arXiv subjects

George K. Karagiannidis

Publications and source records attributed to George K. Karagiannidis.

At least 127 records · Page 7Linked to original sources

arXiv2022

Waveform Design for Joint Sensing and Communications in Millimeter-Wave and Low Terahertz Bands

The convergence of sensing and communication in the millimeter-wave (mmWave) and low terahertz (THz) bands has been envisioned as a promising technology, since it incorporates high-rate data transmission of hundreds of Gbps and mm-level radar sensing in a spectrum- and cost-efficient manner, by sharing both the frequency and hardware resources. However, the joint radar sensing and communication (JRC) system faces considerable challenges in the mmWave and low-THz scale, due to the peculiarities of the propagation channel and radio-frequency (RF) front ends. To this end, the waveform design for the JRC systems in mmWave and low-THz bands with ultra-broad bandwidth is investigated in this paper. Firstly, by considering the JRC design based on the co-existence concept, where both functions operate in a time-domain duplex (TDD) manner, a novel multi-subband quasi-perfect (MS-QP) sequence, composed of multiple perfect subsequences on different subbands, is proposed for target sensing, which achieves accurate target ranging and velocity estimation, whilst only requiring cost-efficient low-rate analog-to-digital converters (A/Ds) for sequence detection. Furthermore, the root index of each perfect subsequence is designed to eliminate the influence of strong Doppler shift on radar sensing. Finally, a data-embedded MS-QP (DE-MS-QP) waveform is constructed through time-domain extension of the MS-QP sequence, generating null frequency points on each subband for data transmission. Unlike the co-existence-based JRC system in TDD manner, the proposed DE-MS-QP waveform enables simultaneous interference-free sensing and communication, whilst inheriting all the merits from MS-QP sequences. Numerical results validate the superiority of the proposed waveforms regarding the communication and sensing performances, hardware cost as well as flexibility of the resource allocation between the dual functions.

eess.SP↗

arXiv2022

Dynamic Programmable Wireless Environment with UAV-mounted Static Metasurfaces

Reconfigurable intelligent surfaces (RISs) are artificial planar structures able to offer a unique way of manipulating propagated wireless signals. Commonly composed of a number of reconfigurable passive cell components and basic electronic circuits, RISs can almost freely perform a set of wave modification functionalities, in order to realize programmable wireless environments (PWEs). However, a more energy-efficient way to realize a PWE is through dynamically relocating static metasurfaces that perform a unique functionality. In this paper, we employ a UAV swarm to dynamically deploy a set of lowcost passive metasurfaces that are able to perform only one electromagnetic functionality, but with the benefit of requiring no power. Specifically, the UAV-mounted static metasurfaces are carefully positioned across the sky to create cascaded channels for improved user service and security hardening. The performance evaluation results, based on

cs.ET↗

arXiv2022

An Open Platform for Simulating the Physical Layer of 6G Communication Systems with Multiple Intelligent Surfaces

Reconfigurable Intelligent Surfaces (RIS) constitute a promising technology that could fulfill the extreme performance and capacity needs of the upcoming 6G wireless networks, by offering software-defined control over wireless propagation phenomena. Despite the existence of many theoretical models describing various aspects of RIS from the signal processing perspective (e.g., channel fading models), there is no open platform to simulate and study their actual physical-layer behavior, especially in the multi-RIS case. In this paper, we develop an open simulation platform, aimed at modeling the physical-layer electromagnetic coupling and propagation between RIS pairs. We present the platform by initially designing a basic unit cell, and then proceeding to progressively model and simulate multiple and larger RISs. The platform can be used for producing verifiable stochastic models for wireless communication in multi-RIS deployments, such as vehicle-to-everything (V2X) communications in autonomous vehicles and cybersecurity schemes, while its code is freely available to the public.

