A context-aware cognitive SIMO transceiver for enhanced ... · ‡Current and future generation...

WIRELESS COMMUNICATIONS AND MOBILE COMPUTINGWirel. Commun. Mob. Comput. 2016; 16:1414–1430Published online 30 June 2016 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/wcm.2705

SPECIAL ISSUE PAPER

A context-aware cognitive SIMO transceiver forenhanced throughput on the downlink of LTE HetNet†

Imen Mrissa*, Faouzi Bellili, Sofiène Affes and Alex Stéphenne

INRS-EMT, Montreal, Quebec, H5A 1K6, Canada

ABSTRACT

In this paper, we design a new single-input multiple-output context-aware cognitive transceiver (CTR) that is able to switchto the best performing modem in terms of link-level throughput. On the top of conventional adaptive modulation andcoding, we allow the proposed CTR to make best selection between three different pilot-utilization modes: conventionaldata-aided (DA) or pilot-assisted, non-DA (NDA) or blind, and NDA with pilots, which is a newly proposed hybrid ver-sion between the DA and NDA modes. We also enable the CTR to make best selection between two different channelidentification schemes: conventional least-squares (LS) and newly developed maximum-likelihood estimators. Dependingon whether pilot symbols can be exploited or not at the receiver, we further enable the CTR to make the best selectionamong two data detection modes: coherent or differential. Owing to exhaustive link-level simulations on the downlink ofa long-term evolution system, we draw out the optimal decision rules in terms of the best combination triplet of pilot-use, channel-identification, and data-detection modes that yield the best link-level throughput as function of channel type,mobile speed, signal-to-noise ratio, and channel quality indicator. The proposed CTR offers a link-level throughput gainsimprovement as high as 700% compared with DA LS for VehB channel type at a mobile speed of 100 km/h in the lowsignal-to-noise ratio region. For VehA channels, its throughput gain improvements can reach 114%. For PedA and PedBchannels, the proposed CTR provides throughput enhancements of about 66% and 330%, respectively. Moreover, realisticsimulations at the system-level of the long-term evolution-HetNet network suggest that the new context-aware CTR out-performs the conventional transceiver (i.e., pilot-assisted LS-type channel estimation with coherent detection) by as muchas 50% and 60% gains in average and cell-edge (i.e., five percentile) throughputs, respectively, in the high-clustering casewith type-B channels. In the low clustering scenario, the average and cell-edge throughput gain improvements offered bythe proposed CTR exceed 80% and 90% for type-B channels. Copyright © 2016 John Wiley & Sons, Ltd.

KEYWORDS

cognitive transceivers; LS and ML channel estimators; data-aided and non-data-aided detection; coherent and differentialmodulations; LTE; HetNet; link; and system-level simulations

*Correspondence

Imen Mrissa, INRS-EMT, 800 De La Gauchetiere Ouest, Suite 6900, Montreal, Quebec, H5A 1K6, Canada.E-mail: [email protected]

1. INTRODUCTION

One of the strongest driving forces for wireless technol-ogy evolution today is 4G (4th Generation), also known asLTE-Advanced (Long Term Evolution) or IMT-Advanced(International Mobile Telecommunications) [2]. 4G allowshigh-speed wireless data delivery at much lower costs and

†A previous version of this paper was invited and presented in the Inter-

national Conference on Wireless Communications and Mobile Com-

puting (IWCMC) 2015 [1]. In this paper, we extend our experimental

assessments to both frequency-selective channels and low-clustering

scenarios.

latency while providing much higher rates, spectrum effi-ciency, and coverage. Most importantly, it promises theprovision of future high-speed wireless data services every-where closer to the mobile user in a seamless and versatilefashion, no matter what the surrounding environment andlink conditions are. This stringent requirement calls forthe development of new cognitive transceivers that arecapable of promptly and properly self-adapting to variableoperating conditions in order to constantly maximize theirperformance.

It is precisely in this vibrant research context that weget onto the emerging cognitive radio [3,4] from a ratheruncommon perspective today. Indeed, cognitive radio isreduced in most recent works to one of its two primary

1414 Copyright © 2016 John Wiley & Sons, Ltd.

I. Mrissa et al. Cognitive SIMO Transceiver for Enhanced DL-LTE-HetNet Throughput

objectives: exploit efficiently the radio spectrum withdynamic spectrum access (DSA) that allocates the leastoccupied frequencies, though licensed and reserved, to sec-ondary users who are short of bandwidth [5–7]. Here,we take up its second primary objective of providinghighly reliable communications anywhere anytime, so faraddressed in a conventional manner, but rarely tackledtoday from a new level of “cognitive wireless com-munications” [4] where cognition could possibly handlemany dynamic reconfiguration dimensions other than spec-trum allocation, the conventional one. In this context,we develop in this paper a new context-aware cognitivetransceiver (CTR) that is able to self-adjust its antenna-array processing structure and air-interface configurationfor optimum performance. More specifically, the proposedCTR is able to select the best combination triplet ofpilot-use, channel-identification, and data-detection modesthat achieve the best link-level performances againstchannel conditions in terms of channel type, mobilespeed, signal-to-noise ratio (SNR), and channel qualityindicator (CQI).

Many recent research works have tackled the problemof channel estimation for LTE systems with the ultimategoal of improving coherent detection. For instance, opti-mizing the pilot symbols pattern was considered in [8] and[9]. Exploiting channel correlations in the time and fre-quency domains to enhance channel estimation in OFDMsystems was also proposed in [10]. Reducing the com-putational complexity of the MMSE channel estimationalgorithm in the LTE standard was also considered in[11]. As a matter of fact, periodic transmission of knownsymbols, according to a predefined and static insertionrate, is assumed by the aforementioned channel estima-tion algorithms as they are designed for coherent detectionreceivers. Yet in order to maximize the system through-put, the required pilot insertion rate and the choice ofthe appropriate channel estimation scheme can be opti-mized depending on user mobility. In fact, if the channelis assumed to be locally constant for low-mobility users, itcan be accurately estimated from pilot observations only,by applying, for instance, the conventional data-aided (DA)least-squares (LS) estimator, and then used to decode thedata at non-pilot observations. For high mobility users,‡

however, the channel varies appreciably between consec-utive pilot positions and increasing the number of pilotsin order to track its variations leads to unacceptably highoverheads. In this situation, by trading computational com-plexity for spectral efficiency, more sophisticated channelestimation approaches that are able to exploit observationsat both pilot and non-pilot positions can be envisaged.

