• 沒有找到結果。

Preamble-Assisted Estimation for Frequency-Dependent I/Q Mismatch in Direct-Conversion OFDM Receivers

N/A
N/A
Protected

Academic year: 2021

Share "Preamble-Assisted Estimation for Frequency-Dependent I/Q Mismatch in Direct-Conversion OFDM Receivers"

Copied!
7
0
0

加載中.... (立即查看全文)

全文

(1)IEICE TRANS. COMMUN., VOL.E92–B, NO.7 JULY 2009. 2426. PAPER. Preamble-Assisted Estimation for Frequency-Dependent I/Q Mismatch in Direct-Conversion OFDM Receivers Ming-Fu SUN†a) , Student Member and Terng-Yin HSU†b) , Nonmember. SUMMARY In direct-conversion orthogonal frequency division multiplexing (OFDM) receivers, the impact of frequency-dependent I/Q mismatch (IQ-M) with carrier frequency offset (CFO) must be considered. A preamble-assisted estimation is developed to circumvent the frequencydependent IQ-M with CFO. The results of a simulation and an experiment show that the proposed method could provide good estimation efficiency and enhance the system performance. Moreover, the proposed scheme is compatible with current wireless local area network standards. key words: carrier frequency offset, direct-conversion receiver, I/Q mismatch, orthogonal frequency division multiplexing (OFDM), wireless local area network (WLAN). 1.. Introduction. Orthogonal frequency division multiplexing (OFDM) is a spectrally efficient technique for high-speed wireless communications. OFDM systems are, however, susceptible to imperfect synchronization and non-ideal front-end effects, which cause serious performance degradation. Most OFDM systems are highly sensitive to carrier frequency offset (CFO), which is the frequency mismatch of local oscillators between the transmitter and the receiver. CFO introduces inter-carrier interference in OFDM systems due to the loss of orthogonality between sub-carriers. Recent research has also focused on developing monolithic OFDM receivers, particularly low-cost technology. Direct-conversion architecture is one potential candidate for simple integration among different architectures. However, direct-conversion receivers suffer from mismatch between the I and Q channels, such as I/Q mismatch (IQ-M) of non-ideal radio frequency (RF) circuits [1]–[3]. Specifically, IQ-M arises when the phase and gain differences between I and Q branches are not exactly 90◦ and 0 dB, respectively. IQ-M introduces the image interference into the desired signal which degrades the system performance. Due to the impairment in the analog components, the low-pass filters (LPFs) of I and Q channels are not identical. The mismatched LPFs result in frequency-dependent IQ-M. Frequency-dependent IQ-M means that the imbalances can vary with frequency. In an OFDM system with frequency-dependent IQ-M, the IQ-M parameters for every sub-carrier are different. Several schemes have been proManuscript received May 26, 2008. Manuscript revised February 4, 2009. † The authors are with the Department of Computer Science, National Chiao-Tung University, Hsinchu 300, Taiwan, R.O.C. a) E-mail: mfsun@csie.nctu.edu.tw b) E-mail: tyhsu@csie.nctu.edu.tw DOI: 10.1587/transcom.E92.B.2426. posed to estimate constant IQ-M [4]–[7]. Although these methods can work well under constant IQ-M, they do not consider non-ideal LPFs and CFO phenomenon. Therefore, these methods are not robust to frequency-dependent IQM and CFO. Estimation for frequency-dependent IQ-M has also been studied in the open literature [8]–[11], [14], [17]. The effect and analysis of frequency-dependent IQ-M are given in [8], [9]. Xing et al. [10] presented a method for frequency-dependent IQ-M. An optimal training sequence for frequency-dependent IQ-M has been proposed in [11]. However, these two methods have specific packet formats, making them incompatible with current wireless local area network (WLAN) standards, such as IEEE 802.11a/g [12], [13]. Cetin et al. proposed an adaptive self-calibrating image rejection receiver [14]. This method needs a long converged time. Therefore it is not suitable for packet-based WLAN systems because the packet length is always limited. In practice, frequency-dependent IQ-M and CFO arise simultaneously. However, only some references consider the frequency-dependent IQ-M with CFO. This study mainly concentrates on the estimation of frequencydependent IQ-M with CFO. To maintain and realize systems with imperfect front-end modules, a preamble-assisted estimation scheme is developed. Simulation results show that the performance loss is in the range of 1.6 to 1.8 dB at 10−4 bit error rate. Experiment results demonstrate that the proposed method could overcome joint impairments of frequency-dependent IQ-M and CFO, enabling a high performance receiver. The remainder of the paper is organized as follows. Section 2 introduces the system model. Section 3 then presents the proposed method. Simulation and experiment results are shown in Sect. 4. Conclusions are finally drawn in Sect. 5. 2.. System Model. Figure 1 depicts the block diagram of a direct-conversion. Fig. 1. Direct-conversion receiver with I/Q mismatch and CFO.. c 2009 The Institute of Electronics, Information and Communication Engineers Copyright .

