## 1 Introduction  
Nowadays, the Internet of Things (IoT) of 5G [1, 2, 3] is one of the crucial applications of wireless communication systems [4, 5, 6]. Namely, wireless transmission services have become an extremely important method of Internet of Things (IoT) information delivery. The applications of the Internet of Things (IoT) include smart homes, healthcare, traffic auxiliary management, industrial automation, and crisis response for natural and artificial disaster prevention. To meet various types of applications of the Internet of Things (IoT), wireless transmission of large amounts of results to data collection centers is an essential need, i.e., 5G technology will be forced to bear massive amounts of data while being more time saving than 4G. Furthermore, the multiple-input multiple-output (MIMO) and the orthogonal frequency-division multiplexing (OFDM) are indispensable technologies of wireless communication systems currently [7, 8]. The former, with multiple antennas at the transmitters and receivers, which can significantly increase the data throughput of the system and transmission distance without increasing the total transmit power expenditure or bandwidth demand, also can efficiently obtain diversity gain, array gain, capacity gain, and beamforming gain [9; 10; 11]; the latter is a favored modulation and transmission scheme that cuts a high-data-rate stream into some lower-rate streams simultaneously transmitted over some narrow-band channels parallelly, which has a lot of advantages, such as robustness against narrow-band co-channel interference, inter-symbol interference (ISI) caused by multipath propagation, and low sensitivity to time synchronization errors [12; 13]. To further promote the merits of the MIMO systems and meet higher spectrum efficiency, the pioneer researchers proposed the massive MIMO (M-MIMO) structure [14; 15; 16; 17], which utilizes spatial diversity to support more users by mounting a large number of antennas in the base station (BS) [18; 19; 20]. It is a pity that, although the M-MIMO systems have brought many benefits, complexity is also appreciably increased. Especially as far as the receiver is concerned, conventional linear detection methods such as zero-forcing (ZF) and minimum mean-square error (MMSE) are adopted [21; 22], even if they show good bit error rate (BER) performance; however, as mentioned by [PERSON] et al. [23], since they involve matrix inversion operations of \(\mathcal{O}(N^{3})\) complexity, the detection complexity is too high to implement when \(N\) is increasing, where \(N\) is the antenna number. Therefore, many iterative methods aim to eschew matrix inversion and not calculate it. In addition, a comparative study of low-complexity linear detectors of M-MIMO is addressed in [24] to discuss the pros and cons of iterative matrix inversion methods. To maintain good performance and low complexity, low-complexity signal detection for the uplink of massive MIMO systems is proposed, such as the Jacobi method [25], Gauss-Seidel method [26], and successive over-relaxation (SOR) method [27] proposed by [PERSON] et al., [PERSON] et al., and [PERSON] et al., respectively, which have lower complexity as \(\mathcal{O}(N^{2})\) through the iterative procedure to avoid the notorious inverse matrix operation. However, the performance is not sufficient to satisfy us. In light of this, [PERSON] et al. [28] considered a modified SOR method to reduce the complexity, but it is strongly confined to lower modulation orders; namely, its performance degrades substantially when the high modulation order becomes more required. In [29], [PERSON] et al. proposed the symmetric successive over-relaxation (SSOR) method, which combines two SOR sweeps in such a way that the resulting iteration matrix is similar to a symmetric matrix and its performance is better than the previous SOR-based method; nevertheless, its complexity will increase drastically compared to the SOR-based method. Compared to the SOR-based method [27], [PERSON] et al.