NSF, Grant No. CCF-1526078, 2015-2018

Goal: Provide theoretical insights and design algorithms for optimal provisioning of large-scale radio access network.

Abstract: The explosive growth in the number of smart consumer devices leads to projections that within 10 years’ time, wireless cellular networks need to offer 1000x throughput increase over the current 4G technology. By that time the network should be able to deliver fiber-like user experience boasting 10 Gb/s individual transmission rate for data intensive cloud-based applications. To move such a huge amount of data from the network to the users’ handheld devices in real time, revolutionary network infrastructure and advanced network provision are required. Two key enablers of the envisioned future mobile networks are the ultra-dense deployment of base stations and centralized cloud-based processing. This project addresses the challenging problem of managing such densely deployed, cloud-based radio access networks.

The proposed research includes the introduction of a unified cross-network framework to manage a cloud-based radio access network. Important aspects of resource management in different sub-networks, including the backhaul and the cloud networks, will be considered. The project focuses on providing theoretical insights as well as designing practical algorithms for the optimal provisioning of a cloud-based radio access network. Fundamental computational issues of key cross-network resource management tasks will be investigated, revealing their intrinsic complexity. Moreover, practical schemes capable of optimally utilizing resources across networks will be developed, using cross-network optimization formulations. These algorithms will be optimized to fully exploit the computational resources offered by the cloud centers, addressing key issues such as parallel/distributed implementation, asynchronous computation, and load balancing. The goal is to determine how computational resources should be deployed through the proposed cross-network framework to dramatically improve the performance of a cloud-based radio access network.

Related Publication

1. Haoran Sun, Xiangyi Chen, Qingjiang Shi, Mingyi Hong and Xiao Fu, “Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management” submitted for publication; available at [PDF] [code].

1. Qingjiang Shi, Mingyi Hong, Enbin Song, Yunlong Cai, Weiqiang Xu, Xiqi Gao, “Joint Source-Relay Design for Full–Duplex MIMO AF Relay Systems”, submitted for journal publication, March 2016

2. Mingyi Hong and Tsung-Hui Chang, “Stochastic Proximal Gradient Consensus Over Random Networks” available at [arXiv.org], Nov, 2015

3. Ya-Feng Liu, Mingyi Hong and Enbin Song, “Sample Approximation Based Deflation Approaches for Chance Constrained Joint Power and Admission Control” IEEE Transactions on Wireless Communication, 2016 ; available at [arXiv.org]

4. Tsung-Hui Chang, Mingyi Hong and Xiangfeng Wang, “Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis”, IEEE Transactions on Signal Processing, Vol. 64, No 12, pages 3118 - 3130, 2016; available at [arXiv.org]

5. Tsung-Hui Chang, Wei-Cheng Liao, Mingyi Hong and Xiangfeng Wang, “Asynchronous Distributed ADMM for Large-Scale Optimization- Part II: Linear Convergence Analysis and Numerical Performance”, IEEE Transactions on Signal Processing, Vol. 64, No. 12, pages 3131 - 3144, 2016; available at [arXiv.org]

6. Qingjiang Shi, Meisam Razaviyayn Mingyi Hong, and Zhi-Quan Luo, “SINR Constrained Beamforming for a MIMO Multi-user Downlink System”,IEEE Transactions on Signal Processing, Vol. 64, No. 11, pages 2920-2933, 2016; available at [arXiv.org]

7. Mingyi Hong, Qiang Li and Ya-Feng Liu, “Decomposition by Successive Convex Approximation: A Unifying Approach for Linear Transceiver Design in Heterogeneous Networks”, IEEE Transactions on Wireless Communication, No. 15, Vol. 2, pages 1377-1392, 2016; available at [arXiv.org]

8. Mingyi Hong *, Meisam Razaviyayn*, Zhi-Quan Luo and Jong-Shi Pang, “A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data”, Feature Article, IEEE Signal Processing Magazine (* equal contribution), Vol. 33, No. 1, pages 57 - 77, Jan. 2016; available at [arXiv.org]

9. Qingjiang Shi, Cheng Peng, Weiqiang Xu, Mingyi Hong, Yunlong Cai, “Energy Efficiency Optimization For MISO SWIPT Systems With Zero-Forcing Beamforming”, IEEE Transactions on Signal Processing, Vol. 64, No. 4 pages 842-854, 2016; available at [IEEE Xplore]

10. Qingjiang Shi, Mingyi Hong, Enbin Song, Yunlong Cai, Weiqiang Xu, “A Penalty-BSUM approach for rate optimization in Full-Duplex MIMO Relay Networks with Relay Processing Delay”, Proc. ICASSP 2016

11. Tsung-Hui Chang, Mingyi Hong, Wei-Cheng Liao, Xiangfeng Wang “Asynchronous Distributed Alternating Direction Method of Multipliers: Algorithm and Convergence Analysis”, Proc, ICASSP 2016