Packet scheduling under imperfect channel conditions in Long Term Evolution (LTE)
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The growing demand for high speed wireless data services, such as Voice Over Internet Protocol (VoIP), web browsing, video streaming and gaming, with constraints on system capacity and delay requirements, poses new challenges in future mobile cellular systems. Orthogonal Frequency Division Multiple Access (OFDMA) is the preferred access technology for downlink Long Term Evolution (LTE) standardisation as a solution to the challenges. As a network based on an all-IP packet switched architecture, LTE employs packet scheduling to satisfy Quality of Service (QoS) requirements. Therefore, efficient design of packet scheduling becomes a fundamental issue. The aim of this thesis is to propose a novel packet scheduling algorithm to improve system performance for practical downlink LTE system. This thesis first focuses on time domain packet scheduling algorithms. A number of time domain packet scheduling algorithms are studied and some well-known time domain packet scheduling algorithms are compared in downlink LTE. A packet scheduling algorithm is identified that it is able to provide a better trade-off between maximizing the system performance and guaranteeing the fairness. Thereafter, some frequency domain packet schemes are introduced and examples of QoS aware packet scheduling algorithms employing these schemes are presented. To balance the scheduling performance and computational complexity and be tolerant to the time-varying wireless channel, a novel scheduling scheme and a packet scheduling algorithm are proposed. Simulation results show this proposed algorithm achieves an overall reasonable system performance. Packet scheduling is further studied in a practical channel condition environment which assumes imperfect Channel Quality Information (CQI). To alleviate the performance degradation due to simultaneous multiple imperfect channel conditions, a packet scheduling algorithm based on channel prediction and the proposed scheduling scheme is developed in downlink LTE system for GBR services. It was shown in simulation results that the Kalman filter based channel predictor can effectively recover the correct CQI from erroneous channel quality feedback, therefore, the system performance is significantly improved.
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