Neural Network Congestion Controller In prioritised ATM ... - IEEE Xplore

where p = {c,,,,, ..., c,,,,+~, c,,,+~,...}. After being interleaved, the soft-value of extrinsic information Q C ( G ) = Qioy.tin~(CtL) - Q(G) :3) is fed back to the demodulator as the u priori value. For further iterations, the extrinsic information of the last iteration is used to obtain a more reliable estimation. Hard decisions are made in the last iteration.

Simulation result: BER performance in a two-user static channel is investigated by computer simulations. The 2l3-rate 16-state convolutional encoder of Ungerboeck code [6] is implemented in the two transmitters, assuming an 8PSK modulator and an L = 300 symbol long interleaver. Fig. 3 shows the BER performance of user1 at OdB power ratio (IAu/A,12 = 1). A performance improvement can be found when the number of iterations increases. With 10 iterations, the BER decreases to 1W at EJN, = 10.5dB. The same coding scheme using an interleaver size of L = 2000 symbols i:i also illustrated in Fig. 3. At E J N , = 9dB, we obtain BER = 10 ‘. For this two-user access channel, we further calculated the channel cutoff rate, which is a good approximation of the channel capacity. It shows that the total cutoff rate becomes 4bitlchannel-use at EJN,, = 9dB. Consequently, the performancc can be consideyed as close to the channel capacity. Conclusions: A multiple-access scheme combining convolutional code and a random interleaver is proposed. With perfect channel estimation. it exhibits a near full capacity performance in a twouser complex valued channel. The iterative symbol-by-symbol decoding scheme is verified to be efficient in decoding interleaved MPSK signals.

0 IEE 1998

17 August I998

Electronics Letters Online No: 19981479

Y. Li, H. Murata and S. Yoshida (Department of’ E/ectronics und Comimmicution, Kyoto Univer-siry,Kyoto, 606-8501 Jappun)

Feedback congestion control has been extensively studied in the literature [ 11. When congestion is detected, control signals are fed back to all sources. The type of feedback signal may vary from one scheme to another. In [2, 31, the use of neural networks (NNs) in congestion control for the scenario of a single buffer with a fixed service rate has been addressed. When a switch with time priorities is considered, the congestion feedback rate will be affected not only by the arrival rate to the buffer to be controlled, but also by the arrival rate in the higher priority buffers. In this Letter we quantify this effect on the NN learning performance and test the NN scheme in a time prioritised ATM switch.

NN tuuining: To create the N N training files, one MPEG video trace from the movie ‘Star Wars’ found in [4] (which we refer to as ‘trace-1’) was fed into the low priority (LP) buffer and one trace-l was fed into the high priority (HP) buffer. The two buffers share the same server, however buffer-1 has full priority over buffer-2. This means that any cells found in buffer-I will be served before cells in buffer-2. Time is divided into intervals with the same length T (T was taken to be 8.3ms as in [2, 31). Two NN models were tested. In model I , a three layer NN (3-8-1) was fed with two inputs representing the number of cell arrivals Al(i-1) and AZ(i-2)measured in the previous time periods T’[i-1) and T(i-2) respectively, from the source feeding the LP buffer. The inputs were normalised between 0-1 by dividing them by the peak number of arrivals to the LP buffer. The final input to the NN was the proportion of the LP buffer space used at the end of the last time period T(i-1). In model 2, a three layer NN (6-8-1) is fed with the three inputs from the LP buffer (as in model l), as well as three inputs from the HP buffer. Both NN models were trained to predict 0, where 0 is defined as the ratio of the number of cells discarded at the LP buffer to the number of cells which arrived at the LP buffer in the time period T(i+2).The NN was trained to predict 2-cycle time periods ahead to include the propagation time period (A) from the buffer to the source which is equal to the sampling time period T for simplicity. In [2], the NN output 0 was fed back to regulate each source from its old rate (R)to its new rate (r), as given in eqn. I : System dexription und

References I‘

‘A survey of multi-way channc:ls in information theory 1961-1976’, IEEE Trans., 1977, IT-23, pp 1-37 CIJEVILLAT,P.R.: “-user trellis coding for a class of multiple access channels’, IEEE Truns., 1981, IT-27, (I), pp. 114-120

VAN

DER MEIJLEN.E.C:

and S U Z U K I , H : ‘Interference cancelling equalizer (ICE) for mobile radio’. Proc. IEEE Int. Conf. Coinmun. (ICC’94),New Orleans, 1994, pp. 1427-1432 MUKATA, H., and YOSHIDA. s.: ‘Trellis-coded co-channel interference canceller for microcellular radio’, I E E E Truns., 1997, COM-45, (c)), pp. 1088-1094 MOHER. M.: ‘An iterative multiuser decoder for asynchronous BPSK users’, Proc. IEEE Int. Symp. Intormation Theory, Cambridge, MA, USA, 1998, pp. 16-21 UNGERBOECK, G.: ‘Channel coding with multilevel/phase signals’, IEEE Trans., 1982, IT-28, ( l ) , pp. 55-67

YOSHINO, H., FUKAWA, K ,

= (1 - 0 ) X R

(1)

Reducing the source rate means increasing the cells inter-arrival time (IAT). When using eqn. 1, the IAT resulting from reducing the source rate might be larger than the sampling time period T. We overcome this problem by using eqn. 2 to regulate the source:

Fe’u_IAT = OldLIAT

+ (0 x T)

(2)

Training files representing the inputs and outputs described above were created when no regulation of the sources was considered. Each file contains 2700 samples representing 0.31% of the whole trace-1 file, The N N used in this Letter was the three layer feedforward NN with error backpropagation learning algorithm [5]. The mean square error (MSE) against the number of passes through the training file for both NN models is shown in Fig. 1.

