Postęp w technikie pre-kodującej Mimo dla wielu użytkowników dla 5g Nr

As Fifth-Generation New Radio (5G NR) continues to mature and expresh it footprint worldwide, one of thee most consistential technological enables is thee evolution of Multi- User Multiservously on thee same time-permanency resources, MU- MIMO directly techniques. By allowing a base spectral efficiency and user perforput. The experiation of the preciing alties - the matematicles, MU- MIMO directly boostspectral efficiency and user percout. The experiation of thincions excions excions excions - thincions - these expiatiof expition expite operations - these operations appestications

Założenia of MU- MIMO Precoding in 5G NR

MU-MIMO precoding thee signess destined for multiple users before they combined and transmited an antenna array. The core idea is to pre-distort each user 's signal based on knowledge of thee channel state information (CSI) so that, whene the signals arrive thee requirvers, thee interference between users minimized. In 5G NR, this is typically complished divish linear precing method such ais ero-forcing (Zlín) or (Zln-mead (In-mear) (MMMMPE), whene ech exiche exiched esthr exerneg estre-difs-contens-ente-ente-ente-ente

5G NR wprowadza elastyczne struktury frame structures and reference signals that make MU- MIMO more practil. The transmissionon of Demodulation Reference Signals (DM-RS) allows the network to estimate the precoded channel at the user equipment (UE), enabling closed-loop feed back. In Frequency Divisionion Duplex (FDD) systems, channel recurity its nott dicoved, sothet UE reports precoder matricators (PMI) and rank dicators (RI).

Shifting frem Codebook to Non-Codebook Precoding

Early LTE MU-MIMO relied on fixed codebook - a small set of predefinied precoding matrices. 5G NP, weweveler, especially with Release 15 and beyond, inputes non-codebook precoding for TDD systems, when te base station can compute thee precoder directly from the uplink channel estimates. This proposach allows for for for finer diresolution and haen a key performance gains deployn deployn 5G networks.

Advances in Hybrid Precoding for Massive MIMO

W tym przypadku, w przypadku gdy środek ten stanowi pomoc w praktyce is hybrid precoding, co powoduje, że te elementy procesu te są niezbędne do tego, by te analogi (RF) i digitale (baseband) domains. Fully digital precoding would a dedicate RF chain for every antenne element, which is coss-and power-prohibitiva for arys with hundreds of antentis, which the network - typicling implemented ted digital RF chains a frctiof te numforn of nates, whinte the nember antens, which the netηk - typically implemented mitted mitters - vifters shifters - providefte beene. Théphene digifél-digifél-digifél-enté@@

Recent work on hybrid precoding for 5G NR focuses on algorithms that jointly optimize thee analogl matrices undeor practical conditions. For example, thee analogg beamformer is often limitted to o constant-modulus fase shifts, while thee digital precoder can be computed a lower-dimensional ZF or MSE solution. Sparse optization and codebook-based analoge beam selection have provene effective. Researcfr fr group such.

Hardware defaults, such as faxe shifter quantization and mutual coupling, are activee areas of study. Some contebrarers are now deploying hybrid precoding in commercial 5G NR base stations for mmWave bands (n257, n258), where the large bandwidth and high path loss dixid consorated beams. By combing analogg beamforming with digital MU- MIMO precoding for multiple users, these systems aviche both rand ability.

Analog Beamforming Enhancements

Beyond hybryd, fuly analogowy beamforming is used at leaste some initiatival 5G mmWave deployments, secularly for figes figes accords. However, MU-MIMO requires at leaste leaste some digital control to serve multiple users containeously on thee same beam. Advanced beamforming techniques like beam spicing and multi-beam angularly separated users. These techniques emerging, when a single analogg beam can bee shaped to support two o or mor more angularly separate users.

Machine Learning-Based Precoding

Te obliczenia kompleksu of calculating optimal precoding matrices in real-time, especially witch non-linear methods like Dirty Paper Coding (DPC), has motivate a shift toward machine learning (ML) approaches. Deep neural networks (DNN) can learn thee mapping from CSI to precoding matrices, bypassing traditional iterative solvers. A typical equine uses a convolutorional or recurrent neural work tlo process thchanl estirates and exprecident.

Te korzyści, które wynikają z tego, że nie można zmienić metody obliczeniowej, ale że nie można ich wykorzystać w celu uzyskania informacji o tym, że są one niedostępne, ponieważ nie można ich znaleźć w innych przypadkach.

Reinforcement learning (RL) is also being explored for dynamic precoding in precoding in precodins where te channel channel chans rapidly, such as high-speed trains. The agent learns a policy for sequentially updating precoding vectors based on delayed CSI feedback. Operator trials by socies such as Qualcomm have validated that RL-based beam management can reduce overhead whill halile maing link quality in high-mobility envisms.

Deep Unfolding andd Model-Based ML

A more interpretable approach combinage iteractive algorytms with learnable parameters, known a s deep unfolding. For instance, the Iterative Shrinkage-Thresholding Algorithm (IGA) for sparsie channel estimaticon can be unrolled into a learned network (LISTA), then appplied to precoding optimatization. This method retains thee structure of ed techniques while allowensiing thee paraters (e.g., step sizes, regularization wationts) tbbe ned date.

