1 citations · 1 across the 6 of their papers we have counts for
6 papers
Low-Complexity OFDM Deep Neural Receivers
Ankit Gupta, Onur Dizdar, Yun Chen +3
Deep neural receivers (NeuralRxs) for Orthogonal Frequency Division Multiplexing (OFDM) signals are proposed for enhanced decoding performance compared to their signal-processing b…
NeuromorphicRx: From Neural to Spiking Receiver
Ankit Gupta, Onur Dizdar, Yun Chen +3
In this work, we propose a novel energy-efficient spiking neural network (SNN)-based receiver for 5G-NR OFDM system, called neuromorphic receiver (NeuromorphicRx), replacing the ch…
Rate-Splitting Multiple Access for 6G: Prototypes, Experimental Results and Link/System level Simulations
Sundar Aditya, Yong Jin Daniel Kim, David Vargas +7
Rate-Splitting Multiple Access (RSMA) is a powerful and versatile physical layer multiple access technique that generalizes and has better interference management capabilities than…
SpikingRx: From Neural to Spiking Receiver
Ankit Gupta, Onur Dizdar, Yun Chen +1
In this work, we propose an energy efficient neuromorphic receiver to replace multiple signal-processing blocks at the receiver by a Spiking Neural Network (SNN) based module, call…
Rate-Splitting Multiple Access for Semantic-Aware Networks: an Age of Incorrect Information Perspective
Onur Dizdar, Stephen Wang
In this letter, we design a downlink multi-user communication framework based on Rate-Splitting Multiple Access (RSMA) for semantic-aware networks. First, we formulate an optimizat…
A Proof of Concept for OTFS Resilience in Doubly-Selective Channels by GPU-Enabled Real-Time SDR
Yi Xien Yap, Neil Bhushan, Onur Dizdar +4
Orthogonal time frequency space (OTFS) is a modulation technique which is robust against the disruptive effects of doubly-selective channels. In this paper, we perform an experimen…