研究多發多收及數位訊號處理技術於高傳輸率之下世代光纖無線通訊系統

Abstract

MIMO(Multi-Input Multi-Output)多發多收技術係於發射端及接收端同時運用多根天線及相關之通訊信號處理技術，它利用通道的空間特性來提高頻譜效率，要求使用多個天線來精確地取樣這些空間特徵，用以有效提昇系統容量。由於該技術可以在不需要增加頻寬或總發送功率耗損的情況下大幅地增加系統的傳輸效率及傳送距離，使得此技術於近幾年受到許多矚目且已被廣泛的應用。3GPP Release8版本中定義的LTE即採用了MIMO技術，其下行的峰值速率最高可達300 Mbp（4×4 MIMO）和150 Mbps（2×2 MIMO）。 本論文主要貢獻在於整合高頻光載微波系統與多發多收技術，並藉由演算法用以更進一步的提升資料傳輸效率。由於高頻光載微波系統將光纖高頻寬的優勢和微波無線的靈活特性結合，具有體積小、重量輕、低成本、低損耗等優點，固可解決傳統無線傳輸在高頻段損耗大等問題。為了實現無縫整合，針對光載微波系統提出了新的光極化多工架構和演算法，實現不需光極化追蹤的光載微波多發多收系統。此外，鑒於巨量天線系統(Massive MIMO)為未來5G的熱門選用技術，本論文也針對此技術結合體育館場景藉由模擬來找出最合適的天線數量和擺放方式，同時導入混和波束成型(Hybrid Beamforming)技術進而降低巨量天線系統的架設成本，其中為了符合天線擺放的方式，亦對混和波束成型技術提出改良。

In this dissertation, two MIMO systems are investigated for indoor or outdoor application in next-generation communication systems. First, it shows the overview of wireless communication systems and introduces RoF technology for the current state of fiber to overcome the challenges of mm-wave transmission in next-generation communication systems. By applying MIMO technology to 60-GHz radio-over-fiber system to increase data rate, we analyse the characteristic of wireless channel by change the location of antenna. From the measurement, some of the antenna location arrangement can cause extremely low performance due to the high condition number of channel. Hence, to mitigate the penalty caused by higher CN, lattice reduction-aided detection with Lenstra-Lenstra-Lovasz algorithm is utilized and experimentally investigated. On the other hand, comparing to our past work, we improve the fiber transmission distance from 4 km to 12 km by choosing proper carrier frequencies of two MZM driving signals. However, the generated 60 GHz OFDM signal will have beat noise after square-law photo-detection. Because the beat noise is analytically analyzed, and the beat noise re-construction technique is derived, the beat noise mitigation algorithm with the beat noise re-construction technique is utilized to reduce beat-noise-induced interference and improve signal-to-noise ratio. Based on the acknowledge of MIMO technology, we combine two transmission data streams and two corresponding SSBIs using multiple-output technology to expand transmission capacity. Training symbols were used to characterize the information of MIMO channels in order to separate the two data streams using their own SSBI. The use of iterative SSBI mitigation for the separation of data streams enables the recovery of the two data streams without affecting the MIMO channel or SSBI. To provide a system which both optical and wireless are operated with MIMO technique, optical layer signal multiplexing with polarization division multiplexing (PDM) has been investigated to transmit the optical signals for wireless MIMO transmission. A new method for tracking the polarization of a DD-PDM-OFDM system without employing extra optical components at the optical receiver is proposed. The key is that two polarization-orthogonal reference optical carriers are separated with an OFDM subcarrier frequency spacing, so that the issue of polarization tracking is converted to the issue of inter-subcarrier interference. With appropriate training symbol design, this approach makes it possible to combine both PDM and MIMO signal processing with the same DSP algorithm. However, noise enhancement due to channel inversion is observed with the proposed system. Therefore, the method of OFDM empty tone insertion is proposed, and the corresponding penalty of increasing overhead is also well analyzed. Also, subcarrier power pre-compensation is also adopted to improve the overall system performance and meet the BER forward error correction (FEC) limit for all states of polarization (SOP). For the last part of our work, to exploit the full potential of MIMO technique, we study the massive MIMO technique which is one of the candidate solutions for next generation mobile communication system. Using the law of large numbers, in a rich scattering environment, the full potential of massive MIMO systems can be achieved by utilizing simple digital beamforming techniques such as zero forcing (ZF) and maximum ratio transmission (MRT). However, using unlimited number of antenna is not realistic. Therefore, we focus on the improvement of system by applying M-MIMO technique with limited number of antenna by utilizing simulation with Matlab. In the simulation, the scenario is outdoor with carrier frequency of 2.1 GHz because most of the massive MIMO system in practice is at this frequency and it helps us to verify the simulation results. Besides, because the cost of setup and implementation are increased with M-MIMO system, we adopt the hybrid beamforming technique to further reduce the cost of implementation.