迭代接收技术在LTE中的应用
摘要
随着无线通信技术的快速发展,LTE技术已逐渐走上商用的道路,LTE技术的提出是为了满足信息社会人们对数据传输的大规模需求,为此,LTE标准采用了MIMO、OFDM等先进技术以在峰值速率、传输时延和频谱利用率等方面达到一个新的高度,LTE的目标是为未来十年或十年以上提供有竞争力的无线通讯解决方案。
在LTE接收端传统的实现方案中,MIMO检测与Turbo译码是单独进行的,随着基于概率的软判决迭代检测思想的广泛应用,若能在LTE接收端将MIMO检测与Turbo译码以迭代接收机的形式进行迭代检测将获得巨大的检测增益,提升接收端的性能,因此有必要研究LTE系统中的迭代接收机技术。
本文首先介绍了LTE系统中迭代接收机的系统模型,然后分别研究了迭代接收机中的Turbo迭代译码技术和软输入软输出MIMO检测技术。
本文给出了Turbo迭代译码中Log-Map译码算法的常见简化方法,并针对LLR计算单元提出了一种简化算法,仿真结果表明,相比Max-Log-Map算法,该算法在增加有限的复杂度下取得了0.2dB的性能提升。
本文采用CUDA平台研究实现了基于GPU的Turbo迭代译码器,测试结果表明该迭代译码器能取得4.8Mbps的净吞吐率,能满足低速实时数据传输需求,采用GPU实现Turbo迭代译码器能降低开发成本同时缩短开发周期,该技术可用于软件无线电中,也可用于Turbo码的仿真加速领域中,能上百倍地降低Turbo码的仿真时间。
针对LTE系统支持高速数据传输的需求,本文研究实现了基于FPGA的高吞吐率Turbo迭代译码器,该译码器能适应LTE系统中的全部188种码长,在该Turbo迭代译码器中,本文针对LTE中QPP交织器的特点提出了一种适合于并行译码的QPP交织器实现方法与硬件架构,相比基于存储的方法所需要的8Mbits左右的存储资源,本文提出的方法只需要1692bits的存储资源,具有很大的优越性；结果表明,本文设计的Turbo迭代译码器在资源消耗,译码延时,数据吞吐率等各项指标上都满足了LTE的需求。
本文最后对迭代接收中软输入软输出MIMO检测技术进行了研究,本文详细介绍了三种软输入软输出MIMO检测算法,包括最大似然检测算法,球形译码算法,以及MMSE-SIC检测算法,通过在LTE系统中的仿真比较了采用以上三种软输入软输出MIMO检测算法的迭代接收机的性能。针对MMSE-SIC检测算法,结合LTE系统调制星座图和多天线传输模式的特点,本文从发射信号的软估计,MMSE滤波,矩阵求逆等方面简化了该算法,得到了一种低复杂度的MMSE-SIC检测算法,非常适合在LTE系统中实现。
With the rapid development of wireless communication technology, LTE technology has gradually on the road of commercial using, LTE technology was proposed to meet the massive demand for data transmission for people in the information society nowadays, in order to reach a new height in peak data rate, transmission delay and spectral efficiency, MIMO, OFDM and other advanced technologies were using in the LTE standard. LTE is aiming to provide a competitive wireless solution for the next decade or more.
LTE adopted Turbo codes as channel coding to obtain a reliable data transmission, since LTE needs to support a peak rate of up to 300Mbps in the downlink, the corresponding Turbo decoding in the receiver also needs to meet this goal and it is very important to design a Turbo decoder with high throughput. In the traditional implementation of receiver, MIMO detection and Turbo decoding are processed separately, however, with the widely using of soft decision iterative detection based on probability, it will be a huge gain and enhance the performance of the receiver in LTE if MIMO detection and Turbo decoding are processed in the form of iterative receiver. So it is necessary to study the iterative receiver in LTE.
At first, this paper introduced the system model of iterative receiver in LTE, and then studied two key technique of iterative receiver:the Turbo iterative decoding and soft-input soft-output MIMO detection.
In this paper, the common simplified methods of Log-Map decoding algorithm in Turbo iterative decoding are given, and a simplified algorithm is presented for the LLR calculation unit. Simulation results show that, compared with Max-Log-Map algorithm, the new algorithm improves the performance about 0.2dB under a limited increase in complexity.
