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This article considers a power allocation problem in energy harvesting downlink non-orthogonal multiple access (NOMA) systems in which a transmitter sends desired messages to their respective receivers by using harvested energy. To tackle this problem, we make use of a reinforcement learning approach based on a shallow neural network structure. We prove that the optimal power allocation policy and the optimal action-value function depend monotonically on some of their input variables and the shallow neural network structure is designed based on properties revealed in the proof. Different from inefficient deep learning methods that tend to require tremendous computational resources, this structure is capable of fully capturing the characteristics of the desired function with a single hidden layer. The optimized structure also allows learning agents to be robust and highly reliable in learning about randomly occurring data. Furthermore, we provide comprehensive experimental results in harsh environments where various arbitrary factors are assumed in order to demonstrate the robustness of the proposed learning approach compared with deep neural networks without proper grounds. It is also shown that the proposed learning process converges to a policy that outperforms existing power allocation algorithms.

In this paper, we consider a power allocation optimization technique for a time-varying fading broadcast channel in energy harvesting communication systems, in which a transmitter with a rechargeable battery transmits messages to receivers using the harvested energy. We first prove that the optimal online power allocation policy for the sum-rate maximization of the transmitter is a monotonically increasing function of harvested energy, remaining battery, and the channel gain of each user. We then construct a lightweight neural network architecture to take advantage of the monotonicity of the optimal policy. This two-step approach, which relies on effective function approximation to provide a mathematical guideline for neural network design, can prevent us from wasting the representational capacity of neural networks. The tailored neural network architectures enable stable learning and eliminate the heuristic network design. For performance assessment, the proposed approach is compared with the closed-form optimal policy for a partially observable Markov problem. Through additional experiments, it is observed that our online solution achieves a performance close to the theoretical upper bound of the performance in a time-varying fading broadcast channel.

This paper deals with the power allocation problem for achieving the upper bound of sum-rate region in energy harvesting downlink channels. We prove that the optimal power allocation policy that maximizes the sum-rate is an increasing function for harvested energy, channel gains, and remaining battery, regardless of the number of users in the downlink channels. We use this proof as a mathematical basis for the construction of a shallow neural network that can fully reflect the increasing property of the optimal policy. This scheme helps us to avoid using big neural networks which requires huge computational resources and causes overfitting. Through experiments, we reveal the inefficiencies and risks of deep neural network that are not optimized enough for the desired policy, and shows that our approach learns a robust policy even with the severe randomness of environments.

In this paper, we consider the problem of minimizing the transmission completion time in energy harvesting devices on time-varying channels with a reinforcement learning approach. Because of the randomness of energy arrival and fading channel in wireless communications, a reinforcement learning algorithm often converges to suboptimal points with a degraded performance. To solve this problem, we first prove that the expected discounted reward sum in the environment is an increasing function of negative time, amount of data sent, channel gain, harvested energy, and remaining battery. We leverage this proof to construct a partially monotonic network that efficiently approximates the optimal action-value function for learning. Experimental results show that our approach with the exploitation of the partial monotonicity of the desired function achieves better performance than existing power allocation policies. Further experiments show that the performance of our learning-based approach is close to the theoretical upper bound over rapidly time-varying channels.

Ovarian cancer is the second most common gynaecological malignancy, and microRNAs (miRNAs) play important role in the cancer development. Here, we found that the level of miR-200b/200a/429 was significantly increased in serum and tumor tissues of patients with stage-I ovarian cancer. Consistent with these results, we detected increased expression levels of miR-200b/200a/429 in ovarian cancer cell lines compared with the human nontumorigenic ovarian epithelial cell line T80. The overexpression of miR-200b/200a/429 in T80 cells stimulated proliferation and caused their growth in soft agar and tumor formation in nude mice. Furthermore, we determined that miR-200b/200a/429 targets inhibitor of growth family 5 (ING5) and that the overexpression of ING5 can block miR-200b/200a/429-induced T80 cell transformation and tumorigenesis. Our findings suggest that miR-200b/200a/429 may be a useful biomarker for the early detection of ovarian cancer and that miR-200b/200a/429 significantly contributes to ovarian cancer development through ING5.

In this paper, we consider a power allocation optimization technique for a time-varying fading broadcast channel in energy harvesting communication systems, in which a transmitter with a rechargeable battery transmits messages to receivers using the harvested energy. We first prove that the optimal online power allocation policy for the sum rate maximization of the transmitter is an increasing function of harvested energy, remaining battery, and each user's channel gain. We then construct an appropriate neural network by relying on increasing behavior of the optimal policy. This two-step approach, by using an effective function approximation as well as providing a fundamental guideline for neural network design, can prevent us from wasting the representational capacity of neural networks. On the basis of the neural network, we apply the policy gradient method to solve the power allocation problem. To validate the performance of our approach, we compare it with the closed-form the optimal policy in a partially observable Markov problem. Through further experiments, it is observed that our online solution achieves a performance close to the theoretical upper bound of the performance in a time-varying fading broadcast channel.

In automotive systems, a radar is a key component of autonomous driving. Using transmit and reflected radar signal by a target, we can capture the target range and velocity. However, when interference signals exist, noise floor increases and it severely affects the detectability of target objects. For these reasons, previous studies have been proposed to cancel interference or reconstruct original signals. However, the conventional signal processing methods for canceling the interference or reconstructing the transmit signals are difficult tasks, and also have many restrictions. In this work, we propose a novel approach to mitigate interference using deep learning. The proposed method provides high performance in various interference conditions and has low processing time. Moreover, we show that our proposed method achieves better performance compared to existing signal processing methods., Accepted in 2018 VTC workshop

In this paper, we consider a power allocation problem for energy harvesting communication systems, where a transmitter wants to send the desired messages to the receiver with the harvested energy in its rechargeable battery. We propose a new power allocation strategy based on deep reinforcement learning technique to maximize the expected total transmitted data for a given random energy arrival and random channel process. The key idea of our scheme is to lead the transmitter, rather than learning the undesirable power allocation policies, by an action-bounding technique using only causal knowledge of the energy and channel processes. This technique helps traditional reinforcement learning algorithms to work more accurately in the systems, and increases the performance of the learning algorithms. Moreover, we show that the proposed scheme achieves better performance with respect to the expected total transmitted data compared to existing power allocation strategies.