5 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…
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…
A Low-Complexity Design for Rate-Splitting Multiple Access in Overloaded MIMO Networks
Onur Dizdar, Ata Sattarzadeh, Yi Xien Yap +1
Rate-Splitting Multiple Access (RSMA) is a robust multiple access scheme for multi-antenna wireless networks. In this work, we study the performance of RSMA in downlink overloaded…
RSMA for Overloaded MIMO Networks: Low-Complexity Design for Max-Min Fairness
Onur Dizdar, Ata Sattarzadeh, Yi Xien Yap +1
Rate-Splitting Multiple Access (RSMA) is a robust multiple access scheme for multi-antenna wireless networks. In this work, we study the performance of RSMA in downlink overloaded…