electronics Article Effective Beamforming Technique Amid Optimal Value for Wireless Communication Zahid Hussain Qaisar 1 , Muhamamd Irfan 2 , Tariq Ali 3,* , Ashfaq Ahmad 4, Ghulam Ali 5 , Adam Glowacz 6,* , Witold Glowacz 6, Wahyu Caesarendra 7 , Aisha Mousa Mashraqi 8 , Umar Draz 9,* and Shafiq Hussain 9 1 Department of Computer Science, NFC Institute of Engineering and Technology Multan, Multan 66000, Pakistan; [email protected] 2 College of Engineering, Electrical Engineering Department, Najran University, Najran 61441, Saudi Arabia; [email protected] 3 Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan 4 Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Defense Road off Raiwind Road, Lahore 54000, Pakistan; [email protected] 5 Department of Computer Science, University of Okara, Okara 56300, Pakistan; [email protected] 6 Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Kraków, Poland; [email protected] 7 Laboratory for Industrial Metrology and Automation, College of Engineering, University of Texas, El Paso, TX 79968, USA; [email protected] 8 Department of computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia; [email protected] 9 Department of Computer Science, University of Sahiwal, Sahiwal 57000, Pakistan; drshafi[email protected] * Correspondence: [email protected] (T.A.); [email protected] (A.G.); [email protected] (U.D.) Received: 23 July 2020; Accepted: 24 October 2020; Published: 6 November 2020 Abstract: In the notion of communication system resource provision specifically, beam-forming is a concept of proficient utilization of the power of transmission. Network densification and massive MIMO allows us to control the power efficiency and can be effectively distributed among different users by reducing cost. We presented a practical scenario for the performance of massive MIMO and multi-small cell system to analyze the overall performance of the system. Our work is based on the resource allocation with optimal structural constraints to maintain the cost effectiveness while considering economic implications. The base stations located far away from the users receive attenuated signals and give rise to path loss, whereas the problems of inter cell interference also arise due to transmission from a base station to others cells. The performance of the cellular system can be enhanced with the combination of massive Mimo and small cells, where we simulate and also provide an analysis on practical system with optimal and low complexity beam-forming. The proposed scenario illustrates a structure with an optimal linear transmit beamforming regarding an efficient number of parameters to not lose optimality, which is extendable to designate any specific cellular network in consideration. Our approach exploited schemes with low complexity that are facilitating in complete solution formation, and tested them in various and all possible cases and scenarios. Keywords: beam-forming techniques; cellular communication; economic implications for communication; resource allocation; MIMO Electronics 2020, 9, 1869; doi:10.3390/electronics9111869 www.mdpi.com/journal/electronics Electronics 2020, 9, 1869 2 of 17 1. Introduction The proposed study provides an approach for the optimization of the energy efficiency in wireless networks. Resources in the communication system are providing the basis for connectivity, coordination and information sharing. If the resource is optimally utilized, the operation will be smooth. previous studied work of the optimal utilization of resources for wireless communication network and signal processing is focused on the functionality of available resources and accomplishment of the tasks of connectivity for data and processing. Recently, data traffic has been increasing rapidly. To cope with the demand of speed in connectivity by the end user, one solution is to provide a separate antenna for each; however, this is not viable. For a recently designed communication system, it is common to facilitate numbered users. Similarly, when facilitating larger users, in some cases a lot of antennas can be observed, but sometimes not. However, the efficient utilization of resources in the existing wireless setup is an effective way forward. The optimal utilization of resources yields maximum throughput. It is a significant and prime consideration to utilize the frequency in an optimal way. Multiple beam-forming techniques such as MRT, XFBF, and SLNR are considered. Resultant effectiveness of the proposed approach is explained with the aid of various dimensions. The focus is to provide the concept of the inevitable importance of resource utilization. Results from cited approaches show a compact analysis of the utilization of the system resources in an established design or architecture. The structural design of the arrangement is explained briefly in the next sections, and a complete analysis of the evaluation in a practical system is mentioned in the following section. The