Adaptive coding and modulation for the reverse link of ... - IEEE Xplore

Adaptive Coding and Modulation for the Reverse Link of Broadband Satellite Networks Stefano Cioni

Riccardo De Gaudenzi and Rita Rinaldo

D.E.I.S. - ARCES University of Bologna Viale V.Toffano 2, 40125 - Bologna, Italy Email: [email protected]

ESTEC TOS-ETC European Space Agency Keplerlaan 1, 2200 AG Noordwijk, The Netherlands Email: [email protected]

Abstract— The effective implementation of Adaptive Coding and Modulation for the reverse (user-to-satellite) link of broadband satellite systems operating at Ka-band and above calls for the resolution of a number of problems. The many-toone reverse link nature and the related distributed interference characteristic make efficient and robust link adaptation challenging, particularly when in presence of bursty traffic and time variant propagation conditions. In this paper channel estimation and adaptation algorithms suited for the system under analysis are devised and their performance investigated in a Kaband multibeam system study case for different multiple access schemes.

I. I NTRODUCTION The potential advantage deriving from the application of Adaptive Coding and Modulation (ACM) to satellite networks has been investigated in-depth in [1] and in [2] for the forward and the reverse link respectively. Thanks to the combination of physical layer adaptation and advanced coding/modulation schemes, the potential for a major (up to four fold) capacity increase compared to current systems has been demonstrated. However, the results shown are based on the assumption of ideal channel estimation and physical layer adaptation. The distributed access nature of the reverse link coupled with the inherent satellite propagation delay, the multibeam system characteristic and the bursty traffic make the subject of channel estimation and physical layer adaptation extremely challenging. A wrong selection of the physical layer may lead to link capacity reduction or unacceptable packet loss probability. The key issue is efficiently achieving link physical layer adaptability with implementation simplicity. In this paper these issues will be investigated and possible solutions outlined. In Sect. II the system model and all its key elements are described in detail. The performance of the proposed approach is then assessed in a realistic Ka-band satellite system scenario for different access schemes in the presence of light and heavy shadowing, and results are shown in Sect. III. In Sect. IV conclusions are drawn. II. S YSTEM M ODEL Today typical satellite systems for distributing multimedia content and providing access to the Internet are based on geostationary satellites operating at high frequency bands (e.g. Kaband). The satellite coverage region is split among a number

IEEE Communications Society Globecom 2004

of beams, which share the available bandwidth according to a pre-defined frequency reuse pattern. The focus will be placed in the following in the user up-link from Satellite Terminal (ST) to satellite, thus both meshed and bent-pipe systems can be accommodated in the analysis. As detailed in [2], the link channel quality is dependent on the fading attenuation affecting the useful link and on the interference level experienced at the satellite. Differently from the forward link, interfering traffic is randomly appearing at different coverage locations in co-frequency beams and is affected by time-variant fading attenuation. The link Signal to Interference Noise Ratio (SNIR) is estimated at the gateway (bent-pipe transponder) or on board the satellite (meshed system) and reported to the ST, which performs the physical layer adaptation. In order to reduce the amount of forward channel signalling, the reporting can occur only in case of SNIR variations exceeding a certain threshold. The today standard approach in the uplink of satellite broadband networks is Time Division Multiple Access (TDMA) combined with fixed coding and modulation format and power control [5]. This approach leads to an increased terminal cost due to ST high power amplifier (HPA) oversizing required to cope with the worst-case link conditions. In the following we will revisit the issue of ST power control and we will investigate how to combine it with the proposed adaptive physical layer. Both TDMA and Quasi-Synchronous Code Division Multiple Access (QS-CDMA) [6] will be investigated, as they apppear the most promising solutions in terms of capacity [2]. Some assumptions on the frame format need to be done for the two multiple access techniques under investigation. For TDMA we consider a frame of duration TFTDMA divided in MsTDMA TDMA slots. Each slot is composed of a preamble of NpTDMA symbols, a signalling information field and a payload field. Each ST may use between one and MsTDMA TDMA slots depending on the required bit rate. For CDMA we assume a frame of duration TFCDMA divided in a preamble of NpCDMA chips, a signalling field and a payload. Spreading is applied to the entire frame. Orthogonal codes with spreading factor L are assumed for users belonging to the same beams. The payload part of the slot (TDMA) or frame (CDMA) carries a data packet characterized by a certain physical layer format, which is indicated by the signalling field. The preamble is

