2. Page110
A Survey on: Energy apportionment approaches in
energy harvesting Wireless Cooperative Networks
1
Vennila. R,
PG scholar/Department of CSE,
Tagore Institute of Engineering and Technology,
Salem, India.
Rvennila1992@gmail.com
2
Narendran. M,
Assistant Professor/ Department of CSE,
Tagore Institute of Engineering and Technology,
Salem, India.
naren0812@gmail.com
Abstract— Wireless sensor networks have gained popular due
to their wide range of attentions. Battery-powered sensor
nodes can rarely meet the design goals of life span, total price,
special power perceiving, transmission coverage and
consistency in wireless sensor networks. Energy harvesting has
seemed as modified to battery-powered nodes. This is
converting the ambient energy into electronic energy. In this
project, a wireless cooperative network is deliberated, where
multiple source-destination pairs communicate with each other
through an energy harvesting relay. Following the water filling
based method and auction based power allocation scheme, next
we consider distributed power allocation strategies with
asymmetric Nash Bargaining algorithm to achieve faster
decay rate, 1/SNR, in which relay allocates power between
users based on their CSI values and users requirements. The
allocation of power is done asymmetric Nash Bargaining
algorithm, it allocates powers to all users based on their
requirements. It uses Frequency Division Multiple Access
technique to divide the bandwidths to all users. It is shown by
simulations that the derived new power allocation strategies
can achieve substantial tradeoff between system performance
and complexity over the conventional methods based on their
CSI values and users requirements.
Index Terms— Power-apportionment strategies,
communication protocols, energy harvesting, Relay selection,
wireless cooperative networks.
I. INTRODUCTION
LOW cost mobile nodes have been known as critical
components of numerous wireless networks. Wireless
sensor networks which have been established for a variety
of appliances, including investigation, environmental
monitoring and health care. Such low price nodes are
typically equipped with stationary energy supplies, such as
batteries with limited process lifespan. Replacing batteries
for such nodes is either difficult or exclusive, mainly in the
situation in which sensors are organized in hostile
environments. Therefore energy harvesting, a system to
gather energy from the nearby atmosphere, has newly
acknowledged considerable attention as a supportable
resolution to overwhelming the blockage of energy
constrained wireless networks [13].
Conventional energy harvesting techniques rely on
outside energy sources that are not part of communication
networks, this based on solar power, wind energy, etc. [13],
[12].Newly a new approach to energy harvesting has been
projected that involves gathering energy from ambient radio
frequency signals [15], [4], so that wireless signals can be
used as a means for the transfer of information and power
concurrently. In addition, such a method can also decrease
the price of communication networks, since peripheral
apparatus to take benefit of external energy sources can be
avoided. This task has motivated a few new works different
from the ideal assumption that a receiver can detect signals
and harvest energy concurrently. In [19], the authors
introduced an overall receiver architecture, in which the
circuits for energy harvesting and signal discovery are
operated in a time distribution or power splitting method.
In this paper, a overall wireless cooperative network is
considered, in which multiple pairs of sources and
destinations communicate over an energy harvesting relay.
Specifically, multiple sources deliver their information to
the relay via orthogonal channels, such as various time slots.
Assuming that the battery of the relay is adequately huge,
the relay can gather a substantial amount of power for
relaying transmissions. The aim of this project is to state
how to efficiently distribute such power among the multiple
users and examine the impact of these power allocation
approaches on the system performance.
The contribution of this paper is three-fold. Firstly, a
more opportunistic power allocation strategy based on the
sequential water filling principle is studied. The key idea of
such a strategy is that the relay will serve a user with a
better channel condition first, and help a user with a worse
channel condition afterwards if there is any power left at the
relay. This sequential water filling scheme can achieve the
optimal performance for the user with the best channel
conditions, and also maximize the number of successful
destinations. Surprisingly, it can also be proved that such a
scheme minimizes the worst user outage probability.
Secondly, an auction based power allocation scheme is
proposed, and the properties of its equilibrium are
discussed. Recall that the sequential water filling scheme
can achieve superior performance in terms of reception
reliability; however, such a scheme requires that channel
state information (CSI) be available at the transmitter,
which can consume significant system overhead in a multi-
user system. As demonstrated by the simulation results, the
3. Page111
auction based distributed scheme can achieve performance
close to the water filling strategy.
