ISSN (Online): 2348 – 3539.
Performance Analysis of MIMO-OFDM System using Energy
Efficiency Algorithm with Source Transmitte Antenna Selection
1
M. Jaya Priya,
2A. Jerin Sujitha,
3M. Zenia Solomon
Department of Electronics and Communication Engineering, Jeppiaar Engineering College, Chennai.
Abstract: This paper a new STAS is based on both channel state information and transmission scheme for the MIMO DF relay networks. It is also shown that the STAS which selects antennas among transmit antennas at the source can achieve full diversity regardless of the value of Simulation results show that the STAS has better average bit error probability (BEP) performance than other STASs. Also, the STAS with has lower cost, complexity, overhead, and BEP than the STAS with using full-rate full diversity space-time block codes with the same total transmit power. Power of the relay node is increased five times above given in base paper, Relay node is linked with energy efficient algorithm and modulation method is with QAM. This will increase packet error rate, throughput and SNR.
Keywords: Energy efficiency, bit error probability (BEP), multiple-input multiple-output (MIMO).
Reference to this paper should be made as follows: 1M. Jaya Priya, 2A. Jerin Sujitha,3M. Zenia Solomon (2015)
„Performance Analysis of MIMO-OFDM System using Energy Efficiency Algorithm with Source Transmitte Antenna Selection ‟, International Journal of Inventions in Computer Science and Engineering, Volume 2 Issue 3 2015.
1 Introduction
When multiple antennas are used at the source, transmit diversity can be achieved by using Space time block codes (STBCs). However, STBCs require multiple antennas associated with radio frequency (RF) chains which are costly in terms of size, power, and hardware. To solve this problem, low-cost and low-complexity transmit antenna selections (TASs) schemes with STBCs have been introduced. Cooperative relay systems utilize two independent source-destination (SD) and source-relaydestination (SRD) paths. To select good transmit antennas at the source, we have to consider both the SD and SRD paths simultaneously. Decode and Forward relay networks of one source, one destination, Assume that the relay-destination (RD) channels are orthogonal, which decreases the data transmission rate. The reason for this assumption is that if the relays transmit signals via the same channel, the potential maximum diversity may be difficult to achieve. Source transmit antenna selection (STAS) of selecting antennas at the source is based on the upper bound on the Pairwise error probability (PEP). The STAS can be performed at the destination, and then the information on the selected transmit antennas is fed back to the source. In the first phase, the source transmits an uncoded single symbol or a codeword of a full-diversity STBC with transmit antennas. During the second phase, the relays decode, re-encode, and re-transmit signals from antennas, and so the relays may transmit erroneous signals. Finally, the destination decodes the received signals from the source and the relays by using the Energy efficient algorithm scheme. Communication systems exploiting multiple antennas at the transmitter and/or the receiver are able to
provide both data rates (capacity) and performance (BER). These two main advantages of the input multiple-output (MIMO) systems can be achieved in two different ways namely diversity methods and spatial multiplexing. Employing diversity methods improves the robustness of the communication systems by exploiting multiple paths between transmit and receive antennas and the performance achieved is in terms of BER. Also when using multiple antennas in a rich scattering environment, it is possible for the receiver to sort out the simultaneously transmitted multiple signals from multiple antennas. Thus by sending parallel independent data streams it is possible to achieve overall system capacities. In other words by resolving these parallel spatial paths very high data rates can be achieved, hence, the name spatial multiplexing.
Ii. Multipleinput Multiple Output (Mimo)
communication standards. A schematic diagram showing simplified MIMO is given fig.1
Fig.1 Multiple input multiple output
III. Relay Network
A relay network is a broad class of network topology commonly used in wireless networks, where the source and destination are interconnected by means of some nodes. In such a network the source and destination cannot communicate to each other directly because the distance between the source and destination is greater than the transmission range of both of them, hence the need for intermediate node(s) to relay. Relays that receive and retransmit the signals between base stations and mobiles can be used to increase throughput extend coverage of cellular networks. Infrastucture relays do not need wired connection to network thereby offering savings in operators‟ backhaul costs. Mobile relays can be used to build local area networks between mobile users under the umbrella of the wide area cellular networks. Furthermore, relays can operate in half-duplex mode, i.e. they do not transmit and receive simultaneously in the same band, or in full-duplex mode. The latter operation requires a spatial separation between transmit and receive antennas to reduce loop-back interference from the transmit antennas to the receive antennas.
There are three fundamental cooperative (diversity) communication schemes in the three- terminal relay network:
(a) Decode and forward
(b) Amplify and forward
(c) Coded cooperation. T
he base-station (BS) can receive signals from two handsets (or nodes or mobile stations). One handset is the source node and the other is the relay node. The BS receives those two handsets‟ signals to obtain the cooperative diversity.
