Adaptive Loading in HF Communications

8/14/2019 Adaptive Loading in HF Communications

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Adaptive Loading in HF CommunicationsXudong Chen, Shuzheng XuDepartment of Electronic Engineering, Tsinghua University, Beijing 100084, P.R.China

Email: [email protected]

Abstract - Adaptive bit loading algorithm is an effectiveway to use spectrum more efficiently when employed bysystems. In this paper, we apply this algorithmfor HF communications adopting OFDM/MIMO scheme withMDAPSK modulations. A MIMO system utilizes multipleantennas at the transmitter and receiver. Such systems havedemonstrated the ability to reliably provide high throughput inrich multipath environment. Simulations ofsystems with/without adaptive loading scheme andOFDMlMIMO with adaptive loading scheme are carried outrespectively. Results justify the advantage of the combinationof an system and the adaptive bit loadingalgorithm, which leads to significant data rates with improvedbit error performance, over traditional HF communicationssystemsIndex Terms -H F Communications, adaptive loading,OFDM, MIMO, DAPSK

INTRODUCTIONThe high-frequency (HF) band communication, usually from3MHz to 30MHz, has regained much attention recentlybecause it has various applications, widely used for military,maritime, and aeronautical systems and long distancebroadcasting. is the only way of achieving global coveragewithout using terrestrial and satellite infrastructure which isextremely expensive and sometimes can be hostilely interfered.The high-frequency band, though covers a wide range, issurely not inexhaustible, so it is crucial to meet the everincreasing demands of using the available radio spectrummore efficiently due to the current and expected continuingdramatic growth in the number of mobile HF communicationservices, systems and users. Because of this, relatively newand largely unexplored theory and techniques must becontinually studied to find innovative ways in order to satisfythe spectrum usage requirements. As the technique ofOFDMlMIMO has the ability to overcome the negative effectresulted from multipath and to obtain high date rate, it isgenerally considered as one of the most effective methods torealize the high-speed digital communication system on HFchannels [1].One of the key questions in the development of future HFcommunication systems concerns about the spectrumutilization characteristics. To better fulfill the requirement, thispaper present adaptive bit loading scheme for HF

scheme adopting DAPSK modulations.Adaptive modulation is an important technique which has theadvantage, in terms of the increased data rates, over non-This work is sponsored by NSFC under Grant #90207001

adaptive schemes. An inherent assumption in channeladaptation is some form of channel knowledge at both thetransmitter and the receiver. Given this knowledge, both thetransmitter and the receiver can have an agreement upon themodulation scheme designed for increased performance. Inthis paper, we put focus on the adaptive bit and powerallocation schemes [2] [3]. Namely, we presuppose a desirednumber of bits to be transmitted by a single OFDM symbol(consisting of N tones), and then we load these bits onto thetones in such a way that minimum energy is allocated to theentire transmission.In addition to adaptive modulation, MIMO is a usefultechnology helping enhance the data rate which is muchhigher than that of a SISO system. Since recent researcheshave reached conclusions that OFDMlSISO has betterperformance when adopting adaptive loading scheme, thispaper seeks to explore adaptive modulation combined withand to see how much benefit we can get fromthis union. A key concept employed here is that every matrixchannel can be decomposed into a set of parallel subchannelsover which data can be transmitted independently, givenappropriate precoding and shaping transformations at thetransmitter and receiver, respectively.The paper is structured as follows. Section II considers the keysystem aspects. Section III details the adaptive modulationtechniques employed. Section IV shows simulation results,and Section V has conclusions.

SYSTEM ANALYSISModulation: MQAM are normally adopted in wirelesscommunications, however, the application of MQAMmodulations has some drawbacks such as [4]: increasedsensitivity to carrier synchronization errors due to the smallangular separation between the constellation points, which isfurther affected by Doppler frequency shift and oscillatorsinstabilities; channel estimation and very complex butnecessary equalization which will bring large amount ofcomputation and cause heavy burden to the receiver; the needfor a precise decision threshold adjustment; and nonlinear(phase and amplitude) distortion caused by the high-poweramplifier, which has to be operated close to saturation toimprove power efficiency. Alternatively, differentialamplitude and phase shift keying (DAPSK), which has beenexplained and analyzed in [5], is a very attractive techniquefor high data-rate transmission in a mobile radio environment.The scheme of MDAPSK is extended to a combineddifferential amplitude and phase modulation in order toachieve a higher bandwidth efficiency without lowering the

