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ИНФОРМАЦИОННЫЕ КАНАЛЫ И СРЕДЫ X
UDC 621.371 Surveys
doi:10.31799/1684-8853-2018-5-94-103
Evolution of multiple-access networks — cellular and non-cellular — in historical perspective. Part 2
A. M. Sergeeva, SeniorLecturer, orcid.org/0000-0002-4788-9869 N. Sh. Blaunsteinb'c, Dr. Sc., Phys.-Math., Professor, [email protected] aSaint-PetersburgState UniversityofAerospace Instrumentation, 67, B. MorskaiaSt., 190000, Saint-Petersburg, Russian Federation
bBen-Gurion University of the Negev, POB 653,1, Ben-Gurion St., Beer-Sheva, 74105, Israel cJerusalem College ofTechnology—LevAcademic Center, 21 Havaad Haleumi, POB 16031, Jerusalem, 91160, Israel
Introduction: The goal of this issue is the analysis of evolution of the current and novel wireless networks, from second generation (2D) to fifth generation (5G), as well as changes in technologies and their corresponding theoretical background and protocols - from Bluetooth, WLAN, WiFi and WiMAXto LTE, OFDM/OFDMA, MIMO and LTE/MIMO advanced technologies with new hierarchy of cellular maps design — femto/pico/micro/macro. Methods: We use new theoretical frameworks for description of the advanced technologies, such as multicarrier diversity technique, OFDM and OFDM novel approach, MIMO aspects description based on multi-beam antennas approach, various cellular maps design based on a new algorithms of femto/pico/micro/macrocell deployment, and a new methodology of a new MIMO/LTE system integration based on multi-beam antennas. Results: We have created a new methodology of multi-carrier diversity description for novel multiple-access networks, of usage of OFDM/OFDMA modulation to obey inter-user and inter-symbol interference in multiple-access networks, of how to obey the multiplicative noises occurring in the multiple-access wireless networks, caused by multi-ray phenomena, and finally, of how to overcome propagation effects occurring in the terrestrial communication links by use combination of MIMO and LTE technologies based on multi-beam antennas. For these purposes we present new stochastic approach that accounts for the terrain features, such as buildings' overlay profile, buildings' density around the base station and each user antennas, and so forth. These parameters allow us to estimate for each situation occurs at the built-up terrain area the effects of fading, as a source of multiplicative noise. Practical relevance: New methodology of how to estimate effects of multiplicative noise, inter-user and inter-symbol interference, occurring in the terrestrial wireless networks, allows us to predict a-priory practical aspects of the current and new multiple-access wireless communication networks, such as: the users' capacity and user's links spectral efficiency for various configurations of cells deployment — femto, pico, micro, and macro, as well as the novel MIMO/LTE system configuration for future networks of 4th and 5th generation deployment.
Keywords — capacity, multiple-input-multiple-output (MIMO), MIMO channel, spectral efficiency, K-factor, space-time diversity, spatial multiplexing urban environment, dense layout ofbuildings.
Citation: Sergeev A. M., Blaunstein N. Sh. Evolution of multiple-access networks — cellular and non-cellular — in historical perspective. Part 2. Informatsionno-upravliaiushchie sistemy [Information and Control Systems], 2018, no. 5, pp. 94-103. doi:10.31799/1684-8853-2018-5-94-103
Continuation.
Start in Informatsionno-upravliaiushchie sistemy [Information and Control Systems], 2018, no. 4, pp. 86-104.
doi:10.31799/1684-8853-2018-4-86-104
MIMO modern networks design in the space and time domains
We will now present some advanced technology concepts based on adaptive multi-beam or phasedarray antenna applications through the prism of the physical layer description accounting for the "reaction" of the multipath outdoor channel with fading on radio propagation within such a channel [22, 49-62]. These techniques are fully described in references [63-82].
