The 21st International Symposium on Mobile Ad Hoc Networking and Computing (ACM MobiHoc 2020)

Technical Sessions

Session S-8

Wireless Communication and Scheduling

10:00 AM — 12:00 PM EDT
Oct 14 Wed, 10:00 AM — 12:00 PM EDT

REFRAIN: Promoting Valid Transmissions in High-Density Modern Wi-Fi Networks

Youngwook Son, Kanghyun Lee (Seoul National University, Korea), Seongwon Kim (SK Telecom, Korea), Jinmyeong Lee (Seoul National University, Korea), Sunghyun Choi (Samsung Research, Korea), Saewoong Bahk (Seoul National University)

For emerging high-density Wi-Fi networks, there have been plenty of studies that claim the need for more aggressive channel access to enhance spatial reuse. Against those previous ideas, this paper presents a different perspective that existing Wi-Fi devices already have excessive transmission opportunities, even without protecting each other in certain scenarios. We shed light on an anomaly within actual carrier sensing (CS) behaviors, which makes some neighboring devices become blind to each other and transmit simultaneously, due to undetected preambles. Through experimental study and analysis, we reveal both sides of the anomaly heavily affecting the overall network performance. Based on the observations, we design REFRAIN, a standard-compliant PHY/MAC framework, which copes with and further exploits the anomaly for better spatial reuse. Our prototype using NI USRP and commercial Wi-Fi devices shows the feasibility and effectiveness of our approach, while extensive simulation results demonstrate that REFRAIN achieves up to 57_ higher average throughput by promoting valid transmissions, without modifying the 802.11 CS specification at all.

Emulating Round-Robin for Serving Dynamic Flows over Wireless Fading Channels

Bin Li (University of Rhode Island), Atilla Eryilmaz (The Ohio State University), R. Srikant (University of Illinois at Urbana-Champaign)

Motivated by the Internet of Things (IoT) and Cyber-Physical Systems (CPS), we consider dynamic wireless fading networks, where each incoming flow has a random service demand and leaves the system once its service request is completed. In such networks, one of the primary goals of network algorithm design is to achieve short-term fairness that characterizes how often each flow is served, in addition to the more traditional goals such as throughput-optimality and delay-insensitivity to the flow size distribution. In wireline networks, all of these desired properties can be achieved by the round-robin scheduling algorithm. In the context of wireless networks, a natural extension of round-robin scheduling has been developed in the last few years through the use of a counter called the Time-Since-Last-Service (TSLS) that keeps track of the time that passed since the last service time of each flow. However, the performance of this round-robin-like algorithm has been primarily studied in the context of persistent flows that continuously inject packets into the network and do not ever leave the network. The analysis of dynamic flow arrivals and departures is challenging since each individual flow experiences independent wireless fading and thus, flows cannot be served in a strict round-robin manner. In this paper, we overcome this difficulty by exploring the intricate dynamics of TSLS-based algorithm and show that flows are provided round-robin-like service with a very high probability. Consequently, we then show that our algorithm can achieve throughput-optimality. Moreover, through simulations, we demonstrate that the proposed TSLS-based algorithm also exhibits desired properties such as delay-insensitivity and excellent short-term fairness performance in the presence of dynamic flows over wireless fading channels.

Portal: Transparent Cross-technology Opportunistic Forwarding for Low-power Wireless Networks

Xiaolong Zheng, Dan Xia (Beijing University of Posts and Telecommunications), Xiuzhen Guo (Tsinghua University), Liang Liu (Beijing University of Posts and Telecommunications), Yuan He (Tsinghua University), Huadong Ma (Beijing University of Posts and Telecommunications)

Opportunistic forwarding seizes early forwarding opportunities in duty-cycled networks to reduce delay and energy consumption. But increasingly serious Cross-Technology Interference (CTI) significantly counteracts the benefits of opportunistic forwarding. Existing solutions try to reserve the channel for low-power networks by implicit avoidance or explicit coordination but ignore the high-power CTIÕs superior capability. In this paper, we propose a new paradigm for low-power opportunistic forwarding in CTI environments. Instead of keeping CTI devices silent, we directly involve them into the forwarding, as cross-technology forwarders. We design Portal to solve the challenges of realizing cross-technology opportunistic forwarding. To be transparent to the low-power networks, Portal adopts cross-technology rebroadcasting to enable the fast overhearing and forwarding of cross-technology data. To maximize the performance gain of using heterogeneous forwarders while minimizing the influence on legacy high-power traffic, we propose a post-forwarding forwarder selection and a traffic scheduling method. We also propose a feature-based ACK recognition method and a jamming-based ACK replying mechanism to forward the unreliable ACKs from asymmetric regions. Extensive experiments demonstrate that Portal not only avoids the CTI but also breaks through the existing performance limit.

TCCI: Taming Co-Channel Interference for Wireless LANs

Adnan Quadri, Hossein Pirayesh, Pedram Kheirkhah Sangdeh, Huacheng Zeng (University of Louisville)

Co-channel interference is a fundamental issue in wireless local area networks (WLANs). Although many results have been developed to handle co-channel interference for concurrent transmission, most of them require network-wide fine-grained synchronization and data sharing among access points (APs). Such luxuries, however, are not affordable in many WLANs due to their hardware limitation and data privacy concern. In this paper, we present TCCI, a co-channel interference management scheme to enable concurrent transmission in WLANs. TCCI requires neither network-wide fine-grained synchronization nor inter-network data sharing, and therefore is amenable to real-world implementation. The enabler of TCCI is a new detection and beamforming method for an AP, which is capable of taming unknown interference by leveraging its multiple antennas. We have built a prototype of TCCI on a wireless testbed and demonstrated its compatibility with commercial Atheros 802.11 devices. Our experimental results show that TCCI allows co-located APs to serve their users simultaneously and achieves up to 113% throughput gain compared to existing interference-avoidance protocol.

