MMSE-based joint source and relay optimization for interference MIMO relay systems

Nguyen, Khoa Xuan; Rong, Yue; Nordholm, Sven

doi:10.1186/s13638-015-0307-4

Cited by 11 publications

(23 citation statements)

References 16 publications

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“…Thus the complexity of the iterative algorithms is often reasonably high. Note that the sum-MSE based iterative algorithms proposed in [8][9][10] have similar complexity orders. Hence in the following subsection, we contrive an algorithm for the joint optimization problem such that the computational overhead is substantially reduced.…”

Section: Iterative Joint Transceiver Optimizationmentioning

confidence: 99%

“…In the first example, we compare the performance of the proposed min-max MSE-based one-way algorithms with that of the sum-MSE minimization algorithm in [8] as well as the NAF approach in terms of the MSE normalized by the number of data streams (NMSE) with K = 3, N s = 3, N r = 9, and N d = 3. Figure 2 shows the NMSE performance of the algorithms versus transmit power P s with fixed P r = 20 dB.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…5, the BER performance of the proposed twoway relaying algorithms has been compared with the sum-MSE-based algorithms originally proposed for oneway relaying in [8][9][10]. QPSK signal constellations were assumed to modulate the transmitted signals.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…We set N s = 2, K = 3, N r = KN s , N d = 6, P r = 20 dB, and transmit 1000N s randomly generated bits from each transmitter in each channel realization. Most importantly, the iterative sum-MSE minimization algorithms in [8][9][10] always penalize the user with the worst channel condition in the two-way relaying system.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…It is no surprise that the NAF algorithm yields much higher MSE compared to the other schemes since the NAF algorithm performs no optimization operation. Most importantly, the iterative sum-MSE minimization algorithm in [8] always penalizes the user with the worst channel condition.…”

Section: Numerical Simulationsmentioning

confidence: 99%

See 4 more Smart Citations

Joint source and relay optimization for interference MIMO relay networks

Khandaker¹,

Wong²

2017

EURASIP J. Adv. Signal Process.

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This paper considers multiple-input multiple-output (MIMO) relay communication in multi-cellular (interference) systems in which MIMO source-destination pairs communicate simultaneously. It is assumed that due to severe attenuation and/or shadowing effects, communication links can be established only with the aid of a relay node. The aim is to minimize the maximal mean-square-error (MSE) among all the receiving nodes under constrained source and relay transmit powers. Both one-and two-way amplify-and-forward (AF) relaying mechanisms are considered. Since the exactly optimal solution for this practically appealing problem is intractable, we first propose optimizing the source, relay, and receiver matrices in an alternating fashion. Then we contrive a simplified semidefinite programming (SDP) solution based on the error covariance matrix decomposition technique, avoiding the high complexity of the iterative process. Numerical results reveal the effectiveness of the proposed schemes.

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Section: Iterative Joint Transceiver Optimizationmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

See 3 more Smart Citations

Joint source and relay optimization for interference MIMO relay networks

Khandaker¹,

Wong²

2017

EURASIP J. Adv. Signal Process.

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Statistically robust transceiver design algorithms for relay aided multiple‐input multiple‐output interference systems

Singh

Chaturvedi

2018

IET signal process.

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In this study, the authors consider robust transceiver design for amplify-and-forward relay aided multiple-input multiple-output interference systems assuming direct transmitter-receiver links and imperfect channel state information (CSI). The imperfect CSI of each link consists of the estimated channel and the covariance matrix of the channel estimation error. The authors address two transceiver optimisation problems: one is minimising the sum of averaged mean squared error (MSE) at all the receivers and the other is minimising the maximum averaged MSE among all the receivers, both of which are subject to power constraints at the transmitter and relay nodes. The formulated sum-MSE minimisation and max-MSE minimisation based optimisation problems are non-convex with matrix variables and therefore a globally optimal solution is difficult to obtain. To solve these non-convex optimisation problems, they develop sub-optimal iterative algorithms based on alternating minimisation approach to jointly optimise the precoding matrices at the transmitter and relay nodes and the receiver filter matrices. Simulation results demonstrate the effectiveness of the proposed algorithms and their improved performance against CSI uncertainties at similar computational cost as the existing non-robust designs.

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Distributed Multi-Stream Beamforming in MIMO Multi-Relay Interference Networks

Yetis

Chang

2019

IEEE Access

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In this paper, multi-stream transmission in interference networks aided by multiple amplify-and-forward (AF) relays in the presence of direct links is considered. The objective is to minimize the sum power of transmitters and relays by beamforming optimization under the stream signal-to-interference-plus-noise-ratio (SINR) constraints. For transmit beamforming optimization, the problem is a well-known non-convex quadratically constrained quadratic program (QCQP) that is NP-hard to solve. After semi-definite relaxation (SDR), the problem can be optimally solved via alternating direction method of multipliers (ADMM) algorithm for distributed implementation. Analytical and extensive numerical analyses demonstrate that the proposed ADMM solution converges to the optimal centralized solution. The convergence rate, computational complexity, and message exchange load of the proposed algorithm outperforms the existing solutions. Furthermore, by SINR approximation at the relay side, distributed joint transmit and relay beamforming optimization is also proposed that further improves the total power saving at the cost of increased complexity.Index Terms-Alternating direction method of multipliers (ADMM), distributed multi-stream beamforming, MIMO multi-relay interference networks with direct links, quality of service assurance.

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MMSE-based joint source and relay optimization for interference MIMO relay systems

Cited by 11 publications

References 16 publications

Joint source and relay optimization for interference MIMO relay networks

Joint source and relay optimization for interference MIMO relay networks

Statistically robust transceiver design algorithms for relay aided multiple‐input multiple‐output interference systems

Distributed Multi-Stream Beamforming in MIMO Multi-Relay Interference Networks

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