(2) SUN and HSU: PREAMBLE-ASSISTED ESTIMATION FOR FREQUENCY-DEPENDENT I/Q MISMATCH IN DIRECT-CONVERSION OFDM RECEIVERS. 2427. where ε and ϕ denote the constant amplitude and phase mismatch, respectively. If neither gain nor phase error exists, α remains at unity, and β decreases to zero. Moreover, the average filter response and response mismatch are defined as [8], [10]   ψ(n) = 0.5 lI (n) + lQ (n)   (3) ξ(n) = 0.5 lI (n) − lQ (n) where lI (n) and lQ (n) denote the LPFs of I and Q channels. If the LPFs of I and Q channels are identical, the response mismatch can be ignored. The effect of constant IQ-M on the 16-QAM constellation is depicted in Fig. 2. As a result of the IQ-M, the constellation is distorted severely. It implies that IQ-M can limit the ability of the receiver to achieve sufficient performance especially for high constellation size, e.g., 64-QAM constellation. 3.. I/Q Mismatch Estimation. Our goal is to estimate the frequency-dependent IQ-M with CFO. The basic strategy for extracting IQ-M parameters is to employ long training symbols. The packet format used in this method is based on IEEE 802.11a/g standards. Let x(n) denote a long training symbol distorted by IQ-M and CFO, as defined in Eq. (1). After N-point fast Fourier transform (FFT), the received signal is given by Yk = FFTN {y(n)} Fig. 2 16-QAM constellation. (a) Without I/Q mismatch. (b) Gain error: 1 dB, Phase error: 10◦ ..   = Ak (Rk Dk + ICIk ) + Bk R∗−k D−k + ICI−k + W(k)   ∗ ∗ = Ak (Hk Xk Dk + ICIk ) + Bk H−k X−k D−k + ICI−k + W(k) (4). architecture. The received RF signal is down-converted to baseband, and then filtered out by the LPFs. Let r(n) and w(n) be the received signal and additive white Gaussian noise, respectively. The baseband signal with CFO Δ f and IQ-M is given by [8], [10]. where Dk is the distortion which is accompanied with intercarrier interference (ICI) due to CFO [15]. Xk is the data sub-carrier and Hk stands for the channel frequency response (CFR). Both X−k and H−k denote the frequency mirror image parts. Ak and Bk can be determined by. y(n) = ((αψ(n) + β∗ ξ(n)) ⊗ r(n)) e j2π·Δ f ·nT s + ((βψ(n) + α∗ ξ(n)) ⊗ r∗ (n)) e− j2π·Δ f ·nT s + w(n). Ak = FFTN {αψ(n) + β∗ ξ(n)} Bk = FFTN {βψ(n) + α∗ ξ(n)} (1). where α and β denote the constant IQ-M parameters, and ψ(n) and ξ(n) represent the frequency-dependent IQ-M parameters. Note that the phase rotation is inversed in the direction between original signal and the conjugate signal if the CFO is present. The signal r(n) can be further expressed as r(n) = h(n) ⊗ x(n), where x(n) and h(n) are the transmitted signal and channel impulse response, respectively. The symbol “⊗” represents the convolution operator. From Eq. (1), the received signal can be regarded as the original signal added to the conjugate signal. The constant IQ-M parameters are expressed as [5]–[7]   α = 0.5 1 + (1 + ε) e− jϕ   β = 0.5 1 − (1 + ε) e jϕ (2). The variables Dk , D−k , ICIk and ICI−k are defined by   (sin πω) Dk = e jπω(N−1)/N N (sin πω/N)   (sin πω) D−k = e jπ(−ω)(N−1)/N N (sin πω/N)   K  (sin πω) ICIk = Xl Hl N (sin π (l − k + ω)/N) k=−K. (5). (6) (7). lk. ICI−k. · e jπω(N−1)/N e− jπ(l−k)/N   K  − (sin πω) ∗ ∗ = Xl Hl N (sin π (l − k − ω)/N) k=−K l−k jπ(−ω)(N−1)/N − jπ(l−k)/N. ·e. e. (8). (9).

