1. 5G NR Link-Level Simulation and Scheduler Evaluation
Advanced
Toolbox: 5G, Communications
Deliverables: Code .m, Report
π― Problem & Objective: Build a full 5G NR link-level simulator in MATLAB to evaluate PHY layer performance, subcarrier spacing numerology trade-offs, and MAC scheduling algorithms (Proportional Fair, Round Robin, QoS-aware) under 3GPP Tapped Delay Line (TDL) channel profiles.
βοΈ Key MATLAB Functions:
nrWaveformGeneratornrPDSCHnrPDSCHDecodenrTDLChannelnrEqualizeMMSE
π Expected Output & Metrics: Throughput (Mbps) vs SNR, Block Error Rate (BLER) performance waterfall curves, user fairness index, and scheduling latency distribution.
carrier = nrCarrierConfig('SubcarrierSpacing', 30, 'NSizeGrid', 51);
pdsch = nrPDSCHConfig('Modulation', '64QAM', 'NumLayers', 2);
[pdschIndices, pdschInfo] = nrPDSCHIndices(carrier, pdsch);
channel = nrTDLChannel('DelayProfile', 'TDL-C', 'DelaySpread', 300e-9, 'MaximumDopplerShift', 10);
channel.SampleRate = nrOFDMInfo(carrier).SampleRate;
dataBits = randi([0 1], pdschInfo.G, 1);
txSymbols = nrPDSCH(carrier, pdsch, dataBits);
[rxWaveform, pathGains] = channel(txSymbols);
rxNoisy = awgn(rxWaveform, 15, 'measured');
Est. Duration: 6β8 Weeks
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2. UAV-Assisted Aerial Base Station Optimization
Advanced
Toolbox: Communications, Optimization
Deliverables: Code .m, Report
π― Problem & Objective: Optimize 3D spatial placement and flight trajectory planning for drone base stations providing emergency cellular coverage to ground users with energy-constrained flight limits and Line-of-Sight (LoS) probability models.
βοΈ Key MATLAB Functions:
fminconrayleighchandistanceplot3optimoptions
π Expected Output & Metrics: 3D flight trajectory plot, ground user coverage probability (>95%), sum-rate throughput (Mbps), and UAV propulsion energy consumption profile.
numUsers = 50;
userLocs = [1000*rand(numUsers, 1), 1000*rand(numUsers, 1), zeros(numUsers, 1)];
objFun = @(uavPos) -sum(log2(1 + (1e-3 * 100 ./ (sum((userLocs - uavPos).^2, 2) + 1e-6))));
initPos = [500, 500, 120];
optUAV = fmincon(objFun, initPos, [], [], [], [], [0 0 50], [1000 1000 300]);
figure; scatter(userLocs(:,1), userLocs(:,2), 'b.'); hold on;
plot(optUAV(1), optUAV(2), 'r^', 'MarkerSize', 12, 'LineWidth', 2);
title(sprintf('Optimal UAV Altitude: %.1f m', optUAV(3)));
Est. Duration: 4β6 Weeks
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3. mmWave Beamforming and Phased Array Prototype Simulation
Advanced
Toolbox: Phased Array System, 5G, Communications
Deliverables: Code .m, Report
π― Problem & Objective: Model hybrid analog/digital beamforming architectures for 28 GHz mmWave Uniform Rectangular Arrays (URA), performing hierarchical beam sweeping, codebook design, and dynamic blockage recovery.
