Publications

In the past ten years, ultra wideband (UWB) technology has attracted great attention from academia and industry to enable a large number of applications. Typically, UWB was candidate to deliver home connectivity, sensor networks and body area networks. As UWB enabled devices operate in unlicensed mode, standardization efforts have been carried out in different parts of the globe to regulate their use in different contexts. ECMA-368 is an industry led standard for high data rate UWB communications. One of the key managing features of this standard is the so called distributed beaconing. In this paper, we analyze the distributed beaconing mechanism of ECMA-368. We focus on the transient phase in which newcomer devices attempt to join a network by accessing the standard defined beacon period. The joining procedure with which devices access the network is modeled by means of an embedded absorbing Markov chain. The relevant issue of modeling the miss-detection of beacon frames transmitted over fading channels is also assessed. Performance evaluation of distributed beaconing is given in terms of energy consumption. The required overhead for network setup is also analyzed.

This paper presents a secure beamforming design to prevent eavesdropping on multiple-input multiple-output (MIMO) device-to-device (D2D) communication. The devices communicate via a trusted relay which performs physical layer network coding (PNC), and multiple eavesdroppers are trying to intercept the device information. The beamforming design is based on minimizing mean square error of the D2D communication while employing signal-to-interference-plus-noise ratio (SINR) threshold constraints to prevent possible eavesdropping. The channel state information of the device-to-eavesdropper and relay-to-eavesdropper channels is imperfect at the devices and relay. The channel estimation errors are assumed with Gaussian Markov uncertainty model. Consequently, robust optimization problems are formulated considering the multiple access and broadcasting stages of the D2D communication. These problems are non-convex, and two algorithms are proposed to solve them. In the numerical analysis, we discuss the convergence of the proposed algorithms, impact of the number of eavesdroppers on the performance, and the SINR distributions at eavesdroppers.

We study the practical phase shift design in a non-ideal reconfigurable intelligent surface (RIS)-aided ultra-reliable and low-latency communication (URLLC) system under finite blocklength (FBL) regime by leveraging a novel deep reinforcement learning (DRL) algorithm named as twin-delayed deep deterministic policy gradient (TD3). First, assuming industrial automation system with multiple actuators, the signal-to-interference-plus-noise ratio (SINR) and achievable rate in FBL regime are identified for each actuator in terms of the phase shift configuration matrix at the RIS. The channel state information (CSI) variations due to feedback delay are also considered that result in channel coefficients' obsolescence. Then, the problem framework is proposed where the objective is to maximize the total achievable FBL rate in all ACs, subject to the practical phase shift constraint at the RIS elements. Since the problem is intractable to solve using conventional optimization methods, we resort to employing an actor-critic policy gradient DRL algorithm based on TD3, which relies on interacting RIS with FA environment by taking actions which are the phase shifts at the RIS elements, to maximize the expected observed reward, which is defined as the total FBL rate. The numerical results show that optimizing the practical phase shifts in the RIS via the proposed TD3 method is highly beneficial to improve the network total FBL rate in comparison with typical DRL methods.

Radio frequency (RF) wireless power transfer (WPT) is a promising charging technology for future wireless systems. However, low end-to-end power transfer efficiency (PTE) is a critical challenge for practical implementations. One of the main inefficiency sources is the power consumption and loss of key components such as the high-power amplifier (HPA) and rectenna, which must be considered for PTE optimization. Herein, we investigate the power consumption of an RF-WPT system considering the emerging dynamic metasurface antenna (DMA) as the transmitter. Moreover, we incorporate the HPA and rectenna non-linearities and consider the Doherty HPA to reduce power consumption. We provide a mathematical framework to calculate each user's harvested power from multi-tone signal transmissions and the system power consumption. Then, the waveform and beamforming are designed using swarm-based intelligence to minimize power consumption while satisfying the users' energy harvesting (EH) requirements. Numerical results manifest that increasing the number of transmit tones enhances the performance in terms of coverage probability and power consumption since the HPAs operate below the saturation region in the simulation setup and the EH non-linearity is the dominant factor. Finally, our findings demonstrate that a properly shaped DMA may outperform a fully-digital antenna of the same size.

Grant-free random access and uplink non-orthogonal multiple access (NOMA) have been introduced to reduce transmission latency and signaling overhead in massive machine-type communication (mMTC). In this paper, we propose two novel group-based deep neural network active user detection (AUD) schemes for the grant-free sparse code multiple access (SCMA) system in mMTC uplink framework. The proposed AUD schemes learn the nonlinear mapping, i.e., multi-dimensional codebook structure and the channel characteristic. This is accomplished through the received signal which incorporates the sparse structure of device activity with the training dataset. Moreover, the offline pre-trained model is able to detect the active devices without any channel state information and prior knowledge of the device sparsity level. Simulation results show that with several active devices, the proposed schemes obtain more than twice the probability of detection compared to the conventional AUD schemes over the signal to noise ratio range of interest.

