Research article Topical Sections

Performance analysis of insertion loss incorporated hybrid precoding for massive MIMO


  • Received: 06 January 2024 Revised: 10 March 2024 Accepted: 04 April 2024 Published: 11 April 2024
  • Due to an increase in the number of users and a high demand for high data rates, researchers have resorted to boosting the capacity and spectral efficiency of the next-generation wireless communication. With a limited RF chain, hybrid analog digital precoding is an appealing alternative. The hybrid precoding approach divides the beamforming process into an analog beamforming network and a digital beamforming network of a reduced size. As a result, numerous hybrid beamforming networks have been proposed. The practical effects of signal processing in the RF domain, such as the additional power loss incurred by an analog beamforming network, were not taken into account. The effectiveness of hybrid precoding structures for massive MIMO systems was examined in this study. In particular, a viable hardware network realization with insertion loss was developed. Investigating the spectral and energy efficiency of two popular hybrid precoding structures, the fully connected structure, and the subconnected structure, it was found that in a massive MIMO, the subconnected structure always performed better than the fully connected structure. Characterizing the effect of quantized analog precoding, it was shown that the subconnected structure was able to achieve better performance with fewer feedback bits than the fully connected structure.

    Citation: Tadele A. Abose, Thomas O. Olwal, Muna M. Mohammed, Murad R. Hassen. Performance analysis of insertion loss incorporated hybrid precoding for massive MIMO[J]. AIMS Electronics and Electrical Engineering, 2024, 8(2): 187-210. doi: 10.3934/electreng.2024008

    Related Papers:

    [1] Qianqian Zhang, Mingye Mu, Heyuan Ji, Qiushi Wang, Xingyu Wang . An adaptive type-2 fuzzy sliding mode tracking controller for a robotic manipulator. Electronic Research Archive, 2023, 31(7): 3791-3813. doi: 10.3934/era.2023193
    [2] Lingling Zhang . Vibration analysis and multi-state feedback control of maglev vehicle-guideway coupling system. Electronic Research Archive, 2022, 30(10): 3887-3901. doi: 10.3934/era.2022198
    [3] Duhui Chang, Yan Geng . Distributed data-driven iterative learning control for multi-agent systems with unknown input-output coupled parameters. Electronic Research Archive, 2025, 33(2): 867-889. doi: 10.3934/era.2025039
    [4] Zhizhou Zhang, Yueliang Pan, Weilong Zhao, Jinchu Zhang, Zheng Zi, Yuan Xie, Hehong Zhang . Frequency analysis of a discrete-time fast nonlinear tracking differentiator algorithm based on isochronic region method. Electronic Research Archive, 2024, 32(9): 5157-5175. doi: 10.3934/era.2024238
    [5] Jian Gong, Yuan Zhao, Jinde Cao, Wei Huang . Platoon-based collision-free control for connected and automated vehicles at non-signalized intersections. Electronic Research Archive, 2023, 31(4): 2149-2174. doi: 10.3934/era.2023111
    [6] Chao Ma, Hang Gao, Wei Wu . Adaptive learning nonsynchronous control of nonlinear hidden Markov jump systems with limited mode information. Electronic Research Archive, 2023, 31(11): 6746-6762. doi: 10.3934/era.2023340
    [7] Tongtong Yu, Zhizhou Zhang, Yang Li, Weilong Zhao, Jinchu Zhang . Improved active disturbance rejection controller for rotor system of magnetic levitation turbomachinery. Electronic Research Archive, 2023, 31(3): 1570-1586. doi: 10.3934/era.2023080
    [8] Xingting Geng, Jianwen Feng, Yi Zhao, Na Li, Jingyi Wang . Fixed-time synchronization of nonlinear coupled memristive neural networks with time delays via sliding-mode control. Electronic Research Archive, 2023, 31(6): 3291-3308. doi: 10.3934/era.2023166
    [9] Ning Li, Yuequn Gao . Modified fractional order social media addiction modeling and sliding mode control considering a professionally operating population. Electronic Research Archive, 2024, 32(6): 4043-4073. doi: 10.3934/era.2024182
    [10] Chao Yang, Juntao Wu, Zhengyang Qiao . An improved fixed-time stabilization problem of delayed coupled memristor-based neural networks with pinning control and indefinite derivative approach. Electronic Research Archive, 2023, 31(5): 2428-2446. doi: 10.3934/era.2023123
  • Due to an increase in the number of users and a high demand for high data rates, researchers have resorted to boosting the capacity and spectral efficiency of the next-generation wireless communication. With a limited RF chain, hybrid analog digital precoding is an appealing alternative. The hybrid precoding approach divides the beamforming process into an analog beamforming network and a digital beamforming network of a reduced size. As a result, numerous hybrid beamforming networks have been proposed. The practical effects of signal processing in the RF domain, such as the additional power loss incurred by an analog beamforming network, were not taken into account. The effectiveness of hybrid precoding structures for massive MIMO systems was examined in this study. In particular, a viable hardware network realization with insertion loss was developed. Investigating the spectral and energy efficiency of two popular hybrid precoding structures, the fully connected structure, and the subconnected structure, it was found that in a massive MIMO, the subconnected structure always performed better than the fully connected structure. Characterizing the effect of quantized analog precoding, it was shown that the subconnected structure was able to achieve better performance with fewer feedback bits than the fully connected structure.



