水声通信中信令检测算法的研究
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摘要
水声信道是一种频谱资源非常受限的通信信道,这成为实现可靠、高速的水声通信的主要障碍之一。为了高效地利用水声通信中有限的频谱资源,需要合理地设计水声通信用户的信道接入方式。
目前,常用的信道接入方式主要有固定分配方式和随机接入方式,但是这两种方式应用于水声通信都存在一定的不足:固定分配方式的各子信道由不同的用户所独占,造成了频谱资源的浪费,且网络的扩展性差;随机接入方式通常将整个信道给竞争胜出的用户使用,而水声信道传输时延大的特性会大大增加竞争冲突的概率,整个信道在冲突期间无法正常使用,同样造成了频谱资源的浪费。
陆地通信中的认知无线电技术可以动态地使用最合适的频谱资源来完成无线传输,实现多用户高效的信道接入,显著地提高频谱利用率。因此,针对传统信道接入方式的不足,可以将认知通信技术应用到水声通信的信道接入过程中,以高效地利用水声信道有限的频谱资源。然而,认知技术应用于水下通信还存在诸多难点,其中,目前的认知通信网络一般采用专用控制信道进行信令的传输,会带来额外的带宽开销,因此专用控制信道并不适合在带宽很窄的水声信道中使用。
鉴于水下认知通信中使用专用控制信道的局限性,本文对无需专用控制信道的水声信道动态接入方法进行了研究,并对传统的信令侦听算法进行了改进,以适应水声信道中较大的多普勒频移。本文先对传统的FFT能量检测和匹配滤波检测等信令侦听算法在水声通信网络中的性能进行了仿真与分析,结果表明它们的性能在多普勒频移较为严重的水声环境下明显下降。针对这一问题,本文提出了一种基于DTW的信令检测算法,并进行了仿真实验,结果表明,与匹配滤波检测和FFT能量检测算法相比,在无需对多普勒进行估计和补偿的前提下DTW检测算法具有更好的性能,它能有效克服多普勒效应的影响,从而降低水声通信网络的错误检测概率,有助于实现高效的信道接入,提高频谱利用率。
Underwater acoustic channel is the most challenging channel in the world due to its time varying and frequency-selective characters. Especially, the extreme limited bandwidth obstructs the realization of most of intelligent and high bit rate underwater communication systems. Therefore, an underwater channel access method should cause little or no bandwidth overhead in order to take full advantage of the limited bandwidth available.
Two kinds of traditional underwater channel access methods are fixed channel assignment and random access channel, but both of them will cause a waste of spectrum resources when used in underwater channel.
In the terrestrial setting, cognitive radio has been of growing interest as it is able to increase spectrum efficiency and reduce collisions by opportunistically accessing a portion of free spectrum. But many cognitive radio techniques that have proven effective in the terrestrial setting can not simply be transplanted underwater. For instance a transmitter and receiver must agree on what frequency is going to be used before the data transmission begins. The classic approach is the use of a dedicated control channel, wherein all parameters can be negotiated before the data transfer begins. But this requires bandwidth overhead that is undesirable in the underwater setting.
In this paper, we introduce a dynamic channel access method based on cognitive techniques, an approach for distributed channel allocation for the underwater acoustic environment that allows for dynamic control channel. This paper proposes the DTW signaling listening algorithm and studies its performance via simulations. The simulation results show that compared with FFT energy detecting algorithm and matched filter detecting algorithm, the DTW detecting algorithm is of much better performance in the underwater channel with doppler shift, which is beneficial to improve the channel utilization.
