Vincent W. S. Wong

University of British Columbia

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Robert Schober

Friedrich-Alexander-Universität Erlangen-Nürnberg

Victor C. M. Leung

University of British Columbia

Vahid Shah‐Mansouri

University of Tehran

Amir-Hamed Mohsenian-Rad

Texas Tech University

Shahab Bahrami

University of British Columbia

Derrick Wing Kwan Ng

UNSW Sydney

Yong Zhou

ShanghaiTech University

Lin Xiang

Technical University of Darmstadt

Man Hon Cheung

City University of Hong Kong

Hamed Shah‐Mansouri

Sharif University of Technology

Cooperative Institutions

University of British Columbia

European Foundation for the Study of Chronic Liver Failure

Inserm

Chinese University of Hong Kong

Assistance Publique – Hôpitaux de Paris

University of Hong Kong

Huazhong University of Science and Technology

Chinese Academy of Sciences

Sun Yat-sen University

University of Florence

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Pinching Antennas: Principles, Applications and Challenges

arXiv (Cornell University) (2025)

Zheng Yang Ning Wang Yanshi Sun Zhiguo Ding Robert Schober

Flexible-antenna systems, such as fluid antennas and movable antennas, have been recognized as key enabling technologies for sixth-generation (6G) wireless networks, as they can intelligently reconfigure the effective channel gains of the users and hence significantly improve their data transmission capabilities. However, existing flexible-antenna systems have been designed to combat small-scale fading in non-line-of-sight (NLoS) conditions. As a result, they lack the ability to establish line-of-sight links, which are typically 100 times stronger than NLoS links. In addition, existing flexible-antenna systems have limited flexibility, where adding/removing an antenna is not straightforward. This article introduces an innovative flexible-antenna system called pinching antennas, which are realized by applying small dielectric particles to waveguides. We first describe the basics of pinching-antenna systems and their ability to provide strong LoS links by deploying pinching antennas close to the users as well as their capability to scale up/down the antenna system. We then focus on communication scenarios with different numbers of waveguides and pinching antennas, where innovative approaches to implement multiple-input multiple-output and non-orthogonal multiple access are discussed. In addition, promising 6G-related applications of pinching antennas, including integrated sensing and communication and next-generation multiple access, are presented. Finally, important directions for future research, such as waveguide deployment and channel estimation, are highlighted.

10.48550/arxiv.2501.10753

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TCP VON: Joint congestion control and online network coding for wireless networks

2015 IEEE Global Communications Conference (GLOBECOM) (2012)

Wei Bao Vahid Shah‐Mansouri Vincent W. S. Wong Victor C. M. Leung

In this paper, we propose TCP Vegas with online network coding (TCP VON), which incorporates online network coding into TCP. It is shown that the use of online network coding in transport layer can improve the throughput and reliability of the end-to-end communication. Compared to generation based network coding, in online network coding, packets can be decoded consecutively instead of generation by generation. Thus, online network coding incurs a low decoding delay. In TCP VON, the sender transmits redundant coded packets when it detects packet losses from acknowledgement. Otherwise, it transmits innovative coded packets. We establish a Markov chain to analytically model the average decoding delay of TCP VON. We also conduct ns-2 simulations to validate the proposed analytical model. Finally, we compare the delay and throughput performance of TCP VON and automatic repeat request (ARQ) network coding based TCP (TCP ARQNC). Simulation results show that TCP VON outperforms TCP ARQNC in terms of the average decoding delay and network throughput.

Linear network coding

TCP Vegas

CUBIC TCP

TCP Westwood plus

10.1109/glocom.2012.6503101

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Citations (7)

Joint Energy Allocation for Sensing and Transmission in Rechargeable Wireless Sensor Networks

IEEE Transactions on Vehicular Technology (2014)

Shaobo Mao Man Hon Cheung Vincent W. S. Wong

Different from a traditional wireless sensor network (WSN) powered by nonrechargeable batteries, the energy management policy of a rechargeable WSN needs to take into account the process of energy harvesting. In this paper, we study the energy allocation for sensing and transmission in an energy harvesting sensor node with a rechargeable battery and a finite data buffer. The sensor aims to maximize the expected total amount of data transmitted until the sensor stops functioning subject to time-varying energy harvesting rate, energy availability in the battery, data availability in the data buffer, and channel fading. Since the lifetime of the sensor is a random variable, we formulate the energy allocation problem as an infinite-horizon Markov decision process (MDP), and propose an optimal energy allocation (OEA) algorithm using the value iteration. We then consider a special case with infinite data backlog and prove that the optimal transmission energy allocation (OTEA) policy is monotonic with respect to the amount of battery energy available. Finally, we conduct extensive simulations to compare the performance of our OEA algorithm, OTEA algorithm, the finite-horizon transmission energy allocation (FHTEA) algorithm extended from [2], and the finite-horizon OEA (FHOEA) algorithm from [1]. Simulation results show that the OEA algorithm transmits the largest amount of data, and the OTEA algorithm can achieve a near-optimal performance with low computational complexity.

