IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL. 12, 2013
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Propagation Characteristics of the LTE Indoor Radio Channel With Persons at 2.6 GHz Ye Wang, Wen-Jun Lu, Member, IEEE, and Hong-Bo Zhu
Abstract—In this letter, the propagation characteristics of an indoor, short-range channel with random distributed persons in a room at 2.6 GHz are studied and modeled. The study and model are based on plenty of data sampled in off-hour and rush-hour scenarios with both line-of-sight (LOS) and non-LOS (NLOS) conditions. The study shows that in NLOS, root mean square (RMS) delay spread has significantly 2.41 ns increased for the rush-hour scenario compared to off-hour. The median value of 3-dB coherence bandwidth in off-hour is wider than that in rush-hour (0.87 MHz wider in LOS and 1.97 MHz wider in NLOS). The 3-dB coherence bandwidth is an empirical exponential inverse relationship to RMS. Moreover, a t-location distribution fits the cumulative distribution function (CDF) of 3-dB coherence bandwidth well in off-hour, while a normal distribution fits appropriately in rush-hour.
Fig. 1. Block diagram of the measurement system.
Index Terms—2.6 GHz, coherence bandwidth, path loss, person, root mean square (RMS) delay spread.
II. MEASUREMENTS A. Equipment
I. INTRODUCTION HE INDOOR propagation channel has been studied for decades [1]–[5]. Previous studies are mainly focusing at 2.4 GHz or lower [i.e., [6] and [7], using three-dimensional (3-D) finite-difference time-domain (FDTD) approach and a site-specific validation of path-loss models] and beyond 3–80 GHz (i.e., [8] and [9] for angular, shadowing, and frequency-domain characteristics at 5.2 GHz, and [10] and [11] for indoor and corridors at 60 GHz). When detailed knowledge of the positions and the electromagnetic characteristics are not available, statistical parameters and models can describe the propagation properties. Moreover, an understanding of the relationship between different characteristics of the multipath channel is necessary for precisely predicting the system performance. As one of the releasing 4G technologies in China, Long Term Evolution (LTE) system operating at 2.5–2.69 GHz will be widely used, and it is essential to investigate the propagation characteristics on this band. However, the studies have not aimed much at the influence of persons at 2.6 GHz [12], [13]. The purpose of this letter is to research the characteristics of path loss and multipath propagation at 2.6 GHz under an indoor office environment with persons based on measured results.
T
Manuscript received July 01, 2013; accepted July 27, 2013. Date of publication July 31, 2013; date of current version August 21, 2013. This work was supported by the National Science and Technology Major Project under Grant No. 2012ZX03001028-005 and 973 Program No. 2013CB329005 and scientific innovation funding of college graduate students in Jiangsu province under Grant No. CXZZ11_0386. The authors are with the Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China (e-mail:
[email protected]). Color versions of one or more of the figures in this letter are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/LAWP.2013.2275811
Fig. 1 depicts the block diagram of the measurement system. The transmit part is composed of a vertical-polarized, diamondlike disc monopole omnidirectional antenna having a gain of 5 dBi, a vector network analyzer (VNA), and a power amplifier. The power amplifier is employed to compensate for cable losses and extend the measurement dynamic range. The VNA generates a 0-dBm signal sweeping a frequency range from 2.1–3.1 GHz at 201 points (centered on 2.6 GHz), with a frequency separation of 5 MHz. The time to sweep the frequency band is 400 ms. In the receive part, the signal is picked up by an antenna the same to the transmit antenna (Tx). The signal from the receive antenna (Rx) returns to the VNA via a 15-m coaxial cable with a 9-dB loss. The noise floor was 100 dBm. The measured complex frequency response is stored on a computer hard drive via a GPIB cable controlled by Agilent-VEE programs. During the measurements, the Tx and Rx were in the same horizontal plane at a height of 1.6 m. B. Measurement Environment The measurements were carried out in an office room (10.8 7.5 3.2 m ) in Nanjing University of Posts and Telecommunications, Nanjing, China. The room is filled with furniture and equipment, like some wooden tables, chairs with metallic legs, wooden cabinets, and computers. The walls are breeze-blocks with plaster, and windows are located in the south wall. The floor is marble, and the ceiling is plastic on laths. C. Measurement Procedure The measurements were conducted in line-of-sight (LOS) and non-LOS (NLOS) conditions for off-hour (i.e., early morning or lunchtime) and rush-hour (i.e., office-hour or meeting time). The Tx was placed behind a wooden cabinet to create an NLOS condition. The height of the cabinet is 2 m.
