Cognitive HF Communication System with AdaptiveComplementary Codes

Johanna Vartiainen, Pekka Pirinen and Risto VuohtoniemiCentre for Wireless Communications, University of Oulu

Oulu, FinlandEmail: firstname.lastname@ee.oulu.fi

Abstract—High frequency (HF) band covers frequencies be-tween 3-30 MHz. With wavelengths of 10-100 m, HF band enablescommunications over a very long distances though a skywavepropagation. As HF band has several benefits like versatility andlarge coverage area, it suffers phenomena like rapidly changingenvironment and strong interference. The cognitive radio conceptwhich adapts to the changes in the environment offers tools tosolve those problems. In this paper, HF band challenges causedby ionospheric break and interference are solved using databasesand complementary codes that adapt to the characteristics ofdifferent data transmission requirements. The goal is to optimizeused frequencies based on the data transmission needs.

Keywords—cognitive; HF communication system; adaptive cod-ing; complementary coding

I. INTRODUCTION

High frequency (HF) bands include frequencies between3-30 MHz, which correspond wavelength of 100-10 m. HFbands have several benefits including versatility, economicand technical advantages, large coverage area even in difficultplaces like mountains and seas, and potential to move datawith only a fraction of the satellite communication costs. Afterthe golden age of HF radio at 1920-1960’s, the resurgenceof HF is due to new advantages including software definedradios, embedded encryption and digital communicationstechnology. In addition, lower frequencies are congestedand satellite transmission is very expensive. Modern HFapplications include civilian, industry, military, defence andpublic safety applications from marine traffic to humanitarianoperations in remote locations.

The drawbacks in HF communication include stronginterference and susceptibility to changes in the ionosphere(non-stability). HF frequencies suffer from three types ofinterfering signals. Those are static signals like broadcast radiostation signals, moderately static signals like analog/digitalvoice links, and variable signals like other HF signals,lightning and ignition especially in old cars [1]. Ionosphericconsists of three layers, and each layer has differentcharacteristics. The challenge is that HF ionospheric changesaccording to the time of day and different season. Thisremarkable changing is caused by the Earth’s geomagneticfield, the radiation of the Sun, time (day/night), location(geographical), season (spring/summer/autumn/winter) and bythe winds in the upper atmosphere.

Traditional frequency management in HF is based onautomatic link establishment (ALE). It is a standard for HFcommunication that selects automatically the best channel fora transmission based on the Link Quality Analysis (LQA)

table [2], [3]. It consists of fixed list of frequencies forcertain days and hours, from where the best available freefrequency is selected for a transmission. As 2G ALE appearedin the 1980s, the 3rd generation ALE was established inthe 1990s. Both the second and the third generation ALEdo not use adaptive or cognitive aspects. The problems inALE include inflexible frequency management and channelselection performance limitations. It is not able to give exactinformation about what is the best frequency to be used inthat time.

One possibility to solve HF communication problems is touse cognitive aspects [4], [5], [6], [7]. Basically, cognitivitymeans that the radio is aware of its environment and able toadapt its parameters according to the spectrum conditions.Cognitive ALE that enhances the system performance has beenconsidered, for example, in [8]. Cognitive HF in general hasbeen considered in [1], where cognitive HF channel choosingwas considered. Therein, channel selection/access was seento be a big problem in traditional HF communications. Itwas discussed that HF radios may use spectrum sensing inorder to increase the HF radio system reliability. CognitiveHF system has several benefits. It offers security, quality,reliability and decreased costs. Cognitive aspects have alreadybeen implemented to HF radio in the 2010s. CognitiveNetworked HF (CNHF) radio system combines HF systemswith cognitive and software-defined technologies [9].

HF communication environment differs from traditionalcognitive radio. In the HF playground there are no occupiedfrequencies, so all the players are equal. It means that thereare no primary (licensed) users (PU). Instead, all the usersare competing with each other for unoccupied frequencies, sothey can be considered to be secondary (non-licensed) users(SU). In that kind of case when there are no PUs but onlySUs, SUs can be called as co-equal users.

HF communication problems can also be solved usingadvanced coding instead of commonly used codes like Barkercoding. Complementary coding is an interesting choicebecause of its excellent correlation properties. The DigisondePortable Sounder 4D (i.e., DPS4D) HF radar system [10]developed in the years 2004-2008 uses complementary coding.Therein, the length of the complementary code is fixed to be16 chips.

