Vol.03, Issue 07, July 2018, Available Online: www.ajeee.co.in/index.php/AJEEE
PERFORMANCE OF MULTIPLE-INPUT MULTIPLE-OUTPUT OFDM WITH ADAPTIVE COHERENT MODULATION
AMY ALICE KUJUR1, RAJEEV SARASWAT2
1M.Tech Scholar BTIRT Sagar M.P.
2H.O.D.E & C Department BTIRT Sagar M.P.
Abstract:- The major requirements in the wireless communication system are to increase the speed, range and reliability of the system by using a Multi User and Multi Carrier Modulation scheme like MIMO-OFDM. The MIMO-OFDM is designed for high speed data rate, higher spectral efficiency and lower latency by using beam forming and multiplexing techniques therefore it is used in Long Term Evolution-Advanced(LTE-A) systems. Multiple Input Multiple Output (MIMO) uses the multiple antennas at the transmitter and receiver. A MIMO antenna adapts itself to pick a user signal, in any direction without the user intervention. OFDM is a popular method for high data wired/wireless transmission. OFDM may be combined with antenna arrays at the transmitter and receiver to increase the diversity gain and /or to enhance the system capacity in a time-variant and frequency selective channels resulting in a MIMO configuration. This paper describes an adaptive coherent QAM/QPSK modulation technique for fast and noise free communication.
Keywords:- OFDM, MIMO, Adaptive modulation, coherent detection.
I. INTRODUCTION
The goal of an ideal digital wireless communication system is to produce the exact replica of transmitted data at the receiver. This has necessitated the corresponding numerous tremendous researches carried out in digital communications industry which leads to rapid growth recorded in the past two decades especially in its various applications. This growth, in turn, has spawned an increasing need to seek automated methods of analyzing the performance of digital modulation types using the latest mathematical software or programming language.
Modulation is the process by which some characteristics, usually the amplitude, frequency or phase of a carrier is varied in accordance with instantaneous value of some other voltage, called the modulating voltage or signal. Forms of digital modulation practically in use now include Amplitude Shift Keying (ASK), Frequency Shift Keying (FSK), Phase Shift Keying (PSK) and Quadrature Amplitude Modulation (QAM) with each having their distinctive features and characteristics. In the case of ASK, the use of amplitude modulated analogue carriers to transport digital information always results in a relatively low quality output. Although it is a low cost type of digital modulation, this is seldom used except for a very low speed telemetry circuits.
FSK has a poorer error performance than PSK or QAM and consequently is not used regularly for high-performance digital radio systems.
The demands for high data rate wireless communication in recent years have continued to increase rapidly for wireless multimedia services. Multiple-input, multiple-output (MIMO) systems are now the popular approaches to meet these demands. The use of multiple antennas at both transmitter and receiver in wireless communication links provides a means of maximizing the system performance of wireless systems. MIMO technology provides diversity by making the receiver to receive multiple replicas of the same information-bearing signal; and this provides a more reliable signal reception.
II. PREVIOUS RESEARCH
2.1. BER Performance Of MPSK And MQAM In 2x2 Almouti MIMO Systems Mindaudu and Miyim (2012) investigated the error performance of the 2x2 MIMO system using the Almouti (1998) space- time coding with M-PSK and M-QAM modulation schemes of modulation orders M = 4, 8, 16, 32 and 64. The problem of increasing error rates and power consumption is associated with using the MIMO for the provision of high speed multimedia wireless services. The aim of the investigation was to develop a MIMO system that would mitigate error rates and also provide better efficiency in power
Vol.03, Issue 07, July 2018, Available Online: www.ajeee.co.in/index.php/AJEEE and bandwidth consumption.. The
simulation results show that the scheme with the M-QAM modulation gives better BER performance compared to the M-PSK modulation. The proposed scheme shows good BER performance, but it is limited to only a 2x2 MIMO antenna configuration.
Also, the energy needed to achieving a given error probability increases with the modulation order.
2.2. Performance Comparison Of MIMO Systems Over AWGN And Rician Channels
A dense multipath fading environment stands as a bottleneck to achieving high data rate wireless transmission. Kaur and Kansal (2013) aimed at exploiting the multipath effect of the wireless communications environment for the enhancement of diversity and capacity gains. The method involved utilizing a MIMO-STBC system with zero-forcing (ZF) equalizer and higher order M-PSK modulation schemes; that is M = 32 and above. The system was simulated over a multipath Rician fading channel.
