225 research outputs found
Measurement-Adaptive Cellular Random Access Protocols
This work considers a single-cell random access channel (RACH) in cellular
wireless networks. Communications over RACH take place when users try to
connect to a base station during a handover or when establishing a new
connection. Within the framework of Self-Organizing Networks (SONs), the system
should self- adapt to dynamically changing environments (channel fading,
mobility, etc.) without human intervention. For the performance improvement of
the RACH procedure, we aim here at maximizing throughput or alternatively
minimizing the user dropping rate. In the context of SON, we propose protocols
which exploit information from measurements and user reports in order to
estimate current values of the system unknowns and broadcast global
action-related values to all users. The protocols suggest an optimal pair of
user actions (transmission power and back-off probability) found by minimizing
the drift of a certain function. Numerical results illustrate considerable
benefits of the dropping rate, at a very low or even zero cost in power
expenditure and delay, as well as the fast adaptability of the protocols to
environment changes. Although the proposed protocol is designed to minimize
primarily the amount of discarded users per cell, our framework allows for
other variations (power or delay minimization) as well.Comment: 31 pages, 13 figures, 3 tables. Springer Wireless Networks 201
Energy Harvesting Wireless Communications: A Review of Recent Advances
This article summarizes recent contributions in the broad area of energy
harvesting wireless communications. In particular, we provide the current state
of the art for wireless networks composed of energy harvesting nodes, starting
from the information-theoretic performance limits to transmission scheduling
policies and resource allocation, medium access and networking issues. The
emerging related area of energy transfer for self-sustaining energy harvesting
wireless networks is considered in detail covering both energy cooperation
aspects and simultaneous energy and information transfer. Various potential
models with energy harvesting nodes at different network scales are reviewed as
well as models for energy consumption at the nodes.Comment: To appear in the IEEE Journal of Selected Areas in Communications
(Special Issue: Wireless Communications Powered by Energy Harvesting and
Wireless Energy Transfer
Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions
The ever-increasing number of resource-constrained Machine-Type Communication
(MTC) devices is leading to the critical challenge of fulfilling diverse
communication requirements in dynamic and ultra-dense wireless environments.
Among different application scenarios that the upcoming 5G and beyond cellular
networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the
unique technical challenge of supporting a huge number of MTC devices, which is
the main focus of this paper. The related challenges include QoS provisioning,
handling highly dynamic and sporadic MTC traffic, huge signalling overhead and
Radio Access Network (RAN) congestion. In this regard, this paper aims to
identify and analyze the involved technical issues, to review recent advances,
to highlight potential solutions and to propose new research directions. First,
starting with an overview of mMTC features and QoS provisioning issues, we
present the key enablers for mMTC in cellular networks. Along with the
highlights on the inefficiency of the legacy Random Access (RA) procedure in
the mMTC scenario, we then present the key features and channel access
mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT.
Subsequently, we present a framework for the performance analysis of
transmission scheduling with the QoS support along with the issues involved in
short data packet transmission. Next, we provide a detailed overview of the
existing and emerging solutions towards addressing RAN congestion problem, and
then identify potential advantages, challenges and use cases for the
applications of emerging Machine Learning (ML) techniques in ultra-dense
cellular networks. Out of several ML techniques, we focus on the application of
low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss
some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future
publication in IEEE Communications Surveys and Tutorial
Optimization of Efficiency and Energy Consumption in p-persistent CSMA-based Wireless LANs
Wireless technologies in the LAN environment are becoming increasingly important. The IEEE 802.11 is the most mature technology for Wireless Local Area Networks (WLANs). The limited bandwidth and the finite battery power of mobile computers represent one of the greatest limitations of current WLANs. In this paper we deeply investigate the efficiency and the energy consumption of MAC protocols that can be described with a p-persistent CSMA model. As already shown in the literature, the IEEE 802.11 protocol performance can be studied using a p-persistent CSMA model [Cal00]. For this class of protocols, in the paper we define an analytical framework to study the theoretical performance bounds from the throughput and the energy consumption standpoint. Specifically, we derive the p values (i.e., the average size of the contention window in the IEEE 802.11 protocol) that maximizes the throughput, , and minimizes the energy consumption, . By providing analytical closed formulas for the optimal values, we discuss the trade-off between efficiency and energy consumption. Specifically, we show that power saving and throughput maximization can be jointly achieved. Our analytical formulas indicate that the optimal values depend on the network configuration, i.e., number of active stations and length of the messages transmitted on the channel
Cross-layer distributed power control: A repeated games formulation to improve the sum energy-efficiency
The main objective of this work is to improve the energy-efficiency (EE) of a
multiple access channel (MAC) system, through power control, in a distributed
manner. In contrast with many existing works on energy-efficient power control,
which ignore the possible presence of a queue at the transmitter, we consider a
new generalized cross-layer EE metric. This approach is relevant when the
transmitters have a non-zero energy cost even when the radiated power is zero
and takes into account the presence of a finite packet buffer and packet
arrival at the transmitter. As the Nash equilibrium (NE) is an
energy-inefficient solution, the present work aims at overcoming this deficit
by improving the global energy-efficiency. Indeed, as the considered system has
multiple agencies each with their own interest, the performance metric
reflecting the individual interest of each decision maker is the global
energy-efficiency defined then as the sum over individual energy-efficiencies.
Repeated games (RG) are investigated through the study of two dynamic games
(finite RG and discounted RG), whose equilibrium is defined when introducing a
new operating point (OP), Pareto-dominating the NE and relying only on
individual channel state information (CSI). Accordingly, closed-form
expressions of the minimum number of stages of the game for finite RG (FRG) and
the maximum discount factor of the discounted RG (DRG) were established. The
cross-layer model in the RG formulation leads to achieving a shorter minimum
number of stages in the FRG even for higher number of users. In addition, the
social welfare (sum of utilities) in the DRG decreases slightly with the
cross-layer model when the number of users increases while it is reduced
considerably with the Goodman model. Finally, we show that in real systems with
random packet arrivals, the cross-layer power control algorithm outperforms the
Goodman algorithm.Comment: 36 pages, single column draft forma
Q-learning Channel Access Methods for Wireless Powered Internet of Things Networks
The Internet of Things (IoT) is becoming critical in our daily life. A key technology of interest in this thesis is Radio Frequency (RF) charging. The ability to charge devices wirelessly creates so called RF-energy harvesting IoT networks. In particular, there is a hybrid access point (HAP) that provides energy in an on-demand manner to RF-energy harvesting devices. These devices then collect data and transmit it to the HAP. In this respect, a key issue is ensuring devices have a high number of successful transmissions.
There are a number of issues to consider when scheduling the transmissions of devices in the said network. First, the channel gain to/from devices varies over time. This means the efficiency to deliver energy to devices and to transmit the same amount of data is different over time. Second, during channel access, devices are not aware of the energy level of other devices nor whether they will transmit data. Third, devices have non-causal knowledge of their energy arrivals and channel gain information. Consequently, they do not know whether they should delay their transmissions in hope of better channel conditions or less contention in future time slots or doing so would result in energy overflow
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