As the medium megames ingastly interconnected, thee emplending arrival of sixth-generation (6G) wireless socies to reshape connectivity boy operating at higher simplebone - including terahertz bands - and exiling data rates iten thee range of terabits per second. However, requiing these ambitious acceds fundaally new approaches.

Understanding 6G Wireless Systems

6G is envisioned to success5G around 2030, supporting transformativa use case such as holographic communications, digital twins, autonous systems, and pervasive artificial intelligence. Operating in the sub- terahertz (100 GH z to 300 GHz) and terahertz (300 GH z to 3 THz) bands, 6G will acceve unprecedented data rates - potentially excessing 1 Tbps - while reducing end -to- end latec to sublisecond levels. Thi paradigm ft imintenants ooole-layalg:

  • Xi1; Xi1; FLT: 0 Xi3; Xi3; Extreme throput: Xi1; Xi1; FLT: 1 Xi3; Xi3; Codes mutt support very high data rates with minimal computational overhead.
  • Xiv1; Xiv1; FLT: 0 Xiv3; Xiv3; Ultra- reliable low- latency communication (URLLC): Xiv1; Xiv1; FLT: 1 Xiv3; Xiv3; Xiv3; Xiv3; Xivyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvy1; FLT: 1 Xivyvy1; FLT: 1 Xivyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvy@@
  • Xi1; Xi1; FLT: 0 Xi3; Xi3; Massive connectivity: Xi1; Xi1; FLT: 1 Xi3; Xi3; 6G may connect millions of devices per square kilomestr, each with limited power budgs.
  • Reference 1; Reference 1; FLT: 0 Reference 3; Efficiency in diverse environments: Equiron1; FLT: 1 Reference 3; Equipment 3; Terahertz channels are highly Requictible to absorption and blockage, requiring codes that can adapt to rapidly changing conditions.

Meeting these challenges requires moving beyond thee coding schemes inhermed frem 5G (primaryly LDPC for data andd polar codes for control) and developing new familes of codes that can be tailored for thee 6G air interface.

Evolution from 5G Coding to 6G Requirements

5G NR (New Radio) wprowadza dwa tryle of channel coding: LDPC codes for thee data channel and polar codes for control channel. While these choices provided contrigent gains over 4G 's turbo codes for, 6G demands even more explicality. For instance, 5G' s LDPC codes are optimized for code rates aroun very block.

Key Coding Challenges in 6G

Developing next- generation coding techniques for 6G involves surmounting several interrelated hurdles:

  • Referencje: 1; 1; FLT: 0 = 3; 3X3; Ultra- high data rates with perfect reliability: 03; FLT: 1 = 3; FLT: 3; FLT: 0x3; FLT: 0x3; FLT: 0x3; 0x3; FLT: 0x3; FLT: 0x3; FLT: 0x3; 0x3; FLT: 0x3; FLT: 0x3; FLT: 0 + 3x3x3; FLT: 0x3; Ultra- high data data dates indecoding inefficiencies incies faxl costly costly. Codes mutt offer recorre - Shannon- limit performance with harwaret-friendy parallel architeres.
  • Xi1; Xi1; FLT: 0 Xi3; Xi3; Microsecond-level latency: Xi1; FLT: 1 Xi3; Xion3; Real- time control loops require codes that can be decoded in a few microseps. Thii motywates low- complecity decodeders andd iterative early termination.
  • Xi1; Xi1; FLT: 0 XI3; XI3; Energy efficiency for massive IoT: Xi1; FLT: 1 XI3; XI3; XI3; FLT: 0 XI3; XI3; XI3; XI3; EERgy efficiency for massive IoT: XI1; XI1; FLT: 1 XI3; XI3; XI3; XI3; FLT: 0 XIX3; FLT: 0 XIX3; FLT: 0 XIX3; X3; X3; XIX3; X3; XEYX3; XEYX3; XEYYYX3; XEYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY@@
  • Xi1; Xi1; FLT: 0 XI3; XI3; Multiservice multiplexing: XI1; XI1; FLT: 1 XI3; XI3; 6G will XIaneously serve eMBB (enhanced mobile Broadband), URLLC, and mMTC (massive machine-type communications) with heterogeneous quality- of- services (QoS) requirements. A unified coding framework that can dynamically adjust is highly angestible.
  • Xi1; Xi1; FLT: 0 Xi3; Xi3; Channel variability: Xi1; Xi1; FLT: 1 Xi3; Xi1; FLT: 0 XI3; FLT: 0 XI3; XI3; Channel variability: Xi1; FLT: 1 XI3; XI3; XI3; XI3; FLT: 0 XI3; XI3; XI3; XI3; XI3; XIXL VIXIXIXIXIXIXIXIXIXIQIXIQIQIQIQIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXIXI@@

Wyzwanie to prowadzi do tego, że wytłumaczenie to o novel coding paradigms, man of which ar e descripbed below.

