In this work, we have studied the index assignment optimization for multiple description quantization and its application to robust DSR over Mobile Ad Hoc Networks. The MDVQ transmission scheme and the corresponding formulation of the optimal decoder design were first presented. The optimization of the index assignment is to place the quantizer output indices into an index matrix in such a way that the expected channel distortion is minimized. The MD-BSA accomplishes this by recursively switching a pair of codevectors until the expected channel distortion can not be further reduced.
However, this exhaustive switching operation is time-consuming especially when the codebook size is large. In order to reduce its computational complexity, we formulated the index assignment problem based on a linear programming framework and then proposed a novel local search algorithm, MD-HA, to quickly find the optimal index assignment. The performance comparison between the MD-BSA and the MD-HA revealed that the slightly better performance of the MD-BSA was achieved at the expense of higher computational complexity. The performances of the MD-BSA and the MD-HA under channel-mismatched conditions were also examined.
For practical application, experiments were conducted on the Mandarin digital string recognition task under different IP network scenarios, including random losses
and Gilbert-model losses. In addition, the ns-2 was introduced to simulate the packet loss characteristics of the MANET. Simulation results indicated that the proposed MDVQ achieves high robustness against random and Gilbert-model packet losses. The ns-2 based MANET simulation was also conducted to examine the DSR performances of the proposed MDVQ under Internet traffic with various numbers of CBR sources.
Finally, we present the future research issue. While, the MD-HA finds the opti-mal index assignment very quickly, it dose not take into consideration any channel information. In contrast, the MD-BSA uses average packet erasure probabilities of the channels to find the channel-matched index assignment. However, when a channel with memory such as the Gilbert-model channel is applied, the MD-BSA does not consider channel memory characteristics during its index assignment optimization. Thus, as a future research work, it is desired to exploit the fast search property of the MD-HA and channel memory characteristics to propose a channel-optimized index assignment algorithm.
Bibliography
[1] V. Weerackody, W. Reichl, and A. Potamianos, “An error-protected speech recog-nition system for wireless communications,” IEEE Trans. Wireless Commun., vol. 1, no. 2, pp. 282-291, Apr. 2002.
[2] C. Boulis, M. Ostendorf, E. A. Riskin, and S. Otterson, “Graceful degradation of speech recognition performance over packet-erasure networks,” IEEE Trans.
Speech and Audio Processing, vol. 10, no. 8, pp. 580-590, Nov. 2002.
[3] V. A. Vaishampayan, “Design of multiple description scalar quantizers,” IEEE Trans. Inform. Theory, vol. 39, pp. 821-834, May 1993.
[4] Y. Zhou and W.-Y. Chan, “Multiple description quantizer design using a channel optimized quantizer approach,” in Proceedings of the 38th Annual Conference on Information Sciences and systems, 2004.
[5] S. D. Voran, “The channel-optimized multiple-description scalar quantizer,” in Proceedings of the 10th IEEE Digital Signal Processing Workshop, Calloway Gar-dens, Pine Mountain, Georgia, Oct. 2002.
[6] P. Yahampath, “On index assignment and the design of multiple description qiantizers,” in Proceedings of 2004 International Conference on Acoustics, Speech, and Signal Processing, Montreal, Quebec, Canada, vol. 4, pp. iv-597-iv-600, May 2004.
[7] V. A. Vaishampayan, N. J. A. Sloane, and S. D. Servetto, “Multiple description vector quantization with lattice codebooks: Design and analysis,” IEEE Trans.
Inform. Theory, vol. 47, pp. 1718-1734, Sep. 2001.
[8] V. K. Goyal, J. A. Kelner, and J. Kovacevic, “Multiple description vector quanti-zation with a coarse lattice,” IEEE Trans. Inform. Theory, vol. 48, pp. 781-788, Mar. 2002.
[9] S. N. Diggavi, N. J. A. Sloane, and V. A. Vaishampayan, “Design of asymmet-ric multiple description lattice vector quantizers,” in Proceedings of IEEE Data Compression Conference, 2000.
[10] K. Zeger and A. Gersho, “Pseudo-Gray coding,” IEEE Trans. Commun., vol. 38, no. 12, pp. 2147-2158, Dec. 1990.
