495 research outputs found
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Energy-efficient Compressive Data Gathering Utilizing Virtual Multi-input Multi-output
Data gathering is an attractive operation for obtaining information in wireless sensor networks (WSNs). But one of important challenges is to minimize energy consumption of networks. In this paper, an integration of distributed compressive sensing (CS) and virtual multi-input multi-output (vMIMO) in WSNs is proposed to significantly decrease the data gathering cost. The scheme first constructs a distributed data compression model based on low density parity check-like (LDPC-like) codes. Then a cluster-based dynamic virtual MIMO transmission protocol is proposed. The number of clusters, number of cooperative nodes and the constellation size are determined by a new established optimization model under the restrictions of compression model. Finally, simulation results show that the scheme can reduce the data gathering cost and prolong the sensor network’s lifetime in a reliable guarantee of sensory data recovery quality
Analysis and Ad-hoc Networking Solutions for Cooperative Relaying Systems
Users of mobile networks are increasingly demanding higher data rates from
their service providers. To cater to this demand, various signal processing
and networking algorithms have been proposed. Amongst them the multiple
input multiple output (MIMO) scheme of wireless communications is one of
the most promising options. However, due to certain physical restrictions,
e.g., size, it is not possible for many devices to have multiple antennas
on them. Also, most of the devices currently in use are single-antenna
devices. Such devices can make use of the MIMO scheme by employing
cooperative MIMO methods. This involves nearby nodes utilizing the antennas
of each other to form virtual antenna arrays (VAAs). Nodes with limited
communication ranges can further employ multi-hopping to be able to
communicate with far away nodes. However, an ad-hoc communications scheme
with cooperative MIMO multi-hopping can be challenging to implement because
of its de-centralized nature and lack of a centralized controling entity
such as a base-station. This thesis looks at methods to alleviate the
problems faced by such networks.In the first part of this thesis, we look,
analytically, at the relaying scheme under consideration and derive closed
form expressions for certain performance measures (signal to noise ratio
(SNR), symbol error rate (SER), bit error rate (BER), and capacity) for the
co-located and cooperative multiple antenna schemes in different relaying
configurations (amplify-and-forward and decode-and-forward) and different
antenna configurations (single input single output (SISO), single input
multiple output (SIMO) and MIMO). These expressions show the importance of
reducing the number of hops in multi-hop communications to achieve a better
performance. We can also see the impact of different antenna configurations
and different transmit powers on the number of hops through these
simplified expressions.We also look at the impact of synchronization errors
on the cooperative MIMO communications scheme and derive a lower bound of
the SINR and an expression for the BER in the high SNR regime. These
expressions can help the network designers to ensure that the quality of
service (QoS) is satisfied even in the worst-case scenarios. In the second
part of the thesis we present some algorithms developed by us to help the
set-up and functioning of cluster-based ad-hoc networks that employ
cooperative relaying. We present a clustering algorithm that takes into
account the battery status of nodes in order to ensure a longer network
life-time. We also present a routing mechanism that is tailored for use in
cooperative MIMO multi-hop relaying. The benefits of both schemes are shown
through simulations.A method to handle data in ad-hoc networks using
distributed hash tables (DHTs) is also presented. Moreover, we also present
a physical layer security mechanism for multi-hop relaying. We also analyze
the physical layer security mechanism for the cooperative MIMO scheme. This
analysis shows that the cooperative MIMO scheme is more beneficial than
co-located MIMO in terms of the information theoretic limits of the
physical layer security.Nutzer mobiler Netzwerke fordern zunehmend höhere Datenraten von ihren
Dienstleistern. Um diesem Bedarf gerecht zu werden, wurden verschiedene
Signalverarbeitungsalgorithmen entwickelt. Dabei ist das "Multiple input
multiple output" (MIMO)-Verfahren für die drahtlose Kommunikation eine der
vielversprechendsten Techniken. Jedoch ist aufgrund bestimmter
physikalischer Beschränkungen, wie zum Beispiel die Baugröße, die
Verwendung von mehreren Antennen für viele Endgeräte nicht möglich. Dennoch
können solche Ein-Antennen-Geräte durch den Einsatz kooperativer
MIMO-Verfahren von den Vorteilen des MIMO-Prinzips profitieren.