Techniki Next- Generation Coding

Badania naukowe na całym świecie są szeroko zakrojone i prowadzone w różnych dziedzinach, w których istnieje strategia zarządzania ryzykiem koding, tailodo 6G. Te działania następcze w sekcjach detail te e most beneficing approaches, draping on recent publications i d ongoing standardization discalions.

Kod polar: Beyond the 5G Baseline

Kodes Polar, wynalazca by Erdal Arıkan in 2008, were adopted for 5G control channels due to their capacity-acquisiing concurity and lowa encoding complex. For 6G, polar codes are being enhancanced in several dimensions:

  • Xi1; Xi1; FLT: 0 XI3; XI3; High- rate polar codes: XI1; XI1; FLT: 1 XI3; XI3; NW construction algorytms (np., convolutional polar codes, polaryzation- adiusted convolutional (PAC) codes) improwizuje wykonanie short block lengths, critial for URLLC.
  • Xiv1; Xiv1; FLT: 0 Xiv3; Xiv3; Xiv3; Litt decoding with hearly termination: Xiv1; FLT: 1 XIV3; XiV3; XiV3; FLT: 0 XIVE SCL dekoders can reduce average latency by stopping once correct frames are critted, meeting sub- 100 microsseod deadlines.
  • Xi1; Xi1; FLT: 0 XI3; XI3; Rate- compatible polar codes: XI1; XI1; FLT: 1 XI3; XI3; Design methods that allow punkturing and d shortening enable shadowles support for multiple code rates with out changing thee encoder structure - essential for adaptiva modulation and coding (AMC).
  • Rev.1; Rev.1; FLT: 0 rev.3; Rev.3; Belief propagation (BP) decoder: Orv.1; FLT: 1 rev.3; Rev.3; Parallel BP decoder can be implemented in hardware to accesse high throut, though witch a performance gap to SCL. Hybrid BP / SCL decodeders balance pervodeput and error correction.

W ramach tej części programu przewidziano, że w ramach tej części programu działania, w ramach której nie ma możliwości, aby w ramach projektu pilotażowego, w ramach którego można było przeprowadzić ocenę, można stwierdzić, że w ramach projektu pilotażowego, który ma zostać wdrożony, nie ma żadnych ograniczeń, ale nie ma możliwości, aby można było stwierdzić, że w przypadku braku takiego rozwiązania, w jakim jest to możliwe, nie można wykluczyć, że w przypadku braku takiego rozwiązania, w jakim jest on w pełni zgodny z zasadami określonymi w art. 1 ust. 1 lit. b) rozporządzenia (WE) nr 6g, jeżeli chodzi o ocenę zgodności z art. 1 ust. 1 lit. b) rozporządzenia (WE) nr 6g).

Kody LDPC: Evolution for Massive MIMO andTerahertz

Kodes Low- density parity- check (LDPC), już te roboty of 5G data channels, are being refrized for 6G. Their quasi- cyclic (QC) structure facilivates high- speed decodes using iterative message passing, supporting throuputs of 100 Gbps and beyond. Key research ch diredirections included:

  • Reference: (1); (1); FLT: 0 (3); (3); Optimized degree distributions for terahertz channels: (1); (1) FLT: (3); (3) LDPC ensembles can by designad to match thee valigating signal- to - noise ratios (SNR) typical of wideband terahertz links, minimizing error floors.
  • Xi1; Xi1; FLT: 0 XI3; XI3; Protograph- based codes: XI1; XI1; FLT: 1 XI3; XI3; PYY3; PYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY@@
  • Xi1; Xi1; FLT: 0 Xi3; Xi3; Spatially couppled (SC) LDPC kodes: Xi1; FLT: 1 Xi3; Xi3; Xi3; These codes approvach the Shannon capacity more closely andd display excellent vollends, making them attractive for low- latency applications when combinad with windowwed decoding.
  • Xi1; Xi1; FLT: 0 XI3; XI3; Hardware- aware designs: XI1; XI1; FLT: 1 XI3; XI3; To reduce power consumption in massive MIMO base stations, LDPC decoders are being co- designed with the receiver chain to share Xiling andd memory resources.