In this paper, the Turbo iterative decoder is implemented by GPU based on the CUDA Platform and the test results show that the iterative decoder can achieve a throughput of 4.8Mbps, which meets the needs of low-speed data transmission in real time. Implementation a Turbo iterative decoder by GPU can reduce the development costs and shorten the development cycle, and this technology can be used in software defined radio and simulation acceleration of Turbo codes with a hundredfold reduction in the simulation time.
With the demand for high-speed data transmission of LTE, this paper also implements a high throughput iterative Turbo decoder based on FPGA, the decoder can adapt to all 188 code block length defined in LTE. In the Turbo decoder, according to the characteristics of QPP interleaver in LTE, a new implementation method and architecture is presented which is very suitable for parallel decoding. Compared with the memory-based approach which required 8Mbits of storage resources, the proposed method only needs 1692bits storage resources. Simulation results show that the Turbo decoder meets the needs of LTE in resource consumption, decoding delay, data throughput and other indicators.
Finally, the SISO MIMO detection techniques in iterative receiver have been studied. This paper introduces three soft input soft output MIMO detection algorithms, including maximum likelihood detection algorithm, sphere decoding, and MMSE-SIC detection algorithm, the performance of the iterative receiver adopting the three soft input soft output MIMO detection algorithm is compared by simulation in the LTE system. For MMSE-SIC detection algorithm, considering the modulation constellation and multi-antenna transmission mode in LTE system, some methods are taken to simplify the algorithm in the soft estimates of the transmitting signals, MMSE filtering, matrix inversion and a low-complexity MMSE-SIC detection algorithm is presented which is very suitable for implementation of iterative receiver in the LTE system.
在LTE接收端传统的实现方案中,MIMO检测与Turbo译码是单独进行的,随着基于概率的软判决迭代检测思想的广泛应用,若能在LTE接收端将MIMO检测与Turbo译码以迭代接收机的形式进行迭代检测将获得巨大的检测增益,提升接收端的性能,因此有必要研究LTE系统中的迭代接收机技术。
本文首先介绍了LTE系统中迭代接收机的系统模型,然后分别研究了迭代接收机中的Turbo迭代译码技术和软输入软输出MIMO检测技术。
本文给出了Turbo迭代译码中Log-Map译码算法的常见简化方法,并针对LLR计算单元提出了一种简化算法,仿真结果表明,相比Max-Log-Map算法,该算法在增加有限的复杂度下取得了0.2dB的性能提升。
本文采用CUDA平台研究实现了基于GPU的Turbo迭代译码器,测试结果表明该迭代译码器能取得4.8Mbps的净吞吐率,能满足低速实时数据传输需求,采用GPU实现Turbo迭代译码器能降低开发成本同时缩短开发周期,该技术可用于软件无线电中,也可用于Turbo码的仿真加速领域中,能上百倍地降低Turbo码的仿真时间。
针对LTE系统支持高速数据传输的需求,本文研究实现了基于FPGA的高吞吐率Turbo迭代译码器,该译码器能适应LTE系统中的全部188种码长,在该Turbo迭代译码器中,本文针对LTE中QPP交织器的特点提出了一种适合于并行译码的QPP交织器实现方法与硬件架构,相比基于存储的方法所需要的8Mbits左右的存储资源,本文提出的方法只需要1692bits的存储资源,具有很大的优越性；结果表明,本文设计的Turbo迭代译码器在资源消耗,译码延时,数据吞吐率等各项指标上都满足了LTE的需求。
本文最后对迭代接收中软输入软输出MIMO检测技术进行了研究,本文详细介绍了三种软输入软输出MIMO检测算法,包括最大似然检测算法,球形译码算法,以及MMSE-SIC检测算法,通过在LTE系统中的仿真比较了采用以上三种软输入软输出MIMO检测算法的迭代接收机的性能。针对MMSE-SIC检测算法,结合LTE系统调制星座图和多天线传输模式的特点,本文从发射信号的软估计,MMSE滤波,矩阵求逆等方面简化了该算法,得到了一种低复杂度的MMSE-SIC检测算法,非常适合在LTE系统中实现。
With the rapid development of wireless communication technology, LTE technology has gradually on the road of commercial using, LTE technology was proposed to meet the massive demand for data transmission for people in the information society nowadays, in order to reach a new height in peak data rate, transmission delay and spectral efficiency, MIMO, OFDM and other advanced technologies were using in the LTE standard. LTE is aiming to provide a competitive wireless solution for the next decade or more.
