Hybrid Precoding-Based Millimeter Wave Massive MIMO-NOMA Systems

Zhu, Zaoxing; Deng, Haixiao; Xu, Fuxin; Zhang, Wenjuan; Liu, Gang

doi:10.3390/sym14020412

Cited by 8 publications

(10 citation statements)

References 22 publications

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“…We consider that both the BS and the RIS are uniform planar arrays (UPA) [ 19 ]; then the channel between the BS-RIS can be expressed as

where L is is the number of paths between the BS-RIS,

is the complex gain of the

th path,

and

is the azimuth angle,

and

is the elevation angle,

, and

is the antenna array response of the base station and RIS. The specific expressions can be expressed as

where

is the wavelength,

is the spacing between the antennas,

, and ⊗ is the Kronecker product.…”

Section: Channel Model and Channel Estimation Protocolmentioning

confidence: 99%

“…In addition, since the adjacent semi-passive RIS elements have stronger channel correlation with each other, we assume that the semi-passive elements are continuously distributed over the RIS to better estimate the CSI. Version July 30, 2022 submitted to Journal Not Specified 4 of 21 We consider that both the BS and the RIS are uniform planar array (UPA) [19], then the channel between the BS-RIS can be expressed as…”

Section: Channel Modelmentioning

confidence: 99%

“…where L r,k is is the number of paths between UE-RIS, α l 2 is the complex gain of the l 2 th path between UE-RIS, φ l 1 and φ l 2 is the azimuth angle, θ l 1 and θ l 2 is the elevation angle, a BS φ R,l 1 , θ R,l 1 and a H RIS φ T,l 1 , θ T,l 1 is the antenna array response of the base station and RIS. Similarily, the specific expressions can be expressed as We consider that both the BS and the RIS are uniform planar arrays (UPA) [19]; then the channel between the BS-RIS can be expressed as…”

Section: Channel Modelmentioning

confidence: 99%

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Two-Stage Channel Estimation for Semi-Passive RIS-Assisted Millimeter Wave Systems

Peng

Deng

Xiao

et al. 2022

Sensors

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In a reconfigurable intelligent surface (RIS) assisted millimeter Wave (mmWave) communication system, the channel coefficient increases exponentially with the number of RIS elements which results in expensive pilot overhead. Most previous works have proposed some channel estimation algorithms for the estimation accuracy of cascaded channels, which have improved the estimation accuracy, but the pilot overhead is discouraging in the estimation process. To improve the channel estimation accuracy with reduced pilot overhead, we propose a two-stage channel estimation protocol by exploiting semi-passive elements and the coherent time difference of the channel, where the quasi-static channel between the base stations (BS) and RIS is estimated at the RIS, and the user (UE)-RIS time-varying channel is estimated at the BS. In the first stage, we formulate the BS-RIS channel estimation as a mathematical optimization problem by an iterative weighting method and then propose a gradient descent (GD)-based algorithm to solve it. In the second stage, we first transform the received the UE-RIS signal model into an equivalent parallel factor (PARAFAC) tensor model and estimate the UE-RIS channel by the least-squares (LS) algorithm. The simulation results show that the proposed method has better estimation accuracy than the LS, compression sensing (CS) and minimum mean square error (MMSE) methods with less pilot overhead, and the spectral efficiency is improved by at least 10.5% compared to the other three methods.

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“…We consider that both the BS and the RIS are uniform planar arrays (UPA) [ 19 ]; then the channel between the BS-RIS can be expressed as

where L is is the number of paths between the BS-RIS,

is the complex gain of the

th path,

and

is the azimuth angle,

and

is the elevation angle,

, and

is the antenna array response of the base station and RIS. The specific expressions can be expressed as

where

is the wavelength,

is the spacing between the antennas,

, and ⊗ is the Kronecker product.…”

Section: Channel Model and Channel Estimation Protocolmentioning

confidence: 99%

Section: Channel Modelmentioning

confidence: 99%

Section: Channel Modelmentioning

confidence: 99%

See 1 more Smart Citation

Two-Stage Channel Estimation for Semi-Passive RIS-Assisted Millimeter Wave Systems

Peng

Deng

Xiao

et al. 2022

Sensors

Self Cite

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show abstract

“…Since the high free-space path loss of the millimeter-wave channel leads to finite tial selectivity or scattering, the channel model used in a conventional MIMO is no lo applicable for millimeter-wave MIMO systems, and we adopt the geometric Saleh-V zuela channel model [17,18]. We set the antennas at the BS to employ a uniform l array (ULA) and the antennas at the IRS to employ a uniform planar array (UPA) then the BS-IRS channel can be expressed as: Since the high free-space path loss of the millimeter-wave channel leads to finite spatial selectivity or scattering, the channel model used in a conventional MIMO is no longer applicable for millimeter-wave MIMO systems, and we adopt the geometric Saleh-Valenzuela channel model [17,18]. We set the antennas at the BS to employ a uniform linear array (ULA) and the antennas at the IRS to employ a uniform planar array (UPA), and then the BS-IRS channel can be expressed as:…”