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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL. 12, 2013
TABLE I ESTIMATED PARAMETERS FOR PATH-LOSS FORMULA IN OFFICES
The transmitter–receiver (T-R) separation, , was ranging from 1.2 to 7.2 m. Fifteen locations were chosen for LOS and NLOS, respectively. In each location, data were collected at 25 points, on a 5 5 square grid with 5-cm spacing. The grid points are numbered from (1, 1) to (5, 5). Then, at each grid point, we collected 32 sets of data. When the 32 snapshots measured at a grid point of a location, the persons in the room were keeping still. The persons could move while the measured grid point was changed in the location. As soon as the measurement in a grid point was performed, the persons kept still again. The densities of randomly distributed persons in the room were about 0.02 persons/m for off-hour and 0.15 persons/m for rush-hour. The average height of the persons was about 1.7 m. III. DATA REDUCTION AND STATISTICAL RESULTS A. Path Loss and Shadowing The path loss (in dB) at a grid point in a location with T-R separation at frequency is computed as [5]
(1) where is the measured complex frequency is 201 discrete frequencies ranging from response, 2.1–3.1 GHz, and represents the number of measured snapshots. Then, the measured path loss at a location is calculated as (2) represents the number of grid points. where A distance-dependent path-loss model applied for an underground environment, such as an underground ladder-type mall consisted of straight corridors with glass or concrete walls, considering the influence of persons is described in Recommendation ITU-R P.1238-7 [14, Section 8]. The path-loss equation is (3) where is the working frequency [MHz], is the distance between the transmitter and receiver [m], is the path-loss exponent, and is a compensation parameter. Four sets of measured path loss of 15 locations at 2.6 GHz for off-hour and rush-hour in LOS and NLOS were obtained by (2). Then, the parameters in (3) were estimated by using the least square method. First, and are estimated by using the off-hour data in LOS and NLOS. Second, is calculated by the rush-hour data in LOS and NLOS at the same and values of off-hour. The obtained parameters are reported in Table I. Fig. 2 shows the fit lines and
Fig. 2. Path loss at 2.6 GHz (a) LOS and (b) NLOS.
the measured snapshots at grid point (3, 3) of each location for instance. It is found that (3) can be applied to an indoor environment equally well. Differing from the outdoor COST-231 model [4], in this model, is used to give the same relationship value between path loss on frequency and distance, and the values are smaller. Additionally, the path loss caused by the persons randomly distributed in the room is accounted for with the linear term . Compared to the parameters values in [14], as the measurement indoor room is much different from the underground mall, so is there some difference between the two values of in different environments. Meanwhile, the additional path loss in NLOS for rush-hour is more obvious in the indoor room. Large-scale fading considers obstruction and person losses as shadowing [1]. The shadowing in dB usually is a zero-mean normal random variable with standard deviation [15]–[17]. However, the shadowing under the influence of persons should be investigated specifically to confirm that it satisfies the normal distribution. is calculated as the difference between the measured path loss at a grid and the path loss estimated by model (3) at the same distance. Fig. 3 shows the cumulative distribution function (CDF) of measured in the office room at 2.6 GHz. The normal distribution fit in dB confirms the log-normality of shadowing in the office room with persons. The values of are similar as that in [8] and listed in Table I. The values show that, on one hand, the shadowing in NLOS is much evident than that in LOS; on the other hand, the shadowing in off-hour is similar with that in rush-hour in NLOS or LOS. B. Time Dispersion Characteristics The multipath channel impulse response with amplitudes, phases, and delays in time domain are depicted as [8] (4) where is the number of tap bins, represents the amplitude of the th tap, is the time delay of the th tap in the channel,
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Fig. 5. CDF of RMS delay spread. Fig. 3. CDF of shadowing in the office room at 2.6 GHz.