LTE adopted Turbo codes as channel coding to obtain a reliable data transmission, since LTE needs to support a peak rate of up to 300Mbps in the downlink, the corresponding Turbo decoding in the receiver also needs to meet this goal and it is very important to design a Turbo decoder with high throughput. In the traditional implementation of receiver, MIMO detection and Turbo decoding are processed separately, however, with the widely using of soft decision iterative detection based on probability, it will be a huge gain and enhance the performance of the receiver in LTE if MIMO detection and Turbo decoding are processed in the form of iterative receiver. So it is necessary to study the iterative receiver in LTE.
At first, this paper introduced the system model of iterative receiver in LTE, and then studied two key technique of iterative receiver:the Turbo iterative decoding and soft-input soft-output MIMO detection.
In this paper, the common simplified methods of Log-Map decoding algorithm in Turbo iterative decoding are given, and a simplified algorithm is presented for the LLR calculation unit. Simulation results show that, compared with Max-Log-Map algorithm, the new algorithm improves the performance about 0.2dB under a limited increase in complexity.
In this paper, the Turbo iterative decoder is implemented by GPU based on the CUDA Platform and the test results show that the iterative decoder can achieve a throughput of 4.8Mbps, which meets the needs of low-speed data transmission in real time. Implementation a Turbo iterative decoder by GPU can reduce the development costs and shorten the development cycle, and this technology can be used in software defined radio and simulation acceleration of Turbo codes with a hundredfold reduction in the simulation time.
With the demand for high-speed data transmission of LTE, this paper also implements a high throughput iterative Turbo decoder based on FPGA, the decoder can adapt to all 188 code block length defined in LTE. In the Turbo decoder, according to the characteristics of QPP interleaver in LTE, a new implementation method and architecture is presented which is very suitable for parallel decoding. Compared with the memory-based approach which required 8Mbits of storage resources, the proposed method only needs 1692bits storage resources. Simulation results show that the Turbo decoder meets the needs of LTE in resource consumption, decoding delay, data throughput and other indicators.
Finally, the SISO MIMO detection techniques in iterative receiver have been studied. This paper introduces three soft input soft output MIMO detection algorithms, including maximum likelihood detection algorithm, sphere decoding, and MMSE-SIC detection algorithm, the performance of the iterative receiver adopting the three soft input soft output MIMO detection algorithm is compared by simulation in the LTE system. For MMSE-SIC detection algorithm, considering the modulation constellation and multi-antenna transmission mode in LTE system, some methods are taken to simplify the algorithm in the soft estimates of the transmitting signals, MMSE filtering, matrix inversion and a low-complexity MMSE-SIC detection algorithm is presented which is very suitable for implementation of iterative receiver in the LTE system.
引文
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[3]R. G. Gallager, Low density parity-check codes[D], Ph.D. dissertation, MIT Press, Cambridge, MA,1963.
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[6]C. Studer, H. Bolcskei, Soft-Input Soft-Output Single Tree-Search Sphere Decoding[J], IEEE Transactions on Information Theory,2010,Vol.56(10), pp.4827-4842.
[7]Shannon C.E., A mathematical theory of communication part 1, Bell System Technical Journal,1948,27 (7), pp.379-423.
[8]Shannon C.E., A mathematical theory of communication part 2, Bell System Technical Journal,1948,27 (10), pp.623-656.
[9]R W Hamming, Error-control coding, Bell Syst. Tech. j, April 1950,29;pp.147-160.
[10]E. Prange, Cyclic Error-Correcting codes in two symbols, AFCRC-TN-57,103,Air Force Cambridge Research Center,Cambridge,Mass., September 1957.
[11]C. Berrou, A. Glavieux, and P. Thitimajshima, Near Shannon Limit Error-Correcting Coding and Decoding:Turbo-Codes[C]. International Conference on Communications, Geneva, Switzerland, May 1993, pp.1064-1070.
[12]H. Song, B. VK. Vijaya Kumar,and E. Kurtas, Effect of transition noise on turbo decoding for optical data storage[C], in Proc. SPIE Int. Symp. Optical Memory and Optical Data Storage 2000, pp.283-288.
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[15]Chaikalis, C., Noras, J.M., Implementation of an improved reconfigurable SOVA/log-MAP turbo decoder in 3GPP[C].3G Mobile Communication Technologies, 2002. Third International Conference on(Conf. Publ. No.489)8-10 May 2002 pp.146-150.
