most citedSmall Cell Transmit Power Assignment Based on Correlated Bandit Learning

15 citations · 15 across the 2 of their papers we have counts for

collaborators

6 papers

eess.SP2018

Towards Optimal Power Control via Ensembling Deep Neural Networks

Fei Liang, Cong Shen, Wei Yu +1

A deep neural network (DNN) based power control method is proposed, which aims at solving the non-convex optimization problem of maximizing the sum rate of a multi-user interferenc…

cs.NI2018

Cost-Aware Learning and Optimization for Opportunistic Spectrum Access

Chao Gan, Ruida Zhou, Jing Yang +1

In this paper, we investigate cost-aware joint learning and optimization for multi-channel opportunistic spectrum access in a cognitive radio system. We investigate a discrete time…

eess.SP2018

How to interconnect for Massive MIMO Self-Calibration?

Fuqian Yang, Hanyu Zhu, Cong Shen +2

In time-division duplexing (TDD) systems, massive multiple-input multiple-output (MIMO) relies on the channel reciprocity to obtain the downlink (DL) channel state information (CSI…

cs.LG2018

Regional Multi-Armed Bandits

Zhiyang Wang, Ruida Zhou, Cong Shen

We consider a variant of the classic multi-armed bandit problem where the expected reward of each arm is a function of an unknown parameter. The arms are divided into different gro…

cs.IT2018

New Results on Multilevel Diversity Coding with Secure Regeneration

Shuo Shao, Tie Liu, Chao Tian +1

The problem of multilevel diversity coding with secure regeneration is revisited. Under the assumption that the eavesdropper can access the repair data for all compromised storage…

cs.NI201715 cited

Small Cell Transmit Power Assignment Based on Correlated Bandit Learning

Zhiyang Wang, Cong Shen

Judiciously setting the base station transmit power that matches its deployment environment is a key problem in ultra dense networks and heterogeneous in-building cellular deployme…