arXiv subjects

Yuguang Fang

Publications and source records attributed to Yuguang Fang.

98 records · Page 6Linked to original sources

arXiv2020

Secure Transmission by Leveraging Multiple Intelligent Reflecting Surfaces in MISO Systems

Due to the broadcast nature of wireless communications, physical layer security has always been a fundamental but challenging problem. Recent advance of Intelligent Reflecting Surface (IRS) introduces a new dimension for secure communications by reconfiguring the transmission environments. In this paper, we devise a secure transmission scheme for multi-user MISO systems by leveraging multiple collaborative IRSs. Specifically, to guarantee the worst-case achievable secrecy rate among multiple legitimate users, we formulate a max-min problem that can be solved by an alternative optimization method to decouple it into multiple sub-problems. Based on semidefinite relaxation and successive convex approximation, each sub-problem can be further converted into convex problem and easily solved. Extensive experimental results demonstrate that our proposed scheme can adapt to complex scenarios for multiple users and achieve significant gain in terms of achievable secrecy rate. The gap between the secrecy rate from our proposed scheme and that from the traditional sum-rate maximization and our results show that the secrecy rate obtained from our scheme converges to that achieved from the traditional sum-rate maximization when increasing the number of elements on IRSs.

eess.SP↗

arXiv2020

Optimizing IoT Energy Efficiency on Edge (EEE): a Cross-layer Design in a Cognitive Mesh Network

Battery-powered wireless IoT devices are now widely seen in many critical applications. Given the limited battery capacity and inaccessibility to external power recharge, optimizing energy efficiency (EE) plays a vital role in prolonging the lifetime of these IoT devices. However, a sheer amount of existing works only focus on the EE design at the infrastructure level such as base stations (BSs) but with little attention to the EE design at the device level. In this paper, we propose a novel idea that aims to shift energy consumption to a grid-powered cognitive radio mesh network thus preserving energy of battery-powered devices. Under this line of thinking, we cast the design into a cross-layer optimization problem with an objective to maximize devices' energy efficiency. To solve this problem, we propose a parametric transformation technique to convert the original problem into a more tractable one. A baseline scheme is used to demonstrate the advantage of our design. We also carry out extensive simulations to exhibit the optimality of our proposed algorithms and the network performance under various settings.

eess.SP↗

arXiv2020

Creating Efficient Blockchains for the Internet of Things by Coordinated Satellite-Terrestrial Networks

Blockchain has emerged as a promising technology that can guarantee data consistency and integrity among distributed participants. It has been used in many applications of the Internet of Things (IoT). However, since IoT applications often introduce a massive number of devices into blockchain systems, the efficiency of the blockchain becomes a serious problem. In this article, we analyze the key factors affecting the efficiency of blockchain. Unlike most existing solutions that handle this from the computing perspective, we consider the problem from the communication perspective. Particularly, we propose a coordinated satellite-terrestrial network to create efficient blockchains. We also derive a network scheduling strategy for the proposed architecture. Simulation results demonstrate that the proposed system can support blockchains for higher efficiency. Moreover, several open research issues and design challenges will be discussed.

cs.NI↗

arXiv2018

IEEE 802.11ay based mmWave WLANs: Design Challenges and Solutions

Millimeter-wave (mmWave) with large spectrum available is considered as the most promising frequency band for future wireless communications. The IEEE 802.11ad and IEEE 802.11ay operating on 60 GHz mmWave are the two most expected wireless local area network (WLAN) technologies for ultra-high-speed communications. For the IEEE 802.11ay standard still under development, there are plenty of proposals from companies and researchers who are involved with the IEEE 802.11ay task group. In this survey, we conduct a comprehensive review on the medium access control layer (MAC) related issues for the IEEE 802.11ay, some cross-layer between physical layer (PHY) and MAC technologies are also included. We start with MAC related technologies in the IEEE 802.11ad and discuss design challenges on mmWave communications, leading to some MAC related technologies for the IEEE 802.11ay. We then elaborate on important design issues for IEEE 802.11ay. Specifically, we review the channel bonding and aggregation for the IEEE 802.11ay, and point out the major differences between the two technologies. Then, we describe channel access and channel allocation in the IEEE 802.11ay, including spatial sharing and interference mitigation technologies. After that, we present an in-depth survey on beamforming training (BFT), beam tracking, single-user multiple-input-multiple-output (SU-MIMO) beamforming and multi-user multiple-input-multiple-output (MU-MIMO) beamforming. Finally, we discuss some open design issues and future research directions for mmWave WLANs. We hope that this paper provides a good introduction to this exciting research area for future wireless systems.