1101

0-7803-8794-5/04/$20.00 © 2004 IEEE

QPSK modulated and used for demodulator synchronization and channel quality estimation. In order to assess the performance of adaptation techniques, an ACM simulator representative of the system under investigation is required. The block diagram of the ACM reverse link simulator is reported in Fig. 1. In case of TDMA there is a maximum of one active user at the time for each carrier while for CDMA a number McCDMA > 1 of active users per beam is assumed. Only the useful user(s) ST(s) are effectively simulated as other beam interferers are emulated through the technique developed in [2]. The co-channel interference Power Spectral Density (PSD) can be approximated thanks to the one-side form of the central limit theorem with a time variant non-central χ-square rv. The statistical characteristics of the distribution (mean and variance) are computed offline for each beam in the coverage, taking into account the random interfering users location and the fading attenuation affecting the interfering links, that for simplicity we assume here incorrelated. Then, for every time slot in case of TDMA or frame in case of CDMA the time variant interference PSD rv I0cc is generated. The ST transmitted signal amplitude is first corrected by the open loop uplink power control (when enabled) which is described in Sect. II-B. The ST signal level is then modified according to the ST antenna gain and other link coefficients like the free space path loss and the satellite antenna gain, which are dependent on the ST location. Then, the fading attenuation and the propagation delay D are applied. The co-channel inter-beam interference is generated and added to the useful signal, together with the satellite input equivalent Additive White Gaussian Noise (AWGN) process. Adjacent channel and inter-system interference are also generated and added to the signal; their PSD is assumed constant in time within the frequency band of interest and is computed as in [2]. For each received uplink frame the preamble information is extracted by the demodulator (at the gateway or on-board the satellite) and used to perform channel estimation. In the CDMA demodulator, despreading is applied to the signal before undergoing channel estimation. Therefore in the system simulator we perform coherent accumulation of the NpCDMA preamble chips and obtain NpCDMA /L symbols as input for the channel estimator. We assume that the reference ST is performing continuous transmission during the observation period to collect the maximum of performance information. In practice, uplink ST activity may be discontinuous on a frame by frame basis. For TDMA we assume the worst case for channel estimation, correspondent to one slot per frame assigned to the useful user. The channel estimator provides a quasi-instantaneous symbol energy over noise plus interference PSD ratio Es /Nt estimate, which is further processed as described in Sect. IIA. The output of the envelope detector drives the physical layer configuration selector. The selected physical layer is then sent to the ST as forward link signaling information with propagation delay D. In order to simulate the uplink power control in a realistic way, the downlink channel (path

IEEE Communications Society Globecom 2004

loss, delay, fading, interference, AWGN) is also simulated. The downlink TDM frame is divided in slots of duration Tsdown , each of them composed of a payload part and a preamble part, which is used for power estimation. The estimated power is then post-processed as discussed in Sect. II-B to determine the uplink power control level. A performance processor is present in the simulator for elaborating the required statistics based on the true and estimated channel information. A. Channel Estimator The reverse link channel estimator is based on the same SNORE estimation algorithm described in [3]. However, differently from the forward link where interference experienced by the pilot is only slowly variant in time for a given ST location, the reverse link Es /Nt dynamic is very high. This is because of the many-to-one nature of this link which makes the interference highly variable. The useful user signal in consecutive time slots (TDMA) or frames (CDMA) is affected by co-channel interference potentially generated by users belonging to very different geographical locations. As explained in depth in [2], this makes the inter-beam interference contribution time-variant on a slot-by-slot or frame-byframe basis spanning a wide range of possible values. From the interference variability perspective TDMA represents a worstcase as only a single ST will be active per beam frequency slot. As a consequence few independent interferes will contribute to the bulk of co-channel interference. In case of CDMA more independent interferers will be active in each beam, thus reducing the aggregated interference power variation in time compared to its mean value thanks to the central limit theorem. The high interference variability makes very difficult if not impossible an accurate channel estimation as the interference level variations can not be tracked by the adaptation loop, whose time constant is dominated by the satellite propagation delay 2D. This reverse link channel estimation problem can be tackled resorting to a modified version of the SNORE estimator [3]. This is indicated in the following as Envelope Detector SNORE (ED-SNORE). The ED-SNORE corresponds to the cascade of the SNORE Es /Nt estimator with a digital (lower) envelope detector. The proposed approach consists in estimating the instantaneous minimum Es /Nt experienced by the demodulator and holding it with an exponential increase constant of time (2βD, β > 1) which is dimensioned to track the maximum slope of the channel fading process and is larger than the total propagation delay 2D. The minimum envelope detector operation can be described by:      
 Es  Eˆs (k) if Eˆs (k) ≤
Es Nt Nt Nt (k − 1) (k) = 