Finally, the proposed system has the relay for data
transmission medium and is trusted to source and
destination nodes. Distributed power allocation strategies
with asymmetric Nash Bargaining algorithm to achieve
faster decay rate 1/SNR, in which relay allocates power
between users based on their CSI values and users
requirements. The proposed method will allocate powers to
all users based on their requirements. Thus to every users
have their allocated bandwidths that can reduce traffic in
transmission. It uses Frequency Division Multiple Access
technique to divide the bandwidths to all users.
II. RELATED WORK
A. Parallel Power Allocation
The main effort that can be cited is the one obtainable by
Min Chen, et al. [5]. This effort derives the power allocation
schemes for parallel relay networks that do not need a
centralized mechanism, and use the limited accessible CSI
at each one node. In exercise, it is possible that the channels
are assessed by training beforehand the Actual data
transmission, when each node functions in TDMA mode.
The simulation outcomes display that by using distributed
power allocation and partial CSI, we can grow power
effectual transmission schemes, decreasing the amount of
control traffic overhead for relay aided communications.
B. Auction Based Power Allocation
In [5], the writers proposes two auction mechanisms for
Supportive communications, the SNR auction and the power
auction, that regulate relay selection and relay power
allocation in a disseminated manner. The SNR auction and
the power auction, to disseminate manage the relay power
allocation amongst users. Under a static price declared by a
single relay, we need exposed that although each user has a
non-smooth, non-concave utility function, its best response
function can nevertheless explicitly be calculated locally
based on a simple threshold procedure. The existence and
uniqueness of the Nash equilibrium in both auctions has
been verified using nonnegative matrix philosophy. Also,
under correctly chosen prices, the power auction has been
exposed to attain the effective allocation, and the SNR
auction has been realized to be flexible in attaining various
tradeoffs between fairness and efficiency depending on the
priority weights. The simulation outcomes prove that
efficiency and robustness of the projected systems.
C. Optimal Power Allocation
Xin Kang,et al [16]. Presents the new scheme based on
the extra channel state information of the main user fading
channel is projected to safeguard the main transmission for
a spectrum allotment based fading cognitive radio network.
This new scheme exploits the detail that in various
circumstances, the main transmission might have a non-zero
outage possibility boundary and is thus able to
accommodate extra interfering from the secondary user.
Subject to the afresh planned main user outage possibility
constraint, along with the secondary user average/peak
transmit power constraint, we state the optimal power
allocation strategies for the secondary link to attain the
ergodic/outage capacity.
D. Efficient Cooperative Protocol
In [2], Behrouz Maham,et al. Three effective
cooperative multihop transmissions are projected, and their
equivalent distributed power allocation schemes, which
relates only on the statistics of the channels, are too derived.
The proposed cooperative protocols proposal different
degrees of energy effectiveness, spectral effectiveness,
complexity and signaling overhead. Simulations illustrate
that, using the proposed cooperative protocols, considerable
energy savings are attainable, compared with non-
cooperative multihop routing.
E. Robust Beamforming For Wireless Information And
Power Transmission
Zhengzheng Xiang, et al. In [18], refer to a robust
beamforming problematic for the multi-antenna wireless
communications system with concurrent information and
power broadcast, under the statement of imperfect channel
state information (CSI) at the transmitter. By means of semi
definite moderation, we convert the original robust design
problem acted into a SDP problem. Then we demonstrate
that such relaxation is tight and we can at all times attain the
optimum resolution of the original problem. The
performance of the proposed beamforming algorithm has
been established by simulations.
F. Green Resource Allocation
In [3], Feng-Seng Chu, Kwang-Cheng Chen, and
Gerhard Fettweis, present a mechanism to decrease UE
energy consumption, we note that in OFDMA cellular
schemes, communications to one UE can be arranged into
fewer time slots due to multiple accessible sub-carriers. It
suggests that the circuit energy of UEs can be reduced if
turning off the circuits for the period of the slots without
receptions is permitted. A nonlinear integer resource
optimization is thus articulated for base stations to
minimalize the receiving energy of UEs, with a projected
system to reach optimal solution powerfully. Numerical
outcomes determine that our computationally effectual
algorithm can easily decrease more than 60% UE energy
consumption, and thus is prepared for practical systems
such as 3GPP LTE/LTE Advanced.