βοΈ Key MATLAB Functions:
phased.URAphased.SteeringVectorpatterncomm.MIMOChannelphased.ArrayResponse
π Expected Output & Metrics: 3D radiation array beam patterns, array directivity gain (dBi), Spectral Efficiency (bits/s/Hz), and beam tracking alignment latency.
fc = 28e9; c = physconst('LightSpeed'); lambda = c/fc;
array = phased.URA('Size', [8 8], 'ElementSpacing', [lambda/2 lambda/2]);
steerVec = phased.SteeringVector('SensorArray', array);
w = steerVec(fc, [30; 15]);
pattern(array, fc, 'PropagationSpeed', c, 'Type', 'directivity', 'Weights', w);
Est. Duration: 4β6 Weeks
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4. Low Power System Design for Emerging Pervasive Platform
Intermediate
Toolbox: Communications, Fixed-Point Designer
Deliverables: Code .m, Report
π― Problem & Objective: Design ultra-low power wireless sensor node communication protocols by optimizing duty-cycling, payload header compression, and fixed-point word length quantization for embedded microcontrollers.
βοΈ Key MATLAB Functions:
finumerictypecomm.AWGNChannelmeanquantize
π Expected Output & Metrics: Energy consumption breakdown per transmitted frame (mJ), battery lifespan projection (years), BER degradation under bit-depth quantization, and packet delivery ratio.
wordLength = 8; fracLength = 6;
T = numerictype(1, wordLength, fracLength);
rawSamples = randn(1000, 1);
quantSamples = fi(rawSamples, T);
activeCurrent = 15e-3; sleepCurrent = 2e-6; V = 3.3;
t_tx = 5e-3; t_sleep = 0.995;
P_avg = (activeCurrent*t_tx + sleepCurrent*t_sleep) * V;
lifespanYears = (2400e-3) / (P_avg / V) / 8760;
fprintf('Projected Battery Lifespan: %.2f Years\n', lifespanYears);
Est. Duration: 2β3 Weeks
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5. LoRaWAN Capacity, Coverage and Scalability Study
Intermediate
Toolbox: Communications, Signal Processing
Deliverables: Code .m, Report
π― Problem & Objective: Simulate city-scale LoRaWAN IoT deployments utilizing Chirp Spread Spectrum (CSS) modulation, evaluating packet collisions, Spreading Factor (SF7-SF12) quasi-orthogonality, capture effects, and multi-gateway coverage.
βοΈ Key MATLAB Functions:
comm.RayleighChannelchirphistogramrandpwelch
π Expected Output & Metrics: Packet Error Rate (PER) vs node density, coverage radius (km) under Okumura-Hata path loss, aggregate network throughput, and optimal SF distribution.
SF = 7; BW = 125e3; Fs = 1e6; Ts = (2^SF)/BW;
t = 0:1/Fs:Ts-1/Fs;
f0 = -BW/2; f1 = BW/2;
baseChirp = chirp(t, f0, Ts, f1);
sym = 45; shift = round((sym / 2^SF) * length(t));
modChirp = circshift(baseChirp, shift);
figure; spectrogram(modChirp, 128, 120, 128, Fs, 'yaxis');
title('LoRa CSS Modulated Spectrogram');
Est. Duration: 2β3 Weeks
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6. Indoor Positioning using Wi-Fi/CSI Fingerprinting and Deep Learning
Advanced
Toolbox: Deep Learning, Communications
Deliverables: Code .m, Report
π― Problem & Objective: Estimate precise indoor 2D Cartesian position coordinates by extracting Wi-Fi subcarrier Channel State Information (CSI) amplitude/phase matrices and training a 2D Convolutional Neural Network (CNN) regressor.
βοΈ Key MATLAB Functions:
trainNetworkconvolution2dLayerpredictrmselayerDimensions
π Expected Output & Metrics: Indoor 2D trajectory estimation map, Cumulative Distribution Function (CDF) of position error, and mean localization accuracy (< 1.2 meters).
layers = [
imageInputLayer([30 3 1], 'Name', 'CSI_Input')
convolution2dLayer(3, 16, 'Padding', 'same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2, 'Stride', 2, 'HasUnpoolingOutputs', false)
fullyConnectedLayer(64)
reluLayer
fullyConnectedLayer(2)
regressionLayer];
options = trainingOptions('adam', 'MaxEpochs', 30, 'MiniBatchSize', 32, 'Plots', 'training-progress');
Est. Duration: 4β6 Weeks
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7. Edge Offloading and Energy-Efficient Protocols for Massive IoT
Intermediate
Toolbox: Communications, Optimization
Deliverables: Code .m, Report
π― Problem & Objective: Formulate multi-user Mobile Edge Computing (MEC) computation offloading decisions, balancing local CPU computation energy against wireless transmission uplink power under strict latency deadlines.
