Limited work has previously been conducted into microwave communication in natural caves and, although modal analysis has given acceptable results in some cave passages, multiple bends and highly irregular passages remained problematic. Recent advances in rapid capture laser scanning technology have permitted accurate 3D models of cave passages to be created, prompting the current study into modeling by ray tracing. Issues of model simplification and handling of roughness, have been studied. The technique has been used in several cave passages – one in which modeling by modal analysis was especially unsuccessful – at three frequencies, and experimental results obtained. Acceptable correlation was demonstrated despite the difficulty in accurately measuring or estimating average roughness.

Free space optical communications (FSO) are fast emerging as the most promising approach for addressing the emerging broadband access network and its "last mile" bottleneck. However, atmospheric attenuations and weather dependence remain the major hurdle in the wide scale acceptability of the technology. This paper proposes performance enhancement using higher state modulation schemes (i.e. 16 and 256-PPM) which promises a gain upto 25 dB when combined with RS codes as compared to the current widely used 2 state modulation mechanisms. The "RS-coded PPM" is shown as an extremely robust and well performing coded-modulation scheme for future FSO systems using a novel approach to terrestrial FSO channel modeling.

The design and optimization of next-generation indoor wireless communication networks require detailed and precise descriptions of the indoor environments. Environmental awareness can serve as a fundamental basis for the dynamic adaptation of the wireless system to channel conditions and can improve the system’s performance. Methods that combine wireless technology with machine learning are promising for identifying the properties of the indoor radio environment (RE) without requiring specialized equipment or manual intervention. In the paper, we propose an approach for identifying the materials of the surfaces using channel impulse response (CIR) and RE identification models built with machine learning. To train the models and assess their performance, we acquired radio propagation data from rooms with different sizes and materials using ray tracing. We explored tree-based methods, ensemble-based methods, kernel-based methods, and neural networks for training the models. The performance of the models is evaluated in three realistic scenarios defined by the location of the radio nodes and the room sizes. The multilayer perceptron models performed best in most of the evaluation settings. The results show that the models are capable of accurately predicting the materials in rooms with sizes that were not included in the training procedure. Including CIRs from a large number of rooms with different sizes and surface materials estimated with different radio node positions in the training process results in models with wider practical applicability.

To enable throughput rates in the terabit-per-second range, future satellite communications will have to utilize a large available bandwidth in the Q-band and above. At these high frequencies, the signal is severely degraded and attenuated by tropospheric phenomena, especially rainfall, which reduces the availability of satellite communication link and degrades the quality of service. Site diversity is an efficient technique used to mitigate the rain attenuation. In this paper, we evaluate the performance of the large-scale site diversity system consisting of ground stations in Ljubljana and Budapest based on long-term satellite signal measurement campaigns of Alphasat at 39.402 GHz. The measured attenuation time series are statistically analyzed and presented for a period of one year and compared with three representative prediction models.