Sophisticated Online Learning Scheme for Green Resource Allocation in 5G Heterogeneous Cloud Radio Access Networks

Abstract

5G is the upcoming evolution for the current cellular networks that aims at satisfying the future demand for data services. Heterogeneous cloud radio access networks (H-CRANs) are envisioned as a new trend of 5G that exploits the advantages of heterogeneous and cloud radio access networks to enhance spectral and energy efficiency. Remote radio heads (RRHs) are small cells utilized to provide high data rates for users with high quality of service (QoS) requirements, while high power macro base station (BS) is deployed for coverage maintenance and low QoS users service. Inter-tier interference between macro BSs and RRHs and energy efficiency are critical challenges that accompany resource allocation in H-CRANs. Therefore, we propose an efficient resource allocation scheme using online learning, which mitigates interference and maximizes energy efficiency while maintaining QoS requirements for all users. The resource allocation includes resource blocks (RBs) and power. The proposed scheme is implemented using two approaches: centralized, where the resource allocation is processed at a controller integrated with the baseband processing unit and decentralized, where macro BSs cooperate to achieve optimal resource allocation strategy. To foster the performance of such sophisticated scheme with a model free learning, we consider users' priority in RB allocation and compact state representation learning methodology to improve the speed of convergence and account for the curse of dimensionality during the learning process. The proposed scheme including both approaches is implemented using software defined radios testbed. The obtained results and simulation results confirm that the proposed resource allocation solution in H-CRANs increases the energy efficiency significantly and maintains users' QoS.

Energy Efficient Traffic Offloading in Multi-Tier Heterogeneous 5G Networks Using Intuitive Online Reinforcement Learning

“…Machine learning is a powerful tool that penetrated the communication and networking field recently [24] [25] [26] [27]. It is envisioned as potential solution for efficient traffic offloading in heterogeneous cellular networks.…”

Resource Allocation for Energy Efficiency and QoS Provisioning

“…In [24], the proportional-fair energy efficient RRA problem for uplink in a small cell scenario is studied and a low-complexity heuristic is proposed. In [25], a scheme using online learning to maximize the EE while maintaining QoS requirements in a heterogeneous C-RAN is proposed. The proposed scheme is implemented in centralized and decentralized scenarios.…”

“…The proposed scheme is implemented in centralized and decentralized scenarios. Although the works [23], [24], [25] directly model QoS constraints, neither multiservice scenarios nor discrete link adaptation are considered. In [26] the authors propose a Dinkelbach-based iterative resource allocation algorithm in a mult-icell OFDMA scenario, which finds a solution to the main problem by solving a sequence of subproblems.…”

Big Data Perspective and Challenges in Next Generation Networks

“…Table 4 briefly summarizes different advanced learning methods and their associated tasks. Recently, Ismail et al [86] proposed an online learning based framework for energy efficient resource allocation in next generation heterogamous CRANs. In this architecture, the spectrum is divided into two resource blocks (RBs), where each RB is assigned to a specific group of users according to their position and QoS requirements.…”

“…Active learning [67,69] Incomplete mobile data processing Representation learning [70,71] Multi dimensional feature data processing Transfer learning [72,73] Data processing of different source domains Deep learning [74,78,79] Complex data processing, automatically learn hierarchical representations Distributed and Parallel learning [82,83] Allocating the learning process among several workstations Online learning [84][85][86][87] On-line mobility predictions…”