Related papers: GNN-based Precoder Design and Fine-tuning for Cell…
This paper introduces a unified deep neural network (DNN)-based precoder for two-user multiple-input multiple-output (MIMO) networks with five objectives: data transmission, energy harvesting, simultaneous wireless information and power…
Cell-free massive MIMO (CF mMIMO) is a promising next generation wireless architecture to realize federated learning (FL). However, sensitive information of user equipments (UEs) may be exposed to the involved access points or the central…
The mobile data traffic has been exponentially growing during the last decades, which has been enabled by the densification of the network infrastructure in terms of increased cell density (i.e., ultra-dense network (UDN)) and/or increased…
Channel state information (CSI) is of pivotal importance as it enables wireless systems to adapt transmission parameters more accurately, thus improving the system's overall performance. However, it becomes challenging to acquire accurate…
Cell-Free (CF) Massive MIMO is an alternative topology for future wireless networks, where a large number of single-antenna access points (APs) are distributed over the coverage area. There are no cells but all users are jointly served by…
We propose a method for channel training and precoding in FDD massive MIMO based on deep neural networks (DNNs), exploiting Downlink (DL) channel covariance knowledge. The DNN is optimized to maximize the DL multi-user sum-rate, by…
Forward channel state information (CSI) often plays a vital role in scheduling and capacity-approaching transmission optimization for massive multiple-input multiple-output (MIMO) communication systems. In frequency division duplex (FDD)…
Cell-free (CF) massive multiple-input multiple-output (MIMO) systems, which exploit many geographically distributed access points to coherently serve user equipments via spatial multiplexing on the same time-frequency resource, has become a…
This paper introduces a practical precoding method for the downlink of Filter Bank Multicarrier-based (FBMC-based) massive multiple-input multiple-output (MIMO) systems. The proposed method comprises a two-stage precoder, consisting of a…
This paper focuses on wireless multiple-input multiple-output (MIMO)-orthogonal frequency division multiplex (OFDM) receivers. Traditional wireless receivers have relied on mathematical modeling and Bayesian inference, achieving remarkable…
Training Graph Neural Networks (GNNs) on real-world graphs consisting of billions of nodes and edges is quite challenging, primarily due to the substantial memory needed to store the graph and its intermediate node and edge features, and…
This paper investigates the near-field (NF) channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Considering the pronounced NF effects in XL-MIMO communications, we first establish a joint…
This paper revisits linear precoding, namely match-filter (MF) and zero-forcing (ZF), in a semantic multiple-input multiple-output (MIMO) system empowered by generative AI. The aim is to examine whether interference, channel state…
This paper considers a downlink cell-free multiple-input multiple-output (MIMO) network in which multiple multi-antenna access points (APs) serve multiple users via coherent joint transmission. In order to reduce the energy consumption by…
Cell-Free (CF) Massive MIMO (mMIMO) is a technology which can potentially augment not only the deployment of 5G, but also the deployment of beyond 5G (B5G) wireless networks. However, the cost for rolling out such systems may be…
Massive MIMO is a promising technology in future wireless communication networks. However, it raises a lot of implementation challenges, for example, the huge pilot symbols and feedback overhead, requirement of real-time global CSI, large…
The literature is abundant with methodologies focusing on using transformer architectures due to their prominence in wireless signal processing and their capability to capture long-range dependencies via attention mechanisms. In particular,…
Cellular communication is a widely used technology in the world where the coverage area is divided into multiple cells. Interference is one of the most important challenges in cellular networks which causes problems by reducing the quality…
A dynamic and flexible generalized spatial modulation (GSM) framework is proposed for massive MIMO systems. Our framework is leveraged on the utilization of machine learning methods for GSM in order to improve the error performance in…
Massive multiple-input multiple-output (MIMO) systems deploying a large number of antennas at the base station considerably increase the spectrum efficiency by serving multiple users simultaneously without causing severe interference.…