Radio environment map-based cognitive Doppler spread compensation algorithms for high-speed rail broadband mobile communications
© Li and Zhao; licensee Springer. 2012
Received: 15 February 2012
Accepted: 26 July 2012
Published: 17 August 2012
Recently, there is an increasing yet challenging demand on broadband mobile communications for high-speed trains. In this article, cognitive Doppler spread compensation algorithms are proposed for high-speed rail broadband mobile communications, which make use of the dedicated radio environment map (REM) for railway to compensate the time-varying Doppler spread. The dedicated REM for high-speed rail can be viewed as a spatial-temporal database consisting of the radio channel parameters along a given railway. The performance of the proposed Doppler spread compensation algorithms are evaluated with a typical OFDM-based broadband mobile system. Simulation results show that the link-level performance of high-speed rail broadband mobile communications can be improved significantly due to the REM-enabled radio channel condition awareness and the cognitive Doppler spread compensation algorithms. The REM-based cognitive radio approach presents a new paradigm for systems design of high-speed rail broadband mobile communications.
High-speed rail is a type of passenger rail transport that operates significantly faster than the normal speed of rail traffic. Specific definitions by the International Union of Railways (UIC) and European Union include 200 km/h for upgraded track and 250 km/h or faster for new track . High-speed rail receives considerable attention recently due to its cost-efficiency, almost all-weather operation, low carbon-dioxide (CO2) emission, and many other advantages. In Japan, Shinkansen lines run at speeds of up to 300 km/h. In China, high-speed conventional rail lines operate at top speeds of 350 km/h and reaches as high as 487.3 km/h during a recent test run [1, 2]. Research on even higher speed train in vacuum tube is also underway in China with target operational speed of 600–1000 km/h in 10 years. Regional or international high-speed rail is under planning or construction in the United States, Europe, Asia, and South America . Therefore, there is an increasing yet challenging demand on broadband mobile communications for high-speed trains, as it is necessary to provide information (such as voice, Internet, video conference) and onboard entertainment services to passengers, support communication-based train control (CBTC), and transmit surveillance video and sensory data from the high-speed train to track-side base stations for the safety and efficient operation of high-speed rail. In addition, positive train control which has been developed in the United States is another major driver for highly reliable high-speed rail broadband mobile communications. Various technologies (such as leaky cable, GSM, satellite, and 802.11 WLAN) have been employed for railway wireless communications. However, each of the above-mentioned technologies has some shortcoming or limitations when applied to high-speed rail broadband mobile communications. The key problem associated with leaky cable is its high attenuation and high cost of deployment and maintenance; the key issue with GSM-based approach is its limited bandwidth and low data rate; the key problems associated with satellite-based approach are the lost of connection in tunnels, high systems cost, and significant delay; whereas the key problems with WLAN-based approach are the limited coverage per access point, modifications required to support smooth handover, and significant performance degradation for higher train speed (e.g., above 300 km/h) .
Key features of high-speed rail broadband mobile communications
Doubly selective channel
The high-speed rail radio channel is both time selective and frequency selective
Fast and frequent (group) handover
All passengers in the train move at the same direction and conduct handover almost simultaneously, resulting high handover failure rate due to signaling storm and long process delay
Predictable train location and channel condition
The high-speed rail train operates on a fixed course repetitively. This is in sharp contrast to traditional cellular systems where the mobile stations move randomly
Currently, there are mainly three approaches in literature to deal with Doppler spread and reduce the inter-subcarrier interference (ICI). The first approach is simply to estimate the dominant frequency offset [7, 8]. Though many existing methods can estimate and remove the frequency offset quite accurately, their performance may degrade sharply under high-speed mobile scenarios with significant Doppler spread. The second approach basically employs signal processing and/or coding to reduce the sensitivity of the OFDM system to the frequency offset. For example, in [9, 10], time windowing has been introduced to reduce the ICI; another method known as the “ICI self-cancellation” has initially been proposed by Zhao and Haggman ; later on, Seyedi and Saulnier  proposed a new self-cancellation scheme with even better performance. In addition, an all phase OFDM system is proposed to improve the OFDM system’s ability to resist frequency offset by employing all-phase FFT . However, all of these methods have considerable computational complexity. In the third approach, sectorized antennas are utilized to divide the Doppler spectrum into a set of sub-spectra and thus reduces the time selectivity [14, 15]. It copes well with large Doppler shift at the cost of increasing system complexity as well as hardware costs.