目前,常用的信道接入方式主要有固定分配方式和随机接入方式,但是这两种方式应用于水声通信都存在一定的不足:固定分配方式的各子信道由不同的用户所独占,造成了频谱资源的浪费,且网络的扩展性差;随机接入方式通常将整个信道给竞争胜出的用户使用,而水声信道传输时延大的特性会大大增加竞争冲突的概率,整个信道在冲突期间无法正常使用,同样造成了频谱资源的浪费。
陆地通信中的认知无线电技术可以动态地使用最合适的频谱资源来完成无线传输,实现多用户高效的信道接入,显著地提高频谱利用率。因此,针对传统信道接入方式的不足,可以将认知通信技术应用到水声通信的信道接入过程中,以高效地利用水声信道有限的频谱资源。然而,认知技术应用于水下通信还存在诸多难点,其中,目前的认知通信网络一般采用专用控制信道进行信令的传输,会带来额外的带宽开销,因此专用控制信道并不适合在带宽很窄的水声信道中使用。
鉴于水下认知通信中使用专用控制信道的局限性,本文对无需专用控制信道的水声信道动态接入方法进行了研究,并对传统的信令侦听算法进行了改进,以适应水声信道中较大的多普勒频移。本文先对传统的FFT能量检测和匹配滤波检测等信令侦听算法在水声通信网络中的性能进行了仿真与分析,结果表明它们的性能在多普勒频移较为严重的水声环境下明显下降。针对这一问题,本文提出了一种基于DTW的信令检测算法,并进行了仿真实验,结果表明,与匹配滤波检测和FFT能量检测算法相比,在无需对多普勒进行估计和补偿的前提下DTW检测算法具有更好的性能,它能有效克服多普勒效应的影响,从而降低水声通信网络的错误检测概率,有助于实现高效的信道接入,提高频谱利用率。
Underwater acoustic channel is the most challenging channel in the world due to its time varying and frequency-selective characters. Especially, the extreme limited bandwidth obstructs the realization of most of intelligent and high bit rate underwater communication systems. Therefore, an underwater channel access method should cause little or no bandwidth overhead in order to take full advantage of the limited bandwidth available.
Two kinds of traditional underwater channel access methods are fixed channel assignment and random access channel, but both of them will cause a waste of spectrum resources when used in underwater channel.
In the terrestrial setting, cognitive radio has been of growing interest as it is able to increase spectrum efficiency and reduce collisions by opportunistically accessing a portion of free spectrum. But many cognitive radio techniques that have proven effective in the terrestrial setting can not simply be transplanted underwater. For instance a transmitter and receiver must agree on what frequency is going to be used before the data transmission begins. The classic approach is the use of a dedicated control channel, wherein all parameters can be negotiated before the data transfer begins. But this requires bandwidth overhead that is undesirable in the underwater setting.
In this paper, we introduce a dynamic channel access method based on cognitive techniques, an approach for distributed channel allocation for the underwater acoustic environment that allows for dynamic control channel. This paper proposes the DTW signaling listening algorithm and studies its performance via simulations. The simulation results show that compared with FFT energy detecting algorithm and matched filter detecting algorithm, the DTW detecting algorithm is of much better performance in the underwater channel with doppler shift, which is beneficial to improve the channel utilization.
引文
[1] Stojanovic M.. Recent Advances in High-Speed Underwater Acoustic Communica -tion[J]. IEEE Journal of Oceanic Engineering, 1996, 21(2): 125-136
[2] Albert F., Zorzi H.M.. Modeling the Underwater Acoustic Channel in ns2[J]. Nstools’07, 2007
[3]王毅,高翔,方世良,等. Aloha-LPD:一种用于水声通信网的MAC协议[J].东南大学学报, 2009, 39(1): 12-17
[4]魏飞.基于跳预约多址接入的认知无线电MAC协议[J].电力系统通信, 2007, 28(176): 31-34
[5] Torres D., Charbiwala Z., Friedman J., et al. Spectrum Signaling for Cognitive Underwater Acoustic Channel Allocatio[A]. Proc. INFOCOM IEEE Conference on Computer Communications Workshops[C], 2010: 1-6
