Abstract-The millimeter wave (mmWave) frequency band spanning from 30 GHz to 300 GHz constitutes a substantial portion of the unused frequency spectrum, which is an important resource for future wireless communication systems in order to fulfill the escalating capacity demand. Given the improvements in integrated components and enhanced power efficiency at high frequencies, wireless systems can operate in the mmWave frequency band. In this paper, we present a survey of the mmWave propagation characteristics, channel modeling and design guidelines, such as system and antenna design considerations for mmWave, including the link budget of the network, which are essential for mmWave communication systems. We commence by introducing the main channel propagation characteristics of mmWaves followed by channel modeling and design guidelines. Then, we report on the main measurement and modeling campaigns conducted in order to understand the mmWave band's properties and present the associated channel models. We survey the different channel models focusing on the channel models available for the 28 GHz, 38 GHz, 60 GHz and 73 GHz frequency bands. Finally, we present the mmWave channel model and its challenges in the context of mmWave communication systems design.

Harnessing the abundant availability of spectral resources at millimeter wave (mmWave) frequencies is an attractive solution to meet the escalating data rate demands. Additionally, it has been shown that full-duplex (FD) communication has the potential of doubling the bandwidth efficiency. However, the presence of significant residual self-interference (SI), which is especially more pronounced at mmWave frequencies because of the non-linearities in the hardware components, erodes the full potential of FD in practice. Conventionally, the residual SI is canceled in the baseband using digital processing with the aid of a transmit precoder. In this work, we propose a hybrid beamforming design for FD mmWave communications, where the SI is canceled by the joint design of beamformer weights at the radio-frequency (RF) and the precoder as well as combiner in the baseband. Our proposed design preserves the dimensions of the transmit signal, while suppressing the SI. We demonstrate that our joint design is capable of reducing the SI by upto 30 dB, hence performing similarly to the interference-free FD system while being computationally efficient. Our simulation results show that the proposed design significantly outperforms eigen-beamforming.

Harnessing the substantial bandwidth available at millimeter wave (mmWave) carrier frequencies has proved to be beneficial to accommodate a large number of users with increased data rates. However, owing to the high propagation losses observed at mmWave frequencies, directional transmission has to be employed. This necessitates efficient beam-alignment for a successful transmission. Achieving perfect beam-alignment is however challenging, especially in the scenarios when there is a rapid movement of vehicles associated with ever-changing traffic density, which is governed by the topology of roads as well as the time of the day. Therefore, in this paper, we take the approach of fingerprint based beam-alignment, where a set of beam pairs constitute the fingerprint of a given location. Furthermore, given the time-varying traffic density, we propose a multi-fingerprint based database for a given location, where the base station (BS) intelligently adapts the fingerprints with the aid of learning. Additionally, we propose multi-functional beam transmission as an application of our proposed design, where the beampairs that satisfy the required received signal strength (RSS) participate in increasing the spectral efficiency or improving the end-to-end performance in some other way. Explicitly, the BS leverages the plurality of beam-pairs to attain both multiplexing and diversity gains. Furthermore, if the plurality of beam-pairs is higher than the number of RF chains, the BS may also employ beam-index modulation to further improve the spectral efficiency. We demonstrate that having multiple fingerprintbased beam-alignment provides superior performance than that of the single fingerprint based beam-alignment. Furthermore, we show that our learning-aided multiple fingerprint design provides a better fidelity compared to that of the benchmark scheme also employing multiple fingerprint but dispensing with learning. Additionally, our reduced-search based learning-aided beam-alignment design performs similarly to beam-sweeping based beam-alignment, even though an exhaustive beam-search is carried out by the latter. More explicitly, our design is capable of maintaining the target performance in dense vehicular environments, while both single fingerprint and line-of-sight (LOS) based beam-alignment suffer from blockages.

Abstract-In this correspondence, we propose a dual-function hybrid beamforming architecture, where the antenna array is split into sub-arrays that are separated by a sufficiently large distance so that each sub-array experiences independent fading. The proposed architecture attains the dual-functions of beamforming and diversity. We then demonstrate that splitting the array into two sub-arrays provides the best performance in terms of the achievable rate as a benefit of the diversity gain obtained in addition to the beamforming gain. However, the performance starts depleting if the array is partitioned into more than two sub-arrays because of diminishing additional diversity gains, which fails to compensate for the beamforming gain erosion due to splitting the antenna arrays. Additionally, we analyze the so-called discrete Fourier transform-mutually unbiased bases (DFT-MUB) aided codebook invoked for the conceived design, which imposes an appealingly low complexity. Explicitly, we show that for the proposed dual-function sub-array-connected design, the DFT-MUB assisted codebook outperforms the state-of-theart precoding benchmarks and performs close to the optimal precoding matrix.

Millimeter Wave (mmWave) technology coupled with full duplex (FD) communication has the potential of increasing the spectral efficiency. However, the self-interference (SI) encountered in the FD mode and the ubiquitous multiuser interference (MI) contaminates the signal. Furthermore, the system performance may also be limited by channel aging that arises because of the time-varying nature of the channel. Therefore, in this paper, we conceive FD hybrid beamforming (HBF) for K-user multipleinput multiple-output (MIMO)-aided orthogonal frequency division multiplexing (OFDM) using learningaided channel prediction. We first derive a joint precoder and combiner design for full duplex K-user MIMO-OFDM interference channels, where we aim for minimizing both the residual SI and the MI, followed by an iterative hybrid decomposition technique developed for OFDM systems. Then, we propose a learning