Learning Based Hardware Design for 5G Systems
- Reference number
- ID17-0114
- Start and end dates
- 180101-221231
- Amount granted
- 2 500 000 SEK
- Administrative organization
- KTH - Royal Institute of Technology
- Research area
- Information, Communication and Systems Technology
Summary
Main Objectives: The focus of this research project will be to develop 5G near-radio signal processing algorithms to mitigate radio hardware impairments that facilitate spectrum sharing specifically in mmWave bands, and that are scalable for massive MIMO systems and can take advantage of multiple antennas both at the base-station and the user-equipment. We envisage that such algorithms should be able to learn which factors need to be taken into account by successively adopting themselves to the specific environment in which they are operating. Brief work-plan: The research project is planned to be executed in three phases, where each phase corresponds to one of the three objectives described above. Phase 1: Characterization of Spectrum Sharing Objectives and Radio HW Impairments in Massive MIMO Systems Phase 2: Radio hardware Impairments Mitigation to facilitate Spectrum Sharing Phase 3: Learning Theory for Spectrum Sharing with Constrained Coordination The above three phases planned to be executed in an iterative, cyclic fashion in order to refine the radio HW aware signal processing algorithms to facilitate spectrum sharing. Expected results: For internal dissemination, the progress will be reported on a monthly basis, and we will organize workshops twice a year. For external dissemination, our ambition is to target the IEEE flagship conferences, magazines, and journals; and plan to summarize the results in Forskning och Framsteg or Elektroniktidningen.
Popular science description
The Fifth Generation Networks (5G) will arguably be the foundation for the connected society and the digital revolution. 5G will be characterised not only by traffic capacity, achievable user data rates, mobility and coverage, but also by reliability, latency, network energy efficiency and scalability in terms of the number connected devices (smart phones, personal computers, sensors and actuators). To meet 5G requirements, the wireless research and standardization communities are investigating various technology enablers including operation in very wide spectrum bands beyond 6 GHz – such as millimeter wave (mmWave) bands – deployment of massive multiple number antennas at both transmitter and receiver, i.e., so-called massive multiple input and multiple output (MIMO), wireless access point densification and smart system design techniques that facilitate the cost efficient deployment of the wireless Internet of Things (IoT). The 5G infrastructure for the networked society will be drastically different from existing broadband network equipment and hardware platforms. The current practice of mobile communications equipment design and deployment assumes static operation conditions, including the spectrum band, in which the demand for specific services, traffic load and technical requirements on latency, reliability and energy efficiency do not change significantly in time. Specifically, the 5G infrastructure, as a platform for the specific vertical industry segments and associated IoT applications must be able to adapt to the specific environment in which it operates. The Learning Based Hardware Design for 5G Systems project addresses the challenges above by applying machine learning for self-adaptable radio hardware design to support the operation of telecommunications equipment in environments whose characteristics and imposed requirements are unknown a-priori. Thus, the current practice of hardware design will be improved by incorporating and adopting the techniques of machine learning that will enable the deployed hardware to analyse its environment, recognize favourable operational conditions and adapt to changing service requirements. We believe that this learning capability for 5G hardware design will become a key distinctive factor beyond the realm of 5G wireless infrastructure.