-
摘要: 動態星座圖擴展(Active constellation extension, ACE)是一種能夠有效降低正交頻分復用(Orthogonal frequency division multiplexing, OFDM)系統峰均功率比(Peak-to-average power ratio, PAPR)的方法，為解決現有ACE算法因選擇固定限幅閾值而限制降低PAPR效果的問題，提出一種最優閾值ACE(Optimal threshold ACE, OTACE)算法，該算法能在每次迭代時根據信號功率求得最合適的限幅閾值，從而增強抑制PAPR的效果。通過數據擬合得到合適的迭代次數，在此基礎上對OTACE算法抑制PAPR的效果進行了仿真分析，仿真結果表明，與凸集映射(Projection onto convex sets, POCS)和智能梯度投影(Smart gradient projection, SGP)算法相比，OTACE分別能提高5 dB和3 dB左右的PAPR增益。在廣電1、廣電6和巴西A三種信道下，分別在多普勒頻移為20 Hz和60 Hz時測試OTACE算法對系統誤碼率(Bit error rate, BER)的影響。實驗結果顯示，采用OTACE可提高系統的BER性能，并且與POCS相比，OTACE可提高1 dB左右的信噪比(Signal-to-noise ratio, SNR)增益；與SGP相比，OTACE在高SNR時有明顯的優勢。Abstract: Orthogonal frequency division multiplexing (OFDM) technology, which can divide the frequency selective fading channel into multiple flat fading sub-channels, is widely used in wireless communication systems because of its robustness to frequency selectivity in wireless channels and the ability to mitigate multipath fading that causes inter-symbol interference. Therefore, it has become one of the key technologies of 5G mobile communication. However, it has a serious shortcoming, i.e., the high peak-to-average power ratio (PAPR), especially when the number of subcarriers is large. High PAPR will make the high-power amplifier work in its nonlinear region, leading to inter-modulation interference among subcarriers and out-of-band interference of OFDM signals. Active constellation extension (ACE) reduces the PAPR of OFDM signals effectively by extending external constellation points outwards. Most of the ACE algorithms currently used set a fixed threshold to limit the amplitude of the OFDM signal during the iteration. As the statistical characteristics of OFDM signals will change after each iteration, the same threshold will reduce the ability of the method to suppress the PAPR of OFDM systems. To solve this problem, an optimal threshold ACE (OTACE) method is proposed, which can determine an appropriate threshold according to the signal power at each iteration to enhance the performance of PAPR reduction. The appropriate number of iterations is obtained by data fitting, and on this basis, the impact of the OTACE algorithm in suppressing the PAPR is simulated and analyzed. The simulation results demonstrate that compared with POCS and SGP, OTACE can increase the performance to reduce PAPR by approximately 5 dB and 3 dB gains, respectively. Under the CDT 1, CDT 6, and Brazil A channels, the impact of the OTACE algorithm on the bit error rate (BER) is tested when the Doppler frequency shift is 20 Hz and 60 Hz, respectively. The experimental results show that the OTACE can achieve better BER performance. Compared with POCS, OTACE has about 1 dB signal-to-noise ratio (SNR) gain in BER performance. OTACE has obvious advantages over SGP at a high SNR.
-
圖 4 不同子載波數下的CCDF曲線. (a)
$ N = 256 $ ; (b)$ N = 512 $ ; (c)$ N = 1024 $ ; (d)$ N = 2048 $ Figure 4. CCDF curves under different subcarriers: (a)
$ N = 256 $ ; (b)$ N = 512 $ ; (c)$ N = 1024 $ ; (d)$ N = 2048 $ 
圖 5 動態CDT 1信道下BER性能曲線. (a) 20 Hz; (b) 60 Hz
Figure 5. BER performance curves under the CDT 1 dynamic channel: (a) 20 Hz; (b) 60 Hz
圖 6 動態CDT 6信道下BER性能曲線. (a) 20 Hz; (b) 60 Hz
Figure 6. BER performance curves under the CDT 6 dynamic channel: (a) 20 Hz; (b) 60 Hz
圖 7 動態Brazil A信道下BER性能曲線. (a) 20 Hz; (b) 60 Hz
Figure 7. BER performance curves under the Brazil A dynamic channel: (a) 20 Hz; (b) 60 Hz