Neural network congestion controller in prioritised ATM switch A . A l - H a m m a d i a n d J. Schormans A neural network (NN) scheme is proposed for congcstion control in an ATM switch with time priorities. It is shown that in a prioritised switch it is necessary to monitor the buffer to be controlled as well as buffers with higher priorities. Furthennorc, it is shown that the N N scheme in a time prioritised switch gives lower cell loss and delay when compared to the conventional

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numtm OT passes through training file 55411

Introduction: One of the fundamental challenges facing broadband

information transport is to design congestion control strategies to support multiple classes of traffic in the future ATM based BISDN. To accomplish this cost-effectively, priority classes ior the different services are required.

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Fig. I Model I und 2 burning curves

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Fig. 1 shows that by monitoring both HP and LP buffers (model 2) a better learning curve is achieved compared to monitoring only the LP buffer (model 1). When the propagation delay from the buffer to the source was increased to 2T, the MSE increases for both, however, model 2 still has the lower MSE curve.

complete the transmission without feedback control to be TO, seconds, and the time to complete the transmission with feedback control as TO,, seconds. Delay is then defined as

0

In test 1, a single trace-I source is fed to the H P buffer, while the LP is fed with a single trace-2 source. The results in Fig. 2 show that the N N scheme gives a three to 10 times improvement in CLR when compared to the CB scheme. Fig. 3 shows that delay in the NN scheme is also lower when compared to that in the CB scheme. From eqn. 3, it is obvious that there is no delay curve for the no-feedback scenario. In test 2, a single trace-1 source was fed to the H P buffer, while the LP was fed with a multiplex of two trace-3 sources and two trace-I sources. The results in Figs. 2 and 3 show that the N N Controller was still perfomiing better with multiple heterogeneous sources in the LP buffer. Conclusion: In this Letter we have proposed a new NN congestion controller for a prioritised ATM switch. We have shown that to control congestion in the low priority buffer using the N N scheme it is necessary to monitor the low priority buffer as well as higher priority buffers. We have also shown that the NN congestion controller outperforms the CB congestion controller in CLR and delay.

LP buffer size

resty I m d 2, CLR uguinyt buflei test -0- test -&lest - - _ _ test -0 test -%teat ~

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0 IEE 1998 7 August I998 Electronics Letters Oiiliiic. No: 19981467 A. AI-Hammadi and J . Schormans (Telecom. Research, Depurtment of’ Electronics Engineering, Queen Mury trnd We.vtfield College, Mile End Road, London E l 4NS, United Kingdoin) E-mail: A.S.Al-Hammadi(gqmmJ.ac.uk

References in ATM networks’, Coinyut. Nerw. ISDN Sjat., 1996, 28. (13), pp. 17231738 YAO-CHING LIU, and DOLLIGERIS.c : ‘Static vs. adaptive reedback congestion controller for A T M nctwork’. Glohecorn’95, Singapore, 13-17 November 1995, pp. 291-295 YAo-CHIPUG LIU, and DOULIGERIS. c : ‘Rate regulator with feedback controller in ATM networks- A neural network approach’, IEEE J. Sei. Areus Coinmun., 1997, SAC-15, (2), pp. 200-208 GARRETT. M , and FERNANDEZ. A : ‘Variable bit rate video bandwidth trace using M P E G code’ (1994), Available FTP: thumper.belkore.com, Directory: publvbr.video.trace RUMELHART. D . IiINToN, G , and WILLIAMS, R : ‘Learning internal representation by error back-propagation’ in ‘Parallel distributed processing: Exploration in microstructure of cognition’ (MIT press, Cambridge, MA, 1986) Directory: Available FTP: ftp-info3.informatik.uni-wuerzburg.de pubiMPEGltraces JAIN, K.: ‘Congestion control and traffic management

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Fig. 3 Tests I and 2, deluy uguinst huJfir size

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Re.mlts; After training the NN on the data files, the weights or the NN were fixed. To validate our scheme we ran several simulations comparing the conventional binary (CB) congestion control scheme, referred to as the static scheme in [2],to our proposed scheme. In the CB scheme, the sources feeding the LP buffer were reduced to 50% of their current rate when more than 50% of the LP buffer spiwp wan useupid, Wn oviiipnrcjd both NiY curd CB

schemes with the no-feedback scenario where n o source regulation is considered. In our simulations we used trace-I as well as trace-? (from ‘Jurassic Park’ movie found in [6]) and trace-3 (from a Football match found in [6])MPEG sources. I n all simulations, the H P buffer size was fixed to 200 cell slots and the bandwidth was set to 3840 celVs giving a cell loss ratio of IO4 in the HP buffer. Simulations were ended when the sources feeding the controlled LP buffer completed transmission. The performance metrics used to compare both N N and CB controllers were the cell loss ratio (CLR) and the transmission delay. In this Letter, we define the CLR as the total number of cells discarded at the LP buffer divided by the total number of cells generated at the sources feeding the LP buffer. For delay, we define the time to

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Polarisation mode dispersion tolerance of 10Gbit/s NRZ and RZ optical signals H. Taga, M. Suzuki and Y. Namihira The authors hnve evaluatcd ihc pcrihrrnance of an optical receiver with polarisation mode dispersion (PMD) for non-return-to-zero (NRZ) and return-to-zero (RZ) signals at 10Gbit/s, and found that the R Z foniiat is more tolerant to P M D than is the NRZ format. Introduction; Polarisation mode dispersion (PMD) is one of the

degradation factors in optical signal transmission. The effect of PMD on optical signal transmission has already been investigated both theoretically and experimentally [l - 41. Recently, PMD has become a more serious problem, because high bit rate systems such as STM-64 are gaining in popularity. Therefore, methods for equalising the PMD and improving the PMD tolerance have been reported [5, 61.

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