    Alcohol dependence (AD) or Alcoholism, also regarded as alcohol use disorder (AUD), is a complex and relapsing neuropsychiatric disorder [1],[2]. World Health Organization (WHO) reported that approximately 140 million individuals addicted to alcohol globally, resulting in to 2.5 million death each year [3]. AD is regarded as a “reward deficiency syndrome” that intemperately affects public health [4],[5]. It has been found to be influenced by both genetic and environmental factors [6],[7]. Exact patho-physiological and molecular mechanism of AD is not known yet. However, molecular genetic studies support that multiple genes determine an individual's predisposition to AD [8]. Heritability of AD likely plays an important role in its development and is determined to be moderate to high [9],[10]. It was reported frequently that alcohol consumption increased homocysteine (Hcy) concentration i.e hyperhomocysteinaemia [11]. However, inconsistent results of the combined effect of both positive and negative association have been reported between alcohol intake and Hcy [12]. Hyperhomocystenemia is already reported as risk factor for several diseases or disorders including neural tube defects, Alzheimer disease, schizophrenia, pregnancy complications, cardiovascular diseases, noninsulin dependent diabetes and end-stage renal disease as evidenced from several studies [13].

    Homocysteine is a sulfur containing amino acid, several genetic and environmental risk factor are reported for higher plasma concentration of homocysteine [14]. Homocysteine (Hcy) is synthesized in methionine and folate cycle by demethylation of methionine. 5,10-methylenetetrahydrofolate reductase (MTHFR) enzyme of folate cycle plays an important role in homocysteine metabolism. MTHFR gene is present on chromosome 1p36.3. Numerous single nucleotide polymorphisms (SNP) are known in MTHFR gene like C677T and A1298C etc [15],[16]. The most clinically important and studies polymorphism is C677T (rs1801133), in which cytosine (C) is substituted with thymine (T) at 677 nucleotide position and consequently alanine is replaced by valine in MTHFR enzyme (Ala 222 Val) [17],[18]. The variant MTHFR enzyme is thermolabile with reduced activity (~70%) and it increased the plasma homocysteine concentrations [15] (Frosst et al., 1995). Globally, frequency of mutant T allele varies greatly [19][23]. Yadav et al. [23] have conducted a comprehensive C667T polymorphism study and reported the highest frequency in European populations ranging from 24.1% to 64.3% and, lowest frequency from African population. Several studies revealed association of MTHFR gene C677T polymorphism with AD. However, findings showed inconsistent results [24][26]. To derive a more precise estimation of the relationship, authors have performed a meta-analysis.

    Present meta-analysis is carried out according to MOOSE (Meta-analysis of observational studies in epidemiology) guidelines.

