- Open Access
Location information-assisted opportunistic beamforming in LTE system for high-speed railway
© Cheng and Fang; licensee Springer. 2012
- Received: 15 February 2012
- Accepted: 28 May 2012
- Published: 3 July 2012
Communication systems of high-speed railway have inherent disadvantages and advantages. The estimation error of direction-of-arrival (DOA) of desired users caused by channel feedback delay and high moving speed make the common beamforming technique of smart antennas work abnormally. Instead, the opportunistic beamforming (OBF) with dumb antennas which does not need channel feedback is proper for high-speed railway. As the system has linear topology, regular movement as well as predictable location and speed information, the OBF can be improved to adapt to this scenario. In this article, conventional OBF is first introduced to the communication systems of high-speed railway. By multiplying random complex on each transmit antenna, the channel fluctuation dynamic range is extended so that multiuser diversity can be exploited. And then location information is employed to assist the conventional one. Communication systems in high-speed railway have the advantage of predictable location and speed information. The improved OBF can perform closer to the performance of coherent beamforming. Numerical analysis and simulation results show that both two algorithms can improve the system performance significantly.
- Location Information
- Channel Gain
- Smart Antenna
- Multiuser Diversity
- Linear Topology
As the industry of high-speed railway develops very fast, people pay a lot of attention to the reliability and efficiency of communication systems for high-speed railway. High mobility of onboard mobile stations brings about Doppler Effect and frequent handovers which are sources of hindrance to high performance communications. However, it has some special characteristics such as linear topology of cells, so the movement is regular and location as well as speed information is predictable . If these features are properly made use of, they can compensate for the disadvantages to some extent, even bring about some gains.
The common beamforming technique we refer to belongs to smart antenna techniques in which desired users’ direction-of-arrival (DOA) is estimated from the channel condition feedback, then the direction of main lobe of the antenna is steered to the desired users . By using this technique, the quality of received signal is improved without interfering undesired users. However, though the smart antennas can adjust the elevation, beam width, and azimuth angle, the changing is very slow . Since the speed of the train is at least above 350 km/h and the estimation error of DOA which are caused by feedback delay is large, beamforming technique of smart antennas cannot achieve expected effect. Instead, OBF is simply realized with dumb antennas by multiplied a complex vector of which the magnitude and phase both are random variables. Simultaneously, there is no need to track the channel vectors of users. Only SNR should be reported to eNodeBs and the user with highest SNR will be scheduled. When there are enough users in the system, multiuser diversity gain will be obtained. Certainly, the amount of OBF overhead is much smaller than that of smart antenna systems. However, compared with omni-directional antenna system, it costs more overhead and complexity to achieve better performance.
In the communication system of high-speed railway, location information is predictable. With the help of location information and length of the train, the range of DOAs of onboard transceivers can be calculated. This angle range can be used to make the generation of random complex weight vector more precise to extend the dynamic range of channel magnitude.
In this article, OBF algorithm is applied to the scenario of high-speed railway under the assumption of equal spacing onboard transceivers deployed on the top of carriages. Simulation results show that conventional OBF can bring some performance gain. With the help of location information, the performance can be improved further. So far as we know, no similar correlated study has been reported on the OBF for high-speed railway as well as its location information assisted version.
The rest of this article is organized as follows. Section 2 introduces the system and signal models of this article; Section 3 describes the principles of OBF and its application to high-speed railway simply; Section 4 analyzes how to improve OBF for high-speed railway with known location information; Section 5 contains simulation results and performance analysis; Section 6 concludes the article.
System and signal model
To obtain the multiuser diversity gain, it is assumed that onboard transceivers should be deployed as many as possible in the condition that the independence of received signals can be achieved. Assume that there is an onboard transceiver on the head of the train as well as on the tail and others are spacing equally distributed between the tail and head of the train. All of them are in a parallel line of the train. If there are U transceivers and the length of the train is known as L, then the equal spacing will be L/(U – 1).
In Equation (1), the scattered component is modeled by , which is an independent complex Gaussian random variable with zero mean and variance one: .
where d m denotes the distance gone through by user m, and the time when the train first arrives at the cell edge is set as 0.
Principle and application of OBF in high-speed railway
Usually, eNodeBs need the users to feedback information to get the individual channel amplitude and phase from transmit antennas in order to beamform to a particular user. However, if there are many users in the system, when a user’s overall channel SNR is near its peak, it will be scheduled a transmission with proper scheduling algorithm to achieve multiuser diversity. In this context, OBF is introduced : by varying the powers and phases allocated to the transmit antennas, a beam is randomly swept and at any time transmission is scheduled to the user currently closest to the beam. With many users, there is likely to be a user very close to the beam at any time.
Let and it is easy to prove that B m (t) is a complex-valued Gaussian random variable with , where .
Location information-assisted OBF for high-speed railway
Communication systems of high-speed railway not only have kinds of disadvantages, but also have some inherent advantages. With the help of predictable location information, that is, at time slot t, the head of train arrives at location s, and the length of train is a constant, the location of the tail of the train can be got. Then, DOA of the users at the head and tail of the train can be calculated which are expressed as and , respectively. If there are U users and the length of the train is known as L, then the equal spacing will be L/(U – 1). For certain U, the distance between two neighboring RSs will increase with L. Higher L also leads to larger angle range of [, ]. Above all, for fixed number of RSs, the performance deteriorates with the increase of L.