Simulation results for 32-PSK, 64-PSK, 128-PSK, 256-PSK and 1024-PSK showed good BER performances due to space diversity provided by the MIMO system.
However, the BER increased with increase in the value of M for M-PSK as a result of decrease in the space between different constellation points, and the energy needed to achieving a given BER increases with the modulation order M.
2.3. Bit Error Rate Performance Of MIMO Spatial Multiplexing With MPSK Modulation
The achievable data rate of the MIMO system with space-time trellis codes (STTC) is limited by the complexity of the ML decoder which grows exponentially with the number of bits per symbol. With a view to reducing the complexity of the ML decoder in a MIMO system, Harjot K., Bindiya J. and Amit V. (2011), investigated the BER performance of MIMO system utilizing a layered space- time coding (LSTC) technique with MPSK modulation schemes and ZF receiver. The system was simulated over the Rayleigh fading channel. Simulation results showed significant improvement in BER for the proposed technique compared to the STTC technique. The BER of the
system, however, increases with increasing modulation order.
2.4. Performance Comparison Of Mimo- Ofdm Transceiver Wireless Communication System
The MIMO system helps to achieve high spectral efficiency, but the system still suffers from the problem of inter-signal interference (ISI) in a frequency-selective mobile communication environment. The aim is to mitigate the problem of ISI in MIMO systems at the same time achieving high spectral efficiency in mobile communication environments. Mangla and Singh (2013) compared the BER performances of higher order M-QAM and M-PSK modulation schemes in a MIMO- OFDM system. The system was simulated for M = 16, 64, 256, 512 and 1024. The results showed that spectral efficiency increases with increasing modulation order M. Also, M-QAM gives better BER performance than M-PSK. The BER of the higher order modulations can be reduced but at the cost of increasing the SNR.
Increasing the SNR is however not advisable because excessive power consumption would adversely affect system lifespan.
III. ADAPTIVE MODULATION BASED ISOLATED MIMO LINKS
In this section we introduce adaptive modulation for isolated MIMO links. We assume that there is no external interference affecting the performance of the system. The performance of a MIMO system is highly dependent on the characteristics of the matrix channel.
Proper selection of antenna weights, modulation index, data rate and transmitted power of each stream based on the channel conditions can ensure a high throughput. In a Single-Input Single- Output (SISO) system, adaptive modulation maximizes the throughput of a link by adjusting the modulation index based on the channel gain and BER threshold. In a MIMO system, since each stream has a different channel gain and processing gain, adaptive modulation can be used to maximize the link throughput, by maximizing the throughput of each stream based on the channel gain and BER threshold.
Vol.03, Issue 07, July 2018, Available Online: www.ajeee.co.in/index.php/AJEEE 3.1. M-QAM BASED OL- MIMO SYSTEM.
In this section we develop a limited- feedback OL-MIMO system with adaptive modulation based on the V-BLAST architecture. An overview of the limited- feedback OL-MIMO system is shown in figure 4.1. The algorithm decides the number of streams to be transmitted and a uniform modulation index for all streams. The use of adaptive modulation for OL-MIMO system has been studied in [18-21]. with adaptive power allocation and modulation index for each stream based on the post-processing gain. But they neglect the effects of error propagation on the post-processing SINR and assume perfect interference cancellation. As shown in Figure 5, the effects of error propagation cannot be neglected. It is seen that the performance of the 1st detected stream is the worst;
hence we adopt a scheme in which we use the post-processing gain of the first detected stream to calculate the number of streams and a uniform modulation index for all streams. With the SIC algorithm, the SNR of the first layer estimated is
Es / No = PT / NTWi2
Where Wi is given by Wi = (G)lT
Denoting the array weight vector for detection of the first detected stream, given by the minimum-norm row of G, the pseudo-inverse of the channel matrix H, nT ≤nt is the number of transmit antennas used and PT is the total noise normalized transmit power. The algorithm for calculating the number of feasible streams is as follows: The number of feasible antennas is assumed to be Nt the SNR of the first stream is calculated and is compared with the SNR threshold in (19). If the SNR is greater than all streams are used, otherwise the number of streams is reduced by 1, the channel matrix is reduced (by setting the column corresponding to detected stream to zero) to reflect the lower number of transmit antennas and the process is repeated. We need to reduce the channel matrix and recalculate the transmitted power since along with the redistribution of the number of streams the dimensions of the
matrix channel also change with the number of transmitting antennas.
