Results 61 to 70 of about 273 (129)
Hybrid PON–RoF LTE Video Transmission with Experimental BLER Analysis and Amplifier Trade-Off
This study evaluates the performance of a hybrid passive optical network–radio over fiber (PON–RoF) architecture for long-term evolution (LTE)-based video transmission, focusing on the analysis of the block error rate (BLER) with and without an external ...
Berenice Arguero +6 more
doaj +1 more source
LoRa technology, renowned for its low-power, long-range capabilities in IoT applications, faces challenges in real-world scenarios, including fading channels, interference, and environmental obstacles.
Boubaker Abdallah +5 more
doaj +1 more source
Optimization of Age of Information in Adaptive FD/HD Cooperative SWIPT NOMA/OMA System
The Age of Information (AoI) is a key metric in monitoring and control applications of Internet of Things (IoT) networks, where real-time decision-making relies on the freshness of the information. This paper presents an innovative approach to optimizing
Simon Kaboyo +4 more
doaj +1 more source
Extreme Value Theory-Based Distributed Interference Prediction for 6G Industrial Sub-Networks
Interference prediction that accounts for extreme and rare events remains a key challenge for ultra-densely deployed sub-networks (SNs) requiring hyper-reliable low-latency communication (HRLLC), particularly under dynamic mobility, rapidly varying ...
Pramesh Gautam +4 more
doaj +1 more source
Pre-Configured Error Pattern Ordered Statistics Decoding for CRC-Polar Codes. [PDF]
Li X, Niu K, Han Y, Dai J, Tan Z, Guo Z.
europepmc +1 more source
Optimizing 5G NR link layer parameters for eMBB and URLLC applications under dynamic channel and transmission configurations. [PDF]
Pateriya S +3 more
europepmc +1 more source
Achieving Ultra-Reliable Low Latency Communication in 5G and Beyond. [PDF]
Bajracharya R, Shrestha R.
europepmc +1 more source
Channel-Agnostic Training of Transmitter and Receiver for Wireless Communications. [PDF]
Davey CP +3 more
europepmc +1 more source
Unified Space-Time-Message Interference Alignment: An End-to-End Learning Approach. [PDF]
Sadeghabadi E, Blostein S.
europepmc +1 more source

