Medical, Pharma, Engineering, Science, Technology and Business

Department of E&TC, D.Y. Patil College of Engineering, Akurdi, Pune, India

^{*}Corresponding Author:- Farhana Mustafa

Department of E&TC, D.Y. Patil

College of Engineering, Akurdi, Pune, India

**Tel:**02027421095

**E-mail:**[email protected]

**Received date:** May 27, 2015; **Accepted date:** May 29, 2015; **Published date:** June 11, 2015

**Citation:** Mustafa F, Lohiya P (2015) Optimization of Resource Allocation in OFDM Communication System for Different Modulation Technique using FRBS and PSO. Sensor Netw Data Commun 4:120. doi: 10.4172/2090-4886.1000120

**Copyright:** © 2015 Mustafa F, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

**Visit for more related articles at** International Journal of Sensor Networks and Data Communications

OFDM is technique that is chosen for high data rate communication and is important for 4th generation communication system. Resources such as power, bandwidth are limited, thus intelligent allocation of these resources to users are crucial for delivering the best possible quality of services. Fuzzy Rule Based System (FRBS) and Particle Swarm Optimization (PSO) algorithm are used for optimization of code rate, modulation and power. FRBS is used for adapting code rate and modulation size while PSO is used for power allocation.

**OFDM**; PSO; FRBS; Optimization; Resource allocation

Orthogonal Frequency Division Multiplexing (OFDM), offers a considerable high spectral efficiency multipath delay spread tolerance, immunity to frequency selective fading channels and power efficiency [1,2]. As a result, OFDM has been chosen for high data rate communication and is used for 4th generation technology. In Orthogonal Frequency Division Multiplexing (OFDM) technique, a single very high data rate stream is divided into several low data rate streams using Inverse Fast Fourier Transform (IFFT). Then these streams are modulated over different orthogonal subcarriers. This is to divide one large frequency selective channel into a number of frequency non-selective sub-channels. Moreover, addition of appropriate cyclic prefix (CP) and interleaver makes the system almost inter-symbol-interference (ISI) free. It has been widely deployed in many wireless communication standard such as based mobile worldwide interoperability for microwave access (mobile WIMAX), 3GPP long term evolution (LTE) based on OFDM access technology. In OFDM every sub channel experiences a different channel condition so the use of same modulation and code rate may not be suitable for all subcarriers. Also, flat power is not beneficial since sub-channels may need different power. This situation demands adaptive resource allocations for an optimum utilization.

In the optimal power allocation and user selection solution was
derived based on Lagrange dual decomposition proposed by Wong et
al. [3] for maximizing the system energy efficiency. A low complexity
algorithm for proportional resource allocation in OFDMA system was
proposed in ref. [4], where linear method and root finding algorithm
were used to allocate power and data rates to users. A gradient based
solution was proposed by Rajendrasingh et al. [5], for downlink OFDM
wireless systems and a 96.6% utility was achieved. A Genetic Algorithm
based adaptive resource allocation scheme was proposed by Reddy [6]
to increase the user data rate where water-filling principle was used as
a fitness function. The water filling theorem is based on a continuous
relationship between the allocated power and the achievable capacity.
OFDM Systems Resource Allocation using Multi-Objective Particle
Swarm **Optimization**. Another paper with adaptive resource allocation
based on modified GA and particle swarm optimization (PSO) for
multiuser OFDM system was propose by Kennedy and Eberhart [7].
In this paper it has shown that MOPSO power optimization is better
than 3GPP LTE and NSGA II **Algorithm**. An optimization problem
for power constraints and use of GA algorithm to maximize the sum capacity of OFDM system with the total power constraint was
investigated in ref. [8-11]. Also it was shown that GA is better than
conventional methods. A scheme for resource allocation in downlink
MIMO OFDMA with proportional fairness where dominant Eigen
channels obtained from MIMO state matrix are used to formulate the
scheme with low complexity in ref. [8], scheme provides much better
capacity gain than static allocation method. A PSO based Adaptive
multi carrier cooperative communication technique which utilizes
the subcarrier in deep fade using a relay node in order to improve the
bandwidth **efficiency **[9] where centralized and distributed versions
of PSO were investigated. Atta-ur-Rahman et al. in ref. [10,11] , used
GA and Water-filling principle in conjunction with FRBS for adaptive
coding, modulation and power in OFDM systems, where GA was used
to adapt the power.

