Joint Uplink and Downlink Resource Allocation Towards Energy-efficient Transmission for URLLC

TL;DR

This paper proposes an energy-efficient resource allocation framework for URLLC in 5G, combining frequency-hopping, proactive dropping, and joint uplink-downlink optimization to meet strict QoS with finite power.

Contribution

It introduces a novel packet delivery mechanism with frequency-hopping and proactive dropping, and develops a joint optimization method for resource allocation in URLLC.

Findings

Significant power savings achieved through joint optimization.

Theoretical analysis confirms convexity and simplifies global optimal solution.

Simulation demonstrates improved reliability and energy efficiency.

Abstract

Ultra-reliable and low-latency communications (URLLC) is firstly proposed in 5G networks, and expected to support applications with the most stringent quality-of-service (QoS). However, since the wireless channels vary dynamically, the transmit power for ensuring the QoS requirements of URLLC may be very high, which conflicts with the power limitation of a real system. To fulfill the successful URLLC transmission with finite transmit power, we propose an energy-efficient packet delivery mechanism incorparated with frequency-hopping and proactive dropping in this paper. To reduce uplink outage probability, frequency-hopping provides more chances for transmission so that the failure hardly occurs. To avoid downlink outage from queue clearing, proactive dropping controls overall reliability by introducing an extra error component. With the proposed packet delivery mechanism, we jointly optimize bandwidth allocation and power control of uplink and downlink, antenna configuration, and subchannel assignment to minimize the average total power under the constraint of URLLC transmission requirements. Via theoretical analysis (e.g., the convexity with respect to bandwidth, the independence of bandwidth allocation, the convexity of antenna configuration with inactive constraints), the simplication of finding the global optimal solution for resource allocation is addressed. A three-step method is then proposed to find the optimal solution for resource allocation. Simulation results validate the analysis and show the performance gain by optimizing resource allocation with the proposed packet delivery mechanism.