149 research outputs found
Closed-form performance analysis of linear MIMO receivers in general fading scenarios
Linear precoding and post-processing schemes are ubiquitous in wireless
multi-input-multi-output (MIMO) settings, due to their reduced complexity with
respect to optimal strategies. Despite their popularity, the performance
analysis of linear MIMO receivers is mostly not available in closed form, apart
for the canonical (uncorrelated Rayleigh fading) case, while for more general
fading conditions only bounds are provided. This lack of results is motivated
by the complex dependence of the output signal-to-interference and noise ratio
(SINR) at each branch of the receiving filter on both the squared singular
values as well as the (typically right) singular vectors of the channel matrix.
While the explicit knowledge of the statistics of the SINR can be circumvented
for some fading types in the analysis of the linear Minimum Mean-Squared Error
(MMSE) receiver, this does not apply to the less complex and widely adopted
Zero-Forcing (ZF) scheme. This work provides the first-to-date closed-form
expression of the probability density function (pdf) of the output ZF and MMSE
SINR, for a wide range of fading laws, encompassing, in particular,
correlations and multiple scattering effects typical of practically relevant
channel models.Comment: 16 pages, 2 figures, contents submitted to IEEE/VDE WSA 201
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MIMO-based Friendly Jamming and Interference Management Techniques for Secure Wireless Communications
The ever-increasing growth of wireless systems has made them an essential part of our daily life. People rely heavily on wireless networks for communications and to conduct critical transactions from their mobile devices, including financial transactions, access to health records, etc. The proliferation of wireless communication devices opens the door for many security breaches, ranging from eavesdropping to jamming attacks. Such a disadvantage stems from the broadcast nature of wireless transmissions, which creates an exposed environment.
In this dissertation, we focus on eavesdropping attacks. While cryptographic techniques can be used to thwart eavesdropping attacks and enable secure wireless communications, they are not sufficient to protect the lower-layer headers of a packet (i.e., PHY and MAC headers). Hence, even though the secret message is encrypted, these unencrypted headers can be exploited by an adversary to extract invaluable information and initiate malicious attacks (e.g., traffic classification). Physical-layer (PHY-layer) security has been introduced as a promising candidate to prevent attacks that exploit unencrypted lower layer headers.
PHY-layer security techniques typically rely on injecting an intentional interference into the medium so as to confuse nearby eavesdroppers (Eve). Specifically, a legitimate transmit-receive (Alice-Bob) pair generates a bogus signal, namely friendly jamming (FJ), along with the information signal, to increase interference at Eve(s) but without affecting the legitimate receiver (Bob). Depending on which end of a legitimate link is responsible for generating the FJ signal, two types of FJ techniques exist: transmitter-based (TxFJ) and receiver-based (RxFJ).
In this dissertation, we propose to advance the state-of-art in PHY-layer security by considering multi-link scenarios, including multi-user multiple-input multiple-output (MU-MIMO) and peer-to-peer (P2P) networks. Specifically, we consider a scenario where one or more external Eve(s) attempt to snoop on communications of various links. In such networks, transmission of one link may be interfered with neighboring links' transmissions. Thus, special care must be dedicated to handling interference.
In our first contribution in this dissertation, we consider a P2P network tapped by external Eve(s) in which each Alice-Bob pair conceals its communications using TxFJ. TxFJ is realized at Alice side using MIMO precoding. The goal is to design the precoders for both information and TxFJ signals at all Alices so as to maximize a given utility (e.g., sum of communication rates) while preventing eavesdropping elsewhere. Because legitimate links do not cooperate with each other and there is no centralized authority to perform optimization, every link selfishly aims at maximizing its secrecy rate. Using non-cooperative game theory, we design a distributed method for maximizing the sum of secrecy rates. Under the exact knowledge of eavesdropping channels, we show that our distributed method has a comparable secrecy sum-rate to a centralized approach.