To mitigate the adverse impact of time-varying Doppler spread on OFDM-based broadband mobile communications, this article proposes cognitive Doppler compensation algorithms based on dedicated radio environment map (REM) for high-speed rail. The basic idea is to obtain fast and accurate channel condition awareness by retrieving the REM and then compensate the time-varying Doppler spread in a cost-efficient manner. The REM can be viewed as a spatial-temporal database that provides multi-domain environmental information (such as prior knowledge about the radio channel parameters and past experience) to cognitive radios [16–18]. Note that the term “cognitive radio” was first coined by Dr. Joseph Mitola III in late 1990s . Over the past 10 years, cognitive radio has enabled a new design paradigm for wireless communications [20–22], mainly owing to its self-learning and situation-awareness capabilities. Compared with the approaches mentioned above, the REM-based cognitive Doppler compensation approach is cost-efficient and requires no additional hardware.
The rest of this article is organized as follows. In the following section, we first review the key challenges encountered by the high-speed rail broadband mobile communications, and then bring forward the system architecture of the proposed cognitive broadband mobile communication systems. In Section “REM-based cognitive Doppler spread compensation algorithms”, two types of REM-based Doppler spread compensation algorithms are presented. The ICI due to Doppler spread is modeled for OFDM-based broadband mobile communications, which is instrumental for the development of cognitive Doppler spread compensation algorithms and link-level performance analysis. In Section “Link-level simulations of high-speed rail broadband mobile communications”, the simulated performances of the proposed algorithms are discussed. Finally, we summarize this article in the last section.
System overview of high-speed rail broadband mobile communications
Challenges of high-speed rail broadband mobile communications
Though the channel estimation problems for high-speed rail have been investigated by some researchers, there is no ideal solution yet. Some proposed algorithms are too sophisticated (with heavy computational load) to meet the requirements of real-time implementation ; some proposed algorithms simply could not produce good enough results [27, and the references therein]. For example, Liu et al.  proposed a channel estimation algorithm based on comb-pilot. This algorithm works well under time-invariant multipath channel. However, it cannot work well under fast time-variant channel (e.g., using COST 259 RA channel model) due to the ICI. There exists an error floor for the minimum square error of channel estimation and the bit error rate (BER) even if the signal-to-noise ratio (SNR) is very high . Hardware-in-the-loop simulation also shows that the performance degrades significantly under Rayleigh fading channels when the train speed reaches 350 km/h .
Note that prior knowledge about high-speed rail channel characteristics has not carefully been exploited for fast channel estimation yet. Considering the repetitive movement of a high-speed train along a given high-speed rail line, the radio channel characteristics could be predictable (to some extent) based on the location of high-speed train, local terrain information from a GIS database and past experience. It is highly possible to exploit such prior knowledge and improve the performance of high-speed rail broadband mobile communication systems by eliminating or reducing the error floor in channel estimation and conducting more effective Doppler compensation thereafter.
System architecture of cognitive broadband mobile communications
Dedicated REM for high-speed rail
Key information elements in the dedicated REM for high-speed rail
Number of paths (Np 1)
Train-top antenna location #1
Channel parameters for each path
Timestamp #1 (time, date, day of the week)
α Np 1– 1
φ Np 1– 1
τ Np 1– 1
Number of paths (Np 2)
Positioning accuracy of the high-speed train
Accurate positioning and speed measurement of the high-speed train is critical for the proposed REM-based cognitive Doppler spread compensation algorithms, as accurate location information is the prerequisite to look up the dedicated REM for HSR and obtain the situation awareness. Odometer is the most widely used in train positioning system because of its low cost and simple-to-implement properties. However, the performance of train odometer-based positioning would easily be influenced by the accumulated counting error due to idling or skidding and the wheel diameter attrition. Integrated train positioning algorithms are developed to leverage both GPS and odometer . Furthermore, the position and speed of the high-speed train can also be periodically calibrated with the balise, which is an electronic beacon or transponder placed between the rails of a railway as part of an automatic train protection system. State-of-the-art train speed and position measurement method such as multi-sensor information fusion in CBTC systems enables very high location accuracy (on the order of 10–2 m) ; for the high-speed maglev train driven by the long-stator synchronous linear motor, even higher location accuracy (with the positioning resolution of 4.3 mm) has been achieved in the lab measurement by using another method based on measuring cog-slots of the long-stator . In addition, other positioning methods (such as RF fingerprinting ) can also be employed as complementary approaches.
