Long-term evolution (LTE) networks frequently confront the issue of meeting the quality of service (QoS) requirements of the many services they offer. When it comes to radio resource scheduling, maintaining a balance between system performance and user fairness is a delicate issue. Different academics have developed several methods to this effect in an attempt to manage the restricted radio resources. The M-LWDF algorithm, for example, is a well-known packet scheduler that can support both real-time and non-real-time services. This algorithm has been proven to be insufficiently supportive of real-time services. This method uses the fuzzy logic uncertainty principle to create new weights for the various flows, taking into account two network input parameters: delay requirement for real-time traffic and throughput need for non-real-time traffic. Using system throughput, packet loss rate, delay, and fairness as performance metrics, this algorithm was compared against PF, M-LWDF, and EXP/PF schemes in an LTE simulator. When the cell was loaded with 60 users, the aggregate throughput for video and VOIP increased significantly by 9.8% and 0.1 percent, respectively, when I-MLWDF was employed, however the throughput for non-real time flow dropped by almost 90% when compared to MLWDF. When compared to EXP/PF and MLWDF, the revised scheme revealed a packet loss rate for video flow of 35.71 percent and 75 percent, respectively. The tolerable packet loss rate for VOIP traffic is less than 3% for both algorithms, with the enhanced system outperforming MLWDF by roughly 20%. In comparison to MLWDF, the packet loss rate for non-real time flow for I-MLWDF was about 95%. When the cell was loaded with 60 users, the enhanced scheme’s video and VOIP delays were under 0.05s and 0.007s, respectively, compared to MLWDF’s delays of 0.07s and 0.02s for video and VOIP, respectively. In addition, when compared to MLWDF, I-MLWDF had a 98 percent longer delay. I-MLWDF has a 20 percent higher video flow fairness index than MLWDF. For the fairness index for VOIP traffic, both algorithms performed between 98.5 percent and 99.5 percent, with I-MLWDF outperforming M-LWDF by roughly 0.2 percent. MLWDF and PF, on the other hand, had the greatest fairness indexes, with scores above 99 percent, while I-MLWDF had the lowest, with a score of around 93 percent.

Cellular communication was created to address the shortcomings of traditional wireless and wired communication networks by allowing communication between mobile and stationary units, as well as vice versa[1]. The mobile units are known as mobile stations, whereas the permanent units are known as land fixed units. Traditional wireless communication networks have a number of faults, including their inability to cope with the high speed of rapid mobile devices, as well as their low capacity and low data rate.
The name “cellular” comes from the fact that cellular communication divides its coverage area into little cells. During the late 1970s, suggestions for cellular communication began to appear in Bell System proposals [1, 2]. The population (traffic) in the area to be covered determines the size of the cells. It is, however, determined by the transmitter’s strength and the cell’s operating frequency. Cells use strategies like frequency hopping, frequency re-use, and cell splitting to manage restricted frequency resources and satisfy the needs of consumers [3].

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