(3) IEICE TRANS. COMMUN., VOL.E92–B, NO.7 JULY 2009. 2428. where ω is the normalized frequency offset, i.e., ω = Δ f /(1/NT s ). Equation (4) clearly indicates that IQ-M results in a mutual interference between symmetric subcarriers in OFDM systems, as shown in Fig. 3. Let x(n) and x(n + Nl ) represent two consecutive long training symbols, as defined in Eq. (1), where Nl is the samples of a long training symbol. In practice, IQ-M not only introduces unwanted image interference into the desired signal, but also restricts the accuracy of CFO estimation. A basic strategy for computing the CFO is to employ two repeated training symbols [15]. However, the estimation error increases when the gain or phase error is not zero. In [16], a pseudo-CFO (P-CFO) algorithm, which rotates three training symbols by adding extra frequency offset into the received sequence, is developed to improve CFO estimation. Since the P-CFO method can estimate the frequency offset more accurately than the two-repeat preamble-based scheme, it is adopted to estimate the CFO value in this work. The two training symbols are then rotated by the estimated CFO. Because the long training symbols are periodic, the received signals, r(n) and r(n + Nl ), can be replaced by r(n). Therefore, the following equations hold: x(n)e− j(2πΔ f nT s ) − x(n + Nl )e− j(2πΔ f (n+Nl )T s ) y1 (n) = e−2 j(2πΔ f nT s ) − e−2 j(2πΔ f (n+Nl )T s ). Fig. 3. Mutual interference due to I/Q mismatch.. Fig. 4. = (βψ(n) + α∗ ξ(n)) ⊗ r∗ (n) (10) ∗ − j(2πΔ f nT s ) ∗ − j(2πΔ f (n+Nl )T s ) − x(n + Nl ) e x(n) e y2 (n) = e−2 j(2πΔ f nT s ) − e−2 j(2πΔ f (n+Nl )T s ) = ((αψ(n) + β∗ ξ(n)) ⊗ r(n))∗ (11) The IQ-M parameters can be obtained as follows. Bk FFTN {y1 (n)} ∗ = A−k FFTN {y2 (n)}. (12). Equations (10) and (11) indicate that the two training symbols are multiplied by the estimated CFO and then are subtracted to eliminate the image interference. Once the IQM parameters are estimated, the effect of IQ-M could be corrected by the frequency-domain compensation. With the estimated IQ-M parameters, the frequency domain compensation is given by. Bk. Zk = FFTN x(n)e− j(2πΔ f nT s ) − ∗ FFTN x∗ (n)e− j(2πΔ f nT s ) A−k. ∗

(4) Bk B = Ak − ∗ −k Hk Xk (13) A−k  compensation gain. The compensation gain could be balanced by the channel equalizer. Moreover, the proposed method could be directly applied to IEEE 802.11a/g standards because it does not require any special packet format. In the open literature [9], [11], [17], most of them consider the IQ-M only. Therefore, these methods may not be suitable for joint CFO and IQ-M. In other words, these methods can tolerate slight CFO value. By contrast, the proposed method is capable of tolerating large CFO value. In the condition of slight CFO value, the inter-carrier interference is smaller than large CFO value. For instance, if the estimated CFO value is within ±2 ppm, the existing methods [9], [11], [17] for frequency-dependent IQ-M could be adopted. In the frequency domain, phase tracking is still performed. Since some rotation operations (CFO estimation and compensation) are eliminated, this approach can maintain the system performance with reasonable complexity.. The proposed frequency-dependent IQ-M estimation architecture..

