[论文解读] Realization of MIMO Channel Model for Spatial Diversity with Capacity and SNR Multiplexing Gains

Subrato Bharati, Prajoy Podder+2 more|arXiv (Cornell University)|Apr 24, 2020

Advanced MIMO Systems Optimization参考文献 31被引用 7

一句话总结

本文提出了一种利用空间分集提升系统可靠性和容量的MIMO信道模型，分析了在收发端硬件损伤情况下的容量与SNR复 multiplexing gain。结果表明，当硬件损伤水平（κ）从0.02降低至0.005时，平均MIMO信道容量提升超过80%；在发射天线数量超过接收天线数量的确定性信道中，高SNR性能表现更优。

ABSTRACT

Multiple input multiple output (MIMO) system transmission is a popular diversity technique to improve the reliability of a communication system where transmitter, communication channel and receiver are the important elements. Data transmission reliability can be ensured when the bit error rate is very low. Normally, multiple antenna elements are used at both the transmitting and receiving section in MIMO Systems. MIMO system utilizes antenna diversity or spatial diversity coding system in wireless channels because wireless channels severely suffer from multipath fading in which the transmitted signal is reflected along various multiple paths before reaching to the destination or receiving section.Parallel transmission of MIMO system has also been implemented where both the real part and imaginary part of the original, detected and the corresponding received data sequence has been described graphically. The MIMO channel average capacity is achieved more than 80% for dissimilar levels of impairments in transceiver when the value of kappa (Level of impairments in transmitter hardware) reduces from 0.02 to 0.005. The finite-SNR multiplexing gain (Proportion of MIMO system capacity to SISO system capacity) has been observed for deterministic and uncorrelated Rayleigh fading channels correspondingly. The core difference is in the high SNR level. It may occur for two reasons: (a) there is a quicker convergence to the limits under transceiver impairments (b) deterministic channels that are built on digital architectural plans or topographical maps of the propagation environment acquire an asymptotic gain superior than multiplexing gain when the number of transmitting antenna is greater than the number of receiving antenna.

研究动机与目标

• 开发一种实用的MIMO信道模型，以利用空间分集提升通信可靠性。
• 量化收发端硬件损伤（以κ表示）对MIMO系统容量与复 multiplexing gain 的影响。
• 比较确定性信道与不相关瑞利衰落信道在高SNR渐近性能方面的表现。
• 在不同信道条件下，评估MIMO系统相对于SISO系统的有限SNR复 multiplexing gain。
• 在确定性环境中，证明当发射天线数量超过接收天线数量时的性能增益。

提出的方法

• MIMO系统在发射端和接收端均采用多天线，以利用空间分集并对抗多径衰落。
• 信道模型结合了发射、检测与接收信号序列的实部与虚部，通过图形化方式呈现以供分析。
• 使用κ因子对收发端损伤进行建模，仿真在不同κ水平（0.02至0.005）下进行。
• 有限SNR复 multiplexing gain 定义为MIMO与SISO系统容量的比值，在确定性信道与瑞利衰落信道中均进行评估。
• 通过理论分析与仿真对比，比较高SNR区域的渐近性能，尤其关注不同天线配置下的表现。
• 该模型利用数字建筑图纸或地形图定义确定性信道响应，从而实现对高SNR增益的精确评估。

实验结果

研究问题

• RQ1 降低收发端损伤因子（κ）对平均MIMO信道容量有何影响？
• RQ2 在确定性信道与不相关瑞利衰落信道中，MIMO相对于SISO的有限SNR复 multiplexing gain 是多少？
• RQ3 为何当发射天线数量超过接收天线数量时，确定性信道在高SNR下表现出更优的渐近增益？
• RQ4 在所提出的MIMO模型中，实部与虚部信号分量在传输、检测与接收过程中的行为如何？
• RQ5 在高SNR条件下，确定性信道与随机（瑞利）衰落信道在容量与复 multiplexing gain 方面的性能差距如何？

主要发现

• 将收发端损伤因子（κ）从0.02降低至0.005，可使MIMO信道平均容量提升至超过80%。
• 在有限SNR下，确定性信道的复 multiplexing gain 高于不相关瑞利衰落信道，尤其在高SNR区域表现更优。
• 当发射天线数量超过接收天线数量时，确定性信道在复 multiplexing gain 的渐近性能上优于其他配置。
• 基于数字建筑图纸或地形图的确定性信道模型，在存在收发端损伤时，能更快收敛至容量极限。
• 所提出的MIMO模型成功捕捉了实部与虚部信号分量在传输与接收过程中的行为，支持对系统性能的图形化分析。
• 研究证实，MIMO系统中的空间分集显著提升了可靠性与频谱效率，尤其在低损伤与高天线数条件下表现更优。