The paper is organized as follows: section III deals with the Multi **Modulation **OFDM system where QAM modulation is taken in
consideration with M=4, 16, 32, 64, 128, 256, system description is
given in section III. FRBS and PSO aspects are discussed for FRBS rule
are define in section IV. Section V describes the **simulation **and results
for OFDM system. VI concludes the paper.

The system model considered is OFDM equivalent baseband model with N number of subcarriers. It is assumed that complete channel state information (CSI) is known at receiver. The frequency domain representation of system is given by

(1)

where amplitude, transmit symbol and the **Gaussian noise **of
sub carrier k=1, 2,…, N respectively. The overall transmit power of
the system and the noise distribution is complex Gaussian with zero
mean and unit variance. It is assumed that signal transmitted on the
k^{th} subcarrier is propagated over Rayleigh at fade channel and each subcarrier faces a different amount of fading independent of each
other. This can be given mathematically

The proposed adaptation model is given in **Figure 1**.

**Coded modulation**

Performance of standard modulation and codes being used in IEEE 802.11n1g/b are analyzed in terms of bit error rate (BER) and SNR. Calculation of coding scheme, modulation scheme and channel is estimated. The code rate are taken from the set C

(2)

Modulation symbol are taken from

(3)

Total number of MCPS can be given by

(4)

**Fuzzy rule Base System and Practicle Swarm
Optimization**

**Fuzzy rule base system**

To maximize the data rate FRBS is used for optimum selection of code modulation pair (CMP) per subcarrier based upon received SNR and QoS. The steps involved in creation of FRBS are described below

**Data acquisition: **The information about SNR and BER obtained
from Coded Modulation can be expressed as “for a given SNR and
specific QOS which modulation code pair can be used.

**Rule formulation:** Rules for every pair are obtained by the
appropriate fuzzy set used.

**Elimination of conflicting rule: **This is used for eliminating
conflicting rules.eg If there are two different pairs with same throughput
like [2,1/2] and[4,1/4], both have same throughput i.e.1×1/2=0.5. Thus
[2,1/2] is chosen since it have less modulation/ demodulation, coding/
decoding cost.

**Completion of Look up Table: **If complete numbers of IO pairs are
not present, then those parts are filled by heuristic or expert knowledge.
Example a modulation code pairs is suggested by rule for a certain SNR
and QOS. Then that rule can also be used for slightly above SNR and
poor QOS (**Table 1**).

Sr. No. | Parameters | Values |
---|---|---|

1 | No. of Subcarriers | 52 |

2 | Code rate | ½ , ¼ |

3 | Modulation | 16, 32, 64, 128,256 |

4 | Channel | AWGN |

5 | BER | 10e-2,10e-3,10e-4,10e-5,10e-6, 10e-7 |

**Table 1:** Parameters.

For instance [64, 1/2] is suggested for 20dB SNR and SER=10^{-2} then
this pair can be used for 21 to 25dB SNR at 10^{-1} SER

**Fuzzy rule base creation: **The input output pair for design of FRBS
are of the form

(5)

where x_{1}s represents received SNR, x_{2}^{s} represents BER (QOS) and
y_{3}s represents the output MCP.

**Fuzzy set:** Input for Fuzzy inference system is given as SNR and
BER or minus log BER.

(6)

(7)

(8)

where q is 0 to 10, there will be one output as MCP. Where BER is of SER

**Membership function: **Membership function used in FIS (fuzzy
inference system) is triangular. Triangular membership function is
simple to implement as well as calculation of arithmetic operation is
easier than Bell, Sigmoidal ,Gaussian.

In FIS system AND is used for MIN and OR as MAX.

**Rule base:** 7 sets of SNR and 10 sets MLBER are taken. Total
number of rules taken is 70 that will be used in FIS system.

**De-fuzzifier: **Standard Center Average Defuzzifier (CAD) is used
for defuzzification. CAD is to perform a linear combination over the
computed weights at the fuzzy inference engine and then modify
this combination by novelization. CAD provide continuity and
homogeneity and has less computational complexity.

Particle **Swarm **Optimization is a stochastic optimization
technique developed by Eberhart and Kennedy inspired by the social
behavior of flocks of bird. Each particle is represented by a position
and velocity vector. Let Dimensions of position and velocity vectors are
defined by the number of decision variables in optimization problem.
Soft PSO has been utilized for finding the optimum power vector for
all the sub carriers depending upon the channel conditions and their
QOS demand. Each sub-channel have different channel condition so
different channel should have different power allocation depending
upon the channel condition. Power allocation will be done with the
help of PSO. Different power allocation is done for different users and
thus the optimization of Power is done for different users in OFDM
system.

