脏纸编码在认知无线电中的应用研究
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摘要
目前,随着信息化进程的深入,业务需求急剧扩张,业务种类日益多元化,无线网络的业务职能和特征发生了深刻的变化。频谱需求呈指数迅猛增长,频谱资源的供需矛盾问题日益突出,对无线业务的影响日益严重,已经成为未来制约无线领域发展的瓶颈之一。因此迫切需要新的无线网络技术,以使无线网络能够基于自身能力,适应电磁环境和业务类型的动态变化,实现频谱资源的高效有序共享。由此,认知无线网络(Cognitive Radio Network, CRN)技术应运而生。
本文研究了认知无线网络的overlay模型之中,从用户在不干扰主用户发送信息的情况之下,还必须通过部分自己的发送功率,中继主用户信息。为了达到信道容量,从用户需要通过脏纸编码(Dirty Paper Coding)技术,消除主用户对从用户的干扰。本文研究了认知无线信道的基本信息,包括通信系统模型,信道模型,信道容量,讨论了当从用户不能完全感知主用户信息时,在广义衰落脏纸编码信道之中,从用户通过脏纸编码所能达到的最大信息速率。
对于脏纸编码的研究,本文通过分析脏纸编码理论模型、脏纸编码信道模型以及在实际通信系统中脏纸编码信道的信道容量,得出了实用化脏纸编码方案设计的两种方式：1.基于优化信源编码的实用化脏纸编码方案；2.基于优化信道编码的脏纸编码实用化方案。
对于基于优化信源编码的实用化脏纸编码方案,我们研究了四种不同的量化算法——标量量化、网格编码量化、Belief Propagation量化和Linear Programming量化,提出了一种基于Belief Propagation量化和Linear Programming量化的实用化脏纸编码方案,该编码方案从优化信源编码角度出发,用Belief Propagation量化器和Linear Programming量化器对信源信息和干扰进行量化,利用高性能量化器来获得更大的信源编码增益。
在基于优化信道编码的脏纸编码实用化方案方向上,我们首先研究了网格编码调制(TCM)的原理以及网格编码调制中的集分割映射理论。随后,通过分析Turbo码和卷积码相比在码字汉明距离和欧式距离上的优势,研究了基于Turbo结构的网格编码调制。最后,提出了一种基于Turbo-TCM的Trellis-Precoding脏纸编码方案,并对提出的脏纸编码方案进行了仿真,验证了相比原有的Trellis-Precoding方案,基于Turbo-TCM的脏纸编码方案能获取更大的信道编码增益。
Nowadays, with the rapid development of the informationization of the whole world, the needs of communication service is increasing rapidly, types of business are increasingly diversified. The application and characteristics of the wireless network has been changed a lot. The needs of the communication spectrum have been growth exponentially and the contradiction between supply and demand issues has become increasingly prominent in the spectrum resources, which impacts the wireless service a lot and turned out to become one of the bottlenecks restricting the development of wireless field. Because of than, nowadays we need new wireless technology which suits today's wireless environment, and adapts to the change of the electromagnetic environment, and achieves the efficient and orderly sharing of spectrum resources. Thus, the cognitive radio network technology has come into being.
In this paper, we firstly studied the overlay model of the cognitive radio networks. In overlay model of the cognitive radio networks, the cognitive user must use some part of its own power to help to send the information of the prime user, and meanwhile, does not affect the signal receiving of the prime user. In order to reach the capacity of the cognitive channel, the cognitive user must use the dirty paper coding technology to cancel the interference of the prime user. In this paper, we studied the channel model of the cognitive radio, the communication model, and analysis the capacity of the cognitive channel. And discuss the communication situation when the cognitive use could sensing some part of the prime user's signal, and in the generalized fading dirty paper channel, how the cognitive user use the dirty paper coding to reach the channel capacity.
