Maximum SINR-Based Beamforming for the MISO OFDM Interference Channel

Lebrun, Yann; Ramon, V.; Bourdoux, André; Pollin, Sofie; Horlin, François; Lauwereins, Rudy

doi:10.1109/icc.2010.5502088

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…In [26, 27], the authors demonstrate that the overall system performance of multicell system can be improved and the outage probability can be reduced by optimal power allocation. In [28], the authors show that the system capacity can be improved using SINR balancing based beamforming in an interference channel. Our problem can be formulated in line with [28].…”

Section: Spatial Multicasting Using Dual Beam Array Formed From Ranmentioning

confidence: 99%

“…In [28], the authors show that the system capacity can be improved using SINR balancing based beamforming in an interference channel. Our problem can be formulated in line with [28]. If P t is the transmit power, P r is the required received power at the destination and P interfere and P noise are the interference and noise powers, respectively, then

\min P_{normalt} = \sum_{i = 1}^{N} | | W_{i} | {falsefalse|}^{2}

such that

\frac{P_{normalr}}{P_{interfere} + P_{noise}} > = K_{1}

where K 1 is the required SINR at the receiver and W i are the weights of the antenna array.…”

Section: Spatial Multicasting Using Dual Beam Array Formed From Ranmentioning

confidence: 99%

See 1 more Smart Citation

Robust spatial multicasting for randomly distributed sensors

Sudha¹,

Vaikundam²

2015

IET wirel. sens. syst.

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Beamforming is used in sensor networks to transfer data to remote stations with enhanced communication. When the position of array nodes and directions are known, it is better to form an approximate uniform linear array (ULA), uniform circular array (UCA) and uniform elliptical array (UEA). Since it is not always possible to find actual sensors matching with the ideal positions of ULA, UCA and UEA, least-square (LS) estimation is used to calculate weights of the array elements. LS estimation does not give realisable weight values and yields transmission in unintended directions. Also, if many single beams arrays are used to transfer data simultaneously to different users, co-channel interference occurs. In this study, to overcome these problems, multibeam array with restriction imposed on the weights is proposed and is reformulated as Tikhonov regularised LS (TRLS) method. This proposed spatial multicasting method is found to be robust up to 90% position variation of sensors for the ULA with weights under control improving the power efficiency and hence the spectral efficiency and data rate. Moreover, it is also observed that when a faulty node is substituted by nearby nodes, the weights calculated using TRLS method need not be recalculated.

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Section: Spatial Multicasting Using Dual Beam Array Formed From Ranmentioning

confidence: 99%

\min P_{normalt} = \sum_{i = 1}^{N} | | W_{i} | {falsefalse|}^{2}

such that

\frac{P_{normalr}}{P_{interfere} + P_{noise}} > = K_{1}

where K 1 is the required SINR at the receiver and W i are the weights of the antenna array.…”

Section: Spatial Multicasting Using Dual Beam Array Formed From Ranmentioning

confidence: 99%

Robust spatial multicasting for randomly distributed sensors

Sudha¹,

Vaikundam²

2015

IET wirel. sens. syst.

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“…The process is proven to converge to an operating point arbitrarily close to the Pareto boundary and dominates the noncooperative outcome of the systems. In [12], the high signal to interference plus noise ratio (SINR) approximation of the achievable sum-rate of a system pair is utilized to determine suboptimal joint transmission strategies. The achieved performance is shown to be better than the joint MRT and joint ZF strategies.…”

Section: Introductionmentioning

confidence: 99%

Optimal Beamforming in Interference Networks with Perfect Local Channel Information

Mochaourab

Jorswieck

2011

IEEE Trans. Signal Process.

104

134

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We consider settings in which T multi-antenna transmitters and K single-antenna receivers concurrently utilize the available communication resources. Each transmitter sends useful information only to its intended receivers and can degrade the performance of unintended systems. Here, we assume the performance measures associated with each receiver are monotonic with the received power gains. In general, the systems' joint operation is desired to be Pareto optimal. However, designing Pareto optimal resource allocation schemes is known to be difficult. In order to reduce the complexity of achieving efficient operating points, we show that it is sufficient to consider rank-1 transmit covariance matrices and propose a framework for determining the efficient beamforming vectors. These beamforming vectors are thereby also parameterized by T (K − 1) real-valued parameters each between zero and one. The framework is based on analyzing each transmitter's power gain-region which is composed of all jointly achievable power gains at the receivers. The efficient beamforming vectors are on a specific boundary section of the power gain-region, and in certain scenarios it is shown that it is necessary to perform additional power allocation on the beamforming vectors. Two examples which include broadcast and multicast data as well as a cognitive radio application scenario illustrate the results.

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