arXiv subjects

George K. Karagiannidis

Publications and source records attributed to George K. Karagiannidis.

At least 145 records · Page 8Linked to original sources

arXiv2021

Learning based signal detection for MIMO systems with unknown noise statistics

This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly detect signals with unknown noise statistics in multiple-input multiple-output (MIMO) systems. In practice, there is little or even no statistical knowledge on the system noise, which in many cases is non-Gaussian, impulsive and not analyzable. Existing detection methods have mainly focused on specific noise models, which are not robust enough with unknown noise statistics. To tackle this issue, we propose a novel ML detection framework to effectively recover the desired signal. Our framework is a fully probabilistic one that can efficiently approximate the unknown noise distribution through a normalizing flow. Importantly, this framework is driven by an unsupervised learning approach, where only the noise samples are required. To reduce the computational complexity, we further present a low-complexity version of the framework, by utilizing an initial estimation to reduce the search space. Simulation results show that our framework outperforms other existing algorithms in terms of bit error rate (BER) in non-analytical noise environments, while it can reach the ML performance bound in analytical noise environments. The code of this paper is available at https://github.com/skypitcher/manfe.

cs.LG↗

arXiv2020

New Results for Pearson Type III Family of Distributions and Application in Wireless Power Transfer

Pearson and log Pearson type III distributions have been considered in several scientific fields, as in hydrology and seismology. In this paper, we present new results for these distributions and we utilize them, for first time in the literature, to investigate the statistical behavior of wireless power transfer (WPT), assuming that the harvested energy follows a well-established nonlinear energy harvesting model based on the logistic function. Specifically, we present new closed-form expressions for the statistical properties of a general form of Pearson and log Pearson type III distributions and we utilize them to introduce a new member of the Pearson type III family, the logit Pearson type III distribution, through which the logit gamma distribution is also defined. Moreover, we derive closed-form expressions for the probability density function, the cumulative distribution function and moments of the distributions of the sum, the log sum and the logit sum of Pearson type III random variables. Furthermore, taking into account that Pearson type III family of distributions is closely related to the considered nonlinear harvesting model the statistical properties of the distribution of the harvested power and derived, for both single input single output and multiple input single output scenarios.

stat.AP↗

arXiv2020

Machine Learning in Nano-Scale Biomedical Engineering

Machine learning (ML) empowers biomedical systems with the capability to optimize their performance through modeling of the available data extremely well, without using strong assumptions about the modeled system. Especially in nano-scale biosystems, where the generated data sets are too vast and complex to mentally parse without computational assist, ML is instrumental in analyzing and extracting new insights, accelerating material and structure discoveries, and designing experience as well as supporting nano-scale communications and networks. However, despite these efforts, the use of ML in nano-scale biomedical engineering remains still under-explored in certain areas and research challenges are still open in fields such as structure and material design and simulations, communications and signal processing, and bio-medicine applications. In this article, we review the existing research regarding the use of ML in nano-scale biomedical engineering. In more detail, we first identify and discuss the main challenges that can be formulated as ML problems. These challenges are classified into the three aforementioned main categories. Next, we discuss the state of the art ML methodologies that are used to countermeasure the aforementioned challenges. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations. Finally, we conclude the article with insightful discussions, that reveal research gaps and highlight possible future research directions.

cs.LG↗

arXiv2020

Optimal Resource Allocation for Delay Minimization in NOMA-MEC Networks

Multi-access edge computing (MEC) can enhance the computing capability of mobile devices, while non-orthogonal multiple access (NOMA) can provide high data rates. Combining these two strategies can effectively benefit the network with spectrum and energy efficiency. In this paper, we investigate the task delay minimization in multi-user NOMA-MEC networks, where multiple users can offload their tasks simultaneously through the same frequency band. We adopt the partial offloading policy, in which each user can partition its computation task into offloading and locally computing parts. We aim to minimize the task delay among users by optimizing their tasks partition ratios and offloading transmit power. The delay minimization problem is first formulated, and it is shown that it is a nonconvex one. By carefully investigating its structure, we transform the original problem into an equivalent quasi-convex. In this way, a bisection search iterative algorithm is proposed in order to achieve the minimum task delay. To reduce the complexity of the proposed algorithm and evaluate its optimality, we further derive closed-form expressions for the optimal task partition ratio and offloading power for the case of two-user NOMA-MEC networks. Simulations demonstrate the convergence and optimality of the proposed algorithm and the effectiveness of the closed-form analysis.

