第五代行動通訊 (Fifth Generation, 5G) 為近年來無線通訊發展的高速無線通訊技術，而3GPP Release 15 也正式定義了 5G 新無線電 (NR) 行動通訊標準。新一代5G無線通訊網路必須搭配目前的通訊系統，大規模多重輸入多重輸出 (Massive Multiple Input Multiple Output System, Massive MIMO System) 為5G的候選技術，我們使用國家儀器公司 (National Instruments, NI) 的軟硬體設備建立巨量多天線系統平台，以LTE為主要架構，設立基地台 (Base Station, BS) 與使用者 (User Equipment, UE) 能即時運算且同時具有上行鏈路 (Uplink, UL) 與下行鏈路 (Downlink, DL) 的系統。
本篇論文，我們在傳送端實現一組編碼器，藉由“雙準循環低密度奇偶校驗碼” (Double Quasi-cyclic Low-Density Parity Check Code) 的特性，其雙準循環低密度奇偶校驗矩陣具有雙移位循環的性質，編碼器不需先求出反矩陣，可以直接用簡單的移位暫存器架構來實現。而在接收端的解碼器，由於矩陣每行1個數均為3的性質，使用行解碼方式能降低解碼器設計的複雜度。在傳統分層解碼演算法中，奇偶校驗矩陣是依順序或一層一層的處理，因此可以同時解碼的最大列或行數會受限於子矩陣大小。為了能夠達到低複雜度、低延遲和高吞吐量的要求，此篇論文使用最小值-總和演算法 (Min-Sum Algorithm) 於行分層解碼，比較平行行解碼和多層平行行解碼兩種方法，討論與分析行解碼結合所提出的接續解碼演算法 (successive decoding algorithm) 對DQC-LDPC解碼性能的影響。我們的結果顯示隨著平行行解碼的層數增加其性能表現略微下降，但可獲得較高的吞吐量，除此之外，結合提出的接續解碼演算法後，當在最大迭代次數及碼率較低時有較好的錯誤率表現。 最後，我們使用上述編解碼器來實現DQC-LDPC碼的大規模MIMO系統。

Fifth generation (5G) is a high-speed wireless communication technology developed in recent years for wireless communication, and 3GPP Release 15 has also officially defined the 5G new radio (NR) mobile communication standard. The next generation 5G wireless communications network must be matched with the current communication system and address not only future capacity constraints but also existing challenges, such as network reliability, coverage, energy efficiency, and latency, with current communication systems. Massive Multiple Input Multiple Output (Massive MIMO) is a candidate technology for 5G. We use National Instruments (NI)'s hardware and software equipment to build a large-scale antenna system platform. With Long Term Evolution (LTE) as the main architecture, base station (BS) and user equipment (UE) can be a real-time operating system for both uplink (UL) and downlink (DL) transmissions.
In this thesis, we implement an encoder at the transmitter. The characteristic of parity check matrix of DQC-LDPC codes has double shift circulant so the encoder can be implemented by simple shift register structure instead of using matrix inverse. Also, the parity check matrix of DQC-LDPC codes with regular column weight three is beneficial to lower total number of degrees, which can achieve low-complexity codes and ease to implement decoder. According to the above properties, we use column decoding algorithm for the decoding at the receiver. In the conventional layered decoding algorithm, the block-rows or block-columns of the parity check matrix are processed sequentially, or layer after layer. The maximum number of rows or columns that can be simultaneously processed by the conventional layered decoder is limited to the sub-matrix size. In order to achieve low complexity, low latency and high throughput requirements, in this paper, we use the min-sum column-layered decoding algorithm in decoder and compare column-layered decoding with multi-layer parallel column decoding performance. Then we discuss the performance evaluation of the proposed successive decoding algorithm for the DQC-LDPC decoding. Simulation results show that keep increasing the level of parallelism, the performance slowly degrades but higher throughput is obtained. The proposed successive decoding algorithm improve about 0.1dB SNR gain compared with random schedule in column-layered decoding. Especially when the maximum number of iteration and the code rate are low, the performance gain gets larger. The results are helpful for the system that require low latency. Finally, we use the codec described above to implement the DQC-LDPC coded massive MIMO system.

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