  Nt  Es (k − 1) exp TF otherwise Nt 2βD

(1)

ˆs /Nt (k) is the ED-SNORE output at time tk = kTF where E and TF is the frame duration. While for TDMA the channel SNIR estimate is the ED-SNORE output, for CDMA it can be easily derived by taking into account spreading operation s /Nt (k). The described algorithm c /Nt (k) = 1/L E as E

1102

0-7803-8794-5/04/$20.00 © 2004 IEEE

provides a relative fast reaction (depending on the SNORE window length W parameter) to a reduction of the estimated Es /Nt . Clearly, the ED-SNORE local worst-case channel estimation approach slightly degrades the system capacity compared to the ideal channel estimation that was assumed in [2]. No effort has been made in this stage to control the packet loss probability. This is obtained by applying shifted thresholds in the physical layer selection with respect to the ideal thresholds, as described in more detail in [3] and recalled in Sect. III. B. Uplink Power Control The rationale for introducing uplink power control (ULPC) in a system with adaptive physical layer was demonstrated in [2] and is mainly related to the potential advantage in increasing the minimum user bit rate, while no major capacity gain is obtained. We assume that in clear sky conditions the ST will transmit a certain nominal power that is called [PTx ]nom . The ST will however be allowed to increase the RF power up to [PTx ]max when the ST demodulator detects some downlink fading. An ULPC based on SNIR is not recommended as slotby-slot Es /Nt fluctuations of several dBs may be experienced by the gateway demodulator. As we are combining ULPC with ACM and the ACM adaptation is based on the short-term worst-case Es /Nt ED-SNORE estimate, we propose to limit the ULPC to the compensation of the propagation impairment. A simple way to do it is to estimate the uplink fading from downlink ST received signal power variations. Techniques providing the fading amplitude estimation based on the knowledge of key link budget parameters are not considered practical as they require the satellite EIRP knowledge (location dependent) and a calibrated receiver measurement point. This approach is not acceptable for consumer applications for which calibrated receivers are not affordable from a ST cost viewpoint. An estimate of the downlink received ST power can be readily obtained as by-product of the SNORE algorithm [3] which estimates the useful signal power PˆS [i] at time ti = iTsdown . The problem is how to reliably derive the current fading amplitude by estimation of the clear sky received power value. This can be solved avoiding the any absolute power measurement at the ST side by estimating the received signal power at every slot and then recording the largest value called PˆREF [i]. As the reference received power value may slowly change over time due to satellite RF power changes, ST antenna small mis-pointing or downlink chain gain variations an exponential decay with ”large” decay constant is suggested to allow for clear-sky power level long-term adaptability. Numerically the reference (clear-sky) ST received power level is computed as:

PˆS [i] if PˆS [i] > Pˆ REF [i −1] (2) PˆREF [i] = ˆ T down PREF [i − 1] exp − τsREF otherwise where τREF is the large reference power estimator time constant. It is now possible to estimate the downlink fading in terms of power as a ˆd [i] = PˆS [i]/PˆREF [i].

