41 research outputs found
Optimal Bayesian Resampling for OFDM Signaling Over Multi-scale Multi-lag Channels
ABSTRACT Underwater acoustic (UWA) communication channels suffer from long delay spreads and significant Doppler effects. Such channels are also ultrawideband in nature. Thus, the typical UWA distortion can be well-described by a multiscale multi-lag (MSML) channel model. Many UWA communication systems employ resampling by a single-scale at the front-end to compensate for the scale effects of UWA channels. In this paper, the optimal resampling factor for OFDM signaling over MSML channels is investigated from a Bayesian perspective. The resampling factor is selected to minimize the inter-carrier interference resulting from the MSML channel. The exact interference power is computed, but is intractable for optimization, thus an upper bound is employed for optimization. Numerical results verify the tightness of the bound and the Bayesian approach is compared to deterministic methods previously derived for resampling in MSML channels
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Space-time-frequency methods for interference-limited communication systems
textTraditionally, noise in communication systems has been modeled as an additive, white Gaussian noise process with independent, identically distributed samples. Although this model accurately reflects thermal noise present in communication system electronics, it fails to capture the statistics of interference and other sources of noise, e.g. in unlicensed communication bands. Modern communication system designers must take into account interference and non-Gaussian noise to maximize efficiencies and capacities of current and future communication networks. In this work, I develop new multi-dimensional signal processing methods to improve performance of communication systems in three applications areas: (i) underwater acoustic, (ii) powerline, and (iii) multi-antenna cellular. In underwater acoustic communications, I address impairments caused by strong, time-varying and Doppler-spread reverberations (self-interference) using adaptive space-time signal processing methods. I apply these methods to array receivers with a large number of elements. In powerline communications, I address impairments caused by non-Gaussian noise arising from devices sharing the powerline. I develop and apply a cyclic adaptive modulation and coding scheme and a factor-graph-based impulsive noise mitigation method to improve signal quality and boost link throughput and robustness. In cellular communications, I develop a low-latency, high-throughput space-time-frequency processing framework used for large scale (up to 128 antenna) MIMO. This framework is used in the world's first 100-antenna MIMO system and processes up to 492 Gbps raw baseband samples in the uplink and downlink directions. My methods prove that multi-dimensional processing methods can be applied to increase communication system performance without sacrificing real-time requirements.Electrical and Computer Engineerin
Intelligent and Secure Underwater Acoustic Communication Networks
Underwater acoustic (UWA) communication networks are promising techniques for medium- to long-range wireless information transfer in aquatic applications. The harsh and dynamic water environment poses grand challenges to the design of UWA networks. This dissertation leverages the advances in machine learning and signal processing to develop intelligent and secure UWA communication networks. Three research topics are studied: 1) reinforcement learning (RL)-based adaptive transmission in UWA channels; 2) reinforcement learning-based adaptive trajectory planning for autonomous underwater vehicles (AUVs) in under-ice environments; 3) signal alignment to secure underwater coordinated multipoint (CoMP) transmissions.
First, a RL-based algorithm is developed for adaptive transmission in long-term operating UWA point-to-point communication systems. The UWA channel dynamics are learned and exploited to trade off energy consumption with information delivery latency. The adaptive transmission problem is formulated as a partially observable Markov decision process (POMDP) which is solved by a Monte Carlo sampling-based approach, and an expectation-maximization-type of algorithm is developed to recursively estimate the channel model parameters. The experimental data processing reveals that the proposed algorithm achieves a good balance between energy efficiency and information delivery latency.
Secondly, an online learning-based algorithm is developed for adaptive trajectory planning of multiple AUVs in under-ice environments to reconstruct a water parameter field of interest. The field knowledge is learned online to guide the trajectories of AUVs for collection of informative water parameter samples in the near future. The trajectory planning problem is formulated as a Markov decision process (MDP) which is solved by an actor-critic algorithm, where the field knowledge is estimated online using the Gaussian process regression. The simulation results show that the proposed algorithm achieves the performance close to a benchmark method that assumes perfect field knowledge.
