79 citations · 207 across the 21 of their papers we have counts for
24 papers
Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps
Thomas M. Hehn, Tribhuvanesh Orekondy, Ori Shental +7
Estimating path loss for a transmitter-receiver location is key to many use-cases including network planning and handover. Machine learning has become a popular tool to predict wir…
Prefix-Free Code Distribution Matching for 5G New Radio
Junho Cho, Ori Shental
We use prefix-free code distribution matching (PCDM) for rate matching (RM) in some 5G New Radio (NR) deployment scenarios, realizing a wide range of information rates from 1.4 to…
"Machine LLRning": Learning to Softly Demodulate
Ori Shental, Jakob Hoydis
Soft demodulation, or demapping, of received symbols back into their conveyed soft bits, or bit log-likelihood ratios (LLRs), is at the very heart of any modern receiver. In this p…
The Penalty in Scaling Exponent for Polar Codes is Analytically Approximated by the Golden Ratio
Ori Shental
The polarization process of conventional polar codes in binary erasure channel (BEC) is recast to the Domany-Kinzel cellular automaton model of directed percolation in a tilted squ…
Sparse NOMA: A Closed-Form Characterization
Benjamin M. Zaidel, Ori Shental, Shlomo Shamai
Understanding fundamental limits of the various technologies suggested for future 5G and beyond cellular systems is crucial for developing efficient state-of-the-art designs. A lea…
Massive BLAST: An Architecture for Realizing Ultra-High Data Rates for Large-Scale MIMO
Ori Shental, Sivarama Venkatesan, Alexei Ashikhmin +1
A detection scheme for uplink massive MIMO, dubbed massive-BLAST or M-BLAST, is proposed. The derived algorithm is an enhancement of the well-known soft parallel interference cance…