notion of evaluation of per user beam-forming posed by Emil et al.’s cited technique also emphasises the optimization of the resources [1]. The resource per user in primary and the average aggregate are used at the end of the analysis. In a collective way, using data analysis from the existing structure enabled us to define the infrastructure of the future communication system. However, in the wireless communication system, a few of their resources are already distributed among the consumers in an unjustifiable way. The cited advance booking-based approach is not too effective in resource exploitation because of the inconsistency in the manner of the packets [1,2]. It is of primary importance for networks with a service of small packets, for a great number of users. Keeping in mind the significance of pilot contamination moderation for ensuring the improvement in enactment of massive MIMO, a lot of arrangements, varying from advanced channel estimation and to down link pre-coding to optimal pilot design and allocation, have been proposed to cater to the stated issue [1,3,4]. User terminals (UTs) which are mobile may obtain wireless facilities from a base station located exclusive in every cell, in a range of the coverage region. There should be the establishment of a networking connection among all base stations (BSs) with a central or critical network wired [5]. This increases the likelihood of a few problems, like inter-cell interference (ICI) and path loss. It should be mentioned that both factors might give rise to other, different issues. For instance, both decrease the (1) signal to interference-plus-noise ratio (SINR), and therefore (2) attainable data rates of the consumer terminals [3,4]. Though massive MIMO and positioning SCA can lead to a difficulty in inter-consumer interference and in a complicated linkage, which is resulting in users being outnumbered and many antennas being installed at the BS [6,7]. This paper examines the possible improvements in energy efficiency when the classical small cell topology is modified by employing massive MIMO at the MS and SISO at the user. We assume perfect channel acquisition and a backhaul network that supports interference coordination; we thus consider an ultimate bound on what is practically achievable. We presented a practical scenario for the performance of massive MIMO and multi small cell system to analyze the overall performance of the system. The goal is to minimize the total power consumption while satisfying quality of service constraints at the users and power constraints at the MS. Our study proceeds in the following way; Section2 provides some state of the art work; Section3 includes system modelling. Section4 discusses multiple beam-forming techniques; Section5 contains data analysis and results about practical scenarios; finally, we conclude our study. Electronics 2020, 9, 1869 3 of 17 2. Related Work Relevant work for our proposed approach was explored. It was observed that a huge number of antennas in the base station can lead to the occurrence of various t orthogonal links from the user side to the site of the base station. If the number of antennas is abundant at BS, the diverse propagation links from the users to the BS are inclined to be orthogonal, and the large extent of spatial degrees of autonomy is handy in mitigating the impact of profligate fading. Generally, a massive MIMO approach offers advanced better spectral, data rate and energy efficiency. Overall, these gains brand massive MIMO as an auspicious approach. It requires highly applied attention to networks posing small packet services to a big number of users [6,8]. The key contest met is the use of the various existing schemes for the utilization of resources and the right communication. This can be a factor such as, in power systems, a practice based on CS for meter reading in a smart grid being proposed; it is supposed to apply to single antenna systems. Moreover, a unique neighbour discovery technique in wireless networks with Reed–Muller codes is listed in the citation, and the CS technique is also implemented [6,9,10]. However, in the situation of the base station, the BS is not practically faultless CSI; it approximates the channels as a typical scenario. Conservatively, it is completed via uplink pilots. Subsequently, together the time-frequency resources assigned for pilot transmission and the channel coherence time are inadequate, the number of conceivable orthogonal pilot sequences is inadequate. Therefore, the pilot sequences need to be recycled in adjacent cells of cellular systems [6,11]. It is crucial that at base station, signals are expected at the same time or with small-time variances. It is a significant job to compute it without assessment; otherwise, data investigation. Consider massive MIMO and small scale networks; there are a large number of enhancements in dynamic parts, but because of complications and dense arrays, they require more hardware.