REM-based cognitive Doppler spread compensation algorithms
The proposed high-speed rail mobile communication systems can obtain channel awareness simply by looking up the dedicated REM for railway, instead of running complicated channel estimation algorithms. The retrieved channel parameters can be exploited by the Doppler spread compensation algorithms at the receiver. In this section, the REM-based Doppler spread compensation algorithms and the analytical performance are presented.
We first derive the mathematics model for both the desired signal and the ICI due to Doppler spread with an OFDM symbol. Then cognitive Doppler compensation algorithms are developed to mitigate or eliminate the ICI.
where N is the number of sub-carriers, L g is the discrete length of the CP, and d i is the transmitted symbol on the i th subcarrier.
where , Tg is the duration of the CP section in an OFDM symbol, and T is the duration of the data section.
REM-based cognitive Doppler spread compensation algorithms
In this section, a simple Doppler spread compensation algorithm is first designed using the Doppler frequency shift information stored in the dedicated REM for high-speed rail. Then a more advanced Doppler spread compensation algorithm is developed by fully utilizing channel information from the REM. First of all, we redefine the symbols to be used in this section: N p is the number of detectable paths in the high-speed rail radio channel, and α n , Δf n , p n are the fading amplitude, Doppler frequency shift, and discrete time delay in the n th path, respectively (0 ≤ n ≤ N p – 1). Note that in this section, the time-discrete transmitted OFDM signal is s(m), which is usually composed of a number of consecutive OFDM symbols, and z(m) is additive Gaussian white noise while r(m) is the received signal sequence.
Note that the REM-based cognitive Doppler spread compensation algorithm is employed on r(m), which is obtained after the A/D conversion of the time-continuous received baseband signal r(t). It exploits the channel parameters stored in the dedicated REM for railway to pre-process the received signal sequence so as to eliminate or mitigate the multipath effects of the wireless channel.
Simple Doppler compensation algorithm (Algorithm 1)
Algorithm 1 aims to estimate the average Doppler frequency shift and then compensate it directly. Since the signal power for each path is different, the path with higher signal power has more significant impact on the average Doppler frequency shift of the received signal and has a higher weight in the average Doppler frequency shift. The algorithm is depicted as follows.
where T s is the sampling interval.
Advanced Doppler spread compensation algorithm (Algorithm 2)
for for where0 ≤ m ≤ M – 1, and M is the length of the transmitted signal sequence.
For the TDD OFDM systems, downlink frames and uplink frames are transmitted alternatively in time. For example, in the downlink communication, the time duration of guard period and uplink frame (as shown in Figure 5) can actually serve as the “guard interval” (in which there is no RF emission from the base station). Therefore, even with the maximum path delay in the high-speed rail mobile communication environment, the samples of the previous frame will not interfere with the samples of the current frame. So, the advanced Doppler spread compensation algorithm is especially good for TDD OFDM communication systems. The iterative approach in (18) can be employed to each “frame” of a TDD OFDM system.
For FDD OFDM systems, in order to employ this advanced Doppler spread compensation algorithm, a guard period (i.e., a pure empty prefix) needs to be inserted at the beginning of each frame.
Analytical performance analysis
The minimum-variance unbiased estimator of S is an efficient estimator, which obtains the Cramer-Rao lower bound of .
Link-level simulations of high-speed rail broadband mobile communications
This section presents the simulated link-level performance of an OFDM-based high-speed rail broadband mobile communication system.
Assumptions and system parameters
TD-LTE downlink parameters
Number of data sub-carriers
601 (including DC sub-carrier)
OFDM symbol duration
Total symbol time duration
71.4 μs (normal CP)
Symbols per slot
Number of protection sub-carriers
212 (left side), 211 (right side)
Simulation methodology and simulation scenarios
To evaluate the performance of a cognitive high-speed rail broadband mobile communication system, dynamically changing channel conditions, including both LOS and non-LOS (NLOS) scenarios, need to be simulated.
