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International Journal of Technological Exploration and Learning (IJTEL) ISSN: 2319-2135 Volume 2 Issue 5 (October 2013)

An Efficient Resource Allocation in MIMO-OFDMA Networks M. Siva Chandra Kumar M.Tech (Communication System) Department of Electronics and Communication Engineering Sir C.R.Reddy College of Engineering India Abstract— Most of the future wireless communication system applications are purely multimedia applications with the different Quality of Services (QoS) and these systems are required to support a variety of data rate for communication services to its end users, such as video streaming and cloud based services.. Therefore for efficiently providing a proper communication among all these wireless communication systems an attractive multiple access scheme is required, which are possible by using OFDMA scheme (Orthogonal Frequency Division Multiple Access). The main objective behind the use of OFDMA is it builds OFDM (Orthogonal Frequency Division Multiplexing) links, which are very immune to inter-symbol interference and very sensitive to frequency selective fading. In this paper, we discusses about the problems of OFDMA system regarding with resource allocation like power allocation, data rate, allocation of active communication links (antenna allocation), and provides a novel solution for all these problems.

In this paper considered problem consists of power consumption of all circuit element (circuit power consumption), CSIT (Imperfect channel state information at the transmitter), and different quality of services like data rate and a maximum tolerable channel outage probability etc.

Keywords- MIMO System; OFDMA, Energy Efficiency; Resource Allocation; Quality of Service; Resource allocation.

I.

INTRODUCTION

Most of the wireless communication systems are designed to support large number subcarriers, offer high data rates and to ensure the fulfillments of quality of service (QoS) requirements, under the constraint frequency spectrum and limited number of channels. The concept of MIMO is evolving technique for wireless communications which fulfills all the requirements of wireless communication systems. MIMO is a promising technique for wireless communication systems to significantly increase the spectral efficiency. The greatest advantage with the MIMO systems is the capacity MIMO channel increases linearly with the minimum number of transmit and receive antennas [1], [2] by efficiently allocating the available power. Here the complexity present in MIMO system limits the gain of the system and linear increment in number of antennas directly affects the MIMO power levels.

Therefore a highly promising alternative is required to support the MIMO, which increases the throughput of MIMO, spectral efficiency and very immune to multipath fading. Therefore all

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CH Madhava Rao Assistant Professor, Department of Electronics and Communication Engineering Sir C.R.Reddy College of Engineering, India these requirements are effectively fulfilled by using the concept of OFDMA (Orthogonal Frequency division multiple access) is a highly promising technique for high speed wireless communication systems.

OFDMA is an attractive technique for wireless communications and it is developed to support several multimedia applications with different Quality-of-Service (QoS) requirements. The basic idea behind the OFDMA system is very simple. OFDMA systems make an Orthogonal Frequency Division Multiplexing (OFDM) links, which are highly immune to inter-symbol interference and highly sensitive to frequency selective fading and it divides total frequency band into several individual frequency bans which are orthogonal in nature and each channel has individual much lower bandwidth than the coherence bandwidth of the channel.

The OFDM systems make the many narrowband systems from a single broad band channel and each sub channel is designed to carry quadrature amplitude modulated (QAM) signal. At the transmitter side the entire sub carriers are beam formed in very efficient manner using the concept of inverse fast Fourier transform (IFFT). A cyclic symbol is pre fixed to the IFFT signal before it transmits from the transmitter. At the receiver side, the receiver removes the pre fixed symbol and performs the FFT, and QAM de-mapping.

Today’s OFDM systems are designed such that only single user can use the entire narrowed band sub channels using either time division or frequency division multiple access schemes.

On the other hand, MIMO OFDM system allows multiple users to transmit simultaneously on the different subcarriers per OFDM symbol. Basically MIMO-OFDMA systems are categorized as two strategies i.e. Single-user and multi-user systems. The wireless communication system with limited feedback or without any feedback is considered as single user MIMO strategy. In multi-user MIMO strategy CSI (channel state information) is known either transmits side or receive side. Most of the capacity achieving schemes is based on multi user MIMO schemes only.