[6] Stojanovic M., Freitag L.. Wideband Underwater Acoustic CDMA: Adaptive Multichannel Receiver Design. http://www.mit.edu/~millitsa/resources/pdfs/oc05.pdf, March. 1st, 2011
[7] Rodoplu V., Park M.K.. An Energy-Efficient MAC Protocol for Underwater Wireless Acoustic Networks. http://www.coralreefeon.org/oceans_05.pdf, March. 1st, 2011
[8] Cabric D., Mishra S., Brodersen R.. Implementation issues in spectrum sensing for cognitive radios[A]. Proc. Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers[C], 2004, 1: 772-776
[9] Cabric D., Tkachenko A., Brodersen R.. Spectrum sensing measurements of pilot, energy, and collaborative detection[A]. Proc. IEEE Military Communication Conference[C], 2006: 1-7
[10] McHenry M., Livsics E., Nguyen T., et al. XG dynamic spectrum sharing field test results[J]. IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2007: 676-684
[11] Blossom E.. GNU radio: tools for exploring the radio frequency spectrum[J]. Linux Journal, 2004, 2004(122)
[12] Peleato B., Stojanovic M.. A channel sharing scheme for underwater cellular networks[J]. OCEANS 2007- Europe, 2007: 1-5
[13] Wang Y.G., Tang J.S., Pan Y., et al.Underwater Communication Goes Cognitive[J].OCEANS 2008, 2008: 1-4
[14] Baldo N., Casari P., Zorzi M.. Cognitive spectrum access for underwater acoustic communications[A]. Proc. IEEE International Conference on Communications Workshops[C] , 2008: 518-523
[15]朱昌平,韩庆邦,李建,等.水声通信基本原理与应用[M].北京:电子工业出版社, 2009: 96-103
[16]魏莉,许芳,孙海信.水声信道的研究与仿真[J].声学技术, 2008, 27(1): 25-29
[17] Thorp. Deep Ocean Sound Attenuation in the Sub And Low-Kilocycle-per-second Region[J]. Acoustic Society of American, 1965, 38: 648
[18]孙博,程恩,欧晓丽.浅海水声信道研究与仿真[J].无线通信技术, 2006, 3: 11-15
[19]魏莉. OFDM水声通信系统FPGA实现初探及多普勒频移补偿研究[D].厦门:厦门大学, 2008
[20]王熹.基于OFDM的高速水声通信系统研究[D].秦皇岛:燕山大学, 2010
[21]张国恒. OFDM水声通信系统中同步技术研究[D].哈尔滨:哈尔滨工程大学, 2008
[22]韩晶,黄建国,苏为,等.水声通信试验信道仿真研究[J].应用声学, 2007, 26(6): 375-380
[23] Cattoni A.F., Minetti I., Gandetto M., et al. A spectrum sensing algorithm based on distributed cognitive models[A]. Proc. SDR Forum Technical Conference[C], 2006
[24] Lee J.H., Baek J.H., Hwang S.H.. Collaborative Spectrum Sensing using Energy Detector in multiple Antenna System[A]. Proc. 10th International Conference on Advanced Communication Technology[C] , 2008, 1: 427-430
[25] Urkowitz H.. Energy detection of unknown deterministic signals[A]. Proc. IEEE[C], 1967, 55(4): 523-531
[26]石磊.认知无线电中空闲频谱检测技术的研究[D].哈尔滨:哈尔滨工业大学, 2010
[27] Digham F., Alouini M., Simon M..On the energy detection of unknown signals over fading channels[A]. Proc. IEEE International Conference Communication[C], 2003, 5: 3575-3579
[28]马伟.认知无线电频谱检测技术研究[D].北京:北京邮电大学, 2010
[29]乔夏君.认知无线电系统的物理层频谱检测算法研究[D].北京:北京交通大学, 2009
[30] Hwang S.J., Schniter P.. Efficient Multicarrier Communication for Highly Spread Underwater Acoustic Channels[J]. IEEE Journal on Selected Areas in Communications, 2008, 26(9): 1674-1683
[31]崔凯峰.扩频通信匹配滤波Rake接收系统分析[D].北京:北京邮电大学, 2009
[32]郑君里,应启珩,杨为理.信号与系统(第二版)[M].北京:高等教育出版社出版, 2000: 358-362
[33]南利平.通信原理简明教程[M].北京:清华大学出版社, 2000: 241-244