cs.ET↗

arXiv2022

Mixed RF-VLC Relaying Systems for Interference-Sensitive Mobile Applications

Due to their Radio-Frequency (RF) immunity, Visible Light Communications (VLC) pose as a promising technology for interference sensitive applications such as medical data networks. In this paper, we investigate mixed RF-VLC relaying systems especially suited for this type of applications that support mobility. In this system setup, the end-user, who is assumed to be on a vehicle that is in dynamic movement, is served by an indoor VLC system, while the outdoor data traffic is conveyed through multiple backhaul RF links. Furthermore, it is assumed that a single backhaul RF link is activated by the mobile relay and due to feedback delay, the RF link activation is based on outdated channel state information (CSI). The performance of this system is analyzed in terms of outage probability and bit error rate (BER), and novel closed form analytical expressions are provided. Furthermore, the analysis is extended for the case where the average SNR over the RF links and/or LED optical power is high, and approximate analytical expressions are derived which determine performance floors. Numerical results are provided which demonstrate that the utilization of multiple RF backhaul links can significantly improve overall RF-VLC system performance when outage/BER floors are avoided. This calls upon joint design of both subsystems. Additionally, the outdated CSI exploited for active RF selection can significantly degrade the quality of system performance.

cs.NI↗

arXiv2022

Improved Grant-Free Access for URLLC via Multi-Tier-Driven Computing: Network-Load Learning, Prediction, and Resource Allocation

Grant-Free (GF) access has been recognized as a promising candidate for Ultra-Reliable and Low-Latency Communications (URLLC). However, even with GF access, URLLC still may not effectively gain high reliability and millimeter-level latency, simultaneously. This is because the network load is typically time-varying and not known to the base station (BS), and thus, the resource allocated for GF access cannot well adapt to variations of the network load, resulting in low resource utilization efficiency under light network load and leading to severe collisions under heavy network load. To tackle this problem, we propose a multi-tier-driven computing framework and the associated algorithms for URLLC to support users with different QoS requirements. Especially, we concentrate on K - repetition GF access in light of its simplicity and well-balanced performance for practical systems. In particular, our framework consists of three tiers of computation, namely network-load learning, network-load prediction, and adaptive resource allocation. In the first tier, the BS can learn the network-load information from the states (success, collision, and idle) of random-access resources in terms of resource blocks (RB) and time slots. In the second tier, the network-load variation is effectively predicted based on estimation results from the first tier. Finally, in the third tier, by deriving and weighing the failure probabilities of different groups of users, their QoS requirements, and the predicted network loads, the BS is able to dynamically allocate sufficient resources accommodating the varying network loads. Simulation results show that our proposed approach can estimate the network load more accurately compared with the baseline schemes. Moreover, our adaptive resource allocation offers an effective way to enhance the QoS for different URLLC services, simultaneously.

cs.IT↗

arXiv2022

On the Ergodic Rate of Cognitive Radio Inspired Uplink Multiple Access

With the exponential increase of the number of devices in the communication ecosystem toward the upcoming sixth generation (6G) of wireless networks, more enabling technologies and potential wireless architectures are necessary to fulfill the networking requirements of high throughput, massive connectivity, ultra reliability, and heterogeneous quality of service (QoS). In this work, we consider an uplink network consisting of a primary user (PU) and a secondary user (SU) and, by integrating the concept of cognitive radio and multiple access, two protocols based on rate-splitting multiple access and non-orthogonal multiple access with successive interference cancellation are investigated in terms of ergodic rate. The considered protocols aim to serve the SU in a resource block which is originally allocated solely for the PU without negatively affecting the QoS of the PU. We extract the ergodic rate of the SU considering a specific QoS for the PU for the two protocols. In the numerical results, we validate the theoretical analysis and illustrate the superiority of the considered protocols over two benchmark schemes.

cs.IT↗

arXiv2022

Learning to Optimize Resource Assignment for Task Offloading in Mobile Edge Computing

In this paper, we consider a multiuser mobile edge computing (MEC) system, where a mixed-integer offloading strategy is used to assist the resource assignment for task offloading. Although the conventional branch and bound (BnB) approach can be applied to solve this problem, a huge burden of computational complexity arises which limits the application of BnB. To address this issue, we propose an intelligent BnB (IBnB) approach which applies deep learning (DL) to learn the pruning strategy of the BnB approach. By using this learning scheme, the structure of the BnB approach ensures near-optimal performance and meanwhile DL-based pruning strategy significantly reduces the complexity. Numerical results verify that the proposed IBnB approach achieves optimal performance with complexity reduced by over 80%.