G. Greedy Power Allocation
In [10], the author presents a proportionate rate-adaptive
resource allocation algorithm for MU-OFDM is obtainable.
Subcarrier and power allocation are carried out in sequence
4. Page112
to decrease the complexity. The low complexity
proportionate subcarriers allocation is shadowed by Greedy
Power Allocation (GPA) to resolve the rate-adaptive re-
source allocation problem with proportionate rate constraints
for MU-OFDM systems. It has been exposed over
simulations that the PRA algorithm attains less complexity
for greater numbers of users.
H. Network Resource Allocation
In [1], this paper studies network resource allocation
between users that organize multiple connections, probably
over several routes, where each connection is subject to
bottleneck control. We express a user-centric Network
Utility Enlargement problem that takes into account the
collective rate a user gets from all connections, and offer
decentralized means to attain this fairness goal. In a first
proposal, cooperative users regulate their number of lively
connections based on congestion cost from the transport
layer, to compete with a appropriate primal-dual dynamics
in the collective rate; we show this control attains
asymptotic convergence to the optimal user centric
allocation. For the instance of non-cooperative users, we
demonstrate that network stability and user-centric fairness
can be prescribed by a utility-based admission control
executed at the network edge. Simulations based on these
implementations demonstrate that the schemes achieve their
objectives.
I. Alternative Optimization Techniques
Tong Wang, et al [14].Presents an algorithm approach to
together design linear receivers and the power allocation
parameters through an alternating optimization method
subject to dissimilar power constraints which contain global,
local and individual ones. Two design standards are
considered: the first one diminishes the mean-square error
and the second one exploits the sum-rate of the wireless
sensor network. We instigate constrained minimum mean-
square error and constrained maximum sum-rate terms for
the linear receivers and the power allocation parameters that
contain the optimal complex amplification coefficients for
each relay node. A study of the computational complexity
and the convergence of the systems are also obtainable.
Computer simulations show good performance of our
proposed approaches in terms of bit error rate and sum rate.
J. Power Allocation In Multi-User Two-Relay AF
Networks
In [6], Linbi Deng, et al. presents the Convex
optimization is a effective mathematical tools which is
appropriate in many dissimilar application fields. In this
project, a convex optimization approach is consequent for
maximizing the weighted-sum channel capacity in a multi-
user two relay Amplify-and-Forward communication
networks over allocating different power in each one relay
node. At present, incline algorithm with fixed step size is
applied, which is a kind of succession method, in this
project; we allocate relay power based on exploiting total
channel capacity of the network. For maximizing the
channel capacity, they just essential to broadcast their
signals to all relay nodes with full power. And first
simulation results show the efficiency of this power
allocation system.
III. ANALYSIS
A. Water Filling Power Allocation
A more opportunistic power allocation strategy based on
the consecutive water filling principle is considered. The
key knowledge of such a strategy is that the relay will help a
user with a better channel condition first, and help a user
with a worse channel condition later if there is any power
left at the relay. This consecutive water filling scheme can
attain the optimal performance for the user with the best
channel conditions, and also maximize the number of
successful destinations. Surprisingly, it can also be showed
that such a scheme minimizes the worst user outage
probability.
B. Auction Based Power Allocation
An auction based power allocation scheme is projected,
and the properties of its equilibrium are deliberated. Recall
that the consecutive water filling scheme can attain greater
performance in terms of reception reliability; however, such
a scheme needs that channel state information (CSI) be
accessible at the transmitter, which can consume significant
system overhead in a multi-user system. As established by
the simulation results, the auction based distributed scheme
can attain performance near to the water filling strategy.
C. Distributed Power Allocation
Our goal in this paper is to discovery power allocation
schemes that do not require a centralized mechanism, and
use the CSI at each node. In practice, it is possible that the
channels are estimated by training beforehand the actual
data transmission, when each node functions in TDMA
mode.
