### IEEE INFOCOM 2022

## 5G and mmW Networks

### A Comparative Measurement Study of Commercial 5G mmWave Deployments

Arvind Narayanan (University of Minnesota, USA); Muhammad Iqbal Rochman (University of Chicago, USA); Ahmad Hassan (University of Minnesota, USA); Bariq S. Firmansyah (Institut Teknologi Bandung, Indonesia); Vanlin Sathya (University of Chicago, USA); Monisha Ghosh (University Of Chicago, USA); Feng Qian (University of Minnesota, Twin Cities, USA); Zhi-Li Zhang (University of Minnesota, USA)

of beams used, number of channels aggregated, and density of deployments, which reflect on the throughput performance. Our measurement-driven propagation analysis demonstrates that narrower beams experience a lower path-loss exponent than wider beams, which combined with up to eight frequency channels

aggregated on up to eight beams can deliver a peak throughput of 1.2 Gbps at distances greater than 100 m.

### AI in 5G: The Case of Online Distributed Transfer Learning over Edge Networks

Yulan Yuan (Beijing University of Posts and Telecommunications, China); Lei Jiao (University of Oregon, USA); Konglin Zhu (Beijing University of Posts and Telecommunications, China); Xiaojun Lin (Purdue University, USA); Lin Zhang (Beijing University of Posts and Telecommunications, China)

### mmPhone: Acoustic Eavesdropping on Loudspeakers via mmWave-characterized Piezoelectric Effect

Chao Wang, Feng Lin, Tiantian Liu, Ziwei Liu, Yijie Shen, Zhongjie Ba and Li Lu (Zhejiang University, China); Wenyao Xu (SUNY Buffalo & Wireless Health Institute, USA); Kui Ren (Zhejiang University, China)

### Optimizing Coverage with Intelligent Surfaces for Indoor mmWave Networks

Jingyuan Zhang and Douglas Blough (Georgia Institute of Technology, USA)

###### Session Chair

Xiaojun Lin (Purdue University)

## Algorithms 1

### Copa+: Analysis and Improvement of thedelay-based congestion control algorithm Copa

Wanchun Jiang, Haoyang Li, Zheyuan Liu, Jia Wu and Jiawei Huang (Central South University, China); Danfeng Shan (Xi'an Jiaotong University, China); Jianxin Wang (Central South University, China)

### Learning for Robust Combinatorial Optimization: Algorithm and Application

Zhihui Shao (UC Riverside, USA); Jianyi Yang (University of California, Riverside, USA); Cong Shen (University of Virginia, USA); Shaolei Ren (University of California, Riverside, USA)

inner optimization problem, which is typically non-convex and entangled with outer optimization. In this paper, we study robust combinatorial optimization and propose a novel learning-based optimizer, called LRCO (Learning for Robust Combinatorial Optimization), which quickly outputs a robust solution in the presence of uncertain context. LRCO leverages a pair of learning-based optimizers - one for the minimizer and the other for the maximizer - that use their respective objective functions as losses and can be trained without the need of labels for training problem instances. To evaluate the performance of LRCO, we perform simulations for the task offloading problem in vehicular edge computing. Our results highlight that LRCO can greatly reduce the worst-case cost, with low runtime complexity.

### Polynomial-Time Algorithm for the Regional SRLG-disjoint Paths Problem

Balázs Vass (Budapest University of Technology and Economics, Hungary); Erika R. Bérczi-Kovács and Ábel Barabás (Eötvös University, Budapest, Hungary); Zsombor László Hajdú and János Tapolcai (Budapest University of Technology and Economics, Hungary)

### Provably Efficient Algorithms for Traffic-sensitive SFC Placement and Flow Routing

Yingling Mao, Xiaojun Shang and Yuanyuan Yang (Stony Brook University, USA)

###### Session Chair

En Wang (Jilin University)

## Algorithms 2

### A Unified Model for Bi-objective Online Stochastic Bipartite Matching with Two-sided Limited Patience

Gaofei Xiao and Jiaqi Zheng (Nanjing University, China); Haipeng Dai (Nanjing University & State Key Laboratory for Novel Software Technology, China)

### Lazy Self-Adjusting Bounded-Degree Networks for the Matching Model

Evgeniy Feder (ITMO University, Russia); Ichha Rathod and Punit Shyamsukha (Indian Institute of Technology Delhi, India); Robert Sama (University of Vienna, Austria); Vitaly Aksenov (ITMO University, Russia); Iosif Salem and Stefan Schmid (University of Vienna, Austria)

We initiate the study of online algorithms for SANs in a more realistic cost model, the Matching Model (MM), in which the network topology is given by the union of a constant number of bipartite matchings (realized by optical switches), and in which changing an entire matching incurs a fixed cost \alpha The cost of routing is given by the number of hops packets need to traverse.

Our main result is a lazy topology adjustment method for designing efficient online SAN algorithms in the MM. We design and analyze online SAN algorithms for line, tree, and bounded degree networks in the MM, with cost O(\sqrt{\alpha}) times the cost of reference algorithms in the uniform cost model (SM). We report on empirical results considering publicly available datacenter network traces, that verify the theoretical bounds.

### Maximizing h-hop Independently Submodular Functions Under Connectivity Constraint

Wenzheng Xu and Dezhong Peng (Sichuan University, China); Weifa Liang and Xiaohua Jia (City University of Hong Kong, Hong Kong); Zichuan Xu (Dalian University of Technology, China); Pan Zhou (School of CSE, Huazhong University of Science and Technology, China); Weigang Wu and Xiang Chen (Sun Yat-sen University, China)

### Optimal Shielding to Guarantee Region-Based Connectivity under Geographical Failures

Binglin Tao, Mingyu Xiao, Bakhadyr Khoussainov and Junqiang Peng (University of Electronic Science and Technology of China, China)

###### Session Chair

Song Fang (University of Oklahoma)

## Algorithms 3

### Ao\(^2\)I: Minimizing Age of Outdated Information to Improve Freshness in Data Collection

Qingyu Liu, Chengzhang Li, Thomas Hou, Wenjing Lou and Jeffrey Reed (Virginia Tech, USA); Sastry Kompella (Naval Research Laboratory, USA)

### CausalRD: A Causal View of Rumor Detection via Eliminating Popularity and Conformity Biases

Weifeng Zhang, Ting Zhong and Ce Li (University of Electronic Science and Technology of China, China); Kunpeng Zhang (University of Maryland, USA); Fan Zhou (University of Electronic Science and Technology of China, China)

To overcome such an issue and alleviate the bias from these two factors, we propose a rumor detection framework to learn debiased user preference and effective event representation in a causal view. We first build a graph to capture causal relationships among users, events, and their interactions. Then we apply the causal intervention to eliminate popularity and conformity biases and obtain debiased user preference representation. Finally, we leverage the power of graph neural networks to aggregate learned user representation and event features for the final event type classification. Empirical experiments conducted on two real-world datasets demonstrate the effectiveness of our proposed approach compared to several cutting-edge baselines.

### Learning from Delayed Semi-Bandit Feedback under Strong Fairness Guarantees

Juaren Steiger (Queen's University, Canada); Bin Li (The Pennsylvania State University, USA); Ning Lu (Queen's University, Canada)

### Optimizing Sampling for Data Freshness: Unreliable Transmissions with Random Two-way Delay

Jiayu Pan and Ahmed M Bedewy (The Ohio State University, USA); Yin Sun (Auburn University, USA); Ness B. Shroff (The Ohio State University, USA)

###### Session Chair

Zhangyu Guan (University at Buffalo)

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