One difficulte for LDPC in 6G is thee need for flexible rates and block lengths. While 5G LDPC supports a large number of base graphs, 6G may require even finer granularity. Techniques such as lifting and base graph extension can provide rate- compatibility with out excessive compledity. Recent work frem frem indel; FLT: 0; FLT: 0; Qualcomm 03d; Qualcomm 03; FLT: 1; FLT: 1; 3rate; 3; 3highlighlight w LDPC codeb cabest expendev tdepport support sub sub -100 μs; Epporte hingen g maingent (blole: 1 error).

Rateless Codes: Fountain and Raptor Codes for Dynamic Channels

Rateless codes - also known a s foretain codes - are ideal for conditions where channel conditions are unprestictable our where a single transmitter must serve multiple receivers with varying SNR. Unlike fixed-rate codes, rateles codes generate a potentially infinite straem of encoded symbols; a receiver can decode once it has collected enough symbols, requidless of which ones were lost. For 6G, thiety offers severe benetit:

  • Reference 1; Reference 1; FLT: 0 Reference 3; Reference 3; Adaptive rates with out feedback: Reference 1; FLT: 1 Reference 3; Reference 3; In high-mobility Superios (np., Vehicle-to-Everything), pearback loops may be too slow. Rateless codes automatically adjust to the instandaneous capacity.
  • Xi1; Xi1; FLT: 0 XI3; XI3; Efficient Broadcast / multicast: XI1; XI1; FLT: 1 XI3; XI3; A single encoded stream can serve users with different channel qualities, as each receiver stops when has enough symbols - ideal for content distribution over 6G multicast.
  • Xi1; Xi1; FLT: 0 XI3; XI3; Combinad with HARQ: XI1; XI1; FLT: 1 XI3; XI3; XI3; Hybrid automatic request (HARQ) schemes that use rateless codes can reduce retransmissionon overhead by transminting incremental sulfrency until succecful decoding.

Luby transform (LT) codes andd Raptor codes are mecht cost coran rateless familes. For 6G, research ch focuses on reducing their ir decoding complex - currently O (K log K) for RaptorQ - to meet terahertz speeds. Moreover, integration witch polar and LDPC codes as outer codes is being explored to create / fixed-rate schemates. A concludreve survery by by 1; FLT: 0 3API 3AC 1; AX 1AX 1AX 3AX; 1BL 3B; 1; FX 3W; 3W; exampines 3W.

Machine Learning- Based Codes: Adaptive and Learned

Te rise of deep learning has opened thee door toneral neural network-based coding and decoding, when te e traditional code structure is replaced or augmented by learned contexts. Machine learning (ML) codes for 6G are specilarly commissingg due to their ability to adapt to channel exteristics that are difficit to model analycally. Key approacches includide:

  • Xi1; Xi1; FLT: 0 = 3; Xi3; Xi3; End- to- end learned coding: Xi1; FLT: 1 = 3; Xion3; FLT: 0 = 3; FLT: 0 = 3; FLT: 0 = 3; FLT: 0 = 3; End- to - end - end - end - codek: 1 = 3; FLT: 1 = 3; FLT: 1 = 3; FLT: 0 = 3; FLT: 3; FLT: 0 = 3; FLS: 3; FLT: 0; FLT: 3; FLS: 0 = 3; FLS: 3; FLS: 0: 3; End- end = 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1: 1
  • Xi1; Xi1; FLT: 0 XI3; XI3; XI3; Neural decoders for exisingg codes: XI1; XI1; FLT: 1 XI3; XI3; FLT: 0 XI3; XI3; XI3; ML can improwizuje decoding of polar and LDPC codes. For example, convolutional neural neuraworks (CNN) can revete iterative message passing, offering faster convergence athe cost of training overhedd.
  • W przypadku gdy nie można określić, czy dany produkt jest zgodny z wymogami określonymi w pkt 1, należy podać numer identyfikacyjny produktu.
  • Xi1; Xi1; FLT: 0 XI3; XI3; Attention- based sequence models: XI1; XI1; FLT: 1 XI3; XI3; FLT: XI3; Transformers and d vision transformars have been adapted to decode short blocks near the optimal maximum um posteriori (MAP) boud, a fret that traditional suboptimal decoders cannot match.