eess.SP↗

arXiv2020

AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems

Accurate channel state information (CSI) feedback plays a vital role in improving the performance gain of massive multiple-input multiple-output (m-MIMO) systems, where the dilemma is excessive CSI overhead versus limited feedback bandwith. By considering the noisy CSI due to imperfect channel estimation, we propose a novel deep neural network architecture, namely AnciNet, to conduct the CSI feedback with limited bandwidth. AnciNet extracts noise-free features from the noisy CSI samples to achieve effective CSI compression for the feedback. Experimental results verify that the proposed AnciNet approach outperforms the existing techniques under various conditions.

eess.SP↗

arXiv2020

Analyzing Grant-Free Access for URLLC Service

5G New Radio (NR) is expected to support new ultra-reliable low-latency communication (URLLC) service targeting at supporting the small packets transmissions with very stringent latency and reliability requirements. Current Long Term Evolution (LTE) system has been designed based on grantbased (GB) (i.e., dynamic grant) random access, which can hardly support the URLLC requirements. Grant-free (GF) (i.e., configured grant) access is proposed as a feasible and promising technology to meet such requirements, especially for uplink transmissions, which effectively saves the time of requesting/waiting for a grant. While some basic GF access features have been proposed and standardized in NR Release-15, there is still much space to improve. Being proposed as 3GPP study items, three GF access schemes with Hybrid Automatic Repeat reQuest (HARQ) retransmissions including Reactive, K-repetition, and Proactive, are analyzed in this paper. Specifically, we present a spatiotemporal analytical framework for the contention-based GF access analysis. Based on this framework, we define the latent access failure probability to characterize URLLC reliability and latency performances. We propose a tractable approach to derive and analyze the latent access failure probability of the typical UE under three GF HARQ schemes. Our results show that under shorter latency constraints, the Proactive scheme provides the lowest latent access failure probability, whereas, under longer latency constraints, the K-repetition scheme achieves the lowest latent access failure probability, which depends on K. If K is overestimated, the Proactive scheme provides lower latent access failure probability than the K-repetition scheme.

eess.SP↗

arXiv2020

Deep Learning based Radio Resource Management in NOMA Networks: User Association, Subchannel and Power Allocation

With the rapid development of future wireless communication, the combination of NOMA technology and millimeter-wave(mmWave) technology has become a research hotspot. The application of NOMA in mmWave heterogeneous networks can meet the diverse needs of users in different applications and scenarios in future communications. In this paper, we propose a machine learning framework to deal with the user association, subchannel and power allocation problems in such a complex scenario. We focus on maximizing the energy efficiency (EE) of the system under the constraints of quality of service (QoS), interference limitation, and power limitation. Specifically, user association is solved through the Lagrange dual decomposition method, while semi-supervised learning and deep neural network (DNN) are used for the subchannel and power allocation, respectively. In particular, unlabeled samples are introduced to improve approximation and generalization ability for subchannel allocation. The simulation indicates that the proposed scheme can achieve higher EE with lower complexity.

