Two-Stage LASSO ADMM Signal Detection Algorithm For Large Scale MIMO

This paper explores the benefit of using some of the machine learning techniques and Big data optimization tools in approximating maximum likelihood (ML) detection of Large Scale MIMO systems. First, large scale MIMO detection problem is formulated as a LASSO (Least Absolute Shrinkage and Selection Operator) optimization problem. Then, Alternating Direction Method of Multipliers (ADMM) is considered in solving this problem. The choice of ADMM is motivated by its ability of solving convex optimization problems by breaking them into smaller sub-problems, each of which are then easier to handle. Further improvement is obtained using two stages of LASSO with interference cancellation from the first stage. The proposed algorithm is investigated at various modulation techniques with different number of antennas. It is also compared with widely used algorithms in this field. Simulation results demonstrate the efficacy of the proposed algorithm for both uncoded and coded cases.Comment: 5 pages, 4 figure

A Quantitative Framework for CDN-Based Over-The-Top Video Streaming Systems

The demand for global video has been burgeoning across industries. With the expansion and improvement of video-streaming services, cloud-based video is evolving into a necessary feature of any successful business for reaching internal and external audiences. Over-the-top (OTT) video streaming, e.g., Netflix and YouTube, has been dominating the global IP traffic in recent years. More than 50% of OTT video traffic are now delivered through content distribution networks (CDNs). Even though multiple solutions have been proposed for improving congestion in the CDN system, managing the ever-increasing traffic requires a fundamental understanding of the system and the different design flexibilities (control knobs) to make the best use of the hardware limitations. In Addition, there is no analytical understanding for the key quality of experience (QoE) attributes (stall duration, average quality, etc.) for video streaming when transmitted using CDN-based multi-tier infrastructure, which is the focus of this thesis. The key contribution of this thesis is to provide a white-box analytical understanding of the key QoE attributes of the enduser in cloud storage systems, which can be used to systematically address the choppy user experience and have optimized system designs. The first key design involves the scheduling strategy, that chooses the subset of CDN servers to obtain the content. The second key design involves the quality of each video chunk. The third key design involves deciding which contents to cache at the edge routers and which content needs to be stored at the CDN. Towards solving these challenges, this dissertation is divided into three parts. Part 1 considers video streaming over distributed systems where the video segments are encoded using an erasure code for better reliability. Part 2 looks at the problem of optimizing the tradeoff between quality and stall of the streamed videos. In Part 3, we consider caching partial contents of the videos at the CDN as well as at the edge-routers to further optimize video streaming services. We present a model for describing a today’s representative multi-tier system architecture for video streaming applications, typically composed of a centralized origin server, several CDN sites and edge-caches. Our model comprehensively considers the following factors: limited caching spaces at the CDN sites and edge-routers, allocation of CDN for a video request, choice of different ports from the CDN, and the central storage and bandwidth allocation. With this model, we optimize different quality of experience (QoE) measures and present novel, yet efficient, algorithms to solve the formulated optimization problems. Our extensive simulation results demonstrate that the proposed algorithms significantly outperform the state-of-the-art strategies. We take one step further and implement a small-scale video streaming system in a real cloud environment, managed by Openstack, and validate our results

A framework for optimal cost media streaming in three-tier wireless networks

Heterogeneous networking is envisioned as a key solution for accommodating the traffic surge resulting from resource demanding applications such as video streaming. In this paper, the diversity in cost, coverage, and resource availability in heterogeneous systems is exploited to minimize the streaming session cost in three-tier integrated systems. A sub-optimal streaming decision engine is developed to overcome the complexity of the original problem whose solution contradicts with both the limited processing capabilities of end-user equipment and short handoff delay requirements. Our results show that the developed framework achieves significant monetary cost savings in comparison to typical greedy streaming behavior. Additionally, our solution can be easily tuned to compromise the tradeoff between monetary, signaling and quality cost components. , Springer Science+Business Media New York.Scopu

Combustion en lit fluidise Paris, 8 juin 1994

Available at INIST (FR), Document Supply Service, under shelf-number : Y 30471 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

Saturation et vote pour la tolerance aux fautes dans les systemes repartis

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : T 78586 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc