Performance Analysis of Space Information Networks with Backbone Satellite Relaying for Vehicular Networks

Jiao, Jian; Gao, Houlian; Wu, Shaohua; Zhang, Qinyu

doi:https://doi.org/10.1155/2017/4859835

Wireless Communications and Mobile Computing

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Abstract Introduction System Model Conclusion Appendix Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

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Emerging Technologies for Vehicular Communication Networks

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Research Article | Open Access

Volume 2017 | Article ID 4859835 | https://doi.org/10.1155/2017/4859835

Performance Analysis of Space Information Networks with Backbone Satellite Relaying for Vehicular Networks

Jian Jiao,¹Houlian Gao,¹Shaohua Wu,¹and Qinyu Zhang¹

Academic Editor: Tao Han

Received09 Sept 2017

Accepted15 Nov 2017

Published10 Dec 2017

Abstract

Space Information Network (SIN) with backbone satellites relaying for vehicular network (VN) communications is regarded as an effective strategy to provide diverse vehicular services in a seamless, efficient, and cost-effective manner in rural areas and highways. In this paper, we investigate the performance of SIN return channel cooperative communications via an amplify-and-forward (AF) backbone satellite relaying for VN communications, where we assume that both of the source-destination and relay-destination links undergo Shadowed-Rician fading and the source-relay link follows Rician fading, respectively. In this SIN-assisted VN communication scenario, we first obtain the approximate statistical distributions of the equivalent end-to-end signal-to-noise ratio (SNR) of the system. Then, we derive the closed-form expressions to efficiently evaluate the average symbol error rate (ASER) of the system. Furthermore, the ASER expressions are taking into account the effect of satellite perturbation of the backbone relaying satellite, which reveal the accumulated error of the antenna pointing error. Finally, simulation results are provided to verify the accuracy of our theoretical analysis and show the impact of various parameters on the system performance.

1. Introduction

Nowadays, the connected vehicles paradigm is to form a vehicular network (VN) to communicate with the surrounding environment and the VN plays a vital role in the next generation intelligent transportation system (ITS) [1]. Generally, the long-term evolution (LTE) can provide reliable access to the Internet for VN communications in the urban areas. However, LTE network has poor coverage in rural areas and highways due to the costly network infrastructure [2, 3]. Moreover, the high mobility of vehicles can suffer from frequent handovers as the networks become even denser.

Space Information Network (SIN) is regarded as an effective strategy to provide diverse vehicular services in a seamless, efficient, and cost-effective manner in rural areas and highways. For instance, satellites and high altitude platforms (HAPs) in SIN can help achieve ubiquitous coverage in rural areas. Further, they can provide road information and transport information to assist ITS, entertainment services dissemination as relays, and relieve the demands on terrestrial networks through data offloading [4].

The return channels of the low/medium Earth orbit (L/MEO) satellites are unstable and discontinuous intrinsically to the ground-based stations and vehicles, which limit the throughput as well as the delay sensitive services of SIN-assisted VN communications. Recently, high throughput backbone satellites (such as the Ka/Q/V-band geostationary Earth orbit (GEO) satellites) relaying for SIN communications are regarded as an effective strategy to improve the continuity of return channels as well as the throughput performance.

Theoretically, three GEO satellites which are apart in the SIN backbone networks can provide coverage of the space between Earth ground and GEO orbit and achieve high-speed data relay through the intersatellite and satellite-terrestrial millimeter/terahertz/laser links.

With the development of high throughput satellites (HTS), several GEO HTS can establish the backbone network of SIN, where the backbone HTS relaying for SIN-assisted VN is able to provide a global seamless broadband transmission by developing the intersatellite links. People believe that the SIN will enable a “terabit data rate capacity” broadband access, which was previously possible only with fiber-optic links, and offer the access availability of “anywhere and anytime” inherent to the satellites [5]. Furthermore, the SIN will be a significant enabling factor as well as an important component of the upcoming 5th-generation (5G) networks [6].

Therefore, considering the backbone HTS relaying communication undergoes the large-scale and complex SIN dual-hop channel properties, such as rain attenuation [7], solar scintillation [8], perturbation factors [9], and interference [10–13], this paper investigates the performance of SIN return channel cooperative communications via an amplify-and-forward (AF) backbone satellite relaying for VN communications.

