SCIENTIA SINICA Informationis, Volume 50 , Issue 6 : 913-930(2020) https://doi.org/10.1360/SSI-2020-0068

Ubiquitous-X: Constructing the Future 6G Networks

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  • ReceivedMar 21, 2020
  • AcceptedApr 21, 2020
  • PublishedJun 8, 2020


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  • Figure 1

    (Color online) The leaps for five generations of mobile communication

  • Figure 2

    (Color online) 6G vision scenario under the concept of UC$^4$

  • Figure 3

    (Color online) The logical structure of Ubiquitous-X

  • Figure 4

    (Color online) The performance requirements of 6G network

  • Figure 5

    (Color online) The bottleneck effect of 6G network

  • Figure 6

    (Color online) The prediction of global Internet of Things connectivity and mobile traffic (data source: protectłinebreak GSMA [8]and ITU [33])

  • Figure 7

    (Color online) The average traffic density distribution per smartphone (source: Ericsson mobility report protectłinebreak 2019 [7])

  • Figure 8

    The evolution trend of information space and dimension

  • Figure 9

    (Color online) The network architecture of Ubiquitous-X

  • Figure 10

    (Color online) The evolution diagram of mobile communication network based on dissipative structure theory

  • Figure 11

    (Color online) The multi-path transmission for multi-modal data orderly access

  • Figure 12

    The performance of multi-path transmission. The simulation was conducted by using the MPTCP (multi-path transmission control protocol) tool [40]on a desktop computer with Intel i7-8700 3.2 GHz CPU, 32G DDR4L 2666 MHz memory and 64-bit Ubuntu 16.04 operating system. Three parallel paths with bandwidth of 8 Mbps and packet loss rate of 0.5% were established, and the overall transmission condition was set with transmission delay ranged from 20 ms to 110 ms. Each experiment was repeated 60 times. The experiment compares DeepCC based on multi-agent reinforcement learning with two kinds of deep reinforcement learning DRL-CC [41], SmartCC [42]and four heuristic multi-path transmission algorithms [43-46]. The results show that DeepCC can greatly improve the throughput and reduce delay jitter

  • Figure 13

    (Color online) The knowledge plane of Ubiquitous-X network realizes accurate control of resources and personalized requirements

  • Figure 14

    The routing performance based on the network topology knowledge by using GAN. Data plane was built with Mininet and deployed on servers with Intel(R) Xeon(R) E5-2407 v2@2.40 GHz CPU and 24 GB DDR3 RAM. The SDN controller of the knowledge plane and the deep reinforcement learning model are deployed on an 8-core Intel(R) i7-9700K@3.60 GHz CPU and 64 GB DDR4 RAM server. When the network changes to a new environment, GAN based transfer learning routing converges in 8.08 and 9.25 times faster speed comparing to the ACKTR (actor critic with Kronecker-factored trust region) [49]and (advantage actor-critic) [50], respectively. The average packet delay after the algorithm convergence: ACKTR is about 29.06 ms, A2C is about 40.06 ms, original transfer learning algorithm is about 39.64 ms, GAN based transfer learning is about 27.83 ms.

  • Figure 15

    (Color online) The shared ecosystem of Ubiquitous-X network

  • Figure 16

    The performance improvement of distributed training by edge computing node. The simulation experiment compares the acceleration ratio of 1 to 64 edge nodes with GPU to train AlexNet network in a distributed way with bandwidth of 10 MHz [52]. General gradient sparse method (GGS) [53]and DGC [54]can achieve nearly 46 multiplier ratio when the distributed node is 64, compared with the baseline method