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quality of performance. In this paper, we choose theMDAPSK modulation scheme due to its advantages overMQAM.A modulator transforms a set of bits into a complex numbercorresponding to an element of a signal constellation. In thispaper, given the adaptive algorithm, the modulator has aninput as a set of bits and an energy value, therefore, the outputof the modulator is a symbol of constellation corresponding tothe number of bits on the input. And now the constellation isappropriately scaled to have a desired energy.The modulator is supposed to have only a finite number ofrates available, which means that only a finite number ofconstellations are available for the modulation. Especiallythese constellations are drawn from the set of constellationswhich have number of symbols equal to an even power of 2.Further, in order to provide robustness against bit errors, Graycoded constellations are employed for each modulation orderavailable.Many demodulation techniques can be employed in thissystem, including maximum-likelihood estimation, MMSE,and zero forcing. In order to simplify the demodulator, thussimplify the receiver. Demodulation algorithm in this paper isimplemented using a zero-forcing approach, and it should begiven the knowledge of the individual flat fading channel gainof each subchannel.Channel: The HF channel can be considered as the WideSense Stationary Uncorrelated Scattering (WSSUS) model[7].Throughout this work, the channel is assumed to be aRayleigh block fading channel, corresponding to a richscattering environment with time variation characterized bythe fade time. In the MIMO case, the channel is a matrixchannel with equation

Yn=LH1Xn-1+nn1=0

where, in general, the values Yk ' k ' nk can be vectors, andk can be a matrix. Thus, the delay spread of the channel is

L symbol periods. An exponentially decaying profile ofchannel taps is modeled by fixing the powers of all theelements in each random matrix H k to a constantE Thesecoefficients form a decaying geometric progression in thevariable i . During a coherence time interval, all matrices H kare constant, and when the channel decorrelates, they are alldrawn newly according to their respective pdfs. Further, forsimplicity it is assumed that the channel decorrelates at theend of an OFDM symbol transmission.Channel estimation inverts the effect of selective fading oneach subcarrier. Usually OFDM systems provide pilot signalsfor channel estimation. In the case of time-varying channels,the pilot signal should be repeated frequently. The spacingbetween pilot signals in time and frequency depends oncoherence time and bandwidth. Throughout this paper, thechannel estimates are assumed to be perfect, and available toboth the transmitter and the receiver. Given full knowledge ofthe channel, the transmitter and receiver can determine the

frequency response of the channel, and the channel gains ateach tone of the OFDM symbol. Furthermore, given thesegains, the adaptive algorithm can proceed to calculate theoptimal bit and power allocation.

ADAPTIVE LOADINGThe advantage of OFDM is that each frequency-band ofsubchannel is relatively narrow, thus can be assumed to havethe character of flat-fading. However, it is entirely possiblethat a given subchannel has a low gain, resulting in a largeBER. Thus, it is desirable to take advantage of subchannelswhich have relatively constant and reliable performance; thisserves as the motivation for adaptive modulation. In thecontext of time-varying channels, there is a decorrelation oftime which is associated with each frequency-selectivechannel instance. Therefore, a new adaptation must beimplemented each time when the channel decorrelates.The optimal adaptive transmission scheme, which nearlyachieves the Shannon capacity with a fixed transmit power, isthe water filling distribution of power over the frequencyselective-fading channel. However, while the water fillingdistribution will indeed yield the optimal solution, it isdifficult to be computed, and this scheme tacitly assumesinfinite granularity in the constellation size, which is notpractically realizable.The adaptive loading technique employed in this paper,however, is an effective technique to achieve power and rateoptimization. This adaptive loading algorithm we concernedabout performs fairly well based on knowledge of thesubchannel gains. Only five different square MDAPSK signalconstellations are used; this scheme is expected to haveefficient performance which is very close to that usingunrestricted constellations.In the discrete bit loading algorithm of [2], we are given a setof N increasing convex functions en that represent theamount of energy necessary to transmit b bits on subchanneln at the desired probability of error using a given codingscheme. We assume en (0)=O.The allocation problem which will be using can be formulatedas: Energy Minimization Problem

min L : ~ l e n ( b Jsubject to L : ~ l b n

bn Z,bn O,n 1,2, ...,N .To initialize the bit allocation, the scheme of [6] is employed.The procedure is summarized as follows:1. Compute the signal to noise ratios(SNR) of each

subchannel2. compute the number of bits for the ith subchannel based

on the formula = log2 (13. round the value of down to



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Fig II BER curves for various schemesFor comparison purposes, the fixed-rate SISO simulator wasimplemented as reference, where the total number of bits pertone was fixed for all tones, and variable power optimizationwas applied. The BER results are plotted versus EblNo and itsperformance of the adaptive SISO, adaptive MIMO, andfixed-rate SISO are shown in Figure 2. In all simulations theMIMO system was held as a 2x 2 link. Clearly, at any givenBER the fixed-rate SISO system will be outperformed by theadaptive SISO system, which in tum will be outperformed bythe adaptive MIMO system as we have expected. For all thesethree systems, the total number of bits per OFDM symbolwere always held constant, to ensure fair comparison.

