Compressive channel estimation for universal filtered multi‐carrier system in high‐speed scenarios
Authors: 
Rong Wang, Jingye Cai, Xiang Yu, Sirui DuanAuthors Info & Claims
Abstract
Due to the high mobility of communication, channel times can vary rapidly and system performance can be decreased. In this study, pseudo‐random noise is used as the guard interval and the training sequence in the time domain in order to estimate the channel‐based compressive sensing scheme. This scheme reduces the number of pilots in the frequency domain and improves spectrum efficiency. By adequately exploiting the sparse characteristics and temporal correlation of the wireless channel, a low complexity compressive channel estimation scheme is proposed. Firstly, the authors average the successive symbols of the channel impulse response in the coherence time to improve the accuracy of the coarse channel estimation. Secondly, a low complexity partial priori information CoSaMP (PPI‐CoSaMP) algorithm is proposed to accurately estimate the channel state information. Finally, based on the precise time delay, the accurate gains are estimated based on the least‐squares algorithm. The simulation results show that compared with the conventional algorithms, the number of observation points required by the PPI‐CoSaMP algorithm is reduced by at least 25%. Moreover, the proposed scheme is more robust at larger multipath channel delays. The complexity of the proposed scheme is reduced by 51.21% compared with the conventional CoSaMP algorithm.
References
[1]
Zhang L., Ijaz A., Xiao P. et al.: ‘Single‐rate and multi‐rate multi‐service systems for next generation and beyond communications’. 2016 IEEE 27th Annual Int. Symp. Personal, Indoor, and Mobile Radio Communications (PIMRC 2016), Valencia, Spain, September 2016, pp. 1–6
[2]
Schaich F., Wild T., Chen Y.: ‘Waveform contenders for 5G‐suitability for short packet and low latency transmissions’. 2014 IEEE 79th Vehicular Technology Conf. (VTC Spring 2014), Seoul, Korea, May 2014, pp. 1–5
[3]
Mukherjee M., Shu L., Kumar V. et al.: ‘Reduced out‐of‐band radiation‐based filter optimization for UFMC systems in 5G’. 11th Int. Wireless Communications and Mobile Computing Conf. (IWCMC), Dubrovnik, Croatia, August 2015, pp. 1150–1155
[4]
Bajwa W.U., Haupt J., Sayeed A.M. et al.: ‘Compressed channel sensing: a new approach to estimating sparse multipath channels’, Proc. IEEE, 2010, 98, (6), pp. 1058–1076
[5]
Gui G., Peng W., Adachi F.: ‘High‐resolution compressive channel estimation for broadband wireless communication systems’, Int. J. Commun. Syst., 2014, 27, (10), pp. 2396–2407
[6]
Peng X., Wu W., Sun J. et al.: ‘Sparsity‐aware, channel order‐blind pilot placement with channel estimation in orthogonal frequency division multiplexing systems’, IET Commun., 2015, 9, (9), pp. 1182–1190
[7]
Lee D.: ‘MIMO OFDM channel estimation via block stagewise orthogonal matching pursuit’, IEEE Commun. Lett., 2016, 20, (10), pp. 2115–2118
[8]
Ding W., Yang F., Dai W. et al.: ‘Time–frequency joint sparse channel estimation for MIMO‐OFDM systems’, IEEE Commun. Lett., 2015, 19, (1), pp. 58–61
[9]
Berger C.R., Wang Z., Huang J. et al.: ‘Application of compressive sensing to sparse channel estimation’, IEEE Commun. Mag., 2010, 48, (11), pp. 164–174
[10]
Ding W., Yang F., Pan C. et al.: ‘Compressive sensing based channel estimation for OFDM systems under long delay channels’, IEEE Trans. Broadcast., 2014, 60, (2), pp. 313–321
[11]
Ma X., Yang F., Ding W. et al.: ‘Novel approach to design time‐domain training sequence for accurate sparse channel estimation’, IEEE Trans. Broadcast., 2016, 62, (3), pp. 512–520