Let represents the position particle *p _{i}* at time t, then it is
given as

(9)

The position of *p _{i }*is then changed by adding a velocity

Each particle know its best position (p-best) and global best (g-best).Thus the particle will tend to attain its g-best at final iteration. g-best will give the optimum allocated power

where C_{1} and C_{2} are constants and is normally equal to 2.0. r_{1} and r_{2} are random
variables.

Simulation is performed to have optimal power allocation.

**Simulation parameters**

**Results**

OFDM system with different code rate and modulation are
simulated. Input of this graph is given to **Fuzzy **inference system and
PSO for optimization of code rate and modulation **Figure 2**.

In **Figure 3** Symbol Error Rate vs Signal to Noise Ratio are calibrated
for different modulation QAM techniques such as 4, 16, 32, 64, and 128
with code rate ½, where EbN0 is taken from 0:33. This represents SER
decrease exponentially with respect to EbN0 for different modulation.

**Figure 4** represent SER vs SNR plot with respect to each modulation
such as 16, 32,64,128 with code rate as ¼ . SER decrease exponentially
with respect to EbN0 for different modulation.

**Figure 5** shows the impact on throughput for different values of
SNR and QoS demand after incorporating the constraint. In this
diagram a higher numbered CMP reflects a high throughput.

Using FRBS in OFDM system it will give optimum modulation and
code rate represented by **Figure 5**.

Power allocation with respect to noise interface is shown in **Figure
6**. This figure indicates that the 3rd user has less interference thus less
power will be allocated.

In this paper FRBS and Particle swarm algorithm are used for optimization of code rate and modulation. FRBS are used for optimization of code rate and modulation. Higher numbered CMP reflects a higher throughput. PSO is used for allocation of power for each sub-channel depending upon the channel condition. After using FRBS in OFDM system it will give optimal modulation and Code rate. Thus y using FRBS and PSO optimal power allocation can be done with specified modulation techniques for specified sub channel.

- Wu Y,Zou WY (1995) Orthogonal frequency division multiplexing: A multi-carrier modulation scheme,IEEE TransConsumer Electonics41: 392-399.
- Derrick Wing Kwan Ng, Ernest SLo, Robert Schober (2010)Energy-Efficient Resource Allocation in Multiuser OFDM Systems with Wireless Information and Power Transfer, University at Erlangen-Nurnberg, Germany.
- Wong IE,ZukangShen, Evans BL, Andrews LG (2004) A low complexity algorithm for proportional resource allocation in OFDMAsystems, Dept of Electr&ComputEng, Texas Univ, Austin, TX, USA.
- Reddy YB,Gajendar N, Taylor, Portia (2007) Computationally Efficient Resource Allocation in OFDM Systems: Genetic Algorithm Approach ,Dept of Math &Comput. Sci, Grambling State Univ, LA, p36-41.
- Rajendrasingh A, Rughooputh Harry CS (2012) OFDM Systems Resource Allocation using Multi-Objective Particle Swarm Optimization.International Journal of Computer Networks & Communications 4.
- Atta-ur-Rahman, Qureshi IM, Malik AN, (2012) A Fuzzy Rule BaseAssisted Adaptive Coding and Modulation Scheme for OFDM Systems.J Basic ApplSci Res 2: 4843-4853
- Kennedy J, Eberhart RC (1995)Particle Swarm Optimization, Proceedings of IEEE Conference on Neural Networks 4: 1942-1948.
- Gheitanchi S, Ali F, Stipidis E (2007)Particle Swarm Optimization for Resource Allocation in OFDMA.Proc International Conference on digital Signal Processing 383-386.
- Atta-ur-Rahman, Qureshi IM,Muzaffar MZ (2011) Adaptive Coding and Modulation for OFDM Systems using Product Codes and Fuzzy Rule Base System. International Journal of Computer Applications (IJCA) 35:41-48.
- Atta-ur-Rahman, Qureshi TM, Malik AN (2012) Adaptive Resource Allocation in OFDM Systems using GA and Fuzzy Rule Base System.World Applied Sciences Journal 18: 836-844.
- Atta-ur-Rahman (2013) Optimum Resource Allocation in OFDM Systems using FRBS and Particle Swarm Optimization. Barani Institute of Information Technology, Rawalpindi, Pakistan Institute of Signals, Systems and Soft-computing (lSSS), Islamabad, Pakistan, 175-181.

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