Convex Transmit Beamforming for Downlink Multicasting to Multiple Co-Channel Groups

Karipidis, Eleftherios; Sidiropoulos, Nicholas D.; Luo, Zhi-Quan

doi:10.1109/icassp.2006.1661440

Cited by 25 publications

(26 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assume the existence of another feasible solution with associated optimal value . This contradicts optimality of for , 2 As for problem Q, there exist special cases of problem F that are not NP-hard: e.g., for Vandermonde channel vectors it admits a SDP reformulation [8], [9] and for independent data transmission a generalized eigenvalue problem reformulation [18]. 3 The meaning will be clear from context.…”

Section: Joint Max-min Fair Beamformingmentioning

confidence: 99%

“…Interestingly, our numerical findings indicate that the solutions generated by our algorithms are often exactly optimal, especially in the case of measured channels. In certain cases, this optimality can be proven beforehand, and alternative convex reformulations of lower complexity can be constructed; see [8] and [9] for further details. In other cases, a theoretical worst-case bound on approximation accuracy can be derived, and shown to be tight; on this issue, see [15].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Quality of Service and Max-Min Fair Transmit Beamforming to Multiple Cochannel Multicast Groups

Karipidis

Sidiropoulos

Luo

2008

IEEE Trans. Signal Process.

554

688

View full text Add to dashboard Cite

Abstract-The problem of transmit beamforming to multiple cochannel multicast groups is considered, when the channel state is known at the transmitter and from two viewpoints: minimizing total transmission power while guaranteeing a prescribed minimum signal-to-interference-plus-noise ratio (SINR) at each receiver; and a "fair" approach maximizing the overall minimum SINR under a total power budget. The core problem is a multicast generalization of the multiuser downlink beamforming problem; the difference is that each transmitted stream is directed to multiple receivers, each with its own channel. Such generalization is relevant and timely, e.g., in the context of the emerging WiMAX and UMTS-LTE wireless networks. The joint problem also contains single-group multicast beamforming as a special case. The latter (and therefore also the former) is NP-hard. This motivates the pursuit of computationally efficient quasi-optimal solutions. It is shown that Lagrangian relaxation coupled with suitable randomization/cochannel multicast power control yield computationally efficient high-quality approximate solutions. For a significant fraction of problem instances, the solutions generated this way are exactly optimal. Extensive numerical results using both simulated and measured wireless channels are presented to corroborate our main findings.

show abstract

Section: Joint Max-min Fair Beamformingmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Quality of Service and Max-Min Fair Transmit Beamforming to Multiple Cochannel Multicast Groups

Karipidis

Sidiropoulos

Luo

2008

IEEE Trans. Signal Process.

554

688

View full text Add to dashboard Cite

show abstract

“…Assuming far-field, line-ofsight conditions, the user channels can be modeled using Vandermonde matrices. For this important special case, the SPC multicast multigroup problem was reformulated into a convex optimization problem and solved in [15], [16]. These results where motivated by the observation that in sum power constrained ULA scenarios, the relaxation consistently yields rank one solutions.…”

Section: A Uniform Linear Arraysmentioning

confidence: 99%

Multicast multigroup beamforming for per-antenna power constrained large-scale arrays

Christopoulos

Chatzinotas

Ottersten

2015

2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

View full text Add to dashboard Cite

Abstract-Large in the number of transmit elements, multiantenna arrays with per-element limitations are in the focus of the present work. In this context, physical layer multigroup multicasting under per-antenna power constrains, is investigated herein. To address this complex optimization problem lowcomplexity alternatives to semi-definite relaxation are proposed. The goal is to optimize the per-antenna power constrained transmitter in a maximum fairness sense, which is formulated as a non-convex quadratically constrained quadratic problem. Therefore, the recently developed tool of feasible point pursuit and successive convex approximation is extended to account for practical per-antenna power constraints. Interestingly, the novel iterative method exhibits not only superior performance in terms of approaching the relaxed upper bound but also a significant complexity reduction, as the dimensions of the optimization variables increase. Consequently, multicast multigroup beamforming for large-scale array transmitters with per-antenna dedicated amplifiers is rendered computationally efficient and accurate. A preliminary performance evaluation in large-scale systems for which the semi-definite relaxation constantly yields non rank-1 solutions is presented.

show abstract

“…Therefore, statistical information about the channel error vectors is not required in this approach, and the rough knowledge of the upper-bound of channel error vector norms is sufficient. 3 To simplify the problem (8a)-(8c), we modify the inequality constraints (8b) and (8c) using an approach similar to the one developed in [23], [26], and [27]. From the triangle inequality, it follows…”

Section: A Transmit Power Minimization Based Beamformingmentioning

confidence: 99%

Spectrum Sharing in Wireless Networks via QoS-Aware Secondary Multicast Beamforming

Phan

Vorobyov

Sidiropoulos

et al. 2009

IEEE Trans. Signal Process.

Self Cite

201

167

View full text Add to dashboard Cite

Abstract-Secondary spectrum usage has the potential to considerably increase spectrum utilization. In this paper, quality-of-service (QoS)-aware spectrum underlay of a secondary multicast network is considered. A multiantenna secondary access point (AP) is used for multicast (common information) transmission to a number of secondary single-antenna receivers. The idea is that beamforming can be used to steer power towards the secondary receivers while limiting sidelobes that cause interference to primary receivers. Various optimal formulations of beamforming are proposed, motivated by different "cohabitation" scenarios, including robust designs that are applicable with inaccurate or limited channel state information at the secondary AP. These formulations are NP-hard computational problems; yet it is shown how convex approximation-based multicast beamforming tools (originally developed without regard to primary interference constraints) can be adapted to work in a spectrum underlay context. Extensive simulation results demonstrate the effectiveness of the proposed approaches and provide insights on the tradeoffs between different design criteria.

show abstract

Convex Transmit Beamforming for Downlink Multicasting to Multiple Co-Channel Groups

Cited by 25 publications

References 9 publications

Quality of Service and Max-Min Fair Transmit Beamforming to Multiple Cochannel Multicast Groups

Quality of Service and Max-Min Fair Transmit Beamforming to Multiple Cochannel Multicast Groups

Multicast multigroup beamforming for per-antenna power constrained large-scale arrays

Spectrum Sharing in Wireless Networks via QoS-Aware Secondary Multicast Beamforming

Contact Info

Product

Resources

About