Learning to Beamform for Minimum Outage

Shi, Yunmei; Konar, Aritra; Sidiropoulos, Nicholas D.; Mao, Xingpeng; Liu, Yongtan

doi:10.1109/tsp.2018.2865408

Cited by 36 publications

(41 citation statements)

References 46 publications

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“…Beamforming for minimum outage [63] has also been proven to be NP-hard even when the channel distribution is known exactly, and in fact no practically good approximation algorithm was known until very recently. Yet, relying on a sample average 'counting' approximation of outage, simple smoothing, and stochastic gradient updates, a lightweight and very effective algorithm was recently designed in [64] that performs remarkably well, using only recent channel data. The problem is formulated as follows:…”

Section: Machine Learning Based Resource Allocationmentioning

confidence: 99%

“…The final step is to construct a smooth approximation of f (w; h), and optimize the resulting function using stochastic gradient descent. As it is shown in [64], this approach works unexpectedly well, on a problem that has challenged many disciplined optimization experts for years. Finally, the sum-rate optimal power control problem is known to be NP-hard, but we have good, albeit computationally expensive, approximation schemes at our disposal.…”

Section: Machine Learning Based Resource Allocationmentioning

confidence: 99%

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Machine Learning in the Air

Gündüz

Kerret

Sidiropoulos

et al. 2019

IEEE J. Select. Areas Commun.

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199

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Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story -ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications.

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Section: Machine Learning Based Resource Allocationmentioning

confidence: 99%

Section: Machine Learning Based Resource Allocationmentioning

confidence: 99%

Machine Learning in the Air

Gündüz

Kerret

Sidiropoulos

et al. 2019

IEEE J. Select. Areas Commun.

Self Cite

199

View full text Add to dashboard Cite

show abstract

“…However, there are few works that focus on the learning approach to optimize the beamforming design in multiantenna communications, with the exception of [42], [43], [44], [45], [46], [47]. The difficulty is partly due to the large number of complex variables contained in the beamforming matrix that need to be optimized.…”

Section: Introductionmentioning

confidence: 99%

“…The difficulty is partly due to the large number of complex variables contained in the beamforming matrix that need to be optimized. An outage-based approach to transmit beamforming was studied in [42] to deal with the channel uncertainty at the BS, however, only a single user was considered. The work in [43] designed a decentralized robust precoding scheme based on a deep neural network (DNN).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Enabled Optimization of Downlink Beamforming Under Per-Antenna Power Constraints: Algorithms and Experimental Demonstration

Zhang

Xia

You

et al. 2020

IEEE Trans. Wireless Commun.

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This paper studies fast downlink beamforming algorithms using deep learning in multiuser multiple-inputsingle-output systems where each transmit antenna at the base station has its own power constraint. We focus on the signal-to-interference-plus-noise ratio (SINR) balancing problem which is quasi-convex but there is no efficient solution available. We first design a fast subgradient algorithm that can achieve near-optimal solution with reduced complexity. We then propose a deep neural network structure to learn the optimal beamforming based on convolutional networks and exploitation of the duality of the original problem. Two strategies of learning various dual variables are investigated with different accuracies, and the corresponding recovery of the original solution is facilitated by the subgradient algorithm. We also develop a generalization method of the proposed algorithms so that they can adapt to the varying number of users and antennas without re-training. We carry out intensive numerical simulations and testbed experiments to evaluate the performance of the proposed algorithms. Results show that the proposed algorithms achieve close to optimal solution in simulations with perfect channel information and outperform the alleged theoretically optimal solution in experiments, illustrating a better performance-complexity tradeoff than existing schemes.Index Terms-Deep learning, beamforming, MISO, SINR balancing, per-antenna power constraints. ).We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan Xp GPU used for this research.

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“…Recently, deep learning (DL) has shown great potentials for improving the performance in communication system. At present, much attempts have been made to apply DL in areas of physical layer [5], resource allocation such as power control [6], [7], and beamforming [8]- [10]. For instance, [6] applied the full connected deep neural network (DNN) to approximate the weighted minimum mean square error (WMMSE) power allocation algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Beamforming in Ultra Dense Network Considering Selfish and Altruistic Strategy

2020

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Coordinated beamforming is very efficient at managing interference in ultra dense network. However, the optimal strategy remains as a challenge task to obtain due to the coupled nature among densely and autonomously deployed cells. In this paper, the deep reinforcement learning is investigated for predicting coordinated beamforming strategy. Formulated as a sum-rate maximization problem, the optimal solution turns out as a balanced combination of selfish and altruistic beamforming. As the balancing coefficients depend on the beamforming vectors of all the cells, iterations are inevitable to get the final solution. To address this problem and improve efficiency, deep reinforcement learning (DL) is proposed to predict the balancing coefficients. Specifically, the agent, on behalf of a base station-user pair, will rely on Deep Q-network to learn the highly complex mapping between the balancing coefficients and signal-interference environment of each user. Subsequently, the beamforming vectors are obtained efficiently through the learned balancing coefficients. Due to the distinguished feature in exploration of the beamforming parameterization, the complexity problem brought by predicting the beamforming matrix directly is avoided. The performance of the proposed scheme is investigated by experiments with arguments regarding multiple input and multiple output configuration, shadow fading and state design. Simulation results indicate the facts that: 1) the theoretically infinite strategy space can be discretized with limited levels and granularity;2)it is feasible to approximate the complex mapping by Q-learning for wireless channel consisting both the large and small scale fading, 3) the balancing coefficients only concerns large scale fading, so the coordinated beamforming can be decomposed to two sub-problems with different time scales: parameterization at large time scales and instant beamforming based on balancing coefficients. INDEX TERMS Deep learning, beamforming, ultra dense network (UDN), Q-learning, interference management.

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Learning to Beamform for Minimum Outage

Cited by 36 publications

References 46 publications

Machine Learning in the Air

Machine Learning in the Air

Deep Learning Enabled Optimization of Downlink Beamforming Under Per-Antenna Power Constraints: Algorithms and Experimental Demonstration

A Machine Learning Approach for Beamforming in Ultra Dense Network Considering Selfish and Altruistic Strategy

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