Satellite Clustering for Non-Terrestrial Networks: Concept, Architectures, and Applications

Recently, mega-constellations with a massive number of low Earth orbit (LEO) satellites are being considered as a possible solution for providing global coverage due to relatively low latency and high throughput compared to geosynchronous orbit satellites. However, as the number of satellites and operators participating in the LEO constellation increases, inter-satellite interference will become more severe, which may yield marginal improvement or even decrement in network throughput. In this article, we introduce the concept of satellite clusters that can enhance network performance through satellites' cooperative transmissions. The characteristics, formation types, and transmission schemes for the satellite clusters are highlighted. Simulation results evaluate the impact of clustering from coverage and capacity perspectives, showing that when the number of satellites is large, the performance of clustered networks outperforms the unclustered ones. The viable network architectures of the satellite cluster are proposed based on the 3GPP standard. Finally, the future applications of clustered satellite networks are discussed.Comment: 7 pages, 7 figures, 1 table, submitted to IEEE Vehicular Technology Magazin

Value of Spectrum for Mobile Communications: Case of Korea

Evaluation of spectrum price is important for most of the regulatory bodies and for most mobile operators. In some cases, regulatory authority faces a situation to evaluate the value of the radio spectrum which has been allocated already. In Korea, there was a reallocation of most of the LTE spectrum bands in 2020. In this paper, we propose a method to evaluate the value of each band based on the band's capacity out of total capacity. In addition, if we can find a reasonable value of the overall spectrum band, we can find a value for each band together with the information in the first stage. This method can be easily applied if only the regulatory authority has detailed geotype information on base transceiver stations(BTS)

Valuation of Mobile Spectrum to be Reassigned

Recently, many countries have been reassigning the mobile communication spectrum and determining their price. In South Korea, the mobile communication spectrum was reassigned in 2021 with a usage period from 2022 to 2026. This study focuses on the methods for calculating reassignment fee. We aimed to measure the value of the spectrum by combining production function approach and engineering approach. First, we estimated the Cobb-Douglas production function and derived the price per spectrum unit using the equal marginal principle. Then, we assessed the appropriateness of this price through a DCF (Discounted Cash Flow) analysis. Next, considering that the value of spectrum varies by band according to their radio wave characteristics, we derived the value of each spectrum band from an investment perspective. We applied each band's relative weight to the per-unit spectrum price obtained earlier to determine each spectrum band's value. The estimation results indicated that the reassignment fee in 2021 was somewhat high in terms of total and band-specific prices. This study might give some insights to regulators facing spectrum reassignment

Deep Learning-Based Prediction of Resource Block Usage Rate for Spectrum Saturation Diagnosis

Strict restrictions on spectrum utilization and the rapid increases in mobile users have brought fundamental challenges for mobile network operators in securing sufficient spectrum resources. In designing reliable cellular networks, it is essential to predict spectrum saturation events in the future by analyzing the past behavior of base stations, especially their frequency resource block (RB) utilization states. This paper investigates a deep learning-based forecasting strategy of the future RB usage rate (RBUR) status of hundreds of LTE base stations deployed in Seoul, South Korea. The dataset consists of real measurement RBUR samples with a randomly varying number of base stations at each measurement time. This poses a difficulty in handling variable-length RBUR data vectors, which is not trivial for state-of-the-art deep learning estimation models, e.g., recurrent neural networks (RNNs), developed for handling fixed-length inputs. To this end, we propose a two-step RBUR estimation approach. In the first step, we extract a useful feature of the RBUR dataset that accurately approximates the behavior of the top quantile base stations. The feature parameters are carefully designed to be fixed-length vectors regardless of the dimensions of the raw RBUR samples. The fixed-length feature parameter vectors are readily exploited as the training dataset of RNN-based prediction models. Thus, in the second step, we propose a feature estimation strategy where the RNN is trained to predict the future RBUR from the input feature parameter sequences. With the estimated RBUR at hand, we can easily predict the spectrum saturation of the future LTE systems by examining the resource utilization states of the top quantile base stations. Numerical results demonstrate the performance of the proposed RBUR estimation methods with the real measurement dataset

Development of flapping wing robot and vision-based obstacle avoidance strategy

Due to the flight characteristics such as small size, low noise, and high efficiency, studies on flapping wing robots are being actively conducted. In particular, the flapping wing robot is in the spotlight in the field of search and reconnaissance. Most of the research focuses on the development of flapping wing robots rather than autonomous flight. However, because of the unique characteristics of flapping wings, it is essential to consider the development of flapping wing robots and autonomous flight simultaneously. In this article, we describe the development of the flapping wing robot and computationally efficient vision-based obstacle avoidance algorithm suitable for the lightweight robot. We developed a 27 cm and 45 g flapping wing robot named CNUX Mini that features an X-type wing and tailed configuration to attenuate oscillation caused by flapping motion. The flight experiment showed that the robot is capable of stable flight for 1.5 min and changing its direction with a small turn radius in a slow forward flight condition. For the obstacle detection algorithm, the appearance variation cue is used with the optical flow-based algorithm to cope robustly with the motion-blurred and feature-less images obtained during flight. If the obstacle is detected during straight flight, the avoidance maneuver is conducted for a certain period, depending on the state machine logic. The proposed obstacle avoidance algorithm was validated in ground tests using a testbed. The experiment shows that the CNUX Mini performs a suitable evasive maneuver with 90.2% success rate in 50 incoming obstacle situations