Algorithm 1 Searching the optimal threshold Variable declaration: $ {A_{{\text{array}}}} $: Array of all possible values of the threshold$ S $: Size of $ {A_{{\text{array}}}} $$ L $: Total number of OFDM symbols$ {M_{\text{P}}} $: Average signal power after clipping$ {P_{\text{P}}} $: Maximum signal power after clipping$ {A_{{\text{opt}}}} $: Optimal threshold Searching procedure: for $ s = 1:S $ do for $ n = 1:L $ do calculate $ z(n) $ according to (12) end for $ {M_{\text{P}}}(s) = {\text{E}}\{ |z(n){|^2}\} $ $ {P_{\text{P}}}(s) = {\text{max}}\{ |z(n){|^2}\} $ $R(s) = \dfrac{{{P_{\text{P}}}(s)}}{{{M_{\text{P}}}(s)}}$ $ M = R(1) $ if $ M > R(s) $ $ M = R(s) $ $ k = s $ end if end for $ {A_{{\text{opt}}}} = {A_{{\text{array}}}}(k) $ Output $ {A_{{\text{opt}}}} $ 
下載: 導出CSV
表 1 系統仿真設置
Table 1. System simulation setting
Meaning Parameters Specifications System model SISO-OFDM Modulation mode QPSK System baseband bandwidth $ {B_{{\text{bw}}}} $/ MHz 7.56 OFDM symbols number $ {N_{{\text{OFDM}}}} $ 100 Pilot pattern Comb Pilot interval $ {P_{\text{i}}} $ 3 CP length $ {N_{{\text{CP}}}} $ 256 Data subcarriers number $ N $ 2048 Total subcarriers number $ {N_{{\text{TS}}}} $ 2304 Doppler spread $ {f_{\text{D}}} $/ Hz 20, 60
下載: 導出CSV
表 2 計算復雜度對比
Table 2. Computational complexity comparison
PAPR suppression algorithms Complex multiplication Complex addition POCS[18] $ N(1 + 2{\log _2}N) $ $ N{\log _2}N $ MACE[19] $ 2N(1 + {\log _2}N) $ $ N(1 + {\log _2}N) $ SGP[20] $ 2N(1 + {\log _2}N) $ $ N(1 + {\log _2}N) $ AST-SLM[21] $ N{\log _2}N $ $ 2N(1 + {\log _2}N) $ EPOCS[22] $ N{\log _2}N $ $ 2N{\log _2}N $ OTACE $ 2N(1 + {\log _2}N) $ $ N(2 + {\log _2}N) $
下載: 導出CSV
www.77susu.com<span id="fpn9h"><noframes id="fpn9h"><span id="fpn9h"></span> <span id="fpn9h"><noframes id="fpn9h"> <th id="fpn9h"></th> <strike id="fpn9h"><noframes id="fpn9h"><strike id="fpn9h"></strike> <th id="fpn9h"><noframes id="fpn9h"> <span id="fpn9h"><video id="fpn9h"></video></span> <ruby id="fpn9h"></ruby> <strike id="fpn9h"><noframes id="fpn9h"><span id="fpn9h"></span> -
參考文獻
[1] Senol H, Tepedelenlioglu C. Subspace-based estimation of rapidly varying mobile channels for OFDM systems. IEEE Trans Signal Process, 2021, 69: 385 doi: 10.1109/TSP.2020.3045562 [2] Hu C, Wang L, Zhou Z. PAPR reduction scheme for OFDM systems based on Arnold transform without side information // Proceedings of the IEEE 24th International Conference on Computer Supported Cooperative Work in Design. Dalian, 2021: 1287 [3] Memisoglu E, Duranay A, Arslan H. Numerology scheduling for PAPR reduction in mixed numerologies. IEEE Wireless Commun Lett, 2021, 10(6): 1197 doi: 10.1109/LWC.2021.3061628 [4] Wang B, Si Q, Jin M. A novel tone reservation scheme based on deep learning for PAPR reduction in OFDM systems. IEEE Commun Lett, 2020, 24(6): 1271 doi: 10.1109/LCOMM.2020.2980832 [5] Wunder G, Fischer R F H, Boche H, et al. The PAPR problem in OFDM transmission: New directions for a long-lasting problem. IEEE Signal Process Mag, 2013, 30(6): 130 doi: 10.1109/MSP.2012.2218138 [6] Hu M X, Wang W, Cheng W, et al. A generalized piecewise linear companding transform for PAPR reduction in OFDM systems. IEEE Trans Broadcast, 2020, 66(1): 170 doi: 10.1109/TBC.2019.2909183 [7] Mestdagh D J G, Monsalve J L G, Brossier J M. Green OFDM: A new selected mapping method for OFDM PAPR reduction. Electron Lett, 2018, 54(7): 449 doi: 10.1049/el.2017.4743 [8] Lim S C, Kim N, Park H. Polar coding-based selective mapping for PAPR reduction without redundant information transmission. IEEE Commun Lett, 2020, 24(8): 1621 doi: 10.1109/LCOMM.2020.2989205 [9] Kumar T D, Venkatesan P. Performance estimation of multicarrier CDMA using adaptive