Dabei schließen sich naheliegende Knoten zusammen um ein sogenanntes
virtuelles Antennen-Array zu bilden. Weiterhin können Knoten mit
beschränktem Kommunikationsbereich durch mehrere Hops mit weiter
entfernten Knoten kommunizieren. Allerdings stellt der Aufbau eines solchen
Ad-hoc-Netzwerks mit kooperativen MIMO-Fähigkeiten aufgrund der dezentralen
Natur und das Fehlen einer zentral-steuernden Einheit, wie einer
Basisstation, eine große Herausforderung dar. Diese Arbeit befasst sich mit
den Problemstellungen dieser Netzwerke und bietet verschiedene
Lösungsansätze.Im ersten Teil dieser Arbeit werden analytisch in
sich geschlossene Ausdrücke für ein kooperatives
Relaying-System bezüglicher verschiedener Metriken, wie das
Signal-Rausch-Verhältnis, die Symbolfehlerrate, die Bitfehlerrate und die
Kapazität, hergeleitet. Dabei werden die "Amplify-and forward" und
"Decode-and-forward" Relaying-Protokolle, sowie unterschiedliche
Mehrantennen-Konfigurationen, wie "Single input single output" (SISO),
"Single input multiple output" (SIMO) und MIMO betrachtet. Diese Ausdrücke
zeigen die Bedeutung der Reduzierung der Hop-Anzahl in Mehr-Hop-Systemen,
um eine höhere Leistung zu erzielen. Zudem werden die Auswirkungen
verschiedener Antennen-Konfigurationen und Sendeleistungen auf die Anzahl
der Hops analysiert. Weiterhin wird der Einfluss von
Synchronisationsfehlern auf das kooperative MIMO-Verfahren herausgestellt
und daraus eine untere Grenze für das
Signal-zu-Interferenz-und-Rausch-Verhältnis, sowie ein Ausdruck für die
Bitfehlerrate bei hohem Signal-Rausch-Verhältnis entwickelt.
Diese Zusammenhänge sollen Netzwerk-Designern helfen die Qualität des
Services auch in den Worst-Case-Szenarien sicherzustellen.
Im zweiten Teil der Arbeit werden einige innovative
Algorithmen vorgestellt, die die Einrichtung und die Funktionsweise von
Cluster-basierten Ad-hoc-Netzwerken, die kooperative Relays verwenden,
erleichtern und verbessern. Darunter befinden sich ein
Clustering-Algorithmus, der den Batteriestatus der Knoten berücksichtigt,
um eine längere Lebensdauer des Netzwerks zu gewährleisten und ein
Routing-Mechanismus, der auf den Einsatz in kooperativen MIMO
Mehr-Hop-Systemen zugeschnitten ist. Die Vorteile beider Algorithmen werden
durch Simulationen veranschaulicht.
Eine Methode, die Daten in Ad-hoc-Netzwerken mit verteilten Hash-Tabellen
behandelt wird ebenfalls vorgestellt. Darüber hinaus wird auch
ein Sicherheitsmechanismus für die physikalische Schicht in
Multi-Hop-Systemen und kooperativen MIMO-Systemen präsentiert. Eine Analyse
zeigt, dass das kooperative MIMO-Verfahren deutliche Vorteile gegenüber dem
konventionellen MIMO-Verfahren hinsichtlich der informationstheoretischen
Grenzen der Sicherheit auf der physikalischen Schicht aufweist
Twin Delayed DDPG based Dynamic Power Allocation for Mobility in IoRT
The internet of robotic things (IoRT) is a modern as well as fast-evolving technology employed in abundant socio-economical aspects which connect user equipment (UE) for communication and data transfer among each other. For ensuring the quality of service (QoS) in IoRT applications, radio resources, for example, transmitting power allocation (PA), interference management, throughput maximization etc., should be efficiently employed and allocated among UE. Traditionally, resource allocation has been formulated using optimization problems, which are then solved using mathematical computer techniques. However, those optimization problems are generally nonconvex as well as nondeterministic polynomial-time hardness (NP-hard). In this paper, one of the most crucial challenges in radio resource management is the emitting power of an antenna called PA, considering that the interfering multiple access channel (IMAC) has been considered. In addition, UE has a natural movement behavior that directly impacts the channel condition between remote radio head (RRH) and UE. Additionally, we have considered two well-known UE mobility models i) random walk and ii) modified Gauss-Markov (GM). As a result, the simulation environment is more realistic and complex. A data-driven as well as model-free continuous action based deep reinforcement learning algorithm called twin delayed deep deterministic policy gradient (TD3) has been proposed that is the combination of policy gradient, actor-critics, as well as double deep Q-learning (DDQL). It optimizes the PA for i) stationary UE, ii) the UE movements according to random walk model, and ii) the UE movement based on the modified GM model. Simulation results show that the proposed TD3 method outperforms model-based techniques like weighted MMSE (WMMSE) and fractional programming (FP) as well as model-free algorithms, for example, deep Q network (DQN) and DDPG in terms of average sum-rate performance
