a design method of cognitive overlay links for satellite...

A Design Method of Cognitive Overlay Links forSatellite Communications

Luciano Barros Cardoso da SilvaIMT Atlantique, Lab-STICC, Toulouse

[email protected]

Tarik BenaddiIMT Atlantique, Lab-STICC, Toulouse

[email protected]

Laurent FranckAirbus Defence and [email protected]

Abstract—In this paper, we present a method to designcognitive overlay links for satellite communications, in orderto allow the primary and the cognitive users to transmitconcurrently while using efficiently the available power re-sources. By means of trellis shaping based dirty paper coding(DPC) and superposition techniques, numerous schemes areinvestigated in various realistic scenarios, and different trade-offs between power efficiency vs complexity are made. Bysimulations, we first show that we are able to design schemeswhere the primary user bit error rate (BER) is maintainedas in the absence of the cognitive user interference. Secondly,thanks to trellis precoding and an appropriate constellationexpansion, we show that the BER of the cognitive user canbe made within 1dB of corresponding the Gaussian channelBER.

Index Terms—Satellite Communications, Cognitive Radio,Overlay Paradigm, Dirty Paper Coding, Trellis Precoding,Flexible Payloads

I. INTRODUCTION

In the past few years, the demand for new risingsatellite services in respect to global connectivity, broad-cast capabilities and mobility aspects has experienced arapid growth [1]. For instance, we point out the use ofsatellite for the support of machine-to-machine (M2M)communications, providing reliable services where terres-trial systems are inaccessible or even unaffordable. Recentprojections highlight a deployment of around 1 milliondevice/km2 in the next couple of years (smart sensors, ve-hicles, healthcare monitoring . . . ) [2]. By contrast, despitetheir advantages when compared to terrestrial networks,the satellite systems also face unique design challenges,such as: (i) low power consumption, (ii) the use, wheneverpossible, of commercial off-the-shelf (COTS) parts, whichmight limits the onboard processing resources, (iii) thedevelopment of technical solutions for enhancing spectrumcoexistence among different users, in order to alleviate theincreasingly spectral scarcity.

It is within this framework that the cognitive radio(CR) techniques have also emerged for satellite com-munications [3], benefiting from the technical advancesin flexible payloads [4] [5] and software defined radiotechnology [6]. On this subject, the recent works [7], [8]and [9] presented contributions to interweave and underlayCR paradigms for satellite communication systems, morespecifically with respect to satellite-terrestrial integratedsystems and dual satellite systems. In a nutshell, theinterweave paradigm is defined by an opportunistic trans-mission of the unlicensed cognitive user (CU) by sensingthe licensed primary user (PU) presence and searching fora gap in frequency, time or polarization. Conversely, in theunderlay paradigm the CU transmits simultaneously with

the PU, while respecting to the power threshold specifiedby an interference temperature sensing method.

With regard to the above contributions, this paper inturn proposes the use of overlay CR paradigm for satellitecommunications. This approach is well suitable for theaforementioned context, especially due to the fact thatthe satellite has a complete and noncausal knowledgeof all users messages prior to the transmission, a keyrequirement for the overlay paradigm. In this sense, bothPU and CU are able to transmit concurrently at the samefrequency, time and space. The main advantage of thisscheme is that the infrastructure of the PU communicationsystem remains unchanged as well as its performance.On the other hand, the CU adapts its transmission, bymeans of advanced encoding strategies, to mitigate thePU interference in the cognitive link and to maintain thesignal to noise ratio of the primary link.

In accordance with the interference model presented in[10], the optimal CU encoding strategy for overlay CRparadigm consists of two independent strategies: superpo-sition and dirty paper coding (DPC) [11]. In the superpo-sition technique, the CU shares part of its power in orderto relay the PU transmission. As a result, considering theproper amount of shared power, the signal-to-interference-plus-noise ratio (SINR) at the PU receiver remains con-stant, as if the interference were absent. Subsequently, withthe remaining available power after superposition, a trellisshaping based DPC scheme, similar to [12] and [13], isimplemented to presubtracted the known PU interferencebefore transmission.

Based on the foregoing techniques, this work presentsa method to control the CU transmitted power in orderto ensure constant SINR at PU receiver, in accordancewith the superposition strategy. In this way, the CU powerresource is efficiently utilized - an important requirementfor satellite communications. We propose to evaluate theshaping gain and use it as a scaling factor for the CU con-stellation minimum distance. Moreover, depending on thestrength of the interference, further constellation expansionas well as modulo quantization are considered. Differentconfigurations and scenarios are simulated and analyzed,leading to design trade-offs between power efficiency andcomplexity.