Motivated by these facts, we develop in this papera cognitive transceiver that switches to the best per-forming channel identification algorithm between LS and

‡Current and future generation multi-antenna systems such as LTE,

LTE-A, and beyond (LTE-B) are indeed expected to support reliable

communications at very high velocities reaching 500 Km/h (e.g., high-

speed trains).

maximum-likelihood (ML), and the best estimation modebetween DA, NDA, and non-DA (NDA) with pilots. Infact, on the top of the conventional LS estimator, we inte-grate the very recent ML algorithm [12] that is tailoredspecifically toward time-varying SIMO channels. More-over, the latter is able to handle the three aforementionedestimation modes on the top of its capabilities to trackfast time variations with user velocities reaching 500 km/h.The underlying ML estimator is based on a piece-wisepolynomial-in-time expansion for the channel involvingvery few unknown coefficients. In the DA scenario wherethe receiver has access to a pilot sequence, the DA estima-tor is derived in closed form. In the NDA case, however,the ML estimates for the unknown channel’s polynomialapproximation coefficients are retrieved iteratively usingthe expectation-maximization concept and the algorithmconverges within few iterations.

The performance of the proposed context-aware CTRis assessed by conducting exhaustive simulations at boththe link and system levels. For link-level assessment pur-poses, we simulate a wireless system consisting of one userequipment (UE) and one base-station (BS). We first iden-tify the performance pertaining to each couple of fchannelestimator (LS or ML), data detection (DA, NDA, or NDAwith pilots)g apart and draw out the optimal decisionrules in terms of the best performing fchannel estimator,detection modeg configuration. Block error rate (BLER)versus SNR results are then fed to a system-level simula-tor, wherein a whole network is simulated in order to assessthe performance of the proposed CTR under more realisticoperating conditions that account for inter-cell and intra-cell interference sources. Link-level simulations reveal thatthe proposed cognition concept offers throughput gains inalmost all operating conditions up to as much as 700%against to the conventional non-cognitive transceiver thatconstantly relies on the pilot-assisted LS channel estimatorwith coherent detection. System-level simulation resultsalso suggest that the new context-aware CTR offers per-formance gains as high as 80% and 90% in terms ofaverage and cell-edge (i.e., five percentile) throughputs,respectively.

The remainder of this paper is structured as follows.In Section 2, we introduce the context-aware CTR modesalong with the different channel estimation schemes. InSection 3, link-level and system-level simulations resultsare presented and discussed in order to illustrate thetremendous performance gains offered by the proposedCTR. Finally, concluding remarks are drawn out inSection 4.

In the sequel, some of the common notations will beused. In fact, vectors and matrices are represented in lower-case and upper-case bold fonts, respectively. Besides,f.g�1, f.gT , and f.g� represent the inverse, the transpose,and the Hermitian (transpose conjugate) operators, respec-tively. We will also denote the probability mass function(PMF) for discrete random variables by PŒ.� and the pdf forcontinuous random variables by p../. The statistical expec-tation with respect to any random variable is denoted as

Wirel. Commun. Mob. Comput. 2016; 16:1414–1430 © 2016 John Wiley & Sons, Ltd. 1415DOI: 10.1002/wcm

Cognitive SIMO Transceiver for Enhanced DL-LTE-HetNet Throughput I. Mrissa et al.

Ef.g. Furthermore, the operators <f.g, f.g� and j.j returnthe real part of any complex number, its conjugate, and itsamplitude, respectively.

2. CONTEXT-AWARE COGNITIVETRANSCEIVER MODES

2.1. Data-aided or pilot-assisted mode

Pilot symbols are reference (i.e., known) symbols insertedaccording to a predefined mapping to be used by thereceiver for channel estimation and synchronization pur-poses. The LTE DL pilot mapping is depicted in Figure 1.An LTE link-level SIMO transceiver block diagram withDA channel estimation is also shown in Figure 2 forantenna array configuration with Nr receiving antenna ele-ments. A brief description of each module is providedhereafter:

Figure 1. Long-term evolution downlink pilot mapping.

� Turbo Coding: Introduces redundancy in thesources’ binary data sequence to mitigate channelerrors. The coding rate is selected according to theChannel Quality Indicator (CQI) as described inTable I.

� Scrambling: Interleaves code bits (we use the scram-bler described in [13]).

� QAM modulation: Maps the code binary sequenceto complex symbols.

� OFDM symbol assembly: Maps symbols to a time-frequency grid of size Nfreq � Nsymb.

� Zero Padding, Cycling prefix, and IFFT: Zeropadding consists in adding NIFFT � Nfreq zeros tothe signal, cycling Prefix consists in prefixing eachOFDM symbol with a portion of its tail whose sizemust be greater or equal to the duration of the multi-path delay spread. It can be configured to normal withdurations of 5.2 and 4.7 �s for the first and remainingOFDM symbols, respectively, or to extended with aduration of 16.7�s for all OFDM symbols. We opt for

Table I. AMC schemes used by LTE downlink simulator.

CQI-CnCQI-D Modulation Coding rate

1 QPSKnDQPSK 0.07622 QPSKnDQPSK 0.11723 QPSKnDQPSK 0.18854 QPSKnDQPSK 0.30085 QPSKnDQPSK 0.43856 QPSKnDQPSK 0.5879

7 16QAMnD16StarQAM 0.36918 16QAMnD16StarQAM 0.47859 16QAMnD16StarQAM 0.6016

10 64QAMnD64StarQAM 0.455111 64QAMnD64StarQAM 0.553712 64QAMnD64StarQAM 0.650413 64QAMnD64StarQAM 0.753914 64QAMnD64StarQAM 0.852515 64QAMnD64StarQAM 0.9258

AMC, adaptive modulation and coding; LTE, long-term evolution.

