172 citations · 1.4k across the 82 of their papers we have counts for
7 papers · 2 filters
Optimal Discrete Spatial Compression for Beamspace Massive MIMO Signals
Zhiyuan Jiang, Sheng Zhou, Zhisheng Niu
Deploying massive number of antennas at the base station side can boost the cellular system performance dramatically. Meanwhile, it however involves significant additional radio-fr…
Task Replication for Deadline-Constrained Vehicular Cloud Computing: Optimal Policy, Performance Analysis and Implications on Road Traffic
Zhiyuan Jiang, Sheng Zhou, Xueying Guo +1
In vehicular cloud computing (VCC) systems, the computational resources of moving vehicles are exploited and managed by infrastructures, e.g., roadside units, to provide computatio…
Optimal Sleeping Mechanism for Multiple Servers with MMPP-Based Bursty Traffic Arrival
Zhiyuan Jiang, Bhaskar Krishnamachari, Sheng Zhou +1
An important fundamental problem in green communications and networking is the operation of servers (routers or base stations) with sleeping mechanism to optimize energy-delay trad…
Joint User Scheduling and Beam Selection Optimization for Beam-Based Massive MIMO Downlinks
Zhiyuan Jiang, Sheng Chen, Sheng Zhou +1
In beam-based massive multiple-input multiple-output systems, signals are processed spatially in the radio-frequency (RF) front-end and thereby the number of RF chains can be reduc…
Elastic Local Breakout Strategy and Implementation for Delay-Sensitive Packets with Local Significance
Yongkang Li, Zhiyuan Jiang, An Xu +2
Explosion of mobile traffic will bring a heavy burden to the core network, and rapid growth of mobile devices, as well as increasing demand for delay-sensitive services poses sever…
How Often Should CSI be Updated for Massive MIMO Systems with Massive Connectivity?
Ruichen Deng, Zhiyuan Jiang, Sheng Zhou +1
Massive multiple-input multiple-output (MIMO) systems need to support massive connectivity for the application of the Internet of things (IoT). The overhead of channel state inform…
Remote Channel Inference for Beamforming in Ultra-Dense Hyper-Cellular Network
Sheng Chen, Zhiyuan Jiang, Jingchu Liu +4
In this paper, we propose a learning-based low-overhead channel estimation method for coordinated beamforming in ultra-dense networks. We first show through simulation that the cha…