Dynamic Resource Allocation for Next Generation Wireless Networks

大多数现代多用户无线通信系统努力通过更高速率、更低延迟传输来提高系统性能，同时延长移动的设备中的电池寿命（或提高能量效率）。信道和队列知识的可用性以及最近的利用已经导致了许多方法来解决部分有效功率分配问题，同时仍然确保其用户/流保证吞吐量和延迟。虽然值得注意，但实际考虑因素（例如过程和计算能量建模以及寿命能量约束）是必不可少的，以便进一步这些改进并在实际无线系统中实现它们，并与现有标准（例如IEEE 802.16e和3GPP LTE/LTE-Advanced）集成。 从先前的工作中已知，当电池资源在存在服务质量（QoS）约束的情况下是有限的时，简单地增加系统吞吐量可能不再是满足QoS的最期望的方法，因为它可能不必要地浪费能量资源，特别是当系统不处于满负荷时。在这里，QoS参数，队列状态信息（QSI）和信道状态信息（CSI）的知识在节能调度器设计中起着至关重要的作用。然而，利用所有或部分这些信息会使任何资源分配算法的设计呈指数级复杂化。将其与多信道资源分配的可能性相结合可以解释设计挑战，因为在具有如此大的自由度的系统中的资源的能量高效最优分配在计算上是高度复杂的。建议的研究计划的目标是解决这些系统的最佳/接近最佳的调度/资源分配方案的建模，分析，设计的问题，以及提出在调度器设计的复杂性降低方法。

Most modern, multiuser wireless communication systems strive to increase system performance through higher rate, lower latency transmissions, while simultaneously extending battery lifetime (or increasing energy efficiency) in mobile devices. The availability, and more recent the exploitation of channel and queue knowledge has led to a number of approaches to solve parts of the efficient power allocation problem while still ensuring their user/stream guarantees for throughput and delay. While notable, practical considerations such as process and computational energy modelling and lifetime energy constraints are essential in order to further these improvements and realize them in practical wireless systems and integration with existing standards such as IEEE 802.16e and 3GPP LTE/LTE-Advanced. It is known from previous work that when battery resources are limited in the presence of quality of service (QoS) constraints, simple increases in system throughput may no longer be the most desirable method to meet QoS as it may unnecessarily waste energy resources, particularly when the system is not under full load. Here, knowledge of QoS parameters, queue state information (QSI) and channel state information (CSI) play a vital role in the energy efficient scheduler design. However, exploiting all or some of this information exponentially complicates the design of any resource allocation algorithm. Combining this, with the potential for multiple channel resource allocation may explain the design challenges, as energy efficient optimal allocation of resources in a system with such a large degrees of freedom is highly computationally complex. The objective of the proposed research program is to address the problem of modeling, analysis, design of optimal/near-optimal scheduling/resource allocation schemes for these systems as well as proposing complexity reduction methods in scheduler design.