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相关论文: Kalman filter based MIMO CSI phase recovery for CO…

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In this letter, we propose an iterative joint detection algorithm of Kalman filter (KF) and channel decoder for the sensor-to-controller link of wireless networked control systems, which utilizes the prior information of control system to…

信息论 · 计算机科学 2025-12-19 Jinnan Piao , Dong Li , Yiming Sun , Zhibo Li , Ming Yang , Xueting Yu

We consider the problem of channel estimation in a multiple-input-multiple-output (MIMO) full-duplex (FD) wireless communication system assisted by a reconfigurable intelligent surface (RIS) with hardware impairments (HI) occurring at the…

信号处理 · 电气工程与系统科学 2023-07-25 Alexander James Fernandes , Ioannis Psaromiligkos

We study the performance of centralized least mean-squares (CLMS) algorithms in wireless sensor networks where nodes transmit their data over fading channels to a central processing unit (e.g., fusion center or cluster head), for parameter…

系统与控制 · 计算机科学 2016-11-18 Reza Abdolee , Benoit Champagne

This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to…

信息论 · 计算机科学 2023-10-20 Zhen Gao , Linglong Dai , Zhaocheng Wang , Sheng Chen

Wi-Fi sensing is gaining momentum as a non-intrusive and privacy-preserving alternative to vision-based systems for human identification. However, person identification through wireless signals, particularly without user motion, remains…

The Kalman filter (KF) is one of the most widely used tools for data assimilation and sequential estimation. In this work, we show that the state estimates from the KF in a standard linear dynamical system setting are equivalent to those…

统计方法学 · 统计学 2021-08-04 Maria Jahja , David C. Farrow , Roni Rosenfeld , Ryan J. Tibshirani

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) systems are investigated under spatially correlated fading channels using realistic…

信息论 · 计算机科学 2025-01-06 Zeping Sui , Hien Quoc Ngo , Michail Matthaiou , Lajos Hanzo

In wireless fading channels, multi-user scheduling has the potential to boost the spectral efficiency by exploiting diversity gains. In this regard, proportional fair (PF) scheduling provides a solution for increasing the users' quality of…

网络与互联网体系结构 · 计算机科学 2016-11-17 R. Fritzsche , P. Rost , G. Fettweis

Millimeter wave is a promising technology for the next generation of wireless systems. As it is well-known for its high path loss, the systems working in this spectrum tend to exploit the shorter wavelength to equip the transceivers with a…

信息论 · 计算机科学 2018-01-12 Sina Shaham , Matthew Kokshoorn , Zihuai Lin , Ming Ding

In many signal processing applications of Kalman filter (KF) and its variants and extensions, accurate estimation of extreme states is often of great importance. When the observations used are uncertain, however, KF suffers from conditional…

信号处理 · 电气工程与系统科学 2019-08-02 Haojing Shen , Haksu Lee , Dong-Jun Seo

Satellite dynamics and tracking remain important challenges in the context of space exploration and communication systems. Accurate state estimation is essential to maintain reliable orbital motion and system performance. This paper…

系统与控制 · 电气工程与系统科学 2026-04-16 Moh Kamalul Wafi

This paper introduces CSI-RFF, a new framework that leverages micro-signals embedded within Channel State Information (CSI) curves to realize Radio-Frequency Fingerprinting of commodity off-the-shelf (COTS) WiFi devices for open-set…

信号处理 · 电气工程与系统科学 2026-02-27 Ruiqi Kong , He Chen

Standard cell-free (CF) multiple-input-multiple-output (mMIMO) systems is a promising technology to cover the demands for higher data rates in fifth-generation (5G) networks and beyond. These systems assume a large number of distributed…

Accurate channel state information (CSI) underpins reliable and efficient wireless communication. However, acquiring CSI via pilot estimation incurs substantial overhead, especially in massive multiple-input multiple-output (MIMO) systems…

信息论 · 计算机科学 2025-12-05 Guangming Liang , Mingjie Yang , Dongzhu Liu , Paul Henderson , Lajos Hanzo

This paper studies the distributed state estimation problem for a class of discrete time-varying systems over sensor networks. Firstly, it is shown that a networked Kalman filter with optimal gain parameter is actually a centralized filter,…

系统与控制 · 计算机科学 2017-11-15 Xingkang He , Wenchao Xue , Haitao Fang

We consider the problem of reconstructing time sequences of spatially sparse signals (with unknown and time-varying sparsity patterns) from a limited number of linear "incoherent" measurements, in real-time. The signals are sparse in some…

信息论 · 计算机科学 2016-11-17 Namrata Vaswani

Owing to the plentiful information released by the commodity devices, WiFi signals have been widely studied for various wireless sensing applications. In many works, both received signal strength indicator (RSSI) and the channel state…

信号处理 · 电气工程与系统科学 2022-03-25 Bo Wei , Hang Song , Jiro Katto , Takamaro Kikkawa

Channel state information (CSI) provided by limited feedback channel can be utilized to increase the system throughput. However, in multiple input multiple output (MIMO) systems, the signaling overhead realizing this CSI feedback can be…

信息论 · 计算机科学 2013-06-18 Mingxin Zhou , Leiming Zhang , Lingyang Song , Merouane Debbah

Integrating the reconfigurable intelligent surface (RIS) into a cell-free massive multiple-input multiple-output (CF-mMIMO) system is an effective solution to achieve high system capacity with low cost and power consumption. However,…

信息论 · 计算机科学 2024-03-27 Jianxin Dai , Jin Ge , Kangda Zhi , Cunhua Pan , Youguo Wang

In this paper, we consider the problem of compressive sensing (CS) recovery with a prior support and the prior support quality information available. Different from classical works which exploit prior support blindly, we shall propose novel…

信息论 · 计算机科学 2015-10-28 Xiongbin Rao , Vincent K. N. Lau