شبکه های دسترسی رادیویی ابر ناهمگن
Abstract
1. Introduction
2. State of the art
3. System model
4. Problem formulation and reference schemes
5. Proposed adaptive beamforming and user clustering (ABUC) algorithm
6. Cost analysis of the proposed ABUC algorithm
7. Optimizing ABUC’s feedback parameters using Q-learning
8. Simulation results
9. Conclusion
Conflicts of Interest
Acknowledgements
Appendix A. Supplementary materials
Research Data
References
Abstract
Heterogeneous Cloud Radio Access Network (H-CRAN) is a promising network architecture for the future 5G mobile communication system to address the increasing demand for mobile data traffic. In this work, we consider the design of efficient joint beamforming and user clustering (user-to-Remote Radio Head (RRH) association) in the downlink of a H-CRAN where users have different mobility profiles. Given the rapidly time-varying nature of such wireless environment, it becomes very challenging to enable optimized beamforming and user clustering without incurring large Channel State Information (CSI) and signaling overheads. The main objective of this work is to investigate and evaluate the trade-off between system throughput and the incurred costs in terms of complexity and signaling overhead, including the impact of different CSI feedback strategies given different user mobility profiles. We propose the Adaptive Beamforming and User Clustering (ABUC) algorithm which adapts its feedback parameters, namely the period of dynamic user clustering and the type of CSI feedback, in function of user mobility. Furthermore, we design a reinforcement-learning framework which enables the proposed ABUC algorithm to optimize its scheduling parameters on-the-fly, given each user mobility profile. Based on computer simulations, an analysis of the effect of mobility on system performance metrics is presented and conclusions are drawn regarding the algorithm’s adequate parameter tuning for different mobility scenarios.
Introduction
Next generation of mobile and wireless communications system (5G) will revolutionize the way people communicate and extend the boundaries of the wireless industry. 5G will move beyond networks that are purpose-built for mobile broadband alone, toward systems that connect far more different types of devices at different speeds. The Internet of Things (IoT) is one of the primary contributors to global mobile traffic growth and this progression will lead to a huge mobile and wireless traffic volume predicted to increase a thousand-fold over the next decade [2]. Besides sustaining the tremendous growth of the traffic load, 5G system will be designed to fulfill diverse application requirements: far more stringent latency and reliability levels are expected to be necessary to support applications related to healthcare, security, logistics, automotive applications, or mission-critical control; Network scalability and flexibility are required to support a large number of devices with very low complexity and to enable long battery lifetimes [3]. 5G system is envisioned to meet such challenges thanks to the combination of several breakthroughs and technological advances such as ultra-dense small-cell deployments, intelligent multi-antenna, full duplex radios, millimeter wave transmissions, and cloud computing abilities. Particularly, the Cloud Radio Access Network (CRAN) is a network architecture based on cloud computing and centralized processing. It has been shown to provide high spectral and energy efficiencies while reducing both capital and operating expenditures [4].