266 documents found, page 1 of 27

On Deep Learning Hybrid Architectures for MIMO-OFDM Channel Estimation

Traditional estimation methods face challenges in adverse conditions in systems such as Multiple Input Multiple Output (MIMO) with Orthogonal Frequency Division Multiplexing (OFDM). To overcome those challenges, Deep Learning (DL) approaches have been proposed as an interesting alternative, thanks to their ability to capture channel features without much complexity. This paper presents a hybrid approach that co...

Streamlined Swift Allocation Strategies for Sequential Fronthaul Cell-Free Mass...

Centralized access point (AP) selection approaches in sequential fronthaul cell-free (SF-CF) networks, such as centralized maximum ratio combining (CMRC) or centralized optimal sequence linear processing (COSLP), often hinder its scalability by increasing fronthaul load and computational complexity as the number of APs and user equipment (UEs) grows. To address this, we propose a novel AP selection scheme that ...

Advanced Channel Decomposition Techniques in OTFS

In this paper, we propose a multi-user downlink system for two users based on the orthogonal time frequency space (OTFS) modulation scheme. The design leverages the generalized singular value decomposition (GSVD) of the channels between the base station and the two users, applying precoding and detection matrices based on the right and left singular vectors, respectively. We derive the analytical expressions fo...

A Survey on Positioning for mmWave Distributed MIMO Systems

Accurate positioning is crucial for the effective design and operation of millimeter Wave (mmWave) distributed MIMO (D-MIMO) systems. It enables reliable connectivity, precise beam alignment, and low-latency communication required in next-generation wireless networks. This research work presents a comprehensive survey of positioning for mmWave D-MIMO systems, examining core techniques, key challenges, and solut...

Nonlinear Massive MIMO–RSMA With Multilinear GSVD Precoding

In this work, we present a novel precoder design for multiuser massive multiple input multiple output (MU-mMIMO) systems impaired by nonlinear effects at the transmitter. By exploiting the rate splitting multiple access (RSMA) technique for user messages, we develop an analytical precoding framework based on the multilinear generalized singular value (ML-GSVD) decompositions for two or more matrices. This frame...

Quasi-Optimum Detection for a Wide Class of Digital Signals With Strong Nonline...

Most of the signals widely employed in wireless communications can have significant envelope fluctuations that make them very prone to nonlinear (NL) effects, leading to significant performance degradation when conventional receivers (designed for ideal linear conditions) are utilized. However, if optimum maximum likelihood (ML) receivers are employed, NL effects do not necessarily lead to performance degradati...

On the Performance of LDPC-Coded Large Intelligent Antenna System

360 Health Analysis (H360) — a comparison of key performance indicators in brea...

Background: The increased focus on quality indicators (QIs) and the use of clinical registries in real-world cancer studies have increased compliance with therapeutic standards and patient survival. The European Society of Breast Cancer Specialists (EUSOMA) established QIs to assess compliance with current standards in breast cancer care. Methods: This retrospective study is part of H360 Health Analysis and aim...

A review on cell-free massive MIMO systems

Cell-free massive multiple-input multiple-output (CF mMIMO) can be considered as a potential physical layer technology for future wireless networks since it can benefit from all the advantages of distributed antenna systems (DASs) and network MIMOs, such as macro-diversity gain, high channel capacity, and link reliability. CF mMIMO systems offer remarkable spatial degrees of freedom and array gains to mitigate ...

Joint Decoding and UE-APs Association for Scalable Cell-Free Systems

User-centric cell-free massive multiple-input multiple-output (UC CF mMIMO) is a key technology for 6G. In UC CF mMIMO each user equipment (UE) is jointly served by a small subset of a large set of cooperating access points (APs). The APs are connected through the fronthaul to a central unit (CU) for joint processing. The key to efficiency is the UE-APs association procedure together with the APs/CU cooperative...