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2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), 7-9 February 2019
978-1-5386-9111-3/19/$31.00 ©2019 IEEE
Maximum Energy Efficiency of Three Precoding
Methods for Massive MIMO Technique in Wireless
Communication System
Md. Humayun Kabir
1
Electronic and Telecommunication
Engineering
mdhkrrabby@gmail.com
Syed Zahidur Rashid
2
Electronic and Telecommunication
Engineering
Abdul Gafur
3
Electronic and Telecommunication
Engineering
Muhammad Nurul Islam
4
International Islamic University Chittagong
Chittagong, Bangladesh
rmdnurulislam921@gmail.com
MD Jiabul Hoque
5
International Islamic University Chittagong
Chittagong, Bangladesh
Abstract—Nowadays, Energy Efficiency (EE) has turned
into a buzzword for 5G. Consequently, Massive MIMO
(MMIMO) technologies have attained a great attention for
Multi-User Communication scenario. In this paper, the
Minimum Mean Square Error (MMSE), the Maximum Ratio
Transmission (MRT) and the Zero Forcing (ZF) precoding
methods are analyzed for MMIMO technique from an EE
perspective. The results show the optimization of EE against
the amount of clients, amount of assigned base stations as well
as launch input power in the system respectively.
Keywords—MMIMO technique; Energy Efficiency (EE);
Wireless Communication System
I. I
NTRODUCTION
EE has turned out to be one of the imperative keys of
the next-generation network (NGN) system. In the next
generation (5G) cellular communication system one of the
winder technologies has MMIMO technology. MMIMO
have smart antenna systems, which serve vast users with
high transfer rate demand in the multiuser antenna system.
For multiuser antenna system, the base stations (BS) are
fitted with energy hungry apparatus. A recent research work
finds that near 80% of the power is consumed by a base
station (BS) in the cellular network [1]. MMIMO
technology has a huge amount of antennas array per BS. So,
power consumption is increments not for this case only but
also as a result of the increases of the transmitted power on
the power enhancer. In any case, MMIMO transmits
antennas systems increase the radiation of energy with a
large amount of energy being increases for uplink and
downlink transmission ends. For this reason, whilst the wide
variety of antenna array will increase in BS, there conjointly
improvement in the range of radio frequency (RF) chains
additionally to the processing load [2]. Therefore, a way to
reduce energy consumption is an important a part of the
studies on the key technologies of 5G. Finally, the linear
precoding method used to decreases the energy of uplink
and downlink transmission ends. In this paper, we go for
mutually planning how to achieve maximum EE whereas
limiting the power consumed within uplink and downlink
MMIMO systems and which linear precoding methods give
the maximal Energy Efficiency (EE). This method
incorporates the transmitted power moreover because of the
basic circuit operation of BS. Thus technologies also depend
on an optimal amount of antennas array (M) and the active
clients (K) in every cell.
II. L
ITERATURE
R
EVIEW
The objective of EE, aside from its environmental
significance, is additionally connected with the decrease in
operational costs for wireless and cellular administrator’s
system, and additionally with more noteworthy consumer
satisfaction. At present, in any case, it isn't clear how 5G
core network technologies, as they are for the most part
centered on higher data transfer capacities and are required
to be conveyed as an overlay over the previous system,
could present a decrease in power utilization. There are
different types of researchers explored the execution of the
International Islamic University
Chittagong
Chittagong, Bangladesh
International Islamic University
Chittagong
Chittagong, Bangladesh
szrashidcce@yahoo.com
International Islamic
University Chittagong
Chittagong, Bangladesh
agafur_cox@yahoo.com
Electronic and Telecommunication Engineering
Electronic and Telecommunication Engineering
jia99cse@yahoo.com
precoding techniques in MMIMO system according to EE
perspective.
Maximizing EE and battery-saving innovation without
relinquishing QoS is progressively essential for cell phones
in downlink MMIMO systems according to different
precoding strategies. They also taking account of the
consumption circuit power ignored because of high transmit
power [1]. The present investigations in monstrous MIMO
alongside a thorough diagram from claiming most recent
exploration issues. They gave future possibility from
claiming massive MIMO toward examining how massive
MIMO might have a chance to be coordinated circuit with
unavoidable advances for more excellent use about assets.
