n-Channel Asymmetric Multiple-Description Lattice Vector Quantization
Jan Ostergaard, Richard Heusdens, Jesper Jensen
Abstract
We present analytical expressions for optimal entropy-constrained multiple-description lattice vector quantizers which, under high-resolutions assumptions, minimize the expected distortion for given packet-loss probabilities. We consider the asymmetric case where packet-loss probabilities and side entropies are allowed to be unequal and find optimal quantizers for any number of descriptions in any dimension. We show that the normalized second moments of the side-quantizers are given by that of an L-dimensional sphere independent of the choice of lattices. Furthermore, we show that the optimal bit-distribution among the descriptions is not unique. In fact, within certain limits, bits can be arbitrarily distributed.
Create a lesson
Related papers
Distributed Consensus, Optimization, and Learning under Communication Constraints
Zheng Chen, Nicolò Michelusi, Themistoklis Charalambous et al.
Energy-Accumulate-Then-Act Backscatter Communications With Retransmission Under Imperfect Feedback Detection
Rui Xu, Yinghui Ye, Guangyue Lu et al.
The Exact Memory-Rate Tradeoff for Three-User Secure Coded Caching
Han Fang, Nan Liu, Wei Kang
Cross-Modal Semantic Successive Refinement for Generative Image Communication over Underwater Acoustic Channels
Xu Pei, Bin Wang, Guangxu Zhu et al.
Fano's Inequality and Channel Capacity in the Individual-Sequence Regime
Neri Merhav
Exact Transport-Information Correspondence for Distribution-Adaptive Discretization
Koretaka Yuge