BPSK BER Simulation in a LoRa sensor radio link

Project info

• Field of studyIoT and Embedded Systems
• LevelMaster's thesis (S2)
• Method typeCommunications and Forecasting
• Estimated build time± 5 working days
• Toolspskmod, pskdemod, awgn, biterr, berawgn (Communications Toolbox)
• PriceUS$25 - US$156

Research goal

Simulate the BPSK bit error rate versus signal-to-noise ratio in a LoRa sensor radio link and compare it with theory.

Variables

Example result

• 37.285BER at 2 dB (x1e-3)
• 2.41BER at 6 dB (x1e-3)
• 2.388Theory at 6 dB (x1e-3)
• 6.83Eb/N0 for BER 1e-3 (dB)
• 200000Bits per SNR point
• 43643Total bit errors

The BPSK simulation with 200000 bits per point gives Bit error rate of 2.4e-03 at 6 dB (theory 2.4e-03) and needs roughly 6.8 dB Eb/N0 for a 1e-3 BER; the Monte Carlo results track the theoretical erfc curve closely.

Computed from illustrative synthetic data, not from a real study.

About BPSK BER Simulation

BPSK BER simulation measures the bit error rate of BPSK on an AWGN channel against Eb/N0 and compares it with the theoretical curve. It is used to validate digital communication system simulations in telecommunications theses.

Steps

1. 1Define the Eb/N0 range and number of simulated bits
2. 2Generate random bits and modulate with pskmod
3. 3Add AWGN noise with awgn
4. 4Demodulate with pskdemod
5. 5Compute BER with biterr
6. 6Compare with berawgn and plot with semilogy

Assumptions to check

• AWGN channel without fading
• Perfect synchronisation
• Enough bits so there are many errors at low BER (at least about 100 errors)

Reading and reporting the results

Report simulated and theoretical BER curves on one plot with their difference. State the number of bits per point so statistical confidence is visible.

Example MATLAB code

A short starting script with this project's variable names. The delivered version is adapted to your data and fully commented.

rng(6);
signal_to_noise_ratio = 0:2:8;                                    % Eb/N0 in dB
N = 3e5;
bit_error_rate = zeros(size(signal_to_noise_ratio));                       % simulated BER
for k = 1:numel(signal_to_noise_ratio)
    bits = randi([0 1], N, 1);
    tx = pskmod(bits, 2);
    rx = awgn(tx, signal_to_noise_ratio(k), 'measured');          % for BPSK, Es/N0 = Eb/N0
    rbits = pskdemod(rx, 2);
    [nerr, bit_error_rate(k)] = biterr(bits, rbits);
    fprintf('Eb/N0 = %d dB: errors = %d, BER = %.2e\n', signal_to_noise_ratio(k), nerr, bit_error_rate(k));
end
theory = berawgn(signal_to_noise_ratio, 'psk', 2, 'nondiff');

figure;
semilogy(signal_to_noise_ratio, bit_error_rate, 'o-', signal_to_noise_ratio, theory, 'r--', 'LineWidth', 1.3);
grid on; xlabel('E_b/N_0 (dB)'); ylabel('BER'); legend('Simulated', 'Theory');

Packages and prices

Choose how much help you need. Each higher package includes everything in the lower ones. Prices exclude 11% VAT.

• Payment: 50% down payment before work starts, balance after the deliverables are handed over
• Tax: Prices exclude 11% VAT
• Data and academic integrity: Results are computed from your own research data; you write and stand behind your manuscript, we help with the analysis and your understanding of it
• Revisions: Up to 2 revisions are included in the price (Package 3: 3 rounds)

Frequently asked questions