Asymmetric Nash Bargaining Algorithm:
The proposed scheme has the relay for data
transmission medium and is reliable to source and
destination nodes. Distributed power allocation strategies
with Asymmetric Nash Bargaining algorithm to attain faster
decay rate 1/SNR, in which relay assigns power between
users based on their CSI values and users requirements. The
proposed method will allocate powers to all users based on
their requirements. Thus to every users have their allocated
bandwidths that can reduce traffic in transmission. It uses
Frequency Division Multiple Access technique to divide the
bandwidths to all users.
Energy
Sources
Energy
Harvesting
Interface
Circuit
Energy
Storage
Relay distributes the harvested
energy among the multiple user
based on CSI value or user
requirements.
Uses Frequency Division Allocation of power is done
Energy Harvesting Relay
5. Page113
Fig. 1. Architectural Diagram
IV. EXPERIMENTAL OUTCOME
Fig. 2. The impact of relay selection on outage probability for the user with
the worst channel conditions. R = 2 BPCU. M = 20
Fig. 3. Average outage performance achieved by the studied transmission
protocols. R = 1/2 BPCU and η = 1
In this segment, computer simulations will be
carried out to assess the performance of those energy
6. Page114
harvesting relaying protocols described in the preceding
sections. As can be observed in Fig. 2.In detail a
straightforward criterion for relay assortment can be labeled
in the following. Each relay informs the worst-user outage
probability it realizes and the relay that minimizes the worst
user outage performance will be designated. The
performance of the cooperative network might be further
enhanced by designing more sophisticated criteria of relay
selection. In Figure 3, we focus on the assessment among
the different power allocation strategies labeled in this
paper. Particularly the path loss factor is 2 and it is
presumed that destinations. That the distance from the
sources to the relay is 2m, the same as the sources to the
relay to the destinations. The water filling scheme can
achieve optimal performance for the destination with the
best channel condition. The distributed power allocation
strategies with Asymmetric Nash Bargaining algorithm to
attain faster decay rate. And simulation based on these
implementations show that the proposals achieve their
goals.
V.CONCLUSION
In this paper, we have measured several power
allocation strategies for a cooperative network in which
multiple source destination pairs interconnect with each
other via an energy harvesting relay. The water filling
scheme can attain optimal performance in terms of a few
criteria. An auction based power allocation scheme has also
been projected to achieve a better tradeoff between the
system performance and complexity. A distributed power
allocation strategies with Asymmetric Nash Bargaining
algorithm to attain faster decay rate. And simulations based
on these implementations demonstrate that the proposals
achieve their goals. Another promising method to further
improve the system performance is that the relay could use
entire signals from one source for energy harvesting, if the
channel from this source to the relay is weak. However,
such an approach cannot be useful to non-coherent detection
receivers and may also root some unfairness among the
users. In addition, a disappointment of decoding is due to
the poor source-relay channel condition, which means the
energy harvested from such a channel could also be limited.
The study of such different energy harvesting methods is a
promising future direction for further performance
enhancement.
ACKNOWLEDGMENT
We greatly thank our anonymous reviewers of their
insightful comments for improving the quality of this paper.
REFERENCES
[1] Andr´es Ferragut and Fernando Paganini, “Network resource
allocation for users with multiple connections: fairness and
stability,” IEEE/ACM Transaction on networking., vol. 22,
no. 1, pp. 349 – 362 , 03 April 2013.
[2] Behrouz Mahamy, Walid Saady, Mérouane Debbahz, Zhu
Hanx, and Are Hjørungnes “Efficient Cooperative Protocols
for General Outage-Limited Multihop Wireless Networks,”
Author manuscript, published in "Personal, Indoor and
Mobile Radio Conference (PIMRC2010), Turkey (2011). pp.
145 – 150..
[3] Feng-Seng Chu, Kwang-Cheng Chen, and Gerhard Fettweis,
“Green Resource Allocation to Minimize Receiving Energy in
OFDMA Cellular Systems, “IEEE Trans. Wireless
Communication., vol. 16, no. 3, pp. 372 – 374 , March. 2012.
[4] P. Grover and A. Sahai, “Shannon meets Tesla: wireless
information and power transfer,” in Proc. 2010 IEEE Int.
Symp. Inf. Theory. pp. 2363 – 2367, June. 2010.
[5] ] Jianwei Huang, Zhu Han, Mung Chiang, and H. Vincent
Poor “Auction-Based Resource Allocation for Cooperative
Communications,” IEEE Journal on Selected Areas in
Communications, Vol. 26, no. 7, PP.1226 - 1237 September.
