Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have been considered a promising solution to provide high-quality data services. To achieve full beamforming gain, the acquisition of accurate channel information is crucial for the success of massive MIMO systems. In recent years, numerous approaches have been suggested to acquire the downlink channel state information (CSI). However, a significant mismatch between the estimated channel and the actual channel for data transmission causes outdated CSI, leading to a severe degradation in spectral efficiency. In this paper, we propose a channel estimation technique for mmWave massive MIMO systems that obtains multipath components of the downlink channel from the previous channel sequence. To be specific, the proposed technique learns the spatio-temporal correlation be-tween multipath components by exploiting a Transformer-based framework. From the numerical results, we demonstrate that the proposed technique outperforms the conventional channel acquisition techniques in terms of normalized mean square error (NMSE).

As a key technology to meet the ever-increasing data rate demand in beyond 5G and 6G communications, millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have gained much attention recently. To make the most of mmWave massive MIMO systems, acquisition of accurate channel state information (CSI) at the base station (BS) is crucial. However, this task is by no means easy due to the CSI feedback overhead induced by the large number of antennas. In this paper, we propose a parametric CSI feedback technique for mmWave massive MIMO systems. Key idea of the proposed technique is to compress the mmWave MIMO channel matrix into a few geometric channel parameters (e.g., angles, delays, and path gains). Due to the limited scattering of mmWave signal, the number of channel parameters is much smaller than the number of antennas, thereby reducing the CSI feedback overhead significantly. Moreover, by exploiting the deep learning (DL) technique for the channel parameter extraction and the MIMO channel reconstruction, we can effectively suppress the channel quantization error. From the numerical results, we demonstrate that the proposed technique outperforms the conventional CSI feedback techniques in terms of normalized mean square error (NMSE) and bit error rate (BER).

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have been considered as a promising solution to provide high-quality data services. To get the most of beamforming gain, an acquisition of accurate channel information is crucial for the success of massive MIMO systems. In recent years, numerous approaches to reduce the excessive feedback overhead due to the large number of antennas have been suggested. However, they do not consider the long-range dependency of the channel state information (CSI) resulting from the correlation between the large-scale antennas and subcarriers. In this paper, we propose a deep learning-based parametric CSI feedback technique for mmWave massive MIMO systems to improve the performance of CSI compression and reconstruction with low feedback overhead. To be specific, the proposed scheme learns the correlation between channel parameters by exploiting Transformer architecture. From the numerical results, we demonstrate that the proposed scheme outperforms the conventional channel feedback schemes in terms of the normalized mean square error (NMSE) and the feedback overhead reduction.

The terahertz (THz) communication systems as-sisted by ultra-massive (UM) number of antennas have been considered as a promising solution for future 6G wireless communications. As a means to overcome the severe propagation loss arising from THz band and thus achieve high beamforming gain, ultra-massive multiple-input-multiple-output (UM-MIMO) sys-tems have received much attention. To realize highly directional communications, acquisition of accurate channel state information is essential but the channel estimation techniques designed for the ideal far-field channel result in severe performance loss in the near-field region. In this paper, we propose an intelligent near-field channel estimation technique for THz UM-MIMO systems. To be specific, we extract the channel parameters, i.e., angles, distances, time delay, and complex gains, by exploiting the convolution neural network (CNN), a deep learning network specialized in capturing the spatially correlated features from the input data. From the simulation results, we demonstrate that the proposed scheme outperforms the conventional channel estimation schemes in terms of the bit error rate (BER) and the pilot overhead reduction.

With the explosive growth in mobile data traffic, ultra-dense network (UDN) where a large number of small cells are densely deployed on top of macro cells has received a great deal of attention in recent years. While UDN offers a number of benefits, an upsurge of energy consumption in UDN due to the intensive deployment of small cells has now become a major bottleneck in achieving the primary goals viz., 100-fold increase in the throughput in 5G+ and 6G. In recent years, an approach to reduce the energy consumption of base stations (BSs) by selectively turning off the lightly-loaded BSs, referred to as the sleep mode technique, has been suggested. However, determining an appropriate active/sleep modes of BSs is a difficult task due to the huge computational overhead and inefficiency caused by the frequent BS mode conversion. An aim of this paper is to propose a deep reinforcement learning (DRL)-based approach to achieve a reduction of energy consumption in UDN. Key ingredient of the proposed scheme is to use decision selection network to reduce the size of action space. Numerical results show that the proposed scheme can significantly reduce the energy consumption of UDN while ensuring the rate requirement of network.

A hybrid Miller-Cascode compensation (HMCC) scheme incorporating Miller compensation (MC) and cascode compensation on a nonsignal path (CCNSP) in the two-stage amplifiers is presented. The proposed HMCC resolves issues in other compensations such as CCNSP, cascode compensation on a signal path (CCSP), and hybrid cascode compensation (HCC) such that the gain peaking near unity gain frequency (UGF) in the open-loop transfer function is alleviated, which results in faster settling. To understand and validate the merit of the proposed HMCC, the locations of poles and a zero are analyzed through the small-signal model and compared with other compensations in terms of settling speed. Moreover, to verify the effect of gain peaking on settling speed, two pipeline ADCs employing HMCC and CCNSP are fabricated in a 0.11-μm CMOS process. In measurement, the ADCs with HMCC achieve higher spurious free dynamic range (SFDR) at the sampling frequencies above 20 MHz than the ADCs with CCNSP, which demonstrates that the proposed HMCC achieves faster settling than CCNSP due to gain peaking suppression.

In orthogonal frequency division multiplexing (OFDM) systems, channel estimation is by far the most important operation in the receiver to ensure the accurate detection and decoding. Over the years, pilot-aided channel estimation has been widely used for this purpose. In open-loop systems, since there is no feedback link between the transmitter and receiver, an approach based on the equi-spaced pilot assignment has been widely employed. In this paper, we propose a closed-loop non-uniform pilot allocation strategy based on deep neural network (DNN) technique. From the numerical evaluations, we show that the proposed autoencoder-based pilot allocation technique outperforms conventional approaches by a large margin, demonstrating its ability to learn the statistical characteristics of the wireless channel.