期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 66, 期 20, 页码 5438-5453出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2018.2866382
关键词
Optimization algorithms approximation; deep neural networks; interference management; WMMSE algorithm
资金
- National Science Foundation [CMMI-1727757, CCF-1526078, CIF-1525194]
- Air Force Office of Scientific Research [15RT0767]
- NSFC [61671411, U1709219, 61374020]
- Fundamental Research Funds for the Central Universities
- Zhejiang Provincial NSF of China [LR15F010002]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1526078] Funding Source: National Science Foundation
Numerical optimization has played a central role in addressing key signal processing (SP) problems. Highly effective methods have been developed for a large variety of SP applications such as communications, radar, filter design, and speech and image analytics, just to name a few. However, optimization algorithms often entail considerable complexity, which creates a serious gap between theoretical design/analysis and real-time processing. In this paper, we aim at providing a new learning-based perspective to address this challenging issue. The key idea is to treat the input and output of an SP algorithm as an unknown nonlinear mapping and use a deep neural network (DNN) to approximate it. If the nonlinear mapping can be learned accurately by a DNN of moderate size, then SP tasks can be performed effectively-since passing the input through a DNN only requires a small number of simple operations. In our paper, we first identify a class of optimization algorithms that can be accurately approximated by a fully connected DNN. Second, to demonstrate the effectiveness of the proposed approach, we apply it to approximate a popular interference management algorithm, namely, the WMMSE algorithm. Extensive experiments using both synthetically generated wireless channel data and real DSL channel data have been conducted. It is shown that, in practice, only a small network is sufficient to obtain high approximation accuracy, and DNNs can achieve orders of magnitude speedup in computational time compared to the state-of-the-art interference management algorithm.
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