    Articles were retrieved through Pubmed, Google scholar, Springer Link, and Science Direct databases up to February 28, 2020, using following key words: “Methylenetetrahydrofolate reductase” or “MTHFR” or “C677T” or “rs1801133” or “polymorphism” and “Alcohol dependence” or “Alcoholism” or “AD” or “Addiction”.

    Inclusion criteria were following: (1) MTHFR C677T polymorphism and alcohol dependence association was investigated in the study, (2) MTHFR C677T genotype/allele numbers in alcohol dependence cases and controls were given in the study, (3) sufficient information for calculating the odds ratio (OR) with 95% confidence interval (CI) and (4) Articles published in English language were only considered. Major reasons for studies exclusion were as follows: (1) no alcohol dependence cases analyzed, (2) the C677T polymorphism details information missing, and (3) duplicate article.

    Name of first author, country name, number of cases and controls, number of genotypes in cases and controls and journal name with full reference from each article were extracted.

    All analysis were done according to the method of Rai et al. [27]. Odds ratio (ORs) with 95% confidence intervals (CIs) were calculated using fixed effect and random effect models [28],[29]. A five genetic models viz. allele contrast, co-dominant, homozygote, dominant and recessive models were calculated. Heterogeneity was investigated and quantified by I2 statistic [30]. Chi-squared analysis was used to determine whether the genotype distribution of control group was in Hardy–Weinberg equilibrium or not. Subetaoup analysis was conducted by ethnicity. In included articles, case samples were not categorized on the basis of gender, so the subetaoup analysis based on gender did not performed in present meta-analysis. Publication bias was investigated by Egger's regression intercept test [31]. P value <0.05 was considered statistically significant. All calculations were done by softwares MIX version 1.7 [32] and MetaAnalyst [33] program.

    Selection of studies is given in fow diagram (Figure 1). Following the exclusion criteria, 10 individual case-control studies with a total of 1676 cases and 1594 controls were included into this meta-analysis [24][26],[34][40]. One author [38] reported their data in to two categories, we included both set of data as different studies. Hence, total number of included studies in present meta-analysis is eleven (Table 1).

    Figure 1.  Flow diagram of study search and selection process.
    Table 1.  Details of included eleven studies in the present meta-analysis.
    Study Ethnicity Control Case Case
    Genotype
    Control Genotype
    HWE p-value of controls
    CC CT TT CC CT TT
    Bonsch, 2006 Caucasian 115 134 64 56 14 60 41 14 0.10
    Lutz, 2006 Caucasian 102 221 95 94 32 53 41 8 0.98
    Lutz, 2007 Caucasian 101 142 65 58 19 53 40 8 0.90
    Saffroy, 2008 Caucasian 93 242 108 113 21 35 41 17 0.41
    Benyamina, 2009 Caucasian 93 120 56 53 11 35 41 17 0.41
    Fabris, 2009 Caucasian 236 63 17 35 11 69 113 54 0.55
    Shin, 2010 Asian 232 68 11 39 18 42 129 61 0.07
    Supic, 2010, Heavy Alcoholic Caucasian 57 32 13 9 10 27 24 6 0.84
    Supic, 2010, Non heavy Alcoholic Caucasian 105 64 37 23 4 53 42 10 0.69
    Singh, 2014 Asian 313 139 91 44 4 228 78 7 0.91
    Singh, 2015 Asian 147 451 312 125 14 107 35 5 0.32

     | Show Table
    DownLoad: CSV

    Overall, eleven studies provided 1676/1594 cases/controls for MTHFR C677T polymorphism. The prevalence of C and T alleles in AD cases was 71.22% and 28.79% respectively. The percentage frequency of TT genotype among cases and controls was 9.43% and 12.98%, respectively whereas prevalence of CT heterozygote among AD cases was 38.72% and 39.21% in controls. The prevalence of CC homozygote among AD cases and controls was 51.85% and 47.80%, respectively. Genotypes were in Hardy-Weinberg equilibrium in all controls. In control group the percentage of C and T allele frequencies was 67.41% and 32.59% respectively (Figure 2).

