MIMO-OFDM WIRELESS COMMUNICATION SYSTEM PERFORMANCE ANALYSIS FOR CHANNEL ESTIMATION: A REVIEW
Sanjay Kumar1 , Mr. Pradeep Pal21M.Tech, Electronic and Communication Engineering, GITM, Lucknow, India
2Assistant Professor Electronic and Communication Engineering, GITM, Lucknow, India
Abstract - MIMO-OFDM (Multiple Input Multiple Output
Orthogonal Frequency Division Multiplexing) is a wireless communication technology that combines two advanced techniques: Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM). MIMO technology involves using multiple antennas at both the transmitter and receiver ends to improve the overall performance and capacity of the wireless communication system. By using multiple antennas, MIMO can increase the data rate, reduce interference, and improve the range of the wireless link. OFDM is a digital multi-carrier modulation technique that divides the available bandwidth into multiple orthogonal subcarriers. Each subcarrier carries a portion of the data, and the subcarriers are modulated and transmitted simultaneously. This makes OFDM an effective technique for mitigating the effects of multipath fading and interference in wireless communication systems. MIMO-OFDM is widely used in many wireless communicationsystems, such as 4G LTE,WiFi, and WiMAX, to provide high-speed and reliable wireless communication. It provides several advantages, including increased data rate, improved spectral efficiency, enhanced reliability, andimprovedresistancetofadingandinterference, compared to traditional single-antenna and single-carrier communication systems.
Key Words: MIMO-OFDM, Channel Estimation, Pilot carriers,MinimumMeansquareerror.
1. INTRODUCTION
Mobilecommunicationsystemsarewirelesscommunication networks that provide communication services to mobile devices, such as smartphones, tablets, and laptops. These systems allow users to communicate with each other and withtheoutsideworld,regardlessoftheirphysicallocation. Mobile communication systems typically consist of a networkofbasestations,eachwithadefinedcoveragearea, and mobile devices that are used by the users to communicate.Thebasestationsareconnectedtothecore network,whichprovidesthenecessaryinfrastructureand servicestosupportcommunicationbetweenmobiledevices. Mobilecommunicationsystemsuseavarietyoftechnologies and standards, including cellular networks, Wi-Fi, and satellite communication, to provide a range of communicationservices,suchasvoice,text,andmultimedia messaging,aswellashigh-speeddataservices.
With the widespread adoption of mobile devices and the growing demand for mobile data services, mobile communication systems have become an integral part of modernsociety,enablingpeopletostayconnectedwitheach otherandtheworldaroundthematalltimes.
1.1. MIMO-OFDM
Multiple Input Multiple Output (MIMO) is a wireless communication technology that involves using multiple antennas at both the transmitter and receiver ends of a wireless link. The goal of MIMO is to improve the performance and capacity of wireless communication systems. In MIMO systems, multiple antennas are used at boththetransmittingandreceivingendstosimultaneously transmitandreceivemultipledatastreams.Thisallowsthe system to effectively exploit the spatial diversity of the wirelesschannel,resultinginincreaseddatarates,reduced interference,andimprovedlinkrange.MIMOtechnologycan beappliedinbothsingle-userandmulti-userscenarios,and itiswidelyusedinmanywirelesscommunicationsystems, suchas4GLTE, Wi-Fi,and WiMAX,to providehigh-speed and reliable wireless communication. MIMO can be implemented in different configurations, such as spatial multiplexing, beamforming, and diversity combining. The choice of MIMO configuration depends on the specific requirementsandconstraintsofthewirelesscommunication system.Overall,MIMOisakeytechnologyforimprovingthe performance and capacity of wireless communication systems,anditwillcontinuetoplayanimportantroleinthe developmentoffuturewirelesscommunicationsystems.
