Decoupling Energy Efficient Approach for Hybrid Precoding-Based mmWave Massive MIMO-NOMA With SWIPT

Jawarneh, Ahlam; Kadoch, Michel; Bataineh, Zaid

doi:10.1109/access.2022.3155485

Cited by 16 publications

(9 citation statements)

References 43 publications

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“…[14] maximized energy efficiency using joint system-level optimization in a massive MIMO SWIPT downlink system based on PS protocol. [15] maximized the overall power and minimum rate values under the set power and rate restrictions for each terminal in millimeter-wave massive MIMO SWIPT systems based on PS protocol.…”

Section: Introductionmentioning

confidence: 99%

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Power allocation optimization for hybrid information and energy transfer with massive MIMO downlink

Sun,

2023

IET Communications

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This paper investigates the power allocation in a massive multiple‐input multiple‐output (MIMO) downlink system, where the base station (BS) simultaneously transmits information and energy to information terminals and energy terminals without resorting to any wireless energy harvesting (EH) protocol, respectively. First, with respect to this system, the closed‐form expressions of achievable rate and harvested energy are, respectively, derived for Rayleigh fading channels. Then, from these expressions, three optimization problems are formulated based on quality‐of‐service (QoS) requirements, with the purposes of maximizing achievable sum rate, harvested sum energy and joint QoS requirements, respectively. The optimization problem of maximizing achievable sum rate is transformed into a sequence of geometric programmings (GPs), which can be solved efficiently with standard convex optimization tools. The optimization problem of maximizing harvested sum energy is proved to be solvable as a linear program. The optimization problem of maximizing joint QoS requirements is shown to be a multi‐objective optimization problem (MOOP) and solved by a one‐dimensional search based on GP. Finally, numerical results manifest that the proposed power allocation algorithms can provide good system performances of achievable sum rate and harvested sum energy, and jointly guarantee QoS fairness in terms of achievable rate and harvested energy.

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Section: Introductionmentioning

confidence: 99%

“…It is noted that the above-mentioned scheme of SWIPT in [8][9][10][11][12][13][14][15] are based on wireless EH protocol. However, with the development of the Internet of Things, another scheme of SWIPT with hybrid signals transfer of information and energy appears in a few literatures.…”

Section: Introductionmentioning

confidence: 99%

Power allocation optimization for hybrid information and energy transfer with massive MIMO downlink

Sun,

2023

IET Communications

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“…However, in order to realize the fully digital signal processing in mmWave MIMO, each antenna usually requires one dedicated radio‐frequency (RF) links, which will result in the requirement for a large number of RF links, increasing the hardware burden and device energy consumption. Fortunately, the emergence of hybrid precoding (HP) technology offers the possibility to solve this problem, which can reduce the number of RF links without causing obvious performance loss 9 . The key idea of HP is to decompose an all‐digital encoder into a high‐dimensional analog encoder and a low‐dimensional digital encoder.…”

Section: Introductionmentioning

confidence: 99%

“…number of RF links without causing obvious performance loss. 9 The key idea of HP is to decompose an all-digital encoder into a high-dimensional analog encoder and a low-dimensional digital encoder.…”

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confidence: 99%

Energy efficient power allocation for MIMO‐NOMA‐based integrated terrestrial‐satellite networks

Li,

Song

et al. 2023

Satell Commun Network

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SummaryMillimeter‐wave (mmWave) massive MIMO and non‐orthogonal multiple access (NOMA) are promising ways for improving energy efficiency of integrated terrestrial‐satellite networks. In this paper, we investigate the performance of integrated terrestrial‐satellite networks, in which MIMO‐NOMA‐based terrestrial networks and satellite network cooperatively provide ubiquitous services for ground users while reusing the entire bandwidth. Both base stations (BSs) and satellite are equipped with multiple antennas, and beamforming technology is utilized to serve multiple users simultaneously. First, a user selection scheme based on channel gain ratio is proposed to select satellite users. Then, we design a user clustering algorithm for reducing the intra‐beam and inter‐beam interference of BSs users. Analog and digital beamforming techniques are also adopted to further increase beam gain and improve system energy efficiency. The terrestrial networks and satellite networks energy efficiency are separately optimized through beamforming design and power allocation scheme, and then, a joint iterative algorithm is proposed to maximize the system energy efficiency. In addition, we introduce the interference temperature threshold to limit the satellite interference to BSs users. Finally, the performance of the proposed algorithm is simulated in comparison with the existing algorithm. The results indicate that the presented algorithm has high superiority in system energy efficiency and it can be applied to integrated terrestrial‐satellite networks.

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“…With some degree of baseband processing already present at the RIS, CSI acquisition is made more accessible. In 31 , 32 , we have an example of this strategy where compressive sensing is used to estimate the relevant channels. In contrast, the second method, which is the one used in this article, assumes a passive structure in which the RIS accomplishes its goals by returning the impeding waves in accordance with a phase-shift rhythm.…”

Section: Introductionmentioning

confidence: 99%

Channel estimation for reconfigurable intelligent surface-assisted mmWave based on Re‘nyi entropy function

Albataineh

Hayajneh

Shakhatreh

et al. 2022

Sci Rep

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This study focuses on channel estimation for reconfigurable intelligent surface (RIS)-assisted mmWave systems, in which the RIS is used to facilitate base-to-user data transfer. For beamforming to work with active and passive elements, a large-size cascade channel matrix should always be known. Low training costs are achieved by using the mmWave channels’ inherent sparsity. The research provides a unique compressive sensing-based channel estimation approach for reducing pilot overhead issues to a minimum. The proposed technique estimates channel data signals in a downlink for RIS-assisted mmWave systems. The mmWave systems often have a sparse distribution of signal sources due to the spatial correlations of the domains. This distribution pattern makes it possible to use compressive sensing methods to resolve the channel estimation issue. In order to decrease the pilot overhead, which is necessary to predict the channel, the proposed method extends the Re‘nyi entropy function as the sparsity-promoting regularizer. In contrast to conventional compressive sensing techniques, which necessitate an initial knowledge of the signal’s sparsity level, the presented method employs sparsity adaptive matching pursuit (SAMP) techniques to gradually determine the signal’s sparsity level. Furthermore, it introduces a threshold parameter based on the signal’s energy level to eliminate the sparsity level requirement. Extensive simulations show that the presented channel estimation approach surpasses the traditional OMP-based channel estimation methods in terms of normalized mean square error performance. In addition, the computational cost of channel estimation is lowered. Based on the simulations, our approach can estimate the channel well while reducing training overhead by a large amount.

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Decoupling Energy Efficient Approach for Hybrid Precoding-Based mmWave Massive MIMO-NOMA With SWIPT

Cited by 16 publications

References 43 publications

Power allocation optimization for hybrid information and energy transfer with massive MIMO downlink

Power allocation optimization for hybrid information and energy transfer with massive MIMO downlink

Energy efficient power allocation for MIMO‐NOMA‐based integrated terrestrial‐satellite networks

Channel estimation for reconfigurable intelligent surface-assisted mmWave based on Re‘nyi entropy function

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