This review goes about as a vital aid to intrigued
bookworms Previously, monstrous MIMO [2]. The
fundamental ideas from claiming massive multiple-input-
multiple-output, to concentrate on the tests also
opportunities, Because of contemporary investigate. basic
concepts of massive multiple-input multiple-output, with a
focus on the challenges and opportunities, based on
contemporary research [3]. The achievable sum rates and EE
of downlink single cell MMIMO systems using linear and
nonlinear precoding strategies also indicated how the
expanding SNR ratio and M antenna array of MMIMO
systems in pico, micro and macro cell conditions when
linear and nonlinear precoding plans are used at the BS have
been illustrated [4]. Also, execution of the MMSE, MRT
and ZF straight precoding strategies for the MMIMO
downlink on a Rayleigh channel show, and spectral
efficiency [5]. A modern SVDbased codebook outline
paradigm for straight zeroforcing (ZF) precoding. What's
more TomlinsonHarashima (TH) precoding with the
restricted sentiment. Over limited sentiment frameworks, the
place main quantized channel state majority of the data will
be available, precoding for transmitter makes sumrate
corruption due to quantization errors. Thus, they recommend
a more significant amount effective codebook that lessen
those quantization slip for easier reaction overheads. They
also created a closedform statement for sumrate of the
suggested codebooks if from claiming TH and ZF precoding
schemes over a multiuser MIMO framework with set input
[6]. Some researchers find out the maximum EE value by
utilizing MRC and ZF precoding strategies in single-cell
correspondence situations. They proposed a new power
consumption model also suggests EE systems can work in
high SNR [7], [8]. EE architecture is recommended that can
progressively dispense assets in light of both current activity
requests and pre-characterized energy arrangements. They
focus on the learning process and predictive analysis process
[9].
All the works described above, and any other papers
didn't show on an optimal amount of antennas array (M), the
active clients (K) in every cell and which methods give the
maximum EE value.
III. S
YSTEM
M
ODELS AND
A
SSUMPTIONS
Uplink data transmission hinges on the K user’s
transmission to the BS. Let
, where‖
‖
=1,
denotes the
user at the transmission site. The
M×1 obtained signal vector at the BS is the blend of
all signals transmitted by all K users [4]:
,
=
∑ℎ
+
(1)
=
+ (2)
Where
is the average SNR, ∈
×
is the additive
noise vector, and≜[
………
]
.
Downlink data transmission is where the BS transmits
the signal to all K users. Let∈
×
, where‖‖
=
1, be the signal vector transmitted from the BS antenna
array. Then, the received signal at the
user is given by
[7]:
,
=
ℎ
+
(3)
Where
the average SNR and
is the additive noise at
the
user. We assume that
is Gaussian distributed unit
variance. On the whole, the got signal vector of the K users
can be composed as [13]
=
+ (4)
Where
≜
,
,
……
,
and[
……
]
.
A. Minimum Mean Square Error (MMSE) Precoding:
The MMSE linear precoding technique is created by
utilizing the mean square error method in the signal. This
linear precoding technique is to minimize the error filtering
between the transmit signal of BS and receive signal of
every user. So, the MMSE precoding matrix can be
composed as [5]:
=
+
(5)
Here is a scalar of Wiener filter.
α=TR(BB
)
P
B =
+
B. Zero Forcing (ZF) Precoding:
ZF is one of the linear precoding strategies in which the
inter-user interference between client obstructions can be
counteracted by every user. This linear precoding technique
is the used of an equalizer is compelled to zero by utilizing
proper linear time-invariant channel filter having appropriate
exchange the function. So, the ZF precoding matrix can be
composed as [5]:
=
(
)
(6)
C. Maximum Ratio Transmission (MRT):
With MRT, the BS aims to maximize signal gain at the
intended user. It is the counterpart of the maximal-ratio
combining receiver for uplink. So, the ZF precoding matrix
can be composed as [5]:
=
(7)
Therefore, the precoding matrix
,
and
for
the three linear precoders mentioned above are given by [7]:
=,
=(
)
=
+
(8)
Where
is the average transmission SNR on the
uplink and downlink. Similar to the case with linear
detection methods, these linear precoders achieve near-
optimal throughput performance in the large M regime. This
completes our discussion on how signal processing
requirements are simplified in MMIMO systems.
D. Energy Eciency (EE):
The EE of a cellular network is the number of bits that
can be dependably transmitted per unit of energy. As
indicated by the above, we can write as [8]:
EE=
[//]
[/]
(9)
Above this equation (9), maximize throughput with fixed
power and minimum transmit power for fixed throughput.
The throughput can be utilize the average Uplink and
Downlink Spectral Efficiency articulation and which
describe the execution of MMIMO systems working over
corresponding data transfer bandwidth. The Power
Consumption (PC) must be computed on the basis of the
ETP (Eective Transmit Power) and of the Circuit Power
(CP) required for running the cellular network [8]:
PC =ETP+CP (10)
This term accounts for the power consumed by the
transmission of the pilot sequences as well as of UL and DL
signals [8]:
ETR=
∑
,
p
+
∑
,
p
+
∑p
(11)
Where µUE, k is the PA eciency at UE k in cell and
µBS, j is that of BS.