In our next contribution, we focus on employing practical precoders in our design for a P2P network. Specifically, we employed a zero-forcing-based (ZF-based) precoder for the TxFJ of each Alice-Bob pair in a P2P network. We also assume that each link has a certain rate demand to be satisfied. In such a scenario, even though the non-cooperative game designed for this P2P network is shown to be convergent to its unique Nash Equilibrium (NE), there is still no guarantee that the resulting NE is Pareto-optimal. Hence, we propose a modified price-based game, in which each link is penalized for generating interference on other legitimate links. We show that the price-based game converges to the Pareto-optimal point of secrecy rate region. We then leverage mixed-strategy games to provide solutions that are robust to uncertainties in knowledge of eavesdropping channels. The proposed ZF-based design of precoders is also implemented on software-defined radios to assess its performance on a single link in real-world scenarios.
In another contribution of this dissertation, we consider to further enhance the secrecy of each link in a P2P network by equipping each receiver with RxFJ. Hence, in addition to the power allocation between TxFJ and information signals, we optimize RxFJ power as well. We show that by using RxFJ at each Bob, we could leverage the well-established concept of concave games, which compared to non-convex games enjoy more simplified game-theoretic analysis. We derive sufficient conditions under which the game admits a unique NE. We also propose another version of our power control algorithm that can be implemented asynchronously, making it robust to transmission delays in the network.
In our last contribution, we consider the downlink of a MU-MIMO network in the presence of an external Eve. No knowledge of Eve's location is assumed at the access point. The network is studied in underloaded and overloaded conditions. In an underloaded (overloaded) network, the number of antennas at the access point is larger (smaller) than the total number of downlink users' antennas. In the overloaded setting, traditional methods of creating TxFJ, such as ZF-based methods, are infeasible. We propose a linear precoding scheme that relaxes such infeasibility in overloaded MU-MIMO networks. In the worst-case scenario where Eve has knowledge of the channels between access point and downlink users, we show that our method imposes the most stringent condition on the number of antennas required at Eve to cancel out TxFJ signals. We also show that choosing the number of independent streams to be sent to downlink users has an important role in achieving a tradeoff between security, reliability, and the achievable rate
Reduced complexity detection for massive MIMO-OFDM wireless communication systems
PhD ThesisThe aim of this thesis is to analyze the uplink massive multiple-input multipleoutput
with orthogonal frequency-division multiplexing (MIMO-OFDM) communication
systems and to design a receiver that has improved performance
with reduced complexity. First, a novel receiver is proposed for coded massive
MIMO-OFDM systems utilizing log-likelihood ratios (LLRs) derived
from complex ratio distributions to model the approximate effective noise
(AEN) probability density function (PDF) at the output of a zero-forcing
equalizer (ZFE). These LLRs are subsequently used to improve the performance
of the decoding of low-density parity-check (LDPC) codes and turbo
codes. The Neumann large matrix approximation is employed to simplify the
matrix inversion in deriving the PDF.
To verify the PDF of the AEN, Monte-Carlo simulations are used to demonstrate
the close-match fitting between the derived PDF and the experimentally
obtained histogram of the noise in addition to the statistical tests and
the independence verification. In addition, complexity analysis of the LLR
obtained using the newly derived noise PDF is considered. The derived LLR
can be time consuming when the number of receive antennas is very large
in massive MIMO-OFDM systems. Thus, a reduced complexity approximation
is introduced to this LLR using Newton’s interpolation with different
orders and the results are compared to exact simulations. Further simulation
results over time-flat frequency selective multipath fading channels demonstrated
improved performance over equivalent systems using the Gaussian
approximation for the PDF of the noise.
By utilizing the PDF of the AEN, the PDF of the signal-to-noise ratio (SNR)
is obtained. Then, the outage probability, the closed-form capacity and three
approximate expressions for the channel capacity are derived based on that
PDF. The system performance is further investigated by exploiting the PDF
of the AEN to derive the bit error rate (BER) for the massive MIMO-OFDM
system with different M-ary modulations. Then, the pairwise error probability
(PEP) is derived to obtain the upper-bounds for the convolutionally coded
and turbo coded massive MIMO-OFDM systems for different code generators
and receive antennas.
