Energy Efficiency Optimization for MIMO Distributed Antenna Systems

Ren, Hong; Liu, Nan; Pan, Cunhua; He, Chunlong

doi:10.1109/tvt.2016.2574899

Cited by 48 publications

(4 citation statements)

References 45 publications

(95 reference statements)

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“…Whereas B is assigned bandwidth and 𝑁 𝑜 is noise. SINR is signal to interference noise ratio and determined as [17]:…”

Section: Methodsmentioning

confidence: 99%

RETRACTED: Energy Efficient Antenna Selection Method for Massive MIMO System in Downlink Scenario

kassahun

2024

Preprint

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Massive MIMO systems provide fast data speeds with a large number of antennas which leads to arise in the power consumption of the system. The basic concept behind massive MIMO is that base stations (BSs) with lots of antenna components supporting terminals over identical time-frequency spectrum. A promising method to improve energy efficiency for both present and future wireless communication systems is the massive multiple-input multiple-output (MIMO) system, which uses a large number of antennas. An increasing amount of energy is required to support the required quality of service due to the exponential expansion and requirement for high-speed wireless communications. As a result, energy-efficient communication has gained a lot of attention in transmission methods for limited energy resources. However, radio frequency (RF) chains can consume a significant amount of power, which presents significant issues. A common feature of numerous antennas is several RF chains, which are made up of filters, ADCs, mixers and amplifiers. Consequently, many RF chains improve the device's cost and hardware complexity. This study addresses the problem of transmitting RF chain configuration under total power limits in large MIMO systems with the goal of optimizing energy efficiency. This creates an optimization problem that decides how many antennas the gadget can employ in order to operate with maximum energy efficiency. The key goal is to choose a fixed number of antennas such that, based on the number of active user terminals that may vary over time, the device can function with a reduced subset of antennas. Thus, the RF chain structure may be ascertained and a minimum number of transmission antenna elements are selected to ensure energy efficiency by using antenna selection methods. The simulation's findings demonstrate how random antenna selection (RANDOM ANPUT SECTION) affects a huge MIMO system's energy efficiency when it uses NOMA and modulation methods to reduce power consumption. To maximize the capacity that was lost as a result of antenna selection, this NOMA and modulation scheme were employed.

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“…Whereas B is assigned bandwidth and 𝑁 𝑜 is noise. SINR is signal to interference noise ratio and determined as [17]:…”

Section: Methodsmentioning

confidence: 99%

RETRACTED: Energy Efficient Antenna Selection Method for Massive MIMO System in Downlink Scenario

kassahun

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Concerning the high energy consumption of 6G massive IoT networks, there are many works that studied the energy efficiency (EE) of massive MIMO systems [33][34][35]. The EE of uplink cellular mMIMO system is evaluated in [33] with distributed base stations (BSs), while [35] considers a multi-slope model for the same network topology.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Network Solution for Improved Efficiency in 6G-Enabled Expanded IoT Network

et al. 2022

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The fast-moving world relies on intelligent connected networks to support the numerous applications of the expanded Internet-of-Things (IoT). The evolving communication requirements of this connected world require a new sixth generation (6G) radio to enable intelligent interaction with the massive number of connected objects. The energy management of billions of connected devices supporting massive Internet-of-Things (IoT) applications is the main challenge. These IoT devices and connected nodes are energy limited, and hence, energy-aware solutions are needed to enable seamless information flow between these communicating nodes. This paper presents an intelligent network solution for improved energy efficiency in a 6G-enabled expanded IoT network. A cell-free massive multiple input multiple output (mMIMO) technology is utilized for maximum energy efficiency with optimum network resource allocation. A practical power consumption model is proposed for the designed network topology which contains all the power components related to data transmission and circuit power. The proposed scheme aims to achieve maximum energy efficiency by the optimal allocation of pilot reuse factor and access point (AP) density for a given number of antennas at each AP and number of users. It is observed that the maximum energy efficiency of 5.2362 Mbit/Joule is achieved at the AP density of 29 and pilot reuse factor of 4 with PMMSE receive combining. In the end, the role of energy efficiency and area throughput tradeoff on the system performance is also evaluated, which suggests that both the energy efficiency and area throughput can be jointly increased until maximum energy efficiency is reached at a point.

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“…According to [22], solving the EE-maximization problem, we can introduce the following three auxiliary subproblems…”

Section: Maximization Of the Eementioning

confidence: 99%

On the energy/spectral efficiency of multi-user full-duplex massive MIMO systems with power control

Wang

Zhang

et al. 2017

J Wireless Com Network

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In this paper, we consider a multi-user full-duplex (FD) massive multiple-input multiple-output (MIMO) system. The FD base station (BS) is equipped with large-scale antenna arrays, while each FD user is equipped with two antennas (one for transmission and the other one for reception). We assume that the channel state information (CSI) is imperfect, and no instantaneous CSI of loop interference (LI) channel is obtained. On the basis of low-complexity uplink beamforming and downlink precoding techniques, i.e. maximum-ratio combining/maximum-ratio transmission (MRC/MRT) and zero-forcing reception/zero-forcing transmission (ZFR/ZFT), the asymptotic expressions of signal-to-interference-plusnoise ratio (SINR) of uplink and downlink are derived respectively when the number of antennas of the BS tends to infinity. Based on the asymptotic SINR expressions, we first analyse the spectral efficiency (SE) performance assuming that the generalized power scaling scenario is adopted. Through the theoretical derivation, it is shown that the detrimental impact of the LI can be eliminated by the very large number of antennas at the BS if the power scaling scenario is appropriately applied, as well as the interference caused by the imperfect channel estimation, the multi-user interference (MUI) and inter-user interference (IUI). Then, we propose power allocation (PA) scenarios to maximize the energy efficiency (EE) and the sum SE with the constraint of maximum powers at the BS and users. The simulation results verify the accuracy of the asymptotic method, we also validate the effectiveness of the EE and the sum SE maximization PA algorithms. We show that adopting the PA scheme to maximize the sum SE can make the multi-user FD massive MIMO system outperform the half-duplex (HD) counterpart regardless of the level of LI.

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Energy Efficiency Optimization for MIMO Distributed Antenna Systems

Cited by 48 publications

References 45 publications

RETRACTED: Energy Efficient Antenna Selection Method for Massive MIMO System in Downlink Scenario

RETRACTED: Energy Efficient Antenna Selection Method for Massive MIMO System in Downlink Scenario

Intelligent Network Solution for Improved Efficiency in 6G-Enabled Expanded IoT Network

On the energy/spectral efficiency of multi-user full-duplex massive MIMO systems with power control

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