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In this paper, we propose deep learning assisted detection for index modulation millimeter wave (mmWave) systems, where we train a neural network (NN) to jointly detect the transmitted data and index information without relying on explicit channel state information (CSI). As a design example, we first employ multi-set space-time shift keying (MS-STSK) combined with beamforming for transmission over the mmWave channel, where the information is conveyed implicitly using the index of the antennas, the dispersion matrix and the M-ary constellation. Then, we analyze our design when MS-STSK transmission is considered in conjunction with beam index modulation (BIM), where the information is also conveyed by the beam index in addition to the MS-STSK information. In contrast to the MS-STSK's conventional maximum likelihood (ML) detector, our learning-assisted detection dispenses with the channel estimation stage. We demonstrate by simulations that the learning assisted detection outperforms the ML-aided detection in the face of channel impairments with low complexity. Furthermore, we show by simulations that ML-aided detection produces an error floor, when the MS-STSK transmission is coupled with BIM, when realistic channel estimation errors are considered. Additionally, we present qualitative discussions on the receiver complexity in terms of its search space as well as the number of computations required.

As a key technique of supporting the fixed backbone network, radio over fiber (RoF) systems transmit the radio frequency signals over optical fiber in order to take advantage of their large available bandwidth. In this context, optical fiber aided phased antenna array (PAA) based beamforming techniques have attracted substantial research interest with the goal of improving the cell-edge coverage of cellular base stations. In this paper, we conceive a novel optical fiber aided beamforming technique based on the fiber's nonlinearity to be applied in cloud radio access network (C-RAN). In our proposed technique, the PAA elements are fed by the phase-shifted signals introduced by our highly nonlinear fiber (HNLF) aided phase-shifting solution, which results in an angular beamsteering range of around 90◦.This can be exploited by sectorization in cellular networks to reduce the cochannel interference imposed. Furthermore, we exploit the proposed RoF-aided phase shifting technique in C-RAN, where our proposed system takes advantage of the centralized signal processing capability of the RoF system to conceive an all-optical processing based tunable beamforming system. While our flexible HNLF-aided phase-shifting process is confined to the central office of the C-RAN, the end users in the C-RAN cellular networks are capable of flexibly choosing the serving remote radio heads and employing diverse wireless transmission techniques. Through integrating our HNLF-aided phase-shifting design into the proposed C-RAN, we impose as little as 0.1 dB signal-to-noise ratio degradation compared to its traditional electronic counterpart, which requires extra phase-shifters. [ABSTRACT FROM AUTHOR]

The millimeter wave (mmWave) frequency band spanning from 30 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, 38, 60, and 73 GHz frequency bands. Finally, we present the mmWave channel model and its challenges in the context of mmWave communication systems design. [ABSTRACT FROM AUTHOR]