Performance Evaluation of Distributed-Antenna Communications Systems Using Beam-Hopping

Digital beamforming (DBF) techniques are capable of improving the performance of communications systems significantly. However, if the transmitted signals are conflicted with strong interference, especially, in the direction of the transmitted beams , these directional jamming signals will severely degrade the system performance. In order to efficiently mitigate the interference of the directional jammers, in this contribution a beam-hopping (BH) communications scheme is proposed. In the proposed BH communications scheme, only one pair of the beams is used for transmission and it hops from one to the next according to an assigned BH pattern. In this contribution a range of expressions in terms of the average SINR performance have been derived, when both the uplink and downlink are considered. The average SINR performance of the proposed BH scheme and that of the conventional single-beam (SB) as well as multiple-beam (MB) assisted beam-processing schemes have been investigated. Our analysis and results show that the proposed BH scheme is capable of efficiently combating the directional jamming, with the aid of utilizing the directional gain of the beams generated by both the transmitter and the receiver. Furthermore, the BH scheme is capable of reducing the intercept probability of the communications. Therefore, the proposed BH scheme is suitable for communications, when several distributed antenna arrays are available around a mobile

Self-organized critical behavior: the evolution of frozen spin networks model in quantum gravity

In quantum gravity, we study the evolution of a two-dimensional planar open frozen spin network, in which the color (i.e. the twice spin of an edge) labeling edge changes but the underlying graph remains fixed. The mainly considered evolution rule, the random edge model, is depending on choosing an edge randomly and changing the color of it by an even integer. Since the change of color generally violate the gauge invariance conditions imposed on the system, detailed propagation rule is needed and it can be defined in many ways. Here, we provided one new propagation rule, in which the involved even integer is not a constant one as in previous works, but changeable with certain probability. In random edge model, we do find the evolution of the system under the propagation rule exhibits power-law behavior, which is suggestive of the self-organized criticality (SOC), and it is the first time to verify the SOC behavior in such evolution model for the frozen spin network. Furthermore, the increase of the average color of the spin network in time can show the nature of inflation for the universe.Comment: 5 pages, 5 figure

Sim-real joint reinforcement transfer for 3D indoor navigation

© 2019 IEEE. There has been an increasing interest in 3D indoor navigation, where a robot in an environment moves to a target according to an instruction. To deploy a robot for navigation in the physical world, lots of training data is required to learn an effective policy. It is quite labour intensive to obtain sufficient real environment data for training robots while synthetic data is much easier to construct by render-ing. Though it is promising to utilize the synthetic environments to facilitate navigation training in the real world, real environment are heterogeneous from synthetic environment in two aspects. First, the visual representation of the two environments have significant variances. Second, the houseplans of these two environments are quite different. There-fore two types of information,i.e. visual representation and policy behavior, need to be adapted in the reinforce mentmodel. The learning procedure of visual representation and that of policy behavior are presumably reciprocal. We pro-pose to jointly adapt visual representation and policy behavior to leverage the mutual impacts of environment and policy. Specifically, our method employs an adversarial feature adaptation model for visual representation transfer anda policy mimic strategy for policy behavior imitation. Experiment shows that our method outperforms the baseline by 19.47% without any additional human annotations

Analysis of synaptic weight distribution in an Izhikevich network

Izhikevich network is a relatively new neuronal network, which consists of cortical spiking model neurons with axonal conduction delays and spike-timingdependent plasticity (STDP) with hard bound adaptation. In this work, we use uniform and Gaussian distributions respectively to initialize the weights of all excitatory neurons. After the network undergoes a few minutes of STDP adaptation, we can see that the weights of all synapses in the network, for both initial weight distributions, form a bimodal distribution, and numerically the established distribution presents dynamic stability

Label Independent Memory for Semi-Supervised Few-shot Video Classification.

In this paper, we propose to leverage freely available unlabeled video data to facilitate few-shot video classification. In this semi-supervised few-shot video classification task, millions of unlabeled data are available for each episode during training. These videos can be extremely imbalanced, while they have profound visual and motion dynamics. To tackle the semi-supervised few-shot video classification problem, we make the following contributions. First, we propose a label independent memory (LIM) to cache label related features, which enables a similarity search over a large set of videos. LIM produces a class prototype for few-shot training. This prototype is an aggregated embedding for each class, which is more robust to noisy video features. Second, we integrate a multi-modality compound memory network to capture both RGB and flow information. We propose to store the RGB and flow representation in two separate memory networks, but they are jointly optimized via a unified loss. In this way, mutual communications between the two modalities are leveraged to achieve better classification performance. Third, we conduct extensive experiments on the few-shot Kinetics-100, Something-Something-100 datasets, which validates the effectiveness of leveraging the accessible unlabeled data for few-shot classification