βοΈ Key MATLAB Functions:
fminconlinprogbarplotgamultiobj
π Expected Output & Metrics: Total system energy reduction (%), task completion latency curves (ms), offloading ratio Pareto frontier, and battery depletion rates.
L = [500e3; 800e3; 300e3]; C = 1000;
f_local = 1e8;
f_edge = 2e9;
B = 10e6; N0 = 1e-13; P_tx = 0.2; h = 1e-5;
T_local = (L .* C) ./ f_local;
E_local = 1e-27 * (f_local^2) .* (L .* C);
R_uplink = B * log2(1 + (P_tx * h) / (N0 * B));
T_edge = (L ./ R_uplink) + (L .* C ./ f_edge);
E_edge = P_tx * (L ./ R_uplink);
Est. Duration: 2β3 Weeks
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8. QPSK and 16-QAM Modulation over AWGN & Rayleigh Fading Channels
Beginner
Toolbox: Communications
Deliverables: Code .m, Report
π― Problem & Objective: Implement end-to-end digital transmission for QPSK and 16-QAM signals over additive white Gaussian noise (AWGN) and flat Rayleigh fading channels. Validate simulated Bit Error Rate (BER) curves against theoretical closed-form bounds.
βοΈ Key MATLAB Functions:
qammodqamdemodpskmodberawgnbiterrscatterplot
π Expected Output & Metrics: Semi-log BER vs Eb/N0 waterfall curves, noisy IQ constellation scatter plots, and symbol error rate comparisons across constellation orders.
M = 16; k = log2(M); numBits = 1e5;
dataBits = randi([0 1], numBits, 1);
txSym = qammod(dataBits, M, 'InputType', 'bit', 'UnitAveragePower', true);
h = (randn(length(txSym), 1) + 1j*randn(length(txSym), 1)) / sqrt(2);
snr_dB = 12;
rxNoisy = awgn(h .* txSym, snr_dB);
rxEq = rxNoisy ./ h;
rxBits = qamdemod(rxEq, M, 'OutputType', 'bit', 'UnitAveragePower', true);
[numErr, ber] = biterr(dataBits, rxBits);
fprintf('Simulated BER at %d dB: %.2e\n', snr_dB, ber);
Est. Duration: 4β6 Hours
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9. 2x2 MIMO Space-Time Block Coding (Alamouti Scheme) BER Simulation
Intermediate
Toolbox: Communications
Deliverables: Code .m, Report
π― Problem & Objective: Implement the orthogonal 2x1 and 2x2 Alamouti Space-Time Block Code (STBC) over independent Rayleigh fading channels. Demonstrate full diversity order (diversity order = 4 for 2x2) without channel knowledge at the transmitter.
βοΈ Key MATLAB Functions:
comm.OSTBCEncodercomm.OSTBCDecodercomm.MIMOChannelberfading
π Expected Output & Metrics: Diversity slope comparison (SISO vs 2x1 MISO vs 2x2 MIMO), channel capacity comparison (bits/s/Hz), and maximum ratio combining (MRC) gain.