eess.SP↗

arXiv2022

Wireless Quantized Federated Learning: A Joint Computation and Communication Design

Recently, federated learning (FL) has sparked widespread attention as a promising decentralized machine learning approach which provides privacy and low delay. However, communication bottleneck still constitutes an issue, that needs to be resolved for an efficient deployment of FL over wireless networks. In this paper, we aim to minimize the total convergence time of FL, by quantizing the local model parameters prior to uplink transmission. More specifically, the convergence analysis of the FL algorithm with stochastic quantization is firstly presented, which reveals the impact of the quantization error on the convergence rate. Following that, we jointly optimize the computing, communication resources and number of quantization bits, in order to guarantee minimized convergence time across all global rounds, subject to energy and quantization error requirements, which stem from the convergence analysis. The impact of the quantization error on the convergence time is evaluated and the trade-off among model accuracy and timely execution is revealed. Moreover, the proposed method is shown to result in faster convergence in comparison with baseline schemes. Finally, useful insights for the selection of the quantization error tolerance are provided.

eess.SP↗

arXiv2022

Towards Optimally Efficient Search with Deep Learning for Large-Scale MIMO Systems

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the decision tree. Unfortunately, the existing optimal search algorithms often involve prohibitively high complexities, which indicates that they are infeasible in large-scale MIMO systems. To address this issue, we propose a general heuristic search algorithm, namely, hyper-accelerated tree search (HATS) algorithm. The proposed algorithm employs a deep neural network (DNN) to estimate the optimal heuristic, and then use the estimated heuristic to speed up the underlying memory-bounded search algorithm. This idea is inspired by the fact that the underlying heuristic search algorithm reaches the optimal efficiency with the optimal heuristic function. Simulation results show that the proposed algorithm reaches almost the optimal bit error rate (BER) performance in large-scale systems, while the memory size can be bounded. In the meanwhile, it visits nearly the fewest tree nodes. This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems. Besides, the code for this paper is available at \url{https://github.com/skypitcher/hats}.

cs.LG↗

arXiv2021

Optimization of Grant-Free NOMA with Multiple Configured-Grants for mURLLC

Massive Ultra-Reliable and Low-Latency Communications (mURLLC), which integrates URLLC with massive access, is emerging as a new and important service class in the next generation (6G) for time-sensitive traffics and has recently received tremendous research attention. However, realizing efficient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in mURLLC, is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs. With multiple CGs (MCG) for UL, UE can choose any of these grants as soon as the data arrives. In addition, non-orthogonal multiple access (NOMA) has been proposed to synergize with GF transmission to mitigate the serious transmission delay and network congestion problems. In this paper, we develop a novel learning framework for MCG-GF-NOMA systems with bursty traffic. We first design the MCG-GF-NOMA model by characterizing each CG using the parameters: the number of contention-transmission units (CTUs), the starting slot of each CG within a subframe, and the number of repetitions of each CG. Based on the model, the latency and reliability performances are characterized. We then formulate the MCG-GF-NOMA resources configuration problem taking into account three constraints. Finally, we propose a Cooperative Multi-Agent based Double Deep Q-Network (CMA-DDQN) algorithm to allocate the channel resources among MCGs so as to maximize the number of successful transmissions under the latency constraint. Our results show that the MCG-GF-NOMA framework can simultaneously improve the low latency and high reliability performances in massive URLLC.

