UE 執行隨機接入過程以建立與 eNB 的無線電鏈路。由於用戶的 隨機性和無線環境的複雜性，這種接入的發起和使用的資源也是隨機 的，這使得終端與網絡建立通信連接成為可能。物聯網傳感器的數量 最近顯著增加。如果多個設備同時嘗試隨機訪問，可能會發生 RA 過 載。非陸地網絡（NTN）可以成為減輕海量接入負載並覆蓋大區域的 潛在解決方案。特別是有前途的低地球軌道（LEO）衛星地面網絡已 被考慮。由於衛星環境的獨特特性，該系統的主要挑戰之一是適應物 聯網設備的大量隨機訪問 (RA) 請求，同時最大限度地減少其訪問過 載。以前的工作主要集中在地面網絡上。他們經常通過 ACB 因子控制 隨機接入信道 (RACH) 資源。然而，這些作品並沒有在短時間內以相 同的物聯網設備服務質量 (QoS) 解決海量訪問問題。它沒有考慮 LEO 場景中的時變多普勒頻移和傳播延遲。 在我們的文章中，我們專注於利用 LEO 衛星場景中物聯網設備的 位置來分配前導碼。通過基於區域分配前導碼，我們減輕了 RA 過載。 我們建立了馬爾可夫過程模型來分析隨機訪問負載。結果表明，我們 的方法可以更有效地減少 LEO 場景中的 RA 負載。;The random access procedure is performed for the UE to establish a radio link with the eNB. Due to the randomness of users and the complexity of the wireless environment, the initiation of this access and the resources used are also random, which makes random access (RA) possible for the terminal to establish a communication connection with the network. The number of IoT sensors is rising significantly recently. If many devices try random access simultaneously, the RA overload may happen. NonTerrestrial Network (NTN) can be a potential solution for alleviating the massive access load and reach the large region. Especially the promising Low Earth Orbit (LEO) satellite terrestrial network has been considered. Due to unique characteristics of satellite environment, one of the main challenges in this system is to accommodate massive random access (RA) requests of IoT devices while minimizing their access overload. Previous works focused on terrestrial network. They often control Random Access Channel (RACH) resources by ACB factor. However, the works did not solve the massive access within a short time with the same Quality of Service (QoS) of IoT devices. It did not consider the time-varying Doppler shift and propagation delay in LEO scenarios. In our article, we focused on allocating preambles by utilizing the location of IoT devices in LEO satellite scenarios. By allocating the preambles based on the region, we alleviate the RA overload. We build the Markov process model for analysing random access load. The results show that our methodology is more efficient to reduce the RA load in LEO scenarios.