enc = comm.OSTBCEncoder('NumTransmitAntennas', 2);
dec = comm.OSTBCDecoder('NumTransmitAntennas', 2, 'NumReceiveAntennas', 2);
data = randi([0 3], 1000, 1);
modData = pskmod(data, 4, pi/4);
encData = enc(modData);
H = (randn(size(encData,1), 4) + 1j*randn(size(encData,1), 4))/sqrt(2);
rxSig = awgn(encData, 10);
decData = dec(rxSig, H);
Est. Duration: 1β2 Weeks
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10. OFDM Transceiver with Cyclic Prefix & ZF/MMSE Equalization
Intermediate
Toolbox: Communications, Signal Processing
Deliverables: Code .m, Report
π― Problem & Objective: Design a complete Orthogonal Frequency Division Multiplexing (OFDM) baseband transceiver with pilot subcarrier insertion, IFFT modulation, cyclic prefix (CP) addition, and Zero-Forcing (ZF) vs Minimum Mean Square Error (MMSE) frequency-domain equalization over frequency-selective channels.
βοΈ Key MATLAB Functions:
comm.OFDMModulatorcomm.OFDMDemodulatorfftifftcomm.RayleighChannel
π Expected Output & Metrics: Channel frequency response estimation, constellation de-mapping diagrams, Peak-to-Average Power Ratio (PAPR) CCDF curves, and BER under inter-symbol interference (ISI).
ofdmMod = comm.OFDMModulator('FFTLength', 64, 'CyclicPrefixLength', 16, 'NumSymbols', 10);
ofdmDemod = comm.OFDMDemodulator('FFTLength', 64, 'CyclicPrefixLength', 16, 'NumSymbols', 10);
data = randi([0 1], 64*10*2, 1);
modSig = qammod(data, 4, 'InputType', 'bit');
modSigReshaped = reshape(modSig, 64, 10);
txWaveform = ofdmMod(modSigReshaped);
multipathChan = [0.8, 0, 0.4, 0.2];
rxSig = filter(multipathChan, 1, txWaveform);
demodData = ofdmDemod(rxSig);
Est. Duration: 1β2 Weeks
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11. Cognitive Radio Spectrum Sensing using Energy Detection & Matched Filtering
Intermediate
Toolbox: Communications, Signal Processing
Deliverables: Code .m, Report
π― Problem & Objective: Implement primary user spectrum sensing in cognitive radio networks using Energy Detection, Cyclostationary Feature Extraction, and Matched Filtering. Generate Receiver Operating Characteristic (ROC) curves across variable SNR levels.
βοΈ Key MATLAB Functions:
qfuncqfuncinvxcorrpwelchmean
π Expected Output & Metrics: Probability of Detection ($P_d$) vs Probability of False Alarm ($P_{fa}$) ROC plots, optimal threshold calculation, and sensing time trade-offs.
N = 1000;
snr_dB = -10; snr_lin = 10^(snr_dB/10);
Pfa = 10.^linspace(-3, 0, 50);
gamma_thresh = qfuncinv(Pfa) / sqrt(N);
Pd_theory = qfunc((gamma_thresh - snr_lin) / sqrt((1 + 2*snr_lin)/N));
figure; semilogx(Pfa, Pd_theory, 'b-', 'LineWidth', 2); grid on;
xlabel('Probability of False Alarm (P_{fa})'); ylabel('Probability of Detection (P_d)');
title(sprintf('Cognitive Radio ROC Curve at SNR = %d dB', snr_dB));
Est. Duration: 1β2 Weeks
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12. Free-Space Optical (FSO) Wireless Link under Atmospheric Turbulence
Intermediate
Toolbox: Communications, Statistics
Deliverables: Code .m, Report
π― Problem & Objective: Model terrestrial laser-based Free-Space Optical communication channels influenced by Log-Normal (weak) and Gamma-Gamma (moderate-to-strong) atmospheric turbulence, pointing errors, and fog/fog attenuation.
βοΈ Key MATLAB Functions:
gamrndintegralbesselkerfclognrnd
π Expected Output & Metrics: Optical irradiance probability density functions (PDF), scintillation index vs link distance, and On-Off Keying (OOK) / PPM bit error rates.