eess.SY↗

arXiv2021

Synergetic UAV-RIS Communication with Highly Directional Transmission

The effective integration of unmanned aerial vehicles (UAVs) in future wireless communication systems depends on the conscious use of their limited energy, which constrains their flight time. Reconfigurable intelligent surfaces (RISs) can be used in combination with UAVs with the aim to improve the communication performance without increasing complexity at the UAV side. In this paper, we propose a synergetic UAV RIS communication system, utilizing a UAV with a highly directional antenna aiming to the RIS. The proposed scenario can be applied in all air-to-ground RIS-assisted networks and numerical results illustrate that it is superior from the cases where the UAV utilizes either an omnidirectional antenna or a highly directional antenna aiming towards the ground node.

cs.IT↗

arXiv2021

Channel modeling for in-body optical wireless communications

Next generation in-to-out-of body biomedical applications have adopted optical wireless communications (OWCs). However, by delving into the published literature, a gap is recognised in modeling the in-to-out-of channel, since most published contributions neglect the particularities of different type of tissues. Towards this direction, in this paper we present a novel pathloss and scattering models for in-to-out-of OWC links. Specifically, we derive extract analytical expressions that accurately describe the absorption of the five main tissues' constituents, namely fat, water, melanin, oxygenated and de-oxygenated blood. Moreover, we formulate a model for the calculation of the absorption coefficient of any generic biological tissue. Next, by incorporating the impact of scattering in the aforementioned model we formulate the complete pathloss model. The developed theoretical framework is verified by means of comparisons between the estimated pathloss and experimental measurements from independent research works. Finally, we illustrate the accuracy of the theoretical framework in estimating the optical properties of any generic tissue based on its constitution. The extracted channel model is capable of boosting the design of optimized communication protocols for a plethora of biomedical applications.

eess.SP↗

arXiv2021

On the Distribution of the Sum of Double-Nakagami-m Random Vectors and Application in Randomly Reconfigurable Surfaces

Meta-surfaces intend to improve significantly the performance of future wireless networks by controlling the wireless propagation and shaping the radio waves according to the generalized Snell's law. A recent application of meta-surfaces is reconfigurable intelligent surfaces which are practically limited by the requirement for perfect knowledge of the user's position. For the case where the user's position cannot be obtained, we introduce randomly reconfigurable surfaces (RRSs) aiming to diffuse the incoming wave. A RRS is defined as a reconfigurable meta-surface that each of its elements induces a randomly selected time-variant phase shift on the reflected signal. To facilitate the performance analysis of a RRS-assisted system, first, we present novel closed-form expressions for the probability density function, the cumulative distribution function, the moments, and the characteristic function of the distribution of the sum of double-Nakagami-m random vectors, whose amplitudes follow the double-Nakagami-m distribution, i.e., the distribution of the product of two Nakagami-m random variables, and phases follow the circular uniform distribution. We also consider a special case of this distribution, namely the distribution of the sum of Rayleigh-Nakagami-m random vectors. Then, we exploit these expressions to investigate the performance of the RRS-assisted composite channel, assuming that the two links undergo Nakagami-m fading and the equivalent phase follows the circular uniform distribution. Closed-form expressions for the outage probability, the average received signal-to-noise ratio, the ergodic capacity, the bit error probability, the amount of fading, and the channel quality estimation index are provided to evaluate the performance of the considered system. These metrics are also derived for the practical special case where one of the two links undergoes Rayleigh fading.

cs.IT↗

arXiv2021

On the symmetric and skew-symmetric K-distributions

We propose a family of four-parameter distributions that contain the K-distribution as special case. The family is derived as a mixture distribution that uses the three-parameter reflected Gamma distribution as parental and the two-parameter Gamma distribution as prior. Properties of the proposed family are investigated as well; these include probability density function, cumulative distribution function, moments, and cumulants. The family is termed symmetric K-distribution (SKD) based on its resemblance to the K-distribution as well as its symmetric nature. The standard form of the SKD, which often proves to be an adequate model, is also discussed. Moreover, an order statistics analysis is provided as well as the distributions of the product and ratio of two independent and identical SKD random variables are derived. Finally, a generalisation of the proposed family, which enables non-zero skewness values, is investigated, while both the SKD and the skew-SKD are proven capable of describing the complex dynamics of machine learning, Bayesian analysis and other fields through simplified expressions with high accuracy.