In our proposal, challenges caused by ionospheric breakand interference are solved using complementary codes thatadapt to the characteristics of different data transmissionrequirements. Adaptivity enables performance monitoring andparameter modifying in order to adapt to the changes in theenvironment. The length of the complementary code adapts to

the channel properties and fulfills different data transmissionneeds. Code design is based on the properties of the channeland transmitted data. Databases collect information fromevery HF frequency at different timeslots to help codeadaptation. This method can be used from simple signalingto data and voice transmission, and in military, public safetyapplications, as well as in civilian uses. Next sections providemore information about the complementary codes and theirusage in the adaptive HF system concept framework.

II. COMPLEMENTARY CODING

Complementary code pairs and sets possess ideal auto- andcross-correlation properties that cannot be achieved by anyunitary spreading codes [11]. Ideal autocorrelation function(ACF) means a high correlation peak at zero delay and zeroelsewhere, i.e., no sidelobes. Correspondingly, ideal cross-correlation function (CCF) means zero correlation at all delays,i.e., no mutual interference. Therefore, it is easy to see thatthis kind of codes have high potential for a wide rangeof applications in wireless communications (e.g., contentionresolution in medium access control layer [12], [13] andphysical layer spreading code based duplexing [14], [15]),radars [16], [10] and other areas. However, complementarycodes have also constraints that should be noted in the systemdesign. Their main restrictions are:1) the granularity of code lengths and the number of distinctsequences are quite limited,2) each code sequence in the code set requires an orthogonaltransmission channel to preserve ideal correlation properties.

The former limits the multi-access capability and the latterexpands the radio resource (bandwidth) demand in the imple-mentation of the system. In addition, complementary codingis sensitive to Doppler shifts because it is assumed that thephase distance of the propagation path between two pulsesis constant. However, in HF systems the maximum Dopplershift of the channel can be estimated and based on that it canbe evaluated is reliable transmission possible or not. Dopplerresilient Golay complementary waveforms have been studiedin [17].

Golay [18] presented the complementary sequence pairsthat were later generalized and extended to complementarycode sets [19] and [20]. More recent contributions haveintegrated these codes to modern communications systemsand proposed techniques to mitigate some of their fundamen-tal limitations. One example is the large area synchronous(LAS) CDMA proposal [21] where the zero auto- and cross-correlation windows (interference-free windows) are scalableaccording to the propagation channel characteristics. Withinthe zero autocorrelation window the multipath interferenceof the desired user is eliminated. Respectively, within thezero cross-correlation window the impact of multiple accessinterference is wiped out completely. Closely related to this,book [22] and articles [23], [24] address CDMA technologyevolution and proposes so-called Real Environment AdaptedLinearization (REAL) methodology for inherent immunityagainst multipath and multi-access interference.

The downsides associated to complementary codes have sofar prevented them from commercial breakthrough in spite oftheir superior features. However, the more the application isinclined towards reliability and robustness in extreme condi-

Fig. 1. Adaptive complementary code design.

Fig. 2. Code adaptation to the channel quality.

tions the more attempting choice they become. That is exactlywhy complementary codes have been selected to be a part ofour cognitive HF system concept.

III. ADAPTIVE COMPLEMENTARY CODING

In this paper, HF communication challenges causedby ionospheric break and interference are solved usingcomplementary codes that adapt to the characteristics ofdifferent data transmission requirements (Figure 1). Usedcomplementary codes adapt to the channel properties andfulfill different data transmission needs from simple signalingto data and voice transmission (Figure 2).

Complementary code design is based on the propertiesof the channel and transmitted data. Databases collectinformation from every HF frequency at different timeslots tohelp code adaptation (Figure 3). The idea is that the codelength adapts according to the quality of the channel. Forexample, in the frequencies with good channel quality, shortcodes are used to achieve high capacity. This enables, forexample, video data transmission. Instead, in the frequencies

Fig. 3. Database.

with poor channel quality, long codes are used. This enablessending low capacity data like signaling.

Approaches [21] and [22] discussed in Section II providedetails and practical mechanisms how to implement theconceptual adaptive complementary coding based systemdepicted in Figures 1-3, so details are not considered here.

The purpose of this proposed method is to optimize usedfrequencies based on the data transmission needs. Thus,frequencies with good channel quality are reserved to highcapacity transmission, as frequencies with poor channelquality can be used to send low capacity transmissions. In thisway, frequencies with poor channel quality are not totally lost.

IV. CONCLUSION

High frequency communication enables long distance com-munication through a skywave propagation. Problems includestrong interference environment and changes to the ionosphere.Cognitivity has been considered to solve problems in HFcommunications. In this paper, the usage of adaptive comple-mentary codes that adapt to the characteristics of different datatransmission requirements is proposed. Used frequencies areoptimized based on the data transmission needs. The length ofthe complementary code adapts to the channel properties andis able to fulfill different data transmission needs from simplesignaling to data and voice transmission.

ACKNOWLEDGMENT

The research of Johanna Vartiainen was funded by theAcademy of Finland.

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