Spectrum Sharing between Cooperative Relay and Ad-hoc Networks: Dynamic Transmissions under Computation and Signaling Limitations

This paper studies a spectrum sharing scenario between a cooperative relay network (CRN) and a nearby ad-hoc network. In particular, we consider a dynamic spectrum access and resource allocation problem of the CRN. Based on sensing and predicting the ad-hoc transmission behaviors, the ergodic traffic collision time between the CRN and ad-hoc network is minimized subject to an ergodic uplink throughput requirement for the CRN. We focus on real-time implementation of spectrum sharing policy under practical computation and signaling limitations. In our spectrum sharing policy, most computation tasks are accomplished off-line. Hence, little real-time calculation is required which fits the requirement of practical applications. Moreover, the signaling procedure and computation process are designed carefully to reduce the time delay between spectrum sensing and data transmission, which is crucial for enhancing the accuracy of traffic prediction and improving the performance of interference mitigation. The benefits of spectrum sensing and cooperative relay techniques are demonstrated by our numerical experiments.Comment: 5 pages, 3 figures, to appear in IEEE International Conference on Communications (ICC 2011

Salicylic acid collaborates with gene silencing to tomato defense against tomato yellow leaf curl virus (TYLCV)

Antiviral research in plants has been focused on RNA silencing (i.e. RNA interference), and several studies suggest that salicylic acid (SA)-mediated resistance is a key part of plant antiviral defense. However, the antiviral defense mechanism of SA-mediation is still unclear, and several recent studies have suggested a connection between SA-mediated defense and RNA silencing, which needs further characterization in TYLCV infection. In this study, both SA-mediated defense and the RNA silencing mechanism were observed to play an important role in the antiviral response against TYLCV. First, we found that SA application enhanced the resistance to TYLCV in tomato plants. The expression of RNA-silencing-related genes, such as SlDCL1, SlDCL2, SlDCL4, SlRDR2, SlRDR3a, SlRDR6a, SlAGO1, and SlAGO4, were significantly triggered by exogenous SA application and inoculation with TYLCV, respectively. Furthermore, silencing of SlDCL2, SlDCL4 in tomato resulted in attenuated resistance to TYLCV, and reduced the expression of defense-related genes (SlPR1 and SlPR1b) in SA-mediated defense after infection with TYLCV, particularly in SlDCL2/SlDCL4-silenced plants. Taken together, we conclude that SA collaborates with gene silencing in tomato defense against TYLCV

A Unified Approach to Optimal Opportunistic Spectrum Access under Collision Probability Constraint in Cognitive Radio Systems

We consider a cognitive radio system with one primary channel and one secondary user, and then we introduce a channel-usage pattern model and a fundamental access scheme in this system. Based on this model and fundamental access scheme, we study optimal opportunistic spectrum access problem and formulate it as an optimization problem that the secondary user maximizes spectrum holes utilization under the constraint of collision tolerable level. And then we propose a unified approach to solve this optimization problem. According to the solution of the optimization problem, we analyze and present optimal opportunistic spectrum access algorithms in several cases that the idle period follows uniform distribution, exponential distribution, and Pareto or generalized Pareto distribution. Theoretical analysis and simulation results both show that the optimal opportunistic spectrum access algorithms can maximize spectrum holes utilization under the constraint that the collision probability is bounded below collision tolerable level. The impact of sensing error is also analyzed by simulation

Decoupled Local Aggregation for Point Cloud Learning

The unstructured nature of point clouds demands that local aggregation be adaptive to different local structures. Previous methods meet this by explicitly embedding spatial relations into each aggregation process. Although this coupled approach has been shown effective in generating clear semantics, aggregation can be greatly slowed down due to repeated relation learning and redundant computation to mix directional and point features. In this work, we propose to decouple the explicit modelling of spatial relations from local aggregation. We theoretically prove that basic neighbor pooling operations can too function without loss of clarity in feature fusion, so long as essential spatial information has been encoded in point features. As an instantiation of decoupled local aggregation, we present DeLA, a lightweight point network, where in each learning stage relative spatial encodings are first formed, and only pointwise convolutions plus edge max-pooling are used for local aggregation then. Further, a regularization term is employed to reduce potential ambiguity through the prediction of relative coordinates. Conceptually simple though, experimental results on five classic benchmarks demonstrate that DeLA achieves state-of-the-art performance with reduced or comparable latency. Specifically, DeLA achieves over 90\% overall accuracy on ScanObjectNN and 74\% mIoU on S3DIS Area 5. Our code is available at https://github.com/Matrix-ASC/DeLA