Section: System Modelmentioning

confidence: 99%

Beamforming Optimization for Intelligent Reflecting Surface-Assisted MIMO Systems

Zhang

Deng

et al. 2022

Symmetry

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To improve the spectral efficiency of symmetry-based intelligent reconfigurable surface (IRS)-assisted MIMO communication systems, this paper investigates the joint precoding and passive beamforming optimization problem for millimeter-wave point-to-point MIMO systems under both ideal and practical IRS phase shifts. For the ideal IRS phase shifts setup, we derive a simplified approximate spectral efficiency expression based on Jensen’s inequality and propose a minimum mean square error (MMSE)-based algorithm to transform the simplified non-convex problem into a solvable convex function. For the practical IRS phase shifts setup, we propose a multi-start stepwise optimization algorithm to obtain the passive beamforming by stepwise iterative search. Finally, with the above-obtained passive beamforming, the optimal precoding is derived by performing the singular value decomposition (SVD) on the effective channel and water-filling power allocation. The simulation results verify our performance analysis and demonstrate that spectral efficiency can be effectively improved compared to various benchmark schemes.

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“…In mmWave-based CR-IoV, beamforming is the additional issue along with the security [21,22]. A symmetry-based hybrid precoder and combiner is an efficient signal processing structure for Non-Orthogonal Multiple Access (NOMA) systems at millimeter wavelengths (mmWave) [23]. Though mmWave achieves better capacity and efficiency, it also demands optimal beamforming and alignment.…”

Section: Introductionmentioning

confidence: 99%

Defense against SSDF Attack and PUE Attack in CR-Internet of Vehicles (IoVs) for Millimeter Wave Massive MIMO Beamforming Systems

Pari

Jaisankar

2022

Symmetry

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The Internet of Vehicles (IoV) is witnessed to play the leading role in the future of Intelligent Transportation Systems (ITS). Though many works have focused on IoV improvement, there is still a lack of performance due to insufficient spectrum availability, lower data rates, and the involvement of attackers. This paper considers all three issues by developing a novel mmWave-assisted Cognitive Radio based IoV (CR-IoV) model. The integration of CR in IoV resolves the issue of spectrum management, while mmWave technology ensures symmetry in acquiring higher data rates for Secondary Users (SUs). With the proposed mmWave-assisted CR-IoV model, symmetric improvements in network performance were achieved in three main areas: security, beamforming, and routing. Optimum detection mechanisms isolate malicious Secondary Users (SUs) in the overall network. First, Spectrum Sensing Data Falsification (SSDF) attack is detected by a Hybrid Kernel-based Support Vector Machine (HK-SVM), which is the lightweight Machine Learning (ML) technique. Then, the Primary User Emulation (PUE) attack is detected by a hybrid approach, namely the Fang Algorithm-based Time Difference of Arrival (FA-TDoA) method. Further, security is assured by validating the legitimacy of each SU through a Lightweight ID-based Certificate Validation mechanism. To accomplish this, we employed the Four Q-curve asymmetric cryptographic algorithm. Overall, the proposed dual-step security provisioning approach assures that the network is free from attackers. Next, beamforming is performed for legitimate SUs by a 3D-Beamforming algorithm that relies on Array Factor (AF) and Beampattern Function. Finally, routing is enabled by formulating Forwarding Zone (FZ) based on the forwarding angle. In the forwarding zone, optimal forwarders are selected by the Multi-Objective Whale Optimization (MOWO) algorithm. Here, a new potential score is formulated for fitness evaluation. Finally, the proposed mmWave-assisted CR-IoV model is validated through extensive simulations in the ns-3.26 simulation tool. The evaluation shows better performance in terms of throughput, packet delivery ratio, delay, bit error rate, and detection accuracy.

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Hybrid Precoding-Based Millimeter Wave Massive MIMO-NOMA Systems

Cited by 8 publications

References 22 publications

Two-Stage Channel Estimation for Semi-Passive RIS-Assisted Millimeter Wave Systems

Two-Stage Channel Estimation for Semi-Passive RIS-Assisted Millimeter Wave Systems

Beamforming Optimization for Intelligent Reflecting Surface-Assisted MIMO Systems

Defense against SSDF Attack and PUE Attack in CR-Internet of Vehicles (IoVs) for Millimeter Wave Massive MIMO Beamforming Systems

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