2,931 research outputs found
Distributed drone base station positioning for emergency cellular networks using reinforcement learning
Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network
Content Placement in Cache-Enabled Sub-6 GHz and Millimeter-Wave Multi-antenna Dense Small Cell Networks
This paper studies the performance of cache-enabled dense small cell networks
consisting of multi-antenna sub-6 GHz and millimeter-wave base stations.
Different from the existing works which only consider a single antenna at each
base station, the optimal content placement is unknown when the base stations
have multiple antennas. We first derive the successful content delivery
probability by accounting for the key channel features at sub-6 GHz and mmWave
frequencies. The maximization of the successful content delivery probability is
a challenging problem. To tackle it, we first propose a constrained
cross-entropy algorithm which achieves the near-optimal solution with moderate
complexity. We then develop another simple yet effective heuristic
probabilistic content placement scheme, termed two-stair algorithm, which
strikes a balance between caching the most popular contents and achieving
content diversity. Numerical results demonstrate the superior performance of
the constrained cross-entropy method and that the two-stair algorithm yields
significantly better performance than only caching the most popular contents.
The comparisons between the sub-6 GHz and mmWave systems reveal an interesting
tradeoff between caching capacity and density for the mmWave system to achieve
similar performance as the sub-6 GHz system.Comment: 14 pages; Accepted to appear in IEEE Transactions on Wireless
Communication
Centralized and Distributed Sparsification for Low-Complexity Message Passing Algorithm in C-RAN Architectures
Cloud radio access network (C-RAN) is a promising technology for
fifth-generation (5G) cellular systems. However the burden imposed by the huge
amount of data to be collected (in the uplink) from the radio remote heads
(RRHs) and processed at the base band unit (BBU) poses serious challenges. In
order to reduce the computation effort of minimum mean square error (MMSE)
receiver at the BBU the Gaussian message passing (MP) together with a suitable
sparsification of the channel matrix can be used. In this paper we propose two
sets of solutions, either centralized or distributed ones. In the centralized
solutions, we propose different approaches to sparsify the channel matrix, in
order to reduce the complexity of MP. However these approaches still require
that all signals reaching the RRH are conveyed to the BBU, therefore the
communication requirements among the backbone network devices are unaltered. In
the decentralized solutions instead we aim at reducing both the complexity of
MP at the BBU and the requirements on the RRHs-BBU communication links by
pre-processing the signals at the RRH and convey a reduced set of signals to
the BBU.Comment: Accepted for pubblication in IEEE VTC 201
Dynamic Uplink/Downlink Resource Management in Flexible Duplex-Enabled Wireless Networks
Flexible duplex is proposed to adapt to the channel and traffic asymmetry for
future wireless networks. In this paper, we propose two novel algorithms within
the flexible duplex framework for joint uplink and downlink resource allocation
in multi-cell scenario, named SAFP and RMDI, based on the awareness of
interference coupling among wireless links. Numerical results show significant
performance gain over the baseline system with fixed uplink/downlink resource
configuration, and over the dynamic TDD scheme that independently adapts the
configuration to time-varying traffic volume in each cell. The proposed
algorithms achieve two-fold increase when compared with the baseline scheme,
measured by the worst-case quality of service satisfaction level, under a low
level of traffic asymmetry. The gain is more significant when the traffic is
highly asymmetric, as it achieves three-fold increase.Comment: 7 pages, 7 figures, ICC 2017 Worksho
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