具備頻譜掃瞄及線性預編碼之感知無線電系統

Linear Precoding and Adaptive Multi-Taper Spectrum Detector for Cognitive Radios

曾義涵(Yi-Han Tseng)

指導教授 ： 馬席彬

國立清華大學/電機資訊學院/電機工程學系/碩士(2011年)

摘要

近年來，感知無線電被視為可解決頻譜使用效率不高和日漸增加的頻譜需求的方法。在本論文中提出了一個具備頻譜掃描以及傳送參數調整的感知無線電系統。在感知無線電中，因為次要使用者(Secondary User)不能影響到主要使用者(Primary User)，所以頻譜掃描是一項非常重要的議題。因此，在本論文中應用了Thomson所提出的適應性多訊窗頻譜(Adaptive Multi-taper Spectrum Estimation)偵測器。此偵測器根據Neyman-Pearson的理論，在一個固定的虛警報機率(False Alarm Rate)下，去達到一個最大的偵測率。就效能來講，可以高出能量接收機40%以上，同時在虛警報機率為0.001，偵測率要達到0.9的條件下，可以減少60%的觀察次數。

在頻譜掃描之後，傳送參數的調整可以讓次要使用者能在不影響主要使用者的情況下去傳輸。在本論文中，考慮一個主要使用者為2x2的閉迴路多輸入多輸出(Close-loop Multiple Input Multiple Output)系統操作在分時雙工(TDD)下。藉由分時雙工，次要使用者可以估測對主要使用者的干擾通道。因此，用在多使用者多輸入多輸出的區塊對角化(Block Diagonalization)可以用來消除對主要使用者的干擾。此方法相較於直接奇異值分解(Direct-Singular Value Decomposition)在訊雜比(Signal to Noise Ratio)14 dB以上、主要使用者的訊雜比0 dB的時候達到更好的通道容量。當主要使用者不在的時候，次要使用者可以直接去使用同個頻帶，並可利用天線選擇來改善效能。

再來，論文中實作了多訊窗頻譜偵測器。透過TSMC90 1P9M製程，此晶片最高可操作在122 MHz下，相對的功率消耗為30.9 mW。電路核心佔的面積為1.057 x 1.057 mm^2，晶片面積為1.56 x 1.56 mm^2。

關鍵字

感知無線電 ； 頻譜掃描 ； 預編碼

並列摘要

In recent years, cognitive radios are regarded as a valid solution to solve the problem of inefficient spectrum utilization and increasing spectrum demand. A cognitive radio system which has the ability of spectrum sensing and transmission adjustment is proposed in this thesis. The spectrum sensing is an important issue because the spirit of cognitive radio is that secondary user’s transmission can not affect primary user. Therefore, an optimal detector
applied for Thomson’s adaptive multitaper spectrum estimation (AMTSE) is presented. The detector is based on Neyman-Pearson Theorem and it can adjust the detection threshold according to the environment change. Also, this detector can reduce the number of observation time and has higher detection rate for a given false alarm rate compared with other methods. The simulation shows that the detection rate can outperform energy detection by 40%. Besides, it reduces the observation time about 60% than energy detection when achieving detection rate 0.9 at false alarm rate 0.001.

Adjusting the transmission parameter is required after spectrum sensing. Here, a primary user which is 2×2 close-loop MIMO system and operates in TDD mode is taken into consideration. The assumption of TDD mode is that the secondary user can estimate the interference channel between its Tx and primary user’s Rx. By knowing the interference channel, block diagonalization which usually used in multiuser MIMO system is applied for canceling the
interference when primary user exists. The limitation of block diagonalization is that the number of transmit antennas should be larger than the total number of receive antennas, so the secondary user needs extra 2 antennas for the cancelation of interference. By the precoding method, the interference to primary user can be eliminated when the CSI is perfectly known by secondary user. The capacity will exceed the Direct-Singular Value Decomposition (D-SVD) when SNR is larger than 14 dB and the SNR of primary user equals to 0 dB. If the primary user is idle, the secondary user’s Tx may can estimate the channel state information (CSI) due to the channel reciprocity. So the extra antennas can be used to antenna selection to improve the secondary user’s BER performance.

Further, the AMTSE spectral detector is implemented by ASIC. We choose the DPSS number K = 2 to reduce the hardware cost. To deal with two paths of spectrum estimation, a novel FFT architecture is proposed. The 1024-point adopts the radix-2 and radix-2/4/8/16 to efficiently reduce the number of nontrivial twiddle factor multipliers. By using timing sharing techniques, the trivial twiddle factor multiplier can be realized by using some adders and
shifters. The chip is implemented by TSMC 90 nm, and the power consumption of this detector is 30.9 mW at 122 MHz. The core area is 1057 × 1057 mm^2 and total chip area is 1557 × 1557 mm^2.

並列關鍵字

Cognitive Radios ； Spectrum Sensing ； Precoding

參考文獻

[2] J. Mitola, "Software Radios: Survey, Critical Evaluation and Future Directions," IEEE Aerosp. Electron. Syst. Mag., vol. 8, pp. 25–36, Apr. 1993.

[3] ——, "The Software Radio Architecture," IEEE Commun. Mag., vol. 33, pp. 26–38, May 1995.

[4] J. Mitola and G. Q. Maguire, "Cognitive Radio: Making Software Radios More Personal," IEEE Personal Commun. Mag., vol. 6, pp. 13–18, Aug. 1999.

[5] J. Mitola, "Software Radio Architecture: A Mathematical Perspective," IEEE J. Sel. Areas Commun., vol. 17, pp. 514–538, Apr. 1999.

[6] ——, "Cognitive Radio for Flexible Mobile Multimedia Communications," in Proc. IEEE MoMuC '99, Nov. 1999, pp. 3–10.