1. 5G NR Link-Level Simulation and Scheduler Evaluation
Advanced
Toolbox: 5G, Communications
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
nrWaveformGeneratornrPDSCHnrPDSCHDecodenrTDLChannelnrEqualizeMMSE
📊 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: 6–8 Weeks
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2. UAV-Assisted Aerial Base Station Optimization
Advanced
Toolbox: Communications, Optimization
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
fminconrayleighchandistanceplot3optimoptions
📊 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: 4–6 Weeks
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3. mmWave Beamforming and Phased Array Prototype Simulation
Advanced
Toolbox: Phased Array System, 5G, Communications
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
phased.URAphased.SteeringVectorpatterncomm.MIMOChannelphased.ArrayResponse
📊 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: 4–6 Weeks
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4. Low Power System Design for Emerging Pervasive Platform
Intermediate
Toolbox: Communications, Fixed-Point Designer
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
finumerictypecomm.AWGNChannelmeanquantize
📊 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: 2–3 Weeks
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5. LoRaWAN Capacity, Coverage and Scalability Study
Intermediate
Toolbox: Communications, Signal Processing
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
comm.RayleighChannelchirphistogramrandpwelch
📊 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: 2–3 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
🎯 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.
⚙️ Key MATLAB Functions:
trainNetworkconvolution2dLayerpredictrmselayerDimensions
📊 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: 4–6 Weeks
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7. Edge Offloading and Energy-Efficient Protocols for Massive IoT
Intermediate
Toolbox: Communications, Optimization
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
fminconlinprogbarplotgamultiobj
📊 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: 2–3 Weeks
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8. QPSK and 16-QAM Modulation over AWGN & Rayleigh Fading Channels
Beginner
Toolbox: Communications
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
qammodqamdemodpskmodberawgnbiterrscatterplot
📊 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: 4–6 Hours
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9. 2x2 MIMO Space-Time Block Coding (Alamouti Scheme) BER Simulation
Intermediate
Toolbox: Communications
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
comm.OSTBCEncodercomm.OSTBCDecodercomm.MIMOChannelberfading
📊 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: 1–2 Weeks
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10. OFDM Transceiver with Cyclic Prefix & ZF/MMSE Equalization
Intermediate
Toolbox: Communications, Signal Processing
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
comm.OFDMModulatorcomm.OFDMDemodulatorfftifftcomm.RayleighChannel
📊 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: 1–2 Weeks
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11. Cognitive Radio Spectrum Sensing using Energy Detection & Matched Filtering
Intermediate
Toolbox: Communications, Signal Processing
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
qfuncqfuncinvxcorrpwelchmean
📊 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: 1–2 Weeks
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12. Free-Space Optical (FSO) Wireless Link under Atmospheric Turbulence
Intermediate
Toolbox: Communications, Statistics
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
gamrndintegralbesselkerfclognrnd
📊 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: 2–3 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
🎯 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).
⚙️ Key MATLAB Functions:
svdpinvcomm.MIMOChannelnrChannelEstimatekron
📊 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: 4–6 Weeks
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14. Visible Light Communication (VLC) / LiFi System Simulation
Beginner
Toolbox: Communications, Signal Processing
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
cosdsindmeshsurfcontourf
📊 Expected Output & Metrics: 3D room optical power distribution heatmaps (lux), SNR distribution across floor coordinates, and maximum achievable data rates.
Est. Duration: 4–8 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
🎯 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).
⚙️ Key MATLAB Functions:
fminconlog2qammodqamdemodraylrnd
📊 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: 3–5 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
🎯 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).
