWave) and massive MIMO systems. This is mainly thanks to their powerful capabilities in learning unknown models and tackling hard optimization problems. To…
In this paper, we propose BeamLLM, a vision-aided millimeter-wave (mmWave) beam prediction framework leveraging large language models (LLMs) to address the challenges of high training overhead and latency in mmWave communication systems. By…
Environmental sensing can significantly enhance mmWave communications by assisting beam training, yet its benefits must be balanced against the associated sensing costs. To this end, we propose a unified machine learning framework that…
This paper introduces a novel neural network framework called M2BeamLLM for beam prediction in millimeter-wave (mmWave) massive multi-input multi-output (mMIMO) communication systems. M2BeamLLM integrates multi-modal sensor data, including…
Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum…
Predicting the millimeter wave (mmWave) beams and blockages using sub-6GHz channels has the potential of enabling mobility and reliability in scalable mmWave systems. These gains attracted increasing interest in the last few years. Prior…
Network traffic prediction techniques have attracted much attention since they are valuable for network congestion control and user experience improvement. While existing prediction techniques can achieve favorable performance when there is…
Beam management (BM), i.e., the process of finding and maintaining a suitable transmit and receive beam pair, can be challenging, particularly in highly dynamic scenarios. Side-information, e.g., orientation, from on-board sensors can…
Mmwave bands are being widely touted as a very promising option for future 5G networks, especially in enabling such networks to meet highly demanding rate requirements. Accordingly, the usage of these bands is also receiving an increasing…
Beamforming techniques are considered as essential parts to compensate severe path losses in millimeter-wave (mmWave) communications. In particular, these techniques adopt large antenna arrays and formulate narrow beams to obtain…
The deep learning trend has recently impacted a variety of fields, including communication systems, where various approaches have explored the application of neural networks in place of traditional designs. Neural networks flexibly allow…
5G is designed to be an essential enabler and a leading infrastructure provider in the communication technology industry by supporting the demand for the growing data traffic and a variety of services with distinct requirements. The use of…