(5) SUN and HSU: PREAMBLE-ASSISTED ESTIMATION FOR FREQUENCY-DEPENDENT I/Q MISMATCH IN DIRECT-CONVERSION OFDM RECEIVERS. 2429. Figure 4 shows the frequency-dependent IQ-M estimation architecture. The estimator begins to work when long training symbols (preambles) arrive. Firstly, the long preambles are rotated by the estimated CFO. To reduce the image interference due to the IQ-M effect, the first training symbol should subtract the second training symbol. At the same time, the conjugate preamble follows the same procedure described above. After calculating the difference between the first and the second preambles, a complex divider is used to eliminate the phase rotation. Finally, these estimated parameters are applied to compensate for the frequencydependent IQ-M in the frequency domain. 4.. legend means that the estimation scheme is applied to the system with frequency-dependent IQ-M and “constant IQM” means that the estimation scheme is applied to the system with constant IQ-M. Figures 6 and 7 show that the degradation in BER and PER owing to IQ-M was significant, particularly for 64-QAM modulation. After compensation, the performance under the condition of constant IQM was close to the case with multipath only. The SNR loss of the proposed scheme for frequency-dependent IQ-M is in the range of 1.6 to 1.8 dB at 10−4 BER. This finding implies that the proposed method is robust to the conditions of both frequency-dependent IQ-M and CFO in fading envi-. Simulation and Experiment Results. On both the simulation and experiment results, the CFO compensation scheme in [16] is applied. A typical OFDM system for wireless LAN is adapted as the referred specification to evaluate the performance [12], [13]. Similar to IEEE 802.11a/g [12], [13], the parameters of the system employed are as follows: FFT size is 64; the cyclic prefix (CP) contains 16 samples; system bandwidth is 20 MHz; the carrier frequency is 2.4 GHz. The packet format is shown in Fig. 5. The preamble includes two parts. The first part is composed of 10 repetitions of a short training signal with a symbol period of 0.8 μs. The second part of the preamble consists of one cyclic prefix with 1.6 μs and two successive long preambles. The period of a long preamble is 3.2 μs. The number of taps of frequency-selective fading is of order 8, simulating as an i.i.d. complex Gaussian random variable. All data are modulated by 16-QAM and 64-QAM. The CFO is set at 50 ppm, and the constant IQ-M parameters are 1 dB gain error and 10◦ phase error. Two-tap FIR filters are adopted to model the frequency-dependent IQ-M [10]. These two LPFs are modeled as lI (n) = 0.1δ(n) + δ(n − 1) lQ (n) = 0.01δ(n) + 0.9δ(n − 1). Fig. 6. BER performance of 16-QAM and 64-QAM modulation.. Fig. 7. PER performance of 16-QAM and 64-QAM modulation.. (14). The Z-transform results of these two LPFs are LI (z) = 0.1 + z−1 and LQ (z) = 0.01 + 0.9z−1 , respectively. Figures 6 and 7 show the bit error rate (BER) and packet error rate (PER) performance. In these two figures, the following scenarios are considered. “Multipath only” legend refers to the system with perfect RF front-end and “without comp” means that the estimation scheme is not applied to the system with frequency-dependent IQ-M. “FD IQ-M”. Fig. 5. The packet format..

(6) IEICE TRANS. COMMUN., VOL.E92–B, NO.7 JULY 2009. 2430. Fig. 8. Experiment setup for verification.. Table 1. Experiment parameters.. ronments. Figure 8 displays the experiment setup for verification. Table 1 lists the experiment parameters. The in-house RF front-ends are used in the transmitter as well as in the receiver. Therefore, there is also IQ-M at the transmitter part. Some compensation methods for transmitter IQ-M are available in the open literature [5], [18]. Since our goal is to estimate the receiver IQ-M with CFO, the pre-compensation scheme is applied to reduce the transmitter IQ-M effect. The basic concept is that a training sequence is utilized to estimate the IQ-M. After calculating the IQ-M parameters, the transmitter can compensate for the transmitter IQ-M using the baseband processing. The CFO is set at 50 ppm. The constant IQ-M parameters at the receiver part are 1 dB gain error and 10◦ phase error. At the receiver part, we assume the frequency-dependent IQ-M. The impulse responses of these two LPFs are lI (n) = 0.1δ(n) + δ(n − 1) and lQ (n)= 0.01δ(n) + 0.9δ(n − 1), respectively. In the verification platform, the functions implemented as the baseband processing include timing synchronization, CFO estimation, IQ-M estimation, channel estimation, phase tracking, and the forward error correction (FEC) decoder. The proposed method is mapped onto the FPGA chips (Xilinx XtremeDSP, Virtex-II) with on-board 14-bit digital-to-analog converters (DACs) to convert the digital data into analog signals. At the transmitter part, the transmitted data are produced using MATLAB and then these data are stored in memory. The signals are then transmitted via an in-house RF front-end. After downconverting the RF signals to baseband at the receiver, analog signals are fed into 14-bit analog-to-digital converters (ADCs). The proposed algorithm then processes the downconverted signals. Figure 9 shows the constellation performance before and after compensation. The performance improvement is clearly seen because the constellation becomes more distinguishable after compensation. After IQ-M compensation,. Fig. 9 Measurement of constellation diagram: (a) Before compensation. (b) After compensation.. the remaining IQ-M parameters are less than 3% (0.13 dB) gain error and 2◦ phase error. In the proposed design, only two long training symbols are acquired to estimate the IQM parameters. If there are more training symbols than currant standards, the accuracy of the IQ-M parameters could be improved further. The error vector magnitude (EVM) after compensation is 8.6% (−21 dB), meeting the standard (IEEE 802.11a/g) allowed 11.2% (−19 dB). The image rejection ratio (IRR) as a function of the mismatch is given by [19]   1+(1+ε)2 +2(1+ε) cos(ϕ) (15) IRR (dB) = 10 · log 1+(1+ε)2 −2(1+ε) cos(ϕ) where ε and ϕ represent the amplitude and phase mismatch, respectively. After compensation, the remaining gain error and phase error are ∼3% and ∼2◦ . An IRR of ∼31 dB is achieved. Figure 10 shows the estimated CFR. The CFR after compensation becomes more smoother than the distorted CFR. In conjunction with the proposed scheme, accuracy CFR could be calculated. The proposed method is compatible with current standards. In contrast, recently proposed methods [10], [11] are incompatible with current standards. In addition, the proposed method can estimate the frequency-dependent IQ-M with CFO, whereas reference.