    Figure 2.  Bar diagram showing percentage of C and T allele frequencies in control group of total 11 studies, 3 Asian studies and 8 Caucasian studies.

    No significant association was observed between the MTHFR C677T polymorphism and the susceptibility to AD in all the genetic models using random effect model (for T vs. C (allele contrast): OR = 1.04, 95% CI = 0.88–1.24; CT vs. CC (co-dominant): OR = 1.02, 95% CI = 0.62–1.68; for TT+CT vs. CC (dominant): OR = 1.10, 95% CI = 0.94–1.29; for TT vs. CC (homozygote): OR = 1.01, 95% CI = 0.66–1.51; for TT vs. CT + CC (recessive): OR = 0.97, 95% CI = 0.66–1.40) (Table 2; Figures 3).

    A true heterogeneity existed between studies for allele contrast (Pheterogeneity = 0.02, Q = 20.64, I2 = 51.56%, t2 = 0.04, z = 0.69), co-dominant genotype (Pheterogeneity < 0.0001, Q = 86.64, I2 = 88.46%, t2 = 0.61, z = 4.29), homozygote genotype (Pheterogeneity = 0.02, Q = 20.93, I2 = 52.24%, t2 = 0.24, z = 0.1), and recessive genotype (Pheterogeneity = 0.02, Q = 21.00, I2 = 52.40%, t2 = 0.20, z = 0.47) comparisons. The “I2” value of more than 50% shows high level of true heterogeneity.

    Table 2.  Summary estimates for the odds ratio (OR) of MTHFR C677T in various allele/genotype contrasts, the significance level (p value) of heterogeneity test (Q test), and the I2 metric and publication bias p-value (Egger Test).
    Genetic Models Fixed effect OR (95% CI), p Random effect OR (95% CI), p Heterogeneity p-value (Q test) I2 (%) Publication Bias (p of Egger's test)
    Allele Contrast (T vs. C) 1.04 (0.92–1.17), 0.48 1.04 (0.88–1.24), 0.60 0.02 51.56 0.62
    Dominant (TT+CT vs. CC) 1.41 (1.20–1.65), <0.0001 1.02 (0.62–1.68), 0.92 <0.0001 88.46 0.06
    Homozygote (TT vs. CC) 0.98 (0.75–1.29), 0.92 1.01 (0.66–1.51), 0.97 0.02 52.24 0.26
    Co-dominant (CT vs. CC) 1.10 (0.94–1.29), 0.21 1.10 (0.94–1.29), 0.21 0.43 0 0.48
    Recessive (CC+CT vs. TT) 0.94 (0.73–1.20), 0.63 0.97 (0.66–1.40), 0.86 0.02 52.4 0.28

     | Show Table
    DownLoad: CSV
    Figure 3.  Random effect Forest plot of allele contrast model (T vs. C) of total 11 studies of MTHFR gene C677T polymorphism.

    Out of 11 studies included in the present meta-analysis, 3 studies were carried out in Asian countries, and 8 studies were carried out on Caucasian (Table 1). The subetaoup analysis by ethnicity did not reveal any significant association between MTHFR C677T polymorphism and AD in Asian population (T vs. C: OR = 1.16; 95% CI = 0.93–1.44; p = 0.17; I2 = 3.1%; Pheterogeneity = 0.65; TT vs. CC: OR = 1.16; 95% CI = 0.62–2.02; p = 0.69; I2 = 3.1%; Pheterogeneity = 0.89; and TT+CT vs. CC: OR = 1.26; 95% CI = 0.96–1.67; p = 0.09; I2 = 3.1%; Pheterogeneity = 0.81); and Caucasian population (T vs. C: OR = 0.99; 95% CI = 0.86–1.14; p = 0.93; I2 = 61.75%; Pheterogeneity = 0.01; TT vs. CC: OR= 0.95; 95% CI = 0.70–1.29; p = 0.75; I2 = 65.63%; Pheterogeneity = 0.004; and TT+CT vs. CC: OR = 1.03; 95% CI = 0.85–1.25; p = 0.75; I2 = 13.93%; Pheterogeneity = 0.32) (Figures 4 and 5).