Figure 2. Block diagram of an long-term evolution downlink link-level single-input multiple-output transceiver with data-aided channelestimation.

1416 Wirel. Commun. Mob. Comput. 2016; 16:1414–1430 © 2016 John Wiley & Sons, Ltd.DOI: 10.1002/wcm


normal cyclic prefix in our simulations. IFFT is theinverse fast Fourier transform modulating the signalbefore transmission.

� Soft Sphere Decoder: The sphere decoder extractssoft bit decisions from the received signal, y, given thechannel estimatebh. The soft sphere decoder computesthe following log-likelihood ratios (LLRs):

Lb D log

0@Phxb D C1 j y,bhi

Phxb D �1 j y,bhi

1A, b D 1, 2, : : : , Q

(1)

where xb is the bth bit among the Q bits of eachtransmitted symbol.

� Hard bit decision: Having the soft bit decision fromthe sphere decoder, this module decides of the binarytransmitted bit.

� Channel estimation: We use either the conventionalLS or the newly proposed ML channel estimatordescribed later.

2.1.1. Least-squares channel estimator.

The LS channel estimates are obtained by minimizingthe squared difference between the vector of received sam-ples and the known pilot symbols. Let yi,DA.q/ denote thereceived signal (at the output of the DFT block) on pilotsubcarrier i among Npilot pilot subcarriers at the OFDM(pilot) symbol of index q. For convenience, we will hence-forth omit the time index q. The transmitted pilot symbol,xi,DA, is related to yi,DA as follows:

yi,DA D hixi,DA C wi i D 0, 1, : : : , Npilot � 1 (2)

where hi is the frequency-domain complex channel coef-ficient and wi is a zero-mean Gaussian noise. The matrixnotation of (2) is

yDA D XDAhC w (3)

where XDA D diag˚x0,DA, x1,DA, : : : , xNpilot�1,DA

�, h D�

h0, h1, : : : , hNpilot�1�T , and w D

�w0, w1, : : : , wNpilot�1

�T .

The LS algorithm minimizes .yDA�XDAh/�.yDA�XDAh/to estimate the channel frequency response at pilot posi-tions thereby leading to [14]:

bhLS D X�1DAyDA (4)

The estimates for the channel coefficients at non-pilot sub-carriers are then obtained by interpolation [15]. However,when the channel is fast fading (i.e., high velocity), the-underlying pilot symbols’ spacing may not be sufficientfor accurate tracking of the channel time variations. Notehere that decreasing pilot spacing increases pilot over-head thereby limiting the effective throughput, which is notdesirable for any communication system.

2.1.2. Maximum-likelihood channel estimator.

Here, we capitalize on the ML channel estimator intro-duced recently in [12]. Over consecutive OFDM symbols,the DA ML estimator captures the channel’s time varia-tions via a polynomial-in-time expansion of order .J � 1/.In fact, the frequency-domain channel over each frthg

NrrD1

antenna branch and ith subcarrier can be modeled asfollows [16]:

hi,r.tn/ DJ�1XjD0

c. j/i,r t j

n C REM.i,r/J .tn/ (5)

where tn D nTs with Ts being the OFDM symbol period.The polynomial order J�1 is a Doppler-dependent parame-ter optimized in [12]. Moreover, c.j/i,r is the jth coefficient ofthe underlying channel polynomial approximation over theith subcarrier and the rth antenna element. The remainderof the Taylor series expansion, REM.i,r/

J .tn/, can be drivento zero by dividing the whole observation window intomultiple local approximation windows of sufficiently smallsizes. Therefore, the channel can be locally approximatedas follows [12]:

hi,r.tn/ DJ�1XjD0

c.j/i,r tjn (6)

In our simulations, ML channel estimation is performedindependently over each pilot subcarrier. For the sakeof simplicity, we also omit the subcarrier index in theremainder of this paper because the very same estimationprocedure applies for all subcarriers.

To use a small J in (5) and avoid costly inversions oflarge-size matrices, the new DA ML estimator partitionsthe whole observation window into K local approximation

windows of the same size. Let ck,r Dhc.0/k,r , c.1/k,r , : : : ,

c.J�1/k,r

iTand y.k/r,DA D

hy.k/r .t1/ y.k/r .t2/ : : : y.k/r

�tPDA

� ibe, respectively, the vectors that contain the Junknown polynomial coefficients and the PDA receivedpilot samples§ corresponding to the rth antenna over the

kth approximation window. Then, by denoting ck DhcT

k,1,

cTk,2, : : : , cT

k,Nr

iTand y.k/DA D

hy.k/1,DA y.k/2,DA : : : y.k/Nr ,DA

iT,

the ML estimator maximizes the probability densityfunction (pdf) of y.k/DA, parametrized by ck as follows:

p�

y.k/DA; ck j Bk

�D

1

.2��2/NDANr�

exp

�

1

2�2

hy.k/DA � Bkck

i� hy.k/DA � Bkck

i(7)

§Note here that PDA is the number of pilot positions within each

approximation window covering NDA pilot and non-pilot received

samples. The approximation window size NDA is another Doppler-

dependent design parameter optimized in [12].



Figure 3. Block diagram of long-term evolution downlink link-level single-input multiple-output transceiver for non-data-aidedchannel estimation.

where Bk is a PDANr � JNr block-diagonal matrix definedas Bk D blkdiagfAkT, AkT, : : : , AkTg. Here, Ak is thePDA � PDA diagonal matrix containing the pilot sym-bols covered by the kth approximation window, that is,Ak D diag

˚ak.t1/, ak.t2/, : : : , ak

�tPDA

��, and T is a

Vandermonde matrix given by the following:

T D

0BBBB@1 t1 : : : tJ�1

1

1 t2 : : : tJ�12

......

. . ....