(7) SUN and HSU: PREAMBLE-ASSISTED ESTIMATION FOR FREQUENCY-DEPENDENT I/Q MISMATCH IN DIRECT-CONVERSION OFDM RECEIVERS. 2431. [7]. [8]. [9]. [10] Fig. 10. Channel frequency response.. [11]. methods [9], [11], [14], [17] consider frequency-dependent IQ-M only.. [12]. 5.. [13]. Conclusions. This study develops a preamble-assisted method for combating the frequency-dependent IQ-M with CFO in directconversion OFDM receivers. The frequency-dependent IQM with CFO can be estimated by taking advantage of the relationship between desired sub-carriers and image subcarriers. Both simulation and experiment results indicate that the proposed method can meet system requirements by preventing frequency-dependent IQ-M from significantly degrading the performance. Furthermore, this method is compatible with current standards because it does not require special packet formats. Acknowledgment This work was supported by the National Science Council under Grant NSC-96-2220-E-009-029 and NSC-97-2220E-009-016. The authors thank the reviewers for their detailed comments that helped improve the quality of the paper.. [14]. [15]. [16]. [17]. [18]. [19]. Man, and M. Moonen, “Compensation of IQ imbalance and phase noise in OFDM systems,” IEEE Trans. Wireless Commun., vol.4, no.3, pp.872–877, May 2005. G.T. Gil, Y.D. Kim, and Y.H. Lee, “Non-data-aided approach to I/Q mismatch compensation in low-IF receivers,” IEEE Trans. Signal Process., vol.55, no.7, pp.3360–3365, July 2007. F. Horlin, S. De Rore, E. Lopez-Estraviz, F. Naessens, and L. Van der Perre, “Impact of frequency offsets and IQ imbalance on MCCDMA reception based on channel tracking,” IEEE J. Sel. Areas Commun., vol.24, no.6, pp.1179–1188, June 2006. L. Anttila, M. Valkama, and M. Renfors, “Frequency-selective I/Q mismatch calibration of wideband direct-conversion transmitters,” IEEE Trans. Circuits Syst. II, Express Briefs, vol.55, no.4, pp.359– 363, April 2008. G. Xing, M. Shen, and H. Liu, “Frequency offset and I/Q imbalance compensation for direct-conversion receivers,” IEEE Trans. Wireless Commun., vol.4, no.2, pp.673–680, March 2005. E. Lopez-Estraviz, S. De Rore, F. Horlin, and L. Van der Perre, “Optimal training sequences for joint channel and frequency-dependent IQ imbalance estimation in OFDM-based receivers,” Proc. IEEE Int. Conf. Commun., pp.4595–4600, June 2006. Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE Std 802.11a, 1999. Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE Std 802.11g, 2003. E. Cetin, I. Kale, and R.C.S. Morling, “Adaptive self-calibrating image rejection receiver,” Proc. IEEE Int. Conf. Commun., pp.2731– 2735, June 2004. P.H. Moose, “A technique for orthogonal frequency division multiplexing frequency offset correction,” IEEE Trans. Commun., vol.42, no.10, pp.2908–2914, Oct. 1994. M.F. Sun, J.Y. Yu, and T.Y. Hsu, “Estimation of carrier frequency offset with I/Q mismatch using pseudo-offset injection in OFDM systems,” IEEE Trans. Circuits Syst. I, Regular Papers, vol.55, no.3, pp.943–952, April 2008. M. Valkama, M. Renfors, and V. Koivunen, “Compensation of frequency-selective I/Q imbalances in wideband receivers: Models and algorithms,” Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications, pp.42–45, March 2001. J. Tubbax, B. Come, L. Van der Perre, S. Donnay, M. Moonen, and H. De Man, “Compensation of transmitter IQ imbalance for OFDM systems,” Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.325–328, May 2004. J.C. Rudell, J.J. Ou, T.B. Cho, G. Chien, F. Brianti, J.A. Weldon, and P.R. Gray, “A 1.9 GHz wide-band IF double conversion CMOS receiver for cordless telephone applications,” IEEE J. Solid-State Circuits, vol.32, no.12, pp.2071–2088, Dec. 1997.. References [1] B. Razavi, “Design consideration for direct-conversion receivers,” IEEE Trans. Circuits Syst. II, Express Briefs, vol.44, no.6, pp.428– 435, June 1997. [2] A.A. Abidi, “Direct-conversion radio transceivers for digital communications,” IEEE J. Solid-State Circuits, vol.30, no.12, pp.1399– 1410, Dec. 1995. [3] P. Kenington, RF and Baseband Techniques for Software Defined Radio, Artech House, Norwood, MA, 2005. [4] T. Yuba and Y. Sanada, “Decision directed scheme for IQ imbalance compensation on OFCDM direct conversion receiver,” IEICE Trans. Commun., vol.E89-B, no.1, pp.184–190, Jan. 2006. [5] A. Tarighat, R. Bagheri, and A.H. Sayed, “Compensation schemes and performance analysis of IQ imbalance in OFDM receivers,” IEEE Trans. Signal Process., vol.53, no.8, pp.3257–3268, Aug. 2005. [6] J. Tubbax, B. Come, L. Van der Perre, S. Donnay, M. Engels, H. De. Ming-Fu Sun received the B.S. and M.S. degrees in computer science and information engineering from National Chiao-Tung University, Hsinchu, Taiwan, R.O.C., in 2003 and 2005, respectively. In 2005, he joined the Institute of Computer Science of National Chiao-Tung University, where he is currently working toward the Ph.D. degree. During 2006, he was a Lecturer in the Department of Electronics Engineering, Ming Hsin University of Science and Technology. His major research interests include signal processing for wireless communications, MIMO-OFDM systems, and associated VLSI architectures..

