Learning Optimal Resource Allocations in Wireless Systems

By Mark Eisen, Clark Zhang, Luiz F. O. Chamon, Daniel D. Lee, Alejandro Ribeiro. 2018

237 citations recorded by the source corpus.

This paper considers the design of optimal resource allocation policies in wireless communication systems, which are generically modeled as a functional optimization problem with stochastic constraints. These optimization problems have the structure of a learning problem in which the statistical loss appears as a constraint, motivating the development of learning methodologies to attempt their solution. To handle stochastic constraints, training is undertaken in the dual domain. It is shown that this can be done with small loss of optimality when using near-universal learning parameterizations. In particular, since deep neural networks (DNNs) are near universal, their use is advocated and explored. DNNs are trained here with a model-free primal-dual method that simultaneously learns a DNN parameterization of the resource allocation policy and optimizes the primal and dual variables. Numerical simulations demonstrate the strong performance of the proposed approach on a number of common wireless resource allocation problems.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Lajos Hanzo
2. Athina P. Petropulu
3. H. Vincent Poor
4. Gan Zheng
5. Kwang-Cheng Chen
6. Qingqing Wu
7. Yong Ren
8. JingJing Wang

In Electrical and Electronic Engineering

1. Lajos Hanzo
2. H. Vincent Poor
3. Rui Zhang
4. Qingqing Wu
5. Tony Q. S. Quek
6. Sang Hyun Lee
7. Hoon Lee
8. Hadi Sarieddeen

Citations in the corpus

• Deep Learning for Distributed Optimization: Applications to Wireless Resource Management (2019, methodology): “Most studies on the DL approach to wireless network optimization require global network information, such as perfect knowledge of channel state information (CSI) at other nodes, and thus the centralized computation process is essential for the operation of the DNN [7], [10]–[12], [19].”
• A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, methodology): “To handle this issue, Liang et al. [62], Eisen et al. [63], and Sun and Yang [26] proposed to use unsupervised deep learning algorithms to search the optimal policy.”
• Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, methodology): “…different traffic distribution [338] traffic prediction CNN traffic modeling and prediction based on a convolutional long short-term memory network [339] resource allocation DNN universal DNN framework for a number of common wireless resource allocation problems [340] PHY representation CNN…”
• A Deep Learning Framework for Optimization of MISO Downlink Beamforming (2019, background): “DL can also facilitate resource management [25, 26], including power allocation [27–31].”
• White Paper on Broadband Connectivity in 6G (2020, background): “A generic resource allocation framework is provided in [120].”