## 5 Key 6G Technologies
### Improved Wireless Interface
#### 5.2.2 New Channel Coding Technologies  
Efficient channel coding technology is pivotal for enhancing reliability, capacity, and service quality in communication systems. Error-correcting codes (ECCs) are shifting from algebraic to probabilistic coding, significantly improving communication system performance [95]. Low-density parity check (LDPC) codes [96], Turbo codes [97], and polar codes [98] are recognized as leading ECCs, utilized as standard codes for 4G data channels, 5G data channels, and 5G control channels, respectively. Despite differences in decoding algorithms and implementations [99], these ECCs share a foundation in Bayes' theorem, making them competitive candidates for 6G requirements of ultra-low-power consumption and ultra-high speed advocating for a unified decoding framework adaptable to complex scenarios [95].  
Being linear block codes, Turbo, LDPC, and polar codes can utilize a belief propagation (BP) decoder, employing the sum-product algorithm on a bipartite Tanner factor graph [100]. For LDPC codes, this is particularly beneficial due to their high sparsity. Turbo codes utilize the Bahl-Cocke-Jelinek-Ravivi Algorithm (BCJR) algorithm, adapting the sum-productalgorithm to a trellis graph for decoding [100; 101]. Polar codes use a special decoder called successive cancellation (SC), which is better than the BP decoder. This SC decoder works by pushing beliefs in a step-by-step message-passing process [102]. For finite code lengths, when channel polarization degrades, SC flip/list algorithms [103; 104] and BP flip/list decoding algorithms [105; 106] become necessary to expand the codeword search space.  
Turbo [107], LDPC, and polar codes [108] share similar decoding rules, but advancements in encoding techniques have resulted in simplified algorithms and factor graphs. These improvements include the generator polynomial for information sets of polar codes and Turbo codes, which have led to better energy efficiency for decoders in 6G [95]. Implementing ECC decoders in circuits involves using various techniques in different communication technologies. For example, in 4G LTE Turbo codes [109], a windowed Max-log-BCJR decoder is used, while in 5G NR, an adaptive min-sum-BP decoder is used for LDPC codes [110]. In 5G NR polar codes, a node-based SC decoder [111] is used. Achieving a consistent ECC design at the circuit level is a crucial component of 6G technology, which includes LDPC/Polar decoders [112] and Turbo/LDPC decoders [113].  
Driven by the ultra-reliability and ultra-low latency requirements of 6G, shorter code lengths are anticipated for ECCs, diminishing the effectiveness of decoding algorithms due to reduced randomness, sparsity, and channel polarization. Using near-maximum decoding techniques for traditional algebraic coding is an alternate strategy. This method, which includes decoding algorithms like ordered statistics decoding (OSD) [114] and the newly proposed guessing random additive noise decoding (GRAND) [115], guarantees uniformity for linear block codes. Newly discovered polarization-adjusted convolutional (PAC) codes [116] are another feasible approach for short-length circumstances. A 2D spatiotemporal coding method uses massive antennas in MIMO systems, resulting in improved transmission rate and reliability with minimal decoding delay [117].