基于matlab使用波束成形对点对点 MIMO-OFDM 系统进行建模

%% Beamforming for MIMO-OFDM Systems % This example shows how to model a point-to-point MIMO-OFDM system with % beamforming. The combination of multiple-input-multiple-output (MIMO) and % orthogonal frequency division multiplexing (OFDM) techniques have been % adopted in recent wireless standards, such as 802.11x families, to % provide higher data rate. Because MIMO uses antenna arrays, beamforming % can be adopted to improve the received signal to noise ratio (SNR) which % in turn reduces the bit error rate (BER). % % This example requires Communications Toolbox(TM). % Copyright 2014-2022 The MathWorks, Inc. %% Introduction % The term MIMO is used to describe a system where multiple transmitters or % multiple receivers are present. In practice the system can take many % different forms, such as single-input-multiple-output (SIMO) or % multiple-input-single-output (MISO) system. This example illustrates a % downlink MISO system. An 8-element ULA is deployed at the base station as % the transmitter while the mobile unit is the receiver with a single % antenna. % % The rest of the system is configured as follows. The transmitter power is % 9 watts and the transmit gain is -8 dB. The mobile receiver is stationary % and located at 2750 meters away, and is 3 degrees off the transmitter's % boresight. An interferer with a power of 1 watt and a gain of -20 dB is % located at 9000 meters, 20 degrees off the transmitter's boresight. % Initialize system constants rng(2014); gc = helperGetDesignSpecsParameters(); % Tunable parameters tp.txPower = 9; % watt tp.txGain = -8; % dB tp.mobileRange = 2750; % m tp.mobileAngle = 3; % degrees tp.interfPower = 1; % watt tp.interfGain = -20; % dB tp.interfRange = 9000; % m tp.interfAngle = 20; % degrees tp.numTXElements = 8; tp.steeringAngle = 0; % degrees tp.rxGain = 108.8320 - tp.txGain; % dB numTx= tp.numTXElements; %% % The entire scene can be depicted in the figure below. helperPlotMIMOEnvironment(gc, tp); %% Signal Transmission % First, configure the system's transmitter. [encoder,scrambler,modulatorOFDM,steeringvec,transmitter,... radiator,pilots,numDataSymbols,frmSz] = helperMIMOTxSetup(gc,tp); %% % There are many components in the transmitter subsystem, such as the % convolutional encoder, the scrambler, the QAM modulator, the OFDM % modulator, and so on. The message is first converted to an information % bit stream and then passed through source coding and modulation stages to % prepare for the radiation. txBits = randi([0, 1], frmSz,1); coded = encoder(txBits); bitsS = scrambler(coded); tx = qammod(bitsS,gc.modMode,'InputType','bit','UnitAveragePower',true); %% % In an OFDM system, the data is carried by multiple sub-carriers that are % orthogonal to each other. ofdm1 = reshape(tx, gc.numCarriers,numDataSymbols); %% % Then, the data stream is duplicated to all radiating elements in the % transmitting array ofdmData = repmat(ofdm1,[1, 1, numTx]); txOFDM = modulatorOFDM(ofdmData, pilots); %scale txOFDM = txOFDM * ... (gc.FFTLength/sqrt(gc.FFTLength-sum(gc.NumGuardBandCarriers)-1)); % Amplify to achieve peak TX power for each channel for n = 1:numTx txOFDM(:,n) = transmitter(txOFDM(:,n)); end %% % In a MIMO system, it is also possible to separate multiple users spatial % division multiplexing (SDMA). In these situations, the data stream is % often modulated by a weight corresponding to the desired direction so % that once radiated, the signal is maximized in that direction. Because in % a MIMO channel, the signal radiated from different elements in an array % may go through different propagation environments, the signal radiated % from each antenna should be propagated individually. This can be achieved % by setting CombineRadiatedSignals to false on the phased.Radiator % component. radiator.CombineRadiatedSignals = false; %% % To achieve precoding, the data stream radiated from each antenna in the % array is modulated by a phase shift corresponding to its radiating % direction. The goal of this precoding is to ensure these data streams add % in phase if the array is steered toward that direction. Precoding can be % specified as weights used at the radiator. wR = steeringvec(gc.fc,[-tp.mobileAngle;0]); %% % Meanwhile, the array is also steered toward a given steering angle, so % the total weights are a combination of both precoding and the steering % weights. wT = steeringvec(gc.fc,[tp.steeringAngle;0]); weight = wT.