    Figure 4.  Random effect Forest plot of allele contrast model (T vs. C) of total 3 Asian studies of MTHFR gene C677T polymorphism.
    Figure 5.  Random effect Forest plot of allele contrast model (A vs. G) of total 8 Caucasian studies of MTHFR gene C677T polymorphism.

    Symmetrical shape of Funnel plots' revealed absence of publication bias. P values of Egger's test were more than 0.05, also provided statistical evidence for the funnel plots' symmetry (p = 0.62 for T vs. C; p = 0.48 for TT vs. CC; p = 0.26 for CT vs. CC; p = 0.48 for TT+AC vs. CC; p = 0.28 for TT vs. CT+CC) (Table 2; Figure 6).

    Figure 6.  Funnel plot- Precision by log odds ratio for allele contrast model (T vs. C) of total 11 studies of MTHFR gene C677T polymorphism.

    In vivo and in vitro studies has demonstrated that homocysteine has neurotoxic effects especially on dopamine neurons of reward pathway [11]. In addition, hyperhomocysteinemia is also reported in AD [11],[41]. In MTHFR gene several polymorphisms are reported but according to deficit hypothesis of addiction, only C677T polymorphism-dependent alteration of the reward system possibly leads to alcohol addiction. Further, homovanillic acid (HVA) is a potential indicator of central dopaminergic neuronal activity [42] and experimentally, it was demonstrated that higher concentration of homocysteine lowers the level of HVA in rat striatum region [43]. On the basis of 11 studies providing data on MTHFR C677T genotype and AD risk in two ethnic populations, including over 3,205 subjects, our meta-analysis provides an evidence that TT and CT genotypes or T allele are not associated with AD risk. Hence the present meta-analysis indicated that C677T is not a risk factor of AD.

    Meta-analysis is a statistical tool to combine the information of independent case-control studies with similar target [44]. Several meta-analysis are published, which evaluated effects of folate pathway genes polymorphisms in susceptibility of diseases/disorders- cleft lip and palate [45], down syndrome [46][48], male infertility [49], bipolar disorder [50], schizophrenia [51],[52], depression [53], obsessive compulsive disorder [54], hyperurecemia [55], epilepsy [56], Alzheimers disease [57], esophageal cancer [58], and ovary cancer [59].

    Several limitations that should be acknowledged like (i) calculated crude Odds ratio, (ii) included the less number of available studies (10 studies) and the limited sample size of each included study, (iii) observed higher between study heterogeneity, (iv) considered only one gene polymorphism and (v) not considered other confounding factors like diet, gender etc. In addition to limitations, current meta-analysis has several strength also such as—higher study power and larger sample size in comparison to individual case control studies, and absence of publication bias etc.

    In conclusion, pooled analysis of data from 11 separate articles indicates that the MTHFR 677TT genotype is not a risk factor for AD. The results of present meta-analysis should be interpreted with certain cautions due to presence of higher heterogeneity and small number of included studies. Future large-scale, population-based association studies from different regions of the world are required to investigate potential gene-gene and gene-environment interactions involving the MTHFR C677T polymorphism in determining AD risk.



