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Analysis of a distributed MIMO channel capacity under a special scenario
EURASIP Journal on Wireless Communications and Networking volume 2019, Article number: 189 (2019)
Abstract
A distributed antenna system enhances the coverage performance of a system by distributing the antennas geographically, as well as by improving the diversity gain and capacity performance. The channel impulse response matrix of a distributed MIMO system in an airplane cabin environment is measured by a selfbuilt channel measurement platform. The transmitting antennas are distributed, and the receiving antennas are placed centrally. The data analysis shows that the distributed MIMO system can significantly increase the system’s channel capacity. The system’s capacity is also closely related to whether the antennas are evenly placed. When the antennas are evenly placed, the capacity is increased and the distribution is more uniform.
Introduction
Network technology has achieved a state of rapid development, and an increasing number of people are paying attention to the development of communication technology. China has achieved diversified and multiangle development in communication technology. The emergence of 5G technology will surely set off a wave of new networks, as 5G technology can not only meet the needs of consumers in terms of speed, but it can also achieve higher levels of efficiency, which is a driving force for the future development of society. In the current situation, many young people are paying more attention to the pursuit of hightech technology, and an increasing number of people have chosen more diversified network systems. The development and progression of Internet communication technology represent not only the development of science and technology, but also the progress of the national economy [1,2,3].
Multiantenna technology, which is the mainstream alternative technology for nextgeneration wireless communication, can greatly improve the capacity and spectrum utilization efficiency of wireless systems. Since the introduction of MIMO technology, theoretical research on MIMO has covered various aspects, including system capacity, precoding technology, and receiver detection. In recent years, a variety of MIMO scenarios have been measured abroad [4,5,6], a large number of channel parameters have been obtained, and the corresponding channel models have been established. Domestic MIMO wireless channel measurements have also been performed [7,8,9]. The two outdoor channels are measured in different frequency bands, and the corresponding largescale channel parameters have been obtained. This paper relies on the channel measurement platform built by Tsinghua University to measure the wireless channel of an indoor distributed antenna system and systematically analyzes its capacity.
Relying on the channel measurement platform independently developed by Tsinghua University, this paper measures the channel impulse response matrix of a distributed MIMO system in an MD82 cabin environment. The measurement platform detects MIMO channels based on the timing of the transceiver antenna timing switching and supports up to 7 transmissions and 7 MIMO channel measurements. Based on the measured channel matrix in the cabin, the channel capacity of the distributed MIMO system and the SISO system is calculated and compared.
Methods/experimental
Measurement platform performance
The measurement platform is a seventransmittingandsevenreceiving system. The center frequency of the system is 3.52 GHz, and the bandwidth is 20 MHz. The specific parameters of the measurement platform are operating frequency 3.51~3.53 GHz, bandwidth 20 MHz, maximum transmit power value 20 dBmw, supported maximum Doppler bandwidth 797.2 Hz, maximum travel speed 123 km/h, minimum resolution delay 50 ns, maximum delay spread 12.8 μs, and an antenna and polarization omnidirectional antenna, with a 4 dBi gain, and a vertical polarization.
The system’s working sequence
Tx1~7 is the transmit antenna switching timing, Rx1~7 is the receiving antenna switching timing, and tp is the duration of the training sequence, as well as the value of the maximum delay spread. Here, tp = 12.8 us is selected. The time for switching the antenna for 1 week is 7 × 7 × 25.6 = 1254.4 us. Therefore, the maximum Doppler frequency that can be measured is 797.2 Hz, which corresponds to a maximum moving speed of 123 km/h.
Measurement environment introduction
Since the originating antenna transmits signals by switching the same antenna through the switches, in order to make the characteristics of each antenna substantially similar, the seven antennas need to be equally spaced. The antennas are fixed to nonmetal plates by coaxial cables, and then, the plates are placed on the top of a luggage cabinet. The spacing is the length of the two luggage cabinets, with the Tx1~7 at the center arrow position of the luggage rack at an interval of 290 cm. Tx1 (the first antenna) is in the front section of the economy class, 159 cm away from the bulkheads of the economy and business class, and then, Tx2~Tx7 are arranged linearly at equal intervals. The height of the bottom of the antenna (i.e., the fixed part without height) is 1.91 m, at 0.53 m from the left side of the luggage rack and 0.56 m from the right side of the luggage rack. The receiving end receives with a linear array of 9 antennas (including 2 dummy antennas).
The table of the receiving antenna array is placed on the seat for a static fixed point measurement, and the reference direction of the receiving antenna array is directed towards the rear. The measurement sequence is from E to A, and the measurement range is from the 4th to the 28th row of the economy class. Due to the limited cable length, measurements are not made in rows 29 to 33. Three groups were measured at each point, with each group being 4 s long. It should be noted that the small table in the fourth row (the first row of the economy class seats) is on the armrests on both sides of the seat, and the rests are in the backrest position of the seat. It should also be noted that there are only three seats (C, D, and E) for rows 26 to 28.
