Low-Complexity Linear Zero-Forcing for the MIMO Broadcast Channel

Guthy, Christian; Utschick, Wolfgang; Dietl, Guido

doi:10.1109/jstsp.2009.2036954

Cited by 24 publications

(18 citation statements)

References 27 publications

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“…As it is desirable to deploy multi-cell systems that operate in the high-SNR regime (for spectral efficiency reasons), many researchers have proposed ZFBF-based transmit strategies for practical use; see [75,97,319] among others. Although perfect interference nulling requires perfect CSI, ZFBF can be implemented under imperfect CSI by avoiding inter-user interference along some of the strongest eigenvectors of E{h j k k h H j k k } of each user k [23,99].…”

Section: Heuristic Coordinated Beamforming 261mentioning

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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Section: Heuristic Coordinated Beamforming 261mentioning

confidence: 99%

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Björnson

2013

FNT in Communications and Information Theory

309

317

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“…In case the receive antennas cannot cooperate, the channel gains in (10) can no longer be achieved but instead compute according tô…”

Section: A Spatial Zero-forcing With Dirty Paper Codingmentioning

confidence: 99%

“…A more advanced approach including the receive filters in the successive optimization can be found in[10].…”

mentioning

confidence: 99%

Large System Analysis of Sum Capacity in the Gaussian MIMO Broadcast Channel

Guthy

Utschick

Honig

2013

IEEE J. Select. Areas Commun.

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We analyze the achievable sum rate of the Gaussian MIMO broadcast channel. We first consider Multiple-Input Single-Output (MISO) channels and derive the large system limit of the sum capacity as the number of users and transmit antennas go to infinity with a fixed ratio. We then consider MultipleInput Multiple-Output (MIMO) broadcast channels and fix the number of users and let the number of transmit and receive antennas tend to infinity with fixed ratio. As in this case an asymptotic expression for sum capacity is hard to obtain, we evaluate the large system sum rate corresponding to successive zero-forcing beamforming with Dirty-Paper Coding. The analysis gives a lower bound on the large system sum capacity, which is numerically observed to be quite close. In addition, large system analysis is applied to estimate the relatively small performance losses with respect to sum capacity of successive zero-forcing beamforming with and without Dirty-Paper Coding in finite MISO systems.Index Terms-Broadcast Channels, Large system analysis, Multiple-Input Multiple-Output (MIMO) systems.

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“…Instead of searching for the globally optimal set of beamformers, we examine a suboptimal Successive Allocation (SA) strategy for K ≤ N users, similar to the scheme in [9]. We begin with the beamformer v 1 and receiver g 1 , which are chosen to maximize the first user's individual rate offset, i. e., the first summand of r in (5) following users k ∈ {2, . .…”

Section: Achievable Rate Offset With K = Nmentioning

confidence: 99%

Large System Performance of Interference Alignment in Single-Beam MIMO Networks

Schmidt

Utschick

Honig

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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Abstract-We consider a network of K interfering transmitterreceiver pairs, where each node has N antennas and at most one beam is transmitted per user. We investigate the asymptotic performance of different beamforming strategies, as characterized by the slope and y-axis intercept (or offset) of the high signal-tonoise-ratio (SNR) sum rate asymptote. It is known that a slope (or multiplexing gain) of 2N − 1 is achievable with interference alignment. On the other hand, a strategy achieving a slope of only N might allow for a significantly higher offset. Assuming that the number of fully aligned beamformer sets that achieve a slope of 2N − 1 is finite for a given channel realization, we approximate the average offset when the best out of a large number L of these sets is selected. We also derive a simple large system approximation for the sum rate of a successive beam allocation scheme when K = N . We show that both approximations accurately predict simulated results for moderate system dimensions and characterize the large-system asymptotes for different relationships between L and N .

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Low-Complexity Linear Zero-Forcing for the MIMO Broadcast Channel

Cited by 24 publications

References 27 publications

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Large System Analysis of Sum Capacity in the Gaussian MIMO Broadcast Channel

Large System Performance of Interference Alignment in Single-Beam MIMO Networks

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