415 research outputs found
Queue-Aware Dynamic Clustering and Power Allocation for Network MIMO Systems via Distributive Stochastic Learning
In this paper, we propose a two-timescale delay-optimal dynamic clustering
and power allocation design for downlink network MIMO systems. The dynamic
clustering control is adaptive to the global queue state information (GQSI)
only and computed at the base station controller (BSC) over a longer time
scale. On the other hand, the power allocations of all the BSs in one cluster
are adaptive to both intra-cluster channel state information (CCSI) and
intra-cluster queue state information (CQSI), and computed at the cluster
manager (CM) over a shorter time scale. We show that the two-timescale
delay-optimal control can be formulated as an infinite-horizon average cost
Constrained Partially Observed Markov Decision Process (CPOMDP). By exploiting
the special problem structure, we shall derive an equivalent Bellman equation
in terms of Pattern Selection Q-factor to solve the CPOMDP. To address the
distributive requirement and the issue of exponential memory requirement and
computational complexity, we approximate the Pattern Selection Q-factor by the
sum of Per-cluster Potential functions and propose a novel distributive online
learning algorithm to estimate the Per-cluster Potential functions (at each CM)
as well as the Lagrange multipliers (LM) (at each BS). We show that the
proposed distributive online learning algorithm converges almost surely (with
probability 1). By exploiting the birth-death structure of the queue dynamics,
we further decompose the Per-cluster Potential function into sum of Per-cluster
Per-user Potential functions and formulate the instantaneous power allocation
as a Per-stage QSI-aware Interference Game played among all the CMs. We also
propose a QSI-aware Simultaneous Iterative Water-filling Algorithm (QSIWFA) and
show that it can achieve the Nash Equilibrium (NE)
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
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
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