[16]E. Agrell, T. Eriksson, A. Vardy, K. Zeger. Closest point search in lattices [J]. IEEE Transactions on Information Theory, vol.48, pp.2201-2214,2002.
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[18]K. J. Kim, J. Yue, R. A. Iltis, et al. A QRD-M/Kalman filter based detection and channel estimation algorithm for MIMO-OFDM systems [J]. IEEE Transactions on Wireless Communications, vol.4, pp.710-721,2005.
[19]H. Kawai, K. Higuchi, N. Maeda, M. Sawahashi. Adaptive control of surviving symbol replica candidates in QRM-MLD for OFDM-MINO multiplexing [J]. IEEE Journal on Selected Areas in Communications, vol.24, pp.1130-1140,2006.
[20]M. Mohaisen, K.H. Chang, B.T. Koo. Adaptive parallel and iterative QRDM algorithms for spatial multiplexing MIMO systems [C]. In Vehicular Technology Conference Fall (VTC 2009-Fall),2009 IEEE 70th,2009, pp.1-5.
[21]L. Boher, R. Legouable and R. Rabineau. Performance Analysis of Iterative Receiver in 3GPP/LTE DL MIMO OFDMA System [C]. In Spread Spectrum Techniques and Applications,2008. ISSSTA'08. IEEE 10th International Symposium on,2008, pp. 103-108.
[22]刘东华,Turbo码原理与应用技术[M],电子工业出版社,2004：79-85
[23]S. Benedetto and G. Montorsi, Unveiling turbo codes:Some results on parallel concatenated coding schemes [J].IEEE Trans. Inf. Theory, Mar.1996, vol.42, no.2, pp. 409-429.
[24]Dolinar S, Divsalar D, Weight Distributions for turbo codes using random and nonrandom permutations, TDA Progress Report, August 15,1995,pp.42-122.
[25]D. Divsalar and F. Pollara, Turbo codes for PCS applications[C], Proc. Int. Conf. Communications, Seattle, WA, Jun.1995.
[26]Ramsey, John L. Realization of Optimum Interleaves [J], IEEE Trans. Inf. Theory,1970, vol.16(3), pp.38-345.
[27]O. Y. Takeshita and D. J. Costello, Jr, New deterministic interleaver designs for turbo codes[J], IEEE Trans. Inf. Theory,2000, vol.46(6), pp.1988-2006.
[28]Sun-J, Takeshita- O.Y. Interleaves for turbo codes using permutation polynomials over integer rings[J]. IEEE Transactions on Information Theory,2005,51(1):101-119.
[29]Papaharalabos, S.,Sybis, M.,Tyczka, P., Mathiopoulos, P.T., Modified Log-MAP Algorithm for Simplified Decoding of Turbo and Turbo TCM Codes[C], IEEE 69th Conference on Vehicular Technology. VTC Spring 2009, pp.1-5.
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[2]X. Wang and H. V. Poor, Iterative (turbo) soft interference cancellation and decoding for coded CDMA[J]. IEEE Trans. On Comm., July 1999,vol.47, no.7, pp.1046-1061.
[3]R. G. Gallager, Low density parity-check codes[D], Ph.D. dissertation, MIT Press, Cambridge, MA,1963.
[4]M. Tuchler, A. C. Singer, and R. Kotter, Minimum mean squared error equalization using a priori information [J], IEEE Trans. on Sig. Proc., vol.50, no.3, Mar.2002, pp. 673-683.
[5]M. Witzke, S. Baro, F. Schreckenbach, and J. Hagenauer, Iterative detection of MEIMO signals with linear detectors[C], in Proc. Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA, USA, Nov.2002, pp.289-293.
[6]C. Studer, H. Bolcskei, Soft-Input Soft-Output Single Tree-Search Sphere Decoding[J], IEEE Transactions on Information Theory,2010,Vol.56(10), pp.4827-4842.
[7]Shannon C.E., A mathematical theory of communication part 1, Bell System Technical Journal,1948,27 (7), pp.379-423.
[8]Shannon C.E., A mathematical theory of communication part 2, Bell System Technical Journal,1948,27 (10), pp.623-656.
[9]R W Hamming, Error-control coding, Bell Syst. Tech. j, April 1950,29;pp.147-160.
[10]E. Prange, Cyclic Error-Correcting codes in two symbols, AFCRC-TN-57,103,Air Force Cambridge Research Center,Cambridge,Mass., September 1957.