IEEE Communications Society Globecom 2004

The uplink fading a ˆu [k] can now be derived by frequency scaling the estimated downlink fading according to the available ITU model [4] and averaging over the up-link frame duration TF . When ULPC is applied the ST transmitted power is then given by:

Tx ]nom [PTx ]nom if a ˆu [k] > [P [PTx ]max [PTx ][k] = (3) [PTx ]max a ˆu [k] otherwise If ULPC is applied the methodology for the interference first order statistics computation described in [2] and used in the simulator is still applicable provided that the the original fading pdf pa (a) is replaced by: 


a pa
(a ) = pa (4) + δ(a ) λPC [PTx ]max where λPC = [P corresponds to the ST ULPC dynamic, Tx ]nom
a is the power controlled fading process power, a is the original fading process power and δ(·) is the delta of Dirac numerical operator.

III. P ERFORMANCE R ESULTS In order to assess the behavior of the proposed channel estimation and physical layer adaptation algorithms a Kaband GEO satellite study case has been investigated. For convenience the same 43-beam Ka-band antenna beam pattern as in [1] has been retained. The key system parameters are summarized in Table I. When not stated otherwise a satellite frequency reuse pattern fR = 3 has been used as it resulted to be the optimum choice according to the capacity optimization results reported in [2]. To ease comparison TFTDMA = TFCDMA = 24 msec and MsTDMA = McCDMA = 10 have been assumed. For the QS-CDMA access a spreading factor L = McCDMA = 10 has been considered. The preamble durations have been fixed to NpTDMA = 256 symbols and NpCDMA = 256L chips for TDMA and CDMA respectively. The same approach described in [3] has been adopted for the fading channel simulator, with a first-order low-pass filter for heavy fading generation and the more complex lowpass filter proposed in [7] for the light fading simulations.. The only difference is in the CDF used for the reverse link as it corresponds to the same geographical location (Nola, Italy), but refers to a carrier frequency of 30 GHz and whose cumulative distribution is reported in [2]. Following the methodology described in Sect. II-A, the envelope detector parameter β has been computed for tracking a worst-case fading slope for a Ka-band link of 0.5 dB/sec. The physical layer selector thresholds have been computed following the methodology outlined in [3] and are superimposed for convenience to simulation results. Only the hysteresis approach will be considered as it is definitely superior to the other methods in terms of signaling requirements and robustness to channel estimation errors. The down thresholds have been computed for each physical layer transition for a packet loss probability target of 10−2 . The output of the envelope detector first order statistics has been accounted

1103

0-7803-8794-5/04/$20.00 © 2004 IEEE

for the threshold computation. To this purpose the standard deviation of the process after the envelope detector has been derived by simulation as a function of Es /Nt for the selected number of preamble symbols. An example of link adaptation for a heavy fading phenomenon is illustrated in Fig. 2 and 3 for TDMA and CDMA respectively. It is apparent that TDMA suffers from the largest co-channel interference PSD (I0cc ) fluctuations due to the limited amount of interference averaging compared to CDMA. However, because of the large value for the other system interference 1 PSD I0other = −203.08dBHz (see Table I) the total noise plus interference PSD Nt = I0cc +I0other +I0ACI +N0 fluctuations are mitigated. The adoption of ULPC leads to a further reduction of the received Es /Nt fluctuations due to the compensation of propagation impairments until the ST HPA power limits are reached. The behavior of the system with ULPC is different between TDMA and CDMA due to the different HPA power dynamic range. TDMA channel bit rate is aggregated, so that an user exploiting a single TDMA slot needs for link closure the RF power corresponding to the TDMA frame aggregated bit rate. On the contrary, for CDMA the RF power required is proportional to the individual user bit rate. Therefore for a fixed HPA maximum power [PTx ]max , CDMA has more power dynamic range to counteract the fading impairments. This can be observed comparing Fig. 2 and Fig. 3. In case of CDMA, the depth of Es /Nt fading (in dB) observed at the gateway demodulator input is minimized jointly with its temporal extension when ULPC is adopted. Thanks to its extra HPA power margin CDMA ULPC is able to completely counteract uplink fading for the initial and final phase of the fade event. The experienced packet loss probabilities are 0.7% and 0.35% for TDMA and CDMA respectively, thus in both cases the required target probability of 1% is satisfied. This proves the ED-SNORE capability of tracking channel Es /Nt variations and the validity of the thresholds computation algorithm. Regarding efficiency performance, the ideal system capacities derived as in [2] are, in the present case, 0.8546 bit/sec/Hz/beam for TDMA and 1.125 bit/sec/Hz/beam for CDMA. The loss introduced by channel estimation and link adaptation is for both TDMA and CDMA close to 12%, thus maintaining the QS-CDMA capacity advantage. Fig. 4 and Fig. 5 show the proposed algorithm performance in case of light fading conditions for TDMA and CDMA respectively. The maximum fading attenuation experienced during the simulation is 5.3 dB. The presence of hysteresis thresholds avoids unwanted ping-pong effects between two adjacent physical layer states due to the estimated channel Es /Nt fluctuations. It can be observed the beneficial presence of ULPC: in the CDMA case the fading is completely cancelled by the ULPC, while for TDMA the received signal is still affected by some residual uncompensated fading. The packet loss probabilities are limited to 0.15% and 0.02% for

