Dynamic Power Splitting Policies for AF Relay Networks with Wireless Energy Harvesting

Wireless energy harvesting (WEH) provides an exciting way to supply energy for relay nodes to forward information for the source-destination pairs. In this paper, we investigate the problem on how the relay node dynamically adjusts the power splitting ratio of information transmission (IT) and energy harvesting (EH) in order to achieve the optimal outage performance. According to the knowledge of channel state information (CSI) at the relay, optimal dynamic power splitting policy with full CSI and partial CSI are both provided. Finally, through simulations, the proposed power splitting policies can improve the outage performances and the policy with full CSI achieves the best performance. It is also shown that the policy with partial CSI can approach the policy with full CSI closely and incurs far less system overhead.Comment: accepted by IEEE ICC 2015 - Workshop on Green Communications and Networks with Energy Harvesting, Smart Grids, and Renewable Energie

Multipliers on a new class of Banach algebras, locally compact quantum groups, and topological centres

We study multiplier algebras for a large class of Banach algebras which contains the group algebra $L_1(G)$, the Beurling algebras $L_1(G, \omega)$, and the Fourier algebra $A(G)$ of a locally compact group $G$. This study yields numerous new results and unifies some existing theorems on $L_1(G)$ and $A(G)$ through an abstract Banach algebraic approach. Applications are obtained on representations of multipliers over locally compact quantum groups and on topological centre problems. In particular, five open problems in abstract harmonic analysis are solved.Comment: 38 page

Beamforming optimisation in energy harvesting cooperative full-duplex networks with self-energy recycling protocol

This study considers the problem of beamforming optimisation in an amplify-and-forward relaying cooperative network, in which the relay node harvests the energy from the radio-frequency signal. Based on the self-energy recycling relay protocol, the authors study the beamforming optimisation problem. The formulated problem aims to maximise the achievable rate subject to the available transmitted power at the relay node. The authors develop a semidefinite programming (SDP) relaxation method to solve the proposed problem. They also use SDP and the full search to solve the beamforming optimisation based on a time-switching relaying protocol as a benchmark. The simulation results are presented to verify that the self-energy recycling protocol achieves a significant rate gain compared with the timeswitching relaying protocol and the power-splitting relaying protocol

Effects of Material Emotional Valence on the Time Course of Massive Repetition Priming

Learning through repetition is a fundamental form and also an effective method of language learning critical for achieving proficient and automatic language use. Massive repetition priming as a common research paradigm taps into the dynamic processes involved in repetition learning. Research with this paradigm has so far used only emotionally neutral materials and ignored emotional factors, which seems inappropriate given the well-documented impact of emotion on cognitive processing. The present study used massive repetition priming to investigate whether the emotional valence of learning materials affects implicit language learning. Participants read a list of Chinese words and made speeded perceptual judgments about the spatial configuration of the two characters in a word. Each word was repeated 15 times in the whole learning session. There were three types of words, negative, positive, or neutral in their emotional valence, presented in separate blocks. Although similar levels of asymptotic performance were reached for different valence conditions showing comparable total effects of learning, learning of the positive words was found to be associated with fewer plateaus of shorter durations and to reach saturation earlier, compared with neutral and negative words. The results showed for the first time that the emotional valence of learning materials has significant effects on the time course of learning so that positive materials are learned faster and more efficiently, relative to negative and neutral materials. The study indicates the importance to explicitly consider the role of emotional factors in implicit language learning research

Beamforming optimization for full-duplex cooperative cognitive radio networks

This paper considers the problem of beamforming optimization in a cognitive cooperative energy harvesting network, in which the secondary transmitter (ST) harvests energy from the primary transmitter (PT) and relays the information for the primary user (PU) with amplify-and-forward (AF) relay protocol. When the channel of the primary system is affected with deep fading or shadowing effects, the ST can assist the primary information transmission. It is particularly useful to employ the energy harvesting protocol to avoid that the ST does not have enough energy to assist the PU. Based on the self-energy recycling relay protocol, we study the beamforming optimization problem. We develop a semidefinite programming (SDP) relaxation method to solve the proposed problem. We also use SDP and one-dimension (1-D) optimization to solve the beamforming optimization based on a time-switching relaying protocol (TSR) as a benchmark. The simulation results are presented to verify that the self-energy recycling protocol achieves a significant rate gain compared to the TSR protocol and the power-splitting relaying (PSR) protocol