Following this introduction, the section II presents asystem description and the general concepts of superpo-sition and DPC techniques. Subsequently, in Section III,the system design is exposed. Simulation and results arediscussed in Section IV. Finally, the Section V is dedicatedto the conclusions with suggestions for future works.

2018 9th Advanced Satellite Multimedia Systems Conference and the 15th Signal Processing for Space Communications Workshop (ASMS/SPSC)

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(a) LEO/MEO satellite scenario (b) Multibeam satellite scenario

Fig. 1: Examples of Satellite Scenarios: PU - solid line; CU - dashed line

II. SYSTEM DESCRIPTION

A. Overlay Model and Superposition Strategy

The main motivation to introduce the overlay CR tech-nique for satellite communications lies in the feasibilityof transmitting both unlicensed and licensed services si-multaneously towards its respective terminals. We pointout that the difference in respect to the broadcast channelis the priority among users, which requires additionalrequirements and countermeasures.

The Fig. 1a and Fig. 1b present examples of scenarioswhere the overlay technique might be applied. The firstone presents a LEO/MEO satellite, where two differentservices are provided. The PU is the licensed one andtakes priority over the CU. In the second example, a GEOmultibeam satellite transmits both licensed and unlicensedservices, utilizing, for instance, flexible payloads tech-niques. In this case, the cognitive user signal is transmittedin the red frequency area but using the blue frequency,in order to avoid interference with the ground terminals.Hence, because of this frequency reuse, the interferenceamong adjacent beams shall be controlled.

In both scenarios, the standard form of the interferencemodel with side information presented in Fig. 2 can beapplied. The primary and cognitive signals are onboard thesatellite. Under this assumption, the cognitive encoder hasfull and noncausal knowledge of the PU signal. Thus, theencoded cognitive signal Xn

c is function of both primaryand cognitive messages mp and mc, respectively. On theother hand, the PU signal Xn

p encoding strategy remainsthe same. Without loss of generality, the cognitive channelgains are defined by (1, a, b, 1), with direct paths assumingunitary gains and the gains of the interfering paths definedas a, b ∈ [0, 1]. The following equations describes thechannel:

Y np = Xn

p + aXnc + Zn

p (1)

Y ns = Xn

c + bXnp + Zn

s , (2)

where the noise component Znp is assumed as N (0, Np)

and Zns as N (0, Ns). Also, the power constraints to be sat-

isfied by the primary and cognitive users are E[|Xnp |2] =

Pp and E[|Xnc |2] ≤ Pc, respectively.

Fig. 2: Channel Model

Given the channel model, the first condition to beaccomplished is to ensure the PU performance in thepresence of CU transmission. To fulfill this requirement,the CU signal shares part of its power to relay the PU,in order for this later to overcome the additional CUinterference. Based on that operation, the CU transmittedsignal is given by:

Xnc = Xn

c +

√αPc

PpXn

p , (3)

where α ∈ [0, 1] is the shared fraction of power Pc.It shall be noticed that a new power constraint

E[|Xnc |2] ≤ (1 − α)Pc is defined, assuming that the

components of Eq. (3) are statistically independents. Byrearranging Eq. (1), the signal-to-interference-plus-noiseratio (SINR) at the primary receiver is given by:

SINRP =E[|(1 + a

√α∗ Pc

Pp)Xn

p |2]

E[|aXnc |2] + E[|Zn

p |2], (4)

Without loss of generality, we assume the noise powerNs at PU receiver as unitary. Under the condition ofstatistical independence among the components in Eq. (3),the superposition factor α∗ ∈ [0, 1] that maximizes thePU SINR, for interference condition (a > 0), is definedby [10]:

α∗ =

(√Pp

(√1 + a2(1 + Pp)Pc − 1

)a√Pc(1 + Pp)

)2

. (5)

It is worth noting from Eq. (4) that, in accordance withinterference channel model, despite of the demodulator at


the PU terminal receives the specified SINR, the receivedsignal power is increased by the superposition component(a√α∗ Pc

Pp)Xn

p . In this paper, emphasizing the practical as-pects, we assumed that the PU infrastructure is unchanged.

B. Dirty Paper Coding

1- Transmitter side

After the superposition, the objective is to design thesignal Xn

c in such way to mitigate the PU interference. Bymeans of the theoretical results presented by Costa [11], onthe assumption that the interference is noncausally knownat transmitter, a transmitter-based interference presubtrac-tion can be implemented without any power increase,reaching the AWGN capacity.

