4.4 Article

Energy-Efficient Resource Allocation With Flexible Frame Structure for Hybrid eMBB and URLLC Services

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGCN.2020.3028202

Keywords

Flexible numerology; flexible frame structure; heterogeneous services; URLLC; energy efficiency; resource allocation

Funding

  1. National Natural Science Foundation of China [61701293, 61871262, 61901251]
  2. National Science and Technology Major Project [2018ZX03001009]
  3. National Key Research and Development Program of China [2017YEF0121400]

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The key objective of 5G wireless technology is to support services with diverse requirements, such as eMBB and URLLC. This article investigates flexible 2-dimensional resource allocation to maximize system energy efficiency. The proposed sliding window algorithm shows significant power consumption reduction and EE improvement compared to baseline systems.
The key objective of the fifth generation (5G) wireless technology is to support services with highly diverse requirements, which necessitates the specification of flexible numerology and frame structure for radio resource allocation in 5G new radio (NR). In this article, flexible 2-dimensional resource allocation is investigated to exploit the frequency and time diversities of the resource grid, and eventually maximize the overall system energy efficiency (EE) in the scenario where enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC) services coexist. A new definition of EE is proposed to capture and fulfill the heterogeneous quality-of-service (QoS) requirements of eMBB and URLLC services. By first sliding a window on the resource grid to determine the candidate resource pool, and then allocating resource blocks (RBs) therein by leveraging linear programming (LP) relaxation, we propose a sliding window (SW) based algorithm with a low complexity for the NP-hard EE optimization problem. In addition, we also analyze the achievable EE performance of the proposed algorithm to demonstrate its scalability with respect to the grid sizes and the maximum transmit power to provide an in-depth understanding. Numerical results show that the proposed algorithm can bring about yas much as 16.7% power consumption reduction, and improve EE by about 29.3% compared to other baseline systems.

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