In the research of the dirty paper, we first studied the dirty paper channel model, the capacity of the dirty paper channel and the capacity of the dirty paper channel in the real communication environment. We analyze the two proposal of designing of the practical dirty paper coding scheme:the practical dirty paper coding scheme based on the better source coding and the practical dirty paper coding scheme based on the better channel coding. In the practical dirty paper coding scheme based on the better source coding, we studied four different types of quantizer, and propose the practical dirty paper coding scheme based on the LPQ and BPQ. In the practical dirty paper coding scheme based on the better channel coding, we first studied the trellis code modulation, and the turbo trellis code modulation. Finally, we propose a new type of practical dirty paper coding scheme, the trellis-precoding scheme based on the turbo trellis code modulation, and design some simulation on it, which could prove that the trellis-precoding scheme based on the turbo trellis code modulation could get more channel coding gain.
本文研究了认知无线网络的overlay模型之中,从用户在不干扰主用户发送信息的情况之下,还必须通过部分自己的发送功率,中继主用户信息。为了达到信道容量,从用户需要通过脏纸编码(Dirty Paper Coding)技术,消除主用户对从用户的干扰。本文研究了认知无线信道的基本信息,包括通信系统模型,信道模型,信道容量,讨论了当从用户不能完全感知主用户信息时,在广义衰落脏纸编码信道之中,从用户通过脏纸编码所能达到的最大信息速率。
对于脏纸编码的研究,本文通过分析脏纸编码理论模型、脏纸编码信道模型以及在实际通信系统中脏纸编码信道的信道容量,得出了实用化脏纸编码方案设计的两种方式：1.基于优化信源编码的实用化脏纸编码方案；2.基于优化信道编码的脏纸编码实用化方案。
对于基于优化信源编码的实用化脏纸编码方案,我们研究了四种不同的量化算法——标量量化、网格编码量化、Belief Propagation量化和Linear Programming量化,提出了一种基于Belief Propagation量化和Linear Programming量化的实用化脏纸编码方案,该编码方案从优化信源编码角度出发,用Belief Propagation量化器和Linear Programming量化器对信源信息和干扰进行量化,利用高性能量化器来获得更大的信源编码增益。
在基于优化信道编码的脏纸编码实用化方案方向上,我们首先研究了网格编码调制(TCM)的原理以及网格编码调制中的集分割映射理论。随后,通过分析Turbo码和卷积码相比在码字汉明距离和欧式距离上的优势,研究了基于Turbo结构的网格编码调制。最后,提出了一种基于Turbo-TCM的Trellis-Precoding脏纸编码方案,并对提出的脏纸编码方案进行了仿真,验证了相比原有的Trellis-Precoding方案,基于Turbo-TCM的脏纸编码方案能获取更大的信道编码增益。
Nowadays, with the rapid development of the informationization of the whole world, the needs of communication service is increasing rapidly, types of business are increasingly diversified. The application and characteristics of the wireless network has been changed a lot. The needs of the communication spectrum have been growth exponentially and the contradiction between supply and demand issues has become increasingly prominent in the spectrum resources, which impacts the wireless service a lot and turned out to become one of the bottlenecks restricting the development of wireless field. Because of than, nowadays we need new wireless technology which suits today's wireless environment, and adapts to the change of the electromagnetic environment, and achieves the efficient and orderly sharing of spectrum resources. Thus, the cognitive radio network technology has come into being.
In this paper, we firstly studied the overlay model of the cognitive radio networks. In overlay model of the cognitive radio networks, the cognitive user must use some part of its own power to help to send the information of the prime user, and meanwhile, does not affect the signal receiving of the prime user. In order to reach the capacity of the cognitive channel, the cognitive user must use the dirty paper coding technology to cancel the interference of the prime user. In this paper, we studied the channel model of the cognitive radio, the communication model, and analysis the capacity of the cognitive channel. And discuss the communication situation when the cognitive use could sensing some part of the prime user's signal, and in the generalized fading dirty paper channel, how the cognitive user use the dirty paper coding to reach the channel capacity.
In the research of the dirty paper, we first studied the dirty paper channel model, the capacity of the dirty paper channel and the capacity of the dirty paper channel in the real communication environment. We analyze the two proposal of designing of the practical dirty paper coding scheme:the practical dirty paper coding scheme based on the better source coding and the practical dirty paper coding scheme based on the better channel coding. In the practical dirty paper coding scheme based on the better source coding, we studied four different types of quantizer, and propose the practical dirty paper coding scheme based on the LPQ and BPQ. In the practical dirty paper coding scheme based on the better channel coding, we first studied the trellis code modulation, and the turbo trellis code modulation. Finally, we propose a new type of practical dirty paper coding scheme, the trellis-precoding scheme based on the turbo trellis code modulation, and design some simulation on it, which could prove that the trellis-precoding scheme based on the turbo trellis code modulation could get more channel coding gain.