Having regard to the practical implementations of DPC,Erez, Shamai and Zamir introduced the pioneer workconcerning this subject [14], pointing out the use ofTomlinson-Harashima precoding (THP) for intersymbolinterference (ISI) cancelling. In addition, also for GaussianISI channels, Eyuboglu and Forney in [15] presented thetrellis precoded (TP), which combines the trellis shaping(TS) technique [16] with THP. In this latter scheme,besides the interference mitigation, the shaping loss couldbe partially recovered. Similarly with our application, anextension of the TP technique for multiuser interferencewas proposed in [12] and further explored in [13].

By rearranging the Eq. (2) and considering the super-position, the received signal is given by:

Y ns = Xn

c +

(b+

√αPc

Pp

)Xn

p︸︷︷︸Sn

+Zns , (6)

where Sn is the transmitter known channel interference.Through the lattice strategy described in [14], it was

observed that the partial interference pre-subtraction (PIP)outperforms the total interference pre-subtraction operat-ing at low or intermediate signal-to-noise ratio regimes.In accordance with this assumption, the signal Xn

c isdesigned as:

Xnc =

[Xn

cc − λSn

]MOD∆ , (7)

where Xncc is the coded signal and the factor λ, to be

properly chosen, controls the fractioned interference to bepresubtracted. Also, MOD∆ is the complex-valued mod-ulo operation. The amplitude is defined by ∆ =

√Mdmin,

where M is the number of points of expanded squareQAM constellation and dmin the minimum intersymboldistance.

The modulo operation presubtracts the interferencewhile assuring a low increasing power. However, its oper-ation introduces some irreversible losses in the system, ascharacterized by [14]. For this reason, its implementationmust be considered according to the interfering scenario.The equations presented in this paper take account ofmodulo operation in order to conserve the notation.

In some practical system, a dither is added at thetransmitter and subtracted at the receiver in order to makethe components of Eq. (7) statistically independents.

Fig. 3: DPC Receiver

2- Receiver side

The receiver operation, considering the elements pre-sented in the last section, is illustrated by Fig. 3. At thedecoder input, the signal is given by:

Y ns =

[(Xn

c + Sn + Zn)λ

]MOD∆ (8)

=

[λXn

c + (Xncc − Xn

c )MOD∆ + λZns

]MOD∆ (9)

=

[Xn

cc − (1− λ)Xnc + λZn

s

]MOD∆, (10)

By Eq. (10), we define the effective noise for cognitiveuser link as (1 − λ)Xn

c + λZns . As a consequence, the

effective signal-to-noise ratio for cognitive user SNReff

is given by:

SNReff =E[|Xn

cc|2]

E[|(1− λ)Xnc |2] + E[|λZn

s |2], (11)

The value of λ that maximize Eq. (11) is given by [11]:

λ =(1− α∗)Pc

(1− α∗)Pc + E[|Zns |2]

. (12)

III. SYSTEM DESIGN

A. CU transmitted power

From this point, the objective is to construct the codedsignal Xn

cc with a good channel coding gain and as closeas possible to the interference λSn.

The Fig. 4 presents the implemented encoder for cog-nitive user. In essence, the upper part is formed by thecoset select code Cc, specified by the generator matrixGc, which encodes the kc bits message into a nc codedsequence. In parallel, in the lower part, the inverse syn-drome former matrix H−Ts , generated for the shaping codeCs, receives the rs-bits syndrome sequence and providesat its output the initial shaping sequence t. Subsequently,by knowledge of w, t and λS, the Viterbi decoder selectsthe closest shaping codeword ys in respect to any metricchosen. The sequence zs, formed by the XOR operationof ys and the original shaping sequence t enters in themapper to form, jointly with w, the shaped transmittedsequence Xn

cc. In the same way as presented in [16], thiswork utilized the trellis shaping on regions strategy forshaped mapping.

The following branch metric is implemented, where theprecoder selects the proper region sequence with minimumaverage energy to steer the scaled interference sequenceλS.

‖[Xn

cc − λSn]MOD∆‖2. (13)

At the decoder, the operation is the same as imple-mented for the trellis coded modulation (TCM) decoder:


Fig. 4: Proposed DPC Encoder

the symbols are decoded using the expanded constellationmapping with a Viterbi decoder for Cc, where the trellisshaping represents the parallel branches between consecu-tive states transitions in the trellis. After that, the shapingbits are estimated by the syndrome mapper for Cs.

However, the possible power reduction of Xnc , caused

by the trellis precoding technique, impacts directly on bothlinks performances, since the value adopted for powersharing at the superposition strategy is no longer exact.It can be noted, by regarding the Eq. (4), that the SINRat primary receiver is increased and, as a result, the PUpresents better bit error rate (BER) performance.

