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

QoE-Driven Video Transmission: Energy-Efficient Multi-UAV Network Optimization

This paper is concerned with the issue of improving video subscribers' quality of experience (QoE) by deploying a multi-unmanned aerial vehicle (UAV) network. Different from existing works, we characterize subscribers' QoE by video bitrates, latency, and frame freezing and propose to improve their QoE by energy-efficiently and dynamically optimizing the multi-UAV network in terms of serving UAV selection, UAV trajectory, and UAV transmit power. The dynamic multi-UAV network optimization problem is formulated as a challenging sequential-decision problem with the goal of maximizing subscribers' QoE while minimizing the total network power consumption, subject to some physical resource constraints. We propose a novel network optimization algorithm to solve this challenging problem, in which a Lyapunov technique is first explored to decompose the sequential-decision problem into several repeatedly optimized sub-problems to avoid the curse of dimensionality. To solve the sub-problems, iterative and approximate optimization mechanisms with provable performance guarantees are then developed. Finally, we design extensive simulations to verify the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm can effectively improve the QoE of subscribers and is 66.75\% more energy-efficient than benchmarks

Flatten a Curved Space by Kernel: From Einstein to Euclid

Einstein’s general theory of relativity fundamentally changed our view about the physical world. Different from Newton’s theory, Einstein’s space and time are not flat but can be warped by matter. For a curved space such as Einstein’s space, Euclidean geometry is no longer suitable, and Riemannian geometry is usually used instead. In parallel with physics, due to an explosion of data from all fields of science, there is an increasing need for pattern analysis tools, which are capable of analyzing patterns of data in a non-Euclidean (curved) space. To handle data in a curved space, linear approaches are not directly applicable, and instead nonlinear approaches are the right weapon. However, early-day nonlinear approaches were usually based on gradient descent or greedy heuristics, and suffered from local minima and overfitting [1]. In contrast, kernel methods provide a powerful means for transforming data in a non-Euclidean curved space (such as Einstein space) into points in a highdimensional Euclidean flat space, so that linear approaches can be applied to the transformed points in the high-dimensional Euclidean space. With this flattening capability, kernel methods combine the best features of linear approaches and nonlinear approaches, i.e., kernel methods are capable of dealing with nonlinear structures while enjoying a low computational complexity like linear approaches. In this column, we provide important insights into kernel methods and illustrate the power of kerne

An Efficient Mobile Authentication Scheme for Wireless Networks

Abstract — In this paper, an efficient authentication scheme is proposed which is suitable for low-power mobile devices. It uses an elliptic-curve-cryptosystem based trust delegation mechanism to generate a delegation passcode for mobile station authentication, and it can effectively defend all known attacks to mobile networks including the denial-of-service attack. Moreover, the mobile station only needs to receive one message and send one message to authenticate itself to a visitor’s location register, and the scheme only requires a single elliptic-curve scalar point multiplication on a mobile device. Therefore, this scheme enjoys both computation efficiency and communication efficiency as compared to known mobile authentication schemes. Index Terms — mobile authentication, denial-of-service attack, message en route attack, false base station attack, elliptic-curve cryptosystems. I

Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers

This paper addresses the problem of cross-modal object tracking from RGB videos and event data. Rather than constructing a complex cross-modal fusion network, we explore the great potential of a pre-trained vision Transformer (ViT). Particularly, we delicately investigate plug-and-play training augmentations that encourage the ViT to bridge the vast distribution gap between the two modalities, enabling comprehensive cross-modal information interaction and thus enhancing its ability. Specifically, we propose a mask modeling strategy that randomly masks a specific modality of some tokens to enforce the interaction between tokens from different modalities interacting proactively. To mitigate network oscillations resulting from the masking strategy and further amplify its positive effect, we then theoretically propose an orthogonal high-rank loss to regularize the attention matrix. Extensive experiments demonstrate that our plug-and-play training augmentation techniques can significantly boost state-of-the-art one-stream and twostream trackers to a large extent in terms of both tracking precision and success rate. Our new perspective and findings will potentially bring insights to the field of leveraging powerful pre-trained ViTs to model cross-modal data. The code will be publicly available