Dealing With Interference in Distributed Large-Scale MIMO Systems: A Statistical Approach

Yin, Haifan; Gesbert, David; Cottatellucci, Laura

doi:10.1109/jstsp.2014.2322583

Cited by 117 publications

(76 citation statements)

References 21 publications

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“…A new promising wireless technology after massive MIMO is the cell free massive MIMO (or distributed massive MIMO) [61,62]. The idea is to distribute the antennas in a large area and make use of all antennas for joint-processing.…”

Section: Future Workmentioning

confidence: 99%

Optimizing Massive MIMO : Precoder Design and Power Allocation

Cheng

2018

Linköping Studies in Science and Technology. Dissertations

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The past decades have seen a rapid growth of mobile data traffic, both in terms of connected devices and data rate. To satisfy the ever growing data traffic demand in wireless communication systems, the current cellular systems have to be redesigned to increase both spectral efficiency and energy efficiency. Massive MIMO (Multiple-Input-Multiple-Output) is one solution that satisfy both requirements. In massive MIMO systems, hundreds of antennas are employed at the base station to provide service to many users at the same time and frequency. This enables the system to serve the users with uniformly good quality of service simultaneously, with low-cost hardware and without using extra bandwidth and energy. To achieve this, proper resource allocation is needed. Among the available resources, transmit power beamforming are the most important degrees of freedom to control the spectral efficiency and energy efficiency. Due to the use of excessive number of antennas and low-end hardware at the base station, new aspects of power allocation and beamforming compared to current systems arises.In the first part of the thesis, new uplink power allocation schemes that based on long term channel statistics is proposed. Since quality of the channel estimates is crucial in massive MIMO, in addition to data power allocation, joint power allocation that includes the pilot power as additional variable should be considered. Therefore a new framework for power allocation that matches practical systems is developed, as the methods developed in the literature cannot be applied directly to massive MIMO systems. Simulation results confirm the advantages brought by the the proposed new framework.In the second part, we introduces a new approach to solve the joint precoding and power allocation for different objective in downlink scenarios by a combination of random matrix theory and optimization theory. The new approach results in a simplified problem that, though non-convex, obeys a v simple separable structure. Simulation results showed that the proposed scheme provides large gains over heuristic solutions when the number of users in the cell is large, which is suitable for applying in massive MIMO systems.In the third part we investigate the effects of using low-end amplifiers at the base stations. The non-linear behavior of power consumption in these amplifiers changes the power consumption model at the base station, thereby changes the power allocation and beamforming design. Different scenarios are investigated and results show that a certain number of antennas can be turned off in some scenarios.In the last part we consider the use of non-orthogonal-multiple-access (NOMA) inside massive MIMO systems in practical scenarios where channel state information (CSI) is acquired through pilot signaling. Achievable rate analysis is carried out for different pilot signaling schemes including both uplink and downlink pilots. Numerical results show that when downlink CSI is available at the users, our proposed NOMA scheme outperforms o...

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Section: Future Workmentioning

confidence: 99%

Optimizing Massive MIMO : Precoder Design and Power Allocation

Cheng

2018

Linköping Studies in Science and Technology. Dissertations

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“…In particular, the existence of the low-rank of the covariance matrix can enable interference mitigation without requiring instantaneous CSI. In [10], this property is evidenced for massive antenna array channels, be they linear arrays or distributed arrays such as RRHs.…”

Section: ) Statistical Mimo Processing For Distributed Rrhsmentioning

confidence: 99%

Enhancing LTE with Cloud-RAN and Load-Controlled Parasitic Antenna Arrays

Artuso

Boviz

Checko³

et al. 2016

IEEE Commun. Mag.

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-Cloud radio access network (C-RAN) systems consisting of remote radio heads (RRHs) densely distributed in a coverage area and connected by optical fibers to a cloud infrastructure with large computational capabilities, have the potential to meet the ambitious objectives of next generation mobile networks. Actual implementations of C-RANs tackle fundamental technical and economic challenges. In this article, we present an end-to-end (E2E) solution for practically implementable C-RANs by providing innovative solutions to key issues such as the design of cost-effective hardware and powereffective signals for RRHs, efficient design and distribution of data and control traffic for coordinated communications and conception of a flexible and elastic architecture supporting dynamic allocation of both the densely distributed RRHs and the centralized processing resources in the cloud to create virtual base-stations (BSs). More specifically, we propose a novel antenna array architecture called load-controlled parasitic antenna array (LCPAA) where multiple antennas are fed by a single RF chain. Energy and spectral-efficient modulation as well as signaling schemes that are easy to implement are also provided. Additionally, the design presented for the fronthaul (FH) enables flexibility and elasticity in resource allocation to support BS virtualization. A layered-design of information control for the proposed E2E solution is presented.The feasibility and effectiveness of such LCPAA-enabled C-RAN system setup has been validated through an over-the-air (OTA) demonstration.

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“…Additionally, a poor scattering environment may also contribute to the spatial correlation. Different from the previous works, which only consider either insufficient inter-element spacing or poor scattering environment, this paper combines the well-known Kronecker correlation model [25] with the geometrical one-ring model [26,27], so as to describe the properties of the spatial correlation of the massive MIMO channel more precisely. Since the MS is equipped with a single antenna, the channel between the k th MS and BS is denoted by a 1 × N t row vector h k (k = 1, 2, .…”

Section: Spatially Correlated Massive Mimo Channelmentioning

confidence: 99%

A limited feedback scheme for massive MIMO systems based on principal component analysis

Zhang

Beaulieu

et al. 2016

EURASIP J. Adv. Signal Process.

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Massive multiple-input multiple-output (MIMO) is becoming a key technology for future 5G cellular networks. Channel feedback for massive MIMO is challenging due to the substantially increased dimension of the channel matrix. This motivates us to explore a novel feedback reduction scheme based on the theory of principal component analysis (PCA). The proposed PCA-based feedback scheme exploits the spatial correlation characteristics of the massive MIMO channel models, since the transmit antennas are deployed compactly at the base station (BS). In the proposed scheme, the mobile station (MS) generates a compression matrix by operating PCA on the channel state information (CSI) over a long-term period, and utilizes the compression matrix to compress the spatially correlated high-dimensional CSI into a low-dimensional representation. Then, the compressed low-dimensional CSI is fed back to the BS in a short-term period. In order to recover the high-dimensional CSI at the BS, the compression matrix is refreshed and fed back from MS to BS at every long-term period. The information distortion of the proposed scheme is also investigated and a closed-form expression for an upper bound to the normalized information distortion is derived. The overhead analysis and numerical results show that the proposed scheme can offer a worthwhile tradeoff between the system capacity performance and implementation complexity including the feedback overhead and codebook search complexity.

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Dealing With Interference in Distributed Large-Scale MIMO Systems: A Statistical Approach

Cited by 117 publications

References 21 publications

Optimizing Massive MIMO : Precoder Design and Power Allocation

Optimizing Massive MIMO : Precoder Design and Power Allocation

Enhancing LTE with Cloud-RAN and Load-Controlled Parasitic Antenna Arrays

A limited feedback scheme for massive MIMO systems based on principal component analysis

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