brain storm optimization for 5G communication system in frequency selective fading channel. Trans Emerg Telecommun Technol, 2020, 31(4): e3829 [10] Harthi N A, Zhang Z F, Kim D, et al. Peak-to-average power ratio reduction method based on partial transmit sequence and discrete Fourier transform spreading. Electronics, 2021, 10(6): 642 doi: 10.3390/electronics10060642 [11] Lahbabi N, Bulusu S S K C, Helard J F, et al. Very efficient tone reservation PAPR reduction fully compatible with ATSC 3.0 standard: Performance and practical implementation analysis. IEEE Access, 2018, 6: 58355 [12] El Hassan M, Crussiere M, Helard J F, et al. EVM closed-form expression for OFDM signals with tone reservation-based PAPR reduction. IEEE Trans Wirel Commun, 2020, 19(4): 2352 doi: 10.1109/TWC.2020.2964196 [13] Ma Z G, Song J Q. Survey of energy efficiency for 5G ultra-dense networks. Chin J Eng, 2019, 41(8): 968馬忠貴, 宋佳倩. 5G超密集網絡的能量效率研究綜述. 工程科學學報, 2019, 41(8):968 [14] Li J Y, Zhao Y K, Xue Z E, et al. A survey of model compression for deep neural networks. Chin J Eng, 2019, 41(10): 1229李江昀, 趙義凱, 薛卓爾, 等. 深度神經網絡模型壓縮綜述. 工程科學學報, 2019, 41(10):1229 [15] Wang X, Jin N D, Wei J D. A model-driven DL algorithm for PAPR reduction in OFDM system. IEEE Commun Lett, 2021, 25(7): 2270 doi: 10.1109/LCOMM.2021.3076605 [16] Ro J, Lee W S, Kang M G, et al. A strategy of signal detection for performance improvement in clipping based OFDM system. Comput Mater Continua, 2020, 64(1): 181 doi: 10.32604/cmc.2020.09998 [17] Kalinov A, Bychkov R, Ivanov A, et al. Machine learning-assisted PAPR reduction in massive MIMO. IEEE Wirel Commun Lett, 2021, 10(3): 537 doi: 10.1109/LWC.2020.3036909 [18] Jones D L. Peak power reduction in OFDM and DMT via active channel modification // Proceedings of the Asilomar Conference. Pacific Grove, 1999: 1076 [19] Samayoa Y, Ostermann J. Modified active constellation extension algorithm for PAPR reduction in OFDM systems // Proceedings of the Wireless Telecommunications Symposium. Washington, 2020: 9198714 [20] Krongold B S, Jones D L. PAR reduction in OFDM via active constellation extension. IEEE Trans Broadcast, 2003, 49(3): 258 doi: 10.1109/TBC.2003.817088 [21] Li G, Li T Y. A low-complexity PAPR reduction SLM scheme for STBC MIMO-OFDM systems based on constellation extension. KSII Trans Internet Inf Syst, 2019, 13(6): 2908 [22] Liu Y Z, Wang Y, Ai B. An efficient ACE scheme for PAPR reduction of OFDM signals with high-order constellation. IEEE Access, 2019, 7: 118322 doi: 10.1109/ACCESS.2019.2936917 [23] Tang R G, Zhou X, Wang C Y. A Haar wavelet decision feedback channel estimation method in OFDM systems. Appl Sci, 2018, 8(6): 877 doi: 10.3390/app8060877 [24] Tang R G, Zhou X, Wang C Y. A novel low rank LMMSE channel estimation method in OFDM systems // Proceedings of the 17th IEEE International Conference on Communication Technology. Chengdu, 2017: 249 [25] Zhang M T, Zhou X, Wang C Y. Noise suppression threshold channel estimation method using RC and SRRC filters in OFDM systems // Proceedings of the 18th IEEE International Conference on Communication Technology. Chongqing, 2019: 176 [26] Tang R G, Zhou X, Wang C Y. Kalman filter channel estimation in 2 × 2 and 4 × 4 STBC MIMO-OFDM systems. IEEE Access, 2020, 8: 189089 doi: 10.1109/ACCESS.2020.3027377 [27] Zhang M T, Zhou X, Wang C Y. A novel noise suppression channel estimation method based on adaptive weighted averaging for OFDM systems. Symmetry, 2019, 11(8): 997 doi: 10.3390/sym11080997 -
點擊查看大圖
圖(7) / 表(3)
計量
- 文章訪問數: 655
- HTML全文瀏覽量: 209
- PDF下載量: 93
- 被引次數: 0