引文
[1]J.Mitola.Cognitive radio:An integrated agent architecture for software defined radio. Ph.D.dissertation. KTH. Stockholm. Sweden. Dec.2000.
[2]FCC Spectrum Policy Task Force:Report of the spectrum efficiency working group. Tech.Rep.02-135. Nov.2002
[3]S.Haykin. Cognitive radio:Brain-empowered wireless communications. IEEE J.Sel.AreasCommun.2005(23):201-220.
[4]M. H. M. Costa. Writing on dirty paper. IEEE Trans. Inf. Theory. vol.IT-29. no. 3. pp.439-441. May 1983.
[5]W.Yu. P. Varodayan. John M. Cioffi. Trellis and Convolutional Precoding for Transmitter-Based Interference Presubtraction. IEEE TRANSACTIONS ON COMMUNICATIONS, VOL.53, NO.7, JULY 2005.
[6]J.Chou, S.S.Paradhan, K.Ramchandran. Turbo Coded Trellis-Based Constructions for Data Embedding:Channel Coding with Side Information.
[7]Y.Sun, Y.Yang, A.D. Liveris, V.Stankovic, Z.Xiong. Near-Capacity Dirty-Paper Code Design:A Source-Channel Coding Approach. IEEE TRANSACTIONS ON INFORMATION THEORY, VOL.55, NO.7, JULY 2009.
[8]Wei Yu, David P.Varodayan, John M. Cioffi. Trellis and Convolutional Precoding for Transmitter Based Interference Presubtraction. IEEE TRANSACTIONS ON COMMUNICATIONS, VOL.53, NO.7, JULY 2005
[9]Shannon, C.E. A Mathematical Theory of Communication. Bell System Technical Journal,27, pp.379-423 & 623-656, July & October,1948.
[10]M.H.M.Costa. Writing on dirty paper. IEEE Trans. Inform. Theory, vol.29, no.3, pp.439-441, May 1983
[11]S. I. Gel' fand and M. S. Pinsker. Coding for channel with random parameters. Problem of Control and Information Theory, vol.9, no. I, pp.19-31, 1980.
[12]U. Erez, S. Shamai (Shitz), and R. Zamir. Capacity and lattice strategies for cancelling known interference, in Proc. Int. Symp. Information Theory and Its Applications (ISITA), Honolulu, HI, Nov.2000, pp.681-684.
[13]M. VedatEyuboglu and G. David Forney. Lattice and Trellis Quantization with Lattice- and Trellis-Bounded Codebooks-High-Rate Theory for Memoryless Sources. IEEE TRANSACTIONS ON INFORMATION THEORY, VOL.39, NO.1, JANUARY 1993
[14]Y.Sun, Y.Yang, A.D. Liveris, V.Stankovic and ZixiangXiong. Near-Capacity Dirty-Paper Code Design:A Source-Channel Coding Approach. IEEE TRANSACTIONS ON INFORMATION THEORY, VOL.55, NO.7, JULY 2009
[15]李太杰,黄文胜,冷建华.栅格编码量化(TCQ)在语音编码中的应用.信息工程学院学报.第17卷第2期.
[16]E. Martinian and J. Yedidia. Iterative quantization using codes on graphs, in Proc.41st Annual Allerton Conference, Aug.2004, arXiv:cs.IT/0408008.
[17]E. Martinian and M. Wainwright. Analysis of LDGM and compound codes for lossy compression and binning, in Workshop on InformationTheory and its Applications, Feb.2006, arXiv:cs.IT/0602046.
[18]——. Low-density constructions can achieve the Wyner-Ziv and Gelfand-Pinsker bounds, in Proc. ISIT 2006, Seattle, WA, Jul.2006, pp. 484-488, arXiv:cs.IT/0605091.
[19]任意,任为民.通用栅格编码量化在图像压缩中的应用.云南师范大学学报.第23卷第五期.2003年9月.
