1.Statically analysis of change of Channel Capacity Rate of
different MIMO system Model.
Abstract:
A good channel capacity is always major issue on modern
wireless communication system. A MIMO system has one key feature that the
channel capacity is dependent on the number of transmitting and receiving
antennas. Here we provide a novel system of channel capacity change curve which
gives when less value of change of channel capacity rate provides a better
channel capacity. Those less value of change in channel capacity rate dependent
on number of transmitting and receiving antennas on MIMO system.
Keywords: Change in Channel Capacity Rate, MIMO System
Model, Outage Probability,
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1. Introduction:
Multiple Input
Multiple Output (MIMO) system is a one of key features of today’s wireless
system. The transmission and reception of signal of MIMO system are under the
Rayleigh Fading. These system are
changing randomly and affected by various phenomena, due to this, channel
capacity are degraded which is serious problem of modern wireless system. Modern
system are leading to the erogdic2 (average) channel capacity as
this is very useful when fluctuation of signal intensity are fast. Maximum data
transmission rate is obtain when error probability becomes arbitrary small and
two successive symbols contains the independent samples of signal intensity
whereas SNR remains constant over duration of large number of symbols.
When signal transmission is random and
fluctuations of these signal also known as quasi-static channels, are slow then
maximum data transmission rate under a specified outage probability can be
known as outage capacity which is more suitable. The outage channel capacity can be calculated
by help of [2]. The presented channel capacity ratio between two successive
models is under the outage probability and some constant SNR level. The
realization of random fading coefficient may be very small when communication
over quasi-static fading channels at a given data rate R. In this case, the
block (frame) error probability is bounded away from zero no matter whether the
block length of symbol tends to infinity. [2]
There are various feature of MIMO system
but one of the key feature that the channel capacity increase as a multiple
antenna system i.e. as numbers of transmit (NT) & receive (NR)
antennas increases then channel capacity increase. No extra additional capacity
the increase factor is Min (NT, NR), can be achieved.[1] In
this paper we provide analysis plot there
which gives a change in channel capacity data rate
with respect to number of antenna increasing. We are interesting on calculating
the channel capacities of Random MIMO system, and statics technique is using
for finding channel capacity rate with different CDF level. The Random MIMO
system has good capacity response when channel state Information (CSI) is not
available on the receiver. In this system H be unknown parameter to the
transmitter so spreading the equal energy to all transmit antennas from one is
possible. The transmitting signal is in autocorrelation function of signal
vector on the receiving side.
2. Channel capacity
of Random MIMO:
In reality, random channel is an erodic2
process and capacity can be known as average channel capacity because it is
result of averaging the instantaneous capacity [3]. So MIMO channel capacity
can be has different statistical notation.
One of them is outage channel capacity with statistical notation which
define on outage probability as
Pout(R) = Pr(C(H)<R) (3)[1]
Within the transmission rate [R bps/Hz],
if the decoding error probability cannot be made arbitrary small then the
system is called outage. The decoder of C(H)
may commit error only when the channel is in outage. We can say that Є-outage
channel capacity is the largest possible data rule which yields the equation of
channel capacity as less than Є [1]. Then outage probability equation be
P(C(H)≤CЄ=Є (4) [1]
From the PDF of transmitting signal
vector we are calculating the CDF of those random capacity channel which is
shown below.
Figure
1: Channel capacity of various MIMO system Model
3. Simulation
Results:
For simulation we assuming those curve
of data bit rate are constantly varying which gives a constant change of data
bit rate. The total probability is always one so we take here change in data
rate is under 0.1 probability difference. Figure 1 gives various value of data
bit change rate when number of transmitting and receiving antennas of MIMO
system Model. So we take interpolation method for finding those resulting curve
which gives as change of data bit rate under 0.1 probability change with
respect to number of transmitting and receiving antennas. Figure 2 shows three
data bit rate changing curves with three SNRs level i.e. 1dB, 5dB, 10dB. This
figure also shows that when number antennas are increasing the curve is
decreasing. The decreasing curve is less
dependent on different SNR levels.
Figure 2: Change in Data bit
rate with respect to numbers of antennas.
4. Conclusion:
The changing data bit rate
curve is decreasing when number of antennas are increasing. This gives lower
change in data bit rate change gives increasing in channel capacity. Hence
above simulation result shows that MIMO system improves his channel capacity when
number of transmitting and receiving antennas are increasing.
5. Acknowledgement
This
research was supported by Basic Science Research Program through the National
Research Foundation of Korea (NRF) funded by the Ministry of Education
(NRF-2009-0093828) and in
part by the Ministry of Science, ICT and Future Planning, Korea, under Grant
IITP-2015-H8601-15-1006 of the Convergence Information Technology Research
Center program supervised by the Institute for Information and Communications
Technology Promotion.
6. References
1) MIMO-OFDMA Wireless Communication with
Matlab, by Yong Soo Cho | Jackwon Kim
Won Young Yang | Chung-Gu Kang http://www.wiley.com
2) Quasi-Static Multiple-AntennaFading Channels at
Finite Blocklength Wei Yang,Student Member, IEEE Giuseppe Durisi,Senior Member,
IEEE Tobias Koch,Member, IEEE and Yury
Polyanskiy,Member, IEEE http://people.lids.mit.edu/yp/homepage/data/qsmimo.pdf
3) Outage capacity evaluation of extended
generalized-KK fading channel in the presence of random blockage Jelena A. Anastasov Nemanja
M. Zdravković Goran T. Djordjevic
4) Ergodic
capacity, outage capacity, and information transmission over Rayleigh fading
channels Sayantan Choudhury and Jerry D. Gibson Department of Electrical and
Computer Engineering.
5) Capacity and error probability
performance analysis for MIMO MC DS-CDMA system in η–μ fading
environmentJames
Osuru Marka Brahim
Belhaouari Samirb, Naufal M.
Saadahttp://www.sciencedirect.com/science/article/pii/S1434841112002014
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