# Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
## 1 Introduction
In an underlay mode, the SUs transmission can coexist with the PUs transmission simultaneously, provided that the caused interference to the primary transmission is below a tolerable threshold. In this way, IoT can transmit at any time without waiting for spectrum holes. By introducing underlay CR into SatComs, IoT users can serve as the SUs and intelligently exploit any available LEO satellite spectrum while avoiding interference with legacy LEO satellite users [22; 23]. In the cognitive LEO satellite system, it is important to prevent and minimize interference between the licensed PUs and SUs. This means that resource allocation and power control are inevitably applied to the secondary system to meet the constraints imposed by the interfering primary system [24; 25; 26; 27]. Specifically, the spectrum sharing between the GEO and LEO SatCom systems is investigated in [28; 29] to maximize the sum rate of the GEO and LEO SatCom systems and the sum rate of the LEO SatCom system, respectively. In [30], the power control algorithms are proposed to maximize the delay-limited capacity of the secondary system for a cognitive LEO satellite with terrestrial systems. In [31], the spectrum sharing in LEO and high-altitude platform (HAP) cognitive system is studied for resource allocation based on the imperfect spectrum sensing. The LEO satellite serves the sensors by sharing the spectrum of the GEO SatCom system [32]. The scenario where the satellite network reuses the spectrum resources of the terrestrial network is considered in [33] and the energy efficiency is maximized by designing the power control scheme. Nevertheless, most of the above works aim to optimize the resource of the secondary system under the fixed parameters for the primary transmission. Actually, the performance of the primary and secondary systems are coupled together. It is significant to propose a joint resource allocation scheme that is applicable to both legacy satellite users and cognitive satellite IoT users. In this paper, we focus on an uplink cognitive LEO SatCom system to support IoT transmissions, in which the IoT communication system is served as the secondary system to share the spectrum of the legacy LEO SatCom system. In addition, the primary and secondary systems share the same receiver, i.e., the satellite base station. As the current LEO SatCom system widely uses code division multiple access (CDMA) technology [34; 35; 36; 37], the spread spectrum signal is distributed over a wide frequency range, making it difficult to detect the power of the primary user range [38]. The collaborative underlay spectrum sharing model is more suitable for the cognitive LEO satellite IoT system than the overlay spectrum, which requires precise spectrum sensing. With perfect power control, the underlying IoT users can use the same spread spectrum as the legacy satellite users without degrading the PU's communication quality, which greatly improves spectrum efficiency. In particular, IoT performance can be improved by using CDMA to successfully transmit more packets per unit time [39] and achieve better spectrum efficiency for IoT transmission than other orthogonal channel allocations [40]. Considering the properties of IoT transmission and in order to be compatible with the existing LEO system, in this paper, we consider supporting the cognitive satellite IoT communication in the CDMA manner. We are interested in the achievable rate and resource allocation in the cognitive LEO satellite networks. Specifically, the minimum mean square error (MMSE) detector is used to recover the information from the legacy LEO satellite users and the IoT users. Due to the randomness of spreading codes, it is difficult to allocate the resources directly. Thus, we use the random matrix theory to analyze the asymptotic signal-to-interference-plus-noise ratio (SINR) and obtain the achievable rate for both IoT and legacy systems.