For determinant Rician K-Factor, to maximize the magnitude of overall channel gain of user m in Equation (7), should be satisfied. At this time, the optimal beamforming is realized which is also called coherent beamforming. This is the upper bound of OBF performance.
Since and , combined with Equation (3), there is a strong probability that the random phase generated by the proposed algorithm is much closer to . When the times of experiment are more enough, performance improvement can be observed.
Channel bandwidth (BW)
Fixed directional antenna gain
Beamforming antenna gain
Number of transmit antennas
Rician factor K
eNodeB transmit power (PeNB)
eNodeB antenna height (h b )
Train antenna height (h m )
Thermal noise density (N0)
Cell radius (R)
Measurement periodic Δτ
The characteristics of communication systems for high-speed railway have both pros and cons. For example, Doppler Effect and frequent handover are hindrances to good performance. The common beamforming technique of smart antennas cannot be normally employed due to the estimation error of DOA of desired users caused by channel feedback delay. On the contrary, OBF with dumb antennas which does not need the channel feedback of users is more proper for the scenario of high-speed railway. The inherent advantage such as regular movement and predictable location and speed information of this system can improve the conventional OBF and obtain multiuser diversity gain. However, current algorithm still cannot achieve or get very close to the performance upper bound set by coherent beamforming. In the future study, scheduling algorithm will be considered and beam selection will be added.
The study was supported partially by the 973 Program under the Grant 2012CB316100, NSFC under the Grant 610711068, 61032002, and the Key Program of Technological R&D of the Ministry of Railway under the Grant 2011X011-A.
- Pascoe RD, Eichorn TN: What is communication-based train control? IEEE Veh. Technol. Mag. 2009, 4(4):16-21.View ArticleGoogle Scholar
- UIC, LTE/SAE: The future railway mobile radio system? Long-term visions on railway mobile radio technologies. 2009., 4:Google Scholar
- White GP, Zakharov YV: Data communications to trains from high-altitude platforms. IEEE Trans. Veh. Technol 2007, 56(4):2253-2266.View ArticleGoogle Scholar
- Abrishamkar F, Irvine J: Comparison of current solutions for the provision of voice services to passengers on high speed trains, in Proc IEEE 52nd Veh Technol Conf (VTC’00 Fall), vol. 5. Boston, MA, Boston, MA; 2000:2068-2075.Google Scholar
- Lu LH, Fang XM, Cheng M, Yang CZ, Luo WT, Di C: Positioning and relay assisted robust handover scheme for high speed railway, in Proc IEEE 73rd Veh Technol Conf (VTC’11 Spring), vol. 1. Budapest, Hungary; 2011:1-5.Google Scholar
- Knopp R, Humblet P: Information capacity and power control in single cell multiuser communications, in Proc IEEE Int Computer Conf (ICC’95), vol. 1. Seattle, WA; 1995:331-335.Google Scholar
- Viswanath P, Tse DNC, Laroia R: Opportunistic beamforming using dumb antennas. IEEE Trans. Inf. Theory 48. 2002, 1277-1294.Google Scholar
- Dahlman E, Ekstrom H, Furuskar A, Jading Y, Karlsson J, Lundevall M, Parkvall S: The 3G long-term evolution radio interface concepts and performance evaluation, in IEEE 63rd Veh Technol Conf (VTC’06), vol. 1. Melbourne, Australia; 2006:137-141.Google Scholar
- Gaire G, Shamai S: On the achievable throughput of a multiantenna Gaussian broadcast channel. IEEE Trans. Inf. Theory 2003, 49(7):1691-1706. 10.1109/TIT.2003.813523View ArticleGoogle Scholar
- Viswanath P, Tse D: Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality. IEEE Trans. Inf. Theory 2003, 49(8):1912-1921. 10.1109/TIT.2003.814483MathSciNetView ArticleGoogle Scholar
- Balanis CA: Antenna Theory. John Wiley, New Jersey; 2005.Google Scholar
- 3G Americas, MIMO and smart antennas for 3G and 4G wireless systems—practical aspects and deployment considerations. (LTEWorld, 2007) 2011.http://lteworld.org/whitepaper/mimo-and-smart-antennas-3g-and-4g-wireless-systems-practical-aspects-and-deployment-consi
- IST-4-027756 WINNER II, D1.1.2 v1.2. WINNER II Channel Models (2007) 2010.https://www.ist-winner.org/
- Kim IM, Yi ZH, Kim DW, Chung WS: Improved opportunistic beamforming in Ricean channels. IEEE Trans. Commun. 2006, 54(12):2199-2211. 10.1109/TCOMM.2006.884851View ArticleGoogle Scholar
- Godara LC: Application of antenna arrays to mobile communications—Part II: beamforming and direction-of-arrival considerations. Proc. IEEE 1997, 85(8):1195-1245. 10.1109/5.622504View ArticleGoogle Scholar
- Godara LC: Smart Antennas. CRC Press, Florida; 2004.View ArticleGoogle Scholar
- Tse DNC, Viswanath P: Fundamentals of Wireless Communications. Cambridge University Press, Cambridge; 2005.View ArticleGoogle Scholar
- Stojmenovic I: Handbook of Wireless Networks and Mobile Computing. John Wiley, New York; 2001.Google Scholar
- ITU-R: Guidelines for evaluation of radio interface technologies for IMT-Advanced. 2011. http://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-M.2135-1-2009-PDF-E.pdfGoogle Scholar
- Paulraj A, Nabar R, Gore D: Introduction of Space-Time Wireless Communications. Cambridge University Press, Cambridge; 2003.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.