Green OFDMA resource allocation in cache-enabled CRAN

Abstract

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 limitedcapacity 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.Index Terms-Caching, cloud radio access network (CRAN), orthogonal frequency division multiple access (OFDMA), resource allocation.

Resource Allocation in Cache-Enabled CRAN with Non-Orthogonal Multiple Access

“…We consider a CRAN scenario where the number of RRHs is 5, and the number of SCs is the same as that of the multicast groups. Following the topology model in [7], we assume that one RRH is located in the center of a square region with the side length of 100 m, while the others are located on the vertices. The users are uniformly and independently distributed within a larger square region with the side length of 200 m, excluding an inner circle of 5 m around each RRH.…”

“…To address this paradigm shift, multicasting and caching are developed as two main techniques to exploit the content diversity. In previous work [6], [7], the content-centric transmission design based on CRAN architectures for efficient content delivery has been investigated. In [6], the joint design of content-centric BS clustering and multicast beamforming in the cache-enabled CRAN was investigated, with the aim of minimizing the total network cost.…”

“…In [6], the joint design of content-centric BS clustering and multicast beamforming in the cache-enabled CRAN was investigated, with the aim of minimizing the total network cost. In [7], the energy-efficient transmission design in an orthogonal frequency-division multiple access (OFDMA)-based CRAN with cache-enabled RRHs was studied. Yet, we note that [6], [7] focused only on the OMA schemes in CRAN, where orthogonality was achieved in the spatial or frequency domain.…”

“…In [7], the energy-efficient transmission design in an orthogonal frequency-division multiple access (OFDMA)-based CRAN with cache-enabled RRHs was studied. Yet, we note that [6], [7] focused only on the OMA schemes in CRAN, where orthogonality was achieved in the spatial or frequency domain. However, the spectrum efficiency can be further improved by incorporating NOMA into CRAN.…”

Fronthaul-Limited Uplink OFDMA in Ultra-Dense CRAN With Hybrid Decoding

“…CRAN significantly improves both the spectral efficiency and energy efficiency compared to conventional cellular networks, due to the centralized resource allocation and joint signal processing over the RRHs at the CP [3]- [17]. Combining a dense network with centralized joint processing as in the CRAN leads to a powerful new network architecture termed ultra-dense CRAN (UD-CRAN) [18], [19], in which the number of RRHs can even exceed the number of users being served in a given area to support wireless connectivity of ultra-high throughput.…”