Multiple-input-multiple-output (MIMO) communication systems with multi-beam or multiple-element antennas arranged at both ends of the communication link have been introduced during the last decade to increase of spectral efficiency and communication link reliability that can be achieved via spatial and time diversity techniques. In Fig. 11 is
shown an example of how to arrange multibeam antennas (4x4 beams, i. e., 16 beams) that manage and control many clients servicing via the mobile/stationary broadband internet or sensors' networking.
In modern MIMO systems, the two basic techniques usually used [63-82] to mitigate multipath fading phenomena and increase efficiency of such networks, are:
— spatial multiplexing as a space-time modulation techniques;
— diversity modulation technique as a special case of a space-time modulation technique.
According to the first procedure, each transmitting antenna element sends to the receiver independent (e. g. non-correlated) streams of signal data accounting for a strong multipath phenomenon occurring in each channel with the Rayleigh fading [63, 64] caused by multiple reflection and diffrac-
ИНФОРМАЦИОННЫЕ КАНАЛЫ И СРЕДЫ
Multibeam antennas M N
Fig. 11. MIMO system based on M x N multibeam antennas
■ Fig. 12. Schematically presented effects of multiray phenomena occurring in the real wireless MIMO channel that causes strong fading on the input/output signals
tion occurring in the real wireless environment (Fig. 12). This idea of spatial multiplexing was first proposed in Reference [47], which then was adapted in practice of MIMO systems deployment in [66, 67]. Initially spatial multiplexing systems used narrowband channel for each antenna element of the MIMO system with a small delay spread (i. e., the large bandwidth of coherency Bcn, see previous section). In modern MIMO systems spatial multiplexing was adapted for wideband channels too in conjunction with OFDM modulation technique [68-72].
In contrast to spatial multiplexing, the diversity modulation technique deals with dependent (e. g. correlated) streams of data from each transmit antenna element of desired MIMO system. There are several diversity modulation techniques, which can be found in references [73-77]. In this book, will be
using the term MIMO diversity technique that combines space-time diversity and spatial-time multiplexing according to references [78-82].
Following the results obtained in [22], we will describe the MIMO system capacity, as an example of space-diversity and time-diversity techniques adapted for the use of the multibeam (e. g., multielement) antennas in various built-up environments. For this case, the desired formulas according to the unified multiparametric stochastic approach described in [21, 22, 95-102], will be rearranged to obtain simple relations between the MIMO antenna element number and the parameters of the terrain.
The spectral efficiency of the MIMO technique strongly depends on the diversity among multiple channels which is determined by the spatial statistical behavior of the MIMO fading channel, partly characterized by the special spatial fading correlation function (or coefficients) described in [22]. It was shown in [22, 49-55] that a sufficiently high diversity in the received multipath replicas of the transmitted signal can be achieved within a "rich" scattering environment where the communication channel capacity linearly increases with the number of the transmitter and the receiver antennas. Moreover, [49] was shown that the spatial fading correlation coefficient for dependent data streams (or de-correlation coefficient for independent data streams) can be determined by the system parameters, such as antenna elements spacing and their number. At the same time, this coefficient can be determined by the propagation conditions, such as the received energy spread in the AOA (angle-of-ar-rival), TOA (time-of-arrival) and Doppler domains, as it is described in [21, 22].
In practice, scattering environment is a scenario-dependent and as a result, spatial de-correlation
characteristics are also scenario dependent [22, 4952, 55]. Therefore, an accurate modeling of the spatial de-correlation characteristics of the MIMO channels is crucial for investigation of the scenario-specific spectral efficiency, which serves as the main criterion for communication systems design and wireless networks planning. Thus, we will derive the MIMO channel capacity for various propagation conditions as a function of the spatial correlation and the received distribution in the AOA-TOA domain. The proposed spatial fading correlation will be introduced via the stochastic multiparametric model of the urban propagation conditions in a joint AOA-TOA domain described in [21, 22, 50-52]. As a result of the proposed stochastic approach, it can be shown that the spatial fading correlation parameters depend on the propagation phenomena, such as multiple scattering, reflection, diffraction, as well as on the waveguide propagation along streets, which characterize urban environment propagation conditions.
