Interference-Aware Decentralized Precoding for Multicell MIMO TDD Systems

Lee, Byong Ok; Je, Hui Won; Sohn, Illsoo; Shin, Oh‐Soon; Lee, Kwang Bok

doi:10.1109/glocom.2008.ecp.858

Cited by 49 publications

(50 citation statements)

References 12 publications

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“…For example, the joint design of beamforming across multiple cells has been considered in [25], [26], [27], [28], [29], [30], [31], [32], but these studies typically focus on the minimization of transmit power for a fixed set of selected users, instead of the optimization of network utility. Likewise, intercell scheduling has been considered in [33], [34], [35], [36], [37], [38], and intercell power control has been considered in [39], [40], but these studies do not include beamforming.…”

Section: E Related Workmentioning

confidence: 99%

Multicell Coordination via Joint Scheduling, Beamforming, and Power Spectrum Adaptation

Kwon

Shin

2013

IEEE Trans. Wireless Commun.

172

147

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Abstract-The mitigation of intercell interference is a central issue for future generation wireless cellular networks where frequencies are reused aggressively and where hierarchical cellular structures may heavily overlap. The paper examines the benefit of coordinating transmission strategies and resource allocation schemes across multiple cells for interference mitigation. For a multicell network serving multiple users per cell sectors and where both the base-stations and the remote users are equipped with multiple antennas, this paper proposes a joint proportionally fair scheduling, spatial multiplexing, and power spectrum adaptation method that coordinates multiple basestations with an objective of optimizing the overall network utility. The proposed scheme optimizes the user schedule, transmit and receive beamforming vectors, and transmit power spectra jointly, while taking into consideration both the intercell and intracell interference and the fairness among the users. The proposed system is shown to significantly improve the overall network throughput while maintaining fairness as compared to a conventional network with per-cell zero-forcing beamforming and with fixed transmit power spectrum. The proposed system goes toward the vision of a fully coordinated multicell network, whereby transmission strategies and resource allocation schemes (rather than transmit signals) are coordinated across the basestations as a first step. I. OVERVIEW A. IntroductionIntercell interference is a fundamental limiting factor in wireless cellular networks. A promising idea for the mitigation of intercell interference in future cellular networks is the coordination of multiple base-stations. In a fully coordinated multicell system, multiple antennas across multiple basestations can be thought of as forming a large antenna array, where intercell interference can be actively exploited. The realization of such a fully coordinated system, however, also requires high-capacity backhaul communication. As antennas from across multiple base-stations need to jointly transmit and receive signals for multiple mobile users, data streams of multiple users must be shared among multiple base-stations. This paper explores a different type of coordination where user transmission strategies and resource allocation schemes, rather than data signals, are coordinated across the basestations. The coordination of transmission strategies clearly requires much less backhaul communication, and is much easier to implement in a practical deployment. The goal of this paper is to show that by jointly setting the scheduling, power allocation, and beamforming strategies of multiple basestations and multiple mobile users within each cell, intercell interference can already be alleviated, and the overall performance of the network can already be improved significantly as compared to the current generation of wireless networks where cells operate independently.Resource management has been the focus of extensive studies for cellular networks in the past, but tr...

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Section: E Related Workmentioning

confidence: 99%

Multicell Coordination via Joint Scheduling, Beamforming, and Power Spectrum Adaptation

Kwon

Shin

2013

IEEE Trans. Wireless Commun.

172

147

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“…An equivalent optimization metric, i.e. signal-togenerating-interference-plus-noise ratio (SGINR), is used by authors in [18]. The maximization of SLNR or SGINR is a generalized eigenvalue problem and hence named as the generalized eigenvalue-based beamformer in [19].…”

Section: B Related Workmentioning

confidence: 99%

Asymptotic Analysis of Area Spectral Efficiency and Energy Efficiency in PPP Networks With SLNR Precoder

Alam

Mary

Baudais

et al. 2017

IEEE Trans. Commun.

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“…Several iterations and rounds of control signaling are generally necessary to achieve the solution, which might not be desirable or feasible in practice. By sacrificing the optimality assurance, noniterative resource allocation algorithms that only utilize local CSI can be obtained; for example, by imitating the structure of optimal beamforming [18,21,23,88,135,142,180,311]. This subsection describes a simple heuristic approach that can be seen as a pragmatic approach to multi-cell coordination but also as a reasonable starting-point for iterative algorithms.…”

Section: Truly Distributed Resource Allocationmentioning

confidence: 99%

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Björnson

2013

FNT in Communications and Information Theory

309

317

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The use of multiple antennas at base stations is a key component in the design of cellular communication systems that can meet high-capacity demands in the downlink. Under ideal conditions, the gain of employing multiple antennas is well-recognized: the data throughput increases linearly with the number of transmit antennas if the spatial dimension is utilized to serve many users in parallel. The practical performance of multi-cell systems is, however, limited by a variety of nonidealities, such as insufficient channel knowledge, high computational complexity, heterogeneous user conditions, limited backhaul capacity, transceiver impairments, and the constrained level of coordination between base stations. This tutorial presents a general framework for modeling different multi-cell scenarios, including clustered joint transmission, coordinated beamforming, interference channels, cognitive radio, and spectrum sharing between operators. The framework enables joint analysis and insights that are both scenario independent and dependent.The performance of multi-cell systems depends on the resource allocation; that is, how the time, power, frequency, and spatial resources are divided among users. A comprehensive characterization of resource allocation problem categories is provided, along with the signal processing algorithms that solve them. The inherent difficulties are revealed: (a) the overwhelming spatial degrees-of-freedom created by the multitude of transmit antennas; and (b) the fundamental tradeoff between maximizing aggregate system throughput and maintaining user fairness. The tutorial provides a pragmatic foundation for resource allocation where the system utility metric can be selected to achieve practical feasibility. The structure of optimal resource allocation is also derived, in terms of beamforming parameterizations and optimal operating points.This tutorial provides a solid ground and understanding for optimization of practical multi-cell systems, including the impact of the nonidealities mentioned above. The Matlab code is available online for some of the examples and algorithms in this tutorial. IntroductionThis section describes a general framework for modeling different types of multi-cell systems and measuring their performance -both in terms of system utility and individual user performance. The framework is based on the concept of dynamic cooperation clusters, which enables unified analysis of everything from interference channels and cognitive radio to cellular networks with global joint transmission. The concept of resource allocation is defined as allocating transmit power among users and spatial directions, while satisfying a set of power constraints that have physical, regulatory, and economic implications. A major complication in resource allocation is the inter-user interference that arises and limits the performance when multiple users are served in parallel. Resource allocation is particularly complex when multiple antennas are employed at each base station. However, the throu...

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Interference-Aware Decentralized Precoding for Multicell MIMO TDD Systems

Cited by 49 publications

References 12 publications

Multicell Coordination via Joint Scheduling, Beamforming, and Power Spectrum Adaptation

Multicell Coordination via Joint Scheduling, Beamforming, and Power Spectrum Adaptation

Asymptotic Analysis of Area Spectral Efficiency and Energy Efficiency in PPP Networks With SLNR Precoder

Optimal Resource Allocation in Coordinated Multi-Cell Systems

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