Thirdly, the dissertation presents a signal alignment method to secure underwater CoMP transmissions of geographically distributed antenna elements (DAEs) against eavesdropping. Exploiting the low sound speed in water and the spatial diversity of DAEs, the signal alignment method is developed such that useful signals will collide at the eavesdropper while stay collision-free at the legitimate user. The signal alignment mechanism is formulated as a mixed integer and nonlinear optimization problem which is solved through a combination of the simulated annealing method and the linear programming. Taking the orthogonal frequency-division multiplexing (OFDM) as the modulation technique, simulation and emulated experimental results demonstrate that the proposed method significantly degrades the eavesdropper\u27s interception capability
Advances in approximate Bayesian computation and trans-dimensional sampling methodology
Bayesian statistical models continue to grow in complexity, driven
in part by a few key factors: the massive computational resources
now available to statisticians; the substantial gains made in
sampling methodology and algorithms such as Markov chain
Monte Carlo (MCMC), trans-dimensional MCMC (TDMCMC), sequential
Monte Carlo (SMC), adaptive algorithms and stochastic
approximation methods and approximate Bayesian computation (ABC);
and development of more realistic models for real world phenomena
as demonstrated in this thesis for financial models and
telecommunications engineering. Sophisticated statistical models
are increasingly proposed for practical solutions to real world problems in order to better capture salient features of
increasingly more complex data. With sophistication comes a
parallel requirement for more advanced and automated statistical
computational methodologies.
The key focus of this thesis revolves around innovation related to
the following three significant Bayesian research questions.
1. How can one develop practically useful Bayesian models and corresponding computationally efficient sampling methodology, when the likelihood model is intractable?
2. How can one develop methodology in order to automate Markov chain Monte Carlo sampling approaches to efficiently explore the support of a posterior distribution, defined across multiple Bayesian statistical models?
3. How can these sophisticated Bayesian modelling frameworks and sampling methodologies be utilized to solve practically relevant and important problems in the research fields of financial risk modeling and telecommunications engineering ?
This thesis is split into three bodies of work represented in
three parts. Each part contains journal papers with novel
statistical model and sampling methodological development. The
coherent link between each part involves the novel
sampling methodologies developed in Part I and utilized in Part II and Part III. Papers contained in
each part make progress at addressing the core research
questions posed.
Part I of this thesis presents generally applicable key
statistical sampling methodologies that will be utilized and
extended in the subsequent two parts. In particular it presents
novel developments in statistical methodology pertaining to
likelihood-free or ABC and TDMCMC methodology.
The TDMCMC methodology focuses on several aspects of automation
in the between model proposal construction, including
approximation of the optimal between model proposal kernel via a
conditional path sampling density estimator. Then this methodology
is explored for several novel Bayesian model selection
applications including cointegrated vector autoregressions (CVAR)
models and mixture models in which there is an unknown number of
mixture components. The second area relates to development of
ABC methodology with particular focus
on SMC Samplers methodology in an ABC context via Partial
Rejection Control (PRC). In addition to novel algorithmic
development, key theoretical properties are also studied for the
classes of algorithms developed. Then this methodology is
developed for a highly challenging practically significant
application relating to multivariate Bayesian -stable
models.
Then Part II focuses on novel statistical model development
in the areas of financial risk and non-life insurance claims
reserving. In each of the papers in this part the focus is on
two aspects: foremost the development of novel statistical models
to improve the modeling of risk and insurance; and then the
associated problem of how to fit and sample from such statistical
models efficiently. In particular novel statistical models are
developed for Operational Risk (OpRisk) under a Loss Distributional
Approach (LDA) and for claims reserving in Actuarial non-life
insurance modelling. In each case the models developed include an
additional level of complexity which adds flexibility to the model
in order to better capture salient features observed in real data.
The consequence of the additional complexity comes at the cost
that standard fitting and sampling methodologies are generally not
applicable, as a result one is required to develop and apply the
methodology from Part I.
Part III focuses on novel statistical model development
in the area of statistical signal processing for wireless
communications engineering. Statistical models will be developed
or extended for two general classes of wireless communications
problem: the first relates to detection of transmitted symbols and
joint channel estimation in Multiple Input Multiple Output (MIMO)
systems coupled with Orthogonal Frequency Division Multiplexing
(OFDM); the second relates to co-operative wireless communications
relay systems in which the key focus is on detection of
transmitted symbols. Both these areas will require advanced
sampling methodology developed in Part I to find solutions to
these real world engineering problems