Latency-Optimal Uplink Scheduling Policy in Training-based Large-Scale Antenna Systems
Kyung Jun Choi, Kwang Soon Kim

TL;DR
This paper introduces an optimal uplink scheduling policy for large-scale antenna systems that significantly reduces network latency, with analysis across different operating regimes and implications for 5G system design.
Contribution
It proposes a polynomial-time optimal scheduling algorithm and provides asymptotic analysis revealing when orthogonal or non-orthogonal pilots are optimal.
Findings
Proposed scheduling policy achieves several times lower latency than conventional methods.
Orthogonal pilots are optimal only in high SNR and small antenna regimes.
Non-orthogonal pilots can substantially reduce latency in low SNR or large antenna regimes.
Abstract
In this paper, an uplink scheduling policy problem to minimize the network latency, defined as the air-time to serve all of users with a quality-of-service (QoS), under an energy constraint is considered in a training-based large-scale antenna systems (LSAS) employing a simple linear receiver. An optimal algorithm providing the exact latency-optimal uplink scheduling policy is proposed with a polynomial-time complexity. Via numerical simulations, it is shown that the proposed scheduling policy can provide several times lower network latency over the conventional ones in realistic environments. In addition, the proposed scheduling policy and its network latency are analyzed asymptotically to provide better insights on the system behavior. Four operating regimes are classified according to the average received signal quality, , and the number of BS antennas, . It turns out that…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced MIMO Systems Optimization · Advanced Wireless Network Optimization · Energy Harvesting in Wireless Networks
