Related Experiment Video
Updated: Nov 27, 2025
09:43
Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
Published on: March 20, 2017
10.2K
Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
Yunus Can Gültekin1, Tobias Fehenberger2, Alex Alvarado1
1Signal Processing Systems Group, Information and Communication Theory Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
Entropy (Basel, Switzerland)
|December 8, 2020
Summary
For short blocklengths, sphere shaping (SpSh) offers superior energy efficiency over distribution matching (DM) techniques like constant composition distribution matching (CCDM). SpSh minimizes rate loss, improving power efficiency by up to 1 dB for additive white Gaussian noise channels.
Area of Science:
- Digital Communications
- Information Theory
- Signal Processing
Background:
- Probabilistic amplitude shaping (PAS) is crucial for optimizing communication systems.
- Traditional distribution matching (DM) algorithms, like constant composition distribution matching (CCDM), face efficiency limitations at short blocklengths due to rate loss.
- The objective for short blocklengths shifts from strict distribution matching to maximizing energy efficiency within the signal space.
Purpose of the Study:
- To systematically compare distribution matching (DM) and sphere shaping (SpSh) for short blocklength PAS.
- To evaluate energy efficiency and rate loss of different shaping algorithms over additive white Gaussian noise (AWGN) channels.
- To analyze the end-to-end decoding performance and computational complexity of these techniques.
Main Methods:
- Comparative analysis of CCDM, multiset-partition DM (MPDM), and SpSh algorithms.
- Numerical simulations to assess rate loss and power efficiency.
- End-to-end decoding performance simulations for blocklengths around 200.
- Discussion on algorithmic complexity (latency, storage, computations).
Main Results:
- MPDM and SpSh demonstrate reduced rate loss compared to CCDM for short blocklengths.
- SpSh achieves the minimum rate loss, particularly for ultra-short blocklengths, by prioritizing energy efficiency.
- Up to 1 dB power efficiency improvement over uniform signaling is observed with MPDM and SpSh at blocklengths around 200.
Conclusions:
- For short blocklength PAS over AWGN channels, energy-efficient signal space design (SpSh, MPDM) is more effective than traditional distribution matching (CCDM).
- Sphere shaping (SpSh) offers the best performance in terms of minimizing rate loss and maximizing power efficiency.
- The choice of algorithm impacts latency, storage, and computational complexity, requiring consideration for practical implementation.
Related Concept Videos
01:19Role of Shaping in Operant Conditioning
749
Shaping is a technique used in operant conditioning to train complex behaviors by rewarding successive approximations toward the target behavior. This method is necessary because organisms are unlikely to perform complex behaviors spontaneously. Instead, shaping breaks down the desired behavior into small, manageable steps.
The steps involved in shaping begin with reinforcing any response that resembles the desired behavior. For example, parents might praise a child for picking up one toy. As...
The steps involved in shaping begin with reinforcing any response that resembles the desired behavior. For example, parents might praise a child for picking up one toy. As...
749
01:32Probability Distributions
11.1K
The probability of a random variable x is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
11.1K
01:25Shape and Texture of Coarse Aggregate
381
Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
381
01:16Precipitate Formation and Particle Size Control
3.4K
In precipitation gravimetry, the precipitating agent should react specifically or selectively with the analyte. While a specific reagent reacts with the analyte alone, a selective reagent can react with a limited number of chemical species.
The obtained precipitate should be either a pure substance of known composition or easily converted to one by a simple process, such as ignition or drying. In addition, the precipitate should be insoluble and easily filterable. In general, filterability...
The obtained precipitate should be either a pure substance of known composition or easily converted to one by a simple process, such as ignition or drying. In addition, the precipitate should be insoluble and easily filterable. In general, filterability...
3.4K
01:25Choosing Between z and t Distribution
3.4K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
3.4K