International Journal of Engineering Trends and Technology (IJETT) – Volume 26 Number 5 - August 2015
Soft Computing Optimization Technique for
Efficient Radio Resource and Power Management at
Base Station
Patel Miteshkumar Manharbhai #1, Shrikant Ahirwar *2, Paritosh Goldar #3
1,2
Electrical Engineering Department,
Computer Science & Engineering Department,
RKDF University, Bhopal, India
3
Abstract— The energy consumption at base station of wireless
network became an critical issue as a wireless access point
transfers high volume of data. In this paper, a new soft
computing optimization technique for efficient radio resource
management and power management is proposed which aims the
minimization of supply energy consumption at the base station
for modern cellular based wireless network. The proposed soft
computing method optimizes energy-saving mechanisms for
power control, antenna adaptation, resource allocation and
discontinuous transmission. In the proposed method the
consumption of supply power at base station is reduced by using
soft computing optimization technique and efficient management
of multi-user resource allocation technique. MATLAB
simulation using soft computing optimization like genetic
algorithm and particle swarm optimization results presents that
our proposed method minimizes the supplied electrical power
consumption at base station.
Keywords— Power Management, Resource Management, Base
Station, Optimization.
I. INTRODUCTION
Developing countries are extensively using 3G and 4G
technologies which are consuming more electrical power
compared to the previous technologies, if no effective solution
will be taken to control this huge power consumption then it
will be critical problem in future. The number of mobile users
and their data downloading, uploading and data transfer is
growing rapidly and due to this high traffic of data and calling
packets transfer, the base station consumes high power in a
cellular network. The power efficiency is becoming major
issue at the base stations of cellular network [1][2]. Depending
on the geographical locations and particular day and time, the
cellular base station may work in full active mode or it may
sometime be in idle mode. In either situation of low-load or
high load data traffic at the cellular base station, power is fully
utilized by the base station, and there is no flexibility
approach for balancing the power efficiency [2]. Generally,
the power-saving mechanism at the cellular base station
adversely affects the user mobile terminal by high power
consumption in mobile terminal device or it affect the quality
of service (QoS) in mobile terminal of the user [1][2].
Today, the power control is the prominent issue in
cellular based wireless network today among the several
ISSN: 2231-5381
power-saving efficient radio resource management methods
and techniques. For interference reduction and link adaption
mechanism, power control and reduction of power
consumption is important and beneficial as well [3]. The
methods and techniques of power control is computationally
complex in context of hardware and it does not affect the
supplied power or a power model of base station [3][4]. The
modern transmission methods are basically Multiple-Input
Multiple-Output (MIMO) transmissions which presents that
the energy efficiency depends on factors like signal-to-noise
ratio, data rate, number of transmit antennas, geographical
area and multiplexing techniques [4]. The power saving
efficient radio resource management and power management
techniques significantly depend on the power consumption in
circuit and its efficiency, transmission methods and antenna
adaptation techniques [4][6].
The system model of cellular system usually consists of
single serving base station connected with multiple mobile
users which establish point to multipoint connection at base
station. The transmitter of base station contains antennas and
total supplied transmit power which is shared among all
antennas. The modern resource and power allocation
management is basically implemented by Orthogonal
Frequency Division Multiple Access (OFDMA) [5]. The
power supply, bandwidth and all resources are equally
distributed among all users through Orthogonal Frequency
Division Multiple Access (OFDMA) [5]. The strategy of
wireless cellular system implemented orthogonally such that
no individual resource unit can be shared among all antennas
of the users terminal.
The total supplied power consumption is calculated and
estimated by the power model of cellular which calculates the
overall power consumption of equipment at the base station.
The power system model allows to calculate RF transmission
power for supplying power consumption. Architectural system
power model is usually mapped from a real-time base station
hardware implementation, which is capable to keep the most
relevant supplied power consumption effects in the base
stations which are available today. This implemented linear
model is verified and compared by a modern realistic model
and detailed actual hardware model [3][4]. This linear power
model usually provides a basic foundational map to analyze
the power efficiency. The RF chain components contains
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International Journal of Engineering Trends and Technology (IJETT) – Volume 26 Number 5 - August 2015
section of a power amplifier, small-signal transceiver and the
antenna interface in this system power model as shown in the
figure 1.