[12]
Ding W., Yang F., Liu S. et al.: ‘Structured compressive sensing‐based non‐orthogonal time‐domain training channel state information acquisition for multiple input multiple output systems’, IET Commun., 2016, 10, (6), pp. 685–690
[13]
Ding W., Yang F., Liu S. et al.: ‘Nonorthogonal time–frequency training‐sequence‐based CSI acquisition for MIMO systems’, IEEE Trans. Veh. Technol., 2016, 65, (7), pp. 5714–5719
[14]
Ma X., Yang F., Liu S. et al.: ‘Structured compressive sensing‐based channel estimation for time frequency training OFDM systems over doubly selective channel’, IEEE Wirel. Commun. Lett., 2017, 6, (2), pp. 266–269
[15]
Dai L., Wang Z., Yang Z.: ‘Next‐generation digital television terrestrial broadcasting systems: key technologies and research trends’, IEEE Commun. Mag., 2012, 50, (6), pp. 150–158
[16]
Kwon B., Kim S., Jeon D. et al.: ‘Iterative interference cancellation and channel estimation in evolved multimedia broadcast multicast system using filter‐bank multicarrier‐quadrature amplitude modulation’, IEEE Trans. Broadcast., 2016, 62, (4), pp. 864–875
[17]
Ding W., Yang F., Liu S. et al.: ‘Approach to suppress out‐of‐band emission for dual pseudo noise padded time‐domain synchronous‐orthogonal frequency division multiplexing systems’, IET Commun., 2015, 9, (13), pp. 1606–1614
[18]
Bajwa W.U., Sayeed A.M., Nowak R.: ‘Learning sparse doubly‐selective channels’. 46th Annual Allerton Conf. Communication, Control, and Computing, September 2008, pp. 575–582
[19]
Telatar I.E., Tse D.N.C.: ‘Capacity and mutual information of wideband multipath fading channels’, IEEE Trans. Inf. Theory, 2000, 46, (4), pp. 1384–1400
[20]
Borade S., Zheng L.: ‘Writing on fading paper, dirty tape with little ink: Wideband limits for causal transmitter CSI’, IEEE Trans. Inf. Theory, 2012, 58, (8), pp. 5388–5397
[21]
Candès E.J., Wakin M.B.: ‘An introduction to compressive sampling’, IEEE Signal Process. Mag., 2008, 25, (2), pp. 21–30
[22]
Gao Z., Dai L., Shen W. et al.: ‘Temporal correlation based sparse channel estimation for TDS‐OFDM in high‐speed scenarios’. Military Communications Conf. (MILCOM 2015), Tampa, Florida, October 2015, pp. 798–803
[23]
Needell D., Tropp J.A.: ‘CoSaMP: iterative signal recovery from incomplete and inaccurate samples’, Appl. Comput. Harmon. Anal., 2009, 26, (3), pp. 301–321
[24]
Kay S.M.: ‘Fundamentals of statistical signal processing, volume I: estimation theory’ (Prentice‐Hall, New Jersey, USA, 1993)
[25]
Tropp J.A., Gilbert A.C.: ‘Signal recovery from random measurements via orthogonal matching pursuit’, IEEE Trans. Inf. Theory, 2007, 53, (12), pp. 4655–4666
[26]
Guideline for Evaluation of Radio Transmission Technology for IMT‐2000, Recommendation ITU‐R M. 1225, 1997
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© 2021 The Institution of Engineering and Technology.
Publisher
John Wiley & Sons, Inc.
United States
Publication History
Published: 29 September 2017
Author Tags
Author Tags
- low complexity compressive channel estimation scheme
- universal filtered multicarrier system
- high‐speed scenarios
- high communication mobility
- pseudo‐random noise
- channel‐based compressive sensing scheme
- frequency domain
- spectrum efficiency
- temporal correlation
- sparse characteristics
- wireless channel
- successive symbols
- channel impulse response
- low complexity partial priori information CoSaMP algorithm
- low complexity PPI‐CoSaMP algorithm
- channel state information estimation
- precise time delay
- least‐squares algorithm
- multipath channel delays
- 5G communication systems
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