Successive Interference Cancellation and Fractional Frequency Reuse For LTE Uplink Communications

Cellular networks are increasingly densified to deal with fast growing wireless traffic. Interference mitigation plays a key role for the dense cellular networks. Successive interference cancellation (SIC) and fractional frequency reuse (FFR) are two representative inter-cell interference (ICI) mitigation techniques. In this paper we study the application of both SIC and FFR for LTE uplink networks, and develop an analytical model to investigate their interactions and impact on network performance. The performance gains with FFR and SIC are related to key system functionalities and variables, such as SIC parameters, FFR bandwidth partition, uplink power control and sector antennas. The ICIs from individual cell sectors are approximated by log-normal random variables, which enables low complexity computation of the aggregate ICI with FFR and SIC. Then network performance of site throughput and outage probability is computed. The model is fast and has small modelling deviation, which is validated by system level simulations. Numerical results show that both SIC and FFR can largely improve network performance, but SIC has an impact over FFR. In addition, most of the network performance gains with SIC could be obtained with a small number of SIC stages applied to a few sectors

Tuning micropillar cavity birefringence by laser induced surface defects

We demonstrate a technique to tune the optical properties of micropillar cavities by creating small defects on the sample surface near the cavity region with an intense focused laser beam. Such defects modify strain in the structure, changing the birefringence in a controllable way. We apply the technique to make the fundamental cavity mode polarization-degenerate and to fine tune the overall mode frequencies, as needed for applications in quantum information science.Comment: RevTex, 7 pages, 4 figures (accepted for publication in Applied Physics Letters

CNOT and Bell-state analysis in the weak-coupling cavity QED regime

We propose an interface between the spin of a photon and the spin of an electron confined in a quantum dot embedded in a microcavity operating in the weak coupling regime. This interface, based on spin selective photon reflection from the cavity, can be used to construct a CNOT gate, a multi-photon entangler and a photonic Bell-state analyzer. Finally, we analyze experimental feasibility, concluding that the schemes can be implemented with current technology.Comment: 4 pages, 2 figure

Free-Form Composition Networks for Egocentric Action Recognition

Egocentric action recognition is gaining significant attention in the field of human action recognition. In this paper, we address data scarcity issue in egocentric action recognition from a compositional generalization perspective. To tackle this problem, we propose a free-form composition network (FFCN) that can simultaneously learn disentangled verb, preposition, and noun representations, and then use them to compose new samples in the feature space for rare classes of action videos. First, we use a graph to capture the spatial-temporal relations among different hand/object instances in each action video. We thus decompose each action into a set of verb and preposition spatial-temporal representations using the edge features in the graph. The temporal decomposition extracts verb and preposition representations from different video frames, while the spatial decomposition adaptively learns verb and preposition representations from action-related instances in each frame. With these spatial-temporal representations of verbs and prepositions, we can compose new samples for those rare classes in a free-form manner, which is not restricted to a rigid form of a verb and a noun. The proposed FFCN can directly generate new training data samples for rare classes, hence significantly improve action recognition performance. We evaluated our method on three popular egocentric action recognition datasets, Something-Something V2, H2O, and EPIC-KITCHENS-100, and the experimental results demonstrate the effectiveness of the proposed method for handling data scarcity problems, including long-tailed and few-shot egocentric action recognition

Cutting Down Electricity Cost in Internet Data Centers by Using Energy Storage

Abstract—Electricity consumption comprises a significant frac-tion of total operating cost in data centers. System operators are required to reduce electricity bill as much as possible. In this paper, we consider utilizing available energy storage capability in data centers to reduce electricity bill under real-time electricity market. Laypunov optimization technique is applied to design an algorithm that achieves an explicit tradeoff between cost saving and energy storage capacity. As far as we know, our work is the first to explore the problem of electricity cost saving using energy storage in multiple data centers by considering both time-diversity and location-diversity of electricity price. Index Terms—Cloud computing, electricity cost, data center, energy storage, Laypunov optimization I

Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks

Graph Neural Networks (GNNs) tend to suffer from high computation costs due to the exponentially increasing scale of graph data and the number of model parameters, which restricts their utility in practical applications. To this end, some recent works focus on sparsifying GNNs with the lottery ticket hypothesis (LTH) to reduce inference costs while maintaining performance levels. However, the LTH-based methods suffer from two major drawbacks: 1) they require exhaustive and iterative training of dense models, resulting in an extremely large training computation cost, and 2) they only trim graph structures and model parameters but ignore the node feature dimension, where significant redundancy exists. To overcome the above limitations, we propose a comprehensive graph gradual pruning framework termed CGP. This is achieved by designing a during-training graph pruning paradigm to dynamically prune GNNs within one training process. Unlike LTH-based methods, the proposed CGP approach requires no re-training, which significantly reduces the computation costs. Furthermore, we design a co-sparsifying strategy to comprehensively trim all three core elements of GNNs: graph structures, node features, and model parameters. Meanwhile, aiming at refining the pruning operation, we introduce a regrowth process into our CGP framework, in order to re-establish the pruned but important connections. The proposed CGP is evaluated by using a node classification task across 6 GNN architectures, including shallow models (GCN and GAT), shallow-but-deep-propagation models (SGC and APPNP), and deep models (GCNII and ResGCN), on a total of 14 real-world graph datasets, including large-scale graph datasets from the challenging Open Graph Benchmark. Experiments reveal that our proposed strategy greatly improves both training and inference efficiency while matching or even exceeding the accuracy of existing methods.Comment: 29 pages, 27 figures, submitting to IEEE TNNL