Vol 7, No 8 (2017)

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Research Article

a

August

2017

Computer Science and Software Engineering

ISSN: 2277-128X (Volume-7, Issue-8)

Study of Power Control in CDMA System on the basis of

optimization Technique

Geetanjli

Department Computer Science and Engineering, SSGI, Shahpur, Haryana, India

DOI: 10.23956/ijarcsse/V7I8/0103

Abstract:- The power control in CDMA systems, grant numerous users to share resources of the system uniformly between each other, leading to expand capacity. With convenient power control, capacity of CDMA system is immense in contrast of frequency division multiple access (FDMA) and time division multiple access (TDMA). If power control is not achieved numerous problems such as the near-far effect will start to monopolize and consequently will reduce the capacity of the CDMA system. However, when the power control in CDMA systems is implemented, it allows numerous users to share resources of the system uniformly between themselves, leading to increased capacity For power control in CDMA system optimization algorithms i.e. genetic algorithm & particle swarm algorithm can be used which regulate a convenient power vector. These power vector or power levels are dogged at the base station and announce to mobile units to alter their transmitting power in accordance to these levels. The performances of the algorithms are inspected through both analysis and computer simulations, and compared with well-known algorithms from the literature.

Key Words:- Power Control, CDMA, Algorithms, Optimization, performance

I. INTRODUCTION

Code in frequency so that the transmitted signal bandwidth is much larger than the original signal’s bandwidth. CDMA offers many benefits that make it more bandwidth efficient than plain FDMA or TDMA. These benefits are obtained by incorporating certain features that are possible due to the noise-like characteristics of the signal waveform. One of the most important of these is universal frequency reuse, that Division multiple access (CDMA) is a spread spectrum technology, in which each user is assigned a pseudo-random spreading code. Using this code, the narrowband data signal of the user is spread is, all users occupy a common frequency spectrum allocation. This increases the spectrum usage, and eliminates the need for planning for different frequency allocation for neighbouring users or cells. Another benefit is the use of the RAKE receive, which can constructively combine multi path components, thus mitigating channel fading-CDMA also enables soft handoffs among base station which improves cell boundary performance and prevents dropped cells. Yet another benefit is the use of voice activity (reducing the transmission rate during silent periods in a conversation), which reduces interference and thus has a direct impact on capacity.

There is no single well-defined definition for wideband CDMA (WCDMA). One proposed definition is based on the coherence bandwidth of the channel, i.e., the minimum distance between two frequencies such that the channel fading at those frequencies is essentially uncorrelated. The CDMA system is termed wideband CDMA if the transmission bandwidth increases the coherence bandwidth. Some interpretations are based on the chip rate or bandwidth as a fraction of centre frequency. Anyway, there is no distinct threshold separating narrowband CDMA from WCDMA.

II. POWER CONTROL

With convenient power control, CDMA offers great capacity in contrast to FDMA and TDMA. Since in CDMA systems there is no need of secluding of time or frequency slots among users, the fundamental mechanism for resource allotment and interference management is power control. So power control is a powerful design problem in CDMA systems. Every user adjusts its approach to the resources by acclimate its transmitting power to the dynamic channel and interference circumstances. Therefore power control also known, as Transmit Power Control (TPC) is a powerful design problem in CDMA systems. Power control encloses the techniques and algorithms used to govern the transmitted power of base stations and mobiles. Power control helps in to overcome co-channel interference, raising the cell capacity by diminish interference and increase the battery life by using a minimal transmitter power. In CDMA systems power control guarantee disposal of resources among users. If power control is not enforce, all mobiles will transmit signal with the same power without taking into attention the fading and the distance from the base station, so mobiles near to the base station will cause a high level of interference to the mobiles that are at the distance from the base station.