Session Chair

Francesco Restuccia (Northeastern University)

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Session S-9

Emerging Topics

2:15 PM — 4:15 PM EDT
Oct 14 Wed, 2:15 PM — 4:15 PM EDT

Private and Communication-Efficient Edge Learning: A Sparse Differential Gaussian-Masking Distributed SGD Approach

Xin Zhang, Minghong Fang, Jia Liu, Zhengyuan Zhu (Iowa State University)

With rise of machine learning (ML) and the proliferation of smart mobile devices, recent years have witnessed a surge of interest in performing ML in wireless edge networks. In this paper, we consider the problem of jointly improving data privacy and communication efficiency of distributed edge learning, both of which are critical performance metrics in wireless edge network computing. Toward this end, we propose a new distributed stochastic gradient method with sparse differential Gaussian-masked stochastic gradients (SDM-DSGD) for non-convex distributed edge learning. Our main contributions are three-fold: i) We theoretically establish the privacy and communication efficiency performance guarantee for our SDM-DSGD method, which outperforms all existing works; ii) We show that SDM-DSGD improves the fundamental training-privacy trade-off by two orders of magnitude compared with the current state-of-the-art; and iii) We reveal theoretical insights and offer practical design guidelines for the interactions between privacy preservation and communication efficiency -- two conflicting performance goals. We conduct extensive experiments with a variety of learning models on MNIST and CIFAR-10 datasets to verify our theoretical findings. Collectively, our results advance privacy-preserving and communication-efficient edge learning.

PolymoRF: Polymorphic Wireless Receivers Through Physical-Layer Deep Learning

Francesco Restuccia, Tommaso Melodia (Northeastern University)

Today's wireless technologies are largely based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. To address this key issue, wireless receivers will need to (i) infer on-the-fly the physical-layer parameters currently used by transmitters; and if needed, (ii) change their hardware and software structures to demodulate the incoming waveform. In this paper, we introduce \emph{PolymoRF}, a deep learning-based polymorphic receiver able to reconfigure itself in real time based on the inferred waveform parameters. Our key technical innovations are (i) a novel embedded deep learning architecture, called \emph{RFNet}, which enables the solution of key waveform inference problems; (ii) a generalized hardware/software architecture that integrates \emph{RFNet} with radio components and signal processing. We prototype \emph{PolymoRF} on a custom software-defined radio platform, and show through extensive over-the-air experiments that \textit{PolymoRF} achieves throughput within 87\% of a perfect-knowledge \emph{Oracle} system, thus demonstrating for the first time that polymorphic receivers are feasible.

Internet Transport Economics: Model and Analysis

Richard T. B. Ma (National University of Singapore)

With the rise of video streaming and cloud services, the Internet has evolved into a content-centric service network. As for the content providers, quality of service (QoS) is still a major concern, because quality degradation is influenced by 1) the capacities of links along the routes used for content delivery and 2) the amount of competing traffic across these links, and therefore, is very difficult to diagnose.

In this paper, we establish a novel model to study how business decisions such as capacity planning, routing strategies and peering agreements affect QoS in terms of end-to-end delay and drop rate of Internet routes. In particular, we take an economics perspective of the Internet transport service and model its supply of network capacities and demands of throughput driven by network protocols. We show that a macroscopic network equilibrium always exists and its uniqueness can be guaranteed under various scenarios. We analyze the impacts of user demands and resource capacities on the network equilibrium and provide implications of Netflix-Comcast type of peering on the QoS of users. We demonstrate that our framework can be used as a building block to understand the routing strategies under a Wardrop equilibrium and to enable further studies such as Internet peering and in-network caching.

Distributed Double Auctions for Large-Scale Device-to-Device Resource Trading

Shuqin Gao, Costas Courcoubetis, Lingjie Duan (Singapore University of Technology and Design)

Mobile users in future wireless networks face limited wireless resources such as data plan, computation capacity and energy storage. Given that some of these users may not be utilizing fully their wireless resources, device-to-device (D2D) resource sharing is a promising approach to exploit usersÕ diversity in resource use and for pooling their resources locally. In this paper, we propose a novel two-sided D2D trading market model that enables a large number of locally connected users to trade resources. Traditional resource allocation solutions are mostly centralized without considering users' local D2D connectivity constraints, becoming unscalable for large-scale trading. In addition, there may be market failure since selfish users will not truthfully report their actual valuations and quantities for buying or selling resources. To address these two key challenges, we first investigate the distributed resource allocation problem with D2D assignment constraints. Based on the greedy idea of maximum weighted matching, we propose a fast algorithm to achieve near-optimal average allocative efficiency. Then, we combine it with a new pricing mechanism that adjusts the final trading prices for buying and selling resources in a way that buyers and sellers are incentivized to truthfully report their valuations and available resource quantities. Unlike traditional double auctions with a central controller, this pricing mechanism is fully distributed in the sense that the final trading prices between each matched pair of users only depend on their own declarations and hence can be calculated locally. Finally, we analyze the repeated execution of the proposed D2D trading mechanism in multiple rounds and determine the best trading frequency.

Session Chair

Tianyi Chen (Rensselaer Polytechnic Institute)

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