CSI (channel state information) is a considerable factor for achieving the improved data rate of wireless communication systems. And achievable data rate of the Wireless communication system purely depends on the amount of CSI available at the receiver side (CSIR) and at the transmitter side

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International Journal of Technological Exploration and Learning (IJTEL) ISSN: 2319-2135 Volume 2 Issue 5 (October 2013) (CSIT). Therefore the performance is purely depends on measuring and providing the feedback of CSI and the process of measuring the CSI depends on communication resources such as power allocation , sub channel assignment, data rate, time and antenna spatial dimensions etc..

Therefore from the above discussion it is clear that the performance of the MIMO communication system depends on the efficient allocation of resources like power, sub channel assignment, data rate, time and antenna spatial dimensions among different antenna elements. For our convenience in this paper we consider only some of the resources only.

The rest of this paper is organized as follows. Section II briefly discuss about the system model which consists of MIMO OFDM down link system and obviously explains about channel state information also in section III. In Section IV different types of resources are discussed and all the resource is discussed mathematically for the convenience of paper description. Finally in section V we clearly shows discuss about the results of this paper.

II.

Figure 2. Downlink MIMO-OFDMA system diagram

The channel state matrix Hk,n for user k (where k=1,2,…,K) on Sub channel n (where n=1,2,…,N), is given as

SYSTEM MODEL

In this section introduce the complete architecture of the MIMO OFDMA Downlink system. For our convenience consider a MIMO OFDMA Downlink system without loss of generality with ‘K’ number of users and ‘N’ sub channel carriers and assume each end user has Mr receiving antennas.

Consider that the base station has Mt transmitting antennas as shown in fig.1

Can be decomposed through singular value decomposition (SVD), Where M=min (Mr, Mt) and M is the rank of and are the singular value decompositions of .

Assume that the data symbols are transmitted from transmitter and received at the receiver successfully i.e. both transmitter and receiver beam forming are conducted successfully then the demodulated signal for user k on subcarrier n is

= Is defined as data vector for user on subcarrier , is the channel matrix, is the transmit beam former matrix, is the receive beam former matrix, and is a complex white Gaussian noise vector.

III.

Figure 1. MIMO-OFDMA Downlink system

The block diagram for OFDMA MIMO system is shown in fig. 2 with all the above considerations.

CHANNEL STATE INFORMATION

In most of wireless communication applications, the required data rate is achieved using the CSI available at the receivers and as well as at the transmitters. In this paper (MIMO OFDM DOWNLINK) consider that The CSI is observed, estimated by the receiver and feed the estimated CSI back to the transmitter. For our convenience consider partial CSI which is statistical in nature with covariance feedback and assume that each transmitter knows the “channel covariance information” of active all transmitting antennas and all active channels. Another important consideration is that consider that the receiver does not contain any physical restrictions and sufficient spacing is present between the antenna elements on the receiver, so that the signals received at different antenna elements at receiver are uncorrelated each other. Therefore the MIMO OFDMA DOWNLINK channel can be modeled as

Is i.i.d., zero-mean, unit-variance complex Gaussian random variables Therefore from the above discussion it is clear that the performance of MIMO OFDMA system depends on measuring www.ijtel.org

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International Journal of Technological Exploration and Learning (IJTEL) ISSN: 2319-2135 Volume 2 Issue 5 (October 2013) of the CSI and providing CSI feedback to the transmitter. But it requires efficient allocation of resources, such as power, sub channel assignment, time and spatial dimensions etc.

IV.

RESOURCE ALLOCATION

In this section we discuss about the allocation different resources. For this purpose consider the following important assumptions: 

Independent downlink fading is experience by each individual sub carrier and user.



Each unique sub carrier is assigned to unique user only, not shared by different users at a time.



Assume that MIMO channel is constant while power allocation and sub carrier allocation process.



At the base stations perfect CSI of all users are available.

A. Power and subcarrier allocation Power allocation is briefly discussed in this section. As we know that the optimal power allocation and sub channel allocation are interleaved each other. Therefore to solve the optimal power allocation it is computationally prohibitive. As a result, some of the sub-optimal power allocations schemes are proposed in [9-10, 12] to achieve an optimal performance.

Therefore the easiest way is to use equal power allocation scheme across all sub channels under different access protocols.

