15,004 research outputs found
Order Statistics Based List Decoding Techniques for Linear Binary Block Codes
The order statistics based list decoding techniques for linear binary block
codes of small to medium block length are investigated. The construction of the
list of the test error patterns is considered. The original order statistics
decoding is generalized by assuming segmentation of the most reliable
independent positions of the received bits. The segmentation is shown to
overcome several drawbacks of the original order statistics decoding. The
complexity of the order statistics based decoding is further reduced by
assuming a partial ordering of the received bits in order to avoid the complex
Gauss elimination. The probability of the test error patterns in the decoding
list is derived. The bit error rate performance and the decoding complexity
trade-off of the proposed decoding algorithms is studied by computer
simulations. Numerical examples show that, in some cases, the proposed decoding
schemes are superior to the original order statistics decoding in terms of both
the bit error rate performance as well as the decoding complexity.Comment: 17 pages, 2 tables, 6 figures, submitted to IEEE Transactions on
Information Theor
Short Block-length Codes for Ultra-Reliable Low-Latency Communications
This paper reviews the state of the art channel coding techniques for
ultra-reliable low latency communication (URLLC). The stringent requirements of
URLLC services, such as ultra-high reliability and low latency, have made it
the most challenging feature of the fifth generation (5G) mobile systems. The
problem is even more challenging for the services beyond the 5G promise, such
as tele-surgery and factory automation, which require latencies less than 1ms
and failure rate as low as . The very low latency requirements of
URLLC do not allow traditional approaches such as re-transmission to be used to
increase the reliability. On the other hand, to guarantee the delay
requirements, the block length needs to be small, so conventional channel
codes, originally designed and optimised for moderate-to-long block-lengths,
show notable deficiencies for short blocks. This paper provides an overview on
channel coding techniques for short block lengths and compares them in terms of
performance and complexity. Several important research directions are
identified and discussed in more detail with several possible solutions.Comment: Accepted for publication in IEEE Communications Magazin
On joint detection and decoding of linear block codes on Gaussian vector channels
Optimal receivers recovering signals transmitted across noisy communication channels employ a maximum-likelihood (ML) criterion to minimize the probability of error. The problem of finding the most likely transmitted symbol is often equivalent to finding the closest lattice point to a given point and is known to be NP-hard. In systems that employ error-correcting coding for data protection, the symbol space forms a sparse lattice, where the sparsity structure is determined by the code. In such systems, ML data recovery may be geometrically interpreted as a search for the closest point in the sparse lattice. In this paper, motivated by the idea of the "sphere decoding" algorithm of Fincke and Pohst, we propose an algorithm that finds the closest point in the sparse lattice to the given vector. This given vector is not arbitrary, but rather is an unknown sparse lattice point that has been perturbed by an additive noise vector whose statistical properties are known. The complexity of the proposed algorithm is thus a random variable. We study its expected value, averaged over the noise and over the lattice. For binary linear block codes, we find the expected complexity in closed form. Simulation results indicate significant performance gains over systems employing separate detection and decoding, yet are obtained at a complexity that is practically feasible over a wide range of system parameters
- …