Fast matrix inversion based on Chebyshev acceleration for linear detection in massive MIMO systems

MASSIVE5G (SAICT‐45‐2017‐02)To circumvent the prohibitive complexity of linear minimum mean square error detection in a massive multiple-input multiple-output system, several iterative methods have been proposed. However, they can still be too complex and/or lead to non-negligible performance degradation. In this letter, a Chebyshev acceleration technique is proposed to overcome the limitations of iterative methods, accelerating the convergence rates and enhancing the performance. The Chebyshev acceleration method employs a new vector combination, which combines the spectral radius of the iteration matrix with the receiver signal, and also the optimal parameters of Chebyshev acceleration have also been defined. A detector based on iterative algorithms requires pre-processing and initialisation, which enhance the convergence, performance, and complexity. To influence the initialisation, the stair matrix has been proposed as the first start of iterative methods. The performance results show that the proposed technique outperforms state-of-the-art methods in terms of error rate performance, while significantly reducing the computational complexity.publishersversionpublishe

Performance Analysis of Massive MIMO-OFDM System Incorporated with Various Transforms for Image Communication in 5G Systems

Modern-day applications of fifth-generation (5G) and sixth-generation (6G) systems require fast, efficient, and robust transmission of multimedia information over wireless communication medium for both mobile and fixed users. The hybrid amalgamation of massive multiple input multiple output (mMIMO) and orthogonal frequency division multiplexing (OFDM) proves to be an impressive methodology for fulfilling the needs of 5G and 6G users. In this paper, the performance of the hybrid combination of massive MIMO and OFDM schemes augmented with fast Fourier transform (FFT), fractional Fourier transform (FrFT) or discrete wavelet transform (DWT) is evaluated to study their potential for reliable image communication. The analysis is carried over the Rayleigh fading channels and M-ary phase-shift keying (M-PSK) modulation schemes. The parameters used in our analysis to assess the outcome of proposed versions of OFDM-mMIMO include signal-to-noise ratio (SNR) vs. peak signal-to-noise ratio (PSNR) and SNR vs. structural similarity index measure (SSIM) at the receiver. Our results indicate that massive MIMO systems incorporating FrFT and DWT can lead to higher PSNR and SSIM values for a given SNR and number of users, when compared with in contrast to FFT-based massive MIMO-OFDM systems under the same conditions.publishersversionpublishe

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Deep unfolding of Chebyshev accelerated iterative method for massive MIMO detection

Abstract The zero-forcing (ZF) and minimum mean square error (MMSE) based detectors can approach optimal performance in the uplink of massive multiple-input multiple-output (MIMO) systems. However, they require inverting a matrix whose complexity is cubic in relation to the matrix dimension. This can lead to the high computational effort, especially in massive MIMO systems. To mitigate this, several iterative methods have been proposed in the literature. In this paper, we consider accelerated Chebyshev SOR (AC-SOR) and accelerated Chebyshev AOR (AC-AOR) algorithms, which improve the detection performance of conventional Successive Over-Relaxation (SOR) and Accelerated Over-Relaxation (AOR) methods, respectively. Additionally, we propose using a deep unfolding network (DUN) to optimize the parameters of the iterative AC-SOR and AC-AOR algorithms, leading to the AC-AORNet and AC-SORNet methods, respectively. The proposed DUN-based method leads to significant performance improvements compared to conventional iterative detectors for various massive MIMO channels. The results demonstrate that the AC-AORNet and AC-SORNet are effective, outperforming other state-of-the-art algorithms. Furthermore, they are highly effective, particularly for high-order modulations such as 256-QAM (Quadrature Amplitude Modulation). Moreover, the proposed AC-AORNet and AC-SORNet require almost the same number of computations as AC-AOR and AC-SOR methods, respectively, since the use of deep unfolding has a negligible impact on the system’s detection complexity. Furthermore, the proposed DUN features a fast and stable training scheme due to its smaller number of trainable parameters

A Novel Hybrid Precoding-Companding Technique for Peak-to-Average Power Ratio Reduction in 5G and beyond

Several high-speed wireless systems use Orthogonal Frequency Division Multiplexing (OFDM) due to its advantages. 5G has adopted OFDM and is expected to be considered beyond 5G (B5G). Meanwhile, OFDM has a high Peak-to-Average Power Ratio (PAPR) problem. Hybridization between two PAPR reduction techniques gains the two techniques’ advantages. Hybrid precoding-companding techniques are attractive as they require small computational complexity to achieve high PAPR reduction gain. Many precoding-companding techniques were introduced to increasing the PAPR reduction gain. However, reducing Bit Error Rate (BER) and out-of-band (OOB) radiation are more significant than increasing PAPR reduction gain. This paper proposes a new precoding-companding technique to better reduce the BER and OOB radiation than previous precoding-companding techniques. Results showed that the proposed technique outperforms all previous precoding-companding techniques in BER enhancement and OOB radiation reduction. The proposed technique reduces the Error Vector Magnitude (EVM) by 15 dB compared with 10 dB for the best previous technique. Additionally, the proposed technique increases high power amplifier efficiency (HPA) by 11.4%, while the best previous technique increased HPA efficiency by 9.8%. Moreover, our proposal achieves PAPR reduction gain better than the most known powerful PAPR reduction technique with a 99% reduction in required computational complexity