Modeling of MIMO channel capacity
Usually, as follows from Fig. 11, there are several output antennas and input antennas assembled at the transmitter and the receiver, which we will denote them by M and N respectively.
For the uncorrelated antennas (e. g. working as separate independent antenna elements) arranged at the MIMO channel, the spectrally normalized to bandwidth Bw [in Hz] capacity C [e. g., spectral efficiency measured in bit/s/Hz] was defined in [50] as
C =
^uncorr
= Nlog2iiKm + U (26a)
^ ^ iv /add JI \ V iv /add j)
where (Pm/N)add is the signal to additive Gaussian noise ratio, which usually is taken into account in the literature. We also accounted the multiplicative noise caused by fading multipath phenomena, occurring in each of N input channels, where Km = (LOS-component)/(Multipath-component) — is the ratio of the coherent (e. g., deterministic) component of the signal and incoherent (stochastic) component of the signal caused the multiplicative noise.
At the same time, for the case of correlated antennas (i. e., working as unified whole transmitter and receiver antenna) the spectral efficiency [measured in bit/s/Hz] can be presented according to [50] as
Ccorr = log2 (1 + MN(Km x
xi—1 l/f Km +f—1 U (26b) I N Jadd J/ I I N Jadd JJ
Thus, mentioned above can be proved by a special numerical simulations carried out for different values of SNR = (Pm /N)add [in dB], and for various amounts of elements M and N at the transmitter (output) and receiver (input) antennas, respectively. We should notice before presenting some results of numerical computations that the uncorrelated arrangement of the MIMO antenna elements at the both terminal sides allows to obtain much higher capacity, e. g., higher spectral efficiency of each M and N channels with respect to the case of the correlated arrangement of the antenna elements into the unique antenna. This can be clearly seen from formulas (26a) and (26b), where in (26a) the number of elements at the receiver N is outside the logarithmic function and C increases linearly with increase of elements N at the input of the receiver. At the same time, in (26b) N and M are inside the logarithm, that is, capacity and the spectral efficiency increases logarithmically (i. e., very slow) with increase of number of elements N and M. In [50, 51] are presented several variants of MIMO system arrangements to proof mentioned above. Therefore, we will present here only the case of the uncorrelat-ed arrangement of the antenna elements, M and N. Thus, Fig. 13 presents a spectral efficiency vs. the fading factor K, as a ratio of the coherent and incoherent (multipath) components of the signal at the input of the multi-element (M = 2) transmitter antenna and multi-element (N = 4) receiving antenna, according to scenario shown in Fig. 12.
As clearly seen from the presented illustration, with increase of the coherent component (e. g. line-of-sight component) of the income signals at the input of the multi-element receiver antenna with respect to the multipath component (caused by multi-diffraction and multi-scattering from obstruc-
C (Spectral efficiency) as a function of C, bit/s/Hz K-parameter (MIMO Uncorrelated)
-SNR = 1 dB -SNR = 5 dB — SNR = 10 dB
■ Fig. 13. Spectral efficiency vs. the K-parameter of fading for various values of signal to additive noise ratio for M = 2 and N = 4
tions located in area of users' service), that is, with increase of K-factor of fading, the spectral efficiency increases sharply till K~4^6, and then a saturation of the process becomes evident.
This effect depends on SNR and becomes for smaller (K~2) with increase of SNR from 1 to 10 dB. In other words, increasing SNR inside the MIMO system, it can be easier to obey effect of fading caused by real conditions of each land-to-land communication channel.
Considering now, according to [51], the uplink scenario where the multiple receiving antennas in the BS are spatially separated, the correlation among the received replicas of the transmitted signal is determined by the propagation conditions and the spatial separation distance, that is,
2rc
P=| gifcrsin((p)/(9)d9,
(27)
where k = 2^/X is a wavenumber, X is a wavelength; r is a spatial spacing between the transmitter or the receiver antennas, which in practice is limited by the physical dimensions of the platform or installation constraints; 9 is AOA; /(9) is an angular spectrum. Conventionally it is assumed that M > N.
