Abstract
Pilot-assisted channel training for acquiring the channel state information (CSI) is a very critical functionality in URLLC to ensure reliable data transmission. In this paper, we consider the joint uplink and downlink URLLC transmission in which the source uploads a packet to the BS, and then BS forwards this packet to the destination. CSI in the uplink can be estimated at BS by sending a training pilot from source while for the downlink, an extra codebook-based channel quantization is required at destination to measure the estimated channel and feedback the limited-precision results to BS for beamforming before data transmission. This could bring about channel estimation errors, quantization errors and decoding errors, respectively caused by limited training symbols, limited feedback overheads and finite blocklength. In view of this, we for the first time investigate the transmission performance of URLLC under this circumstance and design a performance optimization framework to maximize the transmission reliability. Particularly, the impact of channel estimation accuracy on the transmission reliability of URLLC is studied based on the finite blocklength information theory. An analytical closed-form expression of transmission error probability under imperfect CSI is also derived with respect to key parameters, like the training pilot length, feedback bits and data transmission durations. By jointly optimizing those parameters, we could derive the maximum transmission reliability. Simulation results are provided to validate the effectiveness of our proposed performance optimization framework.
Highlights
The fifth generation (5G) mobile networks are expected to support ultra-reliable and low-latency communications (URLLC), which is regarded as one of the enabled technologies for mission-critical Internet-of-Things (IoT) applications, such as intelligent transportation, industrial automation, health monitoring and tactile internet [1]–[4]
The major contributions of this paper are summarized as follows: 1) Based on the finite blocklength information theory, we investigate the impact of channel training and quantized feedback on the transmission reliability of URLLC, and derive a closed-form expression of transmission error probability with imperfect channel state information (CSI), which is a function with respect to the training pilot length, feedback bits and data transmission durations
Channel estimation for acquiring the channel state information (CSI) and data transmission for conveying information bits over wireless channels are two basic components required for URLLC
Summary
The fifth generation (5G) mobile networks are expected to support ultra-reliable and low-latency communications (URLLC), which is regarded as one of the enabled technologies for mission-critical Internet-of-Things (IoT) applications, such as intelligent transportation, industrial automation, health monitoring and tactile internet [1]–[4]. It is a single-input-multi-output (SIMO) link through which the uplink CSI could be estimated at BS via sending a known training pilot from source, and can be directly used for data decoding [6].
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