Physical Layer Design Beyond-5G with Deep-Neural Networks

Deep learning (DL) based auto encoder (AE) has been proposed recently as a promising, and potentially disruptive Physical Layer (PHY) design for beyond-5G communication systems. DL based approaches offer a fundamentally new and holistic approach to the physical layer design problem and hold the promise for performance enhancement in complex environments that are difficult to characterize with tractable mathematical models, e.g., for the communication channel [2]. Compared to a traditional communication system, as shown in Figure 2 (top) with a multiple-block structure, the DL based AE, as shown in Figure 2 (bottom), provides a new PHY paradigm with a pure data-driven and end-to-end learning based solution which enables the physical layer to redesign itself through the learning process in order to optimally perform in different scenarios and environment. As an example, time evolution of the constellations of two auto encoder transmit-receiver pairs are shown in Figure 3 which starting from an identical set of constellations use DL-based learning to achieve optimal constellations in the presence of mutual interference [3].

Spectrum Sharing with AI

The concept of cognitive radio was originally introduced in the visionary work of Joseph Mitola as the marriage between wireless communications and artificial intelligence, i.e., wireless devices that can change their operations in response to the environment and changing user requirements, following a cognitive cycle of observe/sense, learn and act/adapt.  Cognitive radio has found its most prominent application in the field of intelligent spectrum sharing. Therefore, it is befitting to highlight the critical role that AI can play in enabling a much more efficient sharing of radio spectrum in the era of 5G. 5G New Radio (NR) is expected to support diverse spectrum bands, including the conventional sub-6 GHz band, the new licensed millimetre wave (mm-wave)  bands which are being allocated for 5G, as well as unlicensed spectrum. Very recently 3rd Generation Partnership Project (3GPP) Release-16 has introduced a new spectrum sharing paradigm for 5G in unlicensed spectrum. Finally, both in the UK and Japan the new paradigm of local 5G networks are being introduced which can be expected to rely heavily on spectrum sharing. As an example of such new challenges, the scenario of 60 GHz unlicensed spectrum sharing is shown in Figure 4(a), which depicts a beam-collision interference scenario in this band. In this scenario, multiple 5G NR BSs belonging to different operators and different access technologies use mm-wave communications to provide Gbps connectivity to the users. Due to high density of BS and the number of beams used per BS, beam-collision can occur where unintended beam from a “hostile” BS can cause server interference to a user. Coordination of beam-scheduling between adjacent BSs to avoid such interference scenario is not possible when considering the use of the unlicensed band as different  BS operating in this band may belong to different operators or even use different access technologies, e.g., 5G NR versus, e.g., WiGig or Multifire. To solve this challenge, reinforcement learning algorithms can successfully be employed to achieve self-organized beam-management and beam-coordination without the need for any centralized coordination or explicit signalling [4].  As 4(b) demonstrates (for the scenario with 10 BSs and cell size of 200 m) reinforcement learning-based self-organized beam scheduling (algorithms 2 and 3 in the Figure 4(b)) can achieve system spectral efficiencies that are much higher than the baseline random selection (algorithm 1) and are very close to the theoretical limits obtained from an exhaustive search (algorithm 4), which besides not being scalable would require centralised coordination.

Figure 4: Spectrum sharing scenario in unlicensed mm-wave spectrum (left) and system spectral efficiency of 10 BS deployment (right).  Results are shown for random scheduling (algorithm 1), two versions of ML-based schemes (algorithms 2 and 3) and theoretical limit obtained from exhaustive search in beam configuration space (algorithm 4).

Conclusions

In this article, we presented few case studies to demonstrate the use of AI as a powerful new approach to adaptive design and operations of 5G and beyond-5G mobile networks. With mobile industry heavily investing in AI technologies and new standard activities and initiatives, including ETSI Experiential Networked Intelligence ISG [5], the ITU Focus Group on Machine Learning for Future Networks Including 5G (FG-ML5G) and the IEEE Communication Society’s Machine Learning for Communications ETI are already actively working on harnessing the power of AI and ML for future telecommunication networks, it is clear that these technologies will play a key role in the evolutionary path of 5G toward much more efficient, adaptive, and automated mobile communication networks. However, with its phenomenally fast pace of development, deep penetration of Artificial Intelligence and machine-learning may eventually disrupt the entire mobile networks as we know it, hence ushering the era of 6G.  

References

1. ETSI ISG for NFV, “ETSI GS NFV 002: Network Functions Virtualisation (NFV); Architectural Framework,” http://www.etsi.org/deliver/etsi_gs/NFV/001_099/002/01.02.01_60/gs_NFV002v010201p.pdf;  January 2015.
2. T. J. O’Shea and J. Hoydis, “An introduction to deep learning for the physical layer,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563-575, December 2017.
3. D. Wu, M. Nekovee, Y. Wang, An adaptive deep learning based auto encoder for the interference channel, available from https://arxiv.org/abs/1902.06841
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