Analyzing the channel models in the mm-wave bandwidth is critical for 5G system performance. This study investigated the effects of 28 GHz and 73 GHz frequencies, the number of transmitting and receiving antennas, and LOS/NLOS parameters on 5G channel capacity using the NYUSIM channel simulator. As a result of the analysis, changing from a 2x2 to a 64x64 antenna structure for 28 GHz increased capacity by 29.78 times for LOS and 26.91 times for NLOS. When changing the MIMO configuration from 2x2 to 64x64 at 73 GHz, the channel capacity rises 36.88 times for LOS and 29.00 times for NLOS. With a 64x64 antenna structure, the channel capacity for 28 GHz and LOS is 8.81 times higher than for 73 GHz, and it is 12.56 times higher for NLOS. For the 28 GHz 64x64 structure and LOS condition, the channel capacity is 215.69 times higher than the NLOS condition, while this value is 307.7 times for 73 GHz.

No abstract

The analysis of multiple-input multiple-output (MIMO) channel capacity is important for developing and optimizing high-speed wireless communication systems that can meet the growing demand for data-intensive applications. This study aims to analyze the 4 × 4 MIMO channel capacity of outdoor urban and rural environments using the NYUSIM simulator. The channel models are designed for 28 GHz and 39 GHz frequencies for both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Realistic channel models are simulated using annual climate data collected in Samsun province, Turkey in three different environments: urban microcell (UMi), urban macrocell (UMa), and rural macrocell (RMa) areas. According to the annual average channel capacity analysis, it is observed that there is a very small capacity difference between UMi and UMa areas at 28 GHz and 39 GHz frequencies in the LOS region, while the RMa area is found to have a very low capacity compared to the UMi and UMa areas. The channel capacity for RMa is found to be approximately eight times smaller than UMi and UMa. In the NLOS region, channel capacities decrease significantly (between 312 and 3953 times) compared to the LOS region, with the UMa area having the greatest capacity and the UMi area having the lowest capacity. Compared to the UMi channel capacity, the RMa channel capacity is 1.36 times higher for 28 GHz and 1.28 times higher for 39 GHz. When the monthly changes in channel capacity are examined, it is discovered that the amount of precipitation has the greatest impact on channel capacity, and the capacity decreases as the rain rate increases. The highest correlation between channel capacity and rain rate was −0.97 for RMa, with a 28 GHz frequency and LOS conditions. Additionally, it becomes clear that channel capacities increase in the summer months as the temperature rises and humidity and pressure fall.

The wireless communication channel is the critical parameter that affects the throughput in the LTEA network. The user equipment measures the quality of the wireless channel as the channel quality indicator (CQI) value and transmits it to eNodeB. The eNodeB uses the CQI value to select the adaptive modulation and coding method to achieve the highest throughput. This study analyzes the LTE-A network based on actual field measurements. Measurements were taken at 80 different locations in Samsun (Turkey) city center using TEMS Investigation software. RSRP, RSRQ, SINR, CQI, and throughput values were recorded for each measurement in a stationary position while downloading 500 MB of data. Then, the averages of these recorded data were calculated, and detailed analyses were performed. At the end of the study, the effects of RSRP, RSRQ, SINR, and CQI on the throughput value in the LTE-A network were examined, and a novel mathematical model was proposed that gives the relationship between them with 88.85% accuracy. It has also been observed that the accuracy of the proposed model can be increased by 4% with GRNN. In the last stage of the study, a new CQI mapping method based on real-field measurements for the LTE-A system was developed.

The wireless communication channel is the critical parameter that affects the throughput in the LTE-A network. The user equipment measures the quality of the wireless channel as the channel quality indicator (CQI) value and transmits it to eNodeB. The eNodeB uses the CQI value to select the adaptive modulation and coding method to achieve the highest throughput. This study analyzes the LTE-A network based on actual field measurements. Measurements were taken at 80 different locations in Samsun (Turkey) city center using TEMS Investigation software. RSRP, RSRQ, SINR, CQI, and throughput values were recorded for each measurement in a stationary position while downloading 500 MB of data. Then, the averages of these recorded data were calculated, and detailed analyses were performed. At the end of the study, the effects of RSRP, RSRQ, SINR, and CQI on the throughput value in the LTE-A network were examined, and a novel mathematical model was proposed that gives the relationship between them with 88.85% accuracy. It has also been observed that the accuracy of the proposed model can be increased by 4% with GRNN. In the last stage of the study, a new CQI mapping method based on real-field measurements for the LTE-A system was developed.