# Performance Analysis of Downlink 5G Networks in Realistic Environments
## 2 Related Work  
Mobile operators face enormous challenges as a result of the recent transition from 4G to 5G systems, as users are switching from traditional applications to a new network design that supports a variety of applications that virtually connect machines, objects, and devices with multi-gigabit speed, negligible latency, and maximum spectrum efficiency. Studying the capacity bounds of these systems in more accurate scenarios can help us solve the aforementioned difficulties by more accurately reflecting the end-user experience. The literature examines and analyzes capacity bounds from various perspectives and angles. In a previous study [19], [PERSON]'s capacity formula was used to calculate system capacity. The authors used long-term evolution (LTE) as a case study to forecast the LTE spectral efficiency in micro and macro contexts. The authors report a perfect match between the expected and simulated values based on the generated results. In [20], the authors used spectral constraints and quantization techniques to derive capacity boundaries. The authors concluded that although employing low quantization resolution does not affect the possible rates of 5G systems, satisfying out-of-band constraints is a barrier to the future of 5G systems. By employing finite block-length coding, the authors of [21] proposed QoS provisioning with a statistically constrained delay parameter for multimedia mobile wireless networks, such as 5G. The capacity formulation of such systems is influenced by delay-bounded QoS restrictions. The authors used simulation findings to validate the system performance results. The capacity bound has been studied by authors in the recent literature [22; 23; 24; 25; 26; 27; 28] based on various criteria and viewpoints. The error probability and capacity constraints in [22] were calculated by the authors based on the unique modulation symbols of the vertical bell labs space-time system. The upper bound for the average bit error probability was derived by the authors, who also created a successful error correction system. Comparing the proposed detection with a compressive sampling-matching pursuit detector, the authors claimed that the proposed detection is much more practical for spatial modulation schemes. Time-varying channel characteristics and signal loss are the two fundamental issues faced by rail train carriages, and efficient capacity analysis and transceiver design were derived in [23] to address these issues. The authors assert that the recommended strategies perform better than conventional methods currently used to address these issues, such as relay and beamforming. With a suggestion for transceiver design, lower and upper bounds of capacity were established, and the simulation results showed that the prediction method can be very helpful for future channel predictions. In contrast to other works in the literature, a device-to-device communication model was proposed in [24], according to the authors, assuming a run-ideal transceiver with interference and no channel state information. Saddle-point approximations were used to derive the upper and lower capacity limitations in the closed form. The numerical results demonstrate that mmWave communication can resist transceiver noise even at high signal-to-noise ratios (SNRs). The capacity results were tested against a Monte Carlo simulation. The authors of [25] investigated the impact of remaining hardware deficiencies on the ergodic capacity of dual-hop (DH) amplify-and-forward (AF) MIMO relay systems by considering the beneficial effects of the MIMO relay model on the coverage and capacity of next-generation wireless networks. The authors used a simulation to examine the impact of low SNR values on the system capacity, in addition to determining the upper and lower limits of the capacity. According to the modeling results, the capacity reaches saturation when the transceiver degrades. The upper capacity limitations of the large intelligent surface (LIS) antenna were derived in [26].