19 research outputs found
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
Full-Duplex Cloud Radio Access Network: Stochastic Design and Analysis
Full-duplex (FD) has emerged as a disruptive communications paradigm for
enhancing the achievable spectral efficiency (SE), thanks to the recent major
breakthroughs in self-interference (SI) mitigation. The FD versus half-duplex
(HD) SE gain, in cellular networks, is however largely limited by the
mutual-interference (MI) between the downlink (DL) and the uplink (UL). A
potential remedy for tackling the MI bottleneck is through cooperative
communications. This paper provides a stochastic design and analysis of FD
enabled cloud radio access network (C-RAN) under the Poisson point process
(PPP)-based abstraction model of multi-antenna radio units (RUs) and user
equipments (UEs). We consider different disjoint and user-centric approaches
towards the formation of finite clusters in the C-RAN. Contrary to most
existing studies, we explicitly take into consideration non-isotropic fading
channel conditions and finite-capacity fronthaul links. Accordingly,
upper-bound expressions for the C-RAN DL and UL SEs, involving the statistics
of all intended and interfering signals, are derived. The performance of the FD
C-RAN is investigated through the proposed theoretical framework and
Monte-Carlo (MC) simulations. The results indicate that significant FD versus
HD C-RAN SE gains can be achieved, particularly in the presence of
sufficient-capacity fronthaul links and advanced interference cancellation
capabilities
Cross Layer Resource Allocation in H-CRAN with Spectrum and Energy Cooperation
5G and beyond wireless networks are the upcoming evolution for the current
cellular networks to provide the essential requirement of future demands such
as high data rate, low energy consumption, and low latency to provide seamless
communication for the emerging applications. Heterogeneous cloud radio access
network (H-CRAN) is envisioned as a new trend of 5G that uses the advantages of
heterogeneous and cloud radio access networks to enhance both the spectral and
energy efficiency. In this paper, building on the notion of effective capacity
(EC), we propose a framework in non-orthogonal multiple access (NOMA)-based
H-CRAN to meet these demands simultaneously. Our proposed approach is to
maximize the effective energy efficiency (EEE) while considering spectrum and
power cooperation between macro base station (MBS) and radio remote heads
(RRHs). To solve the formulated problem and to make it more tractable, we
transform the original problem into an equivalent subtractive form via
Dinkelbach algorithm. Afterwards, the combinational framework of distributed
stable matching and successive convex algorithm (SCA) is then adopted to obtain
the solution of the equivalent problem. Hereby, we propose an efficient
resource allocation scheme to maximize energy efficiency while maintaining the
delay quality of service (QoS) requirements for the all users. The simulation
results show that the proposed algorithm can provide a non-trivial trade-off
between delay and energy efficiency in NOMA H-CRAN systems in terms of EC and
EEE and the spectrum and power cooperation improves EEE of the proposed
network. Moreover, our proposed solution complexity is much lower than the
optimal solution and it suffers a very limited gap compared to the optimal
method