160 research outputs found
Deep Reinforcement Learning for Real-Time Optimization in NB-IoT Networks
NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based
technology that offers a range of flexible configurations for massive IoT radio
access from groups of devices with heterogeneous requirements. A configuration
specifies the amount of radio resource allocated to each group of devices for
random access and for data transmission. Assuming no knowledge of the traffic
statistics, there exists an important challenge in "how to determine the
configuration that maximizes the long-term average number of served IoT devices
at each Transmission Time Interval (TTI) in an online fashion". Given the
complexity of searching for optimal configuration, we first develop real-time
configuration selection based on the tabular Q-learning (tabular-Q), the Linear
Approximation based Q-learning (LA-Q), and the Deep Neural Network based
Q-learning (DQN) in the single-parameter single-group scenario. Our results
show that the proposed reinforcement learning based approaches considerably
outperform the conventional heuristic approaches based on load estimation
(LE-URC) in terms of the number of served IoT devices. This result also
indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve
almost the same performance with much less training time. We further advance
LA-Q and DQN via Actions Aggregation (AA-LA-Q and AA-DQN) and via Cooperative
Multi-Agent learning (CMA-DQN) for the multi-parameter multi-group scenario,
thereby solve the problem that Q-learning agents do not converge in
high-dimensional configurations. In this scenario, the superiority of the
proposed Q-learning approaches over the conventional LE-URC approach
significantly improves with the increase of configuration dimensions, and the
CMA-DQN approach outperforms the other approaches in both throughput and
training efficiency
Active Versus Passive: Receiver Model Transforms for Diffusive Molecular Communication
This paper presents an analytical comparison of active and passive receiver
models in diffusive molecular communication. In the active model, molecules are
absorbed when they collide with the receiver surface. In the passive model, the
receiver is a virtual boundary that does not affect molecule behavior. Two
approaches are presented to derive transforms between the receiver signals. As
an example, two models for an unbounded diffusion-only molecular communication
system with a spherical receiver are unified. As time increases in the
three-dimensional system, the transform functions have constant scaling
factors, such that the receiver models are effectively equivalent. Methods are
presented to enable the transformation of stochastic simulations, which are
used to verify the transforms and demonstrate that transforming the simulation
of a passive receiver can be more efficient and more accurate than the direct
simulation of an absorbing receiver.Comment: 6 pages, 3 figures, 3 tables. Will be presented at IEEE Globecom 201
Cooperative Deep Reinforcement Learning for Multiple-Group NB-IoT Networks Optimization
NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based
technology that offers a range of flexible configurations for massive IoT radio
access from groups of devices with heterogeneous requirements. A configuration
specifies the amount of radio resources allocated to each group of devices for
random access and for data transmission. Assuming no knowledge of the traffic
statistics, the problem is to determine, in an online fashion at each
Transmission Time Interval (TTI), the configurations that maximizes the
long-term average number of IoT devices that are able to both access and
deliver data. Given the complexity of optimal algorithms, a Cooperative
Multi-Agent Deep Neural Network based Q-learning (CMA-DQN) approach is
developed, whereby each DQN agent independently control a configuration
variable for each group. The DQN agents are cooperatively trained in the same
environment based on feedback regarding transmission outcomes. CMA-DQN is seen
to considerably outperform conventional heuristic approaches based on load
estimation.Comment: Submitted for conference publicatio
Modeling and Simulation of Molecular Communication Systems with a Reversible Adsorption Receiver
In this paper, we present an analytical model for the diffusive molecular
communication (MC) system with a reversible adsorption receiver in a fluid
environment. The widely used concentration shift keying (CSK) is considered for
modulation. The time-varying spatial distribution of the information molecules
under the reversible adsorption and desorption reaction at the surface of a
receiver is analytically characterized. Based on the spatial distribution, we
derive the net number of newly-adsorbed information molecules expected in any
time duration. We further derive the number of newly-adsorbed molecules
expected at the steady state to demonstrate the equilibrium concentration.
Given the number of newly-adsorbed information molecules, the bit error
probability of the proposed MC system is analytically approximated.
Importantly, we present a simulation framework for the proposed model that
accounts for the diffusion and reversible reaction. Simulation results show the
accuracy of our derived expressions, and demonstrate the positive effect of the
adsorption rate and the negative effect of the desorption rate on the error
probability of reversible adsorption receiver with last transmit bit-1.
