Wireless Communications and Mobile Computing
Volume 2018 (2018), Article ID 6581827, 7 pages
https://doi.org/10.1155/2018/6581827

Research Article

Average SEP of AF Relaying in Nakagami- Fading Environments

Dong Qin,1,2 Yuhao Wang,¹ and Tianqing Zhou³

¹School of Information Engineering, Nanchang University, Nanchang, China
²Postdoctoral Research Station of Environmental Science and Engineering, Nanchang University, Nanchang, China
³School of Information Engineering, East China Jiaotong University, Nanchang, China

Correspondence should be addressed to Dong Qin; qindong@seu.edu.cn

Received 7 January 2018; Accepted 26 March 2018; Published 3 May 2018

Academic Editor: Wolfgang H. Gerstacker

Copyright © 2018 Dong Qin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

This paper is devoted to an investigation of an exact average symbol error probability (SEP) for amplify and forward (AF) relaying in independent Nakagami- fading environments with a nonnegative integer plus one-half , which covers many actual scenarios, such as one-side Gaussian distribution (). Using moment generating function approach, the closed-form SEP is expressed in the form of Lauricella multivariate hypergeometric function. Four modulation modes are considered: rectangular quadrature amplitude modulation (QAM), -ary phase shift keying (MPSK), -ary differential phase shift keying (MDPSK), and /4 differential quaternary phase shift keying (DQPSK). The result is very simple and general for a nonnegative integer plus one-half , which covers the same range as integer . The tightness of theoretical analysis is confirmed by computer simulation results.

1. Introduction

When the transceiver is far away, it is not a wise choice to increase transmit power in green communications. Energy efficiency is placed in a very important position in resource optimization configuration. The relay technology is used as one effective means to achieve diversity gain, reduce transmission power, and improve energy efficiency. Therefore, cooperative diversity technology has attracted considerable benefits for enhancing the performance of wireless networks, such as cell network and ad hoc network. SEP is considered to be an important performance measure. Integral-free bit error probability formulas were derived in [1, 2] for decode and forward (DF) cooperative systems. An asymptotic bit error probability formula was provided in [3–5] for all participate and selective AF cooperation. A new approximation to the symbol error rate was derived in [6]. Multiple input multiple output (MIMO) technology was introduced in [7, 8], which considered exact and asymptotic symbol error rate. Selection combining was added in [9] with the same configuration as that in [7]. Mobile to mobile communication scenario was shown in [10], where asymptotic symbol error rate bound was provided. Signal space cooperation was realized in [11] for full rate transmission, where a tight bit error rate was obtained. Error probabilities of AF multihop variable gain relaying systems were analyzed in [12] by generalized hypergeometric functions. While the two-hop scenario was investigated in [13].

Although the error probability of the Nakagami- relay channel has been studied extensively, they mostly addressed integer fading parameter . The error probability formula for arbitrary must resort to infinite series expansion or reasonable approximation in high signal to noise (SNR) region. Some articles provided error probability just by simulation. The actual situation often requires quickly grasping error probability of wireless communication, so it is impossible to carry out a large number of simulation experiments. The importance and generalization of Nakagami- channel have not been fully exploited, which motivates our work from another perspective.

In this paper, we calculate the exact average SEP formulas in an AF relay system over Nakagami- fading environment when is a nonnegative integer plus one-half, while it includes as a special case the one-sided Gaussian distribution (). Such closed-form expressions are urgent because they allow fast and efficient evaluation of system reliability. Note that this parameter covers the same quantity range as the integer in most literature, according to basic knowledge of number theory. Four prevailing modulations are investigated: MQAM, MPSK, MDPSK, and DQPSK. Using the properties of moment generating function, precise expressions are a combination of a series of special functions. Simulation results confirm the tightness compactness of the theoretical analysis. For ease of reading and searching, see Mathematical Notations.

2. System Model

Consider a cooperative system where a source node communicates with a destination node via a relay node . There is not direct link between and due to obstacles. Assume that all links between transceivers are subject to independent but different Nakagami- fading. When is a nonnegative integer plus one-half, the probability density function of the end to end SNR at is given by [14, eq. ]

where and are fading parameter and scale parameter, respectively. The subscripts and represent link and link, respectively. Using [15, eq. (3.381.4)], the moment generating function of the SNR is given by

The above moment generating function is very useful in error probability analysis.

3. Average SEP Analysis

The average SEP is an important measure of communication reliability. Next, we prepare to study the SEP performances of four modulations.

3.1. Rectangular QAM

The average SEP of coherent rectangular QAM is given by

where

is the decision distance ratio between constellations of in phase and quadrature components. Based on moment generating function approach, the error probability is written as

where the first and second integrals correspond to the error probability involving a single function and the third and fourth integrals correspond to the error probability involving the product of two functions. We first begin by the integral containing one Gaussian function. Taking the first integral in (6) as an example, we encounter a kind of integral given by

By change of the variable , after some manipulations, can be written as

Applying [15, eq. (9.100)], (8) can be expressed in closed form in terms of Gauss hypergeometric function.

Next, we cope with the integral containing the product of two Gaussian functions. Making change of the variable , we obtain one kind of integral given by

Using [16, eq. ], can be expressed in closed form in terms of Appell hypergeometric function. Finally, combining the results in (8) and (9), the average SEP of rectangular QAM is given by

where and stands for the Gauss hypergeometric function defined in [15, eq. (9.100)] and the Appell hypergeometric function [17].

3.2. MPSK

Following similar steps in QAM, the average SEP of coherent MPSK is given by

3.3. MDPSK

With the aid of the common moment generating function, the average SEP for MDPSK is given by

where is the Lauricella function [16, eq. ].

3.4. Noncoherent Detection of Equiprobable Correlated Binary Signals and  DQPSK

The average SEP of equal energy, equiprobable, and correlated binary signals with noncoherent detection is given by

where

and is the magnitude of the cross correlation coefficient between the two signals. When and , (13) corresponds to  DQPSK with gray coding.

4. Simulation Results

In this section, the simulation results of the error probability for QAM, MPSK, MDPSK, and DQPSK are evaluated. The average SNR per symbol is defined as , where and represent transmit power of the source and the relay, respectively. is the noise variances. The channel gain is normalized to unit. The theoretical results highly agree with the simulations for  QAM and integer plus one-half . The same coincidence can be deduced for 8 PSK, 8 DPSK, and DQPSK modulation constellations and fading parameters . This demonstrates the accuracy and validity of the proposed formula.

Figures 1–4 show that the diversity gain is an increasing function of the fading parameter . For example, when the SEP of 8 PSK is 0.1, the diversity gain of the case of , is achieved about 4 dB compared with the case of , . Moreover, the diversity gain increased to about 10 dB when , . Similar observations can be found in 8 DPSK modulation in Figure 3. The diversity order is dominated by the worse link between and . For example, let us observe two cases of , and , . From Figure 2, when , in the high SNR region, the SEP is 0.089645629941062 at average SNR = 28 dB and 0.070958408816342 at average SNR = 30 dB. This implies that the diversity gain is

While for , , the SEP is 0.046485872872970 at 28 dB and 0.036583504432831 at 30 dB. The diversity gain becomes

Although the parameters are different, the results are the same. So they achieve the same diversity order.

Figure 1: Comparison of SEP for  QAM.

Figure 2: Comparison of SEP for 8 PSK.

Figure 3: Comparison of SEP for 8 DPSK.

Figure 4: Comparison of SEP for  DQPSK.

Figures 5–7 show the average SEP of -ary modulation schemes, where , . The average SEP increases with the increase of because the minimum distance between symbols becomes smaller. But the slopes of the SEP curves are nearly the same, implying the same diversity gain. The influence of cross correlation coefficient on SEP is drawn in Figure 8. When the correlation between two signals is small, the SEP is relatively small.

Figure 5: Comparison of SEP for MQAM.

Figure 6: Comparison of SEP for MPSK.

Figure 7: Comparison of SEP for MDPSK.

Figure 8: Comparison of SEP for correlated binary signals with noncoherent detection.

5. Conclusion

In this paper, we study the SEP of QAM, MPSK, MDPSK, and DQPSK modulation in cooperative AF system. Exact closed-form expressions for average SEP are obtained over independent Nakagami- fading channels with integer plus one-half . Simulation results agree well with the theoretical analysis.

Mathematical Notations

: | Probability density function of the variable
: | Moment generating function of the variable
: | Floor function
: | Gaussian function
: | Gauss hypergeometric function
: | Appell hypergeometric function
: | Lauricella function.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant nos. 61761030, 61661028, 61701214, 61461030, and 61661032), by the China Postdoctoral Science Foundation (Grant no. 2017M622103), and by the Young Natural Science Foundation of Jiangxi Province (Grants nos. 20171BAB212002 and 20161BAB212038).

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