[20]G. Ungerboeck. Channel coding with multilevel/phase signals. IEEE Trans. Inform. Theory, vol. IT-28, pp.55-67, Jan.1982.
[21]Goff. S. L, Glavieux. A and Berrou. C. Turbo code and high spectral efficiency modulation. Proc ICC'94. New Orleans, May 1994:645-649
[22]Jon Feldman, Martin J. Wainwright and David R. Karger. Using Linear Programming to Decode Binary Linear Codes. IEEE TRANSACTIONS ON INFORMATION THEORY, VOL.51, NO.3, MARCH 2005
[23]A.Jovicic,P.Viswanath. Cognitive Radio:An information theoretic perspective[J].IEEE Transactions on Information Theory,2009,55(9):3945-3958.
[24]N.Devroye,P.Mitran,V.Tarokh. Achievable rates in cognitive radio channels[J].IEEE Transactions on Information Theory.2006,52(5):1813-1827.
[25]A.B.Carleia. A case where interference does not reduce capacity[J]. IEEE Transactions on Information Theory,1975,21(5):569-570.
[26]S.Gelfand and M.Pinsker. Coding for channel with random parameters. Problems of control and information theory, vol.9, no.1, pp. 19-31,1980.
[27]C. Berrou and A. Glavieux. Near Optimum Error Correcting Coding and Decoding:Turbo-Codes. IEEE Trans. Commun, vol.44, no.IO, Oct.1996, pp, 1261-71.
[28]C. Berrou, A. Graell i Amat, Y.Ould Cheikh Mouhamedou, C. Douillard, and Y.Saouter. Improving the Distance Properties of Turbo Codes Using a Third Component Code:3D Turbo Codes. IEEE TRANSACTIONS ON COMMUNICATIONS, VOL.57, NO.9, SEPTEMBER 2009.
[29]C. Berrou, A. Graell i Amat, Y.Ould Cheikh Mouhamedou, C. Douillard, and Y.Saouter. Adding a Rate-1 Third Dimension to Turbo Codes. ITW 2007, Lake Tahoe, California, September 2-6,2007.
[30]Eirik Rosnes, A. Graell i Amat. Performance Analysis of 3-D Turbo Codes. IEEE TRANSACTIONS ON INFORMATION THEORY, VOL.57, NO. 6, JUNE 2011.
[2]FCC Spectrum Policy Task Force:Report of the spectrum efficiency working group. Tech.Rep.02-135. Nov.2002
[3]S.Haykin. Cognitive radio:Brain-empowered wireless communications. IEEE J.Sel.AreasCommun.2005(23):201-220.
[4]M. H. M. Costa. Writing on dirty paper. IEEE Trans. Inf. Theory. vol.IT-29. no. 3. pp.439-441. May 1983.
[5]W.Yu. P. Varodayan. John M. Cioffi. Trellis and Convolutional Precoding for Transmitter-Based Interference Presubtraction. IEEE TRANSACTIONS ON COMMUNICATIONS, VOL.53, NO.7, JULY 2005.
[6]J.Chou, S.S.Paradhan, K.Ramchandran. Turbo Coded Trellis-Based Constructions for Data Embedding:Channel Coding with Side Information.
[7]Y.Sun, Y.Yang, A.D. Liveris, V.Stankovic, Z.Xiong. Near-Capacity Dirty-Paper Code Design:A Source-Channel Coding Approach. IEEE TRANSACTIONS ON INFORMATION THEORY, VOL.55, NO.7, JULY 2009.
[8]Wei Yu, David P.Varodayan, John M. Cioffi. Trellis and Convolutional Precoding for Transmitter Based Interference Presubtraction. IEEE TRANSACTIONS ON COMMUNICATIONS, VOL.53, NO.7, JULY 2005
[9]Shannon, C.E. A Mathematical Theory of Communication. Bell System Technical Journal,27, pp.379-423 & 623-656, July & October,1948.
[10]M.H.M.Costa. Writing on dirty paper. IEEE Trans. Inform. Theory, vol.29, no.3, pp.439-441, May 1983
[11]S. I. Gel' fand and M. S. Pinsker. Coding for channel with random parameters. Problem of Control and Information Theory, vol.9, no. I, pp.19-31, 1980.