    [1] Swindlehurst AL, Ayanoglu E, Heydari P, Capolino F (2014) Millimeter-wave massive MIMO: The next wireless revolution? IEEE Commun Mag 52: 56–62. https://doi.org/10.1109/MCOM.2014.6894453 doi: 10.1109/MCOM.2014.6894453
    [2] Karjalainen J, Nekovee M, Benn H, Kim W, Park J, Sungsoo H (2014) Challenges and Opportunities of mm-Wave Communication in 5G Networks. Proceedings of the 2014 9th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), 372–376. https://doi.org/10.4108/icst.crowncom.2014.255604 doi: 10.4108/icst.crowncom.2014.255604
    [3] Marzetta TL (2010) Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE T Wirel Commun 9: 3590–3600. https://doi.org/10.1109/TWC.2010.092810.091092 doi: 10.1109/TWC.2010.092810.091092
    [4] Larsson EG, Edfors O, Tufvesson F, Marzetta TL (2014) Massive MIMO for next generation wireless systems. IEEE Commun Mag 52: 186–195. https://doi.org/10.1109/MCOM.2014.6736761 doi: 10.1109/MCOM.2014.6736761
    [5] Xie H, Wang B, Gao F, Jin S (2016) A full-space spectrum-sharing strategy for massive MIMO cognitive radio systems. IEEE J Sel Areas Commun 34: 2537–2549. https://doi.org/10.1109/JSAC.2016.2605238 doi: 10.1109/JSAC.2016.2605238
    [6] Feng W, Gao F, Shi R, Ge N, Lu J (2015) Dynamic-cell-based macro coordination for massively distributed MIMO systems. Proceedings of the 2015 IEEE Global Communications Conference (GLOBECOM), 1–6. https://doi.org/10.1109/GLOCOM.2015.7417293 doi: 10.1109/GLOCOM.2015.7417293
    [7] Ayach OE, Rajagopal S, Abu-Surra S, Pi Z, Heath RW (2014) Spatially sparse precoding in millimeter wave MIMO systems. IEEE T Wirel Commun 13: 1499–1513. https://doi.org/10.1109/TWC.2014.011714.130846 doi: 10.1109/TWC.2014.011714.130846
    [8] Roh W, Seol JY, Park J, Lee B, Lee J, Kim Y, et al. (2014) Millimeter-wave beamforming as an enabling technology for 5G cellular communications: Theoretical feasibility and prototype results. IEEE Commun Mag 52: 106–113. https://doi.org/10.1109/MCOM.2014.6736750 doi: 10.1109/MCOM.2014.6736750
    [9] Han S, Chih-Lin I, Xu Z, Rowell C (2015) Large-scale antenna systems with hybrid analog and digital beamforming for millimeter wave 5G. IEEE Commun Mag 53: 186–194. https://doi.org/10.1109/MCOM.2015.7010533 doi: 10.1109/MCOM.2015.7010533
    [10] Gao X, Dai L, Han S, Chih-Lin I, Heath RW (2016) Energy-efficient hybrid analog and digital precoding for mmwave MIMO systems with large antenna arrays. IEEE J Sel Areas Commun 34: 998–1009. https://doi.org/10.1109/JSAC.2016.2549418 doi: 10.1109/JSAC.2016.2549418
    [11] Liang L, Xu W, Dong X (2014) Low-complexity hybrid precoding in massive multiuser MIMO systems. IEEE Wireless Commun Lett 3: 653–656. https://doi.org/10.1109/LWC.2014.2363831 doi: 10.1109/LWC.2014.2363831
    [12] Wan S, Zhu H, Kang K, Qian H (2021) On the Performance of Fully-Connected and Sub-Connected Hybrid Beamforming System. IEEE T Veh Technol 70: 11078–11082. https://doi.org/10.1109/TVT.2021.3109300 doi: 10.1109/TVT.2021.3109300
    [13] Ngo HQ (2015) Massive MIMO: Fundamentals and System Designs. Vol. 1642, Linköping University Electronic Press: Linköping, Sweden. https://doi.org/10.3384/lic.diva-112780
    [14] Luo Z, Luo L, Zhang X, Liu H (2022) Robust Hybrid Beamforming for Multi-user Millimeter Wave Systems with Sub-connected Structure. International Conference on Communications and Networking in China. https://doi.org/10.1007/978-3-031-34790-0_9 doi: 10.1007/978-3-031-34790-0_9