Supply Power
Power Amplifier
Transmit Power
RF
Base
Station
RF
Antenna Interface
Figure 1: Components of Power Modelled Base Station
From the total power supplied to the system, only the power
amplifier consumes the huge portion of the total power due to
the low energy efficiency of power amplifier. The adaptive
antenna performs for adaptation of the relative RF chain in
which all transmit antenna is attached to an RF chain.
Whenever fewer RF chains become active then the base
station model consumes less power. There is no time delay or
extra power cost incurred by implementing a base station into
a discontinuous transmission mode and enabling/disabling an
RF chain in this model. The supplied power consumption
value is calculated when it operates with a single RF chain [4].
II. BACKGROUND
Some realistic modern energy efficient methods are analyzed
and reviewed for further study in this section.
1. The effect of receiver interference cancellation methods
at the base station power consumption [7]:
The estimated effect of receiver interference reduction
and cancellation methods are analyzed and studied at the base
station downlink signal transmission power within the intercell interference for the multiple-input multiple-output
(MIMO) network communication system [7]. The energy
consumption of wireless signal processing module is defined
as a portion of the power consumption at power amplifier
module instead of specifying absolute values. The effect of
wireless signal processing module with the receiver
interference reduction or cancellation techniques are estimated
on total supplied power consumption of the cellular base
station. This technique determines that the supplied power
consumed at the wireless signal processing module is not
overestimated while working with the receiver interference
reduction and cancellation methods, when calculating the total
power consumption of the cellular base station [7]. Whenever
power consumption of wireless signal processing module
exceeds over its limit, then it generally contributes to
maximize the total supplied power consumption of the base
station as compared to the extra power needed by its power
amplifier module for solving inter-cell interference of cellular
system [7].
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Limitation: The receiver interference reduction and
cancellation techniques and received diversity gain is not
justified if wireless signal processing consumes more supplied
power relative to the supplied power in power amplifier.
2. Minimum Mean Square Error (MMSE) Optimization
Method with Per-Base-Station Energy Constraints [8][14]:
The signal interference problem of previous
conventional cellular system is reduced by the cooperative
transmission within multiple cellular base stations in this
minimum mean square error (MMSE) method [8][14]. This
method reduces the limitation of linear transceiver system
design for Multiple-Input Multiple-Output (MIMO) system
with supplied power consumption per base station of cellular
system. There are basically four design goals:
(i). Reducing the total MMSE estimated for per-base-station
power consumption in the system.
(ii). Reducing the total transmit power estimated for total
MMSE target and per-base-station supplied power
consumption.
(iii). Reducing the highest weighted MMSE estimated for perbase-station supplied power consumption.
(iv). Reducing the total transmit power estimated for MMSE
targets and per-base-station supplied power consumption.
For considering these limitations, globally optimized
transmitters are derived by reformulating the limitations. The
iterative methods are proposed for a combined
transmitter/receiver optimization and implementation [8][14].
The combined transceiver optimization employed for the
optimal transmitter, and the receivers are then updated as
linear MMSE filters.
Limitation: Its error rate exponentially increases for
overloading conditions.
3. The Minimized Average Supplied Power Consumption
Downlink Method [9]:
The average supplied power at the base station is
minimized within a single cell multi-user orthogonal
frequency division multiple access (OFDMA) downlink
system resource allocation on the flat-fading channel of
bandwidth [9]. It was estimated that the system wide
transmitted powers allocation is optimal. Whenever only
anyone of the link degrades then the bandwidth resource
allocation method which minimizes the total supplied power
consumption needs the signal transmission power on all the
links which is to be increased. This is determined that for the
mobile stations with equal number of channels with different
data rate requirements, it uses minimum optimized power to
assign equal transmit powers within equal transmit durations
[9]. The power model is taken to correspond transmit power to
the supplied power which usually relate the effectiveness and
correctness of power control for system working operation.
