Variability of radio signal attenuation by single deciduous tree versus reception angle at 80 GHz

Vegetation significantly affects radio signal attenuation, influenced by factors such as signal frequency, plant species, and foliage density. Existing attenuation models typically address specific scenarios, like single trees, rows of trees, or green spaces, with the ITU-R P.833 recommendation being a widely recognized standard. Most assessments for single trees focus on the primary radiation direction of the transmitting antenna. This paper introduces a novel approach to evaluating radio signal scattering by a single deciduous tree. Through measurements at 80 GHz and a bandwidth of approximately 2 GHz, we analyze how total signal attenuation varies with the reception angle relative to the transmitter-tree axis. The findings from various directional measurements contribute to a comprehensive attenuation model applicable to any reception angle and also highlight the impact of bandwidth on the received signal level.

Power angular spectrum versus Doppler spectrum -- Measurements and analysis

In this paper, we present an empirical verification of the method of determining the Doppler spectrum (DS) from the power angular spectrum (PAS). Measurements were made for the frequency of 3.5 GHz, under non-line-of-sight conditions in suburban areas characteristic of a university campus. In the static scenario, the measured PAS was the basis for the determination of DSs, which were compared with the DSs measured in the mobile scenario. The obtained results show that the proposed method gives some approximation to DS determined with the classic methods used so far.

Spectral efficiency for mmWave downlink with beam misalignment in urban macro scenario

In this paper, we analyze the spectral efficiency for millimeter wave downlink with beam misalignment in urban macro scenario. For this purpose, we use a new approach based on the modified Shannon formula, which considers the propagation environment and antenna system coefficients. These factors are determined based on a multi-ellipsoidal propagation model. The obtained results show that under non-line-of-sight conditions, the appropriate selection of the antenna beam orientation may increase the spectral efficiency in relation to the direct line to a user.

Site-Specific Radio Channel Representation -- Current State and Future Applications

A site-specific radio channel representation considers the surroundings of the communication system through the environment geometry, such as buildings, vegetation, and mobile objects including their material and surface properties. In this article, we focus on communication technologies for 5G and beyond that are increasingly able to exploit the specific environment geometry for both communication and sensing. We present methods for a site-specific radio channel representation that is spatially consistent, such that mobile transmitter and receveiver cause a correlated time-varying channel impulse response. When modelled as random, this channel impulse response has non-stationary statistical properties, i.e., a time-variant Doppler spectrum, power delay profile, K-factor and spatial correlation. A site-specific radio channel representation will enable research into emerging 5G and beyond technologies such as distributed multiple-input multiple-output systems, reconfigurable intelligent surfaces, multi-band communication, and joint communication and sensing. These 5G and beyond technologies will be deployed for a wide range of environments, from dense urban areas to railways, road transportation, industrial automation, and unmanned aerial vehicles.

Deep Single Shot Musical Instrument Identification using Scalograms

Musical Instrument Identification has for long had a reputation of being one of the most ill-posed problems in the field of Musical Information Retrieval(MIR). Despite several robust attempts to solve the problem, a timeline spanning over the last five odd decades, the problem remains an open conundrum. In this work, the authors take on a further complex version of the traditional problem statement. They attempt to solve the problem with minimal data available - one audio excerpt per class. We propose to use a convolutional Siamese network and a residual variant of the same to identify musical instruments based on the corresponding scalograms of their audio excerpts. Our experiments and corresponding results obtained on two publicly available datasets validate the superiority of our algorithm by $\approx$ 3\% over the existing synonymous algorithms in present-day literature.