B. Instantaneous Channel Capacity and Outage Capacity Assume that carrier I present between the BS and user k with bandwidth W then the capacity of the channel is given as

is the received signal-to-interference-plus-noise ratio i.e. SINR. The beam forming vector of the base station (BS) is chosen to be the eigenvector corresponding to the maximum Eigen value of

is the constant circuit power consumption per antenna which includes the power dissipations Therefore, the energy efficiency of the system is defined as the total number of bit/Joule successfully delivered to the users which is given by

C. Power allocation policy, , antenna allocation policy, data rate adaption policy, , and subcarrier allocation policy, For the considered system power allocation policy, , antenna allocation policy, data rate adaption policy, , and subcarrier allocation policy, are obtained by solving the

And s.t C1: C2: C3:

(

)

C4: C5:

,

C6: D. Simulation Results and Analysis In this section, we analyze the performance of the system through simulations using Matlab.

E. Outage capacity Vs Transmit Power: Below figure 3 shows the simulation results for the outage capacity of the MIMO system with respective to the transmit power. As discussed in above sections the outage capacity or carrying capacity of the MIMO system increases only when the number of active antennas increases and which is indirectly need to higher power requirements, what is the main problem we consider at the starting of this paper discussion.

Therefore the average weighted system outage capacity is defined as the total average number of bit/s successfully delivered to the mobile users and is given by

are the power, antenna, data rate, and subcarrier allocation policies. is data rate of sheduled sub channel.

The power dissipation of the considered system UTP (P, A, R, S), is the sum of two dynamic terms and one static term [10] + Figure 3.

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Outage capacity Vs Transmit Power

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International Journal of Technological Exploration and Learning (IJTEL) ISSN: 2319-2135 Volume 2 Issue 5 (October 2013) As explained in section II and from the below simulation results it is clear that whenever the transmit power increases then correspondingly the outage capacity of the system increases. Here we have to remember that, the meaning of increasing the transmit power is indirectly increasing the active antennas.

In figure 3, whenever the transmitting and receiving antennas both are 2 then their required transmitting power is small and therefore their corresponding outage capacity is also less. And in other hand whenever the transmitting and receiving antennas both are increased to 4 then automatically the transmitting power also increased and the total outage capacity of the system increases gradually as shown in figure 3.

Thus the simulation results for Outage capacity Vs Transmit Power shows a clear description among the capacity of the MIMO system, Transmit power requirement and number of active antennas.

F. Energy efficiency Vs Number of users: Another important problem what we discussed in above sections is the energy efficiency of the MIMO system.

Generally efficiency of the energy depends on the number of user. In this paper the term energy efficiency is considered as number of bits delivered for unit amount of joule. The objective of this paper is to achieve the maximum energy efficiency to all the users under certain energy constraints. Figure 4 represents the simulation results for energy efficiency

Figure 5. Results for iterative algorithm

As we know noise is a common element for all types of communications. Here we consider only the thermal noise component. Form the simulation results we observes that, whenever the energy efficiency of the MIMO system reaches a peak point, from that onwards the efficiency of the system decreases exponentially due to the cause of thermal noise.

Therefore in our proposed iterative scheme, we consider all these considerations of the MIMO system and proposed an advanced energy-efficient resource allocation scheme for MIMO-OFDMA systems.

Therefore figure 6 shows the simulation results for the entire proposed method.

Figure 4. Energy efficiency Vs Number of users

Figure 4 represents the simulation results for energy efficiency of the MIMO system with respective to number of users. The simulation results are approximately equal for theoretical and as well as for proposed scheme, which means that it is possible to achieve high energy efficiency even the number of users increases under constant energy source.

V.

RESULTS FOR PROPOSED SCHEME

The simulation results for proposed iterative algorithm are shown in figure 5.

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Figure 6. Simulation results for proposed method

VI.

CONCLUSION

From the simulation results of our proposed method it is clear that the objectives our work achieves successfully. From the above simulation results, the energy efficiency of the MIMO system good for high amount of power transmission, compared to less power transmission system under equal 250

International Journal of Technological Exploration and Learning (IJTEL) ISSN: 2319-2135 Volume 2 Issue 5 (October 2013) iterations i.e. from our discussions at equal number of iteration the MIMO system which has more number of active achieves high energy efficiency than with less antenna MIMO system ender available unique power resource only. In simple words, by efficiently allocating the power among all the active antennas and by gradually increasing the outage capacity using proposed iterative scheme the energy efficiency of the MIMOOFDMA system increased which is our “Advanced EnergyEfficient Resource Allocation in MIMO-OFDMA Systems”.

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