[11]C. Berrou, A. Glavieux, and P. Thitimajshima, Near Shannon Limit Error-Correcting Coding and Decoding:Turbo-Codes[C]. International Conference on Communications, Geneva, Switzerland, May 1993, pp.1064-1070.
[12]H. Song, B. VK. Vijaya Kumar,and E. Kurtas, Effect of transition noise on turbo decoding for optical data storage[C], in Proc. SPIE Int. Symp. Optical Memory and Optical Data Storage 2000, pp.283-288.
[13]VOGT, J., KOORA, K, FINGE, A., and FETTWEIS, G:Comparison of different turbo decoder realizations for IMT-2000[C]. Proc.IEEE GLOBECOM'99, Rio de Janeiro, Dec.1999, pp.27062708.
[14]Ahmed, I.., Arslan, T., Baloch, S., Improved memory strategy for Iogmap turbo decoders[C]. SOC Conference,2005. Proceedings. IEEE International 25-28 Sept.2005, pp.103-104.
[15]Chaikalis, C., Noras, J.M., Implementation of an improved reconfigurable SOVA/log-MAP turbo decoder in 3GPP[C].3G Mobile Communication Technologies, 2002. Third International Conference on(Conf. Publ. No.489)8-10 May 2002 pp.146-150.
[16]E. Agrell, T. Eriksson, A. Vardy, K. Zeger. Closest point search in lattices [J]. IEEE Transactions on Information Theory, vol.48, pp.2201-2214,2002.
[17]L. Barbero J. Thompson. Fixing the complexity of the sphere decoder for MIMO detection [J]. IEEE Transactions on Wireless Communications, vol.7, pp.2131-2142, 2008.
[18]K. J. Kim, J. Yue, R. A. Iltis, et al. A QRD-M/Kalman filter based detection and channel estimation algorithm for MIMO-OFDM systems [J]. IEEE Transactions on Wireless Communications, vol.4, pp.710-721,2005.
[19]H. Kawai, K. Higuchi, N. Maeda, M. Sawahashi. Adaptive control of surviving symbol replica candidates in QRM-MLD for OFDM-MINO multiplexing [J]. IEEE Journal on Selected Areas in Communications, vol.24, pp.1130-1140,2006.
[20]M. Mohaisen, K.H. Chang, B.T. Koo. Adaptive parallel and iterative QRDM algorithms for spatial multiplexing MIMO systems [C]. In Vehicular Technology Conference Fall (VTC 2009-Fall),2009 IEEE 70th,2009, pp.1-5.
[21]L. Boher, R. Legouable and R. Rabineau. Performance Analysis of Iterative Receiver in 3GPP/LTE DL MIMO OFDMA System [C]. In Spread Spectrum Techniques and Applications,2008. ISSSTA'08. IEEE 10th International Symposium on,2008, pp. 103-108.
[22]刘东华,Turbo码原理与应用技术[M],电子工业出版社,2004：79-85
[23]S. Benedetto and G. Montorsi, Unveiling turbo codes:Some results on parallel concatenated coding schemes [J].IEEE Trans. Inf. Theory, Mar.1996, vol.42, no.2, pp. 409-429.
[24]Dolinar S, Divsalar D, Weight Distributions for turbo codes using random and nonrandom permutations, TDA Progress Report, August 15,1995,pp.42-122.
[25]D. Divsalar and F. Pollara, Turbo codes for PCS applications[C], Proc. Int. Conf. Communications, Seattle, WA, Jun.1995.
[26]Ramsey, John L. Realization of Optimum Interleaves [J], IEEE Trans. Inf. Theory,1970, vol.16(3), pp.38-345.
[27]O. Y. Takeshita and D. J. Costello, Jr, New deterministic interleaver designs for turbo codes[J], IEEE Trans. Inf. Theory,2000, vol.46(6), pp.1988-2006.
[28]Sun-J, Takeshita- O.Y. Interleaves for turbo codes using permutation polynomials over integer rings[J]. IEEE Transactions on Information Theory,2005,51(1):101-119.
[29]Papaharalabos, S.,Sybis, M.,Tyczka, P., Mathiopoulos, P.T., Modified Log-MAP Algorithm for Simplified Decoding of Turbo and Turbo TCM Codes[C], IEEE 69th Conference on Vehicular Technology. VTC Spring 2009, pp.1-5.
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