The throughput of the cell for computing the consumed
power for backhaul, encoding, and interpreting is gotten
utilizing the UL and DL SE articulations.
TR=B∑SE
+maxSE
,SE
(12)
TheQ
is the addition of the power devoured through
various BTS apparatus [7]. Building on the prior works of [7
- 11], we present a Q
utilization model for MMIMO
systems: Q
=Q
+Q
+Q
+Q
/
+
Q
+Q
(13)
Here the different terms account Q
is the fixed
power for control signaling, load-independent backhaul,
baseband processors of BS. Q
is the power of transceiver
chains for the local oscillator, circuits additives of all
individual terminals. Q
is the power for channel
assessment per coherence block. Q
/
is the power for
channel coding and decoding of BS. Q
is the power for
load-dependent backhaul of BS. Q
is the power required
for computation at the BS. In the accompanying, we give
basic practical design for showing the dependency upon the
fundamental framework parameters (M, K, R). For an EE-
optimal MMIMO this should be disentangled as flows:
∈
,∈
,
EE=
∑
()
()
()
()
,,
(14)
IV. M
ETHODOLOGY
Start
Spectral
Efficiency
of UL
Data Rate
Spectral
Efficiency
of DL
Data Rate
Eective
Transmit
Power
Circuit Power
Consumption
++
Throughput Total Circuit Power
Consumption
/
Calculate EE Value
According to MMSE,
ZF and MRT Linear
Precoding Methods
End
Process 1 Process 2 Process 3 Process 4
Process 5 Process 6
Process 7
Fig. 1. The flowchart for evaluating the maximum EE.
To get the maximum EE value in MMIMO systems and
minimizing the circuit power of BS according to three linear
precoding methods are the major goals of our work. The
Power consumed at the BS depends on an optimal amount of
antennas array (M) and the active clients (K) in every cell.
The figure 1 shows the methodology of this work. In
process 1 and process 2, we have calculated the Spectral
Efficiency data rates for UL and DL communications
scenario. In process 3 and process 4 calculated of Effective
Transmit Power and Circuit Power Consumption of the BS.
In further, process 1 and 2 are combined up to get the
Throughput in process 5 and the process 3 and 4 are
combined up in process 6 to get the Total Circuit Power
Consumed by the entire system. Ultimately, process 7 gives
the proportion of Throughput and Total Circuit Power
Consumption to determine the EE value according to
MMSE, ZF, and MRT linear precoding strategy.
V. S
IMULATION
R
ESULT AND
A
NALYSIS
The corresponding simulation parameters are given in
Table I and are inspired by a variety of prior works [8], [11
– 13]:
TABLE I. S
IMULATION
P
ARAMETE RS
Parameter
Value
Number of Antenna
200
Number of Users
100
Transmission Bandwidth (B)
20 MHz
Coherence Bandwidth (B
C
)
180 kHz
Coherence Time (T
C
)10 ms
Coherence Block (τ
c
)400
UL Transmit Power (P
UL
)20 dBm
DL Transmit Power (P
DL
)20 dBm
Fixed Power Consumption (P
FIX
)
9 W
Power for BS LO: P
LO
0.1 W
Power of Circuit Components (such as converters,
mixers, and filters) (P
BS
)
1 W
Power consumed by Local Oscillator at BSs (P
SYN
)
1 W
Power required of circuits components of each
single-antenna UE. (P
UE
)
0.1 W
Power for data encoding (P
COD
)
0.01 W
Power for data decoding (P
DEC
)
0.08 W
Power for backhaul trac (P
BT
)
0.025 W
Figure 2, 3 and 4 show that the set of achievable Energy
efficiency values with different linear precoding methods. In
figure 1, MMSE precoding method gets the maximum
energy efficiency (EE) values as compared to other
precoding methods. With MMSE, a maximal EE of 24.16
Mbit/Joule is achieved by (M, K) = (50, 30).
Fig. 2. Energy Efficiency with MMSE Precoding Methods.
In figure 3, ZF precoding method gets the slightly less
maximum energy efficiency value than MMSE. The
achievable a maximal EE of 22.96 Mbit/Joule is achieved by
(M, K) = (80, 30). Interestingly, MRT precoding technique
shows a very dissimilar performance the EE value much
smaller than MMSE and ZF which is 11.77 Mbit/joule
achieved at (M, K) = (50, 20). The explanation in the back
of M ≈ K is that MRT/MRC works according to robust inter-
user interference, in this way the rate per UE is little and it
bodes well to arrange for whatever number UEs could be
permitted. The signal processing of multifaceted nature is
decreasing than with ZF in favor of a comparative M and K,
but the energy-saving reserves are sufficient to change for
the lower rates. The MMSE offers better execution
considering the fact that the MMSE can make the MMIMO
framework much less touchy to SNR at an extended amount
of antennas regarding the potential data rate contrasted with
ZF and MRT. Ultimately, EE first increments and after that
reduction with an accelerated amount of antennas, thereby
augments the transmit power.
