Distributed Multicell Beamforming Design Approaching Pareto Boundary with Max-Min Fairness

Huang, Yongming; Zheng, Gan; Bengtsson, Mats; Wong, Kai-Kit; Yang, Liu; Ottersten, Björn

doi:10.1109/twc.2012.061912.111751

Cited by 61 publications

(72 citation statements)

References 31 publications

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“…And the detail of the first 20 alternative iterations is shown in Figure 6b. We can see that the Loop1 represented by blue block has successfully maximize problem (8), and the Loop2 indicated by purple block has accomplished the minimization procedure of (13). Through alternatively manipulating the two loops, the lower bound of network capacity can converge to a saddle point which is equal to the optimal point of (4).…”

Section: Parameters Valuesmentioning

confidence: 99%

“…Furthermore, it can be observed that x ∈ C K×Q is a global variable coupling among different objective functions and the constraint C 3 in (8). The Lagrangian function of (8) can be expressed as:…”

Section: Loop1 Design: Uplink Power and Beamforming Optimizationmentioning

confidence: 99%

“…Several approaches to optimize the WSRMax problem in multi-antenna systems have been proposed. Some of them are centralized methods which include the monotonic optimization method [4,5], uplink-downlink signal-to-interference-plus-noiseratio (SINR) duality [6][7][8], and branch-and-bound technique [9]. Nowadays, a great interest has been sparked in developing distributed methods.…”

Section: Introductionmentioning

confidence: 99%

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Distributed weighted sum-rate maximization with multi-cell uplink-downlink throughput duality

Zhu

et al. 2015

EURASIP J. Adv. Signal Process.

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The weighted sum-rate maximization (WSRMax) problem is important for the radio resource allocation in wireless networks. In this paper, we focus on solving the WSRMax optimization problem in a Home eNodeB (HeNB) wireless network for the multiple input single output (MISO) downlink transmission. Based on an ameliorated max-min multi-cell uplink-downlink throughput duality, we design a distributed algorithm which contains Loop1 and Loop2 associating with the max-min procedure, respectively. Especially, in Loop2 procedure, we propose a new distributed iteration scheme with the complete proof. During the implementation of the proposed algorithm, randomly deployed HeNBs only need to share information with their neighboring nodes. Simulation results show that the two processes can converge to a stable state, and the network capacity is dramatically improved with the coordination among HeNBs.

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Section: Parameters Valuesmentioning

confidence: 99%

Section: Loop1 Design: Uplink Power and Beamforming Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distributed weighted sum-rate maximization with multi-cell uplink-downlink throughput duality

Zhu

et al. 2015

EURASIP J. Adv. Signal Process.

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“…For weighted max-min optimization with a total power constraint, there are computation-ally efficient fixed-point algorithms [42,208,226,228,296] that are also amenable to distributed implementation [42,59,208]. These algorithms are less suitable under general multi-cell power constraints, although such constraints can be handled exactly for single-antenna transmitters [43], by iterative subgradient methods for multi-antenna transmitters [59,308], or by suboptimal approximation of the power constraints [43,108]. Furthermore, the beamforming parametrization with interference-temperature constraints in Theorem 3.2 enables a simple decentralized algorithm for moving an operating point toward the Pareto boundary [325].…”

Section: Algorithm 4: Distributed Optimization With Qos Requirementsmentioning

confidence: 99%

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Björnson

2013

FNT in Communications and Information Theory

298

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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“…Coordinated or cooperative beamforming, where multiantenna reprocessing at neighboring base stations (BSs) are designed cooperatively, has been extensively studied in the existing literatures such as [3][4][5][6][7][8][9] and thereof. To be more specific, the transmit power minimization problem subject to signal-to-interference-and-noise-ratio (SINR) constraints at the remote users was addressed based on *Correspondence: huangym@seu.edu.cn 1 School of Information Science and Engineering, Southeast University, Nanjing 210096, China Full list of author information is available at the end of the article the uplink-downlink duality theorem for multicell multiuser downlink systems [3].…”

Section: Introductionmentioning

confidence: 99%

Coordinated beamforming design for multicell systems with transceiver impairments: from perspective of spectral efficiency to energy efficiency

Huang

Zhang

et al. 2015

J Wireless Com Network

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Physical transceiver implementations for wireless communication systems usually suffer from transmit-radio frequency (Tx-RF) and receiver-RF (Rx-RF) impairments. In this paper, we aim to design efficient coordinated beamforming for multicell multiuser multi-antenna systems by fully taking into account the residual transceiver impairments. Our design objectives include both spectral efficiency and energy efficiency. In particular, we first derive the closed-form expression of the mean square error (MSE) which includes the impact of transceiver impairments. Based on that, we propose an alternating optimization algorithm to solve the coordinated multicell beamforming problems with the goal of minimizing the worst user MSE, and the sum MSE. Then, by exploiting the relationship between the minimum mean square error (MMSE) and the achievable rate, we develop a new algorithm to address the sum rate maximization problem. This approach is further generalized to solve the more intractable energy efficiency optimization problem. We prove that all the proposed iterative algorithms guarantee to converge to a stationary point. Numerical results show that our proposed schemes achieve a better performance than conventional coordinated beamforming algorithms that were designed ignoring the transceiver impairments.

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Distributed Multicell Beamforming Design Approaching Pareto Boundary with Max-Min Fairness

Cited by 61 publications

References 31 publications

Distributed weighted sum-rate maximization with multi-cell uplink-downlink throughput duality

Distributed weighted sum-rate maximization with multi-cell uplink-downlink throughput duality

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Coordinated beamforming design for multicell systems with transceiver impairments: from perspective of spectral efficiency to energy efficiency

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