* wR; %% % The transmitted signal is thus given by txOFDM = radiator(txOFDM,repmat([tp.mobileAngle;0],1,numTx),conj(weight)); %% % Note that the transmitted signal, txOFDM, is a matrix whose columns % represent data streams radiated from the corresponding elements in the % transmit array. %% Signal Propagation % Next, the signal propagates through a MIMO channel. In general, there are % two propagation effects on the received signal strength that are of % interest: one of them is the spreading loss due to the propagation % distance, often termed as the free space path loss; and the other is the % fading due to multipath. This example models both effects. [channel,interferenceTransmitter,toRxAng,spLoss] = ... helperMIMOEnvSetup(gc,tp); [sigFade, chPathG] = channel(txOFDM); sigLoss = sigFade/sqrt(db2pow(spLoss(1))); %% % To simulate a more realistic mobile environment, the next section also % inserts an interference source. Note that in a wireless communication % system, the interference is often a different mobile user. % Generate interference and apply gain and propagation loss numBits = size(sigFade,1); interfSymbols = wgn(numBits,1,1,'linear','complex'); interfSymbols = interferenceTransmitter(interfSymbols); interfLoss = interfSymbols/sqrt(db2pow(spLoss(2))); %% Signal Reception % The receiving antenna collects both the propagated signal as well as the % interference and passes them to the receiver to recover the original % information embedded in the signal. Just like the transmit end of the % system, the receiver used in a MIMO-OFDM system also contains many % stages, including OFDM demodulator, QAM demodulator, descrambler, % equalizer, and Viterbi decoder. [collector,receiver,demodulatorOFDM,descrambler,decoder] = ... helperMIMORxSetup(gc,tp,numDataSymbols); rxSig = collector([sigLoss interfLoss],toRxAng); % Front-end amplifier gain and thermal noise rxSig = receiver(rxSig); rxOFDM = rxSig * ... (sqrt(gc.FFTLength-sum(gc.NumGuardBandCarriers)-1)) / (gc.FFTLength); % OFDM Demodulation rxOFDM = demodulatorOFDM(rxOFDM); % Channel estimation hD = helperIdealChannelEstimation(gc, numDataSymbols, chPathG); % Equalization rxEq = helperEqualizer(rxOFDM, hD, numTx); % Collapse OFDM matrix rxSymbs = rxEq(:); rxBitsS = qamdemod(rxSymbs,gc.modMode,'UnitAveragePower',true,... 'OutputType','bit'); rxCoded = descrambler(rxBitsS); rxDeCoded = decoder(rxCoded); rxBits = rxDeCoded(1:frmSz); %% % A comparison of the decoded output with the original message stream % suggests that the resulting BER is too high for a communication system. % The constellation diagram is also shown below: ber = comm.ErrorRate; measures = ber(txBits, rxBits); fprintf('BER = %.2f%%; No. of Bits = %d; No. of errors = %d\n', ... measures(1)*100,measures(3), measures(2)); %% constdiag = comm.ConstellationDiagram('SamplesPerSymbol', 1,... 'ReferenceConstellation', [], 'ColorFading',true,... 'Position', gc.constPlotPosition); % Display received constellation constdiag(rxSymbs); %% % The high BER is mainly due to the mobile being off the steering direction % of the base station array. If the mobile is aligned with the steering % direction, the BER is greatly improved. tp.steeringAngle = tp.mobileAngle; % Steer the transmitter main lobe wT = steeringvec(gc.fc,[tp.steeringAngle;0]); [txBits, rxBits,rxSymbs] = helperRerunMIMOBeamformingExample(gc,tp,wT); reset(ber); measures = ber(txBits, r