A game theoretic approach for optimizing density of remote radio heads in user centric cloud-based radio access network

In this paper, we develop a game theoretic formulation for empowering cloud enabled HetNets with adaptive Self Organizing Network (SON) capabilities. SON capabilities for intelligent and efficient radio resource management is a fundamental design pillar for the emerging 5G cellular networks. The C-RAN system model investigated in this paper consists of ultra-dense remote radio heads (RRHs) overlaid by central baseband units that can be collocated with much less densely deployed overlaying macro base-stations (BSs). It has been recently demonstrated that under a user centric scheduling mechanism, C-RAN inherently manifests the trade-off between Energy Efficiency (EE) and Spectral Efficiency (SE) in terms of RRH density. The key objective of the game theoretic framework developed in this paper is to dynamically optimize the trade-off between the EE and the SE of the C- RAN. More specifically, for an ultra-dense C- RAN based HetNet, the density of active RRHs should be carefully dimensioned to maximize the SE. However, the density of RRHs which maximizes the SE may not necessarily be optimal in terms of the EE. In order to strike a balance between these two performance determinants, we develop a game theoretic formulation by employing a Nash bargaining framework. The two metrics of interest, SE and EE, are modeled as virtual players in a bargaining problem and the Nash bargaining solution for RRH density is determined. In the light of the optimization outcome we evaluate corresponding key performance indicators through numerical results. These results offer insights for a C-RAN designer on how to optimally design a SON mechanism to achieve a desired trade-off level between the SE and the EE in a dynamic fashion

Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges

As a promising paradigm for fifth generation (5G) wireless communication systems, cloud radio access networks (C-RANs) have been shown to reduce both capital and operating expenditures, as well as to provide high spectral efficiency (SE) and energy efficiency (EE). The fronthaul in such networks, defined as the transmission link between a baseband unit (BBU) and a remote radio head (RRH), requires high capacity, but is often constrained. This article comprehensively surveys recent advances in fronthaul-constrained C-RANs, including system architectures and key techniques. In particular, key techniques for alleviating the impact of constrained fronthaul on SE/EE and quality of service for users, including compression and quantization, large-scale coordinated processing and clustering, and resource allocation optimization, are discussed. Open issues in terms of software-defined networking, network function virtualization, and partial centralization are also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin note: text overlap with arXiv:1407.3855 by other author

Green OFDMA Resource Allocation in Cache-Enabled CRAN

Cloud radio access network (CRAN), in which remote radio heads (RRHs) are deployed to serve users in a target area, and connected to a central processor (CP) via limited-capacity links termed the fronthaul, is a promising candidate for the next-generation wireless communication systems. Due to the content-centric nature of future wireless communications, it is desirable to cache popular contents beforehand at the RRHs, to reduce the burden on the fronthaul and achieve energy saving through cooperative transmission. This motivates our study in this paper on the energy efficient transmission in an orthogonal frequency division multiple access (OFDMA)-based CRAN with multiple RRHs and users, where the RRHs can prefetch popular contents. We consider a joint optimization of the user-SC assignment, RRH selection and transmit power allocation over all the SCs to minimize the total transmit power of the RRHs, subject to the RRHs' individual fronthaul capacity constraints and the users' minimum rate constraints, while taking into account the caching status at the RRHs. Although the problem is non-convex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. We compare the minimum transmit power required by the proposed algorithm with different caching strategies against the case without caching by simulations, which show the significant energy saving with caching.Comment: Presented in IEEE Online Conference on Green Communications (Online GreenComm), Nov. 2016 (Invited Paper