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

Edge Cache-assisted Secure Low-Latency Millimeter Wave Transmission

In this paper, we consider an edge cache-assisted millimeter wave cloud radio access network (C-RAN). Each remote radio head (RRH) in the C-RAN has a local cache, which can pre-fetch and store the files requested by the actuators. Multiple RRHs form a cluster to cooperatively serve the actuators, which acquire their required files either from the local caches or from the central processor via multicast fronthaul links. For such a scenario, we formulate a beamforming design problem to minimize the secure transmission delay under transmit power constraint of each RRH. Due to the difficulty of directly solving the formulated problem, we divide it into two independent ones: {\textit{i)}} minimizing the fronthaul transmission delay by jointly optimizing the transmit and receive beamforming; {\textit{ii)}} minimizing the maximum access transmission delay by jointly designing cooperative beamforming among RRHs. An alternatively iterative algorithm is proposed to solve the first optimization problem. For the latter, we first design the analog beamforming based on the channel state information of the actuators. Then, with the aid of successive convex approximation and $S$-procedure techniques, a semidefinite program (SDP) is formulated, and an iterative algorithm is proposed through SDP relaxation. Finally, simulation results are provided to verify the performance of the proposed schemes.Comment: IEEE_IoT, Accep

Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks with Mobile Users

In this paper, the problem of proactive caching is studied for cloud radio access networks (CRANs). In the studied model, the baseband units (BBUs) can predict the content request distribution and mobility pattern of each user, determine which content to cache at remote radio heads and BBUs. This problem is formulated as an optimization problem which jointly incorporates backhaul and fronthaul loads and content caching. To solve this problem, an algorithm that combines the machine learning framework of echo state networks with sublinear algorithms is proposed. Using echo state networks (ESNs), the BBUs can predict each user's content request distribution and mobility pattern while having only limited information on the network's and user's state. In order to predict each user's periodic mobility pattern with minimal complexity, the memory capacity of the corresponding ESN is derived for a periodic input. This memory capacity is shown to be able to record the maximum amount of user information for the proposed ESN model. Then, a sublinear algorithm is proposed to determine which content to cache while using limited content request distribution samples. Simulation results using real data from Youku and the Beijing University of Posts and Telecommunications show that the proposed approach yields significant gains, in terms of sum effective capacity, that reach up to 27.8% and 30.7%, respectively, compared to random caching with clustering and random caching without clustering algorithm.Comment: Accepted in the IEEE Transactions on Wireless Communication

Joint Beamforming and Admission Control for Cache-Enabled Cloud-RAN with Limited Fronthaul Capacity

Caching is a promising solution for the cloud radio access network (Cloud-RAN) to mitigate the traffic load problem in the fronthaul links. Multiuser downlink beamforming plays an important role for efficient utilization of spectrum and transmission power while satisfying the user’s quality of service (QoS) requirements. When the number of users exceeds the serving capacity of the network, certain users will have to be dropped or re-scheduled. This is normally achieved by appropriate admission control mechanisms. Introducing local storage or cache at the remote radio heads (RRHs) where some popular contents are cached, we propose beamforming and admission control technique for cache-enabled Cloud-RAN in the downlink. This minimizes the total network cost including power and fronthaul cost while admitting as many users as possible. We formulate this multi-objective optimization problem as a single objective optimization problem. The original problem which is mixed-integer non-linear program (MINLP) is first co verted to the mixed-integer second order cone programming form (MISOCP). Branch and Bound (BnB) algorithm is then used to determine the optimal and suboptimal solutions. Simulation study has been conducted to assess the performance of both methods

Spectral and Energy Efficiency Maximization for Content-Centric C-RANs with Edge Caching

This paper aims to maximize the spectral and energy efficiencies of a content-centric cloud radio access network (C-RAN), where users requesting the same contents are grouped together. Data are transferred from a central baseband unit to multiple remote radio heads (RRHs) equipped with local caches. The RRHs then send the received data to each group's user. Both multicast and unicast schemes are considered for data transmission. We formulate mixed-integer nonlinear problems in which user association, RRH activation, data rate allocation, and signal precoding are jointly designed. These challenging problems are subject to minimum data rate requirements, limited fronthaul capacity, and maximum RRH transmit power. Employing successive convex quadratic programming, we propose iterative algorithms with guaranteed convergence to Fritz John solutions. Numerical results confirm that the proposed joint designs markedly improve the spectral and energy efficiencies of the considered content-centric C-RAN compared to benchmark schemes. Importantly, they show that unicasting outperforms multicasting in terms of spectral efficiency in both cache and cache-less scenarios. In terms of energy efficiency, multicasting is the best choice for the system without cache whereas unicasting is best for the system with cache. Finally, edge caching is shown to improve both spectral and energy efficiencies.This work is supported in part by an ECRHDR scholarship from The University of Newcastle, in part by the Australian Research Council Discovery Project grants DP170100939 and DP160101537

Centralized and partial decentralized design for the Fog Radio Access Network

Fog Radio Access Network (F-RAN) has been shown to be a promising network architecture for the 5G network. With F-RAN, certain amount of signal processing functionalities are pushed from the Base Station (BS) on the network edge to the BaseBand Units (BBU) pool located remotely in the cloud. Hence, partially centralized network operation and management can be achieved, which can greatly improve the energy and spectral efficiency of the network, in order to meet the requirements of 5G. In this work, the optimal design for both uplink and downlink of F-RAN are intensively investigated