Exploiting Tradeoff Between Transmission Diversity and Content Diversity in Multi-Cell Edge Caching

Caching in multi-cell networks faces a well-known dilemma, i.e., to cache same contents among multiple edge nodes (ENs) to enable transmission cooperation/diversity for higher transmission efficiency, or to cache different contents to enable content diversity for higher cache hit rate. In this work, we introduce a partition-based caching to exploit the tradeoff between transmission diversity and content diversity in a multi-cell edge caching networks with single user only. The performance is characterized by the system average outage probability, which can be viewed as the sum of the cache hit outage probability and cache miss probability. We show that (i) In the low signal-to-noise ratio(SNR) region, the ENs are encouraged to cache more fractions of the most popular files so as to better exploit the transmission diversity for the most popular content; (ii) In the high SNR region, the ENs are encouraged to cache more files with less fractions of each so as to better exploit the content diversity.Comment: Accepted by IEEE International Conference on Communications (ICC), Kansas City, MO, USA, May 201

Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion Recognition

Long short-term memory (LSTM) is normally used in recurrent neural network (RNN) as basic recurrent unit. However,conventional LSTM assumes that the state at current time step depends on previous time step. This assumption constraints the time dependency modeling capability. In this study, we propose a new variation of LSTM, advanced LSTM (A-LSTM), for better temporal context modeling. We employ A-LSTM in weighted pooling RNN for emotion recognition. The A-LSTM outperforms the conventional LSTM by 5.5% relatively. The A-LSTM based weighted pooling RNN can also complement the state-of-the-art emotion classification framework. This shows the advantage of A-LSTM

A New DoF Upper Bound and Its Achievability for KK-User MIMO Y Channels

This work is to study the degrees of freedom (DoF) for the $K$-user MIMO Y channel. Previously, two transmission frameworks have been proposed for the DoF analysis when $N \geq 2M$, where $M$ and $N$ denote the number of antennas at each source node and the relay node respectively. The first method is named as signal group based alignment proposed by Hua et al. in [1]. The second is named as signal pattern approach introduced by Wang et al. in [2]. But both of them only studied certain antenna configurations. The maximum achievable DoF in the general case still remains unknown. In this work, we first derive a new upper bound of the DoF using the genie-aided approach. Then, we propose a more general transmission framework, generalized signal alignment (GSA), and show that the previous two methods are both special cases of GSA. With GSA, we prove that the new DoF upper bound is achievable when $\frac{N}{M} \in \left(0,2+\frac{4}{K(K-1)}\right] \cup \left[K-2, +\infty\right)$. The DoF analysis in this paper provides a major step forward towards the fundamental capacity limit of the $K$-user MIMO Y channel. It also offers a new approach of integrating interference alignment with physical layer network coding.Comment: 6 pages, 3 figures, submitted to IEEE ICC 2015. arXiv admin note: text overlap with arXiv:1405.071