1 tPDA : : : tJ�1PDA

1CCCCA (8)

The estimates of the polynomial coefficients over all thereceiving antenna branches are obtained by setting the par-

tial derivative of the natural logarithm of p�

y.k/DA; ck j Bk

�in (7) to zero thereby leading to the following:

bck,DA D�

B�k Bk

��1B�k y.k/DA (9)

The DA ML estimates for the channel coefficients, at bothpilot and non-pilot positions, are obtained by injectingbck,DA in (9) back into (5).

2.2. Non-data-aided with pilotsor hybrid mode

Ensuring reliable communications is the purpose of allwireless communication systems. However, user mobilityand surrounding scatters’ motion make accurate estimationof highly time-varying channels a truly challenging task. Infact, relying solely on pilot symbols that are often insertedfar apart, in the time-frequency grid, do not enable accu-rate tracking of fast-varying channels. Samples receivedat non-pilot positions are also exploited hereafter in ahybrid channel identification scheme in order to enhancethe system’s performance.

2.2.1. Recursive Least Squares (RLS)

channel estimator.

The hybrid mode of the LS channel estimator (usingonly pilot symbols to estimate the channel) is the RLSalgorithm that relies on both data and pilot symbols to

Figure 4. Differential modulation block diagram for: (a) QPSK,and (b) 16 QAM (� is a time (or frequency) index for time (or

frequency) differential modulation.

track the channel. The algorithm recursively estimates thechannel polynomial approximation coefficients defined in(5). In fact, stacking the channel coefficients (across allthe antennas) at the pth OFDM symbol and subcarrier i inthe vector hp D

�hi,1.pTs/, hi,2.pTs/, : : : , hi,Nr .pTs/

�T , itfollows from (6) that

hp D Cvp (10)

where vp D Œ1, p, : : : , pJ�1�T and C is a matrix that con-tains the polynomial approximation coefficients over all theantenna branches (normalized by Ts), that is,

C D�Qci,1, Qci,2, : : : Qci,Nr

�T (11)

where Qci,r DhQc.0/i,r , Qc.1/i,r , : : : , Qc.J�1/

i,r

iTand Qc.j/i,r D c.j/i,r Tj

s.

Note here that for ease of exposition, we drop the sub-carrier index i because the same procedure is used for allsubcarrires. At OFDM symbol pC1, the previous detectedsymbols are used as a training sequence of p symbols. Infact, given the previous detected symbols

˚xp0�p

p0D1 and



the corresponding received vectors,˚yp0�p

p0D1 2 CNr�1,over all the antenna branches, the polynomial coefficientsmatrix, C, given in (11) is estimated using the weighted LSmethod as follows [17]:

bCpC1 D arg minC2CNr�J

pXp0D1

ˇp0��yp0 � Cvp0xp0

��2 (12)

where ˇp0 is the p0th weighting coefficient given by ˇp0 D

�p�p0 where � 2 R is referred to as a forgetting factor.The exponential weighted RLS algorithm is implemented

as follows:

�p D ˆ�1p�1vpxp,

˛p D1

�C ��p vpxp

,

ˆ�1p D �

�1ˆ�1p�1 � �

�1˛p�p��p

ep D yp �bCpvpxp,bCpC1 D bCp C ˛pep��p

Figure 5. Decision rules of the cognitive transceiver, CTR, and the throughput gain percentages against the conventional data-aided(DA) least-squares (LS) receiver (with coherent detection) versus SNR for different channel types and mobile speeds.

Table II. Link-level simulations parameters.

Number of user equipments 1Channel bandwidth (MHz) 15Carrrier frequency (GHz) 2.1Frame duration (ms) 10Subframe duration (ms) 1Subcarrier spacing (KHz) 15FFT size 128Number of subcarriers/RB 12DL bandwidth efficiency 77.1%OFDM symbols 14per subframeCP length .�s/ Normal: 5.2 (first symbols)

4.69 (six following symbols)Transmit mode SIMOChannel types PedA, VehA, PedB, and VehBChannel coding Convolutional turbo encoder



For initialization, bC1 is considered to be identically zeroand ˆ�1

0 is set to %I.J/ where % � 1 is a constant withsufficiently large value. Moreover, x1 is assumed to bea pilot symbol. The estimated polynomial approximationmatrix, bCpC1, is then used in (10) along with vpC1 in

order to find bhpC1 that is required to detect the .p C 1/th

symbol xpC1.

2.2.2. Maximum-likelihood channel estimator.

We consider the expectation maximization (EM)-basedML channel estimator recently introduced in [12]. Thisnew estimator jointly exploits both pilot and data sym-bols to track the channel variations. In a first step, weapply over each given subcarrier the ML channel esti-mator that relies on pilot observations only, as describedpreviously in Section 2.1.2, in order to find initial esti-mates for the channel coefficients at both pilot and non-pilot positions. In a second step, we apply the EMalgorithm over all the received samples (i.e., pilots andnon-pilots) in order to jointly estimate the channel coef-ficients and detect the unknown data symbols. The iter-ative EM-based algorithm runs in two main steps anduses as initialization bck,DA obtained in (9) from pilotpositions only.

� Expectation step (E-Step):During the E-Step, the pdf defined in (7) takes into

account all the possible transmitted symbols famgMmD1

where M is the modulation order. In fact, at each EM

iteration l, and within each kth approximation win-dow (of size NNDA symbols), the objective function isupdated as follows:

Q�

ck jbc.l�1/k

�D �NNDANr ln.2��2/ �

1

2�2

NrXrD1

�

M.r/

2,k C

NNDAXnD1

˛.l�1/n,k

ˇ̌cT

r,kt.n/ˇ̌2

�2ˇ.l�1/r,n,k .cr,k/

!(13)

where M.r/2,k D Efjyr,k.n/j2g is the second-order

moment of the received samples over the rth receiv-

ing antenna branch and t.n/ D�1, tn, t2n, : : : , tJ�1

n

�T.

Moreover, by denoting the constellation alphabet asC D fa1, a2, : : : , aMg, the other quantities involved in[12] are given by

˛.l�1/n,k D

MXmD1

P.l�1/m,n,k jamj

2 (14)

ˇ.l�1/r,n,k .cr,k/ D

MXmD1

P.l�1/m,n,k <

˚y�r,k.n/amt.n/T cr,k

�(15)

Table III. Multipath power delay profile.