Orthogonal Frequency Division Multiplexing (OFDM) is a digitalmulti-carriermodulationtechniqueusedinwireless communicationsystems.Itdividestheavailablebandwidth intomultipleorthogonalsubcarriers,eachofwhichcarriesa portion of the data. The subcarriers are modulated and transmittedsimultaneously,andthedataisreconstructedat the receiver. The main advantage of OFDM is its ability to effectively combat the effects of multipath fading and interferenceinwirelesscommunicationsystems.Bydividing theavailablebandwidthintomultiplesubcarriers,eachwith a relatively narrow bandwidth, OFDM can mitigate the effectsoffadingandinterferencebyspreadingthedataover multiplesubcarriers.OFDMiswidelyusedinmanywireless communicationsystems,suchas4GLTE,Wi-Fi,andDigital
Video Broadcasting (DVB), to provide high-speed and reliable wireless communication. It is also used in broadband wired communication systems, such as Digital SubscriberLines(DSL)andcablemodems,toprovidehighspeeddataservicesovercopperandcoaxialcables.OFDM providesseveraladvantages,includingincreaseddatarate, improved spectral efficiency, enhanced reliability, and improvedresistancetofadingandinterference,comparedto traditional single-carrier modulation techniques. Overall, OFDM is an important technology for improving the performance and capacity of wireless communication systems,anditwillcontinuetoplayasignificantroleinthe developmentoffuturewirelesscommunicationsystems.
1.2. MIMO - OFDM SYSTEM
Multiple Input Multiple Output (MIMO) - Orthogonal Frequency Division Multiplexing (OFDM) is a wireless communicationsystemthatcombinesthe benefitsofboth MIMO and OFDM technologies. MIMO-OFDM systems use multipleantennasatboththetransmitterandreceiverends of a wireless link, along with the OFDM modulation technique,toimprovetheperformanceandcapacityofthe wireless communication system. In MIMO-OFDM systems, theavailablebandwidthisdividedintomultipleorthogonal subcarriers,eachofwhichcarriesaportionofthedata.The multipleantennasatboththetransmitterandreceiverare used to transmit and receive multiple data streams simultaneously,effectivelyexploitingthespatialdiversityof the wireless channel to provide improved data rates, reducedinterference,andincreasedlinkrange.
MIMO-OFDM is widely used in many wireless communication systems, such as 4G LTE and Wi-Fi, to provide high-speed and reliable wireless communication. ThecombinationofMIMOandOFDMtechnologiesprovides severaladvantages,includingincreaseddatarate,improved spectral efficiency, enhanced reliability, and improved resistance to fading and interference, compared to traditional single-antenna and single-carrier modulation techniques. Overall, MIMO-OFDM is a key technology for improving the performance and capacity of wireless communication systems, and it will continue to play a significant role in the development of future wireless communicationsystems.
2. LITERATURE REVIEW
In this literature survey section, we have studied the previousresearchpaperrelatedtotheMIMO-OFDMsystem, thesummaryofthesepapersisgivenbelowinthedetail:
Ganesh et.al: MIMO-OFDM channel estimation under Rayleigh fading is studied. Simulations are run using two distinct techniques, LS channel estimation, and MMSE channel estimation. The simulation findings demonstrate thattheBERislowerwhenusingacomb-typepilotcarrier
forchannelestimationinaMIMO-OFDMsystemthanwhen usinga block-typepilotcarrier,andthattheMSEislower whenusingMMSEthanwhenusingLSchannelestimation. The results show that the MMSE channel estimator outperformstheLSchannelestimator.
Abdelhakim, Ridha: We suggest assessing the impact of channellengthontheefficiencyofLSandLMMSEestimation methodsforLTEDownlinknetworks.ToreduceICIandISI,a cyclic prefix is appended to the beginning of each OFDM symbolthatisatleastaslongasthechannel.Unfortunately, thechannelmayexhibitunexpectedbehaviorthatcausesthe CP length to fall short of the channel length. The LMMSE outperforms the LS estimator in simulations when the CP lengthiscomparabletoorgreaterthanthechannellength, butattheexpenseofcomplexityduetoitsdependenceon thechannelandnoisestatistics.Ontheotherhand,LMMSE's superior performance is limited to low SNR values and gradually decreases as SNR increases. Comparatively, LS outperformsLMMSEinthisrangeofSNRvalues.
