International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025 www.irjet.net p-ISSN: 2395-0072
FPGA-Based Adaptive Beamforming for MIMO Systems: A Comprehensive Review
Shreya S. Khochage1 , Mahadev S. Patil 2
1UG Student, Department of Electronics and Telecommunication Engineering, Kasegaon Education Society’s Rajarambapu Institute of Technology, affiliated to Shivaji University, Sakharale, MS-415414, India.
2Professor, Department of Electronics and Telecommunication Engineering, Kasegaon Education Society’s Rajarambapu Institute of Technology, affiliated to Shivaji University, Sakharale, MS-415414, India.
Abstract - Adaptive beamforming is a critical technique in Multiple-Input Multiple-Output (MIMO) wireless systems, improving signal quality and suppressing interference. Field Programmable Gate Arrays (FPGAs) provide an efficient and flexible platform for real-time implementation of these algorithms due to their parallel processing capabilities and reconfigurability.Thispaperpresentsacomprehensivereview of key FPGA-based adaptive beamforming algorithms, including classical approaches such as Least Mean Squares (LMS) and QR Decomposition Recursive Least Squares (QRDRLS), as well as recent machine learning techniques. We critically analyze these methods in terms of computational complexity, hardware resource utilization, latency, and practical applicability. A comparative summary table highlights their advantages and limitations. Furthermore, implementationstrategiesandopenchallengesarediscussed, with suggestions for future research directions to meet the demands of evolving wireless communication systems.
Key Words: Adaptive Beamforming, FPGA, MIMO, LMS, QRD-RLS, Machine Learning, Wireless Communications.
1.
INTRODUCTION
Multiple-Input Multiple-Output (MIMO) technology significantlyenhanceswirelesscommunicationcapacityand reliability by exploiting spatial multiplexinganddiversity. Adaptive beamformingdynamicallyadjustsantenna array weights to enhance the desired signal and suppress interference. Implementing these algorithms in real-time requiresintensivematrixandvectorcomputationswithlow latency.
FieldProgrammableGateArrays(FPGAs)arewell-suitedfor adaptive beamforming due to their inherent parallelism, reconfigurability,andefficientuseofDSPblocks.Thisreview surveysFPGA-basedadaptivebeamformingalgorithmsfor MIMO systems, focusing on classical adaptive methods, advanced matrix factorization, and machine learning approaches. Hardware implementation considerations, challenges,andfutureresearchdirectionsarealsodiscussed.
2. REVIEW OF FPGA-BASED ADAPTIVE BEAMFORMING ALGORITHMS
2.1 Least Mean Squares (LMS) and Variants
The Least Mean Squares (LMS) algorithm is a commonly employedadaptivefilteringtechniquethatadjustsantenna weights iteratively to reduce the mean squared error betweenthedesiredreferencesignalandtheactualoutput. Due to its simplicity and low computational cost, LMS is favoredinFPGAimplementations[1],particularlyusingfixedpoint arithmetic and pipelined DSP blocks to enhance throughput[12],[20]. However, LMS suffers from slow convergenceandsensitivitytostep-sizeparameters,which limitsitseffectivenessinfast-varyingchannels.VariablestepsizeLMSvariantsimproveconvergencebutincreasedesign complexity[17].
2.2 QR Decomposition Recursive Least Squares (QRD-RLS)
QRD-RLS offers faster convergence by minimizing a weightedleastsquarescostfunction,makingitsuitablefor fast-changingchannelenvironments[2].However,itdemands highercomputationalandhardwareresources[3],[4].FPGA implementations typically employ systolic array architectures or CORDIC-based QR decomposition to optimize matrix inversion and reduce latency [5],[11]. Recent designs leverage pipeline and parallelism optimizationstoscaleforlargeantennaarrays[9],[16].QRDRLS remains the preferred choice for precision-critical applicationsdespiteitsresourcedemands.