[12]U. Erez, S. Shamai (Shitz), and R. Zamir. Capacity and lattice strategies for cancelling known interference, in Proc. Int. Symp. Information Theory and Its Applications (ISITA), Honolulu, HI, Nov.2000, pp.681-684.
[13]M. VedatEyuboglu and G. David Forney. Lattice and Trellis Quantization with Lattice- and Trellis-Bounded Codebooks-High-Rate Theory for Memoryless Sources. IEEE TRANSACTIONS ON INFORMATION THEORY, VOL.39, NO.1, JANUARY 1993
[14]Y.Sun, Y.Yang, A.D. Liveris, V.Stankovic and ZixiangXiong. Near-Capacity Dirty-Paper Code Design:A Source-Channel Coding Approach. IEEE TRANSACTIONS ON INFORMATION THEORY, VOL.55, NO.7, JULY 2009
[15]李太杰,黄文胜,冷建华.栅格编码量化(TCQ)在语音编码中的应用.信息工程学院学报.第17卷第2期.
[16]E. Martinian and J. Yedidia. Iterative quantization using codes on graphs, in Proc.41st Annual Allerton Conference, Aug.2004, arXiv:cs.IT/0408008.
[17]E. Martinian and M. Wainwright. Analysis of LDGM and compound codes for lossy compression and binning, in Workshop on InformationTheory and its Applications, Feb.2006, arXiv:cs.IT/0602046.
[18]——. Low-density constructions can achieve the Wyner-Ziv and Gelfand-Pinsker bounds, in Proc. ISIT 2006, Seattle, WA, Jul.2006, pp. 484-488, arXiv:cs.IT/0605091.
[19]任意,任为民.通用栅格编码量化在图像压缩中的应用.云南师范大学学报.第23卷第五期.2003年9月.
[20]G. Ungerboeck. Channel coding with multilevel/phase signals. IEEE Trans. Inform. Theory, vol. IT-28, pp.55-67, Jan.1982.
[21]Goff. S. L, Glavieux. A and Berrou. C. Turbo code and high spectral efficiency modulation. Proc ICC'94. New Orleans, May 1994:645-649
[22]Jon Feldman, Martin J. Wainwright and David R. Karger. Using Linear Programming to Decode Binary Linear Codes. IEEE TRANSACTIONS ON INFORMATION THEORY, VOL.51, NO.3, MARCH 2005
[23]A.Jovicic,P.Viswanath. Cognitive Radio:An information theoretic perspective[J].IEEE Transactions on Information Theory,2009,55(9):3945-3958.
[24]N.Devroye,P.Mitran,V.Tarokh. Achievable rates in cognitive radio channels[J].IEEE Transactions on Information Theory.2006,52(5):1813-1827.
[25]A.B.Carleia. A case where interference does not reduce capacity[J]. IEEE Transactions on Information Theory,1975,21(5):569-570.
[26]S.Gelfand and M.Pinsker. Coding for channel with random parameters. Problems of control and information theory, vol.9, no.1, pp. 19-31,1980.
[27]C. Berrou and A. Glavieux. Near Optimum Error Correcting Coding and Decoding:Turbo-Codes. IEEE Trans. Commun, vol.44, no.IO, Oct.1996, pp, 1261-71.
[28]C. Berrou, A. Graell i Amat, Y.Ould Cheikh Mouhamedou, C. Douillard, and Y.Saouter. Improving the Distance Properties of Turbo Codes Using a Third Component Code:3D Turbo Codes. IEEE TRANSACTIONS ON COMMUNICATIONS, VOL.57, NO.9, SEPTEMBER 2009.
[29]C. Berrou, A. Graell i Amat, Y.Ould Cheikh Mouhamedou, C. Douillard, and Y.Saouter. Adding a Rate-1 Third Dimension to Turbo Codes. ITW 2007, Lake Tahoe, California, September 2-6,2007.
[30]Eirik Rosnes, A. Graell i Amat. Performance Analysis of 3-D Turbo Codes. IEEE TRANSACTIONS ON INFORMATION THEORY, VOL.57, NO. 6, JUNE 2011.