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Book ChapterDOI

Performance Analysis of MIMO and Massive MIMO with Rayleigh, Rician and Nakagami Channels

01 Jan 2022-pp 221-233
TL;DR: In this article, the BER performance and outage capacity of MIMO and massive mIMO on Rayleigh, Rician and Nakagami channels are compared. And the performance analysis between MIMI and Massive MIMOs is analyzed with different channels corresponding to the output and result obtained.
Abstract: Multiple Input Multiple Output (MIMO) provides an environment in wireless communication that offers simple antennas and resource allocation is simplified utilizing Time Division multiplexing. MIMO overcomes bandwidth restrictions with its channel state information and multiple antennas. Massive MIMO uses large-scale antenna systems and are capable of improving the channel capability. Extra antennas improve the throughput and energy efficiency. The performance analysis between MIMO and Massive MIMO is analyzed with different channels corresponding to the output and result obtained. In this paper, the BER performance and outage capacity of MIMO and Massive MIMO on Rayleigh, Rician and Nakagami channels are compared.
Citations
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Journal ArticleDOI
TL;DR: In this paper , the linear precoding schemes were proposed to mitigate interference between the base stations (intercell), which are categorized into linear and non-linear; these schemes are grounded into three types, namely, Zero Forcing (ZF), Block Diagonalization (BD), and Signal Leakage Noise Ratio (SLNR).
Abstract: In Mobile Communication Systems, inter-cell interference becomes one of the challenges that degrade the system’s performance, especially in the region with massive mobile users. The linear precoding schemes were proposed to mitigate interferences between the base stations (inter-cell). These schemes are categorized into linear and non-linear; this study focused on linear precoding schemes, which are grounded into three types, namely Zero Forcing (ZF), Block Diagonalization (BD), and Signal Leakage Noise Ratio (SLNR). The study included the Cooperative Multi-cell Multi Input Multi Output (MIMO) System, whereby each Base Station serves more than one mobile station and all Base Stations on the system are assisted by each other by shared the Channel State Information (CSI). Based on the Multi-Cell Multiuser MIMO system, each Base Station on the cell is intended to maximize the data transmission rate by its mobile users by increasing the Signal Interference to Noise Ratio after the interference has been mitigated due to the usefully of linear precoding schemes on the transmitter. Moreover, these schemes used different approaches to mitigate interference. This study mainly concentrates on evaluating the performance of these schemes through the channel distribution models such as Ray-leigh and Rician included in the presence of noise errors. The results show that the SLNR scheme outperforms ZF and BD schemes overall scenario. This implied that when the value of SNR increased the performance of SLNR increased by 21.4% and 45.7% for ZF and BD respectively.
References
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Journal ArticleDOI
TL;DR: While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios.
Abstract: Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multi-user MIMO, as originally envisioned, with roughly equal numbers of service antennas and terminals and frequency-division duplex operation, is not a scalable technology. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO, full-dimension MIMO, and ARGOS) makes a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation. Extra antennas help by focusing energy into ever smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include extensive use of inexpensive low-power components, reduced latency, simplification of the MAC layer, and robustness against intentional jamming. The anticipated throughput depends on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This article presents an overview of the massive MIMO concept and contemporary research on the topic.

6,184 citations

Journal ArticleDOI
21 Jan 2019
TL;DR: The statistical properties of the minimum mean squared error (MMSE), element-wise MMSE, and least-square channel estimates for this model, where the channels are spatially correlated Rician fading, are derived and analyzed.
Abstract: This paper considers multi-cell massive multiple-input multiple-output systems, where the channels are spatially correlated Rician fading. The channel model is composed of a deterministic line-of-sight path and a stochastic non-line-of-sight component describing a practical spatially correlated multipath environment. We derive the statistical properties of the minimum mean squared error (MMSE), element-wise MMSE, and least-square channel estimates for this model. Using these estimates for maximum ratio combining and precoding, rigorous closed-form uplink (UL) and downlink (DL) achievable spectral efficiency (SE) expressions are derived and analyzed. The asymptotic SE behavior, when using the different channel estimators, are also analyzed. The numerical results show that the SE is higher when using the MMSE estimator than that of the other estimators, and the performance gap increases with the number of antennas.

