Optimal Joint Routing and Scheduling in Millimeter-Wave Cellular Networks

Millimeter-wave (mmWave) communication is a promising technology to cope with the expected exponential increase in data traffic in 5G networks. mmWave networks typically require a very dense deployment of mmWave base stations (mmBS). To reduce cost and increase flexibility, wireless backhauling is needed to connect the mmBSs. The characteristics of mmWave communication, and specifically its high directional- ity, imply new requirements for efficient routing and scheduling paradigms. We propose an efficient scheduling method, so-called schedule-oriented optimization, based on matching theory that optimizes QoS metrics jointly with routing. It is capable of solving any scheduling problem that can be formulated as a linear program whose variables are link times and QoS metrics. As an example of the schedule-oriented optimization, we show the optimal solution of the maximum throughput fair scheduling (MTFS). Practically, the optimal scheduling can be obtained even for networks with over 200 mmBSs. To further increase the runtime performance, we propose an efficient edge-coloring based approximation algorithm with provable performance bound. It achieves over 80% of the optimal max-min throughput and runs 5 to 100 times faster than the optimal algorithm in practice. Finally, we extend the optimal and approximation algorithms for the cases of multi-RF-chain mmBSs and integrated backhaul and access networks.Comment: To appear in Proceedings of INFOCOM '1

Optimal and Approximation Algorithms for Joint Routing and Scheduling in Millimeter-Wave Cellular Networks

Millimeter-wave (mmWave) communication is a promising technology to cope with the exponential increase in 5G data traffic. Such networks typically require a very dense deployment of base stations. A subset of those, so-called macro base stations, feature high-bandwidth connection to the core network, while relay base stations are connected wirelessly. To reduce cost and increase flexibility, wireless backhauling is needed to connect both macro to relay as well as relay to relay base stations. The characteristics of mmWave communication mandates new paradigms for routing and scheduling. The paper investigates scheduling algorithms under different interference models. To showcase the scheduling methods, we study the maximum throughput fair scheduling problem. Yet the proposed algorithms can be easily extended to other problems. For a full-duplex network under the no interference model, we propose an efficient polynomial-time scheduling method, the {\em schedule-oriented optimization}. Further, we prove that the problem is NP-hard if we assume pairwise link interference model or half-duplex radios. Fractional weighted coloring based approximation algorithms are proposed for these NP-hard cases. Moreover, the approximation algorithm parallel data stream scheduling is proposed for the case of half-duplex network under the no interference model. It has better approximation ratio than the fractional weighted coloring based algorithms and even attains the optimal solution for the special case of uniform orthogonal backhaul networks.Comment: accepted for publish in the IEEE/ACM Transactions on Networkin

TDC: Towards Extremely Efficient CNNs on GPUs via Hardware-Aware Tucker Decomposition

Tucker decomposition is one of the SOTA CNN model compression techniques. However, unlike the FLOPs reduction, we observe very limited inference time reduction with Tuckercompressed models using existing GPU software such as cuDNN. To this end, we propose an efficient end-to-end framework that can generate highly accurate and compact CNN models via Tucker decomposition and optimized inference code on GPUs. Specifically, we propose an ADMM-based training algorithm that can achieve highly accurate Tucker-format models. We also develop a high-performance kernel for Tucker-format convolutions and analytical performance models to guide the selection of execution parameters. We further propose a co-design framework to determine the proper Tucker ranks driven by practical inference time (rather than FLOPs). Our evaluation on five modern CNNs with A100 demonstrates that our compressed models with our optimized code achieve up to 3.14X speedup over cuDNN, 1.45X speedup over TVM, and 4.57X over the original models using cuDNN with up to 0.05% accuracy loss.Comment: 12 pages, 8 figures, 3 tables, accepted by PPoPP '2

Improving Quality of Service in Wireless Sensor Networks for Industrial Automation

Monitoring and control systems have played a central role in industry and everyday life, often in a non-intrusive manner. Yet, they will become more ubiquitous, autonomous and distributed, with the rapid development of the envisioned technologies of "smart homes", "smart cities" and "industry 4.0". All these new technologies are built upon the enabling technology of WSN. Their success depends to a large extent on the communication capability of WSNs. Fast reaction and feedback is a common characteristic of these technologies, therefore, how to achieve low-latency, high-reliability and flexibility in WSN communication is a key challenge, and a decisive success factor. WSN refers to a network that connects a number of low-cost, low-power sensor nodes which have sensing and/or actuating capabilities, and can communicate with each other over short distance via a low-power radio. The predominant advantages that WSN offers are 1) distribution and fault tolerance in communication and sensing/actuation by leveraging large number of sensor nodes, 2) cost reduction by removing the cables of communication and power supply, and 3) flexibility in the deployment of tiny cableless sensor nodes. However, one main drawback of the wireless technology, in contrast to the mature wired counterpart, is the much weaker communication capability --- the combined result of stronger interference in the wireless channel, the weak signal strength of low-power radios and the complexity in the scheduling of multi-hop wireless communication. The main goal of the thesis is to facilitate the transition from wired technology to wireless technology for industrial automation. Specifically, I provide solutions for improving and guaranteeing QoS in WSN communication. I tackle the problem for two scenarios where the network topology is either known or not. When the network topology is known, I adopt an approach of reservation-based scheduling, i.e., through centralized scheduling of communication opportunities, in order to optimize various communication metrics. In the thesis, I propose a very efficient multi-channel scheduling algorithm that gives nearly optimal latency performance (within 1.22% of the optimum) for the tree-based convergecast, which is by far the predominant communication pattern, especially for monitoring applications. I also propose very efficient multi-channel scheduling algorithms that offer high schedulability and low overhead for multi-flow periodic real-time communication on an arbitrary network topology with multiple gateways. Such a communication pattern is typical of a multi-loop control system. On the other hand, if the network topology is unknown or changes very dynamically, I optimize the QoS in communication by exploiting concurrent transmission on the physical layer, which is routing-free by nature. First, I proposes a simple model for concurrent transmissions in WSN which accurately predicts the success or failure in the packet reception. Then I design the Sparkle protocol for highly reliable, low latency and energy efficient multi-flow periodic communication. Finally, it presents the Ripple protocol for high throughput, reliable and energy efficient network flooding using pipeline transmissions and forward error correction, which significantly improves the state-of-the-art. Although the thesis assumes WSN as the communication technology and industrial automation as the application scenario, it is by no means restricted to these settings since the proposed solutions can be applied to other wireless networks and other scenarios with similar communication patterns and QoS concerns