1.1. Background and Motivation

In our SIN communication scenario, space-based nodes (i.e., source nodes, like space mission explorers, orbiters and landers, space stations, spacecraft, manned and unmanned aircraft, etc.) can establish cooperative communications via an AF backbone HTS relaying.

Recently, SINs have attracted considerable research interest, and substantial effort has been devoted to investigating the performance of the research works of the hybrid satellite-terrestrial cooperative/relay networks (HSTC/RNs) by analyzing the complex multihop channel models. For that, by applying maximal ratio combining (MRC) at the destination, [14, 15] studied the outage probability (OP) performance of HSTCNs with an AF relaying protocol. In [16], the decode-and-forward (DF) relaying protocols for HSTCNs was investigated. Further, with the help of the moment generating function (MGF), [14, 15, 17] have presented the analytical expression of average symbol error rate (ASER) for HSTCNs with an AF relaying protocol. Besides, the performance of optimal selection algorithm of multiple relays for HSTRNs was presented in [18, 19].

Moreover, to achieve higher system capacity and energy efficiency, multiantenna technique was investigated in [20–22], and HTS with Ka/Q/V-band frequency with multiple antennas have attracted significant attention [23]. Authors in [24–31] investigate the performance of relay-based multiple antenna HSTC/RNs, since relay transmissions can effectively improve the throughput and the coverage of satellite communications. Further, the cognitive radio (CR) needs to be investigated since the HTS already suffer from spectrum scarcity in Ka band [32].

Besides, the SINs backbone GEO satellites are subjected to various satellite perturbation forces (e.g., Earth oblateness perturbation, third-body gravitational perturbation, atmospheric perturbation, and solar perturbation), which leads to position drift and result in the beam center of the ground station antenna unfocused [33]. The accumulated error of the antenna pointing error will cause the satellite elevation error, which may deteriorate the signal-to-noise ratio (SNR), decrease link margin [34] and bit error rate (BER) [35], and so forth. To the best of our knowledge, this is the first work on GEO satellite perturbation that reveals the effect of satellite elevation error for SIN backbone satellite relaying.

1.2. Contributions and Novelty

In this paper, we investigate the performance of SIN return channel cooperative communications via an AF backbone satellite relaying for VN, where both of the source-destination and relay-destination links undergo Shadowed-Rician fading, and the source-relay link follows Rician fading, respectively. By applying MRC at the destination, the equivalent end-to-end SNR of the system is first obtained, and then analytical expressions as well as the satellite perturbation effect are derived to evaluate the system performance. The detailed contributions of this paper are outlined as follows:(i)The system model of SIN return channel cooperative communications via an AF backbone satellite relaying for VN is first built, and we present a new analytical expression for the approximate statistical distributions of the equivalent end-to-end SNR of system (7).(ii)To gain further insight, the effect of the satellite perturbation of the relaying GEO satellite is considered for the first time, which reveals the accumulated error of the antenna pointing error leads to the satellite elevation error. And the accumulated satellite elevation error is taking into account the derivation of the ASER expression.(iii)The closed-form expression for the end-to-end ASER (31) is derived, which can efficiently evaluate the system performance. Moreover, simulation results prove the rationality of our theoretical analysis.

Notations. describes the link from node to node . represents the dual-hop link from node to node through relay node . denotes the expectation operator. denotes a complex Gaussian distribution with mean and variance . represents the exponential function. denotes the moment generating function (MGF) of . and denote the probability distribution function (PDF) and cumulative distribution function (CDF) of , respectively. represents the confluent hypergeometric function of first kind [36, Eq. (9.210.1)]. is Gauss hypergeometric function [36, Eq. (9.100)], and represents the modified Bessel function of the second kind with order [36, Eq. (8.446)]. is the Whittaker function defined as [36, Eq. (9.220.2)].

2. System Model

Our system model of the SIN return channel cooperative communications via an AF GEO HTS relaying for VN is considered as shown in Figure 1, where a source node , that is, space node, communicates with a terrestrial destination via a GEO HTS relay and , , and are the channel gains of the , , and links, respectively.