Fig 1: Open loop MIMO system diagram with limited feedback.
3.2. M·QAM BASED SOLUTIONS FOR CL- MLMO
In this section we investigate the use of adaptive modulation in CL MlMO systems. We modify the existing water- filling and MMSE solutions and propose a rate-maximization scheme using adaptive M-QAM modulation. An overview of the CL-MIMO system is shown in figure 12.
The water filling algorithm and MMSE solutions maximize the capacity and minimize the BER, respectively. But both do not guarantee a target BER. The BER of each individual stream is different and if an average target BER is to be satisfied the stronger streams have to sacrifice their transmission rate in order to lower the BER. This makes both solutions unsuitable for adaptive modulation. In this section we propose a modification to both WF and TWF algorithms in order to maximize the throughput while maintaining a target BER. The constraint in the WF and IWF strategies is that the Lagrange multiplier is accepted only if the power allocated to the weakest eigen- mode is greater than zero, i.e., αi > 0 where i is the weakest eigen-mode of the subset of eigen-modes being considered.
Fig 2: Close Loop-MIMO model with full CSI feedback
Vol.03, Issue 07, July 2018, Available Online: www.ajeee.co.in/index.php/AJEEE We add the following constraint to satisfy
the BER threshold (r) for each stream:
αiλi ≥ -2ln(5ϒ)
This constraint makes sure that the weakest eigen mode can transmit at least QPSK for the given bit error rate threshold ensuring that the stronger streams need not sacrifice the transmission rate in order to maintain overall BER. Next we develop a closed-loop rate maximization scheme that takes a BER constraint into account while calculating the maximum possible throughput. When K streams of data, each utilizing M-QAM modulation with modulation index M" are transmitted, the throughput is given by,
R = Σi=1k log2(Mi) Substituting (20) in (24) we get,
R = Σj log2{1 – 3/2ln(5ϒ) * αiλi} Using the Lagrangian multiplier procedure (32) can be rewritten as
R = Σj log2{1 – 3/2ln(5ϒ) * αiλi} +µ (Σ αi- Pt) + ζ (αiλi +2ln(5ϒ)-v)
Where µ, ϒ are the lagrangian multipliers and v is the dummy variable to convert the inequality constraint into equality constraint.
3.3. ADAPTIVE MODULATION BASED INTERFERENCE-LIMITED MIMO SYSTEM
In this section we combine the adaptive modulation scheme developed in the previous section for isolated MIMO links with a SIC based detection scheme with a new decision statistic for increasing the throughput of interfering MIMO links when the interference power is larger than signal power. Given the increasing number of users and shrinking cell sizes of a cellular system, or multiple simultaneous transmissions in a space- division multiple-access (SDMA) system, interference is an important parameter affecting the performance of the system?
It was shown that a MIMO-SDMA system can yield a greater throughput compared to a time-division multiple- access (TDMA) system. This implies that in a practical system, co-channel
interference among nodes in the network' will have to be accounted for, along with co-channel interference from nodes in neighboring networks (cells). The adaptive modulation schemes presented in the previous section did not consider interference from other links. In OL-MIMO links, the received streams interfered with each other, hence we chose a uniform modulation index based on the first stream detected.
In the CL-MIMO case, the streams were uncoupled from each other using transmit and receive beam forming and had different transmitted power and different constellation. In the presence of external interference from other links, the received streams can have different SNRs and modulation indices, and they will not be decoupled. Assuming the SIC algorithm is employed; the interference will affect the detection order as well as the data rates. When a strong interfering stream with a low modulation index is imminent, SIC can be used to first separate it from the received vector of signals with a low BER, and then, the desired signals can be detected with higher gain due to an increased diversity order.
Consequently, this gain can be used to increase the modulation index constrained by the BER threshold. Thus, when the interference can be separated from the desired signals using the SIC algorithm, the adaptive modulation scheme should be extended to possibly increase the data rate according to the order in which the streams are processed.