TDMA and CDMA respectively, remarkably lower than in the previous case when the system tracking capability was stressed by a deep fading event. The average ideal system efficiency in the TDMA case, 0.9597 bit/sec/Hz/beam, is diminished by 11.78% because of channel estimation and physical layer adaptation errors. For CDMA the efficiency loss is limited to 7.19%, while the ideal system capacity is 1.167 bit/sec/Hz/beam. Therefore QS-CDMA capacity advantage with ideal channel estimation as expected from [2] is further increased when taking into account the loss from the ideal case. This is justified by the CDMA improved interference averaging effect, which limits the Es /Nt standard deviation experienced at the demodulator. IV. S UMMARY AND C ONCLUSIONS In this paper the problem of channel estimation and physical layer adaptation for the reverse link of a broadband satellite system exploiting ACM has been tackled. An overall system simulator has been devised for being able to capture all essential phenomena affecting the ACM system operation. It has been shown that the fast intra-system interference variability characteristic of the reverse link calls for the development of ad-hoc countermeasures that allow to implement robust physical layer adaptation. It has been shown that by properly modifying the channel SNORE estimator adopted for the forward link and combining it with open loop uplink power control, overall good performance can be achieved. The proposed solution has been validated for a realistic Ka-band multibeam satellite system study case for heavy and light fading event. The performance of the two most promising access schemes i.e. TDMA and QS-CDMA have been compared for the same conditions and with identical system parameters. Simulation results confirm the superiority of QS-CDMA compared to TDMA in terms of system capacity and minimum bit rate. R EFERENCES [1] R. Rinaldo, R. De Gaudenzi Adaptive Coding and Modulation for the Forward Link of Satellite Broadband Multimedia Systems, to appear on International Journal of Satellite Communications, 2004. [2] R. Rinaldo, R. De Gaudenzi Adaptive Coding and Modulation for the Reverse Link of Satellite Broadband Multimedia Systems, to appear on International Journal of Satellite Communications, 2004. [3] S. Cioni, R. De Gaudenzi, R. Rinaldo, Adaptive Coding and Modulation for the Forward Link of Broadband Satellite Networks, Globecom 2003, Vol.6, pp. 3311-3315. [4] ITU-R Reccomendation P.618-7 ”Propagation Data Prediction Method Required for the Design of Earth-Space Communication Systems”. [5] ETSI EN 301 790 v1.2.2 Digital Video Broadcasting (DVB); Interaction channel for satellite distribution systems. [6] R. De Gaudenzi, C. Elia, R. Viola, Band-Limited Quasi-Synchronous CDMA BLQS-CDMA a Novel Satellite Access Technique for Mobile and Personal Communication System, IEEE Journal on Selected Areas in Communications, Vol. 10, No.2, February 1992. [7] L. Castanet et al. ”Comparison of Various Methods for Combining Propagation Effects and Predicting Loss in Low-Availability Systems in the 20-50 GHz Frequency Range”, Int. Journ. on Sat. Comm., Vol. 19, pp. 317-334, 2001.

1 The value considered here is considered representative of the possible interference generated by two adjacent GEO satellites following the current regulations for Ka-band systems.

IEEE Communications Society Globecom 2004

1104

0-7803-8794-5/04/$20.00 © 2004 IEEE

Rkk R

-D Z-D

Akk

Ck C k

nnkk

Equivalent Equivalent Interference Interference

Channel Channel Gen. Gen.