Distributed MIMO system capacity analysis
In the case where the origin does not know the channel condition, the best strategy is to transmit the transmitting antennas at equal power. In this case, the MIMO channel capacity is calculated as follows [10,11,12]:
where n_{R} and n_{T} are the number of receiving and transmitting antennas, respectively; \( {I}_{n_{\mathrm{R}}} \) and \( {I}_{n_{\mathrm{T}}} \) are the n_{R} × n_{R} and n_{T} × n_{T} unit matrices, respectively; P is the total transmitting power; and m is the noise power. For ease of analysis, the channel matrix H is normalized according to Eq. (2), and an average received signaltonoise ratio is introduced in the capacity calculation formula. After normalization, the MIMO capacity calculation formula is Eq. (3):
In Eq. (3), ρ is the average received signaltonoise ratio of the n_{R} receiving antennas, and \( \hat{H} \) is the normalized channel matrix. The MIMO channel capacity can also be written in the form of the sum of the equivalent parallel channel capacities:
\( {\lambda}_1^2,{\lambda}_2^2,\dots {\lambda}_m^2 \) are the m nonzero eigenvalues of \( {\hat{H}\hat{H}}^H \) (the eigenvalues of the matrices \( {\hat{H}\hat{H}}^H \) and \( {\hat{H}}^H\hat{H} \) are the same).
For wideband frequency selective fading channels, we take the channel capacity as the average of each frequency point [13, 14]:
where B is the channel bandwidth. There are two criteria for evaluating the MIMO channel capacity for random channels: traversal capacity and interrupt capacity. The ergodic capacity refers to the average channel capacity, and the interrupt capacity can be obtained from the cumulative probability distribution of the capacity.
The effect of different antenna pairs on the channel capacity
The actual measurement is a 7 × 7 distributed MIMO channel matrix, but by selecting different combinations of transmitting and receiving antennas, different MIMO channel matrices can be formed. When one antenna is selected at the transceiver end, a singletransmitting singlereceiving system (SISO) is formed. Based on this, the cumulative probability distribution results of the channel capacity for the 3transmitting3receiving, 5transmitting5receiving, and 7transmitting7receiving distributed MIMO systems in the measured scenario are calculated. To ensure fairness, distributed MIMO systems with different configurations of the number of transmitting and receiving antennas in the same scenario have the same total transmitting power and remain fixed (0 dBm) during the test. Each distributed MIMO system consisting of different transmitting and receiving antennas satisfies the following requirements: the transmitting antennas are evenly distributed in the test scenario, and the spacing of the array elements at the receiving end is half a wavelength.
For each system, we calculated the channel capacity at each test location in the cabin using the measured data according to Eq. (5). Figures 1, 2, and 3 show the channel capacity of the three systems, respectively.
It can be seen from the measured results that the distributed MIMO has a significant capacity gain as the number of antennas increases. From the 3transmitting3receiving system to the 7transmitting7receiving system, the channel capacity is improved by 3.5 bit/s/Hz for each addition of one pair of receiving and transmitting antennas in the distributed MIMO systems.
In Fig. 4, the abscissa indicates the channel capacity in units of bits per second per hertz and the ordinate is the probability of interruption. This indicates that in the case of a distributed antenna placement, the channels of each transmitting antenna arriving at the receiving end are highly irrelevant, and the systems are then able to obtain a large diversity and multiplexing gain.
Influence of different distributions on the channel capacity
If the antenna is selected for the origin, it is evenly distributed indoors. Here, four antennas are uniformly distributed in the nacelle, namely the first, third, fifth, and seventh antennas, and four other antennas are also selected at the receiving end. The capacity distribution of the static test points in the center at the center frequency is shown in Fig. 5. In order to compare this with the uniform antenna distribution, two sets of nonuniformly distributed transmitting antennas are selected, namely 1, 2, 3, and 4, and 2, 4, 5, and 6. The channel capacity is as shown in Figs. 6 and 7.
It can be seen that when the antenna is placed sufficiently and uniformly indoors, the distribution of static measuring points, except for the points measured near the origin, will tend to be uniform. When different antenna combinations are selected at the origin, the capacity distribution is as shown in Fig. 8.
It can be seen from Fig. 8 that when the transmitting antennas are combined into 1, 3, 5, and 7, that is, the distribution of the selected transmitting antennas is more uniform than other combinations, the corresponding capacity becomes larger. The further statistics show that the capacity distribution is also more uniform. Therefore, for a distributed antenna system, having the antennas be evenly distributed is advantageous not only for improving the capacity performance, but also for making the capacity distribution more uniform.
