no code implementations • 26 Jan 2023 • Jinfei Wang, Yi Ma, Na Yi, Rahim Tafazolli
The design of iterative linear precoding is recently challenged by extremely large aperture array (ELAA) systems, where conventional preconditioning techniques could hardly improve the channel condition.
no code implementations • 25 Jan 2023 • Siqi Zhang, Na Yi, Yi Ma
When the number of subgraphs is maximized, the proposed subset selection approach is shown to be optimum in the AWGN channel.
no code implementations • 4 Aug 2022 • Jinfei Wang, Yi Ma, Na Yi, Rahim Tafazolli, Fei Tong
The basic concept of COP is to apply vector perturbation (VP) in the constellation domain instead of symbol domain; as often used in conventional techniques.
no code implementations • 4 Aug 2022 • Jinfei Wang, Yi Ma, Na Yi, Rahim Tafazolli
With imperfect CSIT, the proposed approach can still provide remarkable user capacity at limited cost of transmit-power efficiency.
no code implementations • 19 Jan 2022 • Jinfei Wang, Yi Ma, Na Yi, Rahim Tafazolli, Fan Wang
Finally, it is shown that the network-ELAA can offer significant coverage extension (50% or more in most of cases) when comparing with the single-AP scenario.
no code implementations • 3 Oct 2021 • Jinfei Wang, Yi Ma, Na Yi, Rahim Tafazolli, Zhibo Pang
In addition, a combinatorial approach of the MF beamforming and grouped space-time block code (G-STBC) is proposed to further mitigate the detrimental impact of the CSIT uncertainty.
no code implementations • 4 May 2021 • Songyan Xue, Yi Ma, Na Yi
In this paper, a novel end-to-end learning approach, namely JTRD-Net, is proposed for uplink multiuser single-input multiple-output (MU-SIMO) joint transmitter and non-coherent receiver design (JTRD) in fading channels.
no code implementations • 14 Apr 2020 • Songyan Xue, Yi Ma, Na Yi, Rahim Tafazolli
Otherwise, it is called non-systematic waveform, where no artificial design is involved.
no code implementations • 1 Apr 2020 • Songyan Xue, Yi Ma, Na Yi, Terence E. Dodgson
Motivated by this finding, we propose a novel modular neural network based approach, termed MNNet, where the whole network is formed by a set of pre-defined ANN modules.