This extension requires formulation of the post-processing SNRs of the streams that go through SIC taking into account error propagation. We first develop a detection matrix based on the signal received in the presence of interference and then use it in a SIC based detection scheme with ordering based on the expected error rates.
3.4. RECEIVED SIGNAL AND THE DETECTION MATRIX
We consider an interfering MIMO system with L+1 links, where each link is affected by co-channel interference from other L transmitters. The received complex baseband signal vector of user under consideration is given by
Y = Hd Xd + Σi=1l HiXi + n
Vol.03, Issue 07, July 2018, Available Online: www.ajeee.co.in/index.php/AJEEE Where the subscript d denotes the link
under consideration and the subscript i denote the interfering links. In general, the transmitted vector is written in terms of the independent vector of modulated symbols, s, as
x = V Σ1/2 S
Where V denotes the transmit beam forming matrix and Σ is the power allocation matrix. With the number of transmitted streams from a node denoted by n, E is an Nt x n diagonal matrix. For OL links with Omni-directional transmission V is an identity matrix. In studies analyzing the capacity under interference such as [23-25], a spatial whitening transformation is used that models the interference as colored noise to reduce the capacity formulation to the form in (2).
The whitened channel is given by H =R-1/2 Hd'
Where R is the interference covariance matrix given byL , ,
R = I + Σi=1 l HiVi ΣHi’Vi’
With channel whitening, the diversity gain is reduced as a function of the number of interfering streams and the gain provided by SIC cannot be fully exploited. At strong interference conditions it may be favorable to estimate the interference and separate it from data, as suggested in [?3].
In this thesis, we develop a detection matrix to separate the interference from the data instead of using channel whitening to account for it. Rewriting (27) in a combined matrix form as
Y = HdVdΣd1/2 HlVlΣl1/2………..HLVLΣL1/2
[Sd, Sl,…..SL] +n
We form the following detection matrix:
H = [HdVdΣd1/2 HlVlΣl1/2………..HLVLΣL1/2]
IV. FLOW CHART OF PROPOSED SIMULATION PROCESS
V. RESULT ANALYSIS Simulation Parameter
Transmission scheme OFDM
Modulation QAM/QPSK
Bandwidth 20 MHz
FFT size 64
Number of carriers 64
Guard interval 16 samples (0.8 _s) Number of data symbols 8, 16
Start
Initialize Environmental Variable
Generation of Data
M- QAM Modulation (Coherent)
Recover Signal from STBC Alamouti STBC
Noise Addition MIMO OFDM Channel
with AWGN
Demodulation M- QAM (coherent detection)
Apply Moving Average Filter
Display Result
End
Vol.03, Issue 07, July 2018, Available Online: www.ajeee.co.in/index.php/AJEEE Numbe of transmitting
antenna 4
Numbe of receiving
antenna 4
Doppler frequency 10 Hz Comparison of BER and SNR between Differential and coherent modulation
Modulation
Scheme BER SNR
Differential
0.1 5
0.01 15
0.00316 20 0.001 23
Coherent
0.1 5
0.01 10
0.001 25
Graph between Simulation vs Theoretical analysis of Coherent modulation
simulation Theoretical Analysis
BER SNR BER SNR
0.1 0 0.1 4
0.01 12 0.01 15
0.00316 20 0.00316 23
0.001 25 0.001 26
0.0001 30 0.0001 35
Table of performance analysis of coherent and differential modulation
under MIMO OFDM Coherent
Modulation Differential Modulation S.
No.
Relativ e Delay (ns)
Averag e Power (db)
Relativ e Delay (ns)
Averag e Power (db)
1 0 0 0 0
2 200 -0.9 300 -1.2
3 400 -2.5 500 -3.5
4 600 -3.2 800 -4.6
5 800 -4.2 1100 -5.2
6 1000 -4.9 1300 -6.5
VI. CONCLUSION
In this paper, gives the information about MIMO systems and Beam forming algorithms. MIMO is used to increase the capacity, data rate and spectral efficiency of the system. Here we use M-QAM modulation for lower range communication to avoid Gaussian noise and M-PSK modulation is use for higher range communication because it gives high performance in long distance communication with minimum power consumption. We use coherent
Vol.03, Issue 07, July 2018, Available Online: www.ajeee.co.in/index.php/AJEEE modulation technique for better reliability
and performance.
REFERENCES
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