Furthermore, the effect of the fixed point data representation on the performance
of the massive MIMO-OFDM systems is investigated using reduced
detection implementations for MIMO detectors. The motivation for the fixed
point analysis is the need for a reduced complexity detector to be implemented
as an optimum massive MIMO detector with low precision. Different
decomposition schemes are used to build the linear detector based on
the IEEE 754 standard in addition to a user-defined precision for selected
detectors. Simulations are used to demonstrate the behaviour of several matrix
inversion schemes under reduced bit resolution. The numerical results
demonstrate improved performance when using QR-factorization and pivoted
LDLT decomposition schemes at reduced precision.Iraqi Government and the Iraqi
Ministry of Higher Education and Scientific researc
Chiral Random Matrix Theory: Generalizations and Applications
Kieburg M. Chiral Random Matrix Theory: Generalizations and Applications. Bielefeld: Fakultät für Physik; 2015
Théorie des jeux et apprentissage pour les réseaux sans fil distribués
Dans cette thèse, nous étudions des réseaux sans fil dans lesquels les terminaux mobiles sont autonomes dans le choix de leurs configurations de communication. Cette autonomie de décision peut notamment concerner le choix de la technologie d'accès au réseau, le choix du point d'accès, la modulation du signal, les bandes de fréquences occupées, la puissance du signal émis, etc. Typiquement, ces choix de configuration sont réalisés dans le but de maximiser des métriques de performances propres à chaque terminal. Sous l'hypothèse que les terminaux prennent leurs décisions de manière rationnelle afin de maximiser leurs performances, la théorie des jeux s'applique naturellement pour modéliser les interactions entre les décisions des différents terminaux. Plus précisément, l'objectif principal de cette thèse est d'étudier des stratégies d'équilibre de contrôle de puissance d'émission afin de satisfaire des considérations d'efficacité énergétique. Le cadre des jeux stochastiques est particulièrement adapté à ce problème et nous permet notamment de caractériser la région de performance atteignable pour toutes les stratégies de contrôle de puissance qui mènent à un état d'équilibre. Lorsque le nombre de terminaux en jeu est grand, nous faisons appel à la théorie des jeux à champ moyen pour simplifier l'étude du système. Cette théorie nous permet d'étudier non pas les interactions individuelles entre les terminaux, mais l'interaction de chaque terminal avec un champ moyen qui représente l'état global des autres terminaux. Des stratégies de contrôle de puissance optimales du jeu à champ moyen sont étudiées. Une autre partie de la thèse a été consacrée à des problématiques d'apprentissage de points d'équilibre dans les réseaux distribués. En particulier, après avoir caractérisé les positions d'équilibre d'un jeu de positionnement de points d'accès, nous montrons comment des dynamiques de meilleures réponses et d'apprentissage permettent de converger vers un équilibre. Enfin, pour un jeu de contrôle de puissance, la convergence des dynamiques de meilleures réponses vers des points d'équilibre a été étudiée. Il est notamment proposé un algorithme d'adaptation de puissance convergeant vers un équilibre avec une faible connaissance du réseau.In this thesis, we study wireless networks in which mobile terminals are free to choose their communication configuration. Theses configuration choices include access wireless technology, access point association, coding-modulation scheme, occupied bandwidth, power allocation, etc. Typically, these configuration choices are made to maximize some performance metrics associated to every terminals. Under the assumption that mobile terminals take their decisions in a rational manner, game theory can be applied to model the interactions between the terminals. Precisely, the main objective of this thesis is to study energy-efficient power control policies from which no terminal has an interest to deviate. The framework of stochastic games is particularly suited to this problem and allows to characterize the achievable utility region for equilibrium power control strategies. When the number of terminals in the network is large, we invoke mean field game theory to simplify the study of the system. Indeed, in a mean field game, the interactions between a player and all the other players are not considered individually. Instead, one only studies the interactions between each player and a mean field, which is the distribution of the states of all the other players. Optimal power control strategies from the mean field formulation are studied. Another part of this thesis has been focused on learning equilibria in distributed games. In particular, we show how best response dynamics and learning algorithms can converge to an equilibrium in a base station location game. For another scenario, namely a power control problem, we study the convergence of the best response dynamics. In this case, we propose a power control behavioral rule that converges to an equilibrium with very little information about the network.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF
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