5G NR PDSCH 能效优化：跨层链路抽象框架
Energy Efficiency Optimization for 5G NR PDSCH: A Cross-Layer Link Abstraction Framework
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中文总结 AI 辅助
针对 NR PDSCH 提出跨层链路抽象框架，以标准传输块大小为吞吐量、目标 BLER 为约束优化能效，推导闭式功率分配与 MCS 选择规则，并证明香农容量基线在高 SNR 下高估能效 1.5-1.7 倍。
中文摘要 AI 辅助
蜂窝链路的能效（EE）优化通常假设采用香农容量传输。然而，3GPP 新无线电（NR）物理下行共享信道（PDSCH）传输的传输块大小由调制与编码方案（MCS）、秩以及调度器选择的资源分配决定。传输块还必须满足目标误块率（BLER）。本文以标准传输块大小作为吞吐量，以目标 BLER 作为约束，优化 NR PDSCH 的能效。指数有效信噪比（SNR）映射（EESM）将 BLER 约束转化为每个子载波上的凸功率约束。我们推导出闭式类注水功率分配和能效最大化的 MCS 选择规则。每个 MCS 仅需一个校准参数，该参数在 3GPP 抽头延迟线 C（TDL-C）信道上离线拟合，EESM 无需逐次实现链路级仿真即可预测 BLER。数值结果表明，在发射功率预算内的高 SNR 下，香农容量基线将等功率传输的能效高估了 1.5 至 1.7 倍。
英文摘要
Energy efficiency (EE) optimization for cellular links often assumes Shannon-capacity transmission. The 3GPP New Radio (NR) physical downlink shared channel (PDSCH), however, delivers a transport block whose size is fixed by the modulation and coding scheme (MCS), the rank, and the resource allocation chosen by the scheduler. The transport block must also meet a target block error rate (BLER). In this paper, we optimize EE for the NR PDSCH with the standard transport block size as the throughput and the target BLER as the constraint. Exponential effective signal-to-noise ratio (SNR) mapping (EESM) turns the BLER constraint into a convex per-subcarrier power constraint. We derive a closed-form waterfilling-like power allocation and an EE-maximizing MCS selection rule. With a single calibration parameter per MCS, fitted offline over 3GPP tapped delay line C (TDL-C) channels, EESM predicts the BLER without per-realization link-level simulation. Numerical results show that a Shannon-capacity baseline overestimates the EE of equal-power transmission by 1.5 to 1.7 times at high SNR within the transmit power budget.
发表机构
- Department of Electrical and Computer Engineering, UC San Diego（加州大学圣地亚哥分校电气与计算机工程系）
- School of Integrated Technology, Yonsei University（延世大学综合技术学院）
机构由 AI 辅助整理，请以论文原文为准。
