Entropy-constrained vector quantization

Chou, Philip A.; Lookabaugh, T.; Gray, Robert M.

doi:10.1109/29.17498

Cited by 525 publications

(303 citation statements)

References 36 publications

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“…Here, we want to point out that since we are interested in the distortions obtainable over a whole range of rates, we ran the CECVQ algorithm repeatedly on an increasing sequence of λ's, beginning with λ 0 = 0 (given by the GLA) and increasing in a manner consistent with obtaining sufficient resolution along the rate axis. The final codebook for λ j is the initial codebook for λ j+1 , as suggested by [5] and [7]. Note that this procedure is an heuristic one to decrease the size of CECVQ codebooks.…”

Section: Resultsmentioning

confidence: 99%

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Conditional entropy-constrained vector quantization: high-rate theory and design algorithms

Garrido

Pearlman

1995

IEEE Trans. Inform. Theory

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Section: Resultsmentioning

confidence: 99%

“…Gray [5] and is called ECVQ design algorithm. The algorithm approaches a local minimum of the distortion for a given entropy of the quantizer output.…”

Section: H(q(x))mentioning

confidence: 99%

Conditional entropy-constrained vector quantization: high-rate theory and design algorithms

Garrido

Pearlman

1995

IEEE Trans. Inform. Theory

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“…In [24], Moschopoulos justified the uniform convergence of the single infinite series in (21). With PFPPC, (21) can be rewritten as…”

Section: Appendixmentioning

confidence: 99%

“…And then given a power codebook, we use the nearest neighbor condition of a generalized Lloyd algorithm with a Lagrangian distortion d(h, P j ) = x j + λP j to generate the optimal partition regions [21], given as,…”

Section: Optimal Power Allocation With Limited Feedback Strategymentioning

confidence: 99%

Outage minimization for parallel fading channels with limited feedback

Dey

2012

J Wireless Com Network

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We address an optimal power allocation problem for minimizing the outage probability for M parallel block-Nakagami-fading channels under a long-term average sum transmit power constraint with finite rate feedback of channel state information (CSI). A simulation-based optimization technique called simultaneous perturbation stochastic approximation algorithm (SPSA) is employed first to numerically derive a locally optimal power codebook. Due to the high computational complexity and long convergence time of SPSA, we make an ordering assumption on the power codebook entries and derive effective hyperplane based approximations to the channel quantization regions and present a number of low-complexity suboptimal quantized power codebook design algorithms. Unlike existing work on outage minimization for multiple-input multiple-output (MIMO) channels with limited feedback, we do not assume that identical transmission power is used for all channels within each channel quantization region. We also do not resort to a Gaussian approximation for the instantaneous mutual information in general as used in many existing work. Based on our power ordering assumption and hyperplane based approximations, we show that allocating identical power to all channels within a given channel quantization region in the limited feedback scenario is asymptotically optimal only at high average power (or average signal-to-noise ratio (SNR)) for the Rayleigh fading case, whereas for the general Nakagami case, the transmit power allocation for an individual channel within each quantized region is asymptotically proportional to the corresponding Nakagami fading parameter (severity of fading). We also present a novel diversity order result for the outage probability for the Nakagami fading case. Finally, we derive a suitable Gaussian approximation based low-complexity power allocation scheme for a large number of parallel channels, which has important applications in wideband slow-fading orthogonal frequency-division multiplexing (OFDM) systems. Extensive numerical results illustrate that only a few bits of feedback close the gap substantially in outage performance between the limited feedback case and the full instantaneous CSI at the transmitter case.

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“…Following the entropy-constrained coding tradition (see, for example, [14], [20], [32], [33]), we describe lossy code design as quantization followed by entropy coding. The only loss of generality associated with the entropy-constrained approach is the restriction to solutions lying on the lower convex hull of achievable entropies and distortions.…”

Section: B Network Vector Quantizers (Nvqs)mentioning

confidence: 99%

Network vector quantization

Fleming

Effros

Proceedings DCC 2001. Data Compression Conference

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Abstract-We present an algorithm for designing locally optimal vector quantizers for general networks. We discuss the algorithm's implementation and compare the performance of the resulting "network vector quantizers" to traditional vector quantizers (VQs) and to rate-distortion (R-D) bounds where available. While some special cases of network codes (e.g., multiresolution (MR) and multiple description (MD) codes) have been studied in the literature, we here present a unifying approach that both includes these existing solutions as special cases and provides solutions to previously unsolved examples.

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Entropy-constrained vector quantization

Cited by 525 publications

References 36 publications

Conditional entropy-constrained vector quantization: high-rate theory and design algorithms

Conditional entropy-constrained vector quantization: high-rate theory and design algorithms

Outage minimization for parallel fading channels with limited feedback

Network vector quantization

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