2.3 Machine Learning-Based Beamforming
Machine learning (ML) techniques, such as deep neural networks (DNNs) and reinforcement learning (RL), have shownpromiseformodelingnonlinearchannel behaviors and improving beamforming adaptability [6],[7]. FPGA deploymentofML modelsfaceschallengesrelatedtohigh computational load and memory use. Techniques like quantization, pruning, and fixed-point arithmetic enable practicalimplementations[15].ML-basedbeamformingcan outperform classical algorithms in complex environments
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025 www.irjet.net p-ISSN: 2395-0072
butrequiresefficienthardwarearchitecturesforreal-time inferenceandtraining[8],[15].
2.4 FPGA Implementation Strategies
EfficientimplementationofadaptivebeamformingonFPGAs requires strategic use of hardware features and design methodologies to meet the real-time, high-throughput demandsofMIMOsystems.
a. Fixed-Point Arithmetic and Word-Length Optimization
To reduce hardware resource usage and power consumption,fixed-pointarithmeticiscommonlypreferred over floating-point. Word-length optimization techniques ensurethatsignalprecisionismaintainedwhileminimizing logicusage.Forinstance,SwainandSahoodemonstrateda highly optimized fixed-point LMS implementation that reducedpowerandresourcerequirementsonVirtexFPGAs without compromising convergence speed [20]. Similarly, Rajarajeswarie et al. implemented a scalable and powerefficientLMSsystemusingtailoredfixed-pointdesigns[12].
b. Pipelining and Parallelism
FPGAsenablemassiveparallelismbyinstantiatingmultiple processingelements.Beamformingcomputations suchas weightupdates,correlation,andfiltering arepipelinedto maintain high throughput. The QRD-RLS implementations usingsystolicarrayarchitectures,aspresentedbyRathiand Sharma[2]andLopesetal.[9],leveragethisparallelismto performreal-timeQRdecompositionandmatrixoperations.
c. Systolic Array and CORDIC Architectures
QRdecompositionandothermatrixoperationsbenefitfrom systolic arrays that process data in a rhythmic, pipelinefriendlyfashion.Forinstance,Gao'sworkdemonstratesthe effectivenessofthesearchitecturesforhigh-speedQRD-RLS on FPGAs [3]. Additionally, CORDIC (Coordinate Rotation Digital Computer) units are utilized for efficient trigonometriccomputationswithoutmultipliers,aidingQR decompositionandvectorrotations[5],[11].
d. High-Level Synthesis (HLS) and Reconfigurable Logic
ModernFPGAdesignflowsoftenuseHigh-LevelSynthesis (HLS), enabling designers to code in C/C++ and automatically generate HDL. This approach drastically reduces development time and makes design iteration easier. Vaithianathan et al. used HLS tools to implement a reconfigurable beamforming system that could adapt to changingchannelconditions[16].
e. Memory Optimization and Data Management
Efficient memory handling is critical. FPGA BRAM (Block RAM)anddatabuffersareusedtostoreintermediatevalues withlowlatency.Multi-bankmemoryarchitecturesanddata
streaming via Direct Memory Access (DMA) improve data throughput[8],[16].
f. Power Efficiency Techniques
Techniquessuchasclockgating,partialreconfiguration,and dynamicvoltage/frequencyscaling(DVFS)areemployedto enhancepowerefficiency.Ortegaetal.implementedclock gating for inactive modules in a space-optimized FPGA beamformerdesign[18],whilePulipatietal.utilizedpartial reconfigurationinRF-SoCplatformstoreduceactivepower duringdynamicoperation[8].
g. RF-SoC and Embedded AI Integration
EmergingplatformslikeXilinxRF-SoCintegrateADCs,DACs, ARMprocessors,andprogrammablelogiconasinglechip, allowing tight coupling of analog front-end and digital processing.Pulipati etal.demonstrateda real-timedigital beamformeronsuchaplatformformmWaveapplications [8].TheseplatformsalsosupportembeddedAIcores,paving the way for low-power machine learning-based beamforming[15],[19].
