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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.

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 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