Linear Precoding for Finite-Alphabet Inputs Over MIMO Fading Channels With Statistical CSI

Zeng, Weiliang; Xiao, Chengshan; Wang, Mingxi; Lu, Jianhua

doi:10.1109/tsp.2012.2188717

Cited by 125 publications

(141 citation statements)

References 30 publications

Supporting

Mentioning

134

Contrasting

Order By: Relevance

“…The most complex part of GAMIO algorithm is to estimate the MMSE matrix based on the current precoding matrix. In order to reduce the time-consuming operations, we take the trick which employed in [15] and update the MMSE matrix recursively. Let s be the number of operations in the MMSE matrix evaluation.…”

Section: Optimization Algorithm Of Pr1mentioning

confidence: 99%

“…In order to overcome this shortage, a parameterized iterative algorithm [13], which can work http://asp.eurasipjournals.com/content/2013/1/59 under equip-probable discrete input assumption, was proposed. Though the parameterized iterative algorithm cannot directly be used in CR network because of the interference constraint, an algorithm, named branch and bound-aided mutual information optimization (BAMIO), was proposed in [14,15] to optimize the precoding matrix in CR network. However, these algorithms can work only under the equip-probable finite alphabet inputs assumption.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal linear precoding for opportunistic spectrum sharing under arbitrary input distributions assumption

Zhu

Zhao

et al. 2013

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Cognitive radio network with multiple-input multiple-output is an effective method to improve not only spectrum efficiency, but also energy efficiency. In this article, a linear precoding matrix optimization algorithm, named gradient-aided mutual information optimization (GAMIO), is designed to maximize the secondary users' spectrum efficiency. Unlike the previous algorithms which were developed under a specific input assumption, the GAMIO algorithm can work without imposing any input assumption. Furthermore, a framework is also proposed to develop the energy-efficient algorithm which can work with arbitrary spectrum-efficient algorithm. In this way, an energy-efficient algorithm, which can work under arbitrary input assumption, be developed based on the GAMIO algorithm (EEGAMIO). Numerical results indicate that either the GAMIO algorithm or the EEGAMIO algorithm shows the best performance at the present time.

show abstract

Section: Optimization Algorithm Of Pr1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal linear precoding for opportunistic spectrum sharing under arbitrary input distributions assumption

Zhu

Zhao

et al. 2013

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Multi-User MIMO (MU-MIMO), also known as Precoding, has been studied recently as a way to reduce co-channel interference in the wireless communications (Wi-Fi, LTE) and multi-beam satellite systems [1,2,3,4]. The requirement of broadband services through high throughput satellite (HTS) systems 5 motivated the research of advanced signal processing techniques for interference mitigation to provide a better spectrum utilization with a reasonable complexity.…”

Section: Introductionmentioning

confidence: 99%

Computationally and energy efficient symbol-level precoding communications demonstrator

Krivochiza

Duncán

Andrenacci

et al. 2018

Physical Communication

View full text Add to dashboard Cite

We demonstrate forward link interference mitigation techniques in a precoded multi-user communication scenario for the efficient frequency reuse. The developed test-bed provides an end-to-end precoding demonstration, which includes a transmitter, a multi-beam satellite channel emulator and user receivers. Precoded communications allow efficient frequency reuse in multiple-input multipleoutput (MIMO) channel environments, where several coordinated antennas simultaneously transmit to a number of independent receivers. We implement and demonstrate the new Symbol-Level Precoding (SLP) technique and benchmark it against Zero-Forcing and MMSE techniques in realistic transmission conditions. We show performance of the SLP in various MIMO channel conditions and outline the impact of the modified constellation by the SLP on a conventional receiver.

show abstract

“…However, this approach is not valid at high SNRs due to the large gap and its inability to predict the rate saturation point. In Lozano, et al [7] and Zeng, et al [8], authors derived optimal power allocation using the FSA input in a non-cognitive scenario, whereas in an interference limited CR system, the same power allocation algorithms cannot be applied due to mutual interference. Therefore, in Sohail, et al [9,10], an optimal power scheme given an FSA input distribution is derived for a case of single and multiple antenna techniques.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Radio Systems: Multicarrier Modulation and Power Allocation Challenges

Ahmed

Al-Imari²,

Xiao³

et al. 2014

J Elec Electron Syst

View full text Add to dashboard Cite

show abstract

Linear Precoding for Finite-Alphabet Inputs Over MIMO Fading Channels With Statistical CSI

Cited by 125 publications

References 30 publications

Optimal linear precoding for opportunistic spectrum sharing under arbitrary input distributions assumption

Optimal linear precoding for opportunistic spectrum sharing under arbitrary input distributions assumption

Computationally and energy efficient symbol-level precoding communications demonstrator

Cognitive Radio Systems: Multicarrier Modulation and Power Allocation Challenges

Contact Info

Product

Resources

About