10.1109/tvt.2013.2295603

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Citations (88)

A potential game framework for charging PHEVs in smart grid

Shahab Bahrami Vincent W. S. Wong

Due to the proliferation of plug-in hybrid electric vehicles (PHEVs), the peak load in the power grid is expected to increase in future. The peak load can be reduced by implementing appropriate load scheduling schemes using advanced metering infrastructure (AMI) and smart chargers. In this paper, we formulate the charging problem of PHEVs as a potential game to jointly optimize the cost of the utility company and payoff of the customers. The potential game approach enables us to study the existence and uniqueness of the pure strategy Nash equilibrium and to design a polynomial time distributed algorithm to achieve that equilibrium. It also enables us to define a Lyapunov function to show that the Nash equilibrium is globally asymptotically stable, i.e., the proposed distributed algorithm converges to the Nash equilibrium from any arbitrary initial conditions. To evaluate the efficiency of our proposed algorithm, we compare its running time with an algorithm based on the customers' best response.

Potential game

Demand Response

10.1109/pacrim.2015.7334804

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Citations (27)

Bayesian Meta-Learning for Adaptive Traffic Prediction in Wireless Networks

IEEE Transactions on Mobile Computing (2023)

Zihuan Wang Vincent W. S. Wong

Wireless traffic prediction is indispensable for network planning and resource management. Due to different population distributions and user behavior, there exist strong spatial-temporal variations in wireless traffic across different regions. Most of the conventional traffic prediction approaches can only tackle a particular spatial-temporal pattern and cannot capture such variations in wireless traffic. This motivates us to develop an adaptive approach which can tackle spatial-temporal variations and predict wireless traffic in different regions. In this paper, we formulate an adaptive traffic prediction problem from a probabilistic inference perspective and develop a variational spatial-temporal Bayesian meta-learning (VST-BML) algorithm. We model the traffic prediction in different regions as different prediction tasks. The proposed VST-BML algorithm can learn the common spatial-temporal features shared by all prediction tasks, and adaptively infer the task-specific parameters to tackle spatial-temporal variations. We evaluate the performance of our proposed VST-BML algorithm using a real-world traffic dataset. Experimental results show that the proposed algorithm can quickly adapt to different prediction tasks by using only a small number of data samples and provide accurate traffic prediction in different regions. When compared with five baseline methods, the proposed algorithm can reduce the root- mean-square error (RMSE) and mean absolute error (MAE) by 53.0% and 48.4%, respectively.

Baseline (sea)

10.1109/tmc.2023.3325301

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Security-Constrained Unit Commitment for AC-DC Grids With Generation and Load Uncertainty

IEEE Transactions on Power Systems (2017)

Shahab Bahrami Vincent W. S. Wong

The uncertainties in renewable generators and load demand make it a challenge for system operators to execute the security-constrained unit commitment (SCUC) program in an ac-dc grid. The SCUC is a nonlinear mixed-integer optimization problem due to the power flow equations, constraints imposed by the ac-dc converters, and the binary variables associated with the generators' on/off state. In this paper, we study the SCUC problem in ac-dc grids with generation and load uncertainty. We introduce the concept of conditional value-at-risk to limit the risk of deviations in the load demand and renewable generation. We relax the binary variables and introduce a l ₁ -norm regularization term to the objective function, and then use convex relaxation techniques to transform the problem into a semidefinite program (SDP). We develop an algorithm based on the iterative reweighted l ₁ -norm approximation that involves solving a sequence of SDPs. Simulations are performed on an IEEE 30-bus test system. Results show that the proposed algorithm returns a solution within 2% gap from the global optimal solution for the underlying test system. When compared with the multi-stage algorithm in the literature, our algorithm has a lower running time and returns a solution with a smaller gap from the global optimal solution.

