| 1. |
- Hong, Yuanyuan, et al.
(författare)
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A Multi-Floor Arrayed Waveguide Grating Based Architecture with Grid Topology for Datacenter Networks
- 2020
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Ingår i: IEEE Access. - 2169-3536 .- 2169-3536. ; 8, s. 107134-107145
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a grid topology based passive optical interconnect (POI) architecture that is composed of multiple floors of arrayed waveguide grating routers (AWGRs) to offer high connectivity and scalability for datacenter networks. In the proposed POI signal only needs to pass one AWGR, and thus can avoid the crosstalk accumulation and cascaded filtering effects, which exist in many existing POI architectures based on cascaded AWGRs. Meanwhile, due to high connectivity, the proposed grid topology based POI also has the potential advantage of high reliability. Simulation results validate the network performance. With a proper node degree, the proposed grid topology can achieve acceptable blocking probability. Besides, steady performance is kept when the number of floors increases, indicating good scalability of the proposed POI.
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| 2. |
- Yaghoubi, Forough, 1988-, et al.
(författare)
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Techno-economic and business feasibility analysis of 5G transport networks
- 2019
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Ingår i: Optical and Wireless Convergence for 5G Networks. - : Wiley. ; , s. 273-295
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This chapter introduces a techno-economic framework that provides a complete market analysis of the various business actors for any type of mobile access network deployments. It presents a case study where the proposed business feasibility framework is applied. The chapter presents a comprehensive techno-economic framework for estimating the total cost of ownership (TCO) of a backhaul network segment as well as for analyzing the business viability of a given wireless network deployment. It focuses on two backhaul technologies: microwave and fiber. The chapter addresses the framework proposed specifically only the backhaul segment, but it is general enough to also be applied to the other 5G transport solutions. It also presents the TCO module used in the proposed framework. The module covers both the Capital Expenditure and the Operational Expenditure aspects of the backhaul segment. The backhaul network is responsible for aggregating the users' traffic from the wireless access to the metro/backbone segment of the network.
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| 3. |
- Li, Jun, 1988, et al.
(författare)
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Bandwidth Slicing to Boost Federated Learning over Passive Optical Networks
- 2020
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Ingår i: IEEE Communications Letters. - : Institute of Electrical and Electronics Engineers Inc.. - 1089-7798 .- 1558-2558. ; 24:7, s. 1492-1495
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Tidskriftsartikel (refereegranskat)abstract
- During federated learning (FL) process, each client needs to periodically upload local model parameters and download global model parameters to/from the central server, thus requires efficient communications. Meanwhile, passive optical network (PON) is promising to support fog computing where FL tasks can be executed and the traffic generated by FL needs to be transmitted together with other types of traffic for broadband access. In this letter, a bandwidth slicing algorithm in PONs is introduced for efficient FL, in which bandwidth is reserved for the involved ONUs collaboratively and mapped into each polling cycle. Results reveal that the proposed bandwidth slicing significantly improves training efficiency while achieving good learning accuracy for the FL task running over the PON.
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| 4. |
- Li, Jun, 1988, et al.
(författare)
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Enabling technologies for low-latency service migration in 5G transport networks [Invited]
- 2021
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Ingår i: Journal of Optical Communications and Networking. - : Institute of Electrical and Electronics Engineers Inc.. - 1943-0620 .- 1943-0639. ; 13:2, s. A200-A210
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Tidskriftsartikel (refereegranskat)abstract
- The fifth generation (5G) mobile communications system is envisioned to serve various mission-critical services such as industrial automation, cloud robotics, and safety-critical vehicular communications. To satisfy the stringent end-to-end latency requirement of these services, fog computing has been regarded as a promising technology to be integrated into 5G networks, in which computing, storage, and network functions are provisioned close to end users, thus significantly reducing the latency caused in transport networks. However, in the context of fog-computing-enabled 5G networks, the high mobility feature of users brings critical challenges to satisfy the stringent quality of service requirements. To address this issue, service migration, which transmits the associated services from the current fog server to the target one to follow the users' travel trace and keep the service continuity, has been considered. However, service migration cannot always be completed immediately and may lead to a situation where users experience a loss of service access. In this regard, low-latency service migration plays a key role to reduce the negative effects on services being migrated. In this paper, the factors that affect the performance of service migration are analyzed. To enable low-latency service migration, three main enabling technologies are reviewed, including migration strategies, low-latency, and high-capacity mobile backhaul network design, and adaptive resource allocation. Based on a summary of the reviewed technologies, we conclude that dynamic resource allocation is the worthiest one to research. Therefore, we carry out a use case, where reinforcement learning (RL) is adopted for autonomous bandwidth allocation in support of low-latency service migration in a dynamic traffic environment and evaluate its performance compared to two benchmarks. The simulation demonstrates that the RL-based algorithm is able to self-adapt to a dynamic traffic environment and gets converged performance, which has an obviously smaller impact on non-migration traffic than the two benchmarks while keeping the migration success probability high. Meanwhile, unlike the benchmarks, the RL-based method shows performance fluctuations before getting converged, which may cause unstable system performance. It calls for future research on advanced smart policies that can get convergence quickly, particularly for handling the migration of latency-sensitive services in 5G transport networks.
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| 5. |
- Li, Jun, 1988, et al.