III. PROPOSED METHODOLOGY Optimum Power Control Curve Law

To achieve a uniform service, the transmitted power for a mobile located at (r, θ) can be adjusted to have the same shape as the total interference factor x1(r, θ). Then P1 changes as interference changes with high Pj for large interference and

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ISSN(E): 2277-128X, ISSN(P): 2277-6451, DOI: 10.23956/ijarcsse/V7I8/0103, pp. 126-132

𝑃_{𝐽𝑂𝑃𝑇} 𝑟, 𝜃 = 𝑃_𝑅,𝑃 𝑟, 𝜃 𝑃𝑅.𝑋1 𝑟, 𝜃

𝑋₁ 𝑅, 300 (1.1)

Where p(r, θ) is the optimum transmitted power function given by 𝑃(𝑟, 𝜃) = 𝑋1( 𝑟,𝜃 𝑋1𝑃(𝑅, 300 PR is the power

transmitted to the users located at the corner (R, 300). Then the total power transmitted at the base station is

𝑃_𝑡 = 𝑓𝑃_{𝐽𝑂𝑃𝑇}(r, 𝜃) 𝑃𝑑𝐴

12 𝑋 𝑃𝐽𝑂𝑃𝑇

𝑅

0 300

0

𝑟, 𝜃 𝑀

𝜋𝑅2

𝑅𝑑𝑑𝑟𝑑𝜃 (1.2)

𝑃𝑅𝑀

12 𝜋𝑅2 𝑃_{𝐽𝑂𝑃𝑇} 𝑅 0 300 0

𝑟, 𝜃 𝑅𝑑𝑑𝑟𝑑𝜃

The C/I will be

𝐶 𝐼

𝑃𝐽𝑂𝑃𝑇𝑟𝑚

𝑃_𝑡𝑋₁𝑟𝑚 (1.3) For a required C/I ratio, the capacity can be written as

𝑀 = 1

(𝐶/𝐼)_𝑟𝑒𝑞_𝜋𝑅12₂ ₀300 𝑟, 𝜃 𝑟𝑑𝑑𝑟𝑑𝜃₀𝑋1

(1.4)

Evaluating equation (4.10) numerically, we can obtain the approximate expressions For m=2, 𝑀 = 0.3424

(𝐶/𝐼)𝑟𝑒𝑞

For m=3, 𝑀 = 0.4921

For m=4, 𝑀 = 0.5794

For a given required C/I ratio, M is a constant for users at any location. If M is fixed, C/I is a constant. The system can then provide uniform service for all users within the cell.

Distributed Balancing (DB) Algorithm

Let N1 (positive integer) denote the number of communicating mobile in cell i (I = 0, 1…….18), B1 denote the base j (j =

0, 1……….18), Mjk denote the mobile k (k = 1, 2………, N1) in cell i, Ziki denote the link gain from B1 to Mik, and Pik

denote the down-link power transmitted from Bi to Mik. Since self-jamming is much more dominant on received SIR (=

S/I; signal-to-interference ratio) than background noise, so it is neglected in this simulation. Therefore, the SIR of Mjk,

denoted by SIRik [16] can be expressed as –

𝑆𝐼𝑅_𝐽𝐾 = 𝑃𝑖𝑘𝑍𝑖𝑘𝑖

𝑁 𝑃_𝑖𝑚𝑍_𝑖𝑘𝑖𝑃_𝑖𝑘𝑍_𝑖𝑘𝑖

𝑚 =1 𝑖

(1.5)

If the signal-to-noise ratio at any mobile station has been balanced by our DB power control process, regard that SIRjk is

independent of mobile k in cell i. i.e, SIRjk = SIRj = constant for fixed i. so rearrange Equ. (4.3.1) and obtain Pjk given by

𝑃𝐽𝐾 =

𝑆𝐼𝑅𝑖 𝑗 𝑁𝑚 =1𝑃𝑗𝑚𝑍𝑖𝑘𝑗

1 + 𝑆𝐼𝑅𝑖 𝑍𝑖𝑘𝑗

(1.6)

Equ. (4.3.2) implies that the optimal transmitted power assignment for Mjk is proportional to the ratio of the total received

power of this mobile to the link-gain between its home base and itself. Express above statement as

(𝑃_𝑖𝑘)_𝑜𝑝𝑡𝛼 𝑄𝑡 𝑗𝑍𝑖𝑗𝑘

𝑍_𝑖𝑗𝑘 (1.7)