The overall estimation and measurement of supplied power
control in terms of load dependence factor is more than 50%
in the base stations [11][12][13].
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International Journal of Engineering Trends and Technology (IJETT) – Volume 26 Number 5 - August 2015
Limitations: (i). The effective calculation and estimation of
power control totally depends on the architecture of
underlying hardware and equipment used in the system.
(ii). The load dependence factor is calculated for measurement
of the cellular base station instead of absolute power
consumption values.
4. Power Management in Rural Network [10]:
The rural power researchers have analyzed and discussed
the issue of powering rural networks. This rural network is
generally focused to operate within autonomous renewable
energy resources [10]. In this method and management a
trace-based analysis of a developed power-saving method and
technology are deployed and implemented in a rural area for
utilizing the available renewable power resource. The same
mechanisms are deployed for saving energy in the urban
networks also as these networks are assumed as overlapping
wireless cellular stations in the urban region [10].
Limitation: The major limitation in this powering rural
network is the unreliable grid power.
III. PROPOSED PROTOCOL
In this section we describe an efficient radio resource
management and power management method for base station
power optimization which are simulated and optimized using
soft computing methods like genetic algorithm and particle
swarm optimization method that overcomes the limitations of
the previous methods discussed in the previous section.
The specified solution of OFDMA supplied power
minimization problem is based on a simplification that the
channel gains on all system resources are equal to the central
resource unit instead of time and frequency-selective fading.
In this system, the central power resource unit is selected
because it presents the highest correlation with all the other
resources. The applied resources of mean or median value
over a MIMO channels set are calculated to allocate the
channel within lower capacity. The established link capacity is
determined using equal-power pre-coding and calculating
uncorrelated antennas. This equal-power pre-coding provides
the direct connection target rate and total transmit power.
Time Division Multiple Access (TDMA) and OFDMA are
equivalent on the block fading channels within real-valued
resource and power sharing. The resource allocation method
by TDMA is selected without any loss of generality. To
establish the convex optimization problem these calculations
determines which is efficiently solved.
The system for user n and its spectral efficiency 𝜇𝑛 is
calculated by equation (1).
MT = Number of the active transmit antennas,
𝑁𝑜 = The noise spectral density,
W = Available system bandwidth.
The system resource share μn ∈ (0, 1], this is the normalized
representation of the time sharing.
The transmission power is basically the function of a target
rate, which normally depends on the total number of transmit
and receive antennas in the base station.
The equation 1 can be reduced as equation (2) and (3):
For 1×2 SIMO:
𝑃𝑛 (𝑅𝑛 ) =
𝑅
1
( 𝑛 )
(2 𝑊𝜇𝑛 − 1)
𝜖1
(2)
For 2×2 MIMO:
𝑅𝑛
− (∈1 +∈2 ) + √(∈1 +∈2 )2 + 4 ∈1 ∈2 (2𝑊𝜇 − 1)
𝑃𝑛 (𝑅𝑛 ) =
(3)
∈1 ∈2
where ∈𝑖 is the i-th member of 𝜀𝑛 .
At the base station side, the total number of active RF series
used for signal reception at the user mobile terminal can be
adapted for the power saving. The advantage power-saving at
the received antenna adaption is comparably much smaller
than in the transmitted antenna adaption, and its power
amplifier is represent in each RF series at the base station.
The Power and Resiurce Allocation Method:
The real-valued resource sharing μn is mapped to the
OFDMA resource count per user mn ∈ C as equation (4).
𝑚𝑛 = ⌈𝜇𝑛 𝑁 𝑇⌉
∀ 𝑛 = 1, … … . , 𝑁
(4)
where N = the number of users.
C = the number of subcarriers.