(författare)
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Scalable Federated Learning over Passive Optical Networks
- 2021
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Ingår i: 2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781943580866
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Konferensbidrag (refereegranskat)abstract
- Two-step aggregation is introduced to facilitate scalable federated learning (SFL) over passive optical networks (PONs). Results reveal that the SFL keeps the required PON upstream bandwidth constant regardless of the number of involved clients, while bringing 10% learning accuracy improvement. © 2021 OSA.
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| 6. |
- Skubic, Björn, et al.
(författare)
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Dynamic bandwidth allocation in EPON and GPON
- 2010
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Ingår i: Convergence of Mobile and Stationary Next-Generation Networks. - Hoboken, NJ, USA : John Wiley & Sons. - 9780470543566 - 9780470630976
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Bokkapitel (refereegranskat)
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| 7. |
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| 8. |
- Zhang, Zhuo, et al.
(författare)
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Demonstration of three‐dimensional indoor visible light positioning with multiple photodiodes and reinforcement learning
- 2020
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 20:22, s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- To provide high‐quality location‐based services in the era of the Internet of Things, visible light positioning (VLP) is considered a promising technology for indoor positioning. In this paper, we study a multi‐photodiodes (multi‐PDs) three‐dimensional (3D) indoor VLP system enhanced by reinforcement learning (RL), which can realize accurate positioning in the 3D space without any off-line training. The basic 3D positioning model is introduced, where without height information of the receiver, the initial height value is first estimated by exploring its relationship with the received signal strength (RSS), and then, the coordinates of the other two dimensions (i.e., X and Y in the horizontal plane) are calculated via trilateration based on the RSS. Two different RL processes, namely RL1 and RL2, are devised to form two methods that further improve horizontal and vertical positioning accuracy, respectively. A combination of RL1 and RL2 as the third proposed method enhances the overall 3D positioning accuracy. The positioning performance of the four presented 3D positioning methods, including the basic model without RL (i.e., Benchmark) and three RL based methods that run on top of the basic model, is evaluated experimentally. Experimental results verify that obviously higher 3D positioning accuracy is achieved by implementing any proposed RL based methods compared with the benchmark. The best performance is obtained when using the third RL based method that runs RL2 and RL1 sequentially. For the testbed that emulates a typical office environment with a height difference between the receiver and the transmitter ranging from 140 cm to 200 cm, an average 3D positioning error of 2.6 cm is reached by the best RL method, demonstrating at least 20% improvement compared to the basic model without performing RL.
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| 9. |
- Zhang, Zhuo, et al.
(författare)
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Iterative point-wise reinforcement learning for highly accurate indoor visible light positioning
- 2019
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Ingår i: Optics Express. - : OPTICAL SOC AMER. - 1094-4087 .- 1094-4087. ; 27:16, s. 22161-22172
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Tidskriftsartikel (refereegranskat)abstract
- Iterative point-wise reinforcement learning (IPWRL) is proposed for highly accurate indoor visible light positioning (VLP). By properly updating the height information in an iterative fashion, the IPWRL not only effectively mitigates the impact of non-deterministic noise but also exhibits excellent tolerance to deterministic errors caused by the inaccurate a priori height information. The principle of the IPWRL is explained, and the performance of the IPWRL is experimentally evaluated in a received signal strength (RSS) based VLP system and compared with other positioning algorithms, including the conventional RSS algorithm, the k-nearest neighbors (KNN) algorithm and the PWRL algorithm where iterations exclude. Unlike the supervised machine learning method, e.g., the KNN, whose performance is highly dependent on the training process, the proposed IPWRL does not require training and demonstrates robust positioning performance for the entire tested area. Experimental results also show that when a large height information mismatch occurs, the IPWRL is able to first correct the height information and then offers robust positioning results with a rather low positioning error, while the positioning errors caused by the other algorithms are significantly higher.
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| 10. |
- Cao, Yuan, et al.
(författare)
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Hybrid Trusted/Untrusted Relay Based Quantum Key Distribution over Optical Backbone Networks
- 2021
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Ingår i: IEEE Journal on Selected Areas in Communications. - 0733-8716 .- 1558-0008. ; 39:9, s. 2701-2718
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Tidskriftsartikel (refereegranskat)abstract
- Quantum key distribution (QKD) has demonstrated a great potential to provide future-proofed security, especially for 5G and beyond communications. As the critical infrastructure for 5G and beyond communications, optical networks can offer a cost-effective solution to QKD deployment utilizing the existing fiber resources. In particular, measurement-device-independent QKD shows its ability to extend the secure distance with the aid of an untrusted relay. Compared to the trusted relay, the untrusted relay has obviously better security, since it does not rely on any assumption on measurement and even allows to be accessed by an eavesdropper. However, it cannot extend QKD to an arbitrary distance like the trusted relay, such that it is expected to be combined with the trusted relay for large-scale QKD deployment. In this work, we study the hybrid trusted/untrusted relay based QKD deployment over optical backbone networks and focus on cost optimization during the deployment phase. A new network architecture of hybrid trusted/untrusted relay based QKD over optical backbone networks is described, where the node structures of the trusted relay and untrusted relay are elaborated. The corresponding network, cost, and security models are formulated. To optimize the deployment cost, an integer linear programming model and a heuristic algorithm are designed. Numerical simulations verify that the cost-optimized design can significantly outperform the benchmark algorithm in terms of deployment cost and security level. Up to 25% cost saving can be achieved by deploying QKD with the hybrid trusted/untrusted relay scheme while keeping much higher security level relative to the conventional point-to-point QKD protocols that are only with the trusted relays.
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