A personal learning system for machine learning, RIS, sensing, and wireless research—built to help you decide what to study next, not just where to click.
0 of 37 notes marked complete
Library explorer
Build the foundation of wireless communication: spectrum, carrier frequency, wavelength, bandwidth, channel capacity, and the difference between bandwidth and data rate.
Understand how a signal travels from transmitter to receiver, why power is lost, how a link budget is built, and where different tower and small-cell structures are used.
Learn how multiple antennas create diversity, spatial multiplexing, and beamforming gains—and how massive MIMO serves many users on the same time-frequency resource.
Compare uniform linear and rectangular arrays, derive their steering behaviour, and connect array geometry to 2D/3D beam control and RIS design.
See how controlled phase differences make signals add or cancel, derive the steering phase for an array, and connect phase quantization to practical beamforming.
Connect array phase profiles to electronic steering, beam-search codebooks, sidelobes, calibration, and practical scanning strategies in 5G, 6G, radar, and RIS systems.
Interpret received-power and signal-quality measurements from a user's point of view, and understand why strong signal bars do not always mean high throughput.
Identify electromagnetic field regions, calculate commonly used boundaries, and understand how distance changes coupling, wavefront shape, beam response, and channel modelling.
Build phased-array intuition from two- and three-element examples, derive distance and phase compensation, compare architectures, and connect active arrays with RIS beam control.
A clearer introduction to machine learning, the basic vocabulary, and how classification problems are framed.
A practical guide to the main ways machines learn from labeled, unlabeled, and partly labeled data.
A beginner-friendly guide to common machine learning, deep learning, DNN, and DRL parameters such as seed, batch size, epoch, bias, overfitting, underfitting, learning rate, and exploration.
A deeper but beginner-friendly explanation of gradient descent, learning rate, optimization variants, and common training problems.
A deeper guide to common classification algorithms, their intuition, strengths, weaknesses, and practical use cases.
A deeper explanation of regression models for predicting continuous values, with intuition, assumptions, and practical guidance.
A beginner-friendly explanation of Ridge Regression and Kernel Ridge Regression from first principles, with intuition, equations, kernels, and Python examples.
A practical comparison of classifiers and regressors, including output type, metrics, computational efficiency, and model-selection guidance.
A clearer and deeper guide to neural networks, CNNs, RNNs, transformers, training, and practical limitations.
A clearer and deeper guide to reinforcement learning, value functions, policies, exploration, Q-learning, and deep RL.
A practical guide to transfer learning, frozen layers, fine-tuning, applications, benefits, and limitations.
A detailed beginner-friendly explanation of RIS, smart radio environments, and why RIS is important for 6G wireless networks.
A deeper explanation of RIS physics, architectures, design variables, advantages, and practical limitations.
A structured view of RIS applications in coverage, interference control, security, IoT, sensing, and smart indoor environments.
How RIS helps localization through virtual anchors, controllable paths, near-field wavefronts, and channel-parameter estimation.
Why mmWave and RIS are naturally connected for high-resolution positioning, beam training, and near-field localization.
A 6G-oriented view of RIS in mmWave/sub-THz networks, including deployment, AI control, digital twins, and open research directions.
A detailed introduction to ISAC, why it matters for 6G, and how a radio signal can both communicate and sense.
The signal-processing principles behind ISAC, including waveform design, sensing metrics, communication metrics, and unavoidable trade-offs.
Major ISAC use cases in autonomous mobility, smart factories, healthcare, drones, localization, and network intelligence.
How ISAC supports device-based and device-free localization, tracking, Doppler estimation, and predictive beamforming.
Why mmWave and THz frequencies are central to high-resolution ISAC, and what challenges they introduce.
How ISAC fits into 6G networks with AI, RIS, digital twins, edge computing, and sensing-as-a-service.
Why RIS and ISAC naturally fit together in 6G networks, and what problem each technology solves.
The mechanics of RIS-assisted ISAC, including joint beamforming, RIS phase control, sensing echoes, and communication constraints.
The main ways RIS improves ISAC: blockage recovery, virtual anchors, sensing geometry, interference control, and active/multifunctional operation.
Recent research trends in RIS-assisted ISAC, including active RIS, STAR-RIS, NOMA, learning-based control, near-field sensing, and multifunctional surfaces.
Open research directions for RIS-assisted ISAC, from channel estimation and beamforming to privacy, hardware, standardization, and real-world deployment.