    [15] Hu Y, Qian H, Kang K, Luo X, Zhu H (2023) Joint Precoding Design for Sub-Connected Hybrid Beamforming System. IEEE T Wirel Commun. https://doi.org/10.1109/TWC.2023.3287229 doi: 10.1109/TWC.2023.3287229
    [16] Yu X, Zhang J, Letaief KB (2018) A Hardware-Efficient Analog Network Structure for Hybrid Precoding in Millimeter Wave Systems. IEEE J Sel Top Signal Process 12: 282–297. https://doi.org/10.1109/JSTSP.2018.2814009 doi: 10.1109/JSTSP.2018.2814009
    [17] Zhang Y, Du J, Chen Y, Li X, Rabie KM, Khkrel R (2020) Dual-Iterative Hybrid Beamforming Design for Millimeter-Wave Massive Multi-User MIMO Systems With Sub-Connected Structure. IEEE T Veh Technol 69: 13482–13496. https://doi.org/10.1109/TVT.2020.3029080 doi: 10.1109/TVT.2020.3029080
    [18] Garcia-Rodriguez A, Venkateswaran V, Rulikowski P, Masouros C (2016) Hybrid analog-digital precoding revisited under realistic RF modeling. IEEE Wireless Commun Lett 5: 528–531. https://doi.org/10.1109/LWC.2016.2598777 doi: 10.1109/LWC.2016.2598777
    [19] Venkateswaran V, Krishnan R (2016) Hybrid Analog and Digital Precoding: From Practical RF System Models to Information Theoretic Bounds. 2016 IEEE Globecom Workshops (GC Wkshps). https://doi.org/10.1109/GLOCOMW.2016.7848924 doi: 10.1109/GLOCOMW.2016.7848924
    [20] Song X, Kuhne T, Caire G (2019) Fully-Connected vs. Sub-Connected Hybrid Precoding Architectures for mmWave MU-MIMO. ICC 2019-2019 IEEE International Conference on Communications (ICC). https://doi.org/10.1109/ICC.2019.8761521 doi: 10.1109/ICC.2019.8761521
    [21] Ribeiro LN, Schwarz S, Rupp M, de Almeida AL (2018) Energy Efficiency of mmWave Massive MIMO Precoding with Low-Resolution DACs. IEEE Journal of Selected Topics in Signal Processing 12: 298‒312. https://doi.org/10.1109/JSTSP.2018.2824762 doi: 10.1109/JSTSP.2018.2824762
    [22] Kolawole O, Papazafeiropoulos A, Ratnarajah T (2018) Impact of Hardware Impairments on mmWave MIMO Systems with Hybrid Precoding. Proceedings of the IEEE Wireless Communication and Networking Conference. https://doi.org/10.1109/WCNC.2018.8377045 doi: 10.1109/WCNC.2018.8377045
    [23] Sheikh TA, Bora J, Hussain MA (2021) Capacity maximizing in massive MIMO with linear precoding for SSF and LSF channel with perfect CSI. Digit Commun Netw 7: 92‒99. https://doi.org/10.1016/j.dcan.2019.08.002 doi: 10.1016/j.dcan.2019.08.002
    [24] Sheikh TA, Bora J, Hussain MA (2019) Combined user and antenna selection in massive MIMO using precoding technique. International Journal of Sensors Wireless Communications and Control 9: 214‒223. https://doi.org/10.2174/2210327908666181112144939 doi: 10.2174/2210327908666181112144939
    [25] Sheikh TA, Bora J, Hussain MA (2018) Sum-rate performance of massive MIMO systems in highly scattering channel with semi-orthogonal and random user selection. Radioelectronics and Communications Systems 61: 547‒555. https://doi.org/10.3103/S0735272718120026 doi: 10.3103/S0735272718120026
    [26] Papoulis A, Unnikrishna Pillai S (2012) Probability, Random Variables, and Stochastic Processes, North America: McGraw-Hill, New York, United States.
    [27] Venkateswaran V, Pivit F, Guan L (2016) Hybrid RF and digital beamformer for cellular networks: Algorithms, microwave architectures, and measurements. IEEE T Microw Theory Technol 64: 2226–2243. https://doi.org/10.1109/TMTT.2016.2569583 doi: 10.1109/TMTT.2016.2569583
