Keep the Bursts and Ditch the Interleavers

Abstract

To facilitate applications in IoT, 5G, and beyond, there is an engineering need to enable high-rate, low-latency communications. Errors in physical channels typically arrive in clumps, but most decoders are designed assuming that channels are memoryless. As a result, communication networks rely on interleaving over tens of thousands of bits so that channel conditions match decoder assumptions. Even for short high rate codes, awaiting sufficient data to interleave at the sender and de-interleave at the receiver is a significant source of unwanted latency. Using existing decoders with non-interleaved channels causes a degradation in block error rate performance owing to mismatch between the decoder's channel model and true channel behaviour.Through further development of the recently proposed Guessing Random Additive Noise Decoding (GRAND) algorithm, which we call GRAND-MO for GRAND Markov Order, here we establish that by abandoning interleaving and embracing bursty noise, low-latency, short-code, high-rate communication is possible with block error rates that outperform their interleaved counterparts by a substantial margin. Moreover, while most decoders are twinned to a specific code-book structure, GRAND-MO can decode any code. Using this property, we establish that certain well-known structured codes are ill-suited for use in bursty channels, but Random Linear Codes (RLCs) are robust to correlated noise. This work suggests that the use of RLCs with GRAND-MO is a good candidate for applications requiring high throughput with low latency.

Research on Efficient Channel Decoding Algorithm for Memory Channel and Short Packet Transmission in Smart Grid

“…(Zhan et al, 2022) proposed an efficient noise pattern generation algorithm based on successive addition and subtraction. (An et al, 2022) queried the putative noise error patterns in the Markov order (MO). The GRAND-MO algorithm regards burst errors as a universal existence in discrete channels.…”

“…GRAND-MO has the potential to become an effective decoding scheme. The GRAND-MO algorithm regards burst errors as common in discrete channels by querying the noise error patterns assumed in the Markov order (MO) (An et al, 2022). Compared with the classical channel coding and decoding algorithm, this algorithm considers the correlation of…”

URLLC with Coded Massive MIMO via Random Linear Codes and GRAND

“…Using error-correcting codes with short codewords is one way of achieving the sought low-latency objective, by allowing to do discard the interleavers that are typically employed in wireless links to make the errors after the wireless channel look i.i.d. [2]. However, developing codes that map information words onto large codewords was prioritized in pre-5G systems to reach Shannon's capability.…”

“…Many recent research works have shown that RLCs supersede the performance of polar codes of the same length and rate in the classical case. Most importantly, while off-the-shelf classical or polar codes, or any other structured code, do not exist for any desired pair of code length and desired rate when using RLCs with GRAND, one has great flexibility of choice regarding the length and the code rate [2], [16]- [18]. For greater spectral efficienly, GRAND has been proposed in combination with massive multiple input-multiple-output (mMIMO) in [19].…”

“…Symbol-level GRAND attains significantly faster decoding than the original bit-level GRAND . In [2], they have suggested modifying GRAND to use knowledge about the adopted modulation scheme. Compared to [2], symbol-level GRAND developed a closed-form expression for the probability that the input stream of bits contains a specific combination of binary strings representing various constellation symbols.…”