4,481 research outputs found
Optimal Cooperative Cognitive Relaying and Spectrum Access for an Energy Harvesting Cognitive Radio: Reinforcement Learning Approach
In this paper, we consider a cognitive setting under the context of
cooperative communications, where the cognitive radio (CR) user is assumed to
be a self-organized relay for the network. The CR user and the PU are assumed
to be energy harvesters. The CR user cooperatively relays some of the
undelivered packets of the primary user (PU). Specifically, the CR user stores
a fraction of the undelivered primary packets in a relaying queue (buffer). It
manages the flow of the undelivered primary packets to its relaying queue using
the appropriate actions over time slots. Moreover, it has the decision of
choosing the used queue for channel accessing at idle time slots (slots where
the PU's queue is empty). It is assumed that one data packet transmission
dissipates one energy packet. The optimal policy changes according to the
primary and CR users arrival rates to the data and energy queues as well as the
channels connectivity. The CR user saves energy for the PU by taking the
responsibility of relaying the undelivered primary packets. It optimally
organizes its own energy packets to maximize its payoff as time progresses
On the Stability of Random Multiple Access with Stochastic Energy Harvesting
In this paper, we consider the random access of nodes having energy
harvesting capability and a battery to store the harvested energy. Each node
attempts to transmit the head-of-line packet in the queue if its battery is
nonempty. The packet and energy arrivals into the queue and the battery are all
modeled as a discrete-time stochastic process. The main contribution of this
paper is the exact characterization of the stability region of the packet
queues given the energy harvesting rates when a pair of nodes are randomly
accessing a common channel having multipacket reception (MPR) capability. The
channel with MPR capability is a generalized form of the wireless channel
modeling which allows probabilistic receptions of the simultaneously
transmitted packets. The results obtained in this paper are fairly general as
the cases with unlimited energy for transmissions both with the collision
channel and the channel with MPR capability can be derived from ours as special
cases. Furthermore, we study the impact of the finiteness of the batteries on
the achievable stability region.Comment: The material in this paper was presented in part at the IEEE
International Symposium on Information Theory, Saint Petersburg, Russia, Aug.
201
Performance Modelling and Optimisation of Multi-hop Networks
A major challenge in the design of large-scale networks is to predict and optimise the
total time and energy consumption required to deliver a packet from a source node to a
destination node. Examples of such complex networks include wireless ad hoc and sensor
networks which need to deal with the effects of node mobility, routing inaccuracies, higher
packet loss rates, limited or time-varying effective bandwidth, energy constraints, and the
computational limitations of the nodes. They also include more reliable communication
environments, such as wired networks, that are susceptible to random failures, security
threats and malicious behaviours which compromise their quality of service (QoS) guarantees.
In such networks, packets traverse a number of hops that cannot be determined
in advance and encounter non-homogeneous network conditions that have been largely
ignored in the literature. This thesis examines analytical properties of packet travel in
large networks and investigates the implications of some packet coding techniques on both
QoS and resource utilisation.
Specifically, we use a mixed jump and diffusion model to represent packet traversal
through large networks. The model accounts for network non-homogeneity regarding
routing and the loss rate that a packet experiences as it passes successive segments of a
source to destination route. A mixed analytical-numerical method is developed to compute
the average packet travel time and the energy it consumes. The model is able to capture
the effects of increased loss rate in areas remote from the source and destination, variable
rate of advancement towards destination over the route, as well as of defending against
malicious packets within a certain distance from the destination. We then consider sending
multiple coded packets that follow independent paths to the destination node so as to
mitigate the effects of losses and routing inaccuracies. We study a homogeneous medium
and obtain the time-dependent properties of the packet’s travel process, allowing us to
compare the merits and limitations of coding, both in terms of delivery times and energy
efficiency. Finally, we propose models that can assist in the analysis and optimisation
of the performance of inter-flow network coding (NC). We analyse two queueing models
for a router that carries out NC, in addition to its standard packet routing function. The
approach is extended to the study of multiple hops, which leads to an optimisation problem
that characterises the optimal time that packets should be held back in a router, waiting
for coding opportunities to arise, so that the total packet end-to-end delay is minimised
Fast-Convergent Learning-aided Control in Energy Harvesting Networks
In this paper, we present a novel learning-aided energy management scheme
() for multihop energy harvesting networks. Different from prior
works on this problem, our algorithm explicitly incorporates information
learning into system control via a step called \emph{perturbed dual learning}.
does not require any statistical information of the system
dynamics for implementation, and efficiently resolves the challenging energy
outage problem. We show that achieves the near-optimal
utility-delay tradeoff with an
energy buffers (). More interestingly,
possesses a \emph{convergence time} of , which is much faster than the time of
pure queue-based techniques or the time of approaches
that rely purely on learning the system statistics. This fast convergence
property makes more adaptive and efficient in resource
allocation in dynamic environments. The design and analysis of
demonstrate how system control algorithms can be augmented by learning and what
the benefits are. The methodology and algorithm can also be applied to similar
problems, e.g., processing networks, where nodes require nonzero amount of
contents to support their actions
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