Parameter ST carrier frequency ST HPA nominal power

Noise Noise Gen. Gen.

Open Loop Loop Open Powe rr Control Control Powe

FL-Channel FL-Channel Estimator Estimator

Code Code Demux Demux

Ukk U

Satellite Satellite receiver receiver antenna antenna simulator simulator

Fading Fading generator generator

Interference Interferenceuser userlocation locationgenerator generator

Phy-Layers Phy-Layers of the the Users Users of in reference reference in Beam Beam

True True SNIR SNIR

RL-Channel RL-Channel Estimator Estimator

Ecc Ntt

Eˆcc Ntt Envelope Envelope Detector Detector

ST HPA maximum power ST carrier bandwidth (Rc ) ST transmission antenna gain Satellite antenna peak gain Satellite noise temp. (Teq ) Other systems interference PSD Clear sky ACI isolation (C/I)ACI Intermodulation C/I

Performance Processor

TABLE I S TUDY CASE SYSTEM PARAMETERS .

Inte rference rference Approximation Approximation Inte (OSCLT) (OSCLT)

Fˆkk Phy-Layer Config.

-D Z-D

Fig. 1.

Value 30.0 GHz 0.3 W for TDMA 0.1 W for QS-CDMA 0.5 W 0.4 Mcps fR = 3 45.22 dBi 52.18 dBi 28.48 dBK -203.08 dBW/Hz 19.5 dB 19 dB

Block diagram of the ACM reverse link simulator.

16

Ideal Threshold ( 3/4 − 16QAM ) Ideal Threshold ( 2/3 − 16QAM )

16 Ideal Threshold Ideal ( 3/4 − 16QAM ) Threshold ( 2/3 − 16QAM )

14

15

12

14

13

8

s

t

Ideal Threshold ( 2/3 − 8PSK )

s

E /N [dB]

t

E /N [dB]

Ideal Threshold ( 5/6 − 8PSK )

10

6

12 Ideal Threshold ( 3/4 − QPSK )

4

11

2

Up Threshold Down Threshold

Ideal Threshold ( 5/6 − 8PSK )

Down Threshold Up Threshold

Ideal Threshold ( 1/2 − QPSK )

0

10

Ideal Threshold ( 1/3 − QPSK ) −2

0

2000

4000

6000 12000 10000 8000 Number of frames [1Frame = 24msec]

14000

16000

0

2000

4000

8000 6000 10000 12000 Number of frames [1Frame = 24msec]

14000

16000

18000

18000

Fig. 2. Example of heavy fading in the reverse link, for TDMA and fR = 3: true (blue) and estimated (violet) Es /Nt true and physical layer selector thresholds.

Fig. 4. Example of light fading in the reverse link, for TDMA and fR = 3. Es /Nt true and estimated

20

20 Ideal Threshold ( 3/4 − 32QAM )

19

18

Ideal Threshold ( 3/4 − 32QAM )

18

16

E /N [dB]

14

s

s

12

Ideal Threshold ( 2/3 − 16QAM ) Ideal Threshold ( 5/6 − 8PSK )

10

17

t

Ideal Threshold ( 3/4 − 16QAM )

t

E /N [dB]

Ideal Threshold ( 7/8 − 16QAM )

16 Ideal Threshold ( 7/8 − 16QAM )

15

Up Threshold Down Threshold

6

Up Threshold Down Threshold

Ideal Threshold ( 3/4 − 16QAM )

14

8

Ideal Threshold ( 2/3 − 8PSK )

13 0

2000

4000

6000 10000 8000 12000 Number of frames [1Frame = 24msec]

14000

16000

18000

Fig. 3. Example of heavy fading in the reverse link, for QS-CDMA and fR = 3: true (blue) and estimated (violet) Es /Nt true and physical layer selector thresholds.

IEEE Communications Society Globecom 2004

0

2000

4000

10000 8000 6000 12000 Number of frames [1Frame = 24msec]

14000

16000

18000

Fig. 5. Example of light fading in the reverse link, for QS-CDMA and fR = 3. Es /Nt true and estimated

1105

0-7803-8794-5/04/$20.00 © 2004 IEEE