Table 2.1 Analysisofdifferentmethods
Algorith-m Complexity Convergence FPGA Resource Usage Power Efficiency LMS Low Moderate Low High QRD-RLS High Fast High Moderate ML-Based VeryHigh Variable VeryHigh Low HLS/Systoli/ CORDIC Moderate High High High
Table 2.1 depicts the analysis of different methods. LMS offers simplicity and low power consumption but is less effective in fast-changing channels. QRD-RLS achieves superiorperformancewithhigherFPGAresourceandpower costs. ML approaches provide adaptability in complex scenarios but require careful optimization for FPGA deployment
3. COMPARATIVE SUMMARY OF KEY PAPERS
Table 3.1: ComparativeSummaryofPapers
Ref Algor -ithm FPGA Platform Key Contribution Latency Resource Use Remarks
[1] LMS Xilinx Virtex FixedpointLMS implemen tation Low Low Balance d for sensor nodes
[2],[ QRD- Custo m Highspeed Mode High Scalabl e for
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025 www.irjet.net p-ISSN: 2395-0072
3] RLS FPGA QRD-RLS with systolic arrays rate large antenn a arrays
[5] QRD FPGA Lowlatency inverse QRD architectu re Low Medi um Radar applica tions
[6],[ 7] ML FPGA/ ASIC Deep learning for channel estimatio n & beamfor ming Varia ble Very High Earlystage promisi ng results
[9],[ 16] LMS/ RLS Xilinx RF-SoC Adaptive beamfor ming for massive arrays Low Mode rate Integra tedDSP andRF
The Table.3.1 compares various FPGA-based adaptive beamformingalgorithms.LMSalgorithmsimplementedon Xilinx Virtex offer low latency and low resource usage, making them ideal for energy-efficient applications like sensornodes.QRD-RLSimplementationsoncustomFPGAs utilize systolic arrays to achieve high-speed processing, thoughtheyconsumemoreresources,makingthemsuitable forlargeantennaarrays.AnotherQRD-baseddesignfocuses onlow-latencyinversearchitectures, balancingspeedand resource use, particularly for radar systems. Machine learning-based methods,thoughstill inearlystages,show promiseforadvancedbeamformingandchannelestimation but demand very high FPGA resources and have variable latency. Finally, LMS and RLS algorithms implemented on XilinxRF-SoCplatformsprovideaneffectivetrade-offwith lowlatencyandmoderateresourceusage,benefitingfrom integrated DSP and RF capabilities for massive MIMO systems.
4. OPEN CHALLENGES AND FUTURE DIRECTIONS
Scalability: DesigningFPGAarchitecturescapable of supporting massive MIMO systems with 64 or moreantennasremainsachallengeduetostringent resourceandtimingconstraints
Precision vs. Complexity: Balancing numerical precision and hardware resources requires adaptive quantization and error correction techniquestomaintainalgorithmstabilitywithout excessiveoverhead.
ML Integration: Seamlessintegration ofmachine learningalgorithmswithclassicalbeamformingon FPGA is a nascent area, requiring new hardware architectures to support real-time training and inference.
Reconfigurability: Dynamichardwareadaptation to channel conditions and beamforming modes throughpartialreconfigurationenhancesflexibility andefficiency.
Emerging FPGA Technologies: Leveraging heterogeneous FPGA fabrics with embedded AI processorsandhigh-bandwidthmemorywillenable more intelligent and scalable beamforming solutions.
5. CONCLUSION
FPGA-based adaptive beamforming remains a vibrant research area essential for advancing MIMO wireless communications. Classical LMS and QRD-RLS algorithms provide foundational solutions balancing complexity and performance. Machine learning techniques show great promisebutrequirefurtherhardwareoptimization.Future research should focus on scalable, power-efficient, and intelligentFPGAarchitecturestomeettheevolvingdemands of5G,6G,andbeyond.
