Wireless World Research and Trends Magazine

A Comparative Analysis of 3GPP and ITU-R 5G Channel Models

Nicolò Avarino — 2026-01-01

The design and validation of 5G systems rely heavily on standardized radio channel models to predict real-world performance. The 3GPP TR 38.901 and the ITU-R M.2412 reports are the industry’s cornerstones for this purpose, yet they exhibit significant technical differences, particularly in the sub-6 GHz frequency band and millimeter-wave indoor scenarios. This paper presents a rigorous comparative study of these two standards, quantifying the performance impact of their divergences across canonical deployment scenarios. Using a unified simulation framework implemented in ns-3, we analyze pathloss and Signal-to-Noise Ratio (SNR) in Rural Macro (RMa), Urban Macro (UMa), Urban Micro (UMi), and Indoor Hotspot (InH) environments.

Our results reveal that while the models align in rural scenarios, they diverge fundamentally in complex urban environments. The ITU-R model proves to be a more conservative benchmark in UMa NLOS, predicting a median SNR up to 9.5 dB lower than 3GPP. Conversely, in UMi NLOS, the 3GPP model describes a significantly more volatile channel (higher shadow fading variance), making it a risky predictor for Ultra-Reliable Low-Latency Communications (URLLC). Furthermore, our analysis of the indoor 28 GHz scenario exposes a dramatic discrepancy: the ITU-R’s optional model is exceptionally optimistic, showing a performance gap of up to 34 dB compared to the standard 3GPP model. We conclude that these technical divergences reflect differing design philosophies – optimistic prediction vs. conservative benchmarking – dictating that model selection must be strategically aligned with the specific 5G service pillar (eMBB or URLLC) under evaluation.

Robust and Resilient Terrestrial–Non-Terrestrial Connectivity for In-Flight Connectivity in the Beyond-5G/6G Era

Christoffer Hjalmarsson — 2026-01-01

The integration of terrestrial and non-terrestrial networks is essential for reliable, secure, and ubiquitous connectivity with QoS guarantees, and is a key enabler for supporting emerging applications like in-flight broadband connectivity and mission-critical operations, though several challenges remain. This paper compares three architectural options for providing in-flight broadband connectivity: a DU/CU functional split with the DU on the aircraft and the CU on the ground; a similar split enhanced with integrated access and backhauling to enable 3GPP-compliant flying ad-hoc networks; and a novel architecture in which aircraft act as mobile wireless access and backhaul nodes hosting MEC capabilities for low-latency services and local breakout, a flexible approach that requires sophisticated SON functions and optimised routing.

Reconfigurable Intelligent Surfaces in Cooperative NOMA: A Review of Key Concepts and Applications

M. Ramadevi — 2026-01-01

The purpose of this survey paper is to provide an overview of the different multiple access techniques, the reason for integrating Reconfigurable Intelligent Surfaces (RIS) with Cooperative Non-Orthogonal Multiple Access (C-NOMA) and the system model of RIS-aided C-NOMA with mathematical equations. The study discusses other 6G technologies and contributes to understanding how RIS-aided C-NOMA together with the other 6G technologies comes close to realizing the goals 6G wireless networks. The paper presents the real-world applications of RIS-aided C-NOMA along with challenges and possible solutions on which upcoming researchers can focus on.

Reconfigurable OFDM Transceivers for UAVs

V. C. Madhavi — 2026-01-01

Unmanned Aerial Vehicles (UAVs), commonly known as drones, are becoming an integral part of modern life. They are increasingly used in communication, surveillance, photography and filmmaking, military operations and commercial applications. As drones take on more diverse roles, their communication systems must be highly efficient, reliable, and flexible. One promising way to meet these demands is through Orthogonal Frequency Division Multiplexing (OFDM) transceivers. OFDM is a popular communication technology known for its ability to transmit data effectively, even in challenging environments such as cities or over long distances. It excels at mitigating signal interference caused by reflections (multipath propagation) and efficiently using the available frequency spectrum. These features make OFDM an excellent choice for drone communication. However, UAVs often need to operate across a variety of environments and support multiple communication standards, such as 5G, Wi-Fi, and IoT. This creates a need for transceivers that can adapt to these varied requirements. A reconfigurable OFDM transceiver addresses this need by supporting multiple communication standards and adjusting its parameters in real-time, enabling seamless communication across different networks. This is especially important for drones, as they frequently switch between tasks and environments. For instance, a drone may need high-speed connections for streaming video in one scenario and low-power connections for transmitting sensor data in another. This survey paper focuses on the design, development, and challenges associated with building multi-standard reconfigurable OFDM transceivers for drone applications. It reviews key technologies, recent advancements, and future possibilities.