INVESTIGATION ON ENERGY BASED DATA GATHERING APPROACH FOR WSN
Wireless Sensor Networks plays a vital role in all emerging areas of Wireless Platforms like Interne of Things (IoT), WiFi, WiMAX etc. Sensor nodes are communicated with or without the presence of administrator. Data gathering is a major issue in WSN which influences the throughput, energy and data delivery. In previous research, there was not taken efforts to focus on balanced data gathering. In this research, we propose Reliable Energy Efficient Data Gathering Approach (REEDGA) to balance data gathering and overhead. To achieve this, proposed work consists of three phases. In first phase, estimation of information gathering is implemented through stable paths. Stable paths are found based on link cost. In second phase, data gathering phase is initialized to save energy in the presence of mobile sensor nodes. Overhead is kept low while keeping round trip time of gathered data. From the analytical simulation using NS2, the proposed approach achieves better performance in terms of data delivery rate, data gathering rate, throughput, delay, link availability and control overhead
Unbalanced Expander Based Compressive Data Gathering in Clustered Wireless Sensor Networks
© 2013 IEEE. CConventional compressive sensing-based data gathering (CS-DG) algorithms require a large number of sensors for each compressive sensing measurement, thereby resulting in high energy consumption in clustered wireless sensor networks (WSNs). To solve this problem, we propose a novel energy-efficient CS-DG algorithm, which exploits the better reconstruction accuracy of the adjacency matrix of an unbalanced expander graph. In the proposed CS-DG algorithm, each measurement is the sum of a few sensory data, which are jointly determined by random sampling and random walks. Through theoretical analysis, we prove that the constructedM×N sparse binary sensing matrix is the adjacency matrix of a (k; ") unbalanced expander graph whenM=D O(N=k) and t=D O.Nc=(kq) for WSNs with Nc clusters, where 0 ≤q≤1 and Nc > k. Simulation results show our proposed CS-DG has better performance than existing algorithms in terms of reconstruction accuracy and energy consumption. When hybrid energy-efficient distributed clustering algorithm is used, to achieve the same reconstruction accuracy, our proposed CS-DG can save energy by at least 27:8%
Optimization and Learning in Energy Efficient Cognitive Radio System
Energy efficiency and spectrum efficiency are two biggest concerns for wireless communication. The constrained power supply is always a bottleneck to the modern mobility communication system. Meanwhile, spectrum resource is extremely limited but seriously underutilized.
Cognitive radio (CR) as a promising approach could alleviate the spectrum underutilization and increase the quality of service. In contrast to traditional wireless communication systems, a distinguishing feature of cognitive radio systems is that the cognitive radios, which are typically equipped with powerful computation machinery, are capable of sensing the spectrum environment and making intelligent decisions. Moreover, the cognitive radio systems differ from traditional wireless systems that they can adapt their operating parameters, i.e. transmission power, channel, modulation according to the surrounding radio environment to explore the opportunity.
In this dissertation, the study is focused on the optimization and learning of energy efficiency in the cognitive radio system, which can be considered to better utilize both the energy and spectrum resources. Firstly, drowsy transmission, which produces optimized idle period patterns and selects the best sleep mode for each idle period between two packet transmissions through joint power management and transmission power control/rate selection, is introduced to cognitive radio transmitter. Both the optimal solution by dynamic programming and flexible solution by reinforcement learning are provided. Secondly, when cognitive radio system is benefited from the theoretically infinite but unsteady harvested energy, an innovative and flexible control framework mainly based on model predictive control is designed. The solution to combat the problems, such as the inaccurate model and myopic control policy introduced by MPC, is given. Last, after study the optimization problem for point-to-point communication, multi-objective reinforcement learning is applied to the cognitive radio network, an adaptable routing algorithm is proposed and implemented. Epidemic propagation is studied to further understand the learning process in the cognitive radio network
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