Fig I Energy and Bit Allocation for a channel InstanceAs expected, tones experiencing very poor channel instanceshad few or zero bits allocated to them. Also, it is interesting tonote that the finite number of MDAPSK constellationsavailable means that the rate remains fixed over some intervalswhere the gain does not vary too widely.

T bl I Ie Slmu atlon parametersNumber of carriers 64OFDM symbol time 64 symbol periodsGuard time 16 symbol periodsMDAPSK available 0,1,2,4,6Noise variance lxl0-3Channel HF(WSSUS model)[7]

Given the these parameters, simulations were conducted with100 Monte Carlo iterations for each case. To demonstrate thebit allocation, an instance of the channel was generated andthe adaptive bit loading algorithm help find the optimal bitallocation. Figure 1 shows the channel frequency response, theallocation of bits to each tone, and also the correspondingenergy on each tone.

SIMULATION RESULTSWe consider both a SISO system and a MIMO system, andthese two simulators were built respectively, and the MIMsimulator was updated to have the SISO system occurs as aspecial case. The following parameters set according to ourabove discussion, were held constant throughout thesimulation:

where =6. form a table of energy increments for each subchannel.

For the ith subchannelb- 1

~ e i ( b ) = e i ( b ) - e i ( b - l ) =Now we put our focus on the kth channel. Given the channelgain and noise PSD(Power Spectrum Density) the energyincrement table will provide the required incremental energiesfor the subchannel to transitfrom supporting 0 bits to 1 bit,from 1 bit to 2 bits, from 2 bits to 3 bits and so on. Since werequire our system to have a maximum of 6 bits, the energyincrement required to go from 6 bits to 7 bits is set to a veryhigh value. Additionally, requirement set for the subchannelis to have only 0, 1, 2, 4 or 6 bits. Thus, odd numbers of bitsare not supposed to be supported.Note that we have introduced a new term, namely GAP. Thisparameter is in effect a tuning parameter which helps us a lotsince different values of GAP yield different Eb / No ratiosfor a given desired number of bits B to be transmitted. This issimply because the GAP directly impacts the energy tablevalue calculations. Thus, tuning GAP allows us to characterizethe BER performance of the system.

4. restrict to take the values 0,1,2,4 or 6 only(whichvalue to take is corresponding to available modulationorders)

5. compute the energy for the ith subchannel based on thenumber of bits initially assigned to it using the followingformula



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CONCLUSIONSIn this paper, we have thoroughly analyzed adaptiveoptImIzation algorithms for MIMO/OFDM HFcommunications. Simulation results also indicate that theadaptive algorithm improved a SISO/OFDM system whichoutperforms the SISO system using fixed-rate variable-poweradaptive modulation. We conclude that MIMO/OFDM is avery promising technology, and practical adaptive rate andpower optimization algorithms serve well to improveperformance further. The adaptive scheme can be a feasibleapproach to further improve the performance of HFcommunication systems. A very useful extension of this paperwould be in multiuser MIMO/OFDM systems, andcharacterizing good rate and power sharing algorithms toachieve good mutual BER performance of all users.

REFERENCES[1] Shuzheng Xu, Hui Zhang, Huazhong Yang, etc. NewConsiderations for High Frequency Communications [J]. IEEE10thAPCC/6th MDMC, Sept.2004, pp.444-447[2] J. Campello de Souza, "Discrete Bit Loading for Multicarr ierModulation Systems," PhD Thesis. May, 1999[3] K. Wong et aI, "Adaptive Spatial-Subcarrier Trellis CodedMQAM and Power Optimization for OFDM Transmission",VTC2000, pp. 2049-2053[4] V. Lottici, M. Louise and R. Reggiannini, "Adaptive nonlinearcompensation of satellite transponder distortion for high-leveldata modulations", 7.th Intern. Workshop on DSP Techn. for

Space Commun., Sesimbra-Portugal, Oct. 2001.[5] T.May, H.Rohling and V.Engels, "Performance analysis ofViterbi decoding for 64-DAPSK and 64-QAM modulatedOFDM signals", IEEE Trans. On Comunications, vol 46,No.2,February 1998[6] P. Chow et aI, "A Practical Discrete Multitone TransceiverLoading Algorithm for Data Transmission Over SpectrallyShaped Channels," IEEE Trans. Comm, Vol. 43, No.2,February 1995, Page 773-775.

[7] ETSI ES 201 980 vl.2.2 (2003-04) ETSI standard: digital radiomondiale (DRM); system specification

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Adaptive Loading in HF Communications

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