5G NR Resource Grid Visualizer is an interactive tool that renders the complete time-frequency structure of a 5G New Radio radio frame (10ms) across configurable bandwidths from 5–100 MHz and subcarrier spacings of 15/30/60/120 kHz, automatically computing the correct number of resource blocks per 3GPP TS 38.101. It maps all major physical channels and signals — SSB (PSS/SSS/PBCH), CORESET 0, PDCCH, PDSCH, PUSCH, PUCCH, PRACH, and both DL/UL DMRS — onto the resource grid at t

Introduction Power-saving features in 5G (including O-RAN deployments) are needed mainly to improve energy efficiency and network sustainability. 1. Reduce operational cost (OPEX): Base stations—especially massive MIMO O-RUs—consume significant electricity, so reducing unnecessary transmissions lowers energy bills for operators. 2. Handle variable traffic load: Network traffic varies over time (night vs peak hours). Power-saving allows parts of the radio (RF chains,

1. Introduction: Why RLC Still Matters in 5G NR When discussions around 5G performance arise, attention usually gravitates toward massive MIMO, beamforming, or spectrum efficiency. Yet, in real networks, user experience often degrades due to issues far removed from PHY or antennas. One of the most common root causes lies in the Radio Link Control (RLC) layer. In 5G NR, RLC sits between PDCP and MAC, just like LTE. But assuming it is “unchanged from LTE” is a mistake. While t

1. Introduction Medium Access Control (MAC) scheduling is one of the most critical real-time functions in a 5G NR gNB. It directly determines throughput, latency, fairness, spectral efficiency, and QoS compliance. Unlike LTE, 5G MAC scheduling operates in a much more complex design space due to: Flexible numerology (multiple SCS) Mini-slots and slot aggregation Beam-based transmissions Massive MIMO QoS flows (5QI-driven scheduling) URLLC pre-emption and puncturing Dynamic TDD

1. Introduction Modern 5G radio access networks have reached a level of complexity where traditional automation approaches are no longer sufficient. While machine learning has been widely adopted in telecom analytics, most deployed solutions still behave as passive systems: they ingest data, run inference, and output a score, label, or alert. In practice, experienced RAN engineers do not work this way. They observe symptoms, form hypotheses, validate those hypotheses by check

1.Introduction Cloud and Network Function Virtualization (NFV) for 5G networks represent a fundamental shift in how telecommunication networks are designed and managed. NFV decouples network functions from specialized hardware, allowing them to run as software on standard cloud infrastructure. This virtualization enables 5G networks to be more flexible, scalable, and cost-efficient by leveraging cloud-native principles and automation. It allows operators to dynamically alloca

1. Introduction Radio Access Networks are becoming more software-driven, disaggregated, and automation-centric. To handle the complexity of dense deployments, Massive MIMO, spectrum fragmentation, and diverse traffic profiles, operators are moving toward intelligent control loops in the RAN.This is where O-RAN Alliance’s RAN Intelligent Controller (RIC) architecture—featuring rApps (non-real time, >1 second) and xApps (near-real time, 10 ms–1 s)—comes into play. These applica