Fig. 3. Energy Efficiency with ZF Precoding Methods.
Fig. 4. Energy Efficiency with MRT Precoding Methods.
VI. C
ONCLUSION
The concentration of this work is on selecting the
optimal value of antenna array (M), active number of clients
(K) ought to find out and which linear precoding technique
gives the Maximal Energy Efficiency (EE). The simulation
results indicate that MMSE attains the better Maximal
Energy Efficiency (EE) value than other linear precoding
techniques in MMIMO system. We also noticed that the
maximum EE in an MMIMO can be accomplished via
expanding the amount of antennas at the BS. In future
works, try to find the limitation of these linear precoding
methods and to compare linear and nonlinear new precoding
methods in a MMIMO system.
R
EFERENCES
[1] Salh, A., Audah, L., M. Shah, N. and Hamzah, S. “Maximizing
Energy Efficiency for Consumption Circuit Power in Downlink
Massive MIMO Wireless Networks,” International Journal of
Electrical and Computer Engineering (IJECE), Vol.7, No.6,
December2017, pp. 2977~2985
[2] "Massive MIMO: A survey of recent advances, research issues and
future directions," 2017 International Symposium on Recent Advances
in Electrical Engineering (RAEE), Islamabad, 2017, pp. 1-6.
[3] "Massive MIMO: survey and future research topics," in IET
Communications, vol. 10, no. 15, pp. 1938-1946, October 2016.
[4] Isabona, J. and Srivastava, "Downlink Massive MIMO Systems:
Achievable Sum Rates and Energy Efficiency Perspective for Future
5G Systems," Wireless Personal Communications Vol.96, No.2
May2017, pp. 2779-2796
[5] V. P. Selvan, M. S. Iqbal and H. S. Al-Raweshidy, "Performance
analysis of linear precoding schemes for very large Multi-user MIMO
downlink system," Fourth edition of the International Conference on
the Innovative Computing Technology (INTECH 2014), Luton, 2014,
pp. 219-224
[6] Zeeshan Kaleem, Bing Hui and KyungHi Chang "Achievable rates of
SVD-based codebooks for zero-forcing and Tomlinson-Harashima
precoding schemes with limited feedback MU-MIMO
system," International Journal of Communication Systems, 10 May
2017, vol. 30, no. 7, pp. 1 –15
[7] K. N. R. S. V. Prasad and V. K. Bhargava, "Resource optimization for
energy efficiency in multi-cell massive MIMO with MRC detectors,"
2016 IEEE Wireless Communications and Networking Conference
Workshops (WCNCW), Doha, 2016, pp. 121-126
[8] Björnson, Emil & Sanguinetti, Luca & Hoydis, Jakob & Debbah,
Mérouane. (2015). "Optimal design of energy-efficient multi-user
MIMO systems: Is massive MIMO t he answer?". IEEE Transactions
on Wireless Communications, 2014, pp. 242-247
[9] G. Auer and et al., D2.3: Energy efficiency analysis of the reference
systems, areas of improvements and target breakdown. INFSO-ICT-
247733 EARTH, ver. 2.0, 2012. [Online]. Available: http://www.ict-
earth.eu/
[10] S. Tombaz, A. Vastberg and J. Zander, "Energy- and cost-efficient
ultra-high-capacity wireless access," in IEEE Wireless
Communications, vol. 18, no. 5, pp. 18-24, October 2011
[11] H. Yang and T. L. Marzetta, "Total energy efficiency of cellular large-
scale antenna system multiple access mobile networks," 2013 IEEE
Online Conference on Green Communications (OnlineGreenComm),
Piscataway, NJ, 2013, pp. 27-32.
[12] Shuguang Cui, A. J. Goldsmith and A. Bahai, "Energy-efficiency of
MIMO and cooperative MIMO techniques in sensor networks,"
in IEEE Journal on Selected Areas in Communications, vol. 22, no. 6,
pp. 1089-1098, Aug. 2004
[13] A. Mezghani and J. A. Nossek, "Power efficiency in communication
systems from a circuit perspective," 2011 IEEE International
Symposium of Circuits and Systems (ISCAS), Rio de Janeiro, 2011,
pp.1896-1899.
