Energy Efficiency and Asymptotic Performance Evaluation of Beamforming Structures in Doubly Massive MIMO mmWave Systems

Buzzi, Stefano; D’Andrea, Carmen

doi:10.1109/tgcn.2018.2800537

Cited by 52 publications

(39 citation statements)

References 25 publications

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“…As mentioned in Section I, for the hybrid beamforming of relay system with different connection structures, the power consumption is a key issue which should be considered. In this subsection, we aim at comparing the proposed mixed and full-connected structures in terms of power efficiency of relay [38].…”

Section: E Performance For Power Efficiency Of Relaymentioning

confidence: 99%

Optimal Hybrid Beamforming Design for Millimeter-Wave Massive Multi-User MIMO Relay Systems

Zhang

Yuan-zhi

et al. 2019

IEEE Access

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As a promising technology in the next generation mobile network, millimeter-wave (mmWave) communication can mitigate the spectrum crunch of improving the network capacity by exploiting the large underutilized spectrum bands of the mmWave frequencies. The hybrid (analog/digital) beamforming of multi-data streams are widely used to further the spectrum efficiency of mmWave relay system when faced with the complex environment or long distance communication. This paper investigates the hybrid beamforming scheme for the decode-and-forward (DF) mmWave massive multiple-input multiple-output (MIMO) relay system with mixed structure and full-connected structure. We optimize hybrid beamforming of relay system by maximizing the sum rate of the overall system as an objective function. To reduce the computational complexity, we reformulate the original problem as two single-hop mmWave MIMO sum-rate maximization subproblems. Then, the piecewise successive approximation method is proposed based on the criterion which jointly designs the analog and digital beamforming stages by trying to avoid the loss of information at each stage. The hybrid beamforming of the two subproblems can be solved by the proposed scheme united with the idea of successive interference cancelation (SIC), the baseband block diagonalization (BD) scheme, and waterfilling power allocation method. Finally, simulation results confirm that the proposed optimal method can achieve good performance in hybrid beamforming design of relay system with both mixed and full-connected structures.

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Section: E Performance For Power Efficiency Of Relaymentioning

confidence: 99%

Optimal Hybrid Beamforming Design for Millimeter-Wave Massive Multi-User MIMO Relay Systems

Zhang

Yuan-zhi

et al. 2019

IEEE Access

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“…For example, instead of having 1000 BS antennas to serve single-antenna UEs, we can have 100 BS antennas and 10 antennas per UE. This also opens the door to explore systems with massive arrays at both sides [7].…”

Section: Mmwave: Blessing and Curse Of Attenuation And Directivitymentioning

confidence: 99%

Massive MIMO in Sub-6 GHz and mmWave: Physical, Practical, and Use-Case Differences

Björnson

Perre

Buzzi

et al. 2019

IEEE Wireless Commun.

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230

126

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The use of base stations (BSs) and access points (APs) with a large number of antennas, called Massive MIMO (multiple-input multiple-output), is a key technology for increasing the capacity of 5G networks and beyond. While originally conceived for conventional sub-6 GHz frequencies, Massive MIMO (mMIMO) is ideal also for frequency bands in the range 30-300 GHz, known as millimeter wave (mmWave). Despite conceptual similarities, the way in which mMIMO can be exploited in these bands is radically different, due to their specific propagation behaviors and hardware characteristics. This paper reviews these differences and their implications, while dispelling common misunderstandings. Building on this foundation, we suggest appropriate signal processing schemes and use cases to efficiently exploit mMIMO in both frequency bands. * E. Björnson is with Linköping University, lower frequencies, which is achieved by mMIMO. There are, however, fundamental differences between how mMIMO technology can be designed, implemented, and exploited in sub-6 GHz and mmWave bands. In this paper, we provide a comparative overview, highlighting three main differences:1. The propagation channels build on the same physics, but basic phenomena such as diffraction, attenuation, and Fresnel zones are substantially different.2. The hardware implementation architecture changes with the increasing carrier frequency. More antennas can be integrated into a given area, but the insertion losses, intrinsic power-overhead in radio-frequency (RF) generation, and amplification result in diminishing gains.3. The signal processing algorithms depend on propagation and hardware. Channel estimation is resource-demanding at sub-6 GHz, while beamforming is straightforward. Conversely, mmWave channel estimation and beamforming are theoretically simpler since there are fewer propagation paths, but become challenging if hybrid beamforming is used.In the remainder of this article, we elaborate on these differences, including that they manifest how sub-6 GHz and mmWave bands are to be exploited to target different use-cases in 5G and beyond. Difference I: The propagation channelAn understanding of the electromagnetic propagation is crucial when considering mMIMO systems and frequencies up to mmWave bands. The channels behave fundamentally different from what we are used to in cellular networks, which exposes weaknesses in the channel modeling simplifications conventionally made.

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“…Since a very large antenna array can be realized in a very small volume, it is practical to mount large numbers of antennas at both the transmit and receive terminals. Such a MIMO system is called a mmWave doublymassive MIMO system [4], [5], [6]. In this paper, we shall consider mmWave doubly-massive MIMO systems that are enhanced by making use of an intelligent reflecting surface (IRS).…”

Section: Introductionmentioning

confidence: 99%

mmWave Doubly-Massive-MIMO Communications Enhanced With an Intelligent Reflecting Surface: Asymptotic Analysis

2020

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As a means to control wireless propagation environments, the use of emerging and novel intelligent reflecting surfaces (IRS) is envisioned to enhance and broaden many applications in future wireless networks. This paper is concerned with an IRS-assisted millimeter-wave (mmWave) system in which the IRS consists of multiple subsurfaces, each having the same number of passive reflecting elements, whereas both the transmitter and receiver are equipped with massive antenna arrays. Under the scenario of having very large numbers of antennas at both transmit and receive ends, the achievable rate of the system is derived. Furthermore, with the objective of maximizing the achievable rate, the paper presents optimal solutions of power allocation, precoding/combining, and IRS's phase shifts. Then it is shown that when the number of reflecting elements at each subsurface is very large, the number of favorable and controllable propagation paths provided by the IRS is simply equal to the number of subsurfaces while the received signal-to-noise ratio corresponding to each of the favorable paths increases quadratically with the number of reflecting elements. The problem of minimizing the transmit power subject to the rate constraint is also analyzed for the scenario without direct paths in the pure LOS propagation. In addition, the asymptotic analysis is extended to the multiuser scenario. Finally, numerical results are provided to corroborate the obtained analysis. INDEX TERMS Intelligent reflecting surface, reconfigurable intelligent surface, massive MIMO, millimeter-wave, achievable rate, power allocation.

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Energy Efficiency and Asymptotic Performance Evaluation of Beamforming Structures in Doubly Massive MIMO mmWave Systems

Cited by 52 publications

References 25 publications

Optimal Hybrid Beamforming Design for Millimeter-Wave Massive Multi-User MIMO Relay Systems

Optimal Hybrid Beamforming Design for Millimeter-Wave Massive Multi-User MIMO Relay Systems

Massive MIMO in Sub-6 GHz and mmWave: Physical, Practical, and Use-Case Differences

mmWave Doubly-Massive-MIMO Communications Enhanced With an Intelligent Reflecting Surface: Asymptotic Analysis

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