Efficient Resource Allocation for Emerging
Wireless Networks
B.Sai pradeep
Abstract: In this paper, the performance of energy efficient
power allocation for secure orthogonal frequency division
multiple access (OFDMA) based cognitive radio networks
(CRN‟s) has been analyzed. To maximize the energy efficiency
of the considered CR system with practical constraints, such as
the power budget of the primary users and the interference
thresholds of the primary users with the proportional fairness of
the CR system and also extended to check BER, Channel
utilization of orthogonal frequency division multiple access
(OFDMA) and widely used water filling algorithm to check
channel capacity and its probability distribution function in case
of noise. The non convex optimization problem is transformed
into an equivalent convex optimization problem using by
parametric programming method. The simulation results show
that the proposed optimization algorithm can achieve higher
energy efficiency, less bit error rate with fast barrier method.
Keywords: OFDMA, BER, CRN,
I. INTRODUCTION
Due to the scarcity of radio spectrum and the inefficiency of
the regularized spectrum usage manner, some insightful
spectrum utilization schemes have been introduced to
improve spectrum usage efficiency [1]. As a highly promising
technique, Cognitive Radio (CR) attracts more and more
attentions in recent years [2], which allows Secondary Users
(SUs) to sense radio spectrum environment and dynamically
adjust transmission parameters to access the licensed
spectrum, as long as the interference to Primary Users (PUs)
can be kept under their tolerances, such as interference
temperature [3]. Orthogonal Frequency Division Multiple
Access (OFDMA) is regarded as one of the prime multiple
schemes for wireless networks. Spectral efficiency plays an
important role in achieving high data rate in wireless
communication. Efficient usage of energy is important for
wire communication as well as wireless communications.
Radio resource management schemes are mainly
concentrating on maximizing the spectral efficiency of
OFDMA systems rather than maximizing total throughput of
the system. In wireless Communication, it is important to
minimize the total transmit power consumption.
Since PU channels have to be utilized by SUs in a CR network
without causing any degradation in service to the PUs, OFDM
has been identified as a potential transmission technology for
future CR systems [7]. This is mainly due to its flexibility in
dynamically changing spectral environments and in allocating
unused spectrum among SUs, which allows for simple
adaptation of subcarriers to fast changing conditions in radio
N.Ramanjaneyulu
spectrum. Moreover, OFDM allows for multiuser diversity
while overcoming frequency-selective fading which helps to
enhance the overall spectrum utilization.
A major challenge is to design efficient resource allocation
algorithms (spectrum sharing and power allocation) that work
well in OFDM based CR networks. Subcarrier power
allocation (or power loading) is a common technique to
improve the system performance of OFDM systems by
optimally allocating transmit power to the different
subcarriers. If the channel state information is available at the
transmitter, power loading can be used to optimize the error
rate, the transmission capacity, or the transmit power.
The classical water filling approach [8], which states that the
transmitter should avoid using subcarriers with poor channel
conditions, has been considered as the optimal power
allocation scheme for OFDM systems. A comprehensive
survey on bit and power allo- cation algorithms for single user
OFDM systems was presented in [9]. These algorithms, more
generally known as bit and power loading schemes, are
practical implementations of the classical water filling
scheme. However, the traditional water filling approach is
inefficient for OFDM-based cognitive radio networks due to
the strict requirements on the interference generated to the
primary users (PUs).
II. RELATEDWORK
In [4], optimal and suboptimal power allocation schemes are
proposed to maximize the sum capacity of the CR system
under the interference constraints of the PUs. In [5], the
authors studied the optimal power allocation to achieve
ergodic capacity and outage capacity in fading channel. In [6],
a greedy max-min algorithm is proposed to maximize the
throughput of the CR system with a given power budget.
In this chapter, we specifically deal with the problem of power
allocation for single user OFDMCR system. Recently, authors
in [10–12] observed that classical power allocation
algorithms for OFDM systems such as uniform power loading
or water filling are not optimal for CR networks because of
the special properties of CR networks. Here, we consider the
sub carrier power allocation problem for OFDM-based CR
systems taking into account subcarrier availability, or in other
terms, PU activity in the licensed bands. Including sub carrier
availability in our capacity function and then optimizing this.