The space node is generally on the stratosphere layer and above, and is a GEO HTS in our SIN communication scenario. In the link, since the line of sight (LOS) signal is much stronger than the others, which is different from terrestrial networks, the channel gain of the link is considered as a Rician fading with additive white Gaussian noise (AWGN) [37, 38]. On the other hand, the channel gains and of satellite-terrestrial links and are usually modeled by Shadowed-Rician fading distribution [14–16, 24–27, 35]. It approaches the LOS communication using the Rician fading, whereas the amplitude is Nakagami- distributed [39], and it sufficiently agrees with experimental data and is computationally less complex than other land mobile satellite channel models.

As illustrated in Figure 1, in such a backbone GEO HTS relaying SIN-assisted VN system, the communication occurs during two time phases. In the first time phase, the space node broadcasts its signal to the relay and the destination , where and are the channel gains of the and links, respectively. The received signals at the relay and the destination from are given bywhere is the transmitted signal with unit power, is the transmitted power at , and and are the AWGN of and links with zero mean and variance and , respectively.

During the second time phase, first amplifies the received signal by an amplifying factor and then forwards it to through link of which the channel gain is , and the received signal at the destination is given bywhere is the transmit power at and is in AWGN at obeying .

Assuming that perfect channel state information (CSI) is available at and and MRC is applied at the destination, thus, the end-to-end SNR at can be expressed aswhere is the SNR of link and is the SNR of link. From (2), we haveFrom (3), can be expressed aswhere and and denote the SNR of and link, respectively. Thus, (4) can be rewritten as

2.1. Satellite Perturbation

In this paper, the GEO HTS satellite is considered as backbone relaying node for SIN-assisted VN communications to enhance the continuity as well as the throughput of the return channel. This is the first work to analyze the performance of cooperative communication for SIN dual-hop channel properties. To gain further insight, the effect of the satellite perturbation of the GEO HTS is analyzed, which reveals that the accumulated error of the antenna pointing error leads to the satellite elevation error.

2.1.1. Principle and Law of Satellite Perturbation Drift

The satellite is always subjected to a variety of perturbation forces, especially to the GEO satellites, which will lead to perturbation drift and accumulate the antenna pointing error. The satellite perturbation forces include the Earth oblateness perturbation [40, 41], the third-body attraction perturbation [42] such as lunisolar gravitational perturbation [43], the solar radiation pressure perturbation [44, 45], and the atmospheric drag perturbation.

The Earth oblateness perturbation is caused by the facts that the Earth is not an ideal sphere and it has uneven internal density distribution. It affects the long-term change of the right ascension of ascending node (RAAN) and argument of perigee of satellite orbit. The lunisolar gravitational perturbation can reduce the satellite orbit radius and may increase the orbital inclination, while the semimajor axis changes with half-day cycle. On the contrary, the solar radiation pressure perturbation mainly affects the orbital eccentricity, which directly determines the satellite center distance and the satellite height.

2.1.2. Satellite Elevation Error

The diagram of antenna pointing error is shown in Figure 2, and let denote the elevation of a GEO HTS, where represents the elevation error, which is the angle between centerline of antenna beam pointed to and the real LOS channel between the actual position of the satellite to the destination. denotes the satellite elevation angle if the GEO satellite is not affected by the satellite perturbation. Considering the drift caused by satellite perturbation in the eastwest and northsouth directions, the elevation of satellite can be calculated by [46]where is the latitude of the destination, is the longitude difference between the subsatellite point and the ground station, represents the longitude of subsatellite point, is the longitude of destination, is the drift of satellite in the northsouth direction, and is the drift of satellite in the eastwest direction. Therefore, the elevation error can be calculated as .

In general, can be calculated by the six elements of the satellite orbit [47]. When the satellite orbit is elliptical, the longitude and the latitude of the subsatellite point can be expressed aswhere is the average angular velocity of Earth, is the time, is the time when the satellite passes the ascending node, is the eccentric anomaly, is the inclination of satellite orbit, and is eccentricity. When the satellite orbit is a circle, (9) can be simplified aswhere is the time when the satellite perigee passes.

Therefore, from (9) and (10), it is worth noting that the subsatellite point is related to the parameters , , , and . If these variables have been affected by the satellite perturbation, the subsatellite point will have drift. Let and represent the longitude and latitude of subsatellite point considering the effect of satellite perturbation, respectively, and and denote the longitude and latitude of unperturbed subsatellite point, respectively. It is clear that the drift of subsatellite point affected by satellite perturbation causes the elevation error of satellite, and we have

2.2. Channel Model

2.2.1. Link

As the link is modeled by using the Rician fading distribution, the probability distribution function (PDF) of is given by [48]where is the average power of received signal at and and are known as the Rician factor, is the amplitude of LOS signal, is the average power of multipath component, and is the modified Bessel function of the first kind with order zero.