The influence of the MIMO channel correlation, represented by the transmitter and the receiver spatial correlation matrices on the channel capacity is intensively studied in the literature (see [22, 49-51, 53]). Following [50, 51], we will represent the influence of the spatial fading correlation in (27) on the MIMO channel capacity in (26) in the more convenient and simple manner to understand the matter:
C = N log2
1 + (1 -p)
M SNR
\
N R
(28)
w /
Notice that the spatial fading correlation in (28) decreases the MIMO channel capacity. Moreover, as was shown in addition, note that the received energy spread in the AOA-TOA domain is inversely proportional to the correlation and therefore directly proportional to the achievable channel capacity. Thus, the higher received signal spread is represented by a smaller correlation, p^ 0, which results in a higher MIMO channel capacity according to equation (28).
Fading correlation in space-time doman in urban environment with complicated building layout
considering the below-the-rooftops propagation conditions [51], and using the corresponding formulas presented in [22], the fading correlation in (26) can be rewritten as follows:
2rc
Pbelow =j /below fa)«'™^^cp,
where
/below (9) = j"
x=0
Lv2xd2 (x2 -1) 2tc(x- cos 9)
d(x2-l)|lnx| (x-cos
2Lvtd \
P(cp)«
dx,
(30)
where d is the straight-line distance between the MS (mobile subscriber) and the BS, km; x is the ratio between the actual distance that signal travels from the MS to the BS and d; 9 is the AOA; v is the building density per square km [i. e., in km-2];
„, , .21 1 • ( dsin(9) p(9) = sin j — arcsin1
X =
-L/.
L +1
is the street "discontinuity" parameter,
where L is the average lengths of the buildings, km; I is the average lengths of the slits (gaps)
between the buildings, km; a' = .
I 4aq
XV
+ a , where
a is the average width of streets, km, and n is the street waveguide mode number.
Considering wave propagation above the rooftops, and using formulas derived in [22], the correlation coefficient in (26) can be obtained as follows [51]:
2rc
Pabove =j /above (^
(31)
where
/above (9) = j
P(q>)-^
hR
x=0 2Lv/ n
Lv2Td2 (x2 -1) 2tc(x- cos 9)
- ^ 9) e 2 h
_ d(x2-l)|lnx| (x-cos 9)a'(9)
h d(x2-1) hR 2( x-cos 9)
2Lvr(x,q>)
dx,
(32)
here, assuming a uniform distribution of building heights; h = ^ is the average height of building profiles, where h1 and h2 are the minimum
and maximum building heights; hR is the BS
antenna height, and r(x, 9) =
d(x - 2tcos9 +1)
2(x-cos 9)
Below, we will analyze MIMO channel capacity in specific urban scenarios.
Correlation coefficient analysis in urban scene
The spatial fading correlation is analyzed in various urban propagation conditions simulated by the proposed fbelow and fabove models according to (30) and (32), respectively. Following [51], we analyse an urban environment with the following parameters of the experiment described there: the two BS receiving antennas with a separation distance of r
— = 10 located in the urban scene with the following parameters: y0 = 8 km-1, % = 0.5, d = 0.3 km, pseudo-LOS is at 9 = 0°, h = =15 m, v = 250 km-2.
2
Figure 14 shows the correlation coefficient p from (32) as a function of the BS antenna height hR and
the buildings' density parameter, y0 = , which
K
describes the clutter density in urban scenario.