Expression saves valuable resources such as battery life by
selectively allocating lesser power to those bands which have
higher PU activity. Hence, this is a more energy-efficient
technique for power allocation in OFDM-CR networks. We
approach this problem by first defining an average rate loss
function and then introducing a risk-return model to
incorporate subcarrier availability. This risk-return approach
differs from traditional approaches in such a way that we
could model the randomness in link capacity as a product of
probability of sensing error/PU activity and average rate loss
which is a function of allocated power in the corresponding
subcarriers. Moreover, we also include the effect of the
interference generated by both PUs and SU on each other.
There are two possible co-existence scenarios for the PUs.
The first one proposed in [10] is an underlay scenario which
considers the limit on the amount of interference created to a
PU, which is occupying a particular set of subcarriers also
used by the SU but is geographically located at a certain
distance from the SU. This allows the SU to transmit within
those set of subcarriers by keeping the transmit power level
low enough to avoid unacceptable interference to the PUs that
cannot be detected due to the large distance from SU. This
requirement puts additional power constraint for
OFDM-based CR system on each group of such subcarriers.
The second-coexistence scenario proposed in [11, 12] is for a
downlink OFDM-CR network scenario in which PU bands
are present in the close vicinity or adjacent to SU subcarriers,
i.e., PU and SU are co-located in the same area with
side-by-side bands. Since there is mutual interference
between CR and PUs when both type of users co-exist in the
side by side band, use of classical power loading schemes
such as uniform loading and water filling may result in higher
mutual interference in the PU bands. Optimal power
allocation strategy in this CR system is to maximize the total
transmission capacity of the SU while keeping the amount of
interference generated to the PU bands within tolerable range.
Both these scenarios restrict the SU to keep it’s transmit
power level low without causing any harmful interference to
PUs.
could be another primary user PU2 which is co-located in the
same area as the SU but is using frequency bands which are
adjacent or in between the available sub bands.
Fig 2.1: Cognitive radio system model.
Figure 2.2 shows the spectrum frequency band for PU‟s and
SU‟s. The CR frequency spectrum band is divided into N sub
carriers which are applied to OFDMA system having a
bandwidth of B.The side by side distribution of spectrum
frequency band for PU bands and the CR bands will be
assumed by showing in figure 2.2. The frequency bands B1,
B2, ・・, BN has been occupied by the PU‟s are called as PU
bands while the other bands are called as SU bands (CR
bands). It is assumed that the CR system can use the inactive
PU bands provided that the total interference introduced to
the Mth PU band [10] does not exceed remaining all the
bands. We also achieve a tolerable interference power and
interference temperature limit.
III. PROPOSED WORK
We consider a typical cognitive radio wireless system as
shown in Fig. 2.1 [10]. There is an SU transmitting data in
underlay fashion in an opportunistically available sub-band
licensed to a primary user PU1.
A sub-band is said to be available if the interference caused to
the PU1 receivers due to transmission using this subcarrier is
within the acceptable range. PU1 is located at a certain
distance geographically removed from SU, thus allowing the
SU to transmit while keeping the interference level low
enough. PU1 shields itself from SU interference by defining a
protection area of radius R and adding a requirement on the
SU to keep its interference power level at the margin of this
area within a certain level P (T). As shown in Fig. 2.1, there
Fig 2.2: spectrum frequency band for PU‟s and SU‟s.
i. Fast barrier method: Barrier method is treated as a
standard technique to solve convex optimization problems.
both possible ways will increase power consumption of the
transceiver.
IV. RESULTS AND DISCUSSIONS
Consider a multiuser OFDM-based CR system, where all
users randomly locate in an area of 3 × 3km, and each SU’s
receiver is distributed in a circle within 0.5km from its
transmitter. The performance of proposed algorithm can be
shown in the simulation results by using MATLAB 7.9.The
simulation parameters are Total number of sub channels
N=32,64, Total number of Primary Users =2, Total number of
SU users =4, Itol=1x10- 6,Pmax=1x10-4, BER=1x10 - 1.5,
Bandwidth B= 10 MHz
Generally, barrier method is a standard technique to solve
convex optimization problems. For the barrier method,
original problem is converted into a sequence of
unconstrained minimization problems by defining a
logarithmic barrier function with parameter t which decides
the accuracy of the approximation to the original problem. As
t increases, the approximation will be more and more close to
the optimal solution. In OFDM system, the number of sub
channels is always several thousand and such a complexity is
too high to apply. It is efficient algorithm to compute the
Newton step by exploiting the special structure of the
problem.