Then, the cumulative distribution function (CDF) of can be expressed aswhere is the Marcum Q-function of the th order, which is defined as

Moreover, the relationship between the Rician factor of the Rician fading link (12) and the satellite elevation was simulated in [49] through a large number of experiments. Then the relationship between the Rician factor and the satellite elevation was fitted as an empirical formulawhere , , and are empirical constant and , , and , respectively. Considering the effect of satellite perturbation, (15) can be rewritten aswhere indicates the elevation error affected by the satellite perturbation at time .

2.2.2. and Links

The link is usually modeled as a composite fading distribution to describe the amplitude fluctuation of the signal envelope. Considering the tradeoffs between accuracy and computational complexity, the satellite-destination link and the relay-destination link are modeled by using the Shadowed-Rician fading distribution [11–13] in our SIN-assisted VN system.

Let and denote the SNR of the and links, respectively. The PDF of is given by [39]wherewhere is the confluent hypergeometric function of first kind [36, Eq. (9.210.1)]. Moreover, and are the average power of the LOS and the multipath components, respectively, and is the fading severity parameter.

Recall the definition of , and we have

For the analytical tractability, we retain our focus in the case when the channel severity parameters take integer values in the rest of this paper; that is, . Hence, with the aid of [50, Eq. (07.20.03.0009.01), Eq. (07.02.03.0014.01)], (19) becomeswhere denotes the Pochhammer symbol with [51, Eq. (6.1.22)].

To solve the three parameters , and in (18), we assume is the elevation at GEO HTS , when the center line of the receiving antenna beam in different link aims at . is the elevation error which affects the satellite perturbation. When , , , and can be calculated by the empirical formulas [39, Eq. ]. Therefore, considering the effect of satellite perturbation, , , and can be calculated as follows:

3. Performance Analysis

In order to exactly measure the effect of satellite perturbation on the SIN return channel cooperative communications via an AF GEO HTS relaying, the important quality-of-service (QoS) metric, that is, average symbol error probability (ASER), is analytically studied and evaluated in our proposed SIN-assisted VN systems.

Since MRC is applied at the destination, we derive the close-form expression by using MGF according to [52], where the ASER of an -ary phase-shift keying (MPSK) modulated system is given bywhere and . The MGF of instantaneous SNR is defined as

Considering , , and are independent and the relationship between , , and is presented in (4), we can express aswhere and are the MGF of and .

In the following, we derive the expressions for and . Then, we use (22) and (24) to obtain the ASER of our SIN backbone satellite relaying VN system.

3.1. MGF of the SNR for the Link

By using the definition of MGF and substituting (17) into (23), we can evaluate the MGF of the link as follows

By using [36, Eq. (7.621.4)], the result of (25) can be easily obtained as follows:

3.2. MGF of the SNR for the Link

By substituting in (6) into (23), we can evaluate the MGF of cooperative link as presented in (27). The proof is provided in the Appendix.where the definitions , , and are shown in (A.7).

3.3. Derivation of ASER

Based on the definition of ASER of an MPSK modulated system (i.e., (27) and (24)), (22) can be written as

Alternatively, the following approximation of (28) can be used [37]:where

As shown in (26) and (27), the close forms of and have already been derived. By substituting these close forms into (29), we finally obtain the accurate closed-form expression of the ASER of a space downlink cooperative transmission system with relay GEO satellite as shown in

4. Numerical Results

This section gives the numerical results to demonstrate the validity of the theoretical analysis and the effect of satellite perturbation on the SIN return channel cooperative communications via a GEO HTS relaying.

We assume the node is a space node and its position is N and W. The node is a GEO HTS and, for the initial position , and and velocity vector , and in the Cartesian coordinate system, their initial values are , and = km and , and = km/sec. We adopt high precision orbit propagator (HPOP) model and the parameters of the various perturbations are shown in Table 1. The GEO HTS is mainly affected by the Earth gravity, the third-body gravity, and the solar radiation pressure perturbation [53]. In Table 1, the Earth gravity model [54], the solar radiation pressure perturbation, and the third-body gravity perturbation adopt general settings.