Archana et.al: Theresultsofthisresearchshowthatinthe low-to-mediumsignal-to-noiseratio(SNR)range,theBERis muchlowerintheSTBC-OFDMsystemcomparedtotheSMOFDMsystem.Therefore,STBC-OFDMmaybeemployedto improve performance even at low SNR. Nonetheless, SMOFDMcannotonlydeliverdoublethethroughputbutalsoa negligibleerrorrateathighSNR.Accordingtothefindings, OFDM may be integrated with STBC or SM systems. The qualityofreceivedpicturessentbybothmodelsisassessed withthroughputandBER.Theoutputimagescorroborate previousfindingsthattheSTBC-OFDMsystemcreatesmuch lessnoiseinthereceivedpicturethantheSM-OFDMmodel. ResearchintoMIMO-OFDMhybridmodelsmayberequired to better comprehend the receiver's power consumption pattern and design complexity about bit error rate and throughputrate.
Juhi et.al: Theneedformobiledataserviceshasskyrocketed inrecentyears,andcertainmobilecarriershaveseeneven morerapidexpansionthan theindustryaverage. Arecent prediction indicates that mobile data traffic will double annually through 2014, amounting to a compound annual growthrateofabout100%worldwide.LTE-Advancedand WiMAX- 2 can employ up to 8x8 MIMO, depending on the system'slevelofdevelopment.Demodulation/detectionand channelstateinformationestimationbothbenefitfromthe introductionofnewreferencesignals.That'swhymodern SU/MU-MIMO systems have focused so intently on improving their signaling. However, one of the primary difficultiesinimplementingMIMOincellularnetworksisthe extreme vulnerability of MIMO receivers to channel interference.Ifsystemdesignerswanttolimittheamountof disruptiontheycausetoadjacentcells,they'llneedtodial downthetransmissionpoweranddatarate.However,dueto theirinherentdesign,MIMOsystemsneedahigherreceived signal-to-interference-noise power ratio to transmit the
same amount of data (SINR). Receiver and transmitter advancementsinsignalprocessinghavebeenemployedto mitigate or eliminate interference. Thus, we were able to learnfromthistextthataMIMOsystemonlyinvolvesusinga transmitterandreceiverequippedwithmanyantennas.Its purpose is to improve network stability and data transfer rate without requiring more bandwidth or power to communicate.Processingtechniquesincludingpre-coding, diversity coding and special multiplexing distinguish between the two primary types of MIMO: multi-user and single-user. When it comes to Multiple-Input MultipleOutputcommunications,reconfigurableantennashavebeen employed to create pattern and frequency diversity. This article shows that the majority of these methods have significantpracticallimitations,especiallywhenitcomesto thecomplexityandchannelinformationnecessaryfortheir effectiveapplicationto3Gcellularnetworks.
Vipin, Parveen: ThisstudyrevealsthattheMSKmodulation scheme performs admirably in the scenario of multi-bit transmissionfromtheMIMOOFDMsystem.Throughput,Bit ErrorRate,ErgodicCapacity,SymbolErrorRate,Signal-toNoise Ratio, and Outage Capacity are some of the metrics used to evaluate MSK modulation's efficacy. The ongoing study clears several paths for researchers of the future to explore.
B.K. Mishra et.al: Different modulation schemes, such as QPSK,16-QAM,and64-QAMinaMIMO-OFDMsystem,have beenshowntoperformdifferentlywhenitcomestochannel estimation using Least Mean Squared (LMS), Leaky Least Mean Squared (LLMS), and Modified Leaky Least Mean Square(MLLMS)algorithms.Theprimarygoalistoincrease SNRanddecreaseBERbymanipulatingthestepsize.Ascan beseenfromthedatashownabove,decreasingthestepsize (=0.0025)improvesthesteady-stateerroruptotherangeof 0.96to10-1andtheSNRvalueuptotherangeof15to5dB. also Based on the results of a comparative analysis, it appears that the Modified Leaky Least Mean Square algorithmperformsbetterthantheLeakyLeastMeanSquare algorithmandtheLeastMeanSquarealgorithmwhenused inconjunctionwithaMultipleInputMultipleOutput(MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) system. We found that the MLLMS method had the best improvementpercentileamongthetestedalgorithms.The results show that the Modified Leaky Least Mean Square algorithmhasthelowestBERandmaximumSNR,allowing for the largest possible channel capacity for data transmission. By optimizing for low BER, high SNR, and small MSE, the Modified Leaky LMS (MLLMS) algorithm boostschannelcapacity(MSE).
