2016
DOI: 10.1109/jsac.2015.2452491
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Joint Beamforming and Power Control for Device-to-Device Communications Underlaying Cellular Networks

Abstract: In this paper, we address the issue of joint beamforming (BF) and power control (PC) for a device-to-device (D2D) communication underlaying cellular network (CN), where the wireless channels of D2D link and the base station (BS) to user equipment (UE) link experience Rician and correlated Rayleigh fading, respectively. Based on the property of the integral network, we first formulate a constrained optimization problem to minimize the total transmit power of the devices in the network, while meeting the quality… Show more

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Cited by 97 publications
(51 citation statements)
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References 33 publications
(57 reference statements)
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“…Remarkably, the optimal solution to the convex problem (35) can be found by using the convex optimization solvers, e.g., SeduMi or CVX [44]. Since the rank-one constraint on W has been relaxed in problem (35), the attained optimal solution may not be rank one.…”
Section: B Bernstein-type Inequality Based Methodsmentioning
confidence: 99%
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“…Remarkably, the optimal solution to the convex problem (35) can be found by using the convex optimization solvers, e.g., SeduMi or CVX [44]. Since the rank-one constraint on W has been relaxed in problem (35), the attained optimal solution may not be rank one.…”
Section: B Bernstein-type Inequality Based Methodsmentioning
confidence: 99%
“…Since the rank-one constraint on W has been relaxed in problem (35), the attained optimal solution may not be rank one. In the following, we can prove that the optimal solution W * of (35) satisfies the condition of rankone.…”
Section: B Bernstein-type Inequality Based Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…For this reason, it is more appropriate to model the D2D links as Rician fading channels. Analysis of the performance of D2D communication in Rician fading environment can be found in [11]- [14]. Using stochastic geometry, the above literature derives analytical results for the outage probability and for the average achievable throughput.…”
Section: Introductionmentioning
confidence: 99%
“…In [27], the author investigated the linear precoding and power allocation policies to realize the optimal resource allocation for general multiple-input-multiple-output (MIMO) Gaussian channels with arbitrary input distributions, by capitalizing on the relationship between mutual information and minimum mean-square error (MMSE). Moreover, other joint precoding and power control techniques have been widely studied in different communication scenarios recently [28][29][30][31][32]. However, none of them consider the sum utility of SUs and the revenue of PUs together.…”
Section: Introductionmentioning
confidence: 99%