2008.
[6] Linbi Deng, Xuanyi Dong, Lulu Wang “Power Allocation in
Multi-User Two-Relay AF Networks,” CERBERUS
MIDTERM REPORT 05/21/2014.
[7] L. Liu, R. Zhang, and K.-C. Chua, “Wireless information
transfer with opportunistic energy harvesting,” IEEE Trans.
Wireless Commun., vol. 12, no. 1, pp. 288–300, Jan. 2013.
[8] L. Liu, R. Zhang, and K. C. Chua, “Wireless information and
power transfer: a dynamic power splitting approach,” IEEE
Trans. Commun., to appear in 2013. Available: arXiv:
1302.0585.
[9] Min Chen, Semih Serbetli, “Distributed Power Allocation
Strategies for Parallel Relay Networks” IEEE Transaction on
wireless communication, arXiv:0801.0597v1 [cs.IT] 3 Jan
2008.
[10] Najib A. Odhah, Moawad I. Dessouky, Waleed E. Al-Hanafy,
and Fathi E. Abd El-Samie, “Low Complexity Greedy Power
Allocation Algorithm for Proportional Resource Allocation in
Multi-User OFDM System,” Journal on Selected Areas in
Telecommunications and Information Technology, pp.421 –
428, April.2012.
[11] A. A. Nasir, X. Zhou, S. Durrani, and R. A. Kennedy,
“Relaying protocols for wireless energy harvesting and
information processing,”IEEE Trans. Wireless Commun., vol.
12, no. 7, pp. 3622–3636, 2013.
[12] J. Paradiso and T. Starner, “Energy scavenging for mobile
and wireless electronics,” IEEE Pervasive Comput., vol. 4,
no. 1, pp. 18–27, Jan.– Mar. 2005.
[13] V. Raghunathan, S. Ganeriwal, and M. Srivastava, “Emerging
techniques for long lived wireless sensor networks,” IEEE
Commun. Mag., vol. 44, no. 4, pp. 108–114, Apr. 2006.
[14] Tong Wang, Rodrigo C. de Lamare, and Anke Schmeink,
“Alternating Optimization Techniques for Power Allocation
and Receiver Design in Multihop Wireless Sensor Networks,”
arXiv:1404.6700v1 [cs.It] 7 Apr 2014.
[15] L. R. Varshney, “Transporting information and energy
simultaneously,” in Proc. 2008 IEEE Int. Symp. Inf.
Theory,pp. 1612 – 1616, July. 2008.
[16] Xin Kang, Rui Zhang, Ying-Chang Liang, and Hari Krishna
Garg, “Optimal Power Allocation Strategies for Fading
Cognitive Radio Channels with Primary User Outage
Constraint,” IEEE Journal on Selected Areas in
Communications, vol 29, no. 2, pp. 374 – 383, February.
2011.
[17] R. Zhang and C. K. Ho, “MIMO broadcasting for
simultaneous wireless information and power transfer,” IEEE
7. Page115
Trans. Wireless Commun., vol. 12, no. 5, pp. 1989–2001,
May. 2013.
[18] Zhengzheng Xiang, and Meixia Tao, “Robust Beamforming
for Wireless Information and Power Transmission,” IEEE
Trans. Wireless Communication, vol. 1, no. 4, pp. 372 - 375,
August. 2012.
[19] X. Zhou, R. Zhang, and C. K. Ho, “Wireless information and
power transfer: architecture design and rate-energy tradeoff,”
IEEE Trans. Commu, Volume:61, pp.4754 – 4767, October.
2013.
[20] Zhiguo Ding,Samir M. Perlaza,Inaki Esnaola,and H. Vincent
Poor,“Power Allocation Strategies in Energy Harvesting
Wireless Cooperative Networks,”IEEE transactions on
wireless communication,Vol.13,No.2, pp. 846 - 860 Feb.
2014.
[21] Rajasekar, R., Prakasam, P, “Performance analysis of mobile
sampling and broadcast scheduling in wireless sensor
networks,” IEEE Proceedings of International Conference on
Current Trends in Engineering and Technology (ICCTET),
pp. 270-274, July. 2014.
