Research Data: Quantum Search-Aided Multi-User Detection of IDMA-Assisted Multi-Layered Video Streaming

Research data for figures 7, 9-12, 14-17 of the paper &quot;Quantum Search-Aided Multi-User Detection of IDMA-Assisted Multi-Layered Video Streaming&quot;, published in IEEE Access.</span

Quantum search-aided multi-user detection of IDMA-assisted multi-layered video streaming

Moore’s law is expected to lead to the gates of the quantum world in 2017. Therefore, the emerging quantum computing research is expected to give rise to novel quantum search algorithms, which may replace the currently used classical ones in wireless communications, leading to performance improvements and complexity reduction. In this paper, we demonstrate the benefits of quantum-assisted multi-user detection (QMUD) in the uplink of a multi-user system, where the reference user conveys a multi- layered video stream to the base station, while using adaptive modulation and different rates per video layer. This is the first study, where a QMUD is employed in a video application. The QMUD does not treat the rest of the users as interference, but rather detects the signals transmitted by all the users. We have evaluated the system’s performance both in terms of its bit error ratio and peak signal-to-noise ratio versus the channel’s signal-to-noise ratio, while quantifying the complexity reduction achieved by using the QMUD instead of the optimal classical maximum a posteriori probability MUD. The effect of the number of users on the system’s performance is also quantified

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

The upcoming 5th Generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated Artificial Intelligence (AI) operations. However, fully-intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the 6th Generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performanceandservicetypes.Theincreasinglystringentperformancerequirementsofemergingnetworks may finally trigger the deployment of some interesting new technologies such as large intelligent surfaces, electromagnetic-orbital angular momentum, visible light communications and cell-free communications – tonameafew.Ourvisionfor6Gis–amassivelyconnectedcomplexnetworkcapableofrapidlyresponding to the users’ service calls through real-time learning of the network state as described by the network-edge (e.g., base-station locations, cache contents, etc.), air interface (e.g., radio spectrum, propagation channel, etc.), and the user-side (e.g., battery-life, locations, etc.). The multi-state, multi-dimensional nature of the network state, requiring real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of Machine Learning (ML), Quantum Computing (QC), and Quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensivereviewoftherelatedstate-of-the-artinthedomainsofML(includingdeeplearning),QCand QML, and identify their potential benefits, issues and use cases for their applications in the B5G networks. Subsequently,weproposeanovelQC-assistedandQML-basedframeworkfor6Gcommunicationnetworks whilearticulatingitschallengesandpotentialenablingtechnologiesatthenetwork-infrastructure,networkedge, air interface and user-end. Finally, some promising future research directions for the quantum- and QML-assisted B5G networks are identified and discussed