Est. Duration: 2β3 Weeks
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13. Massive MIMO Channel Estimation & Hybrid Precoding for 5G/6G
Advanced
Toolbox: 5G, Communications, Phased Array
Deliverables: Code .m, Report
π― Problem & Objective: Simulate base stations equipped with 64/128 antenna arrays serving multiple single-antenna users. Implement Pilot Contamination mitigation, Minimum Mean Square Error (MMSE) channel estimation, and hybrid analog/digital precoding via Singular Value Decomposition (SVD).
βοΈ Key MATLAB Functions:
svdpinvcomm.MIMOChannelnrChannelEstimatekron
π Expected Output & Metrics: Sum spectral efficiency (bits/s/Hz) vs number of BS antennas, channel estimation Mean Square Error (MSE), and inter-user interference suppression ratios.
Est. Duration: 4β6 Weeks
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14. Visible Light Communication (VLC) / LiFi System Simulation
Beginner
Toolbox: Communications, Signal Processing
Deliverables: Code .m, Report
π― Problem & Objective: Model an indoor LiFi / VLC communication link utilizing ceiling LED transmitters with Lambertian radiation patterns and PIN photodiode receivers, analyzing received optical power and electrical SNR.
βοΈ Key MATLAB Functions:
cosdsindmeshsurfcontourf
π Expected Output & Metrics: 3D room optical power distribution heatmaps (lux), SNR distribution across floor coordinates, and maximum achievable data rates.
Est. Duration: 4β8 Hours
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15. Non-Orthogonal Multiple Access (NOMA) vs OMA Sum-Rate Capacity Analysis
Advanced
Toolbox: Communications, Optimization
Deliverables: Code .m, Report
π― Problem & Objective: Implement Downlink Power-Domain NOMA with Successive Interference Cancellation (SIC) at near and far mobile users. Compare sum-rate capacity, user fairness, and outage probability against traditional Orthogonal Multiple Access (OMA/OFDMA).
βοΈ Key MATLAB Functions:
fminconlog2qammodqamdemodraylrnd
π Expected Output & Metrics: User achievable rate regions, Sum-rate capacity gain (NOMA vs OMA), power allocation coefficient optimization plots, and SIC decoding error propagation analysis.
Est. Duration: 3β5 Weeks
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16. Channel Estimation in 5G NR using Deep Neural Networks
Advanced
Toolbox: 5G, Deep Learning, Communications
Deliverables: Code .m, Report
π― Problem & Objective: Formulate 5G NR pilot-based channel estimation as an image super-resolution / denoising problem (ChannelNet architecture). Train a deep CNN to reconstruct full 2D time-frequency channel grids from sparse Demodulation Reference Signals (DM-RS).
βοΈ Key MATLAB Functions:
trainNetworknrChannelEstimatetransposedConv2dLayermse
π Expected Output & Metrics: Normalized Mean Square Error (NMSE) vs SNR compared against Least Squares (LS) and LMMSE estimators, and uncoded BER performance curves under high mobility (Doppler).
Est. Duration: 4β6 Weeks
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17. Doppler Shift & Multipath Delay Spread in Vehicular (V2X) Channels
Intermediate
Toolbox: Communications, 5G
Deliverables: Code .m, Report
π― Problem & Objective: Model high-speed vehicle-to-everything (C-V2X / IEEE 802.11p) double-selective fading channels at 5.9 GHz. Implement Jakes' Doppler spectrum, coherence time, and RMS delay spread evaluations.
βοΈ Key MATLAB Functions:
comm.RayleighChanneldopplerpwelchnrCDLChannel
π Expected Output & Metrics: Jakes Doppler power spectral density plots, inter-carrier interference (ICI) power levels, and packet delivery ratio vs vehicle speed (km/h).
