Space Alignment Based on Regularized Inversion Precoding in Cognitive Transmission

Yao, Rugui; Li, G.; Xu, Juan; Wang, L.

doi:10.13164/re.2015.0824

Cited by 3 publications

(3 citation statements)

References 16 publications

(30 reference statements)

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“…

D

gathers both signals as

\begin{matrix} right leftthickmathspace.5em \\ y_{d_{m, n}} & = μ_{m} h_{d_{m}} y_{r_{m}} + μ_{n} h_{d_{n}} y_{r_{n}} + n_{d} \\ = [μ_{m} \sqrt{0.5 P_{normals}} h_{d_{m}} h_{r_{m}} h_{\bar{m}}^{normal⊥} μ_{n} \sqrt{0.5 P_{normals}} h_{d_{n}} h_{r_{n}} h_{\bar{n}}^{normal⊥}] [\begin{matrix} s_{R} \\ s_{I} \end{matrix}] + n_{0} \end{matrix}

where the total equivalent noise at

D

{bold-italicn}_{0} = μ_{m} {bold-italich}_{{normald}_{m}} n_{{normalr}_{m}} + μ_{n} {bold-italich}_{{normald}_{n}} n_{{normalr}_{n}} + {bold-italicn}_{normald}

. With the idea of regularised inversion [13],

D

estimates

…”

Section: Proposed Schemementioning

confidence: 99%

Symbol separation and beamforming to improve secure transmission in multi‐untrusted relay networks

2018

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In multi-untrusted relay networks, the transmission should be carefully devised to guarantee both security and performance. A novel scheme with symbol separation and beamforming is proposed to improve secure transmission. First, the best two relays with the strongest channel gains are selected to deliver the separated real and imaginary parts of the confidential symbols. Then, directional beamforming is carefully designed to maximise the receptions of the real and imaginary parts at the two selected relays and to zero force the receptions at the other relays. The proposed scheme ensures a perfect physical layer security with high BER at untrusted relays, and an improved BER performance at the destination. Numerical simulations show the correctness and efficiency of the proposed scheme. JCYJ20170306155652174), the Aerospace Science and Technology Fund (no. 2017-HT-XG) and the Fundamental Research Funds for the Central Universities [no. 3102017JG02002, 3102017HQZZ016 and 3102015BJ(II)ZS023].

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“…

D

gathers both signals as

\begin{matrix} right leftthickmathspace.5em \\ y_{d_{m, n}} & = μ_{m} h_{d_{m}} y_{r_{m}} + μ_{n} h_{d_{n}} y_{r_{n}} + n_{d} \\ = [μ_{m} \sqrt{0.5 P_{normals}} h_{d_{m}} h_{r_{m}} h_{\bar{m}}^{normal⊥} μ_{n} \sqrt{0.5 P_{normals}} h_{d_{n}} h_{r_{n}} h_{\bar{n}}^{normal⊥}] [\begin{matrix} s_{R} \\ s_{I} \end{matrix}] + n_{0} \end{matrix}

where the total equivalent noise at

D

{bold-italicn}_{0} = μ_{m} {bold-italich}_{{normald}_{m}} n_{{normalr}_{m}} + μ_{n} {bold-italich}_{{normald}_{n}} n_{{normalr}_{n}} + {bold-italicn}_{normald}

. With the idea of regularised inversion [13],

D

estimates

…”

Section: Proposed Schemementioning

confidence: 99%

Symbol separation and beamforming to improve secure transmission in multi‐untrusted relay networks

2018

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show abstract

“…Reference [7] designed the precoders and receive filters to maximize the sum-rate for cognitive MIMO ad-hoc networks with a single PU by using the weighted minimum MSE method. The authors in [9] developed a space alignment algorithm for a CR network with a single PU and single SU. Alternatively, reference [2] introduced the cooperative game theory for spectrum sharing in multiuser multiple-input multiple output (MIMO) cognitive radio networks.…”

Section: Introductionmentioning

confidence: 99%

Min-Max MSE-based Interference Alignment for Transceiver Designs in Cognitive Radio Networks

Kha¹,

Ta²

2017

RADIOENGINEERING

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This paper is concerned with an optimal design of the precoders and receive filters for cognitive radio (CR) networks in which multiple secondary users (SUs) share the same frequency band with multiple primary users (PUs). To cope with interference and to achieve fairness among users, we develop an interference alignment (IA) scheme by minimizing the maximum mean squared error (Min-Max MSE) of the received signals. Since the Min-Max MSE design problems are nonconvex in the design matrix variables of the precoders and receive filters, we develop an alternating optimization algorithm with provable convergence to iteratively find the optimal solutions. In each iteration, the precoder design problems can be recast as second order cone program (SOCP) while the optimal receive filters can be derived in closed-form solutions. Finally, numerical results are provided to demonstrate the superiority of the proposed method as compared to previous work in terms of the information rate and bit error rate.

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“…By adopting a cross-tier precoder (CTP) to project the transmitted signal of an SC onto the null space of a channel from the SC to the MC, VFDM can prevent an SC transmission from generating interference to the MC; therefore, spectrum reuse can be achieved. Actually, VFDM can be regarded as interference alignment (IA) realization within the frequency domain [12] rather than in the spatial domain [5], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Optimized BD-ZF Precoder for Multiuser MIMO-VFDM Cognitive Transmission

Yao

et al. 2016

ETRI J

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In this paper, we study an optimized block-diagonal zero-forcing (BD-ZF) precoder in a two-tiered cognitive network consisting of a macro cell (MC) and a small cell (SC). By exploiting multiuser multiple-input and multipleoutput Vandermonde-subspace frequency-division multiplexing (VFDM) transmission, a cognitive SC can coexist with an MC. We first devise a cross-tier precoder based on the idea of VFDM to cancel the interference from the SC to the MC. Then, we propose an optimized BD-ZF intra-tier precoder (ITP) to suppress multiuser interference and maximize the throughput in the SC. In the case where the dimension of a provided null space is larger than that required by the BD-ZF ITP, the optimized BD-ZF ITP can collect all limited channel gain by optimizing rotating and selecting matrices. Otherwise, the optimized BD-ZF ITP is validated to be equivalent to the conventional BD-ZF ITP in terms of throughput. Numerical results are presented to demonstrate the throughput improvement of the proposed optimized BD-ZF ITP and to discover the impact of imperfect channel state information.

show abstract

Space Alignment Based on Regularized Inversion Precoding in Cognitive Transmission

Abstract: Abstract. For a two-tier Multiple-Input Multiple-Output (MIMO)

Cited by 3 publications

References 16 publications

Symbol separation and beamforming to improve secure transmission in multi‐untrusted relay networks

Symbol separation and beamforming to improve secure transmission in multi‐untrusted relay networks

Min-Max MSE-based Interference Alignment for Transceiver Designs in Cognitive Radio Networks

Optimized BD-ZF Precoder for Multiuser MIMO-VFDM Cognitive Transmission

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