Kipnis et al., 2021 - Google Patents

Gaussian approximation of quantization error for estimation from compressed data

Kipnis et al., 2021

View PDF
Document ID
6843892728499233651
Author
Kipnis A
Reeves G
Publication year
Publication venue
IEEE Transactions on Information Theory

External Links

Snippet

We consider the distributional connection between the lossy compressed representation of a high-dimensional signal X using a random spherical code and the observation of X under an additive white Gaussian noise (AWGN). We show that the Wasserstein distance between a …
Continue reading at ieeexplore.ieee.org (PDF) (other versions)

Classifications

    • HELECTRICITY
    • H03BASIC ELECTRONIC CIRCUITRY
    • H03MCODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M7/00Conversion of a code where information is represented by a given sequence or number of digits to a code where the same information or similar information or a subset of information is represented by a different sequence or number of digits
    • H03M7/30Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00Cryptographic mechanisms or cryptographic arrangements for secret or secure communication
    • H04L9/08Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
    • H04L9/0816Key establishment, i.e. cryptographic processes or cryptographic protocols whereby a shared secret becomes available to two or more parties, for subsequent use
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00Cryptographic mechanisms or cryptographic arrangements for secret or secure communication
    • H04L9/30Public key, i.e. encryption algorithm being computationally infeasible to invert or user's encryption keys not requiring secrecy
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00Cryptographic mechanisms or cryptographic arrangements for secret or secure communication
    • H04L9/06Cryptographic mechanisms or cryptographic arrangements for secret or secure communication the encryption apparatus using shift registers or memories for block-wise or stream coding, e.g. DES systems or RC4; Hash functions; Pseudorandom sequence generators
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00Arrangements for detecting or preventing errors in the information received
    • H04L1/004Arrangements for detecting or preventing errors in the information received by using forward error control

Similar Documents

Publication Publication Date Title
Shlezinger et al. Federated learning with quantization constraints
Kipnis et al. Gaussian approximation of quantization error for estimation from compressed data
US11468355B2 (en) Data compression and communication using machine learning
Oh et al. Communication-efficient federated learning via quantized compressed sensing
Stavrou et al. The role of fidelity in goal-oriented semantic communication: A rate distortion approach
Jalali et al. Minimum complexity pursuit for universal compressed sensing
Khobahi et al. Signal recovery from 1-bit quantized noisy samples via adaptive thresholding
Wimalajeewa et al. Application of compressive sensing techniques in distributed sensor networks: A survey
Kipnis et al. The rate-distortion risk in estimation from compressed data
No et al. Rateless lossy compression via the extremes
Coluccia et al. Compressed sensing for distributed systems
Nemati et al. All-in-one: VQ-VAE for end-to-end joint source-channel coding
Zhe et al. Rate-distortion optimized coding for efficient cnn compression
Leinonen et al. Rate-distortion performance of lossy compressed sensing of sparse sources
Bergström et al. Deep randomized distributed function computation (DeepRDFC): Neural distributed channel simulation
Cheng et al. On strong converse theorems for quantum hypothesis testing and channel coding
Treust et al. Strategic communication with side information at the decoder
Saha et al. Efficient randomized subspace embeddings for distributed optimization under a communication budget
Malik et al. A distributionally robust approach to shannon limits using the wasserstein distance
Shevchuk Theoretical and algorithmic foundations of improving the efficiency of packet data transmission in high-speed and secure radio networks
Leinonen et al. Distributed variable-rate quantized compressed sensing in wireless sensor networks
Wimalajeewa et al. Compressive sensing based signal processing in wireless sensor networks: A survey
Shirazinia et al. Distributed quantization for measurement of correlated sparse sources over noisy channels
Adikari et al. Compressing gradients by exploiting temporal correlation in momentum-SGD
Vempaty et al. The non-regular CEO problem