Channel type Relative delay (ns) Relative power (dB)

PedA

0 0110 �9.7190 �19.2410 �22.8

PedB

0 0200 �0.9800 �4.9

1200 �82300 �7.83700 �23.9

VehA

0 0310 �1710 �9

1090 �101730 �152510 �20

VehB

0 �2.5300 0

8900 �12.812900 �1017100 �25.220000 �16



where P.l�1/m,n,k D P

�am j yk.n/;bc .l�1/

�is the a pos-

teriori probability of am at iteration .l � 1/ that iscomputed using the Bayes’ formula as follows:

P.l�1/m,n,k D

PŒam�p�

yk.n/jam;bc.l�1/k

�p�

yk.n/;bc.l�1/k

� (16)

Since the transmitted symbols are assumed to beequally likely, we have P.am/ D

1M and therefore:

p�

yk.n/;bc.l�1/k

�D

1

M

MXmD1

p�

yk.n/jam;bc.l�1/k

�

D1

M.2��2/Nr

MXmD1

exp

(�

1

2�2

NrXrD1

ˇ̌yr,k.n/

�amcTr,kt.n/

ˇ̌2)(17)

� Maximization step (M-Step):During the M-Step, the objective function obtained

in (13) is maximized with respect to ck:

bc.l/k D argmaxck

Q�

ckjbc.l�1/k

�(18)

yielding the following more refined estimates for thepolynomial approximation coefficients:

bc.l/r,k D

NNDAXnD1

t.n/t.n/T!�1 NNDAX

nD1

�.l�1/r,n,k t.n/ (19)

Figure 6. Block error rate curves for different channel estimation algorithms and data detection modes implemented for our CTR fordifferent channel types and mobile speeds: a) PedA 2 km/h, b) VehA 30 km/h, c) PedB 2 km/h, and d) VehB 100 km/h; and for: 1)

QPSK/CQI D 1, 2) 16QAM/CQI D 7, and 3) 64QAM CQI D 10 MCSs.



Here, �.l�1/r,n,k is given by:

�.l�1/r,n,k D

hba.l�1/k .tn/

i�yr,k.tn/ (20)

in which

ba.l�1/k .tn/ D

MXmD1

P.l�1/m,n,k am (21)

is the soft symbol estimate at iteration l�1. Note herethat the EM-based algorithm described earlier is iter-ative in nature and as such requires accurate initialguesses about the polynomial-approximation coeffi-cients in order to converge to the global maximum ofthe Log-Likelihood function (LLF). For this reason, itis initialized using the pilot-based DA ML estimatesalready obtained in (9) as discussed in some depthin [12].

2.3. Non-data-aided or blind mode

For blind or NDA channel estimation, no pilot symbolsare exploited by the receiver. Phase ambiguity is resolvedby differential modulation. The blind RLS channel estima-tor algorithm is already the one described in Section 2.2.1.However, an arbitrary guess of the first transmitted sym-bol is used to initialize the recursive algorithm. The blindchannel ML estimation algorithm is also the one describedin Section 2.2.2. The only difference, however, is that theinitialization is arbitrary and random. Figure 3 presentsthe block diagram of the SIMO transceiver with the NDAchannel estimation. On the top of those already detailed inSection 2.1, the new modules are described as follows:

� Differential modulation: Modulates the phase dif-ference between two consecutive transmitted symbols[18] as described in Figure 4 .

� STAR QAM Demapper: To fully exploit the poten-tial power of channel coding, soft-decision baseddemodulation is required. For this purpose, we usethe soft decision aided DAPSK detection algorithm

Table IV. System-level simulation parameters.

Macrocell parameters

Cellular layout Hexagonal grid, 7 cell siteswith BTS in the center of the cell

Inter-site distance 500 metersMinimum UE to macro-BS distance 35 metersPath loss model TS 36.942, sub-clause 4.5.2Antenna pattern 3-dimensional TS 36.942TX antennas 1Shadowing Log-normal with 10 dB standard deviationLTE BS antenna gain after cable loss7 17 dBiMacro BS antenna height 32 metersMaximum macro-BS TX power 46 dBm (conventional)MCL 70 dBmScheduling algorithm Proportional FairResource Block width 180 kHz

Pico-cell parametersCellular layout Circular shape with BTS in the center of cellMinimum distance between pico pNodeBs 40 metersMinimum distance between new node and regular nodes 75 metersMinimum UE to pico-BS distance 10 metersPath loss model TS 36.942, sub-clause 4.5.2Antenna pattern OmnidirectionalTX antennas 1shadowing Log-normal with 10 dB standard deviationAntenna gain 5 dBiMaximum pico-DS TX power 30 dBmScheduling algorithm Proportional Fair

UE parametersUE Rx antennas 2UE antenna gain 0 dBiUE noise figure 9 dBNumber of UEs per cell-site area 60



Figure 7. Long-term evolution HetNet empirical CFDs of downlink system-level throughput for (1) 1 pico-cell per macro area, (2) 2pico-cells per macro area, and (3) 4 pico-cells per macro area; a) total cell-site, b) macro-cell, and c) pico-cell in case of low clustering

over type-A channels.

developed in [19] and [20] and referred to hereafteras the STAR QAM Demapper. The a posteriori LLRexpression used in the joint amplitude-phase detectoris given by:

LLR.bm/ D ln

Ps�2sbmD1

exp Œd.'� ,!l/�Ps�2sbmD0

exp Œd.'� ,!l/�

!(22)

where sbmD0 and sbmD1 represent the set of sym-bols went the mth bit, bm, is 0 and 1, respectively.The probability metric d.'� ,!l/ in case of fully dif-ferential version, for which no channel estimation isrequired, is defined as follows:

d.'� ,!l/ D �ky�C1 � '

�!ly�k2

QN�0(23)

where

– y� is the received symbol vector y� D

Œy� ,1; : : : ; y� ,Nr � where � is the time (or fre-quency) index in the case of time (or fre-quency) differential modulation.