170 citations

Proceedings ArticleDOI
24 Apr 2014
TL;DR: This paper establishes that SM has significant signal-to-noise (SNR) advantage over conventional modulation in large-scale multiuser (multiple-input multiple-output) MIMO systems, and proposes two novel algorithms for detection of large- scale SM-MIMO signals at the BS, one based on message passing and the other based on local search.
Abstract: Spatial modulation (SM) is attractive for multi-antenna wireless communications. SM uses multiple transmit antenna elements but only one transmit radio frequency (RF) chain. In SM, in addition to the information bits conveyed through conventional modulation symbols (e.g., QAM), the index of the active transmit antenna also conveys information bits. In this paper, we establish that SM has significant signal-to-noise (SNR) advantage over conventional modulation in large-scale multiuser (multiple-input multiple-output) MIMO systems. Our new contribution in this paper addresses the key issue of large-dimension signal processing at the base station (BS) receiver (e.g., signal detection) in large-scale multiuser SM-MIMO systems, where each user is equipped with multiple transmit antennas (e.g., 2 or 4 antennas) but only one transmit RF chain, and the BS is equipped with tens to hundreds of (e.g., 128) receive antennas. Specifically, we propose two novel algorithms for detection of large-scale SM-MIMO signals at the BS; one is based on message passing and the other is based on local search. The proposed algorithms achieve very good performance and scale well. For the same spectral efficiency, multiuser SM-MIMO outperforms conventional multiuser MIMO (recently being referred to as massive MIMO) by several dBs. The SNR advantage of SM-MIMO over massive MIMO can be attributed to: (i) because of the spatial index bits, SM-MIMO can use a lower-order QAM alphabet compared to that in massive MIMO to achieve the same spectral efficiency, and (ii) for the same spectral efficiency and QAM size, massive MIMO will need more spatial streams per user which leads to increased spatial interference.

78 citations

Proceedings ArticleDOI
22 Mar 2017
TL;DR: Estimating the channel parameters for conventional MIMO and massive MIMo based on training-based and blind channel estimation techniques wherein the performance of both is compared is compared.
Abstract: Multiple-input multiple-output (MIMO) technology is becoming mature in wireless communication systems. It has led to third and fourth generation wireless systems, which has been providing good range, reliability and higher data rates. For the increased demand of much higher data rates, coverage, spectral efficiency, capacity and reduced latency, the evolution of the next generation i.e., the fifth generation technology is necessary. Massive MIMO technology is one of the most promising solution for the above-mentioned challenge. In massive MIMO, the base station is incorporated with hundreds to thousands of antenna array wherein the degrees of freedom can be exploited and the energy can be efficiently used due to the fact that the extra antennas at the base station helps focus the energy into the smaller regions of space. To reap the benefits provided by the extra antennas, the channel information is necessary which makes it possible to have a reliable communication. Therefore, to acquire the channel knowledge, channel state information is required at the base station and estimating the channel parameters plays an important role. In this paper, we concentrate on estimating the channel parameters for conventional MIMO and massive MIMO based on training-based and blind channel estimation techniques wherein the performance of both is compared.

19 citations

Proceedings ArticleDOI
Dan Fei1, Ruisi He1, Bo Ai1, Bei Zhang1, Ke Guan1, Zhangdui Zhong1 
01 Aug 2015
TL;DR: A measurement campaign at the 3.33 GHz frequency band is carried out in an outdoor scenario using a large virtual antenna array with 64 elements, showing that the non-stationary of the channel over the large array size occur both in delay and spatial domains.
Abstract: As an important candidate technologies of 5th generation mobile communication system (5G), massive MIMO is able to make full use of space resources, and effectively improves the spectrum efficiency, thus helps to solve the current condition of the shortage of spectrum resources. In order to study the massive MIMO technology, the radio channel characteristics, which is different to the conventional MIMO systems, must be captured. Therefore, in this paper, a measurement campaign at the 3.33 GHz frequency band is carried out in an outdoor scenario using a large virtual antenna array with 64 elements. Based on the measured data, we analyze the behaviors of the typical channel parameters including power delay profile (PDP), power azimuth-delay spectrum (PADS), power delay spectrum (PDS), power azimuth spectrum (PAS), delay spread (DS) and azimuth spread (AS). The results show that the non-stationary of the channel over the large array size occur both in delay and spatial domains. The results is useful to develop the massive MIMO channel model and design the 5G communication system.

19 citations