On the multiplexing of data and metadata for ultra reliable low latency communications in 5G
Karimi, Ali; Pedersen, Klaus I.; Mahmood, Nurul Huda; Berardinelli, Gilberto; Mogensen, Preben (2020-07-31)
A. Karimi, K. I. Pedersen, N. H. Mahmood, G. Berardinelli and P. Mogensen, "On the Multiplexing of Data and Metadata for Ultra-Reliable Low-Latency Communications in 5G," in IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 12136-12147, Oct. 2020, doi: 10.1109/TVT.2020.3013391
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This paper addresses the problem of downlink radio resource management for ultra-reliable low-latency communications (URLLC) in fifth generation (5G) systems. To support low-latency communications, we study performance of two multiplexing schemes namely in resource control signalling and joint encoding of data and metadata. In the former, the metadata and data are separately encoded and the metadata is sent at the beginning of transmission time prior to the data. Thus, it benefits from a low-complexity receiver structure to decode the data block. The latter takes advantages of transmitting a larger blocklength to enhance the reliability and improve spectrum efficiency by jointly encoding data and metadata as a single codeword. Dealing with small URLLC payloads, the overhead and error of sending metadata are not negligible and have a significant impact on the system performance in terms of resource usage the reliability of transmission. For each scheme, we derive expressions for the outage probability and resource usage by taking into account impacts of the finite blocklength payloads, overhead and error of sending metadata, and probability of error in uplink feedback channel. We propose a novel framework for joint data and metadata link adaptation to minimize the average number of allocated resources, while ensuring the stringent URLLC quality of service requirements. An optimization problem is formulated for each scheme that is mixed-integer nonconvex problem, difficult to solve in polynomial time. Solutions based on successive convex optimization are proposed. Numerical evaluations show that the proposed algorithms perform close to the optimal solution and demonstrate remarkable gains of up to 27% improvement in resource usage. Finally, we present sensitivity analysis of the results for various network parameters.
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