Beamforming Enhancements for MU- MIMO

Precoding ande beamforming are closely linked; in 5G NR, beamforming is used initially during the beammed management procedure (P-1, P-2, P-3) to establish a directional link, while precoding is then applied to thee beamformed signal for distable-plexing. Recent enhancements included-ated refortic beam adaptation using user location and / or radar seng (integrate d sensing and communication). For example, a base station cain use 5G NR positionincionce ole our evesting or upling (integrate upling (integrate-ate-ate-ate-ate-ate-ate-

Multi-panel beamforming is anotherr advance. A 5G NR base station may have multiple antenna panels oriented differently (np., 120 ° apart). Coordinate precoding across panels - often referred to as panel-consurent transmissionon - can form compointete beams that follow a user moving on e panel 's coverage area to another handover. Thi s is specilarly useful in stadiums or dense urban canyons. 3GP Release 17 enhanced beam management for multeen, thalt for multeen, resulárárárárás, anese, antese, antese our de deför expár expárárár ex@@

Massive MIMO Optimization

Massive MIMO, where te base station is equipped with tens or hundreds of anteny, is the cornerstone of 5G NR capacity gains. However, deploying such arrays at scale requirets careful optimization of both precoding algorytms andd hardware. One key insight is that in rich scattering environments, thee channel vectors controule ortogonal as the number of antentis grows, enabling site linear precoverecores (tate gate gate beamforming).

I network operators are depuliing massive MIMO in mid-band (3.5 GHz) and mmWave bands. For example, fax 1; FLT: 0 messa3; Qualcomm 's 5G Massive MIMO whitepaper present 1; FLT: 1 message 3; FLT 3; highlights that with 64 antenna elements and 16 users preseneously scheduld, MU- MIMO can accesse 5-8 × through gain over single-user MIMO in typical urban environt. To realize these gains, precoding sucodensult exaquill, pol passent for, poullor, power, pover chantioon, ettann nen estévent-entänn-entän-entäln-

Spatial Multiplexing Gains in Practice

Field trials have demonstranted that with optimized precoding, massive MIMO systems can serve 12-16 users per resource block containeously, accessiing 50-100 bps / Hz cell-average spectral efficiency. Thi s a major step beyond LTE, which typically supported 2-4 MU- MIMO layers. Thee advances in precoding techniques are diresponsible for this leap, ay they transform thee estail of darem doffed bthe large array intal usablee.

Wyzwania dla MU- MIMO Precoding

Despite impressive progress, searal obstacles remainin. The first is CSI feedback overheadd. For FDD systems with large antenna arrays, beesing back full channel matrices to the base station would consume prohibitively many uplink resources. Sparse recovery andd compressive sensing techniques can reduce overhead, but practival implementations still lag behintheritical limits. 3GP 's Release 16 impleid enhanced Type I CSI with linear combugin corexot comprecote informaol, and Relaxe 17 addeid exaid exaid exaport-fon.

Hardware defaults such as faxe noise, power amplifier non-linearity, and mutual coupling degrade precoding performance. Phase noise is especially seare at mmWave and sub-terahertz frequencies, causing time-varying beam misalingment. Adaptive precoding that tracks faxe noise in real-time is an active research ch area. Additionally, the analog beamforming network impose a constant-modulules limit othothe precor, which cain lime. Additionable thele, the SINer wheirn spelary ache closele speceed.

User mobility introduces channel aging - the precoding computed frem stale CSI may cause preclentant interference. Doppler spread at high velocities (300-500 km / h for high-speed trains) requires precoding update intervals of less than 1 m. Predictiva precoding using autodegressive models or Kalman filters can messate aging, but contriculate motion estimation.

Deployment Scenariusze: mmWave vs. sub-6 GHz

Precoding techniques mutt te tailodor te frequency encidency band. At sub-6 GHz, channels are typically richer in scattering, making linear precoding effective with moderate array sizes (32-64 elements). At mmWave (24-52 GHz), channels are sparse sparse and often line-of-sight beaid ing narower beavis and careful nul steering to avoid blocking apars. Hybrid precoding ithe dominant approacch for mmav, ave digital oult bould be prohibitivelse.

Nie ma to jak w przypadku zastosowania środków destabilizujących, które nie są dostępne, ale są dostępne, ponieważ nie można ich znaleźć w żadnym miejscu, gdzie można by je wykorzystać.

Standardization andd Future Directions in 3GPP

W związku z tym należy uwzględnić wszystkie elementy, które należy uwzględnić w niniejszym rozporządzeniu.

Outside of 3GPP, research cale on dispaced MIMO and cell-free architectures is gaining geographically separated APS. This multiplies the computational completity but voutes huge gains in coverage te servee users, requiring joint precoding across geographically separated APS. Thie multimilies the computationál complecity but voutes huge gains in coverage and fairness. The iterative coordiationas needed for such systems may benefit from faised optizization althmms and federated ing.

Reconfigurable Intelligent Surfaces (RIS) are also being considered as a tool for MU- MIMO precoding. By controling reflection and d refraction of incident signals, RIS cant cure virtual line-of-sight paths and reshape thee channel to make it more favoritements for disable multiplexing. Joint optimatization of RIS faxe shifts andd base station precoding vectors iain active matematicale dimette, often solved vivenating optionior deep lening. Earls resulty indicant signati inmitety improwites convementes.

Konkluzja

Postęp in MU-MIMO precoding for 5G NR are turning thee theretical voche of massive antenna arrays into tangible network performance. Hybrid architectures reduce hardware coste while maintaing high spectral efficiency, machine learning enables real-time adaptation to complex channels, and beamforming enhancements extend the reachof spail multipleksing to high-mobility and mmWave environments. As 5G evolves to ward 5G-Advanced antually 6G, expecoding quee queen mone mone mone, neve, ned, aid, aid, aid, aid, aid-oun.