A Multitask Diffusion Strategy with Optimized Inter-Cluster Cooperation

We consider a multitask estimation problem where nodes in a network are divided into several connected clusters, with each cluster performing a least-mean-squares estimation of a different random parameter vector. Inspired by the adapt-then-combine diffusion strategy, we propose a multitask diffusion strategy whose mean stability can be ensured whenever individual nodes are stable in the mean, regardless of the inter-cluster cooperation weights. In addition, the proposed strategy is able to achieve an asymptotically unbiased estimation, when the parameters have same mean. We also develop an inter-cluster cooperation weights selection scheme that allows each node in the network to locally optimize its inter-cluster cooperation weights. Numerical results demonstrate that our approach leads to a lower average steady-state network mean-square deviation, compared with using weights selected by various other commonly adopted methods in the literature.Comment: 30 pages, 8 figures, submitted to IEEE Journal of Selected Topics in Signal Processin

Data Detection and Code Channel Allocation for Frequency-Domain Spread ACO-OFDM Systems Over Indoor Diffuse Wireless Channels

Future optical wireless communication systems promise to provide high-speed data transmission in indoor diffuse environments. This paper considers frequency-domain spread asymmetrically clipped optical orthogonal frequency-division multiplexing (ACOOFDM) systems in indoor diffuse channels and aims to develop efficient data detection and code channel allocation schemes. By exploiting the frequency-domain spread concept, a linear multi-code detection scheme is proposed to maximize the signal to interference plus noise ratio (SINR) at the receiver. The achieved SINR and bit error ratio (BER) performance are analyzed. A computationally efficient code channel allocation algorithm is proposed to improve the BER performance of the frequency-domain spread ACO-OFDM system. Numerical results show that the frequency-domain spread ACO-OFDM system outperforms conventional ACO-OFDM systems in indoor diffuse channels. Moreover, the proposed linear multi-code detection and code channel allocation algorithm can improve the performance of optical peak-to-average power ratio (PAPR

Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

Many artificial intelligence (AI) applications often require multiple intelligent agents to work in a collaborative effort. Efficient learning for intra-agent communication and coordination is an indispensable step towards general AI. In this paper, we take StarCraft combat game as a case study, where the task is to coordinate multiple agents as a team to defeat their enemies. To maintain a scalable yet effective communication protocol, we introduce a Multiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with a vectorised extension of actor-critic formulation. We show that BiCNet can handle different types of combats with arbitrary numbers of AI agents for both sides. Our analysis demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of advanced coordination strategies that have been commonly used by experienced game players. In our experiments, we evaluate our approach against multiple baselines under different scenarios; it shows state-of-the-art performance, and possesses potential values for large-scale real-world applications.Comment: 10 pages, 10 figures. Previously as title: "Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games", Mar 201