Wireless Communication using Python

Explore wireless communication systems, from cellular networks and WLANs to satellite links, and trace the history from the radio's invention to today’s advanced technologies.

Explore wireless communication technologies, including cellular networks, wifi, satellite communications, Bluetooth, and NFC. Learn how base stations and cells enable mobile devices to connect and transfer data.

Learn how to create and save a Python file, set up Visual Studio Code, and write and explain each line of simple Python scripts.

Use a Python script with the subprocess module to run the airport command on macOS, capture and decode output, and print networks with ssid, bssid, mac address, and signal strength.

Trace evolution of wireless communication from 19th century radio to networks, highlighting Marconi's 1895 radio, amplitude modulation and frequency modulation, 1970s cellular networks, 1990s 802.11 Wi-Fi, and satellite communications.

Create a simple python script that prints the current time using datetime.now from the date and time library. Save as 1.2.py and observe the year‑month‑date hours minutes seconds microseconds format.

Explore the fundamentals of digital modulation in wireless communication using Python, covering amplitude, phase, and quadrature modulation with Python code for generating and demodulating signals.

Encode digital information into an analog signal by amplitude modulation, vary carrier with the digital data, and recover a replica of the original signal via envelope detection with a diode.

Demonstrate amplitude modulation signal generation in Python by composing carrier and message cosines, multiplying them, and plotting the result with NumPy, SciPy, and Matplotlib.

Explore how amplitude modulation encodes digital information into an analog signal, and master demodulation through envelope detection, with Python code for generating and demodulating am signals.

Demonstrate amplitude modulation with a Python script that generates an am signal and uses an envelope detector to demodulate and retrieve the original message in wireless communication systems.

Learn how phase modulation encodes digital data by varying the carrier phase with the modulating signal, generating and demodulating the PM index, and its noise resilience over AM and FM.

Generate a phase modulation signal in python using numpy and matplotlib, with a 10 Hz carrier, 2 Hz message, and a 0.5 modulation index, then plot the result.

Explore Python code for generating and demodulating phase modulation signals, and learn how coherent demodulation uses a phase-locked local oscillator to recover the original message.

Shows how to generate a 10 Hz carrier and 2 Hz message, perform phase modulation with index 0.5, apply coherent demodulation to recover the original signal, and plot the results.

Explore quadrature amplitude modulation (Kwame), encoding digital data into amplitude and phase with multiple carrier waves, exemplified by 16 Kwame using four phase shifts and four amplitude levels.

Demonstrates generating a 16 qualm signal with Python using NumPy and Matplotlib to perform quadrature amplitude modulation and demodulate to recover the original eight-bit message.

Explore Python code for Kwame signals. Kwame encodes digital data into an analog carrier via amplitude and phase modulation, with 16 states from four phase shifts and four amplitudes.

Demonstrate a Python script that creates a 10 Hz carrier, generates an 8-bit binary message, modulates by the carrier, and demodulates to recover the original bits, with plots of signals.

Learn to simulate and analyze digital modulation schemes in python using numpy, scipy, and matplotlib, including amplitude, phase, and quadrature amplitude modulation, under noise, and measure bit error rate.

Explore how NumPy, SciPy, and Matplot library enable simulating digital modulation schemes in Python. Learn to generate and manipulate arrays, analyze noise and interference, and visualize modulation schemes.

Explore a Python script for quadrature phase shift keying modulation. The code uses cupy to compute real and imaginary components and plot the four-point constellation.

Leverage Python code to simulate and analyze performance of various digital modulation schemes under noise and interference, using bit error rate, and preview a script for binary phase shift keying.

Explore a Python script that simulates and analyzes binary phase shift keying performance, generating random data, modulating with bpsk, adding Gaussian noise, demodulating by threshold, and plotting ber vs snr.

Explore error correction coding, including forward error correction with convolutional and block codes, and automatic repeat request schemes, with Python simulations using numpy and scipy.

Explore forward error correction, which detects and corrects errors without retransmission by adding redundant data. Learn convolutional and block codes, including Hamming, Reed-Solomon, and BCD codes for real-time applications.

Encode a four-bit input with a generator matrix to form a seven-bit encoded array, then add random noise and compute the received data modulo two to illustrate forward error correction.

Explore how Python implements convolutional and block codes for error correction, using numpy and scipy to encode data, apply generator polynomials, and perform block-wise redundancy.

A Python script demonstrates encoding data with a simple linear block code—the Hamming code—using a generator matrix, block size three, and matrix multiplication with modulo operations.

Explore automatic repeat request (arq) for reliable data transmission, including stop-and-wait and selective repeat schemes, using an acknowledgement, frame retransmission of corrupted data, and receiver buffers.

Implement a stop-and-wait ARQ scheme using a Python socket to send data across a network, wait for an acknowledgement, and resend if needed, then close the socket.