Results and discussion
The channel measurements of a distributed antenna system were analyzed to determine the cabin capacity characteristics. For a number of fixedpoint measurements, the channel at which the transmitting antennas arrive at the receiving end is highly irrelevant in the case of a distributed placement of the transmitting antennas. In this scenario, the channel is approximately equivalent to an independent and identically distributed complex Gaussian channel, and its capacity increases approximately linearly with the number of antennas. At the same time, when the number of antennas is the same but the placement positions are different, the uniformly placed antennas have a larger channel capacity and a more uniform distribution. In short, in the distributed system, the average access distance of the mobile terminal is small, which enhances the coverage performance of the system. Moreover, due to the distributed placement of the antennas, the decoupling characteristics of each channel are good, which greatly improves the capacity performance of the system.
Availability of data and materials
Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
Abbreviations
 5G:

The 5th generation wireless systems
 MD82:

Douglas MacDonald 82 aircraft
 MIMO:

Multiple input multiple output
 SISO:

Single input single output
References
S. Li, D.X. Li, S. Zhao, 5G internet of things: a survey. J. Ind. Inf. Integr. 10, 1–9 (2018)
C. Campolo, A. Molinaro, A. Iera, et al., 5G network slicing for vehicletoeverything services. IEEE Wirel. Commun. 24(6), 38–45 (2018)
S. Chen, Q. Fei, H. Bo, et al., Usercentric ultradense networks for 5G. IEEE Wirel. Commun. 23(2), 78–85 (2018)
R. IbernonFernandez, J.M. MolinaGarciaPardo, L. JuanLlacer, Comparison between measurements and simulations of conventional and distributed MIMO system. IEEE Antennas Wirel. Propag. Lett. 7, 546–549 (2008)
J. Liang, Q. Liang, Outdoor propagation channel modeling in foliage environment. IEEE Trans. Veh. Technol. 59(5), 2243–2252 (2010)
Q. Liang, Radar sensor wireless channel modeling in foliage environment: UWB versus narrowband. IEEE Sensors J. 11(6), 1448–1457 (2011)
Z. Yan, L. Zhenghui, L. Fengyu, et al., Measurementbased analysis of transmit antenna selection for incabin distributed MIMO system. Int. J. Antennas Propag. 2012, 1–6 (2012)
Xiaonan Z , Chunping H , Qing W, A new SVMbased modeling method of cabin path loss prediction. Int. J. Antennas Propag. 2013, 1–7 (2013)
X. Zhao, C. Hou, Q. Wang, et al., Location refinement and power coverage analysis based on distributed antenna. Trans. Tianjin Univ. 22(1), 7–10 (2016)
Li Z, Luan F, Zhang Y, et al. Capacity and spatial correlation measurements for wideband distributed MIMO channel in aircraft cabin environment. Wireless Communications & Networking Conference. (IEEE, Shanghai, 2012). https://doi.org/10.1109/WCNC.2012.6213954
A.V. Reznichenko, I.S. Terekhov, in Information Theory Workshop. Channel capacity and simple correlators for nonlinear communication channel at large SNR and small dispersion (2018)
Khalili A, Rini S, Barletta L, et al. On MIMO channel capacity with output quantization constraints. 2018
R.D. Vieira, J.C.B. Brandao, G.L. Siqueira, in International Telecommunications Symposium. MIMO measured channels: capacity results and analysis of channel parameters (2008)
A.F. Molisch, M. Steinbauer, M. Toeltsch, et al., Capacity of MIMO systems based on measured wireless channels. IEEE J. Sel. Areas Commun. 20(3), 561–569 (2006)
Acknowledgements
The authors would like to thank Tsinghua University for providing the measurement data. The first author also would like to thank Professor Chunping Hou and Dr. Qing Wang for the helpful instructions.
Funding
This work was supported by the Natural Science Foundation of Tianjin City (No. 18JCYBJC86400), Doctor Fund of Tianjin Normal University (No. 52XB1604), Natural Science Foundation of Tianjin City (No. 18JCQNJC70900), National Natural Science Foundation of China (No. 61704122), and National Natural Science Foundation of China (No. 61801327).
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XZ conducted the channel model research, participated in the channel capacity analysis, and wrote the manuscript. PZ and QZ did the data processing. YL participated in the study design and statistical analysis. RX and YL participated in its design and coordination and helped draft the manuscript. All authors read and approved the final manuscript. All contributors who do not meet the criteria for authorship should be listed in the “Acknowledgements” section.
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Zhao, X., Jia, P., Zhang, Q. et al. Analysis of a distributed MIMO channel capacity under a special scenario. J Wireless Com Network 2019, 189 (2019). https://doi.org/10.1186/s1363801915150
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DOI: https://doi.org/10.1186/s1363801915150
Keywords
 Communication channels
 Modeling
 MIMO
 Channel capacity