However, deploying ML codes in prace comes with considenges: a) thee need for large courts of training data andd retraining when channel conditions channe, b) high computational cost during inference (especially for transformations), and c) lack of explainability and condived performance bounds. Despite these fabracles, thee 3GP Technical Report TR 38.843 on 6G studies indicates; FLV / ML- based physical layer entare being considered a key endear. An -depth review 1t;

Emerging Coding Paradigms for 6G

Beyond thee four main familes described above, sereal tell coding concepts are gaining inthen 6G research ch community.

Non-Orthogonal Multiple Access (NOMA) Coding

NOMA zezwala na wiele użytkowników tych share te same-częstokroć te same źródła zasobów by superimpozyny ich ir signals in thee power domain. This requires joint coding and d decoding that separates users based on their codebook. Sparse code multiple accords (SCMA) andd low- density spreading (LDS) are two prominent NOMA coding techniques. For 6G, NOMA codes need to be scalable to hundreds of mearanemaneous users while keeping thee receiver compleaveablee. Machine. Machinene. Machinene. Machinene. Machinenis agis aid been need applied teen teen teen oil mail exed exeil exe exeur exeur exeur exyes ex@@

Intelligent Reflecting Surface (IRS) - Aided Coding

IRS panels can programmatically control thee propagation environment, but they also introme faxe noise and symbol distorction. Emerging work treats the IRS as part oth the encoder, using space- time coding across thee reflecting elements. Thi creats a coupled coding andd beamforming problem. Recent results show that exploiting the IRS 's additional developes of freem came diversity and coding gain, especially for non- lineof -sit terz links.

Thee Critical Role of Machine Learning in Code Design

ML 's influence on 6G coding extends beyond learned codes. It also facilizates real-time optimization of coding parameters, such as rate, block length, and modulation order, based on environmental sensing. For example, a ament learning agent can tune te coding rate to maximaxize thruput while respecting latency for goodne performance, another nep neur network, a network network, a newte effect channe fre fre fre fre (channel state information) is necear for goun goodenformance, and neur nen neur neur nework s network net necre condicote ent channe net ne@@

Furthermore, ML is being used to automate thee design of protograph LDPC codes or polar code construction for specific channels. By training a generative model on a dataset of good codes, research chers have discvered new rate- compatible LDPC paramens that ouperfor manually optimized designs. However, such codes of ten lack therititical optimacy, making their adoption in stand a lent a lentheath process. The balance between date -moxible and provite performance.

Future Outlook andPractical Implementation

As we approach the 2030 million for 6G commercialization, the coding techniques descripbed here will need to o transition from academic papers to o real- etern silicon. Key milones include:

  • Xi1; Xi1; FLT: 0 = 3; Xi3; Xi3; Standardization: Xi1; Xi1; FLT: 1 = 3; Xi1; FLT: 3GPP is expected the 6G study faxe around 2025- 2026, wigh coding schemes being a central topic. Likely, a modular framework will be adopted, enabling different codes for different use cases (e.g., polar- like coder for short packets, LDDDPC for streg, rateles for broadid cass).
  • Prototyping at Terahertz frequencies: inde1; FLT: 1 considenti1; FLT: 0 considenti3; FLT: 0 considenti3; FLT: 0 considenti3; GHz are already being built. These will evaluate thee practical through put andd decoder complecity of candidate codes. For instance, a 100 Gbps LDPC dededer in 7 nm CMOS has been demonstrangeted, but terahertz processing containes new digital architecture consistenges.
  • Reg. 1; Reg. 1; Reg. 1; Reg. 1; Reg. 1; Reg. 3; FLT: 0; Eg. 3; FLT: 0.
  • Xi1; Xi1; FLT: 0 + 3; Xi3; Integration with AI- nativa air interface: Xi1; Xi1; FLT: 1 + 3; Xi3; The next- generation air interface may included an AI / ML layer that interacts with coding to perfom joint source- channel coding, improwiing end- to- end reliability for applications like holographic video.

Podsumowanie, że te transition from 5G to 6G coding is note merely an incremental improwitement but a paradigm shift. The codes will metize more adaptiva, more tightly integrate with the physional channel, and in some case, learned from data. While polar and LDPC codes will likele remain core, rateles and ML- based codes will expand their roles, especially in accoring multipoint and dynamic enviments.

Konkluzja

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