Feifei Gao

We report two new heteroleptic polypyridyl ruthenium complexes, coded C101 and C102, with high molar extinction coefficients by extending the π-conjugation of spectator ligands, with a motivation to enhance the optical absorptivity of mesoporous titania film and charge collection yield in a dye-sensitized solar cell. On the basis of this C101 sensitizer, several DSC benchmarks measured under the air mass 1.5 global sunlight have been reached. Along with an acetonitrile-based electrolyte, the C101 sensitizer has already achieved a strikingly high efficiency of 11.0−11.3%, even under a preliminary testing. More importantly, based on a low volatility 3-methoxypropionitrile electrolyte and a solvent-free ionic liquid electrolyte, cells have corresponding >9.0% and ∼7.4% efficiencies retained over 95% of their initial performances after 1000 h full sunlight soaking at 60 °C. With the aid of electrical impedance measurements, we further disclose that, compared to the cell with an acetonitrile-based electrolyte, a dye-sensitized solar cell with an ionic liquid electrolyte shows a feature of much shorter effective electron diffusion lengths due to the lower electron diffusion coefficients and shorter electron lifetimes in the mesoporous titania film, explaining the photocurrent difference between these two type devices. This highlights the next necessary efforts to further improve the efficiency of cells with ionic liquid electrolytes, facilitating the large-scale production and application of flexible thin film mesoscopic solar cells.

Machine learning (ML) has been widely applied to the upper layers of wireless communication systems for various purposes, such as deployment of cognitive radio and communication network. However, its application to the physical layer is hampered by sophisticated channel environments and limited learning ability of conventional ML algorithms. Deep learning (DL) has been recently applied for many fields, such as computer vision and natural language processing, given its expressive capacity and convenient optimization capability. The potential application of DL to the physical layer has also been increasingly recognized because of the new features for future communications, such as complex scenarios with unknown channel models, high speed and accurate processing requirements; these features challenge conventional communication theories. This paper presents a comprehensive overview of the emerging studies on DL-based physical layer processing, including leveraging DL to redesign a module of the conventional communication system (for modulation recognition, channel decoding, and detection) and replace the communication system with a radically new architecture based on an autoencoder. These DL-based methods show promising performance improvements but have certain limitations, such as lack of solid analytical tools and use of architectures that are specifically designed for communication and implementation research, thereby motivating future research in this field.

We conjugated 2-(hexylthio)thiophene with bipyridine to construct a new heteroleptic polypyridyl ruthenium sensitizer exhibiting a charge-transfer band at 550 nm with a molar extinction coefficient of 18.7 × 103 M−1 cm−1. In contrast to its analogues Z907 and C101, a mesoporous titania film stained with this new sensitizer featured a short light absorption length, allowing for the use of a thin photoactive layer for efficient light-harvesting and conversion of solar energy to electricity. With a preliminary testing, we have reached 11.4% overall power conversion efficiency measured at the air mass 1.5 global conditions. Transient photoelectrical decays and electrical impedance spectra were analyzed to picture the intrinsic physics of temperature-dependent photovoltage and photocurrent.

When there are a large number of antennas in massive MIMO systems, the transmitted wideband signal will be sensitive to the physical propagation delay of electromagnetic waves across the large array aperture, which is called the spatial-wideband effect. In this scenario, the transceiver design is different from most of the existing works, which presume that the bandwidth of the transmitted signals is not that wide, ignore the spatial-wideband effect, and only address the frequency selectivity. In this paper, we investigate spatial- and frequency-wideband effects, called dual-wideband effects in massive MIMO systems from the array signal processing point of view. Taking millimeter-wave-band communications as an example, we describe the transmission process to address the dual-wideband effects. By exploiting the channel sparsity in the angle domain and the delay domain, we develop the efficient uplink and downlink channel estimation strategies that require much less amount of training overhead and cause no pilot contamination. Thanks to the array signal processing techniques, the proposed channel estimation is suitable for both TDD and FDD massive MIMO systems. Numerical examples demonstrate that the proposed transmission design for massive MIMO systems can effectively deal with the dual-wideband effects.

Intelligent communication is gradually becoming a mainstream direction. As a major branch of machine learning, deep learning (DL) has been applied in physical layer communications and has demonstrated an impressive performance improvement in recent years. However, most existing works related to DL focus on data-driven approaches, which consider the communication system as a black box and train it by using a huge volume of data. Training a network requires sufficient computing resources and extensive time, both of which are rarely found in communication devices. By contrast, model-driven DL approaches combine communication domain knowledge with DL to reduce the demand for computing resources and training time. This article discusses the recent advancements in model-driven DL approaches in physical layer communications, including transmission schemes, receiver design, and channel information recovery. Several open issues for future research are also highlighted.