6. REFERENCES
[1] Ramanna S Havinal a, Girish V Attimarad “Implementation of Smart Antenna Adaptive Beamforming Algorithm in FPGA,” presented at the SPIT-IEEE Colloquium and International Conference, Mumbai,India,2008.
[2] N.RathiandS.Sharma,“QRD-RLSAdaptiveFilterBased AntennaBeamformingforOFDMSystems,”ICGST-PDCS, 2008.
[3] Q. Gao, “A FPGA based low-cost high speed QRD-RLS array processing,” Ph.D. dissertation, Univ. of Strathclyde,2012.
[4] A. Bertheussen, “Adaptive Beamforming Using the Recursive Least Squares Algorithm on an FPGA,” M.S thesis,NorwegianUniv.ofSci.andTechnol.,Trondheim, Norway,2015.
[5] R. Irfan and W. A. Toor, “FPGA-based Low Latency InverseQRDArchitectureforAdaptiveBeamformingin PhasedArrayRadars,”Radioengineering,2017.
[6] A. M. Elbir, K. V. Mishra, M. R. B. Shankar, and B. Ottersten,“AFamilyofDeepLearningArchitecturesfor ChannelEstimationandHybridBeamforminginMulti-
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025 www.irjet.net p-ISSN: 2395-0072
Carrier mm-Wave Massive MIMO,” arXiv:1912.10036, Dec.2019.
[7] X.LiandA.Alkhateeb,“DeepLearningforDirectHybrid PrecodinginMillimeterWaveMassiveMIMOSystems,” arXiv:1905.13212,May2019.
[8] S.Pulipatietal.,“XilinxRF-SoC-basedDigitalMulti-Beam Array Processors for 28/60 GHz Wireless Testbeds,” arXiv:2008.01220,Aug.2020.
[9] F. F. Lopes, S. N. Silva, and M. A. C. Fernandes, “FPGA ImplementationoftheAdaptiveDigitalBeamformingfor MassiveArray,”IEEEVTC2021-Spring,Apr.2021.
[10] J.C.Gentile,“FPGA-BasedAdaptiveDigitalBeamforming UsingMachineLearningforMIMOSystems,”M.S.thesis, JohnsHopkinsUniv.,May2021.
[11] L. Canese, G. C. Cardarilli, L. Di Nunzio, et al., “FPGA implementationofaQRD-RLS-basedequalizerforMIMO systems,”IEEETrans.CircuitsandSystems,2021.
[12] B.Rajarajeswarie,“AnAdaptiveBeamformingAlgorithm BasedonFPGASynthesisforMIMOAntennas,”inProc. SMARTGENCON,2022.
[13] S. Komeylian and C. Paolini, “Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform,”arXiv:2211.01763,Nov.2022.
[14] S. Komeylian and C. Paolini, “Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform,”Sensors,vol.23,no.3,p.1742,Feb.2023.
[15] L.HonrubiaFernández,“OptimizingBeamformingusing NeuralNetworksforWirelessCommunicationswithan FPGA,”Univ.PolitécnicadeMadrid,2023.
[16] M. Vaithianathan, N. S. Frank, M. Patil, et al., “FPGABased Adaptive Beamforming System for Improved Wireless Communication Performance,” ACOIT, Sep. 2024.
[17] N. Tittas and G. Prabha, “FPGA Based Adaptive BeamformingUsingVSSLMSAlgorithm,”inProc.IEEE 5thWomeninTechnologyConference,2024.
[18] E. Ortega et al., “A Digital Beamforming Receiver Architecture Implemented on a FPGA for Space Applications,”arXiv:2405.18992,May2024.
[19] N. R. Dusari and M. Rawat, “Practical Implementation ChallengesinMIMOBeamformingandItsApplications,” IEEEMicrowaveMagazine,2025.
[20] Kaliprasanna Swain and Manoj Kumar Sahoo, “FPGA Implementation of LMS Algorithm Used in Adaptive Equalizer,”InternationalJournalofComputationaland
Electronic Aspects in Engineering, Vol. 1, No. 1, December2014.