## What is/are Mimo Ofdm Systems?

Mimo Ofdm Systems - In this work, we propose a two-stage tensor-based receiver for a joint channel, phase-noise (PN), and data estimation in MIMO-OFDM systems.^{[1]}In this paper, we propose a CSI-based positioning pipeline for wireless LAN MIMO-OFDM systems operating indoors, which relies on NNs that extract a probability map indicating the likelihood of a UE being at a given grid point.

^{[2]}Compared with other non-Hermitian symmetry MIMO-OFDM systems, HU-OFDM has significant advantages in terms of power efficiency, system design flexibility, computational complexity, or hardware cost without losing reliability.

^{[3]}In this paper, we propose a new stream power allocation method for single-user beamforming for BICM MIMO-OFDM systems.

^{[4]}We consider the joint activity detection and channel estimation for massive connectivity in MIMO-OFDM systems.

^{[5]}This paper presents a simple technique for employing multiple-beamforming on the downlink of massive MIMO-OFDM systems.

^{[6]}In this paper, we consider the problem of hybrid precoding and combining for wideband millimeter wave (mmWave) and sub-terahertz (THz) MIMO-OFDM systems with beam squint effects.

^{[7]}In this article, two methods are proposed to further increase the advantages of MIMO-OFDM systems such as high access quality, high data rates and spectral efficiency.

^{[8]}This chapter demonstrates various ways for transmitting an image file using MIMO-OFDM systems which have anti-error ability to reduce BER.

^{[9]}This work proposes a PAPR reduction algorithm for solving the problem of high PAPR in MIMO-OFDM systems.

^{[10]}Simulation results illustrate that the novel semiblind estimators perform much better than existing blind/training-based sparse methods (even including the popular compressed sensing and Bayesian methods), when few training subcarriers are available (which may occur in futuristic, pilot-starved massive MIMO-OFDM systems), at much reduced complexity.

^{[11]}In this work, we propose a two-stage tensor-based receiver for joint channel, phase-noise (PN), and data estimation in MIMO-OFDM systems.

^{[12]}In this paper, we use SecretKey Capacity (SKC) on MIMO and MIMO-OFDM systems with more than one receiver antennas for the eavesdropper.

^{[13]}The BER and the capacity of MIMO-OFDM systems are analyzed by varying the parameters of the system.

^{[14]}Wireless Communications and Mobile Computing has retracted the article titled “Adaptive Blind Channel Estimation for MIMO-OFDM Systems Based on PARAFAC” [1], due to a high level of similarity identified with a previously published article, as confirmed by the editorial board [2]: Ruo-Nan Yang, Wei-Tao Zhang, Shun-Tian Lou, "Joint Adaptive Blind Channel Estimation and Data Detection for MIMO-OFDM Systems", Wireless Communications and Mobile Computing, vol.

^{[15]}This paper proposes a novel two-stage joint hybrid precoder and combiner design for maximizing the average achievable sum-rate of frequency-selective millimeter-wave massive MIMO-OFDM systems.

^{[16]}This paper proposes affine-precoded superimposed pilot (SIP) design, followed by channel state information (CSI) estimation techniques for millimeter wave (mmWave) MIMO-OFDM systems.

^{[17]}The main contribution of the proposed algorithm is enabling comprehensive simulation analyses of ED performance based on the SLC method for versatile combinations of operating parameter characteristics for different working environments of MIMO-OFDM systems.

^{[18]}The forward error correction plays an important role in enhancing the performance of the MIMO-OFDM systems.

^{[19]}To be specific, a spiking reservoir computing (RC) based approach is introduced for spectrum sensing of MIMO-OFDM systems to take advantage of the spatial and temporal correlations of the environment.

^{[20]}In addition to this, the proposed method is utilized to restrict the interference in the MIMO-OFDM systems.

^{[21]}Therefore, this paper proposes a computationally-efficient hybrid precoding algorithm for mmWave MIMO-OFDM systems.

^{[22]}This paper deals with IQ imbalance in wideband MIMO OFDM systems.

^{[23]}This technique has been investigated for MIMO OFDM systems so far.

^{[24]}It is found that around 50% reduction in per-user downlink rate could occur to the majority of users due to asynchronous reception in DM-MIMO OFDM systems.

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