cs.IT↗

arXiv2020

All-Optical Cochlear Implants

In the present work, we introduce a novel cochlear implant (CI) architecture, namely all-optical CI (AOCI), which directly converts acoustic to optical signals capable of stimulating the cochlear neurons. First, we describe the building-blocks (BBs) of the AOCI, and explain their functionalities as well as their interconnections. Next, we present a comprehensive system model that incorporates the technical characteristics and constraints of each BB, the transdermal-optical-channel particularities, i.e. optical path-loss and external-implanted device stochastic pointing-errors, and the cochlear neurons biological properties. Additionally, in order to prove the feasibility of the AOCI architecture, we conduct a link-budget analysis that outputs novel closed-form expressions for the instantaneous and average photon flux that is emitted on the cochlear neurons. Likewise, we define three new key-performance-indicators (KPIs), namely probability of hearing, probability of false-hearing, and probability of neural damage. The proposed theoretical framework is verified through respective simulations, which not only quantify the efficiency of the proposed architecture, but also reveal an equilibrium between the optical transmission power and the patient's safety, as well as the AOCI BBs specifications. Finally, it is highlighted that the AOCI approach is greener and safer than the conventional CIs.

eess.SP↗

arXiv2020

UAV-to-Ground Communications: Channel Modeling and UAV Selection

Unmanned aerial vehicle (UAV)-enabled communications have been proposed as a critical part of the beyond fifth-generation (5G) cellular networks. This type of communications is frequently characterized by line-of-sight (LoS) and dynamic propagation conditions. However, in various scenarios, the presence of large obstacles in the LoS path is unavoidable, resulting in shadowed fading environments. In this paper, a new channel model is proposed, in which the effects of mobility and shadowing are simultaneously considered. In particular, the performance of a UAV-based communication system operating in a shadowed double-scattering channel is analyzed. {The new channel model is generic, since it models various fading/shadowing conditions, while it is in terms of easy-to-evaluate mathematical functions. Moreover, a low complexity UAV selection policy is proposed, which exploits shadowing-related information. The proposed scheme offers a reduction of the signal processing complexity, without any important degradation on the performance, as compared to alternatives approaches.} In this context, a new analytical framework has been developed for investigating the performance of the new strategy. Finally, the main outcomes of this paper are also validated by empirical data, collected in an air-to-ground measurement campaign.

cs.IT↗

arXiv2020

Statistical Modeling of the FSO Fronthaul Channel for UAV-based Communications

In this paper, we investigate the statistics of the free space optics (FSO) communication channel between a hovering unmanned aerial vehicle (UAV) and a central unit. Two unique characteristics make UAV-based FSO systems significantly different from conventional FSO systems with static transceivers. First, for UAV-based FSO systems, the incident laser beam is not always orthogonal to the receiver lens plane. Second, both position and orientation of the UAV fluctuate over time due to dynamic wind load, inherent random air fluctuations in the atmosphere around the UAV, and internal vibrations of the UAV. On the contrary, for conventional FSO systems, the laser beam is always perpendicular to the receiver lens plane and the relative movement of the transceivers is limited. In this paper, we develop a novel channel model for UAV-based FSO systems by quantifying the corresponding geometric and misalignment losses (GML), while taking into account the non-orthogonality of the laser beam and the random fluctuations of the position and orientation of the UAV. In particular, for diverse weather conditions, we propose different fluctuation models for the position and orientation of the UAV and derive corresponding statistical models for the GML. We further analyze the performance of a UAV-based FSO link in terms of outage probability and ergodic rate and simplify the resulting analytical expressions for the high signal-to-noise ratio (SNR) regime. Finally, simulations validate the accuracy of the presented analysis and provide important insights for system design. For instance, we show that for a given variance of the fluctuations, the beam width should be properly adjusted to minimize the outage probability.

eess.SP↗

arXiv2019

A Novel Cross Entropy Approach for Offloading Learning in Mobile Edge Computing

In this paper, we propose a novel offloading learning approach to compromise energy consumption and latency in multi-tier network with mobile edge computing. In order to solve this integer programming problem, instead of using conventional optimization tools, we apply a cross entropy approach with iterative learning of the probability of elite solution samples. Compared to existing methods, the proposed one in this network permits a parallel computing architecture and is verified to be computationally very efficient. Specifically, it achieves performance close to the optimal and performs well with different choices of the values of hyperparameters in the proposed learning approach.