Power system simulation

10.1109/tpwrs.2017.2749303

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Citations (51)

A Blockchain-Empowered Incentive Mechanism for Cross-Silo Federated Learning

IEEE Transactions on Mobile Computing (2024)

Ming Tang Peng Fu Vincent W. S. Wong

In cross-silo federated learning (FL), organizations cooperatively train a global model with their local datasets. However, some organizations may act as free riders such that they only contribute a small amount of resources but can obtain a high-accuracy global model. Meanwhile, some organizations can be business competitors, and they do not trust each other or any third-party entity. In this work, our goal is to design a framework that motivates efficient cooperation among organizations without the coordination of a central entity. To this end, we propose a blockchain-empowered incentive mechanism framework for cross-silo FL. Under this incentive mechanism framework, we develop a distributed algorithm that enables organizations to achieve social efficiency, individual rationality, and budget balance without private information of the organizations. Our proposed algorithm has a proven convergence guarantee and empirically achieves a higher convergence rate than a benchmark method. Moreover, we propose a transaction minimization algorithm to reduce the number of transactions made among organizations in the blockchain. This algorithm is proven to achieve a performance no worse than twice the minimum value. The experimental results in a testbed show that our proposed framework enables organizations to achieve social efficiency within a relatively short iterative process.

Mechanism Design

10.1109/tmc.2024.3361089

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Citations (2)

Coordinated channel selection in cognitive macro-femto networks

Binglai Niu Vincent W. S. Wong

In this paper, we study uplink channel selection in a system where a macro base station (MBS) and a number of cognitive femto base stations (FBSs) share the same spectrum to serve their intended users with quality of service (QoS) requirements. In this system, the MBS may experience significant aggregate interference when multiple FBSs select the same channel to serve their FUs. An FBS also experiences strong interference from nearby femtocells if the same channel is utilized by adjacent FBSs. We investigate how to coordinate the channel selection at FBSs to reduce the interference experienced at the MBS and FBSs. We propose a cluster-based coordination mechanism where the operator groups the set of FBSs into clusters, and FBSs in the same cluster can utilize a set of channels simultaneously without violating the QoS requirements. To find the desired clusters, we employ a graph-theoretic approach and propose an efficient FBS clustering scheme. Simulation results show that the proposed coordination mechanism achieves a better performance compared to the channel selection scheme without coordination.

Femtocell

Selection algorithm

Macro

10.1109/icc.2014.6883759

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Optimal Data Transmission and Channel Code Rate Allocation in Multi-Path Wireless Networks

Keivan Ronasi Amir-Hamed Mohsenian-Rad Vincent W. S. Wong Sathish Gopalakrishnan Robert Schober

Wireless links are often unreliable and prone to transmission error due to varying channel conditions. These can degrade the performance in wireless networks, particularly for applications with tight quality-of-service requirements. A common remedy is to use channel coding where the transmitter node adds redundant bits to the transmitted packets in order to reduce the error probability at the receiver. However, this per-link solution can compromise the link data rate, leading to undesired end-to-end performance. In this paper, we show that this latter shortcoming can be mitigated if the end-to-end transmission rates and channel code rates are selected properly over multiple routing paths. We formulate the joint channel coding and end-to-end data rate allocation problem in multipath wireless networks as a network throughput maximization problem, which is non-convex. We tackle the non-convexity by using function approximation and iterative techniques from signomial programming. Simulation results confirm that by using channel coding jointly with multi-path routing, the end-to-end network performance can be improved significantly.

10.1109/icc.2011.5962917

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Citations (3)

An Online Learning Algorithm for Demand Response in Smart Grid

IEEE Transactions on Smart Grid (2017)

Shahab Bahrami Vincent W. S. Wong Jianwei Huang

Demand response program with real-time pricing can encourage electricity users toward scheduling their energy usage to off-peak hours. A user needs to schedule the energy usage of his appliances in an online manner since he may not know the energy prices and the demand of his appliances ahead of time. In this paper, we study the users' long-term load scheduling problem and model the changes of the price information and load demand as a Markov decision process, which enables us to capture the interactions among users as a partially observable stochastic game. To make the problem tractable, we approximate the users' optimal scheduling policy by the Markov perfect equilibrium (MPE) of a fully observable stochastic game with incomplete information. We develop an online load scheduling learning (LSL) algorithm based on the actor-critic method to determine the users' MPE policy. When compared with the benchmark of not performing demand response, simulation results show that the LSL algorithm can reduce the expected cost of users and the peak-to-average ratio in the aggregate load by 28% and 13%, respectively. When compared with the short-term scheduling policies, the users with the long-term policies can reduce their expected cost by 17%.

Demand Response

Fictitious play

Benchmark (surveying)

10.1109/tsg.2017.2667599

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Citations (140)