## 1 Introduction  
In the past two decades, the use of wireless communication has exceeded the use of personal communication or human-to-human (H2H) communication. Thus, we need to achieve high spectral efficiency, update user experience and reduce latency in this widely-used wireless communication. Nowadays, the information connectivity for the machine-to-machine (M2M) devices is placed under high demand, which plays a major factor in the next generation [1]. A new type of traffic that has arrived in the cellular communication system by interconnecting the large devices is known as the Internet-of-Things (IoT) [2]. The implementation of gigabit ethernet (GigE), Internet of Things (IoT), vision internet and heterogeneous networks (Hetnets) has been aided by the 5 th generation (5G) networks [3]. Of these, the IoT technology supported by M2M has modernized applications, including agriculture, transportation, tracking, metering, e-health and so on. Some of the design aspects related to massive M2M infrastructures include the multiple access system [4], sharing of the resources and the different parts of networking [5]. Multi-user detection (MUD) is a receiver technology dedicated to the detection of all the interfering signals via compressive sensing (CS) [6]. If more devices are not in an active state (user activity is low), the transmitting signal vector has a sparse property due to a large number of non-zero elements. Therefore, the decoding of the transmitted signal becomes a CS problem [7]. The long-term evolution is appropriate for a system that provides a small number of high activity of users. However, this shifts for machine-type communication (MTC) where a higher number of users with fewer activity sporadically sends a small number of packets [8]. Recently, researchers have focused more on OFDM systems compared to the existing air-interface techniques due to its low complexity. In OFDM systems, the subcarriers are sent through multiple channels, which permits ease of equalization in the case of low complexity during the implementation. [PERSON] et al. [9] considered various types of noises, such as Additive white Gaussian noise (AWGN), phase noise (PN), Rayleigh fading, Rician fading and Doppler shift with the turbo coding technique. The simulation platform consisted of three modules (transmitter, channel and receiver). In the transmitter module, turbo coding is performed, which makes the system more immune to the effects of noise with excellent BER results. The channel model is constituted by multipath fading, Doppler shift, AWGN and PN. In reference [10] the simulation is carried under various noise types, such as complex Rayleigh fading, complex Rician noise, AWGN and phase noise. [PERSON] et al. [11] split an information stream into multiple frequency carriers, which joins OFDM in the simulation platform with turbo codes to find a better turbo scheme compared to a typical parallel concatenated convolutional codes and serial concatenated convolutional codes (PCCCs and SCCCs) are a class of Forward error correction codes suitable for turbo decoding and a convolutional encoder. Some of the evolutionary algorithms (EAs) that have been useful for both CE and MUD investigation includes repeated weighted boosting search (RWBS), differential evolution algorithms (DEAs) and fuzzy adaptive differential evolution (FADE) algorithm [9; 10]. Normally, the continuous search space is considered for the CE problem and a discrete search space for the MUD optimization problem. Multiuser-MIMO (MU-MIMO) broadcast approaches are frequently used to improve the spectral efficiency. The quality of transmitting pre-coding to dominate the multiuser interference becomes degraded because of the coarse knowledge of channel state information (CSI) at the transmitter. Therefore, the interference from co-scheduled user equipment can affect the system throughput. [PERSON] et al.