[11] N. Gortz and P. Leelapornchai, “Optimization of the index assignments for mul-tiple description vector quantizers,” IEEE Trans. Commun., vol. 51, no. 3, pp.
336-340, Mar. 2003.
[12] J. Broch, D. A. Maltz, D. B. Johnson, Y.-C. Hu, and J. Jetcheva, “A performance comparison of multi-hop wireless ad hoc network routing protocols,” in Proceed-ings of the 4th International Conference on Mobile Computing and Networking (ACM/IEEE MOBICOM ’98), Dallas, Texas, USA, pp. 85-97, Oct. 1998.
[13] C. E. Perkins, E. M. Royer, S. R. Das, and M. K. Marina, “Performance compar-ison of two on-demand routing protocols for ad hoc networks,” IEEE Personal Communications, vol. 8, no. 1, pp. 16-28, Feb. 2001.
[14] S. R. Das, R. Castaneda, and J. Yan, “Simulation-based performance evaluation of routing protocols for mobile ad hoc networks,” Mobile Networks and Applica-tions, vol. 5, pp. 179-189, Sep. 2000.
[15] Y. Lu, Y. Zhong, and B. Bhargava, “Packet loss in mobile ad hoc networks,”
Purdue University, West Lafayette, IN, 47904, Tech. Rep. CSD-TR 03-009.
[16] P. Johansson, T. Larsson, N. Hedman, B. Mielczarek, and M. Degermark,
“Scenario-based performance analysis of routing protocols for mobile ad-hoc net-works,” in Proceedings of the 5th International Conference on Mobile Computing and Networking (ACM/IEEE MOBICOM ’99), Seattle, Washington, USA, pp.
195-206, Aug. 1999.
[17] C. E. Perkins and P. Bhagwat, “Highly dynamic Destination-Sequenced Distance-Vector routing (DSDV) for mobile computers,” in Proceedings of the SIG-COMM ’94 Conference on Communications Architecture, Protocols, and Appli-cations, pp. 234-244, Aug. 1994. A revised version of the paper is available from http://www.cs.umd.edu/projects/mcml/papers/Sigcomm94.ps.
[18] C. E. Perkins, “Ad Hoc On Demand Distance Vector (AODV) routing,” Internet draft, draft-ietf-manet-aodv-00.txt, Nov. 1997. Work in progress.
[19] D. Eckhardt and P. Steenkiste, “Measurement and analysis of the error char-acteristics of an in-building wireless network,” in Proceedings of the SIGCOMM
’96 Conference on Applications, Technologies, Architecture, and Protocols for Computer Communication, pp. 243-254, Aug. 1996.
[20] B. Tuch, “Development of WaveLan, an ISM band wireless LAN,” AT &T Tech.
J., vol. 72, no. 4, pp. 27-33, Jul./Aug. 1993.
[21] ETSI ES 202 212 v1.1.1. Digital speech recognition; extended advanced front-end feature extraction algorithm; compression algorithms; back-front-end speech re-construction algorithm. Nov. 2003.
[22] J. Cardinal, “Entropy-constrained index assignments for multiple description quantizers,” IEEE Trans. Signal Processing, vol. 52, no. 1, pp. 265-270, Jan.
2004.
[23] J. P. Ignizio and T. M. Cavalier, Linear Programming, Prentice Hall, Upper Saddle River, New Jersey, 1994.
[24] H. W. Kuhn, “The Hungarian method for the assignment problem,” Naval Re-search Logistics Quarterly, vol. 2, no. 1-2, pp. 83-97, 1955.
[25] CoCentric System Studio-Reference Design Kits. Mountain View, CA: Synopsys, Inc., 2003.
[26] S. Lin and D. J. Costello, Error Control Coding, Prentice Hall, Upper Saddle River, New Jersy, Apr. 2004.
[27] H. Sanneck and N. T. L. Le, “Speech property-based FEC for Internet telephony applications,” in Proceedings of the SPIE/ACM SIGMM Multimedia Computing and Networking Conference 2000 (MMCN 2000), San Jose, CA, Jan. 2000.