On Analytical Achievable Rate for MIMO Linear Interference Alignment with Imperfect CSI

Dong, Anming; Zhang, Haixia; Zhou, Xiaotian; Yuan, Dongfeng

doi:10.1007/s11277-016-3824-5

Cited by 2 publications

(3 citation statements)

References 38 publications

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“…We evaluate the total achievable sum rate of the system as

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ R = \sum_{g = 1}^{G} \sum_{k = 1}^{K} {log}_{2} (I_{d} + (λ^{- 1} I_{d} + {normalΨ}_{k_{g}})^{- 1} {normalΦ}_{k_{g}}), \end{matrix}

where

Φ_{k_{g}}

and

Ψ_{k_{g}}

are the desired signal and the interference signal covariance matrices for the

k th

user in cell g . In all scenarios, the number of transmit and receive antennas is selected such that the conditions of the IA feasibility are satisfied [19].…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The availability of the perfect channel state information (CSI) at each node is one of the main pre‐requirements to perform IA, while the imperfect CSI degrades the IA performance in terms of sum rate, achievable DoF and bit error rate (BER). A lot of research efforts have focused on analysing the performance of IA in MIMO ICs [14–20]; for example in [14] the impact of channel error on the achievable rate of symmetric K‐user MIMO IA networks is studied and the lower and upper bounds of the sum rate are derived analytically. In [20], a non‐iterative two‐stage pre‐processing filter scheme is presented to reduce the CSI feedback overhead of interfering links for the massive MIMO IC.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Interference alignment in MIMO interference broadcast channels with imperfect CSI

Atbaei

Tadaion

2019

IET Communications

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“…We evaluate the total achievable sum rate of the system as

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ R = \sum_{g = 1}^{G} \sum_{k = 1}^{K} {log}_{2} (I_{d} + (λ^{- 1} I_{d} + {normalΨ}_{k_{g}})^{- 1} {normalΦ}_{k_{g}}), \end{matrix}

where

Φ_{k_{g}}

and

Ψ_{k_{g}}

are the desired signal and the interference signal covariance matrices for the

k th

user in cell g . In all scenarios, the number of transmit and receive antennas is selected such that the conditions of the IA feasibility are satisfied [19].…”

Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interference alignment in MIMO interference broadcast channels with imperfect CSI

Atbaei

Tadaion

2019

IET Communications

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“…and it is uncorrelated with s k [45]. Then, (12) means that the system can be equivalent to a point-to-point MIMO system after IA, and the ergodic achievable rate for the k-th user is written as:…”

Section: Interference Alignmentmentioning

confidence: 99%

Simultaneous Wireless Information and Power Transfer for MIMO Interference Channel Networks Based on Interference Alignment

Dong

Zhang

Shu

et al. 2017

Entropy

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This paper considers power splitting (PS)-based simultaneous wireless information and power transfer (SWIPT) for multiple-input multiple-output (MIMO) interference channel networks where multiple transceiver pairs share the same frequency spectrum. As the PS model is adopted, an individual receiver splits the received signal into two parts for information decoding (ID) and energy harvesting (EH), respectively. Aiming to minimize the total transmit power, transmit precoders, receive filters and PS ratios are jointly designed under a predefined signal-to-interference-plus-noise ratio (SINR) and EH constraints. The formulated joint transceiver design and power splitting problem is non-convex and thus difficult to solve directly. In order to effectively obtain its solution, the feasibility conditions of the formulated non-convex problem are first analyzed. Based on the analysis, an iterative algorithm is proposed by alternatively optimizing the transmitters together with the power splitting factors and the receivers based on semidefinite programming (SDP) relaxation. Moreover, considering the prohibitive computational cost of the SDP for practical applications, a low-complexity suboptimal scheme is proposed by separately designing interference-suppressing transceivers based on interference alignment (IA) and optimizing the transmit power allocation together with splitting factors. The transmit power allocation and receive power splitting problem is then recast as a convex optimization problem and solved efficiently. To further reduce the computational complexity, a low-complexity scheme is proposed by calculating the transmit power allocation and receive PS ratios in closed-form. Simulation results show the effectiveness of the proposed schemes in achieving SWIPT for MIMO interference channel (IC) networks.

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On Analytical Achievable Rate for MIMO Linear Interference Alignment with Imperfect CSI

Cited by 2 publications

References 38 publications

Interference alignment in MIMO interference broadcast channels with imperfect CSI

Interference alignment in MIMO interference broadcast channels with imperfect CSI

Simultaneous Wireless Information and Power Transfer for MIMO Interference Channel Networks Based on Interference Alignment

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