在未來的世界中，我們對於通訊系統的服務品質要求(QoS) 越來越高。如何
在有限的頻寬之中，有效的提升服務品質成為一個很重要的課題。近年來，感知
無線電(Cognitive Radio) 的概念開始廣為討論，許多人認為這是一個解決頻寬擁擠的好方法。在感知無線電系統中有次要使用者(Cognitive Users) 和主要使用者(Primary Users)，分別屬於兩種不同的通訊系統，像是數位電視跟手機系統。一般來說，這兩種系統要同時使用必須要用到兩個不同的頻段，但是在感知無線電系統中，這兩種不同的通訊系統能夠同時使用同樣的頻段來進行通訊，只要次要使用者(Cognitive Users) 不會對主要使用者造成影響。一般使用波束成型技術(Transmit Beamforming) 來消去特定傳送端的訊號。但是我們沒有辦法不同的兩個系統中得到彼此的完整通道資訊(Full CSI)。在這篇研究中，我們建立一個感知無線電系統，可以不用知道完整的通道資訊(Channel State Information) 就可以進行傳送端波束成型(Transmit Beamforming) 來降低次要使用者(Cognitive Users)對主要使用者(Primary Users) 的干擾。在這個感知無線電系統中，我們首先利用MUSIC 演算法來進行訊號到達角度(Direction of arrival, DOA ) 的估計, 接下來根據估計到的角度來進行傳送端波束成型(Transmit Beamforming)。同時我們也使用KR-MUSIC 演算法來克服天線數少於訊號到達個數(number of DOAs) 的情況。我們首先使用電腦模擬來驗證估算角度的正確性，接著我們驗證傳送端波束成型(Transmit Beamforming) 的確可以使主要使用者(PUs) 受到的干擾變小，使錯誤率
(Bit Error Rate, BER) 降低。在完成模擬驗證之後，我們使用通用軟體無線電週邊設備平台(Universal Software Radio Peripheral, USRP) 來建立實際(Real-Time) 的系統並且驗證結果。

In the future world, we require higher QoS in communication systems. How to imporve QoS efficiently with limited bandwidth has become an important issue. Recently, the
concept of cognitive radio has emerged and is regard this as a solution to the limitation of bandwidth. There are cognitive users(CRs) and primary users(PUs) in a cognitive radio system.In this thesis, two kinds of users are in different communication system i.e. Digital TV and mobile system. Generally, two different communication systems need two different frequency bands for these two different systems so that we need lots of bandwidth to support them. However, in cognitive radio system these two systems can use the same frquency band as long as CRs will not cause interference to PUs. To null the interference, we used to adopt Transmit Beamforming technology with full CSI, but we cannot obtain full CSI in Cognitive Radio system since PUs and CRs are two different systems. In this thesis, we construct a Cognitive Radio system with Transmit Beamforming to reduce the interference between CRs to PUs with only DOA information without full Channel State
Information(CSI). At first, MUSIC estimation algorithm is applied to estimate the Direction of Arrivals(DOAs), and then we utilize this space information to apply Transmit Beamforming. Moreover, KR-MUSIC algorithm is applied to alleviate the constraint that the number of antenna is less than the number of DOAs. The accuracy of our DOA estimation algorithm is shown by simulation, and then we verify Transmit Beamforming can indeed reduce the interference to PUs so that the BER would be lower. After verifying
with simulation, in this thesis we have constructed a real-time Cognivive Radio system with Universal Software Radio Peripheral(USRP) and demonstrate its performance.

[1] Stephen Poythress Boyd and Lieven Vandenberghe. Convex optimization. Cambridge
university press, 2004.
[2] Federal Communications Commission et al. Second report and order and memorandum
opinion and order, in the matter of unlicensed operation in the tv broadcast
bands (et docket no. 04-186) and additional spectrum for unlicensed devices below
900 mhz and in the 3 ghz band (et docket no. 02-380), fcc 08-260, 2008.
[3] Tanuja S Dhope. Application of music, esprit and root music in doa estimation.
[4] Richard B Ertel, Paulo Cardieri, Kevin W Sowerby, Theodore S Rappaport, and Jeffrey
H Reed. Overview of spatial channel models for antenna array communication
systems. IEEE, Personal Communications, 5(1):10--22, 1998.
[5] Feifei Gao, Rui Zhang, Ying-Chang Liang, and Xiaodong Wang. Design of learningbased
mimo cognitive radio systems. IEEE Transactions on Vehicular Technology,
59(4):1707--1720, 2010.
[6] Lal Chand Godara. Application of antenna arrays to mobile communications. ii.
beam-forming and direction-of-arrival considerations. Proceedings of the IEEE,
85(8):1195--1245, 1997.
[7] Simon Haykin. Cognitive radio: brain-empowered wireless communications. IEEE
Journal , on Selected Areas in Communications, 23(2):201--220, 2005.
[8] Roger A Horn and Charles R Johnson. Matrix analysis. Cambridge university press,
2012.
[9] Friedrich K Jondral. Software-defined radio: basics and evolution to cognitive radio.
EURASIP Journal on Wireless Communications and Networking, 2005(3):275--283,
2005.
[10] Jean Philippe Kermoal, Laurent Schumacher, Klaus Ingemann Pedersen, Preben Elgaard
Mogensen, and Frank Frederiksen. A stochastic mimo radio channel model