As follows from results presented in Fig. 14, the spatial fading correlation is directly proportional to the BS antenna height. We can outline that the results shown in Fig. 14 agree with those obtained in [52, 55]. In addition, Fig. 14 shows that the spatial fading cor-
Correlation coefficient p as a function of BS antenna height and Y0
1
0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2
0,130 35 40 45 50
BS antenna heigh, m
-Y0=10 -x- Ï0=8 -V— Ï0=6 -Ô- Ï0=4
55
60
-Yo=2
■ Fig. 14. Correlation coefficient vs. the BS antenna height hR for various y0 = 2, 4, 6, 8, 10 km-1 in the urban environment with r=10X, % = 0.5, d = 0.3 km, pseudo-LOS is at 0°, h = 15m, v = 250 km-2
relation is inversely proportional to the buildings' density parameter, y0 (e. g., clutter density), which determines the received signal diversity in the AOA-TOA domain. Therefore, the denser urban environment (higher y0) results in lower correlation among the received replica of the transmitted signal. Finally, we notice that the influence of the parameter y0 on the spatial fading correlation decreases with increasing BS antenna height. This observation can be explained by the fact that the significance of the built-up environment structure for a particular urban scene (distribution of the scatterers) decreases with increasing BS antenna height. In practice, the result shown in Fig. 14 can be used as a guideline for the BS antenna height selection required to achieve the predefined correlation (and as a result the predefined MIMO channel capacity) in a specific urban scenario.
MIMO channel capacity estimation
Now, we will analyze the effect of the spatial fading correlation on the MIMO channel capacity by using (29) and (31) in (32). In Fig. 15, a shows the MIMO channel capacity as a function of the BS antenna heights for a variety of the buildings' density y0 in the simulated urban environment with the following parameters: SNR = 10 dB, separation distance between two BS receiver antennas of r = 10X, X = 0.5, d = 0.5 km, pseudo LOS is at 9 = 0°, and h = 25 m.
Channel capacity analysis
30 35 40 45 50 55 60 BS antenna heigh, m
-0-Ï0 = 2 —A— Y0= 5 -Y0= 8 Ï0= 11
6)
ö a •SIS
tien ai
<h
5 a
o 0 Cc
0,8 0,6 0,4
o,2 0
60
30 35 40 45 50 55
BS antenna heigh, m -0-Y0 = ^ -A- Y0 = 5 -Y0= 8 Y0 = 11
■ Fig. 15. MIMO channel capacity vs. the correlation coefficient and y0 in the urban scenario with SNR = = 10 dB, r/X^ 10, % = 0.5, d = 0.5 km, pseudo LOS is at 9 = 0°, and h = 25 m
We show that the MIMO channel capacity in Fig. 15, a, to be inversely proportional to the spatial fading correlation in Fig. 15, b. Thus, the increase in the spatial fading correlation p from 0.2 to 0.8 results in the normalized channel capacity degradation from approximately 6.5 bit/s/Hz to approximately 3.2 bit/s/Hz.
Notice that the MIMO channel capacity is directly proportional to the buildings' density y0 in the urban environment. Thus, the increase in the density y0 from 2 to 11 km-1, results in the channel capacity improvement.
This simulations show that in additional to the conventional dependence of the MIMO channel capacity on SNR, it strongly depends on the location-specific parameters of the urban environment (via the statistical model of the urban propagation conditions), such as BS antenna height, buildings' density, average buildings' height, and so forth.
MIMO channel capacity analysis in predefined urban scenario
Now we will evaluate the MIMO channel capacity in a simulated "virtual" urban scenario, using an urban topographic plan taken from [22, 51]. The MIMO channel capacity was evaluated in 11 representative locations characterized by the SNR of 20 dB. The SNR was obtained using the ray-tracing software tool [54] as a ratio between the received signal strength and the noise with an equivalent bandwidth of 5 MHz. A BS antenna height of 15 m was simulated. The separation distance of r = 10 X was simulated between 4 receiving antennas. The urban environment with the following parameters was simulated using (30) and (32): x = 0.8, h = 25 m, y0 = 6 km-1. The inter-user interference was neglected in this simulation.
Figure 16 shows the simulated urban scene. Colored dots in Fig. 16 show the tested MS locations with the SNR of 20 dB. The color pattern repre-
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■ Fig. 16. The MIMO channel capacity is simulated urban scenario with the BS antenna height of 15 m, MIMO system is with 4 x 4 antenna elements, and SNR = 20 dB; the numbers show places where numerical experiment was carried out
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The above obtained results allow us to summarize that the MIMO channel capacity in an urban environment has the BS antenna elevation and location specific factor and strongly depends on the spatial fading correlation determined by the scattering, reflection, diffraction and waveguide propagation phenomena.