TABLE II MEDIAN AND STANDARD DEVIATION OF
in rush-hour, the power of multipath components arriving the receiver among 23 and 40 ns nearly kept steady. Root mean square (RMS) delay spread, , is the statistical measure of the time dispersion of the channel and shows the flatness of the channel in frequency. It is the square root of the second central moment of the PDP and is defined as [10] Fig. 4. Power delay profile in NLOS (a) off-hour and (b) rush-hour.
is the phase associated with the th tap, and is the Dirac delta function. Since the measurements were conducted in the frequency domain, a 1241-point inverse discrete Fourier transform (IDFT) by zero pading from 0–2.1 GHz (including conjugate reflection) is performed the measured frequency response to obtain the channel impulse response. The time delay is ranging from 0 to 200 ns, with a bin size of 161.3 ps. For a grid point in a location, the instantaneous power delay profile (PDP) is computed as (5) with . where Then, by averaging 32 snapshots over time, at each grid point PDP is given as (6) where is the expectation operator over time. Thus, at each location, 25 time-averaged PDPs are derived from which the time dispersion parameters will be determined. The persons in the office room have effect on the multipath propagation characteristics at 2.6 GHz. Fig. 4 shows two typical time-averaged PDPs of the channel, which are measured for off-hour and rush-hour in NLOS condition, respectively, at the same grid of a location. There is no significant multipath that was observed above 100 ns beyond the noise floor. In off-hour scenario, Fig. 4(a), the strongest power peak corresponds to the first remarkable path followed by gradual exponential decrease of power originating from multipath components coming within several clusters [1], [16]. However, in rush-hour scenario [see Fig. 4(b)], the multipath components cannot be obviously distinguished in several clusters. Moreover, the strongest path is about 2.2 ns after the first detected path. As persons in the room
(7) is the mean excess delay, and is the received where power (in linear units) at corresponding arrival time . The various channel delays are spanned between the first crossing above the preset threshold level and the last crossing below it. The choice of threshold was considered to be relevant. After several measurements of thresholds by considering all experimental PDPs, the threshold of a fixed 15 dB with respect to the noise floor was chosen. For each grid point, the is computed by the time-averaged PDP. The CDFs of computed at each grid point of all locations in LOS and NLOS for off-hour and rush-hour are plotted in Fig. 5. The statistical values of are summarized in Table II. In LOS, the direct path usually includes most energy of the channel. Then, it is found that the influence of persons for is not obvious in LOS as the median value of for rush-hour is slightly larger than that for off-hour. However, in NLOS, the median value of for rush-hour is increased about 2.41 ns than that for off-hour. The median RMS delay spread value is comparable to the proportion to the area of the office space described in [14]. The RMS delay spread ranges from 10 to 30 ns, which is accordance with previous results in [15]. It is also corresponding with values in the stochastic radio channel model for a room environment [18]. C. Coherence Bandwidth The coherence bandwidth is a measure of the frequency interval over which the frequency response does not change significantly. The complex autocorrelation function of the frequency response is computed for the measured frequency responses in frequency range given as [2] (8)
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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL. 12, 2013
rush-hour. For off-hour, t-location distribution fits the CDF of well, and for rush-hour, normal distribution is suitable. Furthermore, the interdependency of RMS delay spread and has been investigated as well. The proposed model and statistic characteristics may be useful for network node spacing, power control, frequency spectrum utilizing, etc., in indoor 2.6-GHz-band LTE mobile communications. REFERENCES Fig. 6. CDF of 3-dB coherence bandwidth.
Fig. 7. Fit curves of RMS delay spread versus 3-dB coherence bandwidth.
TABLE III VALUES OF COHERENCE BANDWIDTH AND FIT CURVES
The 3-dB coherence bandwidth, , of is defined as the frequency lag for which equals 50% . Fig. 6 depicts the CDF of in LOS and of NLOS for the off-hour and rush-hour. It shows that satisfies t-location distribution for off-hour and normal distribution for rush-hour. Meanwhile, in LOS, the median value of for off-hour is 0.87 MHz wider than that for rush-hour. In NLOS, it is about 1.97 MHz wider. Previously, an uncertainty relation of the same kind as the one by Heisenberg was established between the coherence bandwidth and RMS delay spread [19]. The coherence bandwidth is inversely proportional to the RMS delay spread of the channel. The equation fitted by using the minimum mean square error (MMSE) method, , describes the inverse relationship between and . Fig. 7 and the esdepicts the fit curves. The statistic parameters of timates of and are given in Table III. IV. CONCLUSION In this letter, measurements of indoor short-range radio channel for off-hour and rush-hour in LOS and NLOS conditions in the room from 2.1 to 3.1 GHz have been reported. The path loss is fitted by the ITU model for off-hour and rush-hour, respectively. The additional path loss caused by the persons in the room is accounted for with the linear term . Then, the statistic characteristics of shadowing, RMS delay spread, and coherence bandwidth have been described. The median value of for off-hour is wider than that for
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