With the help of a ceiling operation in the equation (4), the
estimated rounding effects are compensated with adjusting the
number of discontinuous signal transmission time slots which
can be represented by equation (5) as the idle time slot:
𝑇 𝑁𝜇𝑛+1 − 𝑁
𝑁
𝑇𝑖𝑑𝑙𝑒 = ⌊
⌋ = ⌊𝑇𝜇𝑛+1 − ⌋
𝐶
𝐶
(5)
The remaining time slots which are available for signal
transmission as an active time slot an be represented by the
equation (6):
𝑇𝑎𝑐𝑡𝑖𝑣 𝑒 = 𝑇 − 𝑇𝑖𝑑𝑙𝑒
(6)
|𝜀𝑛 |
𝑅𝑛
𝑃𝑛 𝜀𝑛 (𝑒)
𝜇𝑛 =
= ∑ 𝑙𝑜𝑔2 (1 +
)
𝑊𝜇𝑛
𝑀𝑇 𝑁𝑜 𝑊
𝑒=1
(1)
The remaining resource which is unassigned can be
represented by the equation (7):
where, 𝑅𝑛 = target rate,
Pn = Transmission power for n users,
𝜀𝑛 = Vector of channel eigenvalues per user,
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𝑁
𝑚𝑟𝑒𝑚 = 𝑁𝑇 − ∑ 𝑚𝑛 − 𝑁𝑇𝑖𝑑𝑙𝑒
(7)
𝑛=1
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International Journal of Engineering Trends and Technology (IJETT) – Volume 26 Number 5 - August 2015
The number of allocated assigned resource units per user mn at
particular base station can be equally sub-divided into number
of resources per user with time slot mn,t as in equation (8):
𝑚𝑛,𝑡 = ⌊
𝑚𝑛
𝐶⌋
∑𝑁
𝑙=1 𝑚𝑙
Fitness Function Formulation:
The fitness function is formulated for optimization of the total
power (Pt) consumed by the base stations of network is
presented in equation 10.
(8)
𝑥𝑖 𝑦𝑖
𝑃𝑡 = 𝑚𝑖𝑛 [∑(𝑚𝑖 )−1 (
)𝐿 ]
𝐺𝑇𝑖 𝑖
The remaining resources which is unassigned is represented in
equation (9):
𝑁
𝑚𝑡,𝑟𝑒𝑚 = 𝐶 − ∑ 𝑚𝑛,𝑡
(9)
𝑛=1
Basically the round-robin method is used for resource
allocation to different user with time slot mk,t.
Resources
R1
R2
R3
……….
Rn
Scheduler
Output
Figure 2 presents that the procedure of round robin resource
scheduler works within rounds by serving the resources in
each priority queue in sequence according to the precedence
of resources till all resources in queues are served and then it
restarts works over to the next resource in each queue.
The soft computing optimization techniques like particle
swarm optimization and genetic algorithm can be embedded
in resource allocation method. PSO is a swarm intelligence
algorithm, which is inspired by an emergent behavior and the
social dynamics that arises generally in socially organized
colonies. PSO method exploits the population of individuals
to analyze promising regions of search path. In this paper, the
population is called the swarm and the individuals are called
the particles or agents. Genetic algorithm is basically unique
comparing to other optimization and search methods in a
number of ways. The conventional optimization and search
method work within the solution itself, Genetic algorithm
works with a coding of present solution set. Genetic algorithm
also has the ability to search from the set of solutions.
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mi = Base station power efficiency
GTi = Antenna gain
Li = Cable loss
IV. SIMULATION AND PERFORMANCE EVALUATION
For performance evaluation of the efficient radio resource and
power management technique at base station for power
optimization, these simulations are conducted using
MATLAB. For MATLAB simulations, the important
parameters are configured as follows: the mobiles users are
uniformly distributed on a circular cell around the base station
with radius of 400 m and the minimum distance of 40 m to the
base station for eliminating the peak signal to noise ratio.
With shadowing standard deviation of 10 dB, fading is
calculated, and the frequency-selective channel with 3 to 4
m/s mobile user velocity. Transmitting and receiving antennas
are assumed to be mutually uncorrelated to each other. The
system configuration parameters for MATLAB simulation are
listed in the Table (1).
