bokaixu5/low-complexity-precoding-algorithm-for-xl-mimo

Low-Complexity Precoding for Extremely Large-Scale MIMO Over Non-Stationary Channels

This simulation code package is mainly used to reproduce the results of the following paper [1]:

[1] Xu, Bokai, Zhe Wang, Huahua Xiao, Jiayi Zhang, Bo Ai and Derrick Wing Kwan Ng. “Low-Complexity Precoding for Extremely Large-Scale MIMO Over Non-Stationary Channels.” in Proc. IEEE International Conference on Communications (ICC), to appear, 2023.

Available on:https://arxiv.org/abs/2302.00847

---

If you use this simulation code package in any way, please cite the original paper [1] above.

The author in charge of this simulation code package is BoKai Xu (email: [email protected]).

---

Abstract of the paper:

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technology for the future sixth-generation (6G) networks to achieve higher performance. In practice, various linear precoding schemes, such as zero-forcing (ZF) and regularized zero-forcing (RZF) precoding, are capable of achieving both large spectral efficiency (SE) and low bit error rate (BER) in traditional massive MIMO (mMIMO) systems. However, these methods are not efficient in extremely large-scale regimes due to the inherent spatial non-stationarity and high computational complexity. To address this problem, we investigate a low-complexity precoding algorithm, e.g., randomized Kaczmarz (rKA), taking into account the spatial non-stationary properties in XL-MIMO systems. Furthermore, we propose a novel mode of randomization, i.e., sampling without replacement rKA (SwoR-rKA), which enjoys a faster convergence speed than the rKA algorithm. Besides, the closed-form expression of SE considering the interference between subarrays in downlink XL-MIMO systems is derived. Numerical results show that the complexity given by both rKA and SwoR-rKA algorithms has $51.3 %$ reduction than the traditional RZF algorithm with similar SE performance. More importantly, our algorithms can effectively reduce the BER when the transmitter has imperfect channel estimation.

---

How to use this simulation code package?

1. Fig. 3 can be generated by running "BER_generate.m" and "Figure3.m".

---

Enjoy the reproducible research!