於下行鏈路NOMA系統之即時功率分配;Enhanced Real-Time Power Allocation on Downlink NOMA

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Communication Engineering > Electronic Thesis & Dissertation > Item 987654321/83830

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/83830

Title:	於下行鏈路NOMA系統之即時功率分配;Enhanced Real-Time Power Allocation on Downlink NOMA
Authors:	羅宗益;Luo, Zong-Yi
Contributors:	通訊工程學系
Keywords:	非正交多重存取技術;正交多重存取技術;連續干擾消除;使用者分群;功率分配;瑞雷通道;NOMA;OMA;SIC;User Grouping;Power Allocation;Rayleigh Channel
Date:	2020-07-29
Issue Date:	2020-09-02 17:10:59 (UTC+8)
Publisher:	國立中央大學
Abstract:	近年來，全世界對於資料的需求正以驚人的速度持續成長。無論身在何處，我們都需要保持連網，並期待無接縫的豐富內容服務。目前的裝置使用越來越多的資料，具備網際網路功能的裝置數量也越來越可觀，現有網路基礎架構確實需要重大的改變，才能追上需求的遽增。在目前的 4G LTE (OFDMA) 網路中，一般要求自我干擾迴避採用的是嚴格基於時間的方法，這導致了較高的功耗，並嚴重浪費控制平面傳訊資源。目前正在密集進行評估可取代目前使用之 OFDM 的 5G 抗干擾存取方案。更有效率的正交方案，例如 FBMC、UFMC 或 GFMD，可更有效地利用頻譜，也是未來 5G 存取頻波形的理想候選技術。一般而言，正交傳輸可避免自我干擾，如此可帶來較高的系統容量。但是，若要快速存取小型酬載，將正交資源指派至不同使用者的程序可能需要大量的傳訊並導致額外的延遲。因此，目前也考慮採用支援非正交存取以補足正交存取。這些例子包括非正交多重存取 (NOMA) 與稀疏編碼多重存取 (SCMA)。本文專注討論於 NOMA 方面。在使用 NOMA 時，接收端必須考慮到消除干擾的問題，目前較常見的方法有連續消除干擾 (Successive Interference Cancellation, SIC)和聯合偵測 (Joint-detection)，不管使用哪種方法都會增加接收端的運算複雜度且更加耗電，其中 SIC 對使用者的傳送功率尤為敏感，假若使用者的功率相當，SIC 就很難運作，正常的消除干擾。本文提出即時功率分配演算法 (Enhanced real-time power allocation)來提升在使用者功率相當時的傳送品質，並於模擬顯示，在運用演算法後，可以在不變動系統通道容量的前提下，降低接收的錯誤率，改善 SIC 因為使用者功率差，所造成的位元錯誤率 (bit error rate, BER)提升。;In recent years, the world′s demand for data has continued to increase at an alarming rate. No matter where we are, we need to stay in touch and look forward to a seamless and rich service. Current devices use more and more data, and the number of Internet-enabled devices is also increasing. The existing network infrastructure does require major changes to adapt to growing demand. In the current 4G LTE (OFDMA) network, a time-based method is generally required to avoid self-interference, which results in higher power consumption and a serious waste of control plane communication resources. An intensive evaluation is currently under way to replace the currently used OFDM 5G anti-interference access scheme. More efficient orthogonal schemes, such as FBMC, UFMC or GFMD, can make more efficient use of spectrum and are also ideal candidates for future 5G access frequency waveforms. Generally speaking, orthogonal transmission can avoid self-interference, which can bring higher system capacity. However, to quickly access small payloads, processes that assign orthogonal resources to different users may require a large amount of messaging and cause additional delays. Therefore, it is currently considered to support non-orthogonal access to complement orthogonal access. Examples include non-orthogonal multiple access (NOMA) and sparse code multiple access (SCMA). This thesis focuses on NOMA. To transmit each UE’s signal, the base station uses different transmission power and add them together which method is also known as power domain. Hence, every UE’s data can be transmitted in the same spectrum at the same time. But with NOMA, receivers have to face interference cancellation problem. Currently, the common method to solve the problem are successive interference cancellation (SIC) and joint-detection. No matter which method you use, they always increase the computation complexity of receiver side and consume more electricity. SIC is very sensitive to transmission power. If the transmission power of each UE is almost equivalent, SIC cannot cancel interference completely. In this paper, we propose a Enhanced real-time power allocation algorithm to improve the transmission performance when UEs’ transmission power is almost equivalent. Simulation results show that after introduce algorithm, it can reduce error rate at receiver side and improve SIC performance without changing any system channel capacity.
Appears in Collections:	[Graduate Institute of Communication Engineering] Electronic Thesis & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	125	View/Open

社群 sharing

Loading...