The elevation error affected by the satellite perturbation is shown in Figure 3, which is mainly considering the Earth nonspherical perturbation, the lunisolar gravitational perturbation, and the solar radiation pressure perturbation. The simulation duration is one lunar month and each step is 60 seconds.

As shown in Figure 3, the elevation error accumulates on the link which is about 1 degree after one lunar month. The elevation error fluctuates on the link, where the range of fluctuation increases gradually and the simulation time accumulates, and the maximum of fluctuation is about 0.1 degrees at the end of the simulation.

We assume and . In and links, the elevations of ignoring satellite perturbation at nodes and are equal; that is, . In order to simulate the influence of the elevation error caused by perturbation on the system ASER performance, the elevation error data is sampled at intervals of 12 hours due to the large amount of elevation error in Figure 3. The amplifying factor is and and QPSK modulation is implemented, and the rest of the simulation parameters are the same as above. The end-to-end ASER with satellite perturbation in the SIN return channel cooperative communications via an AF GEO HTS are shown in Figures 4, 5, and 6, respectively.

(a)

(b)

(a)

(b)

Figures 4 and 5 indicate the ASER performance is improving with the increasing of receiving SNR . As one can expect, with the increasing of and average power of received signal at node in the and links, the ASER performance is improving. Comparing with the cases of ignoring the effect of the satellite perturbation, the ASER performance deteriorates a little due to the satellite perturbation.

Moreover, three subfigures in Figures 4 and 5 show the fluctuation and error accumulation process of the ASER with satellite perturbation with = 5 dB, under different and , respectively. In Figure 4, when , and , the fluctuation of ASER is the same, and the fluctuation ranges are 5, , and , respectively, and the fluctuation range becomes tighter and increases. Similar to Figure 5, when = 2 W, 5 W, and 10 W, the fluctuation of ASER is the same, and the fluctuation ranges are , , and , respectively, and the fluctuation range declines when increases. To be clearer, we show the ASER performance with the increasing of and as in Figure 6.

5. Conclusion

In this paper, we investigate the ASER performance of SIN return channel cooperative communications via an AF GEO HTS relaying for VN, where both of the and links undergo the Shadowed-Rician fading, and the link follows Rician fading, respectively. By applying MRC at , the equivalent end-to-end SNR of the system is first obtained, then the analytical expressions of ASER and the satellite perturbation effect are derived. The effect of the satellite perturbation of the relaying GEO satellite is considered for the first time, which reveals that the accumulated error of the antenna pointing error leads to the satellite elevation error. And the accumulated satellite elevation error is taking into account the derivation of the ASER expression. The closed-form expression for the end-to-end ASER can efficiently evaluate the system performance, and simulation results prove the rationality of our theoretical analysis.

Appendix

Derivation the MGF of the Cooperative Link

By the definition of MGF, from (6) and (23), the MGF of the cooperative link can be evaluated:

Considering and are independent, we first calculate the following integral of variable such that

By using [36, Eq. (6.614.3)], we getwhere is the Whittaker functions defined as [36, Eq. (9.220.2)]where is a second notation of confluent hypergeometric function, and when , has the relationship [36, Eq. (9.215)] as follows:

Thus, (A.3) can be rewritten as

In order to make the derivation more clear, we define

After some algebra manipulations, we can rewrite (A.6) as

Now, we can rewrite as the integral of variable

Let , and then we have and . After some algebra manipulations, (A.9) can be written as the integral of variable

By substituting (20) into (A.10), we have

Then, by using the Binomial expansion for , we can rewrite (A.11) as

The integral part of (A.12) can be solved by using [36, Eq. (3.471.9)] as follows:Therefore, by plugging (A.13) into (A.12), the result of can be obtained as presented in (27).

Conflicts of Interest

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

Authors’ Contributions

Jian Jiao, Houlian Gao, and Qinyu Zhang contributed equally to this work.

Acknowledgments

This work was supported in part by the National Natural Sciences Foundation of China (NSFC) under Grants 61771158, 61701136, 61525103, and 61371102, the National High Technology Research & Development Program no. 2014AA01A704, the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology under Grant HIT.NSRIF.2017051, and the Shenzhen Fundamental Research Project under Grants JCYJ20160328163327348 and JCYJ20150930150304185.

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Copyright © 2017 Jian Jiao 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.

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