Rank-two transmit beamformed Alamouti space-time coding for physical-layer multicasting

Wu, Sissi Xiaoxiao; So, Anthony Man–Cho; Ma, Wing-Kin

doi:10.1109/icassp.2012.6288497

Cited by 37 publications

(57 citation statements)

References 12 publications

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“…In essence, Theorem 1 states that the gap between the worst-user SINR achieved by our proposed scheme and the best achievable worst-user SINR scales on the order of √ MS log MP . This result generalizes that in [14], which applies only to the single-group multicast scenario (i.e., G = 1 and MP = 0). Moreover, Theorem 1 shows that in the multi-group multicast scenario (i.e., when MP = 0), the gap scales only on the order of √ MS.…”

Section: Rank-2 Beamformed Alamouti Scheme For Mm-cr Systemssupporting

confidence: 80%

“…In the multi-group multicast scenario, the factor can be improved to O( √ MS). Our results not only unify and generalize those for single-group multicasting [11,12,14] and multi-group multicasting [10], but also yield the first provable bound on the performance of a beamforming scheme in a general MM-CR system. Finally, we will demonstrate by simulations that the proposed rank-2 beamformed Alamouti scheme can indeed enhance the performance of an MM-CR system.…”

supporting

confidence: 71%

“…In this paper, we shall depart from the above BF scheme and consider the beamformed (BF) Alamouti scheme, which was introduced independently by Wu et al [14] and Wen et al [15] and can be viewed as a generalization the BF scheme. In the BF Alamouti scheme, the unit-power data symbol stream sk(t) is parsed into blocks via sk(n) = [ sk(2n) sk(2n+1) ] T .…”

Section: Preliminariesmentioning

confidence: 99%

“…On another front, recent research shows that it is possible, both theoretically and practically, to improve upon the performance of transmit beamforming by modifying the transmit structure [13][14][15][16]. In particular, by combining transmit beamforming with the Alamouti space-time code, one obtains a so-called rank-2 transmit beamformed Alamouti scheme, which in the single-group multicast scenario can achieve a worst-user SINR that is only at most a factor of O( √ M ) away from the best achievable worst-user SINR [14]. However, there is no prior work that incorporates the rank-2 transmit beamformed Alamouti scheme in MM-CR systems, let alone its performance analysis.…”

mentioning

confidence: 99%

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Multi-group multicast beamforming in cognitive radio networks via rank-two transmit beamformed Alamouti space-time coding

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper, we consider transmit design in multiple-input singleoutput (MISO) multi-group multicast (MM) cognitive radio (CR) systems. Previously, semidefinite relaxation (SDR)-based transmit beamforming has been very successful in transmit design. However, recent research shows that further performance gain is possible by suitably modifying the transmit structure. Here, we propose a transmit beamformed Alamouti space-time code scheme for MM-CR systems, whose corresponding transmit design problem can be reformulated as a rank-2 constrained fractional semidefinite program. We then develop an SDR framework for this scheme and study its signal-to-interference-and-noise ratio (SINR) performance via both theoretical analysis and simulations. Specifically, we show that the worst-case approximation accuracy of the proposed scheme scales on the order of √ MS log MP , where MP (resp. MS) is the number of primary (resp. secondary) users in the CR network. This unifies and generalizes a number of results in the literature and is, to the best of our knowledge, the first provable bound on the performance of a beamforming scheme in a general MM-CR system. Finally, simulation results show that our proposed scheme indeed has a better performance in both MM and MM-CR scenarios than the traditional beamforming scheme.

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Section: Rank-2 Beamformed Alamouti Scheme For Mm-cr Systemssupporting

confidence: 80%

supporting

confidence: 71%

Section: Preliminariesmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Multi-group multicast beamforming in cognitive radio networks via rank-two transmit beamformed Alamouti space-time coding

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…Empirical evidence and theoretical analysis were provided by Sidiropoulos, Davidson, and Luo (2006); Luo et al (2007); and Jindal and Luo (2006) to show the viability of the SDR-based techniques. Also, the SDR-based techniques have been applied to many different types of beamforming problems ) and have motivated the study of multicast methods with non-rank-one nature (Schad, Law, and Pesavento 2012;Wen et al 2012;Wu, So, and Ma 2012;Wu, Ma, and So 2013).…”

Section: Introductionmentioning

confidence: 99%

A two-user approximation-based transmit beamforming for physical-layer multicasting in mobile cellular downlink systems

Hsiao

Guey

Sheen

et al. 2015

Journal of the Chinese Institute of Engineers

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Multicast has been known as an efficient transmission technique for group-oriented applications such as multi-party video conferencing, video streaming for paid users, online gaming, and social networking. In this paper, we investigate physical-layer multicasting in mobile cellular downlink systems, where the antennas at base station are employed to transmit common signals to multiple users simultaneously. A central design problem of downlink physical-layer multicasting is the search for the optimal beamforming vector that maximizes the multicast rate. Traditionally, the problem has been formulated as a quadratically constrained quadratic programming problem and shown to be NP-hard in general. In this paper, starting from examining the Karush-Kuhn-Tucker stationary conditions, a new method based on two-user approximation is proposed for the search for the optimal beamforming vector. The method is able to achieve a much higher multicast rate than the existing methods and provides an attractive trade-off between performance and complexity, especially for the case of using a large number of antennas. Using a large number of antennas at base station, also known as the large-scale multiple-input and multiple-output technique, has been regarded widely as one of the most promising technologies to increase system capacity, coverage, and user throughput for future generations of mobile cellular systems.

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Robust joint signal and interference alignment in cognitive radio networks with ellipsoidal channel state information uncertainties

Ratnarajah

et al. 2013

IET Communications

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The authors propose a distributed robust joint signal and interference alignment design for multiple-input-multipleoutput cognitive radio (CR) networks where single primary link coexists with multiple secondary links. Considering two practical challenges of interference alignment: imperfect channel state information (CSI) and finite signal-to-noise ratio, the proposed scheme aims to minimise both the leakage of interference signals and that of the desired signals, while maintaining interference to the primary user below a permissible level. Under the assumption of the ellipsoidal CSI uncertainties, the joint worst-case optimisation problem is decomposed and reformulated as semi-definite programming form by using S-lemma, orthogonal relaxation and semi-definite relaxation. Simulation results verify the effectiveness of the joint design, and robustness of the worst-case design against channel uncertainties.

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Rank-two transmit beamformed Alamouti space-time coding for physical-layer multicasting

Cited by 37 publications

References 12 publications

Multi-group multicast beamforming in cognitive radio networks via rank-two transmit beamformed Alamouti space-time coding

Multi-group multicast beamforming in cognitive radio networks via rank-two transmit beamformed Alamouti space-time coding

A two-user approximation-based transmit beamforming for physical-layer multicasting in mobile cellular downlink systems

Robust joint signal and interference alignment in cognitive radio networks with ellipsoidal channel state information uncertainties

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