# On the Performance of LDPC-Coded Large Intelligent Antenna System
## 1 Introduction  
### Motivation  
The future of communications is going to become intelligent and interactive as it tries to make human-made surfaces electronically active, improving wireless communication. In this regard, the emergence of the Internet of Things (IoT) and billions of terminals that require access to wireless communications have made researchers put a lot of effort into solving communication problems [1]. m-MIMO (Massive MIMO--Multiple Input Multiple Output), UM-MIMO (Ultra Massive-MIMO), and ELAA (Extremely Large Antenna Arrays) are three of the most significant developments in communication system design in recent decades, and they have significantly improved data rate, network capacity, and performance. In this regard, the LIS concept can be viewed as a beyond-massive MIMO in a telecommunications network with increased capacity and data rate, where the number of antennas is even higher. Traditionally, wireless communications are established in the far-field, that is, with propagation distances beyond the Fraunhofer distance (the Fraunhofer distance is only a few wavelengths). The LIS system comprises several panels, and each panel includes several antenna elements. The LIS system acts as a near-field beamforming; that is, the communication is established behind the Fraunhofer distance [2, 3]. In this case, the individual array elements are in the far-field but not the array as a whole. Consequently, the focus is established not only in the bearing and elevation planes but also in the distance dimension. This allows for the reduction of interferences between users that are aligned but located at different ranges, bringing another advantage, as compared to traditional beamforming. The typical distance between the antenna elements is \(\lambda/2\). The channel correlation between the antenna elements allows for the creation of the above-described beam. A block transmission strategy has already been presented. Single carrier with frequency domain equalization [4; 5] is also discussed and analyzed in this article, but it is applied to the Large Intelligent Surface (LIS). Furthermore, this paper analyzes the use of Low-Density Parity-Check (LDPC) codes to this combined system. Training sequences are used in this article to make the results more realistic. There are substantial concerns with high-frequency transmission, such as enormous free-space path losses, massive losses because of obstacles, and practical difficulties, particularly with power amplification. Adopting LIS as a novel approach allows us to have a large number of small-sized antenna aggregates with a large number of panels [6]. In addition, the significant reflection effects in LIS can be used to increase coverage [7]. Using these qualities, we could develop communications with several orders of magnitude larger capacities than current wireless networks. In fact, new communications approaches such as LIS can be fully utilized to improve performance [8]. The LIS system can be viewed as an extension of m-MIMO, which can receive and transmit from a man-made structure. As a result, user terminals establish communications from a close distance, which reduces battery consumption and reduces interference [1; 6]. The LIS system has better channel estimation capabilities than m-MIMO, which typically requires Channel State Information (CSI) for hundreds of antennas. In addition, the LIS system is reliable and efficient [9]. Four different receivers are studied in this article and compared in terms of performance: namely, Zero Forcing (ZF), Maximum Ratio Combining (MRC), Equal Gain Combining (EGC), and Minimum Mean Squared Error (MMSE) [6; 10]. Moreover, low-density parity-check codes (LDPC) coding is studied in the scenario of the LIS system combined with SC-FDE transmission, leading to a reduction in the Bit Error Rate (BER) [11; 12]. The integration of LDPC in the scenario of the LIS system combined with SC-FDE transmission is the novelty of the article.