⚙️ Key MATLAB Functions:
trainNetworknrChannelEstimatetransposedConv2dLayermse
📊 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: 4–6 Weeks
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17. Doppler Shift & Multipath Delay Spread in Vehicular (V2X) Channels
Intermediate
Toolbox: Communications, 5G
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
comm.RayleighChanneldopplerpwelchnrCDLChannel
📊 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: 2–3 Weeks
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18. Physical Layer Security (PLS) via Artificial Noise & Cooperative Jamming
Advanced
Toolbox: Communications, Optimization
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
nullorthsvdcomm.MIMOChannelfmincon
📊 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: 3–5 Weeks
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19. Reconfigurable Intelligent Surface (RIS / IRS) Assisted Wireless Transmission
Advanced
Toolbox: Communications, Optimization, Phased Array
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
fminconexpangleabsnorm
📊 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: 4–6 Weeks
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20. Satellite-to-Ground LEO Constellation Link Budget & Doppler Tracking
Intermediate
Toolbox: Communications, Aerospace
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
fsplaer2eceflookAtplot
📊 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: 1–2 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
🎯 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.
⚙️ Key MATLAB Functions:
oqpskmodoqpskdemodrcosdesignbiterr
📊 Expected Output & Metrics: DSSS chip sequence autocorrelation plots, eye diagrams, packet error rate curves in AWGN and multipath fading channels.
Est. Duration: 5–8 Hours
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22. Rician Fading Channel Simulator with Varying K-Factors
Beginner
Toolbox: Communications
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
comm.RicianChannelricerndhistogramberfading
📊 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: 4–6 Hours
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23. Cooperative Relay Networks: Decode-and-Forward vs Amplify-and-Forward
Intermediate
Toolbox: Communications
Deliverables: Code .m, Report
🎯 Problem & Objective: Implement dual-hop cooperative diversity transmission protocols—Decode-and-Forward (DF) and Amplify-and-Forward (AF). Compare end-to-end outage probability and cooperative diversity gains over independent Rayleigh links.
⚙️ Key MATLAB Functions:
comm.RayleighChannelqammodqamdemodsemilogy
📊 Expected Output & Metrics: End-to-end BER vs SNR curves, Outage probability comparisons, and optimal relay location positioning trade-offs.
Est. Duration: 1–2 Weeks
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24. Underwater Acoustic Wireless Communication Link Simulation
Intermediate
Toolbox: Communications, Signal Processing
Deliverables: Code .m, Report
🎯 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).
⚙️ Key MATLAB Functions:
pwelchconvfiltercomm.DPSKModulator
📊 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: 2–3 Weeks
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25. RF Energy Harvesting & Wireless Information and Power Transfer (SWIPT)
Advanced
Toolbox: Communications, Optimization
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
fmincongamultiobjlog2plot
📊 Expected Output & Metrics: Energy-Information rate trade-off curves, optimal power splitting ratio $\rho^*$, and rectenna non-linear conversion efficiency profiles.
Est. Duration: 3–5 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
🎯 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.
⚙️ Key MATLAB Functions:
bleWaveformGeneratorbleChannelbleIdealReceiverbiterr
📊 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: 5–8 Hours
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27. Turbo Coding & LDPC Code Performance in 5G Communications
Advanced
Toolbox: 5G, Communications
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
nrLDPCEncodenrLDPCDecodecomm.TurboEncodercomm.TurboDecoder
📊 Expected Output & Metrics: Coded vs Uncoded BER waterfall plots, decoding iteration convergence curves, and hardware throughput latency metrics.
Est. Duration: 3–5 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
🎯 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.
⚙️ Key MATLAB Functions:
comm.PhaseFrequencyOffsetanglexcorrcomm.PhaseNoise
📊 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: 1–2 Weeks
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29. Millimeter-Wave Radar and Communications Coexistence (ISAC)
Advanced
Toolbox: Phased Array, Radar, 5G, Communications
Deliverables: Code .m, Report
🎯 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.
⚙️ Key MATLAB Functions:
phased.FMCWWaveformphased.RangeDopplerResponsefft2
📊 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: 4–6 Weeks
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30. Full-Duplex Transceiver with Self-Interference Cancellation (SIC)
Advanced
Toolbox: Communications, Signal Processing
Deliverables: Code .m, Report
🎯 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).
⚙️ Key MATLAB Functions:
dsp.LMSFilterdsp.RLSFiltercomm.MemorylessNonlinearitypwelch
📊 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: 4–6 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
🎯 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.
⚙️ Key MATLAB Functions:
rlDQNAgentrlDDPGAgenttrainrlNumericSpecstep
📊 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: 4–6 Weeks
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