Resource Allocation for URLLC and eMBB Traffic in Uplink Wireless Networks

D. Lee, Cheng-Shang Chang, Ruhui Zhang, Mao-Pin Lee
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引用次数: 1

Abstract

In this paper we consider two resource allocation problems of URLLC traffic and eMBB traffic in uplink 5G networks. We propose to divide frequencies into a common region and a grant-based region. Frequencies in the grant-based region can only be used by eMBB traffic, while frequencies in the common region can be used by eMBB traffic as well as URLLC traffic. In the first resource allocation problem we propose a two-player game to address the size of the grant-based region and the size of the common region. We show that this game has specific pure Nash equilibria. In the second resource allocation problem we determine the number of packets that each eMBB user can transmit in a request-grant cycle. We propose a constrained optimization problem to minimize the variance of the number of packets granted to the eMBB users. We show that a water-filling algorithm solves this constrained optimization problem. From simulation, we show that our scheme, consisting of resource allocation according to Nash equilibria of a game, persistent random retransmission of URLLC packets and allocation of eMBB packets by a water-filling algorithm, works better than four other heuristic methods.
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无线上行网络中URLLC和eMBB业务的资源分配
本文研究了5G上行网络中URLLC业务和eMBB业务的资源分配问题。我们建议将频率划分为公共区域和基于授权的区域。授权区域的频率只能由eMBB流量使用,而公共区域的频率可以由eMBB流量和URLLC流量使用。在第一个资源分配问题中,我们提出了一个双人博弈来解决基于拨款的区域的大小和公共区域的大小。我们证明了这个博弈有特定的纯纳什均衡。在第二个资源分配问题中,我们确定每个eMBB用户在请求授予周期中可以传输的数据包数量。我们提出了一个约束优化问题,以最小化授予eMBB用户的数据包数量的方差。我们证明了一种充水算法解决了这个约束优化问题。仿真结果表明,基于博弈纳什均衡的资源分配、URLLC报文的持续随机重传和灌水算法的eMBB报文分配方案比其他四种启发式方法效果更好。
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