arXiv subjects

Jiadong Yu

Publications and source records attributed to Jiadong Yu.

24 records · Page 2Linked to original sources

arXiv2022

IRS Assisted NOMA Aided Mobile Edge Computing with Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning

By employing powerful edge servers for data processing, mobile edge computing (MEC) has been recognized as a promising technology to support emerging computation-intensive applications. Besides, non-orthogonal multiple access (NOMA)-aided MEC system can further enhance the spectral-efficiency with massive tasks offloading. However, with more dynamic devices brought online and the uncontrollable stochastic channel environment, it is even desirable to deploy appealing technique, i.e., intelligent reflecting surfaces (IRS), in the MEC system to flexibly tune the communication environment and improve the system energy efficiency. In this paper, we investigate the joint offloading, communication and computation resource allocation for IRS-assisted NOMA MEC system. We firstly formulate a mixed integer energy efficiency maximization problem with system queue stability constraint. We then propose the Lyapunov-function-based Mixed Integer Deep Deterministic Policy Gradient (LMIDDPG) algorithm which is based on the centralized reinforcement learning (RL) framework. To be specific, we design the mixed integer action space mapping which contains both continuous mapping and integer mapping. Moreover, the award function is defined as the upper-bound of the Lyapunov drift-plus-penalty function. To enable end devices (EDs) to choose actions independently at the execution stage, we further propose the Heterogeneous Multi-agent LMIDDPG (HMA-LMIDDPG) algorithm based on distributed RL framework with homogeneous EDs and heterogeneous base station (BS) as heterogeneous multi-agent. Numerical results show that our proposed algorithms can achieve superior energy efficiency performance to the benchmark algorithms while maintaining the queue stability. Specially, the distributed structure HMA-LMIDDPG can acquire more energy efficiency gain than centralized structure LMIDDPG.

eess.SP↗

arXiv2022

Distributed Intelligence in Wireless Networks

The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, security and privacy concerns caused by billions of connected wireless devices and typically zillions bytes of data they produce at the network edge. A blend of edge computing and Artificial Intelligence (AI) techniques could optimally shift the resourceful computation servers closer to the network edge, which provides the support for advanced AI applications (e.g., video/audio surveillance and personal recommendation system) by enabling intelligent decision making on computing at the point of data generation as and when it is needed, and distributed Machine Learning (ML) with its potential to avoid the transmission of large dataset and possible compromise of privacy that may exist in cloud-based centralized learning. Therefore, AI is envisioned to become native and ubiquitous in future communication and networking systems. In this paper, we conduct a comprehensive overview of recent advances in distributed intelligence in wireless networks under the umbrella of native-AI wireless networks, with a focus on the basic concepts of native-AI wireless networks, on the AI-enabled edge computing, on the design of distributed learning architectures for heterogeneous networks, on the communication-efficient technologies to support distributed learning, and on the AI-empowered end-to-end communications. We highlight the advantages of hybrid distributed learning architectures compared to the state-of-art distributed learning techniques. We summarize the challenges of existing research contributions in distributed intelligence in wireless networks and identify the potential future opportunities.

eess.SY↗

arXiv2021

Low half-wave-voltage, ultra-high bandwidth thin-film LiNbO3 modulator based on hybrid waveguide and periodic capacitively loaded electrodes

A novel thin-film LiNbO3 (TFLN) electro-optic modulator is proposed and demonstrated. LiNbO3-silica hybrid waveguide is adopted to maintain low optical loss for an electrode spacing as narrow as 3 μm, resulting in a record low half-wave-voltage length product of only 1.7 V*cm. Capacitively loaded traveling-wave electrodes (CL-TWEs) are employed to reduce the microwave loss, while quartz substrate is used in place of silicon substrate to achieve velocity matching. The fabricated TFLN modulator with a 5-mm-long modulation region exhibits a half-wave-voltage of 3.4 V and merely 1.3 dB roll-off in electro-optic response up to 67 GHz, and a 3-dB modulation bandwidth over 110 GHz is predicted.

physics.app-ph↗

arXiv2020

Soliton dynamics in microresonators with XPM induced negative thermo-optic effect

Optical frequency comb generation in microresonators has attracted significant attention over the past decade, as it offers the promising potential for chip-scale optical frequency synthesis, optical clocks and precise optical spectroscopy. However, accessing temporal dissipative Kerr soliton (DKSs) is known to be severely hampered by thermal effects. Furthermore, due to the degeneracy of soliton existence range with respect to soliton number, deterministically accessing single soliton state is another challenge. Here, we demonstrate stable and deterministic single soliton generation in AlN-on-sapphire platform via auxiliary laser pumping scheme without the requirement of fast control of the pump power and detuning. Moreover, we reveal the underlying physics of soliton switching in a dual-pumped microcomb, which is fully described by the Lugiato - Lefever equation. The switching process is attributed to cross-phase modulation (XPM) induced degeneracy lifting of the soliton existence range, corresponding to an effective negative thermo-optic effect.

physics.optics↗

arXiv2020

Integrated Gallium Nitride Nonlinear Photonics

Gallium nitride (GaN) as a wide-band gap material has been widely used in solid-state lighting. Thanks to its high nonlinearity and high refractive index contrast, GaN-on-insulator (GaNOI) is also a promising platform for nonlinear optical applications. Despite its intriguing optical proprieties, nonlinear applications of GaN have rarely been studied due to the relatively high optical loss of GaN waveguides (2 dB/cm). In this letter, we report GaNOI microresonator with intrinsic quality factor over 2 million, corresponding to an optical loss of 0.26 dB/cm. Parametric oscillation threshold power as low as 8.8 mW is demonstrated, and the experimentally extracted nonlinear index of GaN at telecom wavelengths is estimated to be n2 = 1.2*10 -18 m2W-1, which is comparable with silicon. Single soliton generation in GaN is implemented by an auxiliary laser pumping scheme, so as to mitigate the high thermorefractive effect in GaN. The large intrinsic nonlinear refractive index, together with its broadband transparency window and high refractive index contrast, make GaNOI a most balanced platform for chip-scale nonlinear applications.

physics.optics↗

arXiv2020

Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing

To support popular Internet of Things (IoT) applications such as virtual reality, mobile games and wearable devices, edge computing provides a front-end distributed computing archetype of centralized cloud computing with low latency. However, it's challenging for end users to offload computation due to their massive requirements on spectrum and computation resources and frequent requests on Radio Access Technology (RAT). In this paper, we investigate computation offloading mechanism with resource allocation in IoT edge computing networks by formulating it as a stochastic game. Here, each end user is a learning agent observing its local environment to learn optimal decisions on either local computing or edge computing with the goal of minimizing long term system cost by choosing its transmit power level, RAT and sub-channel without knowing any information of the other end users. Therefore, a multi-agent reinforcement learning framework is developed to solve the stochastic game with a proposed independent learners based multi-agent Q-learning (IL-based MA-Q) algorithm. Simulations demonstrate that the proposed IL-based MA-Q algorithm is feasible to solve the formulated problem and is more energy efficient without extra cost on channel estimation at the centralized gateway compared to the other two benchmark algorithms.

eess.SP↗