Table 1: System Parameters for Simulation
Time
Figure 2: Round Robin Resource Scheduler
(10)
𝑖∈𝑆
Variables
Name of Variables
Values
N
C
T
PS
Number of Users
Number of Subcarriers
Number of Time Slots
Power Consumption in
Discontinuous Transmission
Number of Transmit
Antennas
Number of Receive
Antennas
Noise Power Spectral
Density
Circuit Power Consumption
Slope Power Increment
Maximum Transmission
Power
Frame Duration
Time Slot Duration
System Bandwidth
Subcarrier Bandwidth
25
55
10
150 W
MT
MR
No
PO
ΔPM
Pmax
Tframe
T
W
w
3
3
4 × 1021
W/Hz
200 W
4.6
46 dBm
10 ms
1 ms
10 MHz
180 KHz
The following signal transmission techniques are calculated
for this simulation:
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International Journal of Engineering Trends and Technology (IJETT) – Volume 26 Number 5 - August 2015
 The maximum supplied power utilization at base station is
calculated by a constant transmission at the maximum
power consumption by the equation (10):
𝑃𝑠𝑢𝑝𝑝𝑙𝑦,𝑚𝑎𝑥 = 𝑃𝑂,𝑀𝑇 + ∆𝑃𝑀 𝑃𝑚𝑎𝑥
(10)
• The channel bandwidth utilization can be conducted by the
minimum subcarriers for approaching the rate target. The
utilized idle modes are not used and all scheduled
subcarriers transmit at the transmission power of Pmax/N
spectral density.
This MATLAB simulation represents the results with using
system parameters taken in Table 1 by taking genetic methods
as well as particle swarm optimization (PSO) methods for
optimization, which are shown in figure 3 to figure 8.
Figure 5: Simulation of the fitness value by using genetic algorithm
method
Figure 3: Simulation of radio resource allocation with respect to the
number of users by using genetic algorithm method.
Figure 6: Simulation of resource allocation with respect to the
number of users by using PSO method
Figure 4: Simulation of power allocation with respect to the number
of users by using genetic algorithm method
ISSN: 2231-5381
These figures from 3 to 8 plot the graph for radio resource
allocation and system power allocation with respect to the
number of users who share the base station of a cellular
system. In this optimization method, two optimization
techniques the genetic algorithm optimization technique and
the particle swarm optimization (PSO) technique are used to
simulate the results. The graphs and results of MATLAB
simulation are summarized by these following points:
 The simulation of radio resource allocation with respect to
the number of users by using genetic algorithm method is
represented in figure (3).
 The graph presented in figure (4) simulates the of power
allocation of system with respect to the number of users by
using genetic algorithm method.
 The simulation of the fitness value by using genetic
algorithm method is represented in figure (5) here the
mean fitness values are represented as blue dots whereas
the best fitness values are represented as black dots.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 26 Number 5 - August 2015
Figure 7: Simulation of power allocation with respect to the number
of users by using PSO method
power consumption at base station for its downlink of
multiuser MIMO-OFDM are defined and presented. Our
simulated efficient radio resource and power management
methods for power optimization at the base station is proposed
to provide a solution of the resource scheduling problem. The
results of MATLAB simulation represents that the proposed
method is applicable of reducing the supplied power
consumption over the users utilized data rates by significantly
large scale. This power saving methods can be implemented at
low target data rates for users which are almost attributed to
discontinue signal transmission and discontinuous adaptive
antennas. This soft computing based efficient radio resource
and power management method for base station power
optimization can be implemented to several types of modern
base stations, and this simulation results are normally the used
power model dependent.
The future generation of base stations will have different
enhanced properties with its several power consumption
components. The future direction of this method will assist
into efficient embedded hardware designs for power
optimization. Another directions can be designed towards the
improvement of hardware switching times of resources and
hardware limitations on antennas which are deployed into the
limited operating region.
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