## 3 4-SSB System Design
### The Proposed New Scheme of 4-SSB with Low Complexity Equalizer for Compensating ISI  
The quality of service (QoS) is a key network metric of a 5G wireless system, i.e., the successful transmission scheme which recovers the reserved signal in the receiver is important for measuring performance quality in the wireless environment. However, most of the current researches have focused on enabling the receiver to compensate for the effect of noise on the wireless channel. In particularly, the inter-symbol interference (ISI) is used to identify and compensate the symbol interference between orthogonal symbols in the receiver side for 4-SSB by utilizing the Hilbert transform. ISI is produced by the channel impulse response duration less than the time symbol modulation. In this context, channel coding, MIMO technology, and channel equalization are mainly designed for improving QoS performance in wireless environments. In this research, we propose a new transmission scheme of 4-SSB with low equalizer complexity and low energy consumption. Before presenting our architectural design, we briefly present the types of equalizer category [22]: First type of equalizer is the zero-forcing (ZF) channel equalization. The main idea of forcing equalization is that the inversed channel impulse responds \(H^{-1}\) is used in the equalizer of the receiver side to equalize the original channel impulse response \(H\). However, the disadvantage of this technology is increased in the implication noise, especially when the channel impulse response is very small which results in high attenuation. To minimize this effect, the MMSE equalizer is applied to improve the zero forcing equalizer performance. The second type of equalizer is the decision feedback channel equalizer. This method of equalization includes the first equalization of zero forcing with first symbol entry which should be known to reduce the order of complexity. An advancement for providing high performance is to add coding channel by applying the convolution encoder and interleaving in transmission and vice versa in a receiver to minimize the error propagation in the receiving channel. This technique is called turbo equalizer, which is mainly designed to compensate the ISI by using an iterative algorithm. The system uses a decoding and equalizing scheme to feed the prior information and, in this way, the equalizer can be used for compensating ISI. In our prior work, turbo equalization was applied to realize the high order M-QAM 4-SSB [12] by designing the equalizer for the soft input soft output (SISO) MMSE equalizer. The result showed that we can successfully transmit M-QAM through a variety of channel environments such as AWGN and multipath fading channel. The presented article is then dedicated to decreasing the complexity of the equalizer to be applied in the MIMO scheme. The third type of equalization aims to decrease the complexity of the equalization which can be considered as an iterative MMSE algorithm corresponding to the principle of the shadow area. This equalization is designed to make the decision of the first symbol by estimating whether the signal is strong or weak. If it is weak, we will cancel the estimation when it is considered not close to the estimated value of the original signal to prevent the error propagation. Then, many researches examine how to make optimal estimation with low complexity. For example, the maximum likelihood (ML) algorithm is applied for the sphere decoder (SD) [22] and lattice code [23]. However, these two algorithms produce high complexity for high order modulation when the channel is not in good condition where the signal to noise ratio (SNR) is low. For MIMO, the optimum maximum likelihood detection (MLD) shows a good performance by increasing the number of antennas, users and modulation level.