– ' is the amplitude ratio of two consecutivetransmitted symbols and � 2 Œ1, : : : , 2MA� 1�where MA is the number of amplitude rings(MA D 1 for DQPSK, MA D 2 for D16STARQAM, and MA D 4 for D64STAR QAM).

– ! is the phase difference of two consecutivetransmitted symbols and l 2 Œ1, .., Mp� whereMp is the number of phases in the DAPSKconstellation.

– QN�0 is the equivalent noise power defined asQN�0 D Œ1C.'

� /2�N0 for � 2 f1, : : : , 2MA�1gwhere N0 is the observation noise power.

The probability metric dNDA.'� ,!l/ under NDA

channel estimation is given by

dNDA.'� ,!l/ D �

��y�C1 � '�!l Oh� Ox�

��2

N0(24)

where Oh� is the blind estimate channel vector and Ox�is the estimated transmitted symbol at the time (or fre-quency) index � obtained by NDA RLS or NDA MLchannel estimators.



Figure 8. Long-term evolution HetNet empirical CFDs of downlink system-level throughput for (1) 1 pico-cell per macro area, (2) 2pico-cells per macro area, and (3) 4 pico-cells per macro area; and: a) total cell-site, b) macro-cell, and c) pico-cell in the case of low

clustering over type-B channels.

2.4. Data detection modes

On the top of selecting the appropriate channel estimatorand pilot-use modes among DA ML, NDA w. pilot ML,NDA ML, DA LS, NDA w. pilot RLS and NDA RLS, thenew context-aware CTR selects one of the following datadetection schemes:

� Coherent if pilot symbols are used; or� Non coherent or differential if no pilot symbols

are used.

We also implement a “fully differential” transceiver ver-sion for which no channel estimation is required. Datadetection is based on differential modulation-demodulationonly as described in Equation (23). We use time differ-ential modulation as it provides better performance thanfrequency differential modulation in case of frequencyselective channels (type-B channels) [21,22].

3. SIMULATION SETUP ANDRESULTSA 1 � 2 antenna configuration (1 transmit antenna at theeNodeB and 2 receive antennas at the mobile) is considered

as a SIMO configuration example for discussion in therest of this paper. In the following, exhaustive computersimulations will be conducted in order to assess the per-formance of the newly proposed CTR at both the link- andsystem-levels.

3.1. Link-level simulations

In this section, link-level simulations assuming only onebase station and a single mobile user are used to draw outdecision rules regarding the following:

(1) The best channel estimation algorithm betweenthe conventional LS and the newly proposed MLestimators.

(2) The best channel identification mode [among DA,NDA w. pilot (i.e., hybrid), and completely NDA]that yields the highest link-level throughput.

(3) The best detection scheme between coherent anddifferential detection depending on whether pilotsymbols can be properly exploited or not at thereceiver, respectively.

(4) The best modulation-coding CQI couple amongthe conventional coherent (CQI-C) and the newly-designed differential (CQI-D) ones (cf. Table I).



Figure 9. System-level total cell-site throughput for (1) low and (2) high clustering; and (a) type-A, and (b) type-B channels.

The CTR selects the best output quadruplet among thefour processing dimensions earlier that offers the best link-level throughout performance for any given input tripletof channel conditions, that is, SNR/CQI, mobile speed,and channel type. The relationships governing the latteroutputs versus inputs, referred to as decision rules, aredetermined in Figure 5 offline by link-level simulations in aso-called learning phase. During online operation, the CTRselects according to these decision rules the best quadru-plet of processing modes that best copes in link-levelthroughput with the experienced triplet of operating chan-nel conditions; actually in the same manner conventionaltransceivers switch today between adaptive modulation andcoding schemes (MCSs) versus the SNR. From this per-spective, the proposed CTR actually extends the simpleconventional adaptive modulation and coding (AMC) con-cept to far larger dimensions, from a single input CSIparameter to three, and from a single output process-ing mode to four. Furthermore, the CTR online operationrequires a priori the estimation of the SNR or SINR, theDoppler (i.e., mobile speed), and the channel type. How-ever, the results disclosed in this paper assume perfectknowledge of these channel parameters. More elaborate

simulation results that integrate the estimation of theseparameters, currently under investigation, fall beyond thescope of the present work.

Most significant LTE DL link-level parameters are sum-marized in Table II.

We consider Pedestrian A and B (PedA and PedB) slow-fading channel models for users with a mobile speed of2 km/h and Vehicular A (VehA) and B (VehB) for fast-fading channels for users with mobile speeds of 30 and100 km/h, respectively. Their power delay profiles (PDPs)are given in Table III.

In order to account for adaptive modulation and coding(AMC), a CQI value indicates to the eNodeB the modu-lation order and the channel coding rate adopted in eachsubframe.

As highlighted in Table I, the CQI value ranges between1 and 15 defining, respectively, six, three, and six possiblecoding rates for QPSK/DQPSK, 16QAM/D16Star-QAM,and 64QAM/D64Star-QAM modulations [23]. Note thatCQI-C and CQI-D stand for coherent and differentialdetection modulations, respectively. In our simulations, theCQI values as well as the SNR are assumed to be perfectlyknown to the receiver. Assessment of CQI feedback and



delay errors are beyond the scope of this work. BLERperformances results are presented in Figure 6 for differ-ent channel types and mobile speeds. As seen there, thebest performing channel estimator (among LS and ML)and data detection mode (among DA, NDA and NDA withpilots) both depend on the environment conditions charac-terized by the channel type, mobile speed, and SNR/CQIvalues. Based on these results, we identify the optimalCTR’s decision rules that correspond to the best channelestimation scheme and data detection mode along withthe associated throughput gain for each channel conditiontriplet as depicted in Figure 5. We define three differentCTRs depending on the implemented channel estimationalgorithms. The first one (denoted hereafter as “CTR LS”)switches between the fully differential detection mode andthe different LS channel estimation schemes, namely, DALS, NDA w. pilot LS, and NDA LS. The second one(referred as “CTR ML”) switches between the fully dif-ferential detection mode and the different ML channelestimation schemes, namely DA ML, NDA w. pilot ML,and NDA ML. The third CTR which is a smarter cognitivetransceiver (denoted hereafter simply as “CTR”) is able to

