64,187 research outputs found
Buffer Management and Hybrid Probability Choice Routing for Packet Delivery in Opportunistic Networks
Due to the features of long connection delays, frequent network partitions, and topology unsteadiness, the design of opportunistic networks faces the challenge of how to effectively deliver data based only on occasional encountering of nodes, where the conventional routing schemes do not work properly. This paper proposes a hybrid probability choice routing protocol with buffer management for opportunistic networks. A delivery probability function is set up based on continuous encounter duration time, which is used for selecting a better node to relay packets. By combining the buffer management utility and the delivery probability, a total utility is used to decide whether the packet should be kept in the buffer or be directly transmitted to the encountering node. Simulation results show that the proposed routing outperforms the existing one in terms of the delivery rate and the average delay
Decentralized Delay Optimal Control for Interference Networks with Limited Renewable Energy Storage
In this paper, we consider delay minimization for interference networks with
renewable energy source, where the transmission power of a node comes from both
the conventional utility power (AC power) and the renewable energy source. We
assume the transmission power of each node is a function of the local channel
state, local data queue state and local energy queue state only. In turn, we
consider two delay optimization formulations, namely the decentralized
partially observable Markov decision process (DEC-POMDP) and Non-cooperative
partially observable stochastic game (POSG). In DEC-POMDP formulation, we
derive a decentralized online learning algorithm to determine the control
actions and Lagrangian multipliers (LMs) simultaneously, based on the policy
gradient approach. Under some mild technical conditions, the proposed
decentralized policy gradient algorithm converges almost surely to a local
optimal solution. On the other hand, in the non-cooperative POSG formulation,
the transmitter nodes are non-cooperative. We extend the decentralized policy
gradient solution and establish the technical proof for almost-sure convergence
of the learning algorithms. In both cases, the solutions are very robust to
model variations. Finally, the delay performance of the proposed solutions are
compared with conventional baseline schemes for interference networks and it is
illustrated that substantial delay performance gain and energy savings can be
achieved
GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing
Network slicing is a key technology in 5G communications system. Its purpose
is to dynamically and efficiently allocate resources for diversified services
with distinct requirements over a common underlying physical infrastructure.
Therein, demand-aware resource allocation is of significant importance to
network slicing. In this paper, we consider a scenario that contains several
slices in a radio access network with base stations that share the same
physical resources (e.g., bandwidth or slots). We leverage deep reinforcement
learning (DRL) to solve this problem by considering the varying service demands
as the environment state and the allocated resources as the environment action.
In order to reduce the effects of the annoying randomness and noise embedded in
the received service level agreement (SLA) satisfaction ratio (SSR) and
spectrum efficiency (SE), we primarily propose generative adversarial
network-powered deep distributional Q network (GAN-DDQN) to learn the
action-value distribution driven by minimizing the discrepancy between the
estimated action-value distribution and the target action-value distribution.
We put forward a reward-clipping mechanism to stabilize GAN-DDQN training
against the effects of widely-spanning utility values. Moreover, we further
develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to
learn the action-value distribution by estimating the state-value distribution
and the action advantage function. Finally, we verify the performance of the
proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive
simulations
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