In this paper, we propose a method for controlling theCU output power such as E[|Xn

c |2] = Pc, or equiva-lently, E[|Xn

c |2] = (1 − α)Pc. This is reached by properscaling of the minimum distance dmin of the transmittedconstellation.

According to the trellis precoding theory [15], theexactly same shaping gain generated by a shaping codeCs is obtained for the multiuser precoding [12]. In linewith this analysis, we define the power of the baseline,without considering the shaping operation, as:

P⊕ =2R

6d2min′, (14)

where R is the data rate in bits per two dimensions. For theproposed scheme, the shaping gain is defined as follows:

γs =P⊕

(1− α)Pc. (15)

Thus, the scaled minimum distance dmin′, such that theavailable power after the shaping operation is equals to(1− α)Pc, is defined by:

dmin′ =√

[(1− α)Pc]6γs2R

. (16)

B. Constellation Expansion

When the interference is much higher than the signalconstellation, the output power after the presubtraction isincreased and the dirty paper technique can not be applied.In this case, two solutions can be implemented: either weadd a modulo operation to confine the transmitted signal

or, alternatively, we expand the CU constellation to getcloser to the interference.

As an example, following the notation in Fig. 4, con-sider that the original DPC constellation is a 16-QAM,where each symbol is defined by the tupple (z1z2c1c2)where z1 and z2 are the sign bits while c1 and c2 arethe coset bits. This constellation can be expanded to a64-QAM by considering an additionally two ’auxiliary’bits (not information bits) z3 and z4. Now, each symbolis defined by the tupple (z1z2z3z4c1c2). That way, theCU constellation can be made arbitrarily larger, enough toconfine the interference signal (here the PU symbols).

This operation could be seen as an extension of the trel-lis shaping procedure. The Viterbi decoder at transmitteracts as an usual TCM decoder, where the auxiliary shap-ing bits zaux represent the parallel branches transitionsbetween consecutive state transitions in the trellis for Cs.

IV. SIMULATION AND RESULTS

The PU is implemented as a 4-QAM signal encodedby a 16-state, rate 1/2, convolutional code Cc, specifiedin the octal notation by the generators g1(D) = 31 andg2(D) = 33. The transmitted power is variable accordingto the specific scenario.

For the CU, in all simulated cases, the available trans-mitted power is assumed as 10 W and the transmissionrate is Rcu = 2 bits/symbol. The proposed DPC encoderpresented in the Fig. 4 is implemented for some configu-rations.

In the upper part of the encoder, for all cases, the samesystematic 64-state, rate 1/2, channel trellis convolutionalcode Cc, given by feedforward polynomial h1(D) = 54and the feedback polynomial h0(D) = 161, is considered.

In the lower part of the encoder, for all cases, the same4-state, rate 1/2, shaping code Cs, specified by generatorsgs,1(D) = 7 and gs,2(D) = 5 is assumed. We investigatethe constellation expansion in 4 (with ns = 2) and 16regions (with ns = 4, where 2 are auxiliary bits). It isimportant to point out that the minimum distance scalingmethod, previously exposed in Section III, was imple-mented in all simulated schemes. Additionally, the impactof modulo operation in the system is also discussed.


-10 -5 0 5 10

In-Phase

-10

-5

0

5

10

Qu

ad

ratu

re

(a) PP = 3 dB

-10 -5 0 5 10

In-Phase

-10

-5

0

5

10

Qu

ad

ratu

re

(b) PP = 7 dB

-10 -5 0 5 10

In-Phase

-10

-5

0

5

10

Qu

ad

ratu

re

(c) PP = 15 dB

Fig. 5: Scatter plots of signal constellations at different PU interfering powers

In all cases, and without loss of generality, the interfer-ing path gains a and b are assumed real values and equalto 0.2, which represent the attenuation factors (antennaspatterns, mismatches, earth station locations etc). Thefollowing scenarios, with respect of the PU power Pp,are evaluated at (i) Pp = 3 dB; (ii) Pp = 7 dB and (iii)Pp = 15 dB.

In other words, since the Pp changes, a different value ofthe superposition factor α∗ is computed at the CU designfor each scenario, according to Eq. (5). As a consequence,the remaining power of the signal Xn

c as well as theamount of interference Sn at CU receiver vary from caseto case.