Where Q1 denotes the total transmitted power from Bj for all j which is given by

𝑃𝑗𝑚

𝑚

= 𝑄1 (1.8)

Define a new parameter 𝐶_𝑖𝑘 𝑎𝑠

𝐶_𝑖𝑘 = 𝑄𝑗 𝑗𝑍𝑖𝑘𝑗

𝑍_𝑖𝑗𝑘 (1.9)

Thus we could find that the optimal transmitted power assignment for mobile k in cell i is proportional to the parameter Cjk. Consider the total transmitted power of each base station is fixed for any time, then we can obtain the following

results for cell i

𝑃_𝑖𝑘𝛼𝐶_𝑖𝑘

𝑃_𝑗𝑘

𝐾

= 𝑄₁ (1.10)

Solve the above simultaneous equations, and then the optimal power allocation method is obtained by

𝑃𝑖𝑘 = 𝑄𝑖

𝐶_𝑖𝑘 𝐶𝑚 𝑖𝑚

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IV. PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHM

Particle Swarm Optimization (PSO) is a method for global optimization [7] and it is different from other well-known Evolutionary Algorithms. As in Evolutionary Algorithms, a population of potential solutions is used to probe the search space, but no operators are applied on the population to generate new solutions. In PSO, every single particle, of the community, called swarm, adapt its orbit toward its own previous best position, and toward the previous best position attained by any member of its topological neighbourhood. In the universal variant of PSO, the whole swarm is treated as the neighbourhood. Thus, universal distribution of instruction takes place and the particles profit from the discoveries and previous experience of all other companions during the search for promising regions of the landscape. For example, in the single-objective minimization case, such regions possess lower function values than others, visited previously. In the local variant of PSO, the neighbourhood of each particle in the swarm is restricted to a certain number of other particles but the migration rules for each particle are the same in the two variants. The basic algorithm of PSO has two distinct variants. The first variant is the one where the particles are characterized by binary strings and the other is the one where the particles are characterized by real numbers in n dimensional space where n is the dimension of the optimization problem under discussion. Initially describes the basic algorithm of PSO in real number problems. The Pseudo-code for this algorithm is:

For i = 1 to number of individuals

If G (𝑥_𝑖) >G (𝑝_𝑖) then do // G() evaluates fitness For d = 1 to dimensions

𝑝_𝑖𝑑 = 𝑥_𝑖𝑑 // 𝑝_𝑖𝑑 is best so far Next d

End do

g = I //arbitrary for j = indices of neighbours

if G 𝑥𝑖 > 𝐺(𝑝 )𝑔 then g = j // g is index of best performer next j

For d = 1 to dimensions

𝑣𝑖𝑑 𝑡 = 𝑣𝑖𝑑 𝑡 − 1 + 𝜑1 𝑝𝑖𝑑− 𝑥𝑖𝑑 (𝑡 − 1)) + 𝜑2(𝑝𝑔𝑑 − 𝑥𝑖𝑑(𝑡 − 1))

𝑣𝑖𝑑 ∈ (−𝑣𝑚𝑖𝑛 ,+ 𝑣𝑚𝑎𝑥)

𝑥_𝑖𝑑 𝑡 = 𝑥_𝑖𝑑 𝑡 − 1 + 𝑣_𝑖𝑑(𝑡)

Next d Next i

Here 𝑥𝑖 is the position of particle i, 𝑣𝑖 is the velocity of particle i, 𝜑₁ is a random number that gives the size of the step towards personal best, 𝜑2 is a random number that gives the size of the step towards global best (the best particle in the neighbourhood) and G is the fitness function which we are trying to minimize. For the binary PSO, the component value of 𝑥𝑖 , 𝑝𝑖 and 𝑝𝑔 are restricted to the set {0, 1}. The velocity 𝑣𝑖 is interpreted as a probability to change a bit from 0 to 1, or from 1 to 0 when updating the position of particles.

