Rate Maximization and Beamforming Design for Relay-Aided Multiuser Cognitive Networks

Luan, Tianxiang; Gao, Feifei; Zhang, Xian-Da; Li, J. C. F.; Lei, Ming

doi:10.1109/tvt.2012.2184312

Cited by 24 publications

(18 citation statements)

References 13 publications

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“…Multi-antenna beamforming is an effective means to mitigate co-channel interference and has been widely used in traditional fixed spectrum based wireless systems [6][7][8]. In the context of a CR, beamforming techniques have been investigated for the secondary network for various objectives such as controlling interference [9], capacity maximization [10], Signal to Interference plus Noise Ratio (SINR) balancing [11]. The beamforming design problem in the context of an underlay CR is challenging since the underlay technique requires the interference caused by the SUs to be below the interference threshold level required by the PUs.…”

Section: Introductionmentioning

confidence: 99%

“…The beamforming design problem in the context of an underlay CR is challenging since the underlay technique requires the interference caused by the SUs to be below the interference threshold level required by the PUs. In the existing CR literature, the beamforming techniques have been considered mostly in the coexistence scenario of two terrestrial networks [9][10][11]. In the context of cognitive satellite communications, SS techniques for dual polarized channels have been proposed in [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Transmit Beamforming for Spectral Coexistence of Satellite and Terrestrial Networks

Sharma¹,

Chatzinotas²,

Ottersten³

2013

Proceedings of the 8th International Conference on Cognitive Radio Oriented Wireless Networks

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Abstract-Herein, we study transmit beamforming techniques in an underlay cognitive mode for the coexistence of satellite and terrestrial networks with the satellite forward link as primary and the terrestrial downlink as secondary. Since geostationary satellite terminals have predetermined propagation characteristics so that they always point towards the geostationary satellite, the interference received by the satellite terminals from the terrestrial Base Station (BS) is confined in an angular sector. Based on this a priori knowledge, we propose transmit beamforming techniques at the BS to maximize the Signal to Interference plus Noise Ratio (SINR) towards the desired secondary user and to mitigate the interference towards the primary satellite terminals. Different types of Linearly Constrained Minimum Variance (LCMV) techniques have been proposed for our considered scenario where the exact locations and the number of satellite terminals within a specific angular sector are not known while designing the beamformer. Furthermore, an optimization problem is formulated for maximizing the Secondary User (SU) rate and it is shown that the worst case SU rate depends on the Primary User (PU) distance, PU interference threshold and the angular separation of the desired SU from the region of interest.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transmit Beamforming for Spectral Coexistence of Satellite and Terrestrial Networks

Sharma¹,

Chatzinotas²,

Ottersten³

2013

Proceedings of the 8th International Conference on Cognitive Radio Oriented Wireless Networks

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show abstract

“…where δ i,j denotes the Kronecker's function, i.e., δ i,j = 1 if i = j and δ i,j = 0 otherwise, and p = [p TĪ T ] T = Algorithm 1 The proposed numerical algorithm to solve (6) Initinalization: Q k := I n k , ψ := 1, µ 1 = µ 2 = 1, t := t 0 , γ, and tolerance ǫ > 0 1: repeat {Outer iteration} 2: repeat {inner iteration (centering step)} 3: Solve (27) to find Σ (i) k for 1 ≤ k ≤ K and 0 ≤ i ≤ N + M + 1.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…2 demonstrate that Algorithm 1 exhibits a superlinear convergence rate, i.e., it converges very fast when approaching the optimal solution. The complexity of Algorithm 1 is mostly due to solving (27) and (33), which require complexity O(n 3 k ) and O (N + M ) 3 , respectively. That is to say, the proposed algorithm requires significantly lower complexity, compared to a generic method that has complexity of O(K 3 N 6 ) as mentioned previously.…”

Section: Convergence and Complexity Analysismentioning

confidence: 99%

An Efficient Precoder Design for Multiuser MIMO Cognitive Radio Networks with Interference Constraints

Nguyen

Tran

Shin

et al. 2016

IEEE Trans. Veh. Technol.

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Abstract-We consider a linear precoder design for an underlay cognitive radio multiple-input multiple-output broadcast channel, where the secondary system consisting of a secondary base-station (BS) and a group of secondary users (SUs) is allowed to share the same spectrum with the primary system. All the transceivers are equipped with multiple antennas, each of which has its own maximum power constraint. Assuming zero-forcing method to eliminate the multiuser interference, we study the sum rate maximization problem for the secondary system subject to both per-antenna power constraints at the secondary BS and the interference power constraints at the primary users. The problem of interest differs from the ones studied previously that often assumed a sum power constraint and/or single antenna employed at either both the primary and secondary receivers or the primary receivers. To develop an efficient numerical algorithm, we first invoke the rank relaxation method to transform the considered problem into a convex-concave problem based on a downlink-uplink result. We then propose a barrier interior-point method to solve the resulting saddle point problem. In particular, in each iteration of the proposed method we find the Newton step by solving a system of discrete-time Sylvester equations, which help reduce the complexity significantly, compared to the conventional method. Simulation results are provided to demonstrate fast convergence and effectiveness of the proposed algorithm.

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“…For example, with AF relaying, beamforming and power allocation for overlay [20]- [24] and underlay [11], [25]- [29] cognitive relay systems have been developed. References [25] and [26] studied one-way multi-antenna relays. And [27] investigated one-way single-antenna relays.…”

Section: Introductionmentioning

confidence: 99%

Joint Distributed Beamforming and Power Allocation in Underlay Cognitive Two-Way Relay Links Using Second-Order Channel Statistics

Cao

Tellambura

2014

IEEE Trans. Signal Process.

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Cognitive underlay amplify-and-forward (AF) two-way relay links require signal-to-interference-and-noise-ratio (SINR) balancing and maximizing strategies. And both secondary-to-primary and primary-to-secondary interference must be taken into consideration. To achieve these goals, we develop several joint distributed beamforming and power allocation algorithms. The development is based on the assumption of the availability of channel state information (CSI) of the relay-transceiver channels and the second-order-statistics (SOS) of all channels. For single-relay and multi-relay systems, a closed-form solution and an exhaustive-search optimal algorithm, respectively, are developed. This optimal algorithm, despite its high computational complexity, serves as a useful benchmark. Moreover, two low-complexity suboptimal algorithms are proposed, both of which compute sub-optimal power allocations and beamformers. A 10-dB SINR improvement by the optimal algorithm and similar gains by both proposed suboptimal algorithms are achievable.Index Terms-Amplify-and-forward relaying, cognitive radio, distributed beamforming, power allocation, second-order statistics of channel gains, SINR balancing, two-way relay, underlay.

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Rate Maximization and Beamforming Design for Relay-Aided Multiuser Cognitive Networks

Cited by 24 publications

References 13 publications

Transmit Beamforming for Spectral Coexistence of Satellite and Terrestrial Networks

Transmit Beamforming for Spectral Coexistence of Satellite and Terrestrial Networks

An Efficient Precoder Design for Multiuser MIMO Cognitive Radio Networks with Interference Constraints

Joint Distributed Beamforming and Power Allocation in Underlay Cognitive Two-Way Relay Links Using Second-Order Channel Statistics

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