[34]张群英,杨学贤,韩月秋.数字匹配滤波系统输出信噪比与采样速率的关系[J].系统工程与电子技术, 1999, 21(10): 61-63
[35]刘四喜.基于认知无线电网络的频谱检测技术研究[D].长沙:湖南大学, 2010
[36]刘子琦.认知无线电网络中频谱检测算法的研究[D].北京:北京邮电大学, 2009
[37]王君伟.基于DTW的红外自动乘客计数方法研究[D].上海:上海交通大学, 2008
[38] Li G.L., Wang Y.Z., Li M., et al. Similarity Match in Time Series Streams under Dynamic Time Warping Distance[A]. Proc. International Conference on Computer Science and Software Engineering[C], 2008, 4: 399-402
[39] Sakurai Y., Faloutsos C., Yamamuro M.. Stream Monitoring under the Time Warping Distance[A]. Proc. IEEE 23rd International Conference on Data Engineering[C], 2007: 1046-1055
[40] Zhou M., Wong M.H.. Efficient Online Subsequence Searching in Data Streams under Dynamic Time Warping Distance[A]. Proc. IEEE 24th International Conference on Data Engineering[C], 2008: 686-695
[41] Berndt D.J., Clifford J.. Using Dynamic Time Warping to Find Patterns in Time Series[J]. KDD Workshop, 1994: 359-370
[42]张俊.基于VQ和DTW相结合的语音识别算法研究[D].武汉:武汉理工大学, 2007
[43] Chu M.K., Sohn Y.S.. A user friendly interface operated by the improved DTW method[A]. Proc. The 10th IEEE International Conference on Fuzzy Systems[C], 2001, 3: 1187-1190
[44]文翰,黄国顺.语音识别中DTW算法改进研究[J].微计算机信息, 2010, 26(7-1): 195-197
[45]李永健.基于DTW和HMM的语音识别算法仿真及软件设计[D].哈尔滨:哈尔滨工程大学, 2009
[46]郭建奎.数据流相似性查询及模式挖掘研究[D].上海:复旦大学, 2008
[47]吴枫.数据流挖掘若干关键技术研究[D].长沙:国防科技大学, 2009
[48]冯红伟,李战怀,张稳保.时间序列的模糊匹配方法[J].计算机科学, 2002, 29(4): 138-140
[2] Albert F., Zorzi H.M.. Modeling the Underwater Acoustic Channel in ns2[J]. Nstools’07, 2007
[3]王毅,高翔,方世良,等. Aloha-LPD:一种用于水声通信网的MAC协议[J].东南大学学报, 2009, 39(1): 12-17
[4]魏飞.基于跳预约多址接入的认知无线电MAC协议[J].电力系统通信, 2007, 28(176): 31-34
[5] Torres D., Charbiwala Z., Friedman J., et al. Spectrum Signaling for Cognitive Underwater Acoustic Channel Allocatio[A]. Proc. INFOCOM IEEE Conference on Computer Communications Workshops[C], 2010: 1-6
[6] Stojanovic M., Freitag L.. Wideband Underwater Acoustic CDMA: Adaptive Multichannel Receiver Design. http://www.mit.edu/~millitsa/resources/pdfs/oc05.pdf, March. 1st, 2011
[7] Rodoplu V., Park M.K.. An Energy-Efficient MAC Protocol for Underwater Wireless Acoustic Networks. http://www.coralreefeon.org/oceans_05.pdf, March. 1st, 2011
[8] Cabric D., Mishra S., Brodersen R.. Implementation issues in spectrum sensing for cognitive radios[A]. Proc. Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers[C], 2004, 1: 772-776
[9] Cabric D., Tkachenko A., Brodersen R.. Spectrum sensing measurements of pilot, energy, and collaborative detection[A]. Proc. IEEE Military Communication Conference[C], 2006: 1-7
[10] McHenry M., Livsics E., Nguyen T., et al. XG dynamic spectrum sharing field test results[J]. IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2007: 676-684
[11] Blossom E.. GNU radio: tools for exploring the radio frequency spectrum[J]. Linux Journal, 2004, 2004(122)
[12] Peleato B., Stojanovic M.. A channel sharing scheme for underwater cellular networks[J]. OCEANS 2007- Europe, 2007: 1-5
[13] Wang Y.G., Tang J.S., Pan Y., et al.Underwater Communication Goes Cognitive[J].OCEANS 2008, 2008: 1-4
[14] Baldo N., Casari P., Zorzi M.. Cognitive spectrum access for underwater acoustic communications[A]. Proc. IEEE International Conference on Communications Workshops[C] , 2008: 518-523
