Abstract
Massive multi-input-multi-output (MIMO) systems are the future of the communication system. The proper design of the MIMO system needs an appropriate choice of detection algorithms. At the same time, Lattice reduction (LR)-aided equalizers have been well investigated for MIMO systems. Many studies have been carried out over the Korkine–Zolotareff (KZ) and Lenstra–Lenstra–Lovász (LLL) algorithms. This paper presents an analysis of the channel capacity of the massive MIMO system. The mathematical calculations included in this paper correspond to the channel correlation effect on the channel capacity. Besides, the achievable gain over the linear receiver is also highlighted. In this study, all the calculations were further verified through the simulated results. The simulated results show the performance comparison between zero forcing (ZF), minimum mean squared error (MMSE), integer forcing (IF) receivers with log-likelihood ratio (LLR)-ZF, LLR-MMSE, KZ-ZF, and KZ-MMSE. The main objective of this work is to show that, when a lattice reduction algorithm is combined with the convention linear MIMO receiver, it improves the capacity tremendously. The same is proven here, as the KZ-MMSE receiver outperforms its counterparts in a significant margin.
Highlights
The continuous demand for high speed and reliable communication systems puts the MIMO systems the most demanding research topic [1]
The main objective of this paper is to present the capabilities of the KZ aided linear receiver to improve the capacity of a massive MIMO system
We demonstrated the effectiveness of zero forcing (ZF) and minimum mean squared error (MMSE) algorithms when they were combined with the likelihood ratio (LLR) and KZ algorithms and proved they are better than integer forcing (IF), as presented in [34]
Summary
The continuous demand for high speed and reliable communication systems puts the MIMO systems the most demanding research topic [1]. As a further enhancement of MIMO technology, massive MIMO can increase the capacity in many folded and enhance the system energy efficiency [2,3]. All the benefits come with the burden of challenges. In the MIMO system, the design of low complex signal processing techniques is the main challenge of concern. It is critical to address the challenge of developing low complex signal processing techniques for increasing the capacity of the massive MIMO system. The authors discuss this critical problem and examine the low complex LR-aided receivers for the channel capacity enhancement
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