Dynamic Switching of Scheduling Algorithm for Uplink LTE System

Abstract

Packet Scheduling (PS) is essential in Long Term Evolution (LTE) cellular system to handle huge traffic via allocating common resources among users. The main target of PS, in LTE systems, is the Radio Resource Management (RRM) by including strategies, algorithms, and schemes to control important parameters and factors such as transmit power, data rates, modulation scheme, and error coding scheme. The paper presents a performance comparison of two scheduling schemes for LTE uplink, Best Channel Quality Indicator BCQI, and Round Robin RR schedulers. Next, a new packet scheduler for uplink LTE networks, that adapts dynamically between the two mentioned packet scheduling algorithms is proposed, evaluated and compared with the performance of the two schedulers, BCQ and RR. The results of the performance, throughput, and fairness indicate that the proposed scheme gives compromise fairness between BCQI and RR in resource distributions in the LTE and maintains the system's capacity as high as possible. The same improvement has also been observed with delay-sensitive services traffic.

5G Downlink Throughput Enhancement by Beams Consolidating at Vacant Traffic

“…Additional arrays focusing the signal energy into narrow beams provide improvements in the UE throughput, particularly, when combined with simultaneous resources scheduling to numerous UEs [17]. The more the BS antennas utilized, the more beams will be added to serve more UEs within enhanced data rate [17].…”

“…Additional arrays focusing the signal energy into narrow beams provide improvements in the UE throughput, particularly, when combined with simultaneous resources scheduling to numerous UEs [17]. The more the BS antennas utilized, the more beams will be added to serve more UEs within enhanced data rate [17]. Moreover, massive MIMO utilizes a suitable usage of beamforming methods to reduce fading which increases the SINR that reflects high Channel Quality Indicator (CQI) and reduces latency [17].…”

“…The more the BS antennas utilized, the more beams will be added to serve more UEs within enhanced data rate [17]. Moreover, massive MIMO utilizes a suitable usage of beamforming methods to reduce fading which increases the SINR that reflects high Channel Quality Indicator (CQI) and reduces latency [17]. The CQI indicates the MCS that will be assigned to each user according to its SNR [17].…”

“…Moreover, massive MIMO utilizes a suitable usage of beamforming methods to reduce fading which increases the SINR that reflects high Channel Quality Indicator (CQI) and reduces latency [17]. The CQI indicates the MCS that will be assigned to each user according to its SNR [17]. When the number of BS antennas exceeds the number of UEs, the throughput will increase [18].…”

Deep Learning Approaches for Intrusion Detection

“…Various methods are used to detect a suspicious attack, such as artificial intelligence and machine learning. Researchers forward to employ and investigate the deep learning method with technology development rather than traditional machine learning techniques [12][13][14]. Deep Learning is a modern technique for dealing with massive amounts of data that can extract useful features from big data and build models for inference, decision making, and prediction [15,16].…”