switch between all the detection modes and channel esti-mation schemes listed previously for both CTR ML andCTR LS. Figure 5 depicts the link-level throughput gainsof CTR against the conventional non-cognitive transceiverthat applies the DA LS channel estimator regardless of thechannel conditions. The gains are presented for each triplet(channel type, mobile speed, SNR/CQI value). Note herethat link-level throughput gains for CTR-LS and CTR-MLare not shown due to space limitations. Their performancewill, however, be assessed later in Section 3.2. As seenfrom Figure 5, CTR offers tremendous link-level through-put gains over the conventional DA LS transceiver thatcan reach up to 700%, in the low-SNR region for VehBchannels and a mobile speed of 100 km/h. For flat-fadingchannels (type-A channels), Figure 5 shows that the DAML channel estimator outperforms the DA LS estimatorat low mobile speeds (PedA channel). This result confirmsthat pilot-only channel estimation is still reliable in the caseof low-speed flat-fading channels. However, for fast-fadingtype-A channels (VehA with 30 and 100 km/h), the NDAwith pilots ML channel estimator exhibits better perfor-mance for most of the CQIs values. Figure 5 also reveals

Figure 10. (1) Average and (2) cell-edge system-level throughput gains for the pico-cell, macro-cell, and the whole cell-site in thecases of: a) low, and b) high clustering over type-A channels.



Figure 11. (1) Average and (2) cell-edge system-level throughput gains for the pico-cell, macro-cell, and the whole cell-site in thecases of: (a) low, and (b) high clustering over type-B channels.

that blind channel estimation (NDA) and differential detec-tion outperforms the pilot-assisted channel estimators (DAand NDA with pilots) for high-order modulations overfrequency-selective channels (type-B channels) at mediumand high mobile speeds (VehB with 30 and 100 km/h).This fact is due to the fast channel variations along succes-sive subcarriers which makes linear interpolation no longerreliable. At low mobile speeds (PedB channel), the newlyproposed DA ML outperforms all other channel estimationalgorithms and achieves a throughput gain of about 330%in the low-SNR region.

3.2. System-level simulations

The link-level-based decision rules, identified in the previ-ous section, are then fed to a HetNet LTE DL system-levelsimulator wherein a whole network is simulated in order toassess the performance of the proposed CTR under morerealistic operating conditions that account for inter-cell andintra-cell interference sources. More specifically, we sim-ulate a 7-hexagonal-cell network with pico-cells droppedrandomly in each macro area. An exhaustive list of the

system-level parameters used in our simulations is pro-vided in Table IV. We also assume that 50% of the usersmove with a speed of 2 km/h and experience PedA/PedBchannel type. The remaining 30% and 20% of the users areassumed to have a speed of 30 and 100 km/h, respectively,and experience a VehA/VehB channel type. Furthermore,we consider the case of f1, 2, 4g pico-cells in each macroarea as defined in [24]. We also consider both low and highclustering distributions for the UEs. For high clusteringscenario, 2/3 of UEs are dropped in the hotspots. In caseof low clustering scenario, four UEs are dropped in eachhotspot with other UEs are dropped uniformly over themacro-cell area (including hotspots). Hereafter, we showthe simulation results for the whole cell site, the centralmacro-cell as well as its pico-cells. Figures 7 and 8 showsystem-level LTE DL throughput CDFs for DA ML, DALS, CTR ML, CTR LS, and CTR transceivers for type-A and -B channels with low clustering¶. In all considered

¶Throughput CDFs in high clustering are not shown here due to space

limitations. However, this case will be assessed later in Figures 9, 10,

and 11.



cases, CTR always delivers the highest throughput withoverwhelming probability among all transceivers. Figure 9shows the impact of the number of dropped pico cellsper-macro area on the different cognitive transceivers. Theaverage DL system-level throughput increases with thenumber of picocells with maximum values achieved bythe smartest (ML-LS-based) CTR. Figures 10 and 11,however, show the average and fifth percentile (i.e., cell-edge) DL throughput gains at the system-level over type-Aand -B channels, respectively. Huge performance gainsare achieved by combining the cognitive transceiver con-cept and the new channel estimator recently introducedin [12]. In fact, in terms of cell-site performance, CTRexhibits about 25% and 50% DL throughput gains (aver-age and fifth percentile) over type-A and type-B channels,respectively. In terms of pico-cell performance, the fifthpercentile (i.e., cell-edge) and average gains exceed 50%and 40%, respectively, in the low clustering type-A chan-nel scenario. These gains become as high as 80% and90% in the low clustering type-B channel scenario, respec-tively. Based on these results, the proposed CTR turns outto be extremely advantageous in HetNet small cells whereusers suffer from serious interference problem due to weaktransmitted power.

4. CONCLUSION

In this paper, we developed a new SIMO context-aware transceiver that is able to switch to the best per-forming modem in terms of link-level throughput. Onthe top of conventional AMC, we allow the proposedCTR to make best selection among three different pilot-utilization modes: conventional (DA) or pilot-assisted,non-DA (NDA) or blind, and NDA with pilots which isa newly proposed hybrid version between the DA andNDA modes. We also enable the CTR to make best selec-tion between two different channel identification schemes:conventional least-squares (LS) and newly developed MLestimators. Depending on whether pilot symbols can beproperly exploited or not at the receiver, we further enablethe CTR to make best selection among two data detectionmodes: coherent or differential. Owing to extensive andexhaustive link-level simulations on the DL of the LTE sys-tem, we were able to draw out the decision rules of thenew CTR that identify the best combination triplet of pilot-use, channel-identification, and data-detection modes thatdeliver the best link-level throughput at any operating con-ditions in terms of channel type, mobile speed, SNR, andCQI. The proposed CTR offers a link-level throughput gainin almost all operating conditions up to as much as 700%against the conventional non-cognitive DA LS transceiver(i.e., pilot-assisted LS-type channel estimation with coher-ent detection). Realistic extensive simulations of LTEheterogeneous network (HetNet) at the system-level alsosuggest that the new proposed CTR outperforms the con-ventional DA LS transceiver by as much as 60% gains inaverage and cell-edge (i.e., five percentile) total throughput

per macro-area. Applying our new cognition concept ishence very promising to enhance LTE HetNet network.