In order to analyze the system performance, the Fig. 5presents the scatter plot of the signal constellations inte-grated in the partial interference pre-subtraction process-ing. The expanded constellation signal Xn

cc is shown ingreen ”x”. The Gaussian distributed version of the scaledinterference λSn is superposed in red points and thetransmitted signal Xn

c is shown in blue dots.In the first scenario (i), for PU transmitted power Pp of

3 dB, the scheme with slight constellation expansion of 16-QAM has proved effective. The CU output power remainsconstant (10 W), which implies accurate superpositionstrategy. By the Fig. 5a, we notice that all interference isconfined within the expanded constellation. In this case,we may infer that a significant part of shaping loss isrecovered.

In such conditions, the scheme with more expansion, i.e.64-QAM, is not be recommended, since the exactly same16-QAM constellation points are going to be selected bythe trellis shaping operation. Thus, the system complexitywould be increased (more branches in the shaping code)without any effective gain.

The performance can be expressed, in a quantitativeway, by the BER curves shown in Fig. 6. As expected, thescheme presented a gap less than 1 dB from the AWGNtrellis shaping curve. In addition, we presented the samescheme with the modulo operation, coined as 16-QAMTP-M. It is clear that the modulo induces the so-calledmodulo losses in the system, as stated in [12] and [14].

Furthermore, in order to verify that PU operates prop-erly, the simulated BER for PU at Pp 3 dB is depicted inthe presence and the absence of CU interference, i.e. 16-

QAM TP-M scheme. Thanks to the superposition and tothe method of d′min scaling, the BER PU, in the presenceof CU interference, remains exactly the same as in thecase of no CU interference.

In the second scenario (ii), for Pp of 7 dB, the inter-ference can not be confined within the 16-QAM scheme.In this case, the CU output power is increased and theDPC condition is not satisfied. In Fig. 5b, we note that,by adopting the solution with 64-QAM, the interferenceremains inside of the expanded constellation region, whichled us to expect that significant part of shaping loss couldbe recovered by the system.

The Fig. 7 presents the BER performance for thisscenario. The results for 64-QAM scheme and 16-QAMTP-M are exposed. We observe that 64-QAM schemeperforms within 1.5 dB to the AWGN trellis shapingBER curve. In addition, the 16-QAM TP-M scheme hasa similar behaviour in relation to the previous scenario.Finally, as in the previous case, by using the 16-QAMTP-M signal as an interfering signal, the BER of the PUremains the same in comparison with the interference-freePU channel.

In the last scenario (iii), i.e. Pp of 15 dB, we canobserve in Fig. 5c that a higher constellation expansion(ns about 5 or 6) is necessary in order to accommodatethe strong amount of interference. However, this expansionmight not be reasonable for satellite communications dueto the induced high complexity of the transmitter (herethe satellite) and to the high 64-QAM peak-to-averageratio (PAPR). In this case, the 16-QAM TP-M should beadopted as depicted in Fig. 5c. Despite of the exact controlof the CU output power, a near-uniform distribution ofthe transmitted signal is observed, which indicates thatno shaping gain is reached. The Fig. 8 presents the BERcurves for this scenario. We observe that the modulo lossis not significantly high when compared to the 64-QAMscheme.

V. CONCLUSION

This paper presents a design of cognitive overlay linkfor satellite communications. Different schemes were im-plemented in different realistic scenarios. The results pre-sented the exactly same bit error rate for the primary userwhen compared with its performance on the correspondingAWGN channel. On the other hand, the cognitive user


2 4 6 8 10 12

1e-05

1.0E-4

0.001

0.01

0.1

SNReff at CU Receiver

BE

R

DPC (PU=3 dB) 16-QAM TP-MDPC (PU=3 dB) 16-QAMTrellis Shaping AWGNPU=3 dB + InterferencePU=3 dB AWGN

Fig. 6: BER of the Cognitive User at PP = 3 dB

2 4 6 8 10 12 14

1e-05

1.0E-4

0.001

0.01

0.1


BE

R

DPC (PU=7 dB) 16-QAM TP-MDPC (PU=7 dB) 64-QAMTrellis Shaping AWGNPU=7 dB + InterferencePU=7 dB AWGN


2 4 6 8 10 12 14 16

1e-05

1.0E-4

0.001

0.01

0.1


BE

R

DPC (PU=15 dB) 16-QAM TP-MDPC (PU=15 dB) 64-QAMTrellis Shaping AWGN


presented a gap of around 1 dB in comparison to trellisshaping scheme over the AWGN channel.

In the future research, the typical impairments of satel-lite communications will be investigated for this trans-mission scheme. Moreover, we will analyze the overlaytechniques considering nonlinear modulations.

ACKNOWLEDGMENT

This work was supported by National Council for Sci-entific and Technological Development (CNPq/Brazil) andby National Institute for Space Research (INPE/Brazil).

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