# Estimating the Performance of MIMO SC-FDE Systems Using SISO Measurements
## 1 Introduction  
The evolution of telecommunications has enabled operators to offer services increasingly adapted to users' needs, such as the possibility of voice and video communications, with a major concern regarding the Quality of Service (QoS) they provide to customers [1]. Despite the constant evolution of technologies towards the Fifth Generation (5G) of telecommunications, the Fourth Generation (4G) is only expected to reach its peak in 2022, being subsequently replaced by 5G [2]. To ensure QoS, operators initially plan their networks and analyse the signal coverage for a given geographical area, which can be validated by carrying out Drive Tests (DTs). One of the major drawbacks of DTs is the expensive way in which DT campaigns are carried out, since they are time-consuming, costly, and, after all, they only depict the network status at the time they were performed. Despite these disadvantages, the information collected by DTs is very valuable as it allows for detecting regions where the signal received power level is lower than expected, therefore allowing us to optimise network planning [3]. There are many tools to assist operators in network planning; however, the starting point is always the signal coverage, which can be estimated using appropriate propagation models and tools. For example, a propagation model that uses terrain morphology data, network Key Performance Indicators (KPIs), and other data, has been implemented using AWS [4] services, which can be found in a network management tool called Metric[5], developed by Multivision [6]. The referred model, named Automatically Calibrated Standard Propagation Model (ACSPM) is technology agnostic and can be used in any existing technology, such as third generation or 4G. The description of all details involved in the estimation of the ACSPM signal, as well as its implementation, are discussed in [7], where it is also possible to find validation metrics for the model. The implementation of this model was a base for new cellular planning algorithms, such as the estimation of neighbouring cells [8], which allows not only frequency planning [9], but also Scrambling Code (SC) planning for Universal Mobile Telecommunications System networks (UMTS) networks [10]. This ACSPM model was initially designed and implemented for Single Input Single Output (SISO) communication systems, which are now changing to Multiple Input Multiple Output (MIMO) systems due to the increase in required user's data rate and QoS [11]. The concept of MIMO in this research is related to spacial diversity, as multiple antennas are used in order to improve the quality of the transmission link and thus offer a better QoS to the user [12; 13]. If this system changeover is key to improve the QoS it is therefore essential to know the impact on signal propagation when moving to the MIMO system and how it can be appropriately foreseen by the propagation model used. Operator's DT campaigns using MIMO systems are still very limited and they only depict specific scenarios (e.g., two transmitting antennas and two receiving antennas), while DT campaigns for SISO systems are more widely available. The motivation for this study lies in the possibility of using these SISO DT campaigns, so as they can be used with propagation models for MIMO systems, which immediately brings a great cost advantage. The use of this methodology enables a simple estimation of coverage for a MIMO system, allowing for the application of algorithms for network planning and optimisation. In Figure 1, the motivation for this study is depicted. In this paper, the authors present a theoretical methodology, with some simulation results, that allows us to verify the impact of the Bit Error Rate (BER) performance when changing from a SISO system to a MIMO system, which can also be used to estimate the received power level with the MIMO system in a given area.