Performance evaluation and implementation complexity analysis framework for ZF based linear massive MIMO detection

Ouameur, Messaoud Ahmed; Massicotte, Daniel; Akhtar, Auon Muhammad; Girard, Reno

doi:10.1007/s11276-020-02318-y

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…In [9] proposed a low-complexity detection system with the sphere decoder (SD) scheme, which confirms the reliability of the ZF equalized observations via some predefined regions and thresholds attained by the channel realization. [10] studied the performance evaluation and implementation complexity analysis framework for ZF based linear massive MIMO detection. A framework for algorithmarchitecture synergy for the performance scheming and FPGA application complexity analysis of linear massive MIMO detection systems are discussed with three low complexity implementation systems of the Zero-Forcing ZF based linear detection.…”

Section: Introductionmentioning

confidence: 99%

A new Transmission and Reception Algorithms for Improving the Performance of SISO/MIMO- OFDM Wireless Communication System

Shahab¹,

Hardan²,

Hammoodi³

2021

Tikrit j. eng. sci.

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The future wireless communication requires a reliable transmission at high data rates, so the transmission over frequency-selective fading Multiple-Input–Multiple-Output MIMO channels become interesting since the capacity of "MIMO" channels expressions enormous gains above that of their essential single-input–single-output "SISO" channels. This paper examines the performance of the Low Complexity Zero Forcing "LCZF" equalizer for both systems single-input–single-output-Orthogonal Frequency Division Multiplexing" SISO-OFDM" and spatially multiplexed-Multiple-Input–Multiple-Output "SM-MIMO-OFDM" with different "QAM" modulations. It is exploring a new algorithm to improve the performance of the "BER", spectral efficiency, and power efficiency and to reduce the complexity of the "RF" communication system under the effect of the Additive White Gaussian Noise "AWGN" and multipath fading channel. It is also improves an efficient channel by developing a Low Complexity Zero Forcing "LCZF" equalizer for both "SISO-OFDM" and "SM-MIMO-OFDM" wireless Communication systems. This is done by proposing a new algorithm at the receiver side to covert the Linear Convolution in to Cyclic Convolution by adding Zero Padding "ZP" to the channel impulse response in such a way to be the same length to the transmitted signal in the time domain which is of length N, where N is the length of "IFFT".

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Section: Introductionmentioning

confidence: 99%

A new Transmission and Reception Algorithms for Improving the Performance of SISO/MIMO- OFDM Wireless Communication System

Shahab¹,

Hardan²,

Hammoodi³

2021

Tikrit j. eng. sci.

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“…On the other hand, it has also been argued that these centralized processing techniques still impose stringent constraints on the interconnects' bandwidth between the massive MIMO radio heads and the central processing unit. Distributed, or decentralized, massive MIMO processing has been introduced to overcome such limitations [13,14]. On the other hand, to support ultra-reliable low-latency communications, low latency and high throughput processing is required.…”

Section: Introductionmentioning

confidence: 99%

Early results on deep unfolded conjugate gradient‐based large‐scale MIMO detection

Ouameur

Massicotte

2021

IET Communications

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Deep learning (DL) is attracting considerable attention in the design of communication systems. This paper derives a deep unfolded conjugate gradient (CG) architecture for large‐scale multiple‐input multiple‐output detection. The proposed technique combines the advantages of a model‐driven approach in readily incorporating domain knowledge and deep learning in effective parameters learning. The parameters are trained via backpropagation over a data flow graph inspired from the iterative conjugate gradient method. We derive the closed‐form expressions for the gradients for parameters training and discuss early results on the performance in a statistically identical and independent distributed channel where the training overhead is considerably low. It is worth noting that the loss function is based on the residual error that is not an explicit function of the desired signal, which makes the proposed algorithm blind. As an initial framework, we will point to the inherent issues and future directions.

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A learning-based uplink massive-MIMO decoder

Kumar,

Mahapatra,

Singh

2024

Wireless Netw

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Performance evaluation and implementation complexity analysis framework for ZF based linear massive MIMO detection

Cited by 4 publications

References 21 publications

A new Transmission and Reception Algorithms for Improving the Performance of SISO/MIMO- OFDM Wireless Communication System

A new Transmission and Reception Algorithms for Improving the Performance of SISO/MIMO- OFDM Wireless Communication System

Early results on deep unfolded conjugate gradient‐based large‐scale MIMO detection

A learning-based uplink massive-MIMO decoder

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