cs.IT↗

arXiv2019

A Unified Spatial Framework for UAV-aided MmWave Networks

For unmanned aerial vehicle (UAV) aided millimeter wave (mmWave) networks, we propose a unified three-dimensional (3D) spatial framework in this paper to model a general case that uncovered users send messages to base stations via UAVs. More specifically, the locations of transceivers in downlink and uplink are modeled through the Poisson point processes and Poisson cluster processes (PCPs), respectively. For PCPs, Matern cluster and Thomas cluster processes, are analyzed. Furthermore, both 3D blockage processes and 3D antenna patterns are introduced for appraising the effect of altitudes. Based on this unified framework, several closed-form expressions for the coverage probability in the uplink and downlink, are derived. By investigating the entire communication process, which includes the two aforementioned phases and the cooperative transmission between them, tractable expressions of system coverage probabilities are derived. Next, three practical applications in UAV networks are provided as case studies of the proposed framework. The results reveal that the impact of thermal noise and non-line-of-sight mmWave transmissions is negligible. In the considered networks, mmWave outperforms sub-6 GHz in terms of the data rate, due to the sharp direction beamforming and large transmit bandwidth. Additionally, there exists an optimal altitude of UAVs, which maximizes the system coverage probability.

cs.IT↗

arXiv2019

Ultra-small Cell Networks with Collaborative RF and Lightwave Power Transfer

This paper investigates a hybrid radio frequency (RF)/visible light communication (VLC) ultra-small cell network consisting of multiple optical angle-diversity transmitters, one multi-antenna RF access point (AP), and multiple terminal devices. In the network, the optical transmitters play the primary role and are responsible for delivering information and power over the visible light, while the RF AP acts as a complementary power transfer system. Thus, we propose a novel collaborative RF and lightwave resource allocation scheme for hybrid RF/VLC ultra-small cell networks. The proposed scheme aims to maximize the communication quality-of-service provided by the VLC under a constraint of total RF and light energy harvesting performance, while keeping illumination constant and ensuring health safety. This scheme leads to the formulation of two optimization problems that correspond to the resource allocation at the optical transmitters and the RF AP. Both problems are optimally solved by appropriate algorithms. Moreover, we propose a closed-form suboptimal solution with high accuracy to tackle the optical transmitters' resource allocation problem, as well as an efficient semi-decentralized method. Finally, simulation results illustrate the achievable performance of the investigated system and the effectiveness of the proposed solutions.

cs.IT↗

arXiv2019

Shadowed FSO/mmWave Systems with Interference

We investigate the performance of mixed free space optical (FSO)/millimeter-wave (mmWave) relay networks with interference at the destination. The FSO/mmWave channels are assumed to follow Malaga-M/Generalized-K fading models with pointing errors in the FSO link. The H-transform theory, wherein integral transforms involve Fox's H-functions as kernels, is embodied to unifying the performance analysis framework that encompasses closed-form expressions for the outage probability, the average bit error rate (BER) and the average capacity. By virtue of some H-transform asymptotic expansions, the high signal-to-interference-plus-noise ratio (SINR) analysis reduces to easy-to-compute expressions for the outage probability and BER, which reveals inside information for the system design. We finally investigate the optimal power allocation strategy, which minimizes the outage probability.

cs.IT↗

arXiv2019

Optimal Task Assignment and Power Allocation for NOMA Mobile-Edge Computing Networks

