4 citations · 4 across the 6 of their papers we have counts for
8 papers · 1 filter
Quantum-Native Maximum Likelihood Detection in Random Access Channel with Overloaded MIMO
Hyoga Iizumi, Naoki Ishikawa, Shunsuke Uehashi +3
In this paper, we propose a quantum-native formulation of maximum likelihood detection (MLD) for overloaded multiple-input multiple-output (MIMO) systems in a random access channel…
Sparsification of Precoding Codebooks for PAPR Reduction via Grassmannian Representations
Joe Asano, Yuto Hama, Hiroki Iimori +2
In this letter, we propose a sparsification method for precoding codebooks that reduces the peak-to-average power ratio (PAPR) while preserving the achievable rate. By exploiting t…
Low-Complexity and Power-Efficient Precoding Codebook Design on Sparse Grassmannian
Joe Asano, Yuto Hama, Hiroki Iimori +3
We propose a sparse Grassmannian design for precoding codebooks. Due to their sparse structure, our proposed codebooks achieve low peak-to-average power ratio (PAPR), low complexit…
Sparse Grassmannian Design for Noncoherent Codes via Schubert Cell Decomposition
Joe Asano, Yuto Hama, Hiroki Iimori +3
In this paper, we propose a method for designing sparse Grassmannian codes for noncoherent multiple-input multiple-output systems. Conventional pairwise error probability formulati…
Maximizing Spectrum Efficiency of Data-Carrying Reference Signals via Bayesian Optimization
Taiki Kato, Hiroki Iimori, Chandan Pradhan +2
Data-carrying reference signals are a type of reference signal (RS) constructed on the Grassmann manifold, which allows for simultaneous data transmission and channel estimation to…
Superimposed Pilot-Based OTFS: Will It Work?
Yuta Kanazawa, Hiroki Iimori, Chandan Pradhan +2
Orthogonal time frequency space (OTFS) modulation is a promising solution to handle doubly-selective fading, but its channel estimation is a nontrivial task in terms of maximizing…