Grouping Based Blind Interference Alignment for KK-user MISO Interference Channels

We propose a blind interference alignment (BIA) through staggered antenna switching scheme with no ideal channel assumption. Contrary to the ideal assumption that channels remain constant during BIA symbol extension period, when the coherence time of the channel is relatively short, channel coefficients may change during a given symbol extension period. To perform BIA perfectly with realistic channel assumption, we propose a grouping based supersymbol structure for $K$-user interference channels which can adjust a supersymbol length to given coherence time. It is proved that the supersymbol length could be reduced significantly by an appropriate grouping. Furthermore, it is also shown that the grouping based supersymbol achieves higher degrees of freedom than the conventional method with given coherence time.Comment: 5 pages, 3 figures, to appear in IEEE ISIT 201

Fully private and secure coded matrix multiplication with colluding workers

In this paper, we propose a new coded computation scheme that can alleviate straggler effects in distributed computing. We consider data security and master’s privacy for matrix multiplication tasks. The proposed scheme, called fully private and secure coded matrix multiplication (FPSCMM), ensures data security and master’s privacy on two data matrices for multiplication tasks from colluding workers. We also show that the storage overhead at workers can be reduced by FPSCMM, since it is enough for workers to store the encoded matrices with sub-blocks. Lastly, we compare FPSCMM with the existing master’s privacy-preserving coded matrix multiplication schemes

Action-Bounding for Reinforcement Learning in Energy Harvesting Communication Systems

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.N

A Novel Relay-aided Successive Aligned Interference Cancellation for X-Channels with Blind Transmitters

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Relay-aided successive aligned interference cancellation for wireless X networks with full-duplex relays

In this work, we propose a simple yet effective beam-forming technique for multiple-input multiple-output (MIMO) multi-pair two-way relay channels. Two key ingredients in our technique are adoption of signal space alignment (SSA) for transmit and receive beamforming and amplify-and-forward (AF) relay beamforming based on advanced zero-forcing (ZF) criterion. From the sum-rate analysis on MIMO multi-pair two-way relay channels, we show that the proposed method achieves full multiplexing gain and substantial beamforming gain in realistic multi-pair two-way relay scenario.1114Nsciescopu

Repair Rates for Multiple Descriptions on Distributed Storage

In a traditional distributed storage system, a source can be restored perfectly when a certain subset of servers is contacted. The coding is independent of the contents of the source. This paper considers instead a lossy source coding version of this problem where the more servers that are contacted, the higher the quality of the restored source. An example could be video stored on distributed storage. In information theory, this is called the multiple description problem, where the distortion depends on the number of descriptions received. The problem considered in this paper is how to restore the system operation when one of the servers fail and a new server replaces it, that is, repair. The requirement is that the distortions in the restored system should be no more than in the original system. The question is how many extra bits are needed for repair. We find an achievable rate and show that this is optimal in certain cases. One conclusion is that it is necessary to design the multiple description codes with repair in mind; just using an existing multiple description code results in unnecessary high repair rates