cs.NI↗

arXiv2017

A Robust Dynamic Edge Network Architecture for the Internet-of-Things

A massive number of devices are expected to fulfill the missions of sensing, processing and control in cyber-physical Internet-of-Things (IoT) systems with new applications and connectivity requirements. In this context, scarce spectrum resources must accommodate a high traffic volume with stringent requirements of low latency, high reliability and energy efficiency. Conventional centralized network architectures may not be able to fulfill these requirements due to congestion in backhaul links. This article presents a novel design of a robust dynamic edge network architecture (RDNA) for IoT which leverages the latest advances of mobile devices (e.g., their capability to act as access points, storing and computing capabilities) to dynamically harvest unused resources and mitigate network congestion. However, traffic dynamics may compromise the availability of terminal access points and channels and, thus, network connectivity. The proposed design embraces solutions at physical, access, networking, application, and business layers to improve network robustness. The high density of mobile devices provides alternatives for close connectivity which reduces interference and latency, and thus, increases reliability and energy efficiency. Moreover, the computing capabilities of mobile devices project smartness onto the edge which is desirable for autonomous and intelligent decision making. A case study is included to illustrate the performance of RDNA. Potential applications of this architecture in the context of IoT are outlined. Finally, some challenges for future research are presented.

cs.NI↗

arXiv2017

Enhanced Random Access and Beam Training for mmWave Wireless Local Networks with High User Density

As low frequency band becomes more and more crowded, millimeter-wave (mmWave) has attracted significant attention recently. IEEE has released the 802.11ad standard to satisfy the demand of ultra-high-speed communication. It adopts beamforming technology that can generate directional beams to compensate for high path loss. In the Association Beamforming Training (A-BFT) phase of beamforming (BF) training, a station (STA) randomly selects an A-BFT slot to contend for training opportunity. Due to the limited number of A-BFT slots, A-BFT phase suffers high probability of collisions in dense user scenarios, resulting in inefficient training performance. Based on the evaluation of the IEEE 802.11ad standard and 802.11ay draft in dense user scenarios of mmWave wireless networks, we propose an enhanced A-BFT beam training and random access mechanism, including the Separated A-BFT (SA-BFT) and Secondary Backoff A-BFT (SBA-BFT). The SA-BFT can provide more A-BFT slots and divide A-BFT slots into two regions by defining a new `E-A-BFT Length' field compared to the legacy 802.11ad A-BFT, thereby maintaining compatibility when 802.11ay devices are mixed with 802.11ad devices. It can also reduce the collision probability in dense user scenarios greatly. The SBA-BFT performs secondary backoff with very small overhead of transmission opportunities within one A-BFT slot, which not only further reduces collision probability, but also improves the A-BFT slots utilization. Furthermore, we propose a three-dimensional Markov model to analyze the performance of the SBA-BFT. The analytical and simulation results show that both the SA-BFT and the SBA-BFT can significantly improve BF training efficiency, which are beneficial to the optimization design of dense user wireless networks based on the IEEE 802.11ay standard and mmWave technology.

cs.NI↗

arXiv2017

Session-Based Cooperation in Cognitive Radio Networks: A Network-Level Approach

In cognitive radio networks (CRNs), secondary users (SUs) can proactively obtain spectrum access opportunities by helping with primary users' (PUs') data transmissions. Currently, such kind of spectrum access is implemented via a cooperative communications based link-level frame-based cooperative (LLC) approach where individual SUs independently serve as relays for PUs in order to gain spectrum access opportunities. Unfortunately, this LLC approach cannot fully exploit spectrum access opportunities to enhance the throughput of CRNs and fails to motivate PUs to join the spectrum sharing processes. To address these challenges, we propose a network-level session-based cooperative (NLC) approach where SUs are grouped together to cooperate with PUs session by session, instead of frame by frame as what has been done in existing works, for spectrum access opportunities of the corresponding group. Thanks to our group-based session-by-session cooperating strategy, our NLC approach is able to address all those challenges in the LLC approach. To articulate our NLC approach, we further develop an NLC scheme under a cognitive capacity harvesting network (CCHN) architecture. We formulate the cooperative mechanism design as a cross-layer optimization problem with constraints on primary session selection, flow routing and link scheduling. To search for solutions to the optimization problem, we propose an augmented scheduling index ordering based (SIO-based) algorithm to identify maximal independent sets. Through extensive simulations, we demonstrate the effectiveness of the proposed NLC approach and the superiority of the augmented SIO-based algorithm over the traditional method.

cs.IT↗

arXiv2016

A Novel Collaborative Cognitive Dynamic Network Architecture

Increasing mobile data demands in current cellular networks and proliferation of advanced handheld devices have given rise to a new generation of dynamic network architectures (DNAs). In a DNA, users share their connectivities and act as access points providing Internet connections for others without additional network infrastructure cost. A large number of users and their dynamic connections make DNA highly adaptive to variations in the network and suitable for low cost ubiquitous Internet connectivity. In this article, we propose a novel collaborative cognitive dynamic network architecture (CDNA) which incorporates cognitive capabilities to exploit underutilized spectrum in a more flexible and intelligent way. The design principles of CDNA are perfectly aligned to the functionality requirements of future 5G wireless networks such as energy and spectrum efficiency, scalability, dynamic reconfigurability, support for multi-hop communications, infrastructure sharing, and multi-operator cooperation. A case study with a new resource allocation problem enabled by CDNA is conducted using matching theory with pricing to illustrate the potential benefits of CDNA for users and operators, tackle user associations for data and spectrum trading with low complexity, and enable self-organizing capabilities. Finally, possible challenges and future research directions are given.

cs.GT↗