Browsing by Author "Huang, Wan-Jen"

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Joint User Clustering and Content Caching with Heterogeneous User Content Preferences
(2019-01-01)
Chiu, Feng
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Kuo, Ting-Yu
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Chien, Feng-Tsun
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Huang, Wan-Jen
;
Chang, Min-Kuan
In this paper, we consider a joint design of the user clustering and content caching in the cache-enabled heterogenous network (HetNet) in which users in the network have distinct content preferences. The joint clustering and caching in the HetNet relies on multitude of factors, such as channel gains in all links, which may not be fully known in practice. Besides, clustering and caching may exhibit a fundamental tradeoff between the content hit probability and the spectral efficiency. We are therefore motivated to tackle this challenging problem by the deep reinforcement learning (DRL). In particular, the deep deterministic policy gradient (DDPG) algorithm is employed to manage the dynamics of clustering and caching in the HetNet with a sizable action space. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.
Robust Far-end Channel Estimation Design for MIMO Relay Systems
(2013-01-01)
Tseng, Fan-Shuo
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Huang, Wan-Jen
;
Wu, Wen-Rong
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電信工程研究所
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Institute of Communications Engineering
In this paper, we propose a robust far-end channel estimation for a multiple-input multiple-output (MIMO) relay system, where channel estimation is accomplished in two phases. In the first phase, the relay-to-destination channel is estimated. In the second phase, the destination estimate the far-end channel, i.e., the source-to-relay link, based on the source pilots, relay precoder, and the estimated channel in the first phase. This work aims to conduct a robust design by deriving the optimum source pilots and relay precoder according to the minimum mean-squared error (MMSE) criterion. Although the optimization problem can be formulated mathematically; however, the problem is not convex and difficult to be solved. Thus, we replace the objective function with its lower bound, and simplify the matrix optimization problem as a scalar-convex optimization. We show that the optimum solution of the optimization with the lower bound equals to that of the original problem when the channel correlation matrices satisfy certain structures. Simulations show that our proposed method outperforms existing non-robust methods.