Monika, Mahendra: ItisstatedthatMIMO-OFDMsystems, with the help of channel estimate methods, have the potential to meet the requirements of future wireless communication systems. The effectiveness of different channel estimating methods, including those based on
trainingdata,channelobservation,andhybridmethods,are alsoreviewed.IncontrasttotheLSandALSestimators,the MMSEchannelestimatormayprovideestimatesinashorter amountoftimedespiteitscomplexity.
Yu et al: recommended benefiting from the synergy of MIMO, cognitive radio, and orthogonal frequency division multiplexing. Using orthogonal space-time block codes in MIMO, they claim to be able to maximize the digital transmission rate while reducing the effects of multipath fading and improving BER performance. Since the combination increases the signal-to-noise ratio, their findingsarealsopositive(SNR).
Gupta et al: We propose combining MIMO and OFDM systemswithwidebandtransmissiontoreduceintersymbol interferenceandboostperformance.Theyhavearguedthat the system's performance may be enhanced by including spatial and frequency variety. A variety of equalizers and space-frequency (SF) block coding has been studied for MIMO-OFDM.ThebestequalizationstrategyforBERanalysis hasbeenproposed.
Jie et al: advocatedfora MIMO-OFDMsystemhybridas a means to increase data transmission speeds. Specifically, theyhaveproposedascenarioinwhichmulti-patheffects andfrequencyselectivefadingcoexist.ComparedtoMIMO OFDM without STBC, they discovered that the BER performanceofthelatterwassuperior.
Darsena et al: DiscusstheuseofthecyclicprefixOFDMin MIMObroadbandwirelesscommunicationsystems(CP).It playsaroleintheSTFBCformula.Asasolution,theyhave offeredtwoMIMOchannelshortens.Itisdependentonthe linearlyrestrictedminimizationofthemeanoutputenergy ofthesignalatthereducedchannel'soutput.Theirfindings demonstrate that the suggested blind channel shortens approach suffers just a small performance hit when compared to non-blind channel shorteners, and this is achievedwithoutsignificantlyincreasingthecomputational complexityofthesystem.
Xin et al: In this presentation, we analyzed MIMO-OFDM systems with Rayleigh fading channels using adaptive modulation(AM)withadiscreterate.Inthiscase,thefading gain value is segmented into many areas for each subchannel.Themodulationdeterminestheadjustmentsmade. The average BER and spectral efficiency (SE) have been gathered.Asaconsequenceoftheirwork,wenowknowthat theenhancedswitching-basedSEapproachissuperiortothe conventionalone.
Seyman et al: The authors propose a feed-forward multilayerperceptron(MLP)neuralnetwork,trainedusing the Levenberg-Marquardt method, for estimating channel parameters in MIMO-OFDM systems. Comparing the suggestedapproachforperformanceassessmentwiththebit
errorrate(BER)andmeansquareerror(MSE)performances ofleastsquare(LS)andleastmeansquare(LMS)algorithms, they find that the neural network channel estimator outperformsboth.
Vakilian et al: Anantennae-basedMIMOOFDMsystemwith space-frequency(SF)blockcodingwasproposed.Radiation patternsthatcanbechangedareemployed.Theythinkthe reconfigurationmustbeperformedautomaticallyontheir part. Their suggested method may support frequency diversity, reconfigurable radiation patterns, and spatial variety over fading channels that are selective in one or morefrequencies.Accordingtotheirfindings,theirmethod outperforms competing SF codes in terms of variety and codinggain.
Doi et al: Joint decoding of block-coded signals in an overloaded MIMO-OFDM system was presented, and its complexity was kept to a minimum. For the block-coded signals,theysuggestacollaborativedecodingapproachthat usestwostages.Themeasurestheyusetonarrowdownthe poolofpossiblecodewordsareefficientlycalculated.Thisis becausejointmaximumlikelihoodsymboldetectionisused inthedecodingprocess.Thenumberofpossiblecodewords iscutdowneffectivelyusingtheirsuggestedapproach.They haveshownacomplexityreductionofaround1/174while transmitting4signalstreams.
