Max-min fair transmit beamforming for multi-group multicasting

Schad, Adrian; Pesavento, Marius

doi:10.1109/wsa.2012.6181191

Cited by 25 publications

(43 citation statements)

References 6 publications

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“…While (6) and (27) can be formulated in terms of the SINRs and solved using existing methods, e.g. [1], [2], [13], this cannot be applied to the RS problem in (32) as the performance of each user cannot be represented by a single SINR expression. As seen from (32), each achievable user-rate (and ultimately group-rate) is in fact expressed as a sum-rate.…”

Section: Optimizationmentioning

confidence: 99%

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Rate-Splitting for Max-Min Fair Multigroup Multicast Beamforming in Overloaded Systems

Joudeh

Clerckx

2017

IEEE Trans. Wireless Commun.

168

150

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Abstract-In this paper, we consider the problem of achieving max-min fairness amongst multiple co-channel multicast groups through transmit beamforming. We explicitly focus on overloaded scenarios in which the number of transmitting antennas is insufficient to neutralize all inter-group interference. Such scenarios are becoming increasingly relevant in the light of growing lowlatency content delivery demands, and also commonly appear in multibeam satellite systems. We derive performance limits of classical beamforming strategies using DoF analysis unveiling their limitations; for example, rates saturate in overloaded scenarios due to inter-group interference. To tackle interference, we propose a strategy based on degraded beamforming and successive interference cancellation. While the degraded strategy resolves the rate-saturation issue, this comes at a price of sacrificing all spatial multiplexing gains. This motivates the development of a unifying strategy that combines the benefits of the two previous strategies. We propose a beamforming strategy based on rate-splitting (RS) which divides the messages intended to each group into a degraded part and a designated part, and transmits a superposition of both degraded and designated beamformed streams. The superiority of the proposed strategy is demonstrated through DoF analysis. Finally, we solve the RS beamforming design problem and demonstrate significant performance gains through simulations.

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Section: Optimizationmentioning

confidence: 99%

“…In the existing literature on multigroup multicast beamform-ing, overloaded transmissions have been implicitly considered through simulations with system parameters that correspond to such scenarios [2], [6], [12], [13]. However, a comprehensive analysis and explicit treatment of interference in overloaded scenarios is absent.…”

Section: Introductionmentioning

confidence: 99%

Rate-Splitting for Max-Min Fair Multigroup Multicast Beamforming in Overloaded Systems

Joudeh

Clerckx

2017

IEEE Trans. Wireless Commun.

168

150

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“…In parallel to [10], the independent work of [12] involved complex dirty paper coding methods. Also, a convex approximation method was proposed in [13] that exhibits superior performance as the number of users per group grows. Finally, in [14] the multicast multigroup problem under SPC, was solved based on approximations and uplink-downlink duality [3].…”

Section: Weighted Fair Multicast Multigroup Beamformingmentioning

confidence: 99%

“…It is especially noticed that the PAC system suffers more than the SPC of [11] as the number of users per multicast group increases. An attempt to solve this inaccuracy, but only under sum power constraints, is presented in [13].…”

Section: A Multigroup Multicasting Over Rayleigh Channelsmentioning

confidence: 99%

Weighted Fair Multicast Multigroup Beamforming Under Per-antenna Power Constraints

Christopoulos

Chatzinotas

Ottersten

2014

IEEE Trans. Signal Process.

172

187

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A multiantenna transmitter that conveys independent sets of common data to distinct groups of users is considered. This model is known as physical layer multicasting to multiple cochannel groups. In this context, the practical constraint of a maximum permitted power level radiated by each antenna is addressed. The per-antenna power constrained system is optimized in a maximum fairness sense with respect to predetermined quality of service weights. In other words, the worst scaled user is boosted by maximizing its weighted signal-to-interference plus noise ratio. A detailed solution to tackle the weighted max-min fair multigroup multicast problem under per-antenna power constraints is therefore derived. The implications of the novel constraints are investigated via prominent applications and paradigms. What is more, robust per-antenna constrained multigroup multicast beamforming solutions are proposed. Finally, an extensive performance evaluation quantifies the gains of the proposed algorithm over existing solutions and exhibits its accuracy over per-antenna power constrained systems.Index Terms-Gaussian randomization, per-antenna power constraints, physical multigroup multicasting, semidefinite relaxation, weighted max-min fair optimization.

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“…The QoS problem is also considered in [8]- [10] with diverse extensions to the formulation. A related formulation known as the maxmin fair (MMF) problem is studied in [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Precoding for Multi-Group Multicasting in mmWave Systems

Abanto-Leon

Hollick

Sim

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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Multicast beamforming is known to improve spectral efficiency. However, its benefits and challenges for hybrid precoders design in millimeter-wave (mmWave) systems remain understudied. To this end, this paper investigates the first joint design of hybrid transmit precoders (with an arbitrary number of finite-resolution phase shifts) and receive combiners for mmWave multi-group multicasting. Our proposed design leverages semidefinite relaxation (SDR), alternating optimization and Cholesky matrix factorization to sequentially optimize the digital/analog precoders at the transmitter and the combiners at each receiver. By considering receivers with multiple-antenna architecture, our design remarkably improves the overall system performance. Specifically, with only two receive antennas the average transmit power per received message improves by 16.8% while the successful information reception is boosted by 60%. We demonstrate by means of extensive simulations that our hybrid precoder design performs very close to its fully-digital counterpart even under challenging scenarios (i.e., when colocated users belong to distinct multicast groups).

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Max-min fair transmit beamforming for multi-group multicasting

Cited by 25 publications

References 6 publications

Rate-Splitting for Max-Min Fair Multigroup Multicast Beamforming in Overloaded Systems

Rate-Splitting for Max-Min Fair Multigroup Multicast Beamforming in Overloaded Systems

Weighted Fair Multicast Multigroup Beamforming Under Per-antenna Power Constraints

Hybrid Precoding for Multi-Group Multicasting in mmWave Systems

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