Dynamic Radio Resource Management for Advanced Wireless Communication Systems

先进无线通信系统的动态无线电资源管理

• 批准号: RGPIN-2014-03777
• 负责人: Zhao, Lian
• 金额: $ 3.06万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688456
• 关键词: Dynamic; Radio; Resource; Management; Advanced

The main theme of the research program is radio resource management (RRM) for advanced wireless communication systems. For a wireless network operating in a fading environment, power and bandwidth are precious radio resources that need detailed and careful planning. Reducing power consumption while satisfying a targeted Quality of Service (QoS) requirement leads to an enlarged overall capacity for the communication system and a prolonged battery life for the communication devices. In this proposal, we will investigate the RRM problems in wireless communication systems involving Cognitive Radio (CR), Multiple-input Multiple-output (MIMO), Orthogonal Frequency Division Multiplexing (OFDM), and energy harvesting green communication techniques. With the evolution to a more sophisticated wireless communication system, the issues of optimal RRM problems have become ever so important to achieve an overall optimal system design.**Water-filling (WF) has been an important algorithm to solve RRM problems in fading channels. With water-filling, more power is allocated to the channels with higher gains to maximize the system throughput. A conventional way that utilizes the WF algorithm (CWF) to solve the optimal power allocation problem is to solve the Karush-Kuhn-Tucker (KKT) conditions, and then find the water-level(s) and the solutions. Recently, we proposed a Geometrically approached WF algorithm (GWF). Comparing with the CWF approach, GWF provides a closed form solution to the power allocation problem and avoids the computational complexity of solving a set of non-linear equations. In addition, we have demonstrated that GWF can be extended to solve more generalized and hence more complicated RRM problems. In our most recent research, we explored this beneficial property even further by demonstrating a set of recursive power allocation algorithms for energy harvesting green communication systems in fading channels. **The proposed research is built upon the outcomes of our earlier research, i.e. to investigate the water-filling mechanism and to develop a series of optimal RRM algorithms to accomplish the dynamic and optimal features for different system settings. We will focus on three main research topics in the proposal: 1) optimal power allocation algorithms for wireless communication with energy harvesting nodes in CR networks; 2) dynamic RRM optimization for energy harvesting and power grid coexisting systems with fair power allocation constraints; and 3) optimal algorithms for OFDM wireless networks with CRs. The long-term research objectives are to extend the proposed algorithms to multi-user cases, and further to real-time resource allocation algorithms. Both will be effectively utilized in practice. And they will push the performance boundary of the current state of art technology. The proposed research builds on the success of water-filling algorithms, and more generally, the application of optimization theory in the field of communications and signal processing. This research will propel the associated academic research and advance this technological innovation to the next level. The successful completion of these research projects will contribute to the overall growth of the fundamental knowledge in the field of multi-user information theory and push the engineering frontier of the next-generation wireless communication systems.

该研究计划的主题是先进无线通信系统的无线电资源管理（RRM）。对于在衰落环境中操作的无线网络，功率和带宽是需要详细和仔细规划的宝贵无线电资源。在满足目标服务质量（QoS）要求的同时降低功耗导致通信系统的总容量增大以及通信设备的电池寿命延长。在这个提议中，我们将研究无线通信系统中的RRM问题，涉及认知无线电（CR），多输入多输出（MIMO），正交频分复用（OFDM）和能量收集绿色通信技术。随着向更复杂的无线通信系统的演进，最优RRM问题对于实现整体最优系统设计变得非常重要。注水算法是解决衰落信道下无线资源管理问题的一种重要算法。在注水的情况下，更多的功率被分配给具有更高增益的信道以最大化系统吞吐量。利用WF算法（CWF）求解最优功率分配问题的传统方法是求解Karush-Kuhn-Tucker（KKT）条件，然后找到水位和解。最近，我们提出了一种几何逼近WF算法（GWF）。与CWF方法相比，GWF方法提供了功率分配问题的封闭形式解，避免了求解一组非线性方程组的计算复杂性。此外，我们已经证明，GWF可以扩展到解决更广义，因此更复杂的RRM问题。在我们最近的研究中，我们通过演示一组用于衰落信道中的能量收集绿色通信系统的递归功率分配算法，进一步探索了这一有益特性。 ** 建议的研究是建立在我们早期研究的成果之上，即研究注水机制并开发一系列最佳RRM算法，以实现不同系统设置的动态和最佳功能。我们将集中在三个主要的研究课题的建议：1）最优功率分配算法的无线通信与能量收集节点在CR网络; 2）动态RRM优化能量收集和电网共存系统的公平功率分配约束;和3）最优算法的OFDM无线网络与CR。 长期的研究目标是将所提出的算法扩展到多用户的情况下，并进一步实时资源分配算法。两者都将在实践中得到有效利用。它们将推动当前最先进技术的性能边界。所提出的研究建立在注水算法的成功基础上，更广泛地说，是优化理论在通信和信号处理领域的应用。本研究将推动相关学术研究，推动这一技术创新更上一层楼。这些研究项目的成功完成将有助于多用户信息理论领域基础知识的全面增长，并推动下一代无线通信系统的工程前沿。