with experimental validation. IEEE Journal , on Selected Areas in Communications,
20(6):1211--1226, 2002.
[11] CG Khatri and C Radhakrishna Rao. Solutions to some functional equations and their
applications to characterization of probability distributions. Sankhyā: The Indian
Journal of Statistics, Series A, pages 167--180, 1968.
[12] Joseph B Kruskal. Three-way arrays: rank and uniqueness of trilinear decompositions,
with application to arithmetic complexity and statistics. Linear algebra and
its applications, 18(2):95--138, 1977.
[13] Wei Liu and Stephan Weiss. Wideband beamforming: concepts and techniques,
volume 17. Wiley, 2010.
[14] Zhi-Quan Luo, Wing-Kin Ma, A.M.-C. So, Yinyu Ye, and Shuzhong Zhang.
Semidefinite relaxation of quadratic optimization problems. IEEE, Signal Processing
Magazine, 27(3):20--34, 2010.
[15] Wing-Kin Ma, Tsung-Han Hsieh, and Chong-Yung Chi. Doa estimation of quasistationary
signals via khatri-rao subspace. In IEEE International Conference,
ICASSP 2009., on Acoustics, Speech and Signal Processing., pages 2165--2168,
2009.
[16] C.S. MacInnes. Source localization using subspace estimation and spatial filtering.
IEEE Journal of Oceanic Engineering, 29(2):488--497, 2004.
[17] Shuo Pan, Salman Durrani, and Marek E Bialkowski. Mimo capacity for spatial
channel model scenarios. Proc. of Aust. Comm. Thr. Wkshp, pages 25--29, 2007.
[18] Lucas Parra and Clay Spence. Convolutive blind separation of non-stationary
sources. IEEE Transactions on Speech and Audio Processing, 8(3):320--327, 2000.
[19] Dinh-Tuan Pham and J-F Cardoso. Blind separation of instantaneous mixtures of
nonstationary sources. IEEE Transactions on Signal Processing, 49(9):1837--1848,
2001.

[20] Kamran Rahbar and James P Reilly. A frequency domain method for blind source
separation of convolutive audio mixtures. IEEE Transactions on Speech and Audio
Processing, 13(5):832--844, 2005.
[21] Kamran Rahbar, James P Reilly, and Jonathan H Manton. Blind identification of
mimo fir systems driven by quasistationary sources using second-order statistics: A
frequency domain approach. IEEE Transactions on Signal Processing, 52(2):406--
417, 2004.
[22] Farrokh Rashid-Farrokhi, KJ Ray Liu, and Leandros Tassiulas. Transmit beamforming
and power control for cellular wireless systems. IEEE Journal on, Selected Areas
in Communications, 16(8):1437--1450, 1998.
[23] R Roy, A Paulraj, and T Kailath. Comparative performance of esprit and music
for direction-of-arrival estimation. In IEEE International Conference on ICASSP'87
Acoustics, Speech, and Signal Processing., volume 12, pages 2344--2347. IEEE,
1987.
[24] Richard Roy and Thomas Kailath. Esprit-estimation of signal parameters via rotational
invariance techniques. IEEE Transactions, on Acoustics, Speech and Signal
Processing, 37(7):984--995, 1989.
[25] Ralph Schmidt. Multiple emitter location and signal parameter estimation. IEEE
Transactions, on Antennas and Propagation, 34(3):276--280, 1986.
[26] Nicholas D Sidiropoulos and Rasmus Bro. On the uniqueness of multilinear decomposition
of n-way arrays. Journal of chemometrics, 14(3):229--239, 2000.
[27] Nicholas D Sidiropoulos, Rasmus Bro, and Georgios B Giannakis. Parallel factor
analysis in sensor array processing. IEEE Transactions on Signal Processing, 48(8):
2377--2388, 2000.
[28] Petre Stoica and Randolph L Moses. Spectral analysis of signals. Pearson/Prentice
Hall Upper Saddle River, NJ, 2005.
[29] Walter HW Tuttlebee. Software defined radio: enabling technologies. Wiley. com,
2003.

[30] Lieven Vandenberghe and Stephen Boyd. Semidefinite programming. SIAM review,
38(1):49--95, 1996.
[31] Shuzhong Zhang. Quadratic maximization and semidefinite relaxation. Mathematical
Programming, 87(3):453--465, 2000.