Iv. Energy Efficient Algorithm
In this paper, SAS using upper bound on PEP scheme and DF using Energy efficient (EE) algorithm is to be studied, EE algorithm is a finite set of steps that are used with a system in order to reduce energy consumption when number of relay nodes increases for consuming the more power to transmit the signal, the EE provide a solution that may be far from optimal. In existed system, Maximum
likelihood (ML) decoder is used in relay for decoding the signal. ML decoder is difficult to design and delay in processing and also decreasing the performance of diversity gains. EE algorithm is used in relay for improving the performance of diversity gain and decrease the delay in processing. Energy consumption of relay node can be solved with the EE, it also propose to achieve diversity gain and throughput and decreases the power of relay node without scarifying system performance. For the selection of good source antennas considering both the SD and SRD paths simultaneous. Unlike AF relay networks, it is difficult to find the optimal solution for the SAS in the DF relay networks due to the difficulty in deriving their error probabilities. Instead, the union bound on BEP can be used as a criterion of selecting good source antennas by deriving PEPs. However, it is still difficult to derive the exact PEP. Number of relay can be increased, so power consuming of each relay can be increased. To reduce the power allocation of relay by using EE algorithm in each relay networks. Not only reducing the power allocation of relay networks and also increasing the diversity gain and throughput without scarifying system performance. Proposed system model has been discussed below.
A. System Model
The data transmission is over two time-slots of equal length. In the first time-slot, the source transmits and the relay and destination receive. If the relay decodes the received signal correctly, the relay transmits the re-encoded signal to the destination in the second time-slot. The transmissions are based on the Alamouti scheme. Two transmit antennas at the source and relay respectively are selected out according to a certain rule. The destination performs selection combining (SC) on the signals from the source and relay.
(a) Transmitter
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(C) Receiver
Fig.2 Block Diagram Of SAS For DF Relay
Networks
In fig.2 shown that transmitter to convolutionally encode data start with k memory registers each holding 1 input bit. Unless otherwise specified, all memory registers start with a value of 0. The encoder has n modulo-2 adder and n generator polynomials one for each adder. An input bit m1 is fed into the leftmost register. Using the generator polynomials and the existing values in the remaining registers, the encoder outputs n bits. Now bit shift all register values to the right (m1 moves to m0, m0 moves to m-1) and wait for the next input bit. If there are no remaining input bits, the encoder continues output until all registers have returned to the zero state. The figure below is a rate 1/3 (m/n) encoder with constraint length (k) of 3. Generator polynomials are
G1= (1,1,1),
G2 = (0,1,1), and
G3 = (1,0,1).
Therefore, output bits are calculated (modulo 2) as follows:
n1 = m1 + m0 + m-1
n2 = m0 + m-1 ( 1)
n3 = m1 + m-1
where n denotes number of encoder output bits
m denotes number of input bits
Inter leaver is used to permutes symbols according to a mapping. spread-spectrum techniques are methods by which a signal (e.g. an electrical, electromagnetic, or acoustic signal) generated with a particular bandwidth is deliberately spread in the frequency domain, resulting in a signal with a wider bandwidth. Modulation is the process of varying one or more properties of a periodic waveform, called the carrier signal, with a modulating signal which typically contains information to be transmitted. Quadrature amplitude modulation (QAM) is both an analog and a digital modulation scheme. It conveys two analog message signals, or two digital bit streams, by changing (modulating) the amplitudes of two carrier waves, using the amplitude-shift
keying (ASK) digital modulation scheme or amplitude modulation (AM) analog modulation scheme. The two carrier waves, usually sinusoids, are out of phase with each other by 90° and are thus called quadrature carriers or quadrature components hence the name of the scheme.
The modulated waves are summed, and the resulting waveform is a combination of both phase-shift keying (PSK) and amplitude-shift keying (ASK), or (in the analog case) of phase modulation (PM) and amplitude modulation. In the digital QAM case, a finite number of at least two phases and at least two amplitudes are used. PSK modulators are often designed using the QAM principle, but are not considered as QAM since the amplitude of the modulated carrier signal is constant. QAM is used extensively as a modulation scheme for digital telecommunication systems. Arbitrarily high spectral efficiencies can be achieved with QAM by setting a suitable constellation size, limited only by the noise level and linearity of the communications channel.
Additive white Gaussian noise (AWGN) is a channel model in which the only impairment to communication is a linear addition of wideband or white noise with a constant spectral density (expressed as watts per hertz of bandwidth) and a Gaussian distribution of amplitude. The model does not account for fading, frequency selectivity, interference, nonlinearity or dispersion. However, it produces simple and tractable mathematical models which are useful for gaining insight into the underlying behaviour of a system before these other phenomena are considered. In this process a corresponding deinterleaver uses the inverse mapping to restore the original sequence of symbols. A decoder is a device which does the reverse operation of an encoder, undoing the encoding so that the original information can be retrieved.
