Abstract--CSI feedback is an important problem of massive multiple-input multiple-output (MIMO) technology because the feedback overhead is proportional to the number of sub-channels and the number of antennas, both of which scale with the size of the massive MIMO system.Deep learning-based CSI feedback methods have been widely adopted recently owing to their superior performance. Despite the success, current approaches have not fully exploited the relationship between the characteristics of CSI data and the deep learning framework. Generally, DL-based methods utilize systems, e.g., 5G and above. Unlike traditional cellbased the auto-encoder framework [7], where the encoder learns to communication paradigms, the massive MIMO makes compress the original CSI at the UE side and the decoder better use of spatial diversity and serves users in a cellfree learns to reconstruct the original CSI at the BS side. A massive MIMO system typically is equipped is trained in unsupervised manner without the need with a large number of antennas at the base station (BS), for labeled data and only requires a single run upon deployment which aims to make full use of spatial diversity by conducting for continuous CSI reconstruction, which overcomes the beamforming to concentrate signal energy to a specific user computation inefficiency of traditional CS-based approaches.

Artificial Intelligence has been developed for decades with the achievement of great progress. Recently, deep learning shows its ability to solve many real world problems, e.g. image classification and detection, natural language processing, playing GO. Theoretically speaking, an artificial neural network can fit any function and reinforcement learning can learn from any delayed reward. But in solving real world tasks, we still need to spend a lot of effort to adjust algorithms to fit task unique features. This paper proposes that the reason of this phenomenon is the sparse feedback feature of the nature, and a single algorithm, no matter how we improve it, can only solve dense feedback tasks or specific sparse feedback tasks. This paper first analyses how sparse feedback affects algorithm perfomance, and then proposes a pattern that explains how to accumulate knowledge to solve sparse feedback problems.

Chatbots rarely make great conversationalists. With the exception of perhaps Microsoft's Xiaoice in China, which has about 40 million users and averages 23 back-and-forth exchanges, and Alibaba's Dian Xiaomi, an automated sales agent that serves nearly 3.5 million customers a day, most can't hold humans' attention for much longer than 15 minutes. But that's not tempering bot adoption any -- in fact, Gartner predicts that they'll power 85 percent of all customer service interactions by the year 2020. Fortunately, continued advances in the field of AI research promise to make conversant AI much more sophisticated by then. In a paper published this week on the preprint paper Arxiv.org