73 research outputs found
Energy-Efficient Cooperative Cognitive Relaying Schemes for Cognitive Radio Networks
We investigate a cognitive radio network in which a primary user (PU) may
cooperate with a cognitive radio user (i.e., a secondary user (SU)) for
transmissions of its data packets. The PU is assumed to be a buffered node
operating in a time-slotted fashion where the time is partitioned into
equal-length slots. We develop two schemes which involve cooperation between
primary and secondary users. To satisfy certain quality of service (QoS)
requirements, users share time slot duration and channel frequency bandwidth.
Moreover, the SU may leverage the primary feedback message to further increase
both its data rate and satisfy the PU QoS requirements. The proposed
cooperative schemes are designed such that the SU data rate is maximized under
the constraint that the PU average queueing delay is maintained less than the
average queueing delay in case of non-cooperative PU. In addition, the proposed
schemes guarantee the stability of the PU queue and maintain the average energy
emitted by the SU below a certain value. The proposed schemes also provide more
robust and potentially continuous service for SUs compared to the conventional
practice in cognitive networks where SUs transmit in the spectrum holes and
silence sessions of the PUs. We include primary source burstiness, sensing
errors, and feedback decoding errors to the analysis of our proposed
cooperative schemes. The optimization problems are solved offline and require a
simple 2-dimensional grid-based search over the optimization variables.
Numerical results show the beneficial gains of the cooperative schemes in terms
of SU data rate and PU throughput, average PU queueing delay, and average PU
energy savings
Optimal Energy Allocation For Delay-Constrained Traffic Over Fading Multiple Access Channels
In this paper, we consider a multiple-access fading channel where users
transmit to a single base station (BS) within a limited number of time slots.
We assume that each user has a fixed amount of energy available to be consumed
over the transmission window. We derive the optimal energy allocation policy
for each user that maximizes the total system throughput under two different
assumptions on the channel state information. First, we consider the offline
allocation problem where the channel states are known a priori before
transmission. We solve a convex optimization problem to maximize the
sum-throughput under energy and delay constraints. Next, we consider the online
allocation problem, where the channels are causally known to the BS and obtain
the optimal energy allocation via dynamic programming when the number of users
is small. We also develop a suboptimal resource allocation algorithm whose
performance is close to the optimal one. Numerical results are presented
showing the superiority of the proposed algorithms over baseline algorithms in
various scenarios.Comment: IEEE Global Communications Conference: Wireless Communications
(Globecom2016 WC
3D Placement of an Unmanned Aerial Vehicle Base Station (UAV-BS) for Energy-Efficient Maximal Coverage
Unmanned Aerial Vehicle mounted base stations (UAV-BSs) can provide wireless
services in a variety of scenarios. In this letter, we propose an optimal
placement algorithm for UAV-BSs that maximizes the number of covered users
using the minimum transmit power. We decouple the UAV-BS deployment problem in
the vertical and horizontal dimensions without any loss of optimality.
Furthermore, we model the UAV-BS deployment in the horizontal dimension as a
circle placement problem and a smallest enclosing circle problem. Simulations
are conducted to evaluate the performance of the proposed method for different
spatial distributions of the users
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