    [28] Pozar DM (2009) Microwave Engineering, John Wiley & Sons: Hoboken, NJ, USA.
    [29] Alkhateeb A, Leus G, Heath RW (2015) Limited feedback hybrid precoding for multi-user millimeter wave systems. IEEE T Wireless Commun 14: 6481–6494. https://doi.org/10.1109/TWC.2015.2455980 doi: 10.1109/TWC.2015.2455980
    [30] Fozooni M, Matthaiou M, Jin S, Alexandropoulos GC (2016) Massive MIMO relaying with hybrid processing. Proceedings of the 2016 IEEE International Conference on Communications (ICC), 1–6. https://doi.org/10.1109/ICC.2016.7510972 doi: 10.1109/ICC.2016.7510972
    [31] Du J, Xu W, Shen H, Dongy X, Zhao C (2017) Quantized Hybrid Precoding for Massive Multiuser MIMO with Insertion Loss. GLOBECOM 2017-2017 IEEE Global Communications Conference. https://doi.org/10.1109/GLOCOM.2017.8254815 doi: 10.1109/GLOCOM.2017.8254815
    [32] Du J, Xu W, Shen H, Dong X, Zhao C (2018) Hybrid Precoding Architecture for Massive Multiuser MIMO with Dissipation: Sub-Connected or Fully-Connected Structures? IEEE T Wirel Commun 17: 5465‒5479. https://doi.org/10.1109/TWC.2018.2844207 doi: 10.1109/TWC.2018.2844207
    [33] Ratnam VV, Molisch AF, Bursalioglu OY, Papadopoulos HC (2018) Hybrid beamforming with selection for multiuser massive mimo systems. IEEE T Signal Process 66: 4105–4120. https://doi.org/10.1109/TSP.2018.2838557 doi: 10.1109/TSP.2018.2838557
    [34] Wei N (2007) MIMO Techniques for UTRA Long Term Evolution, Citeseer: Princeton, NJ, USA.
    [35] Liu J, Bentley E (2017) Hybrid-beamforming-based millimeter-wave cellular network optimization. IEEE J Sel Area Commun 37: 2799‒2813. https://doi.org/10.23919/WIOPT.2017.7959916 doi: 10.23919/WIOPT.2017.7959916
    [36] Méndez-Rial R, Rusu C, González-Prelcic N, Alkhateeb A, Heath RW (2016) Hybrid mimo architectures for millimeter wave communications: Phase shifters or switches? IEEE Access 4: 247–267. https://doi.org/10.1109/ACCESS.2015.2514261 doi: 10.1109/ACCESS.2015.2514261
    [37] Jedda H, Ayub MM, Munir J, Mezghani A, Nossek JA (2015) Power-and spectral efficient communication system design using 1-bit quantization. Proceedings of the 2015 International Symposium on Wireless Communication Systems (ISWCS), 296–300 https://doi.org/10.1109/ISWCS.2015.7454349 doi: 10.1109/ISWCS.2015.7454349
    [38] Jing J, Xiaoxue C, Yongbin X (2016) Energy-efficiency based downlink multi-user hybrid beamforming for millimeter wave massive mimo system. J China Univ Posts Telecommun 23: 53–62. https://doi.org/10.1016/S1005-8885(16)60045-6 doi: 10.1016/S1005-8885(16)60045-6
    [39] Zhang Y, Yang Y, Dai L (2016) Energy efficiency maximization for device-to-device communication underlying cellular networks on multiple bands. IEEE Access 4: 7682–7691. https://doi.org/10.1109/ACCESS.2016.2623758 doi: 10.1109/ACCESS.2016.2623758
    [40] Abose TA, Olwal TO, Hassen MR, Bekele ES (2022) Performance Analysis and Comparisons of Hybrid Precoding Scheme for Multi-user mmWave Massive MIMO System. 2022 3rd International Conference for Emerging Technology (INCET), 1‒6. https://doi.org/10.1109/INCET54531.2022.9824401 doi: 10.1109/INCET54531.2022.9824401
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1394) PDF downloads(119) Cited by(1)

Figures and Tables

Figures(8)  /  Tables(2)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog