We study spectrum sharing between two dense low-earth orbit (LEO) satellite constellations, an incumbent primary system and a secondary system that must respect interference protection constraints on the primary system. In particular, we propose a secondary satellite selection framework and algorithm that maximizes capacity while guaranteeing that the time-average interference and absolute interference inflicted upon each primary ground user never exceeds specified thresholds. We solve this NP-hard constrained, combinatorial satellite selection problem through Lagrangian relaxation to decompose it into simpler problems which can then be solved through subgradient methods. A high-fidelity simulation is developed based on public FCC filings and technical specifications of the Starlink and Kuiper systems. We use this case study to illustrate the effectiveness of our approach and that explicit protection is indeed necessary for healthy coexistence. We further demonstrate that deep learning models can be used to predict the primary satellite system associations, which helps the secondary system avoid inflicting excessive interference and maximize its own capacity.

This work provides a rigorous assessment of the feasibility of spectrum sharing between large low-earth orbit (LEO) satellite constellations. For concreteness, we focus on the existing Starlink system and the soon-to-be-launched Kuiper system, the latter of which is prohibited from inflicting excessive interference onto incumbent Starlink ground users. We carefully model and study the potential downlink interference between the two systems at 20 GHz and investigate how strategic satellite selection may be used by Kuiper to serve its own ground users while also protecting Starlink ground users. We then extend this notion of satellite selection to the case where Kuiper has limited knowledge of Starlink’s serving satellite. Throughout our analysis, we examine the distribution of interference and SINR over time as each constellation orbits the globe. Our findings reveal that there is virtually always the potential for very high or extremely low interference, depending on which Starlink and Kuiper satellites are being used to serve their ground users. Consequently, we show that Kuiper can protect Starlink ground users, with high probability, by strategically selecting which of its satellites are used to serve its ground users. Simultaneously, Kuiper is capable of delivering near-maximal downlink SINR to its own ground users. This highlights a potential feasible route to the coexistence of two dense LEO satellite systems, even in scenarios where one system has limited knowledge of the other’s serving satellites.

This work investigates the in-band coexistence between two dense low-earth orbit (LEO) satellite communication systems by analyzing two preeminent large-scale constellations, namely Starlink and Kuiper, both which have been granted non-exclusive rights to operate at 20 GHz. Through extensive simulation of Starlink and Kuiper based on their public filings, we examine downlink performance of both systems when Kuiper is obliged to protect Starlink by not inflicting prohibitive interference onto its ground users. We show that Kuiper is capable of reliably satisfying a strict protection constraint at virtually all times by strategically selecting which overhead satellites are used to serve its ground users. In fact, while protecting Starlink users in this way, our results show that Kuiper can remarkably also deliver near-maximal downlink SINR to its own ground users, revealing a feasible route to fruitful coexistence of both systems. For instance, as the constellations orbit the globe, we show that Kuiper is always capable of keeping its inflicted interference at least 12 dB below noise and in doing so sacrifices only about 1 dB in SINR over 80% of the time.

The coming extension of cellular technology to base-stations in low-earth orbit (LEO) requires a fresh look at terrestrial 3GPP channel models. Relative to such models, sky-to-ground cellular channels will exhibit less diffraction, deeper shadowing, larger Doppler shifts, and possibly far stronger cross-cell interference: consequences of high elevation angles and extreme “sectorization” of LEO satellite transmissions into partially-overlapping spot beams. To permit forecasting of expected signal-to-noise ratio (SNR), interference-to-noise ratio (INR), and probability of outage, we characterize the powers of desired and interference signals as received by ground users from such a LEO satellite. In particular, building on the Shadowed Rician (SR) channel model, we observe that co-cell and cross-cell sky-to-ground signals travel along similar paths, whereas terrestrial co- and cross-cell signals travel along very different paths. We characterize SNR, signal-to-interference ratio (SIR), and INR using transmit beam profiles and linear relationships that we establish between certain SR random variables. These tools allow us to simplify certain density functions and moments, facilitating future analysis. Numerical results yield insight into the key question of whether emerging LEO systems should be viewed as interference- or noise-limited.

In cognitive radio, spectrum sensing is used to find the white spectrum or protect the primary user from interference caused by the secondary user (SU). There are two conventional spectrum sensing approaches: quiet and active. However, these conventional approaches have several problems. In quiet sensing, the quiet period degrades the SU capacity. With active sensing, the SU capacity is also degraded by the need for additional resource consumption and the mismatch in feedback information. In order to mitigate these problems, the structure of simultaneous PU sensing and data transmission is introduced. This structure is equipped with antenna isolation and self-interference cancellation in which the communication and the sensing radios are already assumed to be significantly isolated. This approach is designed so that the SU transmitter can sense PU signals and transmit data signals at the same time by dividing its spatial resources. Expanding on this work, we propose a concept of "TranSensing" which adaptively uses spatial resource according to the surrounding environments. To effectively use TranSensing, we propose a two-stage algorithm (TSA). Finally, the impact of residual interference on TranSensing is investigated. Simulation results show that TranSensing with TSA enhances the SU capacity over the conventional quiet or active sensing.

Portable unlicensed devices operating in TV white space must adhere to FCC rules designed to protect licensed devices. These rules decrease the achievable throughput for the portable unlicensed devices because the portable unlicensed devices are required to perform a channel availability query (CAQ) before transmitting. In order to reduce the number of CAQs, the Federal Communications Commission allows a region based mobility procedure (RMP). In this paper, we propose a new-RMP using directional information in order to enhance the achievable throughput. Directional information enables an unlicensed portable device to increase its achievable throughput by considering a uni- directional region that is smaller than an omni-directional region. Simulation results show that the proposed RMPs enhance the achievable throughput compared to the conventional RMPs. In addition, the achievable throughput for the proposed RMP using a navigation system is greatly enhanced compared to the conventional RMPs.

As the volume and diversity of scientific resources grows, trend detection and analysis have become much more important issues. A variety of trend detection, characterization, evaluation and visualization methodologies have been introduced for various application domains. In this paper, we consider detection of temporal trends for topics using feature selection and extraction of additional information from the subtopics of them, for the Global Trends Briefing (GTB) dataset. Thus, we propose a novel trend detection method using feature selection based on the Improved Gini-Index (I-GI) algorithm, which can obtain representative features for given topics. Second, with those features, we extract subtopics for the topics and visualize temporal/emerging/upward/ downward trends with them. Third, utilizing the relations among the subtopics, we obtain relevant documents and seed sentences that co-occur with the upper features for the topic. In addition, we can extract information to forecast future trends relevant to the issues: for example, financial market or emerging technology. In the experimental results, we could obtain good representative features, more specific trends for the topics, and additional useful information.