Est. Duration: 2β3 Weeks
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18. Physical Layer Security (PLS) via Artificial Noise & Cooperative Jamming
Advanced
Toolbox: Communications, Optimization
Deliverables: Code .m, Report
π― Problem & Objective: Protect confidential wireless transmissions against passive and active eavesdroppers by injecting Artificial Noise (AN) into the null-space of legitimate channels and deploying cooperative jamming nodes.
βοΈ Key MATLAB Functions:
nullorthsvdcomm.MIMOChannelfmincon
π Expected Output & Metrics: Achievable Secrecy Rate (bits/s/Hz), secrecy outage probability curves, and power splitting ratio trade-offs between information signal and artificial noise.
Est. Duration: 3β5 Weeks
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19. Reconfigurable Intelligent Surface (RIS / IRS) Assisted Wireless Transmission
Advanced
Toolbox: Communications, Optimization, Phased Array
Deliverables: Code .m, Report
π― Problem & Objective: Model smart radio environments enhanced by passive metamaterial RIS elements. Jointly optimize transmit beamforming at the base station and discrete phase-shift matrix at the reflecting surface to bypass non-line-of-sight (NLoS) blockage.
βοΈ Key MATLAB Functions:
fminconexpangleabsnorm
π Expected Output & Metrics: Received SNR enhancement (dB) vs number of reflecting elements ($N$), user spectral efficiency gains, and discrete phase quantization loss analysis.
Est. Duration: 4β6 Weeks
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20. Satellite-to-Ground LEO Constellation Link Budget & Doppler Tracking
Intermediate
Toolbox: Communications, Aerospace
Deliverables: Code .m, Report
π― Problem & Objective: Simulate Low Earth Orbit (LEO, 600 km altitude) satellite pass over a ground terminal, computing dynamic slant range, free-space path loss, atmospheric absorption, $G/T$, and Doppler frequency shift tracking curves.
βοΈ Key MATLAB Functions:
fsplaer2eceflookAtplot
π Expected Output & Metrics: Carrier-to-Noise ratio ($C/N_0$) profile across elevation pass, Doppler shift curve (±50 kHz S-band), contact window duration, and link margin availability.
Est. Duration: 1β2 Weeks
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21. ZigBee (IEEE 802.15.4) PHY Layer Simulation for WSNs
Beginner
Toolbox: Communications, Signal Processing
Deliverables: Code .m, Report
π― Problem & Objective: Implement the 2.4 GHz IEEE 802.15.4 / ZigBee physical layer featuring 4-bit to 32-chip Direct Sequence Spread Spectrum (DSSS) symbol mapping and Offset-QPSK (O-QPSK) half-sine pulse shaping modulation.
βοΈ Key MATLAB Functions:
oqpskmodoqpskdemodrcosdesignbiterr
π Expected Output & Metrics: DSSS chip sequence autocorrelation plots, eye diagrams, packet error rate curves in AWGN and multipath fading channels.
Est. Duration: 5β8 Hours
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22. Rician Fading Channel Simulator with Varying K-Factors
Beginner
Toolbox: Communications
Deliverables: Code .m, Report
π― Problem & Objective: Build a Rician fading channel simulator with tunable Rice factor ($K = 0$ dB for Rayleigh to $K = 20$ dB for dominant Line-of-Sight). Evaluate envelope probability distributions and BPSK/QPSK bit error rates.
βοΈ Key MATLAB Functions:
comm.RicianChannelricerndhistogramberfading
π Expected Output & Metrics: Empirical vs theoretical PDF curves for Rician envelope, BER waterfall comparisons across $K \in \{0, 3, 6, 12\}$ dB, and level crossing rate (LCR) statistics.
Est. Duration: 4β6 Hours
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23. Cooperative Relay Networks: Decode-and-Forward vs Amplify-and-Forward
Intermediate
Toolbox: Communications
Deliverables: Code .m, Report
π― Problem & Objective: Implement dual-hop cooperative diversity transmission protocolsβDecode-and-Forward (DF) and Amplify-and-Forward (AF). Compare end-to-end outage probability and cooperative diversity gains over independent Rayleigh links.
