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Your search keyword '"Su, Hsuan-Jung"' showing total 7 results
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7 results on '"Su, Hsuan-Jung"'

1. Trade-Off Between Beamforming and Macro-Diversity Gains in Distributed mMIMO

Author

Tominaga, Eduardo Noboro, Su, Hsuan-Jung, Du, Jinfeng, Venkatesan, Sivarama, Souza, Richard Demo, and Alves, Hirley

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract

Industry and academia have been working towards the evolution from Centralized massive Multiple-Input Multiple-Output (CmMIMO) to Distributed mMIMO (DmMIMO) architectures. Instead of splitting a coverage area into many cells, each served by a single Base Station equipped with several antennas, the whole coverage area is jointly covered by several Access Points (AP) equipped with few or single antennas. Nevertheless, when choosing between deploying more APs with few or single antennas or fewer APs equipped with many antennas, one observes an inherent trade-off between the beamforming and macro-diversity gains that has not been investigated in the literature. Given a total number of antenna elements and total downlink power, under a channel model that takes into account a probability of Line-of-Sight (LoS) as a function of the distance between the User Equipments (UEs) and APs, our numerical results show that there exists a ``sweet spot" on the optimal number of APs and of antenna elements per AP which is a function of the physical dimensions of the coverage area., Comment: 6 pages, 3 figures. Manuscript submitted to the IEEE Wireless Communications and Networking Conference (WCNC) 2024, Dubai, United Arab Emirates

Published
2023

2. A Conditional ℓ1 Regularized MMSE Channel Estimation Technique for IBI Channels

Author

Takano, Yasuhiro, Su, Hsuan-Jung, Juntti, Markku, and Matsumoto, Tad

Subjects
inter-block-interference (IBI), turbo equalization, ℓ1 norm regularization, compressive sensing, Astrophysics::High Energy Astrophysical Phenomena, Inter-block-interference (IBI), subspace-based channel estimation, l1 norm regularization, 1 norm regularization, Computer Science::Information Theory
Abstract

Inter-block-interference (IBI) caused as a result of pursuing the spectral efficiency can deteriorate channel estimation performance. For this problem, previously-proposed chained turbo estimation (CHATES) performs IBI cancelation by using the soft replica of the transmitted signal. The IBI cancelation technique can, however, suffer from a mean squared error (MSE) floor problem, since the soft replica is unavailable at the first turbo iteration. The IBI problem can be avoided by using channel impulse response (CIR) length constraint. Nevertheless, as shown in this paper, the IBI avoidance approach is difficult to be performed independently since it requires unbiased second-order statistics. This paper proposes, therefore, a new conditional ℓ1 regularized minimum mean square error (MMSE) channel estimation algorithm by jointly utilizing the IBI avoidance/cancelation and subspace techniques. Simulation results verify that the proposed algorithm solves the MSE floor problem, and, hence, improves the bit error rate (BER) convergence performance in realistic IBI channels including the effect of pulse shaping filters.

Published
2018

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