[15]朱昌平,韩庆邦,李建,等.水声通信基本原理与应用[M].北京:电子工业出版社, 2009: 96-103
[16]魏莉,许芳,孙海信.水声信道的研究与仿真[J].声学技术, 2008, 27(1): 25-29
[17] Thorp. Deep Ocean Sound Attenuation in the Sub And Low-Kilocycle-per-second Region[J]. Acoustic Society of American, 1965, 38: 648
[18]孙博,程恩,欧晓丽.浅海水声信道研究与仿真[J].无线通信技术, 2006, 3: 11-15
[19]魏莉. OFDM水声通信系统FPGA实现初探及多普勒频移补偿研究[D].厦门:厦门大学, 2008
[20]王熹.基于OFDM的高速水声通信系统研究[D].秦皇岛:燕山大学, 2010
[21]张国恒. OFDM水声通信系统中同步技术研究[D].哈尔滨:哈尔滨工程大学, 2008
[22]韩晶,黄建国,苏为,等.水声通信试验信道仿真研究[J].应用声学, 2007, 26(6): 375-380
[23] Cattoni A.F., Minetti I., Gandetto M., et al. A spectrum sensing algorithm based on distributed cognitive models[A]. Proc. SDR Forum Technical Conference[C], 2006
[24] Lee J.H., Baek J.H., Hwang S.H.. Collaborative Spectrum Sensing using Energy Detector in multiple Antenna System[A]. Proc. 10th International Conference on Advanced Communication Technology[C] , 2008, 1: 427-430
[25] Urkowitz H.. Energy detection of unknown deterministic signals[A]. Proc. IEEE[C], 1967, 55(4): 523-531
[26]石磊.认知无线电中空闲频谱检测技术的研究[D].哈尔滨:哈尔滨工业大学, 2010
[27] Digham F., Alouini M., Simon M..On the energy detection of unknown signals over fading channels[A]. Proc. IEEE International Conference Communication[C], 2003, 5: 3575-3579
[28]马伟.认知无线电频谱检测技术研究[D].北京:北京邮电大学, 2010
[29]乔夏君.认知无线电系统的物理层频谱检测算法研究[D].北京:北京交通大学, 2009
[30] Hwang S.J., Schniter P.. Efficient Multicarrier Communication for Highly Spread Underwater Acoustic Channels[J]. IEEE Journal on Selected Areas in Communications, 2008, 26(9): 1674-1683
[31]崔凯峰.扩频通信匹配滤波Rake接收系统分析[D].北京:北京邮电大学, 2009
[32]郑君里,应启珩,杨为理.信号与系统(第二版)[M].北京:高等教育出版社出版, 2000: 358-362
[33]南利平.通信原理简明教程[M].北京:清华大学出版社, 2000: 241-244
[34]张群英,杨学贤,韩月秋.数字匹配滤波系统输出信噪比与采样速率的关系[J].系统工程与电子技术, 1999, 21(10): 61-63
[35]刘四喜.基于认知无线电网络的频谱检测技术研究[D].长沙:湖南大学, 2010
[36]刘子琦.认知无线电网络中频谱检测算法的研究[D].北京:北京邮电大学, 2009
[37]王君伟.基于DTW的红外自动乘客计数方法研究[D].上海:上海交通大学, 2008
[38] Li G.L., Wang Y.Z., Li M., et al. Similarity Match in Time Series Streams under Dynamic Time Warping Distance[A]. Proc. International Conference on Computer Science and Software Engineering[C], 2008, 4: 399-402
[39] Sakurai Y., Faloutsos C., Yamamuro M.. Stream Monitoring under the Time Warping Distance[A]. Proc. IEEE 23rd International Conference on Data Engineering[C], 2007: 1046-1055
[40] Zhou M., Wong M.H.. Efficient Online Subsequence Searching in Data Streams under Dynamic Time Warping Distance[A]. Proc. IEEE 24th International Conference on Data Engineering[C], 2008: 686-695
[41] Berndt D.J., Clifford J.. Using Dynamic Time Warping to Find Patterns in Time Series[J]. KDD Workshop, 1994: 359-370
[42]张俊.基于VQ和DTW相结合的语音识别算法研究[D].武汉:武汉理工大学, 2007
[43] Chu M.K., Sohn Y.S.. A user friendly interface operated by the improved DTW method[A]. Proc. The 10th IEEE International Conference on Fuzzy Systems[C], 2001, 3: 1187-1190
[44]文翰,黄国顺.语音识别中DTW算法改进研究[J].微计算机信息, 2010, 26(7-1): 195-197
[45]李永健.基于DTW和HMM的语音识别算法仿真及软件设计[D].哈尔滨:哈尔滨工程大学, 2009
[46]郭建奎.数据流相似性查询及模式挖掘研究[D].上海:复旦大学, 2008
[47]吴枫.数据流挖掘若干关键技术研究[D].长沙:国防科技大学, 2009
[48]冯红伟,李战怀,张稳保.时间序列的模糊匹配方法[J].计算机科学, 2002, 29(4): 138-140