ii. MIMO system capacity: The MIMO system capacity is
defined as total bits per second per total number of active
channels [4]. Generally the capacity of the system is nothing
but the maximum throughput of the system while maintaining
the lowest probability of the error. The capacity of the wire
line Single Input Single Output channel is given by [6]
Where P is the transmission power
Therefore from the above equation it is clear that
for the wire line SISO channel, the capacity will be increased
only if the transmission power ‘P’ is increased. But this is not
the case for wireless MIMO channels. The capacity of the
wireless MIMO channel is
Where
P is the total transmission power radiating from the all active
transmitting antennas at transmitter side. Therefore it is clear
that higher transmit power ‘P’ or large number of active
antennas at both the sides i.e. at transmitter side and receiver
side can increases the channel capacity ‘C’. However, these
Fig.3.1: The EE of CR system as a function of the
transmission power limit=4, L=2.
The above figure .illustrates the EE of the CR system versus
the transmission power limit for different numbers of sub
channels. The numbers of SUs and PUs are set to 4 and 2,
respectively. Initially Energy efficiency of the Cognitive
Radio system varies linearly at the beginning stages with
respect to transmission power limit due to outage in CR
system can be reduced as the increase of the transmission
power budget. When the transmission power budget is
reaches threshold, all SUs’ rate requirements can be satisfied
and the Energy efficiency of the CR system maintains nearly
constant.
Energy efficiency of the CR system versus the interference
threshold for different numbers of PUs (L=1, L=2 and L=4) in
Fig. 3.2. The number of sub channels is N=64. Energy
efficiency increases linearly to certain interference threshold
than it becomes constant with the increasing of the
interference threshold due to the lower the interference
threshold is, the more frequently the CR system suffers
outage. Additionally, we can observe that more Pus can lower
energy-efficiency. The reason is that for either lower
interference threshold or more PUs scenarios, more sub
channels will be interference limited and fail to maintain the
rate requirements.
Fig 3.4: Increased data rate of MIMO system
By using this schema i.e. Antenna Management we show that
by efficiently managing the power we can increases the
capacity of MIMO system.
Fig 3.2: The EE of CR system as a function of interference threshold
of PUs. N=64, K=4, Pt=1W.
Fig 3.5: Performance comparison of MIMO OFDM system
Figure 3.5 represents the comparison among 4 different
combinations of single MIMO systems i.e. the channel
utilization of MIMO system is compared when antenna
selection increases linearly by 1 (like 1, 2, 3 and 4), they are
having same characteristics.
Fig 3.3 Bit error rate for Gaussian broadcast channel
V. CONCLUSION
The Fig 3.3 represents the bit error rate for Gaussian
broadcast channel. For OFDM modulation we are taking
BPSK modulation technique. The OFDM uses Fourier
transform and Inverse Fourier transform for modulation and
demodulation respectively. Taking count of errors in
modulation and demodulation in OFDM the bit error rate
decreases in terms of white Gaussian noise.
Fig 3 4 shows the results for MIMO system for different
combinations of antennas under proposed scheme
considerations. From the results we clearly analyzes that the
capacity of the MIMO-OFDM system increases gradually by
increasing the number of active antennas linearly, without
increasing the input power levels which is efficiently achieved
by using proposed method only.
In this paper we studied the “Efficient Resource Management
for Emerging wireless networks” which can be need for green
communication design. Our network model is low bit error
rate, maximum channel utilization, efficient utilization of
channel capacity and optimal power allocation by showing
the simulation results using different optimization techniques.
Our optimization techniques are covers many practical
constrains by performing the series of equivalent
transformations, converting it into a convex optimization
problem which can be solved by efficient algorithms to obtain
optimal solution. our proposed algorithm is quickly, reducing
computation complexity and stably.
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Mr. B. Sai Pradeep received the B.Tech
degree in Electronics and Communications
engineering
from
Jawaharlal
Nehru
Technological Univesity, Anantapur, A.P,
India. Currently he is scholar of M.Tech
degree in Digital Systems and Computer
Electronics from JNTU, Anantapur, A.P,
India. His areas of interest include
communications and Embedded systems
PH-+919491944851.
E-mail: berilasaipradeep@gmail.com
Mr. N.Ramanjaneyulu received the B.Tech
degree in Electronics and Communications
engineering from JNTU, Hyderabad, A.P,
India in 2004. He awarded M.Tech degree
from the Dept of Electronics and
Communications engineering from JNTU,
Ananthapur, A.P, India in 2010. currently he
is working as an Associate professor in