To be continued.
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УДК 621.371
doi:10.31799/1684-8853-2018-5-94-103
Эволюция многопроцессорных систем связи — сотовых и несотовых — в исторической перспективе. Часть 2
А. М. Сергеева, старший преподаватель, orcid.org/0000-0002-4788-9869,
Н. Ш. Блаунштейн6, в, доктор физ.-мат. наук, профессор, [email protected]
аСанкт-Петербургский государственный университет аэрокосмического приборостроения, Санкт-Петербург, РФ 6Негевский университет им. Бен-Гуриона, ПОБ 653, Бен-Гуриона ул., 1, г. Беэр-Шева, 74105, Израиль вИерусалимский технологический колледж, Хавад Халейми, 21, ПОБ 16031, Иерусалим, 91160, Израиль
Постановка проблемы: целью данного обзора является анализ эволюции систем беспроводной связи от второй генерации (2D) до пятой генерации (5G), а также изменения технологий и их существующих теоретических основ и протоколов — от Bluetooth, WLAN, WiFi и WiMAX до LTE, OFDM/OFDMA, MIMO и LTE/MIMO — продвинутых технологий с новой иерархической структурой дизайна сотовых карт femto/pico/micro/macro. Методы: использованы новые теоретические подходы для описания продвинутых технологий, таких как многопользовательская техника разделения пользователей, OFDM и OFDM-новейший подход, новые аспекты описания MIMO-систем на базе использования многолучевых антенн, дизайн различных сотовых карт на основе новых алгоритмов построения фемто/пико/микро/макро сот, а также новой методологии интегрирования новой MIMO/LTE-системы с помощью многолучевых антенн. Результаты: создана новая методология описания многопользовательского разделения, использования комбинированной OFDM/OFDMA-модуляции для обхождения интерференции между пользователями и между символами в новых многопроцессорных системах, мультипликативных шумов, имеющих место в беспроводных многопроцессорных системах связи, вызванных явлениями многолучевости. В итоге предложено, как обойти эффекты распространения, имеющие место в наземных каналах связи, используя комбинацию MIMO- и LTE-технологий, основанных на применении многолучевых антенн. Для этих целей разработан новый стохастический подход к проблеме, учитывающий особенности застройки земной поверхности, такие как профиль застройки домов, плотность застройки домов вокруг антенн базовой станции и пользователей и т. д. Эти характеристики позволяют в итоге оценить эффекты фединга как источника мультипликативного шума. Практическая значимость: новая методология оценки эффектов, созданных мультипликативным шумом, интерференцией между пользователями и между символами, имеющими место в наземных системах беспроводной связи, позволяет прогнозировать практические аспекты существующих и новых многопроцессорных беспроводных систем связи, такие как емкость (количество) пользователей и спектральная эффективность каналов пользователей для различных конфигураций построения сот — фемто/пико/микро/макро, а также новейших конфигураций систем MIMO/LTE для построения будущих систем 4-го и 5-го поколений.
Ключевые слова — пропускная способность, многочисленный вход-многочисленный выход (MIMO), канал MIMO, пространственное мультиплексирование, спектральная эффективность, фактор K, городская среда, плотная застройка домов.
Цитирование: Sergeev A. M., Blaunstein N. Sh. Evolution of multiple-access networks — cellular and non-cellular — in historical perspective. Part 2. Информационно-управляющие системы, 2018, № 5, с. 94-103. doi:10.31799/1684-8853-2018-5-94-103 Citation: Sergeev A. M., Blaunstein N. Sh. Evolution of multiple-access networks — cellular and non-cellular — in historical perspective. Part 2. Informatsionno-upravliaiushchie sistemy [Information and Control Systems], 2018, no. 5, pp. 94-103. doi:10.31799/1684-8853-2018-5-94-103
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