ACKNOWLEDGEMENTS

Work supported by the CREATE PERSWADE www.create-perswade.ca and the Discovery Grants Programs ofNSERC and a Discovery Accelerator Supplement (DAS)Award from NSERC.

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AUTHORS’ BIOGRAPHIES

Imen Mrissa was born in Tunis,Tunisia. She received the EngineeringDiploma in Telecommunications fromthe École Nationale d’Ingénieurs deTunis (ENIT), Tunisia, in 2006 and theM.Sc. and Ph.D. degrees in telecom-munications from INRS-EMT, Univer-sité du Québec, Montreal, Canada, in

2009 and 2016, respectively. Her research focuses on chan-nel estimation, data demodulation, cognitive transceiverdesigns, LTE, HetNet, and both link- and system-level sim-ulations. She received for both her M.Sc. and Ph.D. pro-grams the National Grant of Excellence from the TunisianGovernment. She was selected to receive a scholarshipfrom the NSERC CREATE PERSWADE Program for theyear 2015.

Faouzi Bellili was born in Tunisia onJune 16, 1983. He received the B.Eng.degree (with Hons.) in signals andsystems from the Tunisia PolytechnicSchool in June 2007, and the M.Sc. andPh.D. degrees (both with the highesthonours) from the National Instituteof Scientific Research (INRS-EMT),

University of Quebec, Montreal, QC, Canada, in Decem-ber 2009 and August 2014, respectively. He is currentlyworking as a Research Associate at INRS-EMT. In Febru-ary 2016, he was awarded a PDF Grant from NSERC fora two-year tenure at the University of Toronto starting inSeptember 2016. Dr. Bellili’s research focuses on statisticalsignal processing and array processing with an emphasison parameters estimation for wireless communications.He authored/co-authored over 40 peer-reviewed papers inreputable IEEE journals and conferences. He was selectedby INRS as its candidate for the 2009–2010 competition ofthe very prestigious Vanier Canada Graduate Scholarshipsprogram. He also received the Academic Gold Medal ofthe Governor General of Canada for the year 2009–2010and the Excellence Grant of the Director General of INRSfor the year 2009–2010. He also received the award of thebest M.Sc. thesis of INRS-EMT for the year 2009–2010and twice — for both the M.Sc. and Ph.D. programs — theNational Grant of Excellence from the TunisianGovernment. He was awarded in 2011 the Merit Schol-arship for Foreign Students from the Ministère del’Éducation, du Loisir et du Sport (MELS) of Québec,Canada. Dr. Bellili recently served as TPC member forIEEE GLOBECOM and IEEE ICUWB and regularly actsas a reviewer for many international scientific journals andconferences.



Sofiène Affes received the Diplômed’Ingénieur in telecommunications,and the Ph.D. degree (Hons.) in sig-nal processing, from École NationaleSupérieure des Télécommunications(ENST), Paris, France, in 1992 and1995, respectively. He was a ResearchAssociate with INRS, Montreal,

QC, Canada, until 1997, an Assistant Professor, until2000, and an Associate Professor, until 2009. Currently,he is a Full Professor and Director of PERWADE, aunique 4M$ research training program on wireless inCanada involving 27 faculty from eight universities andten industrial partners. From 2003 to 2013, he was aCanada Research Chair in wireless communications.In 2006 and 2015, he served as a General Co-Chair ofIEEE VTC’2006-Fall and IEEE ICUWB’2015, respec-tively, both held in Montreal, QC, Canada. He will actas General Chair of IEEE PIMRC’2017 to be held in Mon-treal, QC, Canada. Currently, he is an Associate Editor forthe IEEE TRANSACTIONS ON COMMUNICATIONSand the Wiley Journal on Wireless Communicationsand Mobile Computing. He was an Associate Editor forthe IEEE TRANSACTIONS ON WIRELESS COM-MUNICATIONS, from 2007 to 2013, and the IEEETRANSACTIONS ON SIGNAL PROCESSING, from2010 to 2014. He has been twice the recipient of a Discov-ery Accelerator Supplement Award from NSERC, from2008 to 2011 and from 2013 to 2016. In 2008 and 2015,he received the IEEE VTC Chair Recognition Award fromIEEE VTS and the IEEE ICUWB Chair Recognition Cer-tificate from IEEE MTT-S for exemplary contributions tothe success of IEEE VTC and IEEE ICUWB, respectively.

Alex Stéphenne was born in Quebec,Canada, on May 8, 1969. He receivedthe B.Ing. degree in electrical engineer-ing from McGill University, Montreal,Quebec, in 1992, and the M.Sc. degreeand Ph.D. degrees in telecommunica-tions from INRS-Télécommunications,Université du Québec, Montreal, in

1994 and 2000, respectively. In 1999 he joined SITA Inc.,in Montreal, where he worked on the design of remotemanagement strategies for the computer systems of airlinecompanies. In 2000, he became a DSP Design Special-ist for Dataradio Inc. (now part of CalAmp), Montreal,a company specializing in the design and manufactur-ing of advanced wireless data products and systems formission critical applications. In January 2001 he joinedEricsson and worked for over two years in Sweden, wherehe was responsible for the design of baseband algorithmsfor WCDMA commercial base station receivers. From2003 to 2008, he is still working for Ericsson, but is nowbased in Montreal, where he is a researcher focusing onissues related to the physical layer of wireless commu-nication systems. From 2009 to 2014, he is a researcherfor Huawei, in Ottawa, working on radio resource man-agement for 4G and 5G systems. Since 2014, he is withEricsson in Ottawa, as a wireless system designer. He isalso an adjunct professor at INRS since 2004 and a Seniormember of the IEEE.


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