For the sake of simplicity, an abstract two-path channel model is used in our simulation, while it is fairly straightforward to extend it to more generic channel models with multiple paths. The number of paths, path-loss coefficient, time delay, Doppler frequency shift, and the noise level for each path are the main parameters stored in the dedicated REM for high-speed rail.
Scenario type 1: LOS scenarios
As depicted by Figure 3, LOS scenarios are the most common scenarios for high-speed rail broadband mobile communication, as the cell radius of track-side base stations is usually less than 3 km and extensive use of viaducts and bridges along the high-speed rail line.
Parameters for the LOS scenario
Travel distance of high-speed train (L)
Speed of train
Distance between track-side base station antenna and high-speed rail (d)
Antenna height at base station (hBS)
Antenna height on top of high-speed train (hMS)
12 m (= 2 m + 10 m)
where hBS is the antenna height of the track-side base station and hMS is the antenna height of the wireless gateway on the train; c is the velocity of light in vacuum. is the environment break-point length, which is set as 120 m (which is different from dbp).
For the high-speed rail LOS scenarios, it is assumed that the signal power of the second path is at least 10 dB weaker than that of the dominant LOS path; with regarding to the LOS path, the time delay of the second path varies from 65 ns to 1.3 μs (according to the WINNER II D2a model); the angle of arrival for the first path is determined according to the position of the wireless gateway antenna on the train and the position of track-side base station, whereas the angle of arrival for the second path varies from 0 to 2π.
Scenario type 2: NLOS scenarios
The link-level performance is also simulated under the NLOS scenarios where LOS path is either blocked by buildings/hills or shadowed by foliages. Rayleigh fading is assumed for NLOS scenarios, where the signal strength of each path follows the Rayleigh distributions [36, 37]. Time delay for the second path still follows a uniform distribution between 65 ns and 1.3 μs. The angle of arrival for the first path follows a normal distribution with the mean value same as that for the LOS scenario (as if the LOS path were not blocked), whereas the angle of arrival for the second path follows a uniform distribution between 0 and 2π.
Simulation results and analysis
Simulation results and analysis for LOS scenarios
where γ is the SNR.
Simulation results and analysis for NLOS scenarios
In Figure 16, it is assumed that the main path has a Doppler frequency shift of 577 Hz, whereas the second path has a Doppler shift of −577 Hz (which is the worst-case scenario for Doppler compensation in our simulations as the Doppler spread is maximized with such assumption). Figure 16 shows that the advanced Doppler compensation algorithm (marked as Algorithm 2 in this figure) can achieve significantly better performance than the simple Doppler compensation algorithm (marked as Algorithm 1 in this figure). Figure 16 also indicates that, even under the serious Doppler spread scenario (corresponding to the case of α1 = 0.9α 0 ), the advanced Doppler compensation algorithm can still work (no BER error floor), whereas there is an obvious BER error floor resulting from a mild Doppler spread (corresponding to the case of α1 = 0.2α 0 ) if the simple Doppler compensation algorithm is employed.
Broadband mobile communications are critical to the quality of passenger experience, safety, and operational efficiency for high-speed trains. The demand on highly reliable broadband mobile communications for high-speed rail is ever increasing as more and more high-speed railways are under construction around the world.
Considering the unique features of high-speed rail broadband mobile communications, especially the repetitive movement of high-speed train along a fixed course, this article presents novel Doppler spread compensation algorithms based on the dedicated REM for railway. REM-based Doppler spread compensation algorithms have significant advantages, e.g., it can reduce the processing load resulting from repetitive and complicated channel estimations along the high-speed rail. Link-level performances of an OFDM-based high-speed rail broadband mobile communication system are evaluated under dynamically changing radio channels (including both LOS and NLOS scenarios), emulating the movement of high-speed train along a given course. Both analytical analysis and simulation results show that the proposed REM-based Doppler spread compensation algorithm can significantly improve the link performance of OFDM-based broadband mobile communication systems under various high-speed rail scenarios. The impact of imperfect channel information and train positioning errors on the link BER performance has also been simulated and analyzed in this article. For the future work, the performance of REM-based cognitive Doppler spread compensation algorithms need to be further validated with simulations and/or field test, taking other practical issues (such as the carrier frequency offset and phase noise of the local oscillators) into account.
This study was supported in part by the NSFC project under grants Nos. 61132003 and 61032002, and Beijing Jiaotong University (2011JBM013 and 2011RC004).
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