Accident Detection and Prevention System Using IoT and VANET

Harsh H. Mangroliya — 2026-01-01

Road accidents are a major global concern, often caused by limited driver awareness and sudden traffic changes. This paper presents an advanced accident detection and prevention system featuring a novel IoT-enabled Onboard Unit (OBU) integrated with a Vehicular Ad-hoc Network (VANET) architecture. The OBU uses ultrasonic sensors and an Arduino-based controller to monitor the proximity and speed of nearby objects, predicting potential collisions and issuing real-time alerts to drivers. Upon detecting a threat or accident, the OBU communicates with Road Side Units (RSUs) to disseminate alerts across the network, enabling timely warnings and emergency response. The system supports both accident prevention and post-accident management through real-time V2V and V2I communication. The research also evaluates network protocols to ensure reliable data exchange in dynamic traffic conditions. This integrated approach aims to reduce accident frequency and severity while improving traffic safety and response efficiency.

Maximum Ratio Transmission for Pedestrians’ Safety at Crosswalks in An Outdoor V2X Environment at 28 GHz

Sai Radha Abhigna Maturi — 2026-01-01

Vulnerable Road Users (VRUs) include pedestrians, bicyclists, and motorized two-wheelers operators. According to the World Health Organization’s (WHO) 2023 Global Status Report on Road Safety, pedestrians account for 23% of fatalities, or at least 31 deaths every hour. In developed countries, the number of road accidents is significantly declining, but a significant percentage of accidents involving VRUs still remains high. Vehicle-to-everything (V2X) communication is one such safety feature that fosters communication amongst numerous elements on the road for cooperative safety and, consequently, pedestrians. Communication between vehicles and pedestrians (V2P), infrastructure and vehicles (V2I), and vehicles themselves (V2V) is a part of vehicle-to-everything (V2X). Pedestrians carrying mobile devices and wearable technology can depend upon V2X communications to improve their safety and situational awareness. To transmit and receive signals efficiently, this paper compares and evaluates the performance of Linearly Polarized Antenna array (LPA) with and without Maximum Ratio Transmission (MRT) technique in the context of V2P communications. The focus is on the pedestrian’s point of view at crosswalks in outdoor V2X scenarios. In conclusion, LPA with MRT technique surpasses in signal reception at pedestrians near crosswalks in a V2X outdoor environment.

Channel Modeling using Deep Neural Network with RIS-powered Wireless Communication Systems

Kumud S. Altmayer — 2026-01-01

This work deals with Reconfigurable Intelligent Surfaces or (also known as Intelligent Reflecting Surfaces) which provide an improvement of performance of wireless communications by utilizing software-controlled meta-surfaces for the reflected signals from the source to destination, especially when the direct path is weak and hence improving the antenna array.

The performance evaluation is done by one of the techniques that utilizes the very high rates and/or large meta-surfaces to outperform the classic method of decode and forward, both in terms of the total transmit power and the energy efficiency. The channel measurements used by classifying them as a function of the frequency band, and usage of deployment scenarios such as indoor/outdoor, and system configuration. So, the 5G and beyond which is 6G use several antennas including the algorithms to make use of signal processing. This improves the antenna array technology.

In the next steps, we work on machine learning-based performance prediction using a deep neural network (DNN) to evaluate the performance of the RIS-aided system in the low-frequency range. This would provide a greater influence of scatterers with a weaker signal attenuation allowing the neural network to pick up a larger number of features, e.g. accurately predicting the energy efficiency (EE), and outage probability (OP).

Pragmatic 6G

Gerhard P. Fettweis — 2026-02-02

Cellular communication is the backbone of our digital society. Today’s excitement around AI is a result of the whole planet having access to newest developments instantaneously via the mobile Internet. However, the cellular industry is currently not in a good shape. It therefore is time to wake-up and draw a picture of opportunities ahead that could make 6G a real game changer.

This paper is not about presenting math, or a technically detailed vision about technologies that might enter the 6G stan- dard as e.g. [1]. It is about presenting a vision and roadmap towards a possibly amazing future of the cellular industry.