V. RESULT & DISCUSSION

A. Outage Consideration Real Application

In wireless CDMA system, the outage probability is an important measure of communication quality. When the received power is less than reception threshold value, the communication quality becomes so poor that the communication link could be jeopardized. The outage measure can also be used to evaluate the performance of power control algorithms [9]. The outage can be defined as following:

With

Pout = prob (Pr < Pthres) (dB)

Where

Pthres - Pσ – P Δ (dB)

Pr = received signal power

Pthres = power threshold

Pd = desired received power

ΔP = power threshold setting

Threshold setting ΔP = 2dB. Power control period is 1.25 msec. adjustment coefficient α = 2 and control resolution is 1 dB.

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ISSN(E): 2277-128X, ISSN(P): 2277-6451, DOI: 10.23956/ijarcsse/V7I8/0103, pp. 126-132 B. Performance Analysis and Comparison Downlink Power Control Algorithms

Following is a list of assumption common to the entire algorithm simulated. 1. Characteristics of each forward link are independent and identical. 2. There are a fixed number (M) of mobile stations per cell.

3. The mobiles are located uniformly within the cell. 4. All mobiles are listening at all times.

5. Outage is defined as the condition when the observed value of the SIR is below the threshold value (SIRthreshold =

14dB) [11]

C. Simulation Results and Analysis

Initial Phase

Figure.1 Average probability that received Eb/N0, is less than θ Vs. Number of users

Resultant Phase

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Performance Analysis Graph 1

Figure.3 Distributed Balancing – Percentage of mobiles in Outage vs. Number of mobiles in cell

Graph 2

Figure.4. BER curve for 32 QAM using matrix intrlv Rayleigh

Result Analysis

 Simulation for showing GAME behaviour is done for a single cell, where the base is situated at its centre. The cell radius is 1 distance unit. Mobile users are distributed uniformly over the cell space. We adopt the distance loss model. The link gain, Gij is modelled as a product of two variables Gij = Aij x Dij. Aijis the variation in the received signal due to shadow fading, and assumed to be independent and log normally distributed with a mean of 0 dB and a standard deviation of 8 dB. The variable Dij is the large-scale propagation loss, which depends on the transmitter and the receiver location, and on the type of geographical environments.

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ISSN(E): 2277-128X, ISSN(P): 2277-6451, DOI: 10.23956/ijarcsse/V7I8/0103, pp. 126-132  Respecting the moderate transmitting power for the mobile units, PSO algorithm comes to the solutions with much smaller values of transmitting power to serve the same number of users. The author says that the resulting values of PSO algorithm are about 65% on average from those of GA algorithm. This means that PSO algorithm was much better in searching the search space when we fixed the maximum number of iterations.

 Regarding the average received Eb/N0 found that the results obtained by GA algorithm are slightly better than

those obtained by PSO algorithm. This was an expected result because the solutions of GA algorithm used greater values for transmitting power and thus they probably will achieve better average received In spite of the fact that GA results are better than PSO results, the results for both algorithms are too close to each other.

VI. CONCLUSION

 In a CDMA network, resource allocation is critical in order to provide stabile quality of service (QoS) for each user and achieve channel efficiency. An increase in the transmission power of a user increases its Eb/No (bit error

rate) but increases the interference to other users, causing a decrees in their Eb/No. on the other hand an increase

in the transmission rate of a user deteriorates its own Eb/No. controlling powers and rates of the user therefore

amounts to directly controlling the QoS that usually specified as a pre-specified target Eb/No. Demand for the

wireless communication shows the need for technology to further increase the capacity of cellular communication system and improve the system performance. CDMA was developed to increase the capacity of cellular network. Unlike FDMA and TDMA whose capacities are primarily bandwidth limit ed, the capacity of CDMA is only interference limited, any reduction in interference converts directly and linearly into an increase in capacity. It has been show that for terrestrial cellular network, the interference suppression feature of CDMA can result in many-fold increase in the capacity of the cellular network. However, the above conclusion is based on the assumption that all signals arriving at the receiver with the same power. So power control is crucial to cellular CDMA system.

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