βοΈ Key MATLAB Functions:
comm.RayleighChannelqammodqamdemodsemilogy
π Expected Output & Metrics: End-to-end BER vs SNR curves, Outage probability comparisons, and optimal relay location positioning trade-offs.
Est. Duration: 1β2 Weeks
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24. Underwater Acoustic Wireless Communication Link Simulation
Intermediate
Toolbox: Communications, Signal Processing
Deliverables: Code .m, Report
π― Problem & Objective: Simulate underwater acoustic sensor network (UWASN) physical layer links incorporating Thorp's frequency-dependent absorption model, severe multipath delay spreads, ambient noise (waves, shipping, thermal), and slow acoustic propagation (1500 m/s).
βοΈ Key MATLAB Functions:
pwelchconvfiltercomm.DPSKModulator
π Expected Output & Metrics: Acoustic transmission loss (TL in dB) vs frequency/distance, ambient noise PSD curves, and achievable bit rate vs depth and salinity.
Est. Duration: 2β3 Weeks
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25. RF Energy Harvesting & Wireless Information and Power Transfer (SWIPT)
Advanced
Toolbox: Communications, Optimization
Deliverables: Code .m, Report
π― Problem & Objective: Implement Simultaneous Wireless Information and Power Transfer (SWIPT) for self-sustaining IoT nodes using Time-Switching (TS) and Power-Splitting (PS) receiver architectures. Optimize harvested energy vs channel capacity.
βοΈ Key MATLAB Functions:
fmincongamultiobjlog2plot
π Expected Output & Metrics: Energy-Information rate trade-off curves, optimal power splitting ratio $\rho^*$, and rectenna non-linear conversion efficiency profiles.
Est. Duration: 3β5 Weeks
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26. Bluetooth Low Energy (BLE 5.0) Long-Range PHY Throughput Analysis
Beginner
Toolbox: Bluetooth Toolbox, Communications
Deliverables: Code .m, Report
π― Problem & Objective: Simulate Bluetooth Low Energy 5.0 PHY modes (LE 1M, LE 2M, and LE Coded S=2 / S=8). Compare effective application throughput, receiver sensitivity gain, and communication range extension.
βοΈ Key MATLAB Functions:
bleWaveformGeneratorbleChannelbleIdealReceiverbiterr
π Expected Output & Metrics: Throughput (kbps) vs SNR curves for 1M, 2M, and Coded PHYs, sensitivity threshold comparison (-93 dBm to -105 dBm), and packet delivery ratio.
Est. Duration: 5β8 Hours
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27. Turbo Coding & LDPC Code Performance in 5G Communications
Advanced
Toolbox: 5G, Communications
Deliverables: Code .m, Report
π― Problem & Objective: Implement and compare 3GPP 5G NR Low-Density Parity-Check (LDPC) codes with base graphs BG1/BG2 against 4G LTE Turbo Codes. Evaluate belief propagation iterative decoding converging toward the Shannon limit.
βοΈ Key MATLAB Functions:
nrLDPCEncodenrLDPCDecodecomm.TurboEncodercomm.TurboDecoder
π Expected Output & Metrics: Coded vs Uncoded BER waterfall plots, decoding iteration convergence curves, and hardware throughput latency metrics.
Est. Duration: 3β5 Weeks
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28. Carrier Frequency Offset (CFO) and Phase Noise Estimation in OFDM
Intermediate
Toolbox: Communications, Signal Processing
Deliverables: Code .m, Report
π― Problem & Objective: Compensate for local oscillator drift and Doppler induced Carrier Frequency Offset (CFO) in OFDM receivers using Schmidl-Cox and Moose preamble estimation algorithms, followed by Common Phase Error (CPE) tracking.