(8) IEICE TRANS. COMMUN., VOL.E92–B, NO.7 JULY 2009. 2432. Terng-Yin Hsu received the B.S. and M.S. degrees from Feng Chia University, Taichung, Taiwan, in 1993 and 1995, respectively, and the Ph.D. degree from National Chiao-Tung University, Hsinchu, Taiwan, R.O.C., in 1999, all in electronic engineering. In 2003, he joined Dept. of Computer Science, National ChiaoTung University, where he is currently an Assistant Professor. His research interests mainly include VLSI architectures, wireless communications, multi-spec transmissions, high-speed networking, analog-like digital circuits, SoC design technology, and related ASIC designs..

(9)

參考文獻

相關文件

Other instruments and appliances, used in dental sciences, with low-frequency, high-frequency, ultrasonic and ultra-short wave

In contrast, FARA exploits this frequency diversity via a frequency-aware rate adaptation scheme that picks different bitrates for different frequencies depending on their SNRs. 6.1

change it with different boards o #define CHANNEL 26 // check correspond frequency in SpectrumAnalyzer o #define TX_DO_CARRIER_SENSE 1. o #define

– Set the corresponding bits in TCCRnX(n=1,2,3 X=A,B) re gisters for mode selection and timer prescaler. – Set OCRnX for “frequency”(OCRnA) or

An OFDM signal offers an advantage in a channel that has a frequency selective fading response.. As we can see, when we lay an OFDM signal spectrum against the

• If the cursor scans the jth position at time i when M is at state q and the symbol is σ, then the (i, j)th entry is a new symbol σ

This research used GPR detection system with electromagnetic wave of antenna frequency of 1GHz, to detect the double-layer rebars within the concrete.. The algorithm

Direct Digital Frequency Synthesizer has many advantages of faster frequency switching, lower memory size, lower circuit complication, lower noise, higher frequency