An Efficient Two-Stage Receiver Base on AOR Iterative Algorithm and Chebyshev Acceleration for Uplink Multiuser Massive-MIMO OFDM Systems

Abstract

The massive multiple-input multiple-output systems (M-MIMO) and orthogonal frequency-division multiplexing (OFDM) are considered to be some of the most promising key techniques in the emerging 5G and advanced wireless communication systems nowadays. Not only are the benefits of applying M-MIMO and OFDM for broadband communication well known, but using them for the application of the Internet of Things (IoT) requires a large amount of wireless transmission, which is a developing topic. However, its high complexity becomes a problem when there are numerous antennas. In this paper, we provide an effective two-stage multiuser detector (MUD) with the assistance of the accelerated over-relaxation (AOR) iterative algorithm and Chebyshev acceleration for the uplink of M-MIMO OFDM systems to achieve a better balance between bit error rate (BER) performance and computational complexity. The first stage of the receiver consists of an accelerated over-relaxation (AOR)-based estimator and is intended to yield a rough initial estimate of the relaxation factor ω, the acceleration parameter γ, and transmitted symbols. In the second stage, the Chebyshev acceleration method is used for detection, and a more precise signal is produced through efficient iterative estimation. Additionally, we call this proposed scheme Chebyshev-accelerated over-relaxation (CAOR) detection. Conducted simulations show that the developed receiver, with a modest computational load, can provide superior performance compared with previous works, especially in the MU M-MIMO uplink environments.

Novel Hybrid SOR- and AOR-Based Multi-User Detection for Uplink M-MIMO B5G Systems

“…Now, we will use the Rayleigh fading channel matrix [53] to simulate the outdoor environment for the above matrix H. Without loss of generality, considering that the realistic environment has many external factors, we set up a channel with two independent and identically distributed (i.i.d.) paths and a Gaussian distribution that obeyed unit variance and a zero mean to align with the compatible actual environment [44]. The noise vector adopted additive white Gaussian noise (AWGN) that conformed to an i.i.d.…”

“…To clarify our proposed method, we will briefly describe the conventional SOR [39,56] and AOR [40,44] methods, including their convergence behavior. Immediately afterward, we will introduce our proposed MOR method, which allows improved BER performance and complexity balance with fewer iterations.…”

“…Even so, their performance results are unsatisfactory. Therefore, Yu et al [43] and the authors in our previous work [44], through the two-stage structure proposed SOR method and AOR method combined with the Chebyshev algorithm, namely Chebyshev successive over-relaxation (CSOR) and Chebyshev accelerated over-relaxation (CAOR) techniques, respectively, produced efficient performance.…”

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

“…Additionally, channel hardening can simplify channel estimation at the users' terminals (i.e., downlink (DL)), while in uplink (UL) the BS runs the channel estimation with the aid of received pilots from each user relying on channel reciprocity between UL and DL in the time division duplexing (TDD) mode [20]. Due to their ability to deliver enhanced quality of the transmission along with very high throughput, many research works advocating the use of mMIMO with OFDM can be found in the literature [21][22][23] and therefore mMIMO-OFDM becomes an attractive candidate for B5G and 6G networks [24]. However, to the best of our knowledge, no previous work regarding performance evaluation of the image transmission over mMIMO-OFDM link has been reported till date.…”

High-Precision Iterative Preconditioned Gauss–Seidel Detection Algorithm for Massive MIMO Systems

“…That is, its computational complexity is higher compared to the exact matrix inversion for more iterations. To further reduce the computational cost, numerous implicit methods such as the Gauss-Seidel (GS) detector [21][22][23][24], Jacobi method [25], Richardson iteration [26,27], accelerated over-relaxation (AOR) [28,29], symmetric successive over-relaxation (SSOR) [30], the Lanczos-method-based detector [31], and the conjugate gradient detector [32] have been introduced. These methods compute the estimates of the transmitted symbol without ever computing the matrix inverse.…”