This section aims to increase the energy-efficiency of cooperative relaying. First analyze the packet complexity and energy consumption of a cooperative relaying protocol using distributed timers to select the best relay. We refer to this protocol as basic relay selection (RS basic) hereafter. The chosen this protocol because it minimizes the packet exchange during relay selection. The considerations do not regard for any MAC issues and focus solely on the selection process.
To introduce two simple modifications to RS basic which increase the overall energy-efficiency as compared to the basic protocol:
(i) Common neighbours of {S, D} determine if they are suitable relay candidates based on their channel qualities,
Fig.3 Packet exchange in RS basic
Formula used to calculate the energy consumed by a node when it receives and forwards kilobits
(2)
(3)
Where
ET (k,d) is energy consumed by a node of distance d
ET−eleck is energy tuning of k
ER (k) is energy consumed by receiver
K is number of message types
V. Simulation Model
The performance analysis of proposed system and existing system is compared with following parameters
(a) Average BEP vs SNR
(b) Throuput vs Probability of detection
A. Comparison of average Bit Error probability proposed SAS with various Mss in DF relay networks with Ms=2 and MR=1
The bit error probability is the expectation value of the BER. The BER can be considered as an approximate estimate of the bit error probability. This estimate is accurate for a long time interval and a high number of bit errors. Signal-to-noise ratio (SNR) is a measure that compares the level of a desired signal to the level of back ground noise. It is defined as the ratio of signal power to the noise power, often expressed in decibels. The fig.4 shows that the x-axis representing SNR (dB) and the yaxis representing average BEP. Here the diversity gain of 10dB is achieved at the bit error rate of of Mss=1 than alamouti scheme Mss=2.
Fig.4 Existing System (average BEP VS SNR)
The fig.5 performance comparison of SAS with Mss=1 of proposed energy efficient scheme with existed alamouti scheme. The diversity gain is the increase due to proposed energy efficient scheme than existed alamouti scheme.
Fig.5 Proposed System (average BEP VS SNR)
B. Comparison of average Bit Error probability proposed SAS with various Mss in DF relay networks with Ms=4 and MR =2
The fig.6 shows the performance comparison of SAS with Mss=1 of DF relay with alamouti scheme. Here the diversity gain of 15dB is achieved at the bit error rate of 10-6 of Mss=1 and MD=4 which is comparatively better than the alamouti scheme of diversity gain 16dB of bit error rate 10-6 of Mss=2 and MD =4.
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The fig.7 shows the performance comparison of SAS with Mss=1 of proposed energy efficient scheme with existed alamouti scheme. The diversity gain is the increase due to proposed energy efficient scheme than existed alamouti scheme. Here the diversity gain of 9dB is achieved at the bit error rate of 10-4 of Mss=1 which is comparatively better than the existed system of diversity gain 10dB of bit error rate 10-4 . The BEP improve due to the decode and forward cooperative techniques with energy efficient.
Fig.7 Proposed System (average BEP vs SNR)
C. Comparison of throughput proposed SAS with Mss=1 in DF relay networks with existed SAS
Throughput is the rate of successful message delivery over a communication channel. This data may be delivered over a physical or logical link, or pass through a certain network node. The throughput is usually measured in bits per second (bit/s or bps), and sometimes in data packets per second or data packets per time slot.
Probability of detection is the ratio of number of events correctly forecast to total number of events observed. The Fig.8 shows Comparison of throughput proposed SAS with Mss=1 in DF relay networks with existed SAS scheme.
Fig.8 Comparison of throughput proposed SAS with Mss=1 in DF relay networks with existed SAS scheme
The x-axis representing the probability of detection and the y-axis representing throughput. The performance comparison of proposed SAS with Mss=1 of energy efficient scheme with existed alamouti scheme. The throughput of 2 is obtained at probability of detection as 1 of proposed system than existed system of throughput 1. The performance can be further improved by using cooperative scenario combined with energy efficient.
D. Comparison of throughput proposed SAS with Mss=2 in DF relay networks with existed SAS
The fig.9 shows Comparison of throughput proposed SAS with Mss=2 in DF relay networks with existed SAS scheme.
Fig.9 Comparison of throughput proposed SAS with Mss=2 in DF relay networks with existed SAS scheme
The performance comparison of proposed SAS with Mss=2 of energy efficient scheme with existed alamouti scheme. The throughput of 1.8 is obtained at probability of detection as 0.9 of proposed system than existed system of throughput 0.9. The performance can be further improved by using cooperative scenario combined with energy efficient.
IV. Conclusion
The proposed Source antenna selection (SAS) for Decode and forward (DF) relay network can achieve the maximum diversity and throughput regardless of the number of the selected antennas. Energy efficient (EE) algorithm liked with the relay network reduces the delay in decoding process and also the power of the relay.
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