Performance difference between zero‐forcing and maximum likelihood detectors in massive MIMO systems

Massive multi-input–multi-output (MIMO) systems are crucial to maximizing energy efficiency (EE) and battery-saving technology. Achieving EE without sacrificing the quality of service (QoS) is increasingly important for mobile devices. We first derive the data rate through zero forcing (ZF) and three linear precodings: maximum ratio transmission (MRT), zero forcing (ZF), and minimum mean square error (MMSE). Performance EE can be achieved when all available antennas are used and when taking account of the consumption circuit power ignored because of high transmit power. The aim of this work is to demonstrate how to obtain maximum EE while minimizing power consumed, which achieves a high data rate by deriving the optimal number of antennas in the downlink massive MIMO system. This system includes not only the transmitted power but also the fundamental operation circuit power at the transmitter signal. Maximized EE depends on the optimal number of antennas and determines the number of active users that should be scheduled in each cell. We conclude that the linear precoding technique MMSE achieves the maximum EE more than ZF and MRTbecause the MMSE is able to make the massive MIMO system less sensitive to SNR at an increased number of antennas.

Download Full-text

Hyper-power zero forcing detector for massive MIMO systems

Wireless Networks ◽

10.1007/s11276-019-02099-z ◽

2019 ◽

Vol 25 (7) ◽

pp. 4349-4357 ◽

Cited By ~ 1

Author(s):

Juan Minango ◽

Celso de Almeida

Keyword(s):

Massive Mimo ◽

Mimo Systems ◽

Zero Forcing

Download Full-text

Interference Suppression in Heterogeneous Massive MIMO Systems with Imperfect CSI

10.21203/rs.3.rs-1114129/v1 ◽

2021 ◽

Author(s):

Rana Sedghi ◽

masoumeh azghani

Keyword(s):

Massive Mimo ◽

Mimo Systems ◽

Estimation Error ◽

Interference Management ◽

Small Cells ◽

Channel Estimation Error ◽

Zero Forcing ◽

Channel State ◽

Error Matrix ◽

Mimo Networks

Abstract Interference management is of paramount importance in heterogeneous massive mimo networks (HetNet). In this paper, an algorithm has been suggested to suppress the interference in large-MIMO HetNets with imperfect channel state information(CSI). The proposed technique controls both the intra-tier and cross-tier interference of the macrocell as well as the small cells. The intra-tier interference of the macrocell as well as the cross-tier interference have been minimized under maximum transmission power and minimum signal to interference and noise ratio (SINR) constraint. The channel estimation error matrix has also been modeled using the joint sparsity property. The precoding algorithm is thus achieved through the application of semi-definite relaxation and block coordinate descent techniques. The intra-tier interference of the small cells are addressed with the aid of the zero forcing scheme. The proposed method has been validated through various simulations which confirm the superiority of the algorithm over its counterparts.

Download Full-text

Spectral and Energy Efficiency of Distributed Massive MIMO with Low-Resolution ADC

Electronics ◽

10.3390/electronics7120391 ◽

2018 ◽

Vol 7 (12) ◽

pp. 391 ◽

Cited By ~ 2

Author(s):

Jiamin Li ◽

Qian Lv ◽

Jing Yang ◽

Pengcheng Zhu ◽

Xiaohu You

Keyword(s):

Energy Efficiency ◽

Massive Mimo ◽

Mimo Systems ◽

Numerical Results ◽

Quantization Noise ◽

Noise Model ◽

Low Resolution ◽

Zero Forcing ◽

Analog To Digital ◽

Linear Receivers

In this paper, considering a more realistic case where the low-resolution analog-to-digital convertors (ADCs) are employed at receiver antennas, we investigate the spectral and energy efficiency in multi-cell multi-user distributed massive multi-input multi-output (MIMO) systems with two linear receivers. An additive quantization noise model is provided first to study the effects of quantization noise. Using the model provided, the closed-form expressions for the uplink achievable rates with a zero-forcing (ZF) receiver and a maximum ratio combination (MRC) receiver under quantization noise and pilot contamination are derived. Furthermore, the asymptotic achievable rates are also given when the number of quantization bits, the per user transmit power, and the number of antennas per remote antenna unit (RAU) go to infinity, respectively. Numerical results prove that the theoretical analysis is accurate and show that quantization noise degrades the performance in spectral efficiency, but the growth in the number of antennas can compensate for the degradation. Furthermore, low-resolution ADCs with 3 or 4 bits outperform perfect ADCs in energy efficiency. Numerical results imply that it is preferable to use low-resolution ADCs in distributed massive MIMO systems.

Download Full-text