Explore Python code for simulating the performance of various ARQ schemes, including the stop-and-wait ARQ, where the receiver's negative acknowledgement prompts retransmission.

simulate the stop and wait arq scheme with a Python script that retransmits on errors and calculates efficiency as the ratio of successful transmissions to total transmissions.

Explore wireless channel properties, including path loss, fading, and multipath in free space, indoor, and outdoor environments, and learn Python-based simulations of Rayleigh, Raytheon, and Nakagami channels.

Explore the wireless channel and its properties, including free space, indoor and outdoor types, and learn how path loss, fading, and multipath affect signal quality and channel modeling.

Demonstrates a Python script that simulates a Rayleigh fading channel with numpy, plots a histogram with the Matplot library, and mentions extending to Resian or Nakagami models.

Learn about wireless channel models such as relay channel and Nakagami, with Python code to simulate multipath fading, and preview the Rayleigh channel pdf in the next lecture.

Generate Rayleigh fading samples for a channel using NumPy, manipulating the scale and mean, and visualize the distribution with a Matplotlib histogram.

Explore Mimo communications, including spatial multiplexing, beamforming, and diversity, and learn to simulate and evaluate Mimo techniques with Python using numpy and scipy to assess capacity and diversity gain.

Explore how mimo systems use multiple antennas to boost wireless performance, covering spatial multiplexing, beamforming, and diversity, with a forthcoming Python script based on this mimo communications.

Learn to simulate mimo systems in python using numpy, generate a random channel matrix and symbols with noise for spatial multiplexing, and compute the received signal.

Explore capacity and diversity gain in MIMO systems, including the channel capacity formula using the channel matrix and noise power, and the diversity gain formula with receive antennas and snr_0.

Compute MIMO channel capacity and diversity gain in Python using a random channel matrix, NumPy, determinant, and log2 with a concrete example and notes on dependence on channel conditions.

Explore wireless network simulation with Python, covering ad hoc and sensor networks, routing protocols, energy-efficient strategies, and performance analysis. Use Python to simulate protocols and evaluate metrics.

Learn to simulate wireless ad hoc networks with Python, exploring routing protocols such as AODV, DSR, and DSDV in disaster relief and military operation scenarios.

Explore a Python script that simulates ad hoc routing protocols using the Sympy library, printing hello world with the simulation time in a five-step loop.

Learn how routing protocols determine the best data paths in wireless ad hoc networks and simulate their performance using Python and the SymPy library.

Explore a Python script that simulates an ad hoc routing protocol in a wireless network using Sympy, updating routing tables, discovering routes, and sending data packets over ten time units.

Learn to simulate wireless sensor networks with python, focusing on energy efficient routing protocols to extend network lifetime. Use the Gns3 library to model deployment of wireless sensor nodes.

Simulate deploying wireless sensor nodes with a Python script using the Gns3 library, evaluating each node's distance to the sink against a communication range to determine active or inactive status.

Explore Python-based simulation of energy efficient routing protocols for wireless sensor networks, using Python libraries to evaluate routing strategies and extend device lifetimes.

Run a Python script that simulates an energy efficient routing protocol for wireless sensor networks using directed diffusion, with sensor nodes deactivating as energy falls below a threshold.

Explore the internet of things with Python by reviewing IoT wireless protocols, their characteristics, and use cases. See practical Python code examples for Zigbee, Bluetooth, BLE, and LoRa.

Explore IoT wireless protocols like Zigbee, Bluetooth and LoRaWAN, and learn how they balance range, power, data rate, and security for home automation, industrial control, and other applications.

Explore IoT wireless protocols by running a Python script that defines a protocol list (Wi-Fi, Bluetooth, Zigbee, Lorawan, NFC) and an overview function that prints each protocol.

Explore Python code for working with various IoT wireless protocols and learn how devices from smartphones to sensors communicate via Bluetooth, Zigbee, Z-wave, and Lorawan.

Explore a Python script that lists IoT wireless protocols—wi-fi, bluetooth, zigbee, lorawan, and nfc—and simulates sending and receiving data, including handling invalid protocols.

Explore how machine learning enhances wireless communications, applying supervised and unsupervised methods to channel estimation, modulation, classification, and interference cancellation, with Python implementations using scikit-learn and TensorFlow.

Examine how machine learning techniques improve wireless communication systems, using supervised and unsupervised learning for modulation classification, channel estimation, and interference cancellation, with a future Python example using scikit-learn.

Explore a Python script using a k-nearest neighbor classifier from scikit-learn to classify modulation types. The script generates 1000 samples, uses an 80/20 split, three neighbors, and reports accuracy.

Explore implementing machine learning algorithms for wireless communications with Python, using scikit-learn, TensorFlow, and Keras, including loading data, pre-processing, training a model, and evaluating performance.

Train a decision tree classifier with scikit-learn on a generated data set to classify wireless modulation types, using train-test split and evaluating accuracy on the test set.