Moreover, our analytical results simplify to the special cases of a full
adsorption receiver and a partial adsorption receiver, both of which do not
include desorption.Comment: 14 pages, 8 figures, 1 algorithm, submitte
Joint Spatial and Spectrum Cooperation in Wireless Network.
PhDThe sky-rocketing growth of multimedia infotainment applications and broadband-hungry
mobile devices exacerbate the stringent demand for ultra high data rate and more spectrum resources. Along with it, the unbalanced temporal and geographical variations
of spectrum usage further inspires those spectral-efficient networks, namely, cognitive
radio and heterogeneous cellular networks (HCNs). This thesis focuses on the system
design and performance enhancement of cognitive radio (CR) and HCNs. Three different
aspects of performance improvement are considered, including link reliability of cognitive
radio networks (CNs), security enhancement of CNs, and energy efficiency improvement
of CNs and HCNs.
First, generalized selection combining (GSC) is proposed as an effective receiver design
for interference reduction and reliability improvement of CNs with outdated CSI. A uni-
ed way for deriving the distribution of received signal-to-noise ratio (SNR) is developed
in underlay spectrum sharing networks subject to interference from the primary trans-
mitter (PU-Tx) to the secondary receiver (SU-Rx), maximum transmit power constraint
at the secondary transmitter (SU-Tx), and peak interference power constraint at the
PU receiver (PU-Rx), is developed. Second, transmit antenna selection with receive
generalized selection combining (TAS/GSC) in multi-antenna relay-aided communica-
tion is introduced in CNs under Rayleigh fading and Nakagami-m fading. Based on
newly derived complex statistical properties of channel power gain of TAS/GSC, exact
ergodic capacity and high SNR ergodic capacity are derived over Nakagami-m fading.
Third, beamforming and arti cial noise generation (BF&AN) is introduced as a robust
scheme to enhance the secure transmission of large-scale spectrum sharing networks
with multiple randomly located eavesdroppers (Eves) modeled as homogeneous Poisson
Point Process (PPP). Stochastic geometry is applied to model and analyze the impact of
i
BF&AN on this complex network. Optimal power allocation factor for BF&AN which
maximizes the average secrecy rate is further studied under the outage probability con-
straint of primary network. Fourth, a new wireless energy harvesting protocol is proposed
for underlay cognitive relay networks with the energy-constrained SU-Txs. Exact and
asymptotic outage probability, delay-sensitive throughput, and delay-tolerant through-
put are derived to explore the tradeoff between the energy harvested from the PU-Txs
and the interference caused by the PU-Txs. Fifth, a harvest-then-transmit protocol is
proposed in K-tier HCNs with randomly located multiple-antenna base stations (BSs)
and single antenna mobile terminals (MTs) modeled as homogeneous PPP. The average
received power at MT, the uplink (UL) outage probability, and the UL average ergodic
rate are derived to demonstrate the intrinsic relationship between the energy harvested
from BSs in the downlink (DL) and the MT performance in the UL. Throughout the
thesis, it is shown that link reliability, secrecy performance, and energy efficiency of
CNs and HCNs can be signi cantly leveraged by taking advantage of multiple antennas,
relays, and wireless energy harvesting
CSK Realization for MC via Spatially Distributed Multicellular Consortia
The design and engineering of molecular communication (MC) components capable
of processing chemical concentration signals is the key to unleashing the
potential of MC for interdisciplinary applications. By controlling the
signaling pathway and molecule exchange between cell devices, synthetic biology
provides the MC community with tools and techniques to achieve various signal
processing functions. In this paper, we propose a design framework to realize
any order concentration shift keying (CSK) systems based on simple and reusable
single-input single-output cells. The design framework also exploits the
distributed multicellular consortia with spatial segregation, which has
advantages in system scalability, low genetic manipulation, and signal
orthogonality. We also create a small library of reusable engineered cells and
apply them to implement binary CSK (BCSK) and quadruple CSK (QCSK) systems to
demonstrate the feasibility of our proposed design framework. Importantly, we
establish a mathematical framework to theoretically characterize our proposed
distributed multicellular systems. Specially, we divide a system into
fundamental building blocks, from which we derive the impulse response of each
block and the cascade of the impulse responses leads to the end-to-end response
of the system. Simulation results obtained from the agent-based simulator BSim
not only validate our CSK design framework but also demonstrate the accuracy of
the proposed mathematical analysis.Comment: 30 pages, 13 figure
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