![Page110
A Survey on: Energy apportionment approaches in
energy harvesting Wireless Cooperative Networks
1
Vennila. R,
PG scholar/Department of CSE,
Tagore Institute of Engineering and Technology,
Salem, India.
Rvennila1992@gmail.com
2
Narendran. M,
Assistant Professor/ Department of CSE,
Tagore Institute of Engineering and Technology,
Salem, India.
naren0812@gmail.com
Abstract— Wireless sensor networks have gained popular due
to their wide range of attentions. Battery-powered sensor
nodes can rarely meet the design goals of life span, total price,
special power perceiving, transmission coverage and
consistency in wireless sensor networks. Energy harvesting has
seemed as modified to battery-powered nodes. This is
converting the ambient energy into electronic energy. In this
project, a wireless cooperative network is deliberated, where
multiple source-destination pairs communicate with each other
through an energy harvesting relay. Following the water filling
based method and auction based power allocation scheme, next
we consider distributed power allocation strategies with
asymmetric Nash Bargaining algorithm to achieve faster
decay rate, 1/SNR, in which relay allocates power between
users based on their CSI values and users requirements. The
allocation of power is done asymmetric Nash Bargaining
algorithm, it allocates powers to all users based on their
requirements. It uses Frequency Division Multiple Access
technique to divide the bandwidths to all users. It is shown by
simulations that the derived new power allocation strategies
can achieve substantial tradeoff between system performance
and complexity over the conventional methods based on their
CSI values and users requirements.
Index Terms— Power-apportionment strategies,
communication protocols, energy harvesting, Relay selection,
wireless cooperative networks.
I. INTRODUCTION
LOW cost mobile nodes have been known as critical
components of numerous wireless networks. Wireless
sensor networks which have been established for a variety
of appliances, including investigation, environmental
monitoring and health care. Such low price nodes are
typically equipped with stationary energy supplies, such as
batteries with limited process lifespan. Replacing batteries
for such nodes is either difficult or exclusive, mainly in the
situation in which sensors are organized in hostile
environments. Therefore energy harvesting, a system to
gather energy from the nearby atmosphere, has newly
acknowledged considerable attention as a supportable
resolution to overwhelming the blockage of energy
constrained wireless networks [13].
Conventional energy harvesting techniques rely on
outside energy sources that are not part of communication
networks, this based on solar power, wind energy, etc. [13],
[12].Newly a new approach to energy harvesting has been
projected that involves gathering energy from ambient radio
frequency signals [15], [4], so that wireless signals can be
used as a means for the transfer of information and power
concurrently. In addition, such a method can also decrease
the price of communication networks, since peripheral
apparatus to take benefit of external energy sources can be
avoided. This task has motivated a few new works different
from the ideal assumption that a receiver can detect signals
and harvest energy concurrently. In [19], the authors
introduced an overall receiver architecture, in which the
circuits for energy harvesting and signal discovery are
operated in a time distribution or power splitting method.
In this paper, a overall wireless cooperative network is
considered, in which multiple pairs of sources and
destinations communicate over an energy harvesting relay.
Specifically, multiple sources deliver their information to
the relay via orthogonal channels, such as various time slots.
Assuming that the battery of the relay is adequately huge,
the relay can gather a substantial amount of power for
relaying transmissions. The aim of this project is to state
how to efficiently distribute such power among the multiple
users and examine the impact of these power allocation
approaches on the system performance.
The contribution of this paper is three-fold. Firstly, a
more opportunistic power allocation strategy based on the
sequential water filling principle is studied. The key idea of
such a strategy is that the relay will serve a user with a
better channel condition first, and help a user with a worse
channel condition afterwards if there is any power left at the
relay. This sequential water filling scheme can achieve the
optimal performance for the user with the best channel
conditions, and also maximize the number of successful
destinations. Surprisingly, it can also be proved that such a
scheme minimizes the worst user outage probability.
Secondly, an auction based power allocation scheme is
proposed, and the properties of its equilibrium are
discussed. Recall that the sequential water filling scheme
can achieve superior performance in terms of reception
reliability; however, such a scheme requires that channel
state information (CSI) be available at the transmitter,
which can consume significant system overhead in a multi-
user system. As demonstrated by the simulation results, the](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)