Chen et al: Data concealment using the orthogonal frequency division multiplexing (OFDM) technique was demonstratedoveranerror-correctingcodedchannel.The effectsofantennadiversity,maximummultipathdelay,and Dopplershifthavebeendemonstrated.Thisdemonstrates theBERperformanceoftheMIMO-OFDM.Whencompared to other coding channels like SISO and OFDM as well as MIMO, the MIMO-OFDM system is less accurate. The data concealing capability, the BER of the carrier data, and the BERofthesecretdataarealltakenintoaccount.
Sharma et al: ItishypothesizedthattheMIMOsystemis effectiveinthelikelihoodofinformationdetectionandcan send and receive data using several antennas at once. To increase spectral efficiency and decrease ISI, they have combinedOFDMandMIMOsystems.
Sezer et al.: Taking into account both average and peak powerlimits,weprovideasolutiontotheoptimumchannel switching issue to maximize the average capacity of the transmitter's connection with the main receiver. For the optimization problem where the solution meets the conditionsequally,theauthorspresentanalternativesimilar optimizationproblem.Theirtheoreticalfindingshavebeen backedupbynumericalinstances.
Li et al: We spoke about the k-user MIMO interference channel, which requires M transmit antennas and N reception antennas. They suggest an interference
cancellation strategy to boost receivers' access to global channelstateinformation(CSI).Ifsimplythelimitationon degreesoffreedomwasimposed,theirsuggestedstrategy would perform better than existing methods in terms of resisting the correlation of transmitters and tolerating interferences.
EI et al: Advisedacomprehensiveandrealisticsimulation for forecasting RoF system performance. A 60 GHz 2x2 MIMO-OFDMRoFsystemhasbeenproposed.Itispredicated on spatial variety (SD). Spatial multiplexing (SMX), which boosts data ratebutnotnecessarilyreliability,isaided by this.Theytestedthissysteminaline-of-sight(LOS)desktop environmentandcompareditsperformanceusingavariety of approaches, such as diversity, modulation, and channel codingrate.Agreaterdataratemaybeattainedwitha22 MIMOOFDMSMXsystem,theydiscovered.
Namitha et al: The high peak-to-average power ratio (PAPR) across separate antennas has been proposed as a problem with the MIMO-OFDM technology. Selective mapping(SLM)hasbeenproposedasamethodforlowering PAPR in OFDM and MIMO-OFDM without introducing any signal distortion. They see the need of delivering side information (SI) with each OFDM data symbol as a shortcomingoftheSLMapproachtotransmission.Usingthe Hadamardsequence,theyhaveshownasimpleSLMmethod. They theorized that without communicating SI, it might significantlylowerPAPRinMIMO-OFDMsystems.
Qiao et al: WeproposeacombinationofMIMOandOFDMto increase the efficiency of the necessary bandwidth for underwateracoustic(UWA)transmissions.Inthedirection ofMIMO-OFDMcommunicationatUWA,theyhavesurveyed andreportedtheirfindings.Thecomplexityandefficiencyof the algorithms have been compared across the various papers.
3. CONCLUSION
The principal component analysis (PCA) technique for channel transformation and the benefits of applying BSS methodsinMIMOmultiuserdetectionarehighlighted.The usefulnessandefficacyofDWTshavealsobeenproved in the context of signal de-noising, which is shown in the results section. As shown, the proposed Enhanced ICA systemisbothmoresuccessfulatsignalseparationandmore resistant to channel noise, whether it is BER or impulsive noise.Thefindingsshowthatthesuggestedsystemimproves in terms of BER performance and robustness, while the complexityofthedetectorsystemisreduced.Furthermore, the suggested technique is successful regardless of the lengthoftheinputdata,makingitaparticularlyattractive option for large datasets. The suggested Enhanced ICA is utilizedtoaddresstheseproblems,anditismoresensitiveto thestartingsettingsfortheinputweightoftheseparation matrixthanthecurrentMN-IAMOandCOAtechniques.
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