math.ST↗

arXiv2021

Non-Orthogonal Multiple Access (NOMA) With Multiple Intelligent Reflecting Surfaces

In this paper, non-orthogonal multiple access (NOMA) networks assisted by multiple intelligent reflecting surfaces (IRSs) with discrete phase shifts are investigated, in which each user device (UD) is served by an IRS to improve the quality of the received signal. Two scenarios are considered according to whether there is a direct link between the base station (BS) and each UD, and the outage performance is analyzed for each of them. Specifically, the asymptotic expressions for the upper and lower bounds of the outage probability in the high signal-to-noise ratio (SNR) regime are derived. Following that, the diversity order is obtained. It is shown that the use of discrete phase shifts does not degrade diversity order. More importantly, simulation results reveal that a 3-bit resolution for discrete phase shifts is sufficient to achieve near-optimal outage performance. Simulation results also imply the superiority of IRSs over full-duplex decode-and-forward relays.

eess.SP↗

arXiv2021

Wireless Federated Learning (WFL) for 6G Networks -- Part II: The Compute-then-Transmit NOMA Paradigm

As it has been discussed in the first part of this work, the utilization of advanced multiple access protocols and the joint optimization of the communication and computing resources can facilitate the reduction of delay for wireless federated learning (WFL), which is of paramount importance for the efficient integration of WFL in the sixth generation of wireless networks (6G). To this end, in this second part we introduce and optimize a novel communication protocol for WFL networks, that is based on non-orthogonal multiple access (NOMA). More specifically, the Compute-then-Transmit NOMA (CT-NOMA) protocol is introduced, where users terminate concurrently the local model training and then simultaneously transmit the trained parameters to the central server. Moreover, two different detection schemes for the mitigation of inter-user interference in NOMA are considered and evaluated, which correspond to fixed and variable decoding order during the successive interference cancellation process. Furthermore, the computation and communication resources are jointly optimized for both considered schemes, with the aim to minimize the total delay during a WFL communication round. Finally, the simulation results verify the effectiveness of CT-NOMA in terms of delay reduction, compared to the considered benchmark that is based on time-division multiple access.

cs.IT↗

arXiv2021

Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trends

Conventional machine learning techniques are conducted in a centralized manner. Recently, the massive volume of generated wireless data, the privacy concerns and the increasing computing capabilities of wireless end-devices have led to the emergence of a promising decentralized solution, termed as Wireless Federated Learning (WFL). In this first of the two parts paper, we present the application of WFL in the sixth generation of wireless networks (6G), which is envisioned to be an integrated communication and computing platform. After analyzing the key concepts of WFL, we discuss the core challenges of WFL imposed by the wireless (or mobile communication) environment. Finally, we shed light to the future directions of WFL, aiming to compose a constructive integration of FL into the future wireless networks.

cs.DC↗

arXiv2021

Large Scale Global Optimization Algorithms for IoT Networks: A Comparative Study

The advent of Internet of Things (IoT) has bring a new era in communication technology by expanding the current inter-networking services and enabling the machine-to-machine communication. IoT massive deployments will create the problem of optimal power allocation. The objective of the optimization problem is to obtain a feasible solution that minimizes the total power consumption of the WSN, when the error probability at the fusion center meets certain criteria. This work studies the optimization of a wireless sensor network (WNS) at higher dimensions by focusing to the power allocation of decentralized detection. More specifically, we apply and compare four algorithms designed to tackle Large scale global optimization (LGSO) problems. These are the memetic linear population size reduction and semi-parameter adaptation (MLSHADE-SPA), the contribution-based cooperative coevolution recursive differential grouping (CBCC-RDG3), the differential grouping with spectral clustering-differential evolution cooperative coevolution (DGSC-DECC), and the enhanced adaptive differential evolution (EADE). To the best of the authors knowledge, this is the first time that LGSO algorithms are applied to the optimal power allocation problem in IoT networks. We evaluate the algorithms performance in several different cases by applying them in cases with 300, 600 and 800 dimensions.

cs.NE↗