βοΈ Key MATLAB Functions:
comm.PhaseFrequencyOffsetanglexcorrcomm.PhaseNoise
π Expected Output & Metrics: CFO estimation Mean Square Error (MSE) vs SNR, constellation rotation correction plots, and BER degradation vs residual CFO.
preamble = [randn(32,1)+1j*randn(32,1); randn(32,1)+1j*randn(32,1)];
cfo_actual = 2500; Fs = 1e6;
t = (0:length(preamble)-1)' / Fs;
rx_preamble = preamble .* exp(1j*2*pi*cfo_actual*t);
r1 = rx_preamble(1:32); r2 = rx_preamble(33:64);
cfo_est = angle(sum(conj(r1) .* r2)) / (2*pi*(32/Fs));
fprintf('Actual CFO: %.1f Hz | Estimated CFO: %.1f Hz\n', cfo_actual, cfo_est);
Est. Duration: 1β2 Weeks
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29. Millimeter-Wave Radar and Communications Coexistence (ISAC)
Advanced
Toolbox: Phased Array, Radar, 5G, Communications
Deliverables: Code .m, Report
π― Problem & Objective: Design an Integrated Sensing and Communications (ISAC) waveform at 77 GHz where dual-functional radar-communication signals simultaneously estimate target range/velocity while transmitting QAM data streams.
βοΈ Key MATLAB Functions:
phased.FMCWWaveformphased.RangeDopplerResponsefft2
π Expected Output & Metrics: Range-Doppler 2D radar heatmaps, communication data throughput (Gbps), and radar sensing resolution vs communication constellation interference.
fc = 77e9; B = 150e6; c = physconst('LightSpeed');
radarWave = phased.FMCWWaveform('SweepBandwidth', B, 'SampleRate', 2*B);
sig = radarWave();
dataBits = randi([0 1], 1024, 1);
qamSym = qammod(dataBits, 16, 'InputType', 'bit');
txISAC = sig(1:length(qamSym)) .* exp(1j * angle(qamSym));
Est. Duration: 4β6 Weeks
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30. Full-Duplex Transceiver with Self-Interference Cancellation (SIC)
Advanced
Toolbox: Communications, Signal Processing
Deliverables: Code .m, Report
π― Problem & Objective: Double wireless spectral efficiency by operating in In-Band Full-Duplex (IBFD) mode. Design multi-stage analog domain RF cancellation and digital adaptive LMS/Volterra non-linear Self-Interference Cancellation (SIC).
βοΈ Key MATLAB Functions:
dsp.LMSFilterdsp.RLSFiltercomm.MemorylessNonlinearitypwelch
π Expected Output & Metrics: Total SIC depth (> 110 dB cancellation), residual self-interference power levels, and full-duplex vs half-duplex throughput doubling validation.
Est. Duration: 4β6 Weeks
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31. Deep Reinforcement Learning for Wireless Resource Allocation & Power Control
Advanced
Toolbox: Reinforcement Learning, Communications, 5G
Deliverables: Code .m, Report
π― Problem & Objective: Train Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) agents in MATLAB to dynamically assign sub-bands and adjust transmit power in multi-cell interfering wireless networks.
βοΈ Key MATLAB Functions:
rlDQNAgentrlDDPGAgenttrainrlNumericSpecstep
π Expected Output & Metrics: Episode reward convergence curves, network energy efficiency (Mbits/Joule), cell-edge user throughput gains, and computational inference delay per frame.
obsInfo = rlNumericSpec([3 1], 'LowerLimit', [-inf -inf 0]', 'UpperLimit', [inf inf 100]');
actInfo = rlNumericSpec([1 1], 'LowerLimit', 0, 'UpperLimit', 1);
criticNet = [
featureInputLayer(4, 'Name', 'StateActionInput')
fullyConnectedLayer(64)
reluLayer
fullyConnectedLayer(1)];
agent = rlDDPGAgent(actor, critic);
Est. Duration: 4β6 Weeks
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