Mobile edge computing (MEC) can enhance the computing capability of mobile devices, and non-orthogonal multiple access (NOMA) can provide high data rates. Combining these two technologies can effectively benefit the network with spectrum and energy efficiency. In this paper, we investigate the task completion time minimization in NOMA multiuser MEC networks, where multiple users can offload their tasks simultaneously via the same frequency band. We adopt the \emph{partial} offloading, in which each user can partition its computation task into offloading computing and locally computing parts. We aim to minimize the maximum task latency among users by optimizing their tasks partition ratios and offloading transmit power. By considering the energy consumption and transmitted power limitation of each user, the formulated problem is quasi-convex. Thus, a bisection search (BSS) iterative algorithm is proposed to obtain the minimum task completion time. To reduce the complexity of the BSS algorithm and evaluate its optimality, we further derive the closed-form expressions of the optimal task partition ratio and offloading power for two-user NOMA MEC networks based on the analysed results. Simulation results demonstrate the convergence and optimality of the proposed a BSS algorithm and the effectiveness of the proposed optimal derivation.

cs.IT↗

arXiv2018

Interference-Free Transceiver Design and Signal Detection for Ambient Backscatter Communication Systems over Frequency-Selective Channels

In this letter, we study the ambient backscatter communication systems over frequency-selective channels. Specifically, we propose an interference-free transceiver design to facilitate signal detection at the reader. Our design utilizes the cyclic prefix (CP) of orthogonal frequency-division multiplexing (OFDM) source symbols, which can cancel the signal interference and thus enhance the detection accuracy at the reader. Meanwhile, our design results in no interference on the existing OFDM communication systems. We also suggest a maximum likelihood (ML) detector for the reader and derive two detection thresholds. Simulations are then provided to corroborate our proposed studies.

cs.IT↗

arXiv2018

Optical Wireless Cochlear Implants

In the present contribution, we introduce a wireless optical communication-based system architecture which is shown to significantly improve the reliability and the spectral and power efficiency of the transcutaneous link in cochlear implants (CIs). We refer to the proposed system as optical wireless cochlear implant (OWCI).In order to provide a quantified understanding of its design parameters, we establish a theoretical framework that takes into account the channel particularities, the integration area of the internal unit, the transceivers misalignment, and the characteristics of the optical units. To this end, we derive explicit expressions for the corresponding average signal-to-noise-ratio, outage probability, ergodic spectral efficiency and capacity of the transcutaneous optical link (TOL). These expressions are subsequently used to assess the dependence of the TOL's communication quality on the transceivers design parameters and the corresponding channels characteristics. The offered analytic results are corroborated with respective results from Monte Carlo simulations. Our findings reveal that OWCI is a particularly promising architecture that drastically increases the reliability and effectiveness of the CI TOL, whilst it requires considerably lower transmit power compared to the corresponding widely-used radio frequency (RF) solution.

eess.SP↗

arXiv2018

Entropy and Energy Detection-based Spectrum Sensing over F Composite Fading Channels

In this paper, we investigate the performance of energy detection-based spectrum sensing over F composite fading channels. To this end, an analytical expression for the average detection probability is firstly derived. This expression is then extended to account for collaborative spectrum sensing, square-law selection diversity reception and noise power uncertainty. The corresponding receiver operating characteristics (ROC) are analyzed for different conditions of the average signal-to-noise ratio (SNR), noise power uncertainty, time-bandwidth product, multipath fading, shadowing, number of diversity branches and number of collaborating users. It is shown that the energy detection performance is sensitive to the severity of the multipath fading and amount of shadowing, whereby even small variations in either of these physical phenomena can significantly impact the detection probability. As a figure of merit to evaluate the detection performance, the area under the ROC curve (AUC) is derived and evaluated for different multipath fading and shadowing conditions. Closed-form expressions for the Shannon entropy and cross entropy are also formulated and assessed for different average SNR, multipath fading and shadowing conditions. Then the relationship between the Shannon entropy and ROC/AUC is examined where it is found that the average number of bits required for encoding a signal becomes small (i.e., low Shannon entropy) when the detection probability is high or when the AUC is large. The difference between composite and traditional small-scale fading is emphasized by comparing the cross entropy for Rayleigh and Nakagami-m fading. A validation of the analytical results is provided through a careful comparison with the results of some simulations.

eess.SP↗