Radio frequency (RF) wireless power transfer (WPT) is a promising technology for sustainable support of massive Internet of Things (IoT). However, RF-WPT systems are characterized by low efficiency due to channel attenuation, which can be mitigated by precoders that adjust the transmission directivity. This work considers a multi-antenna RF-WPT system with multiple non-linear energy harvesting (EH) nodes with energy demands changing over discrete time slots. This leads to the charging scheduling problem, which involves choosing the precoders at each slot to minimize the total energy consumption and meet the EH requirements. We model the problem as a Markov decision process and propose a solution relying on a low-complexity beamforming and deep deterministic policy gradient (DDPG). The results show that the proposed beamforming achieves near-optimal performance with low computational complexity, and the DDPG-based approach converges with the number of episodes and reduces the system's power consumption, while the outage probability and the power consumption increase with the number of devices.

This paper investigates solutions that combine relay selection algorithms and opportunistic geographic routing strategies for autonomous wireless sensor networks, wherein transmissions often occur over multiple hops. In order to design and assess such contention-based geographic forwarding strategies, we establish an analytical framework that incorporates concepts from probability generating functions (through power series expansion), spatial point processes, and auction theory, as well as Monte Carlo simulations to corroborate analytical results. The investigated strategies are built upon three constituent parts: 1) relay selection algorithm; 2) contention resolution mechanism; and 3) geographic forwarding strategy. We then propose an auction-based algorithm for selecting the relay node that relies on the network topology as side-information. Our results show that geographic solutions that iteratively exploit the local knowledge of the topology to ponder the algorithm operation outperforms established random approaches. Although, there is an intrinsic tradeoff between the time needed to identify a suitable next hop relay and the respective progress that is achieved toward the final destination.

We study the problem of how autonomous cognitive nodes (CNs) can arrive at an efficient and fair opportunistic channel access policy in scenarios where channels may be non-homogeneous in terms of primary user (PU) occupancy. In our model, a CN that is able to adapt to the environment is limited in two ways. First, CNs have imperfect observations (such as due to sensing and channel errors) of their environment. Second, CNs have imperfect memory due to limitations in computational capabilities. For efficient opportunistic channel access, we propose a simple adaptive win-shift lose-randomize (WSLR) strategy that can be executed by a twostate machine (automaton). Using the framework of repeated games (with imperfect observations and limited memory), we show that the proposed strategy enables the CNs (without any explicit coordination) to reach an outcome that: 1) maximizes the total network payoff and also ensures fairness among the CNs; 2) reduces the likelihood of collisions among CNs; and 3) requires a small number of sensing steps (attempts) to find a channel free of PU activity. We compare the performance of the proposed autonomous strategy with a centralized strategy and also test it with real spectrum data collected at RWTH Aachen.

The paper presents results of a co-existence measurement study between multiple ultra wideband (UWB) transmitters and a niversal Mobile Telecommunications System (UMTS) mobile phone. Two environments were explored: an anechoic chamber with one operating UMTS link and a true operational UMTS network. A large number of FCC compatible UWB transmitters were used to generate interference for an active UMTS connection. In an anechoic chamber, only the voice service was studied between a radio communication analyser (used as a base station) and a mobile terminal. In the true commercial UMTS network case, both the voice and data services were investigated in a shelter having a low UMTS signal level. The results clearly show that UMTS and high pulse repetition rate UWB devices can co-exist at link level when a moderate number of simultaneously active UWB devices operate in close proximity of the UMTS ‘victim’ receiver. The results also show that the activity factor of the UWB transmitter disproportionately impacts the effective interference. When using low activity factors, even with high pulse repetition rates and very large numbers of UWB devices, it is difficult to detect UMTS link performance degradation. As the activity factors grow beyond 5%, the impact of the UWB interference becomes visible. However, this study covers only one active UMTS link in both environments but does not report on the UWB impact on the UMTS radio access network.

5G will change the mobile communication business ecosystem by introducing location specific high-quality wireless networks that can by operated by different stakeholders.This development will change the traditional business models and ecosystem roles, as well as open the market for new local mobile network operators.These operators, such as recently introduced micro operators, can target specific customers in different vertical sectors with closed 5G networks, serve mobile network operator's (MNO) customers in high-demand areas on behalf of the MNO as a neutral host with open 5G networks, or mix different types of customers and offerings through various hybrid business models.This paper discusses business model options for local 5G micro operators, addressing also the different network deployment options.Three generic 5G business models and respective value ecosystems are presented: Vertical business model and ecosystem, Horizontal business model and ecosystem, and Oblique business model and ecosystem.Finally, the scalability, adaptability and sustainability of the business models and ecosystems are examined.

We characterize three near-field sub-regions for phased array antennas by elaborating on the boundaries {\it Fraunhofer}, {\it radial-focal}, and {\it non-radiating} distances. The {\it Fraunhofer distance} which is the boundary between near and far field has been well studied in the literature on the principal axis (PA) of single-element center-fed antennas, where PA denotes the axis perpendicular to the antenna surface passing from the antenna center. The results are also valid for phased arrays if the PA coincides with the boresight, which is not commonly the case in practice. In this work, we completely characterize the Fraunhofer distance by considering various angles between the PA and the boresight. For the {\it radial-focal distance}, below which beamfocusing is feasible in the radial domain, a formal characterization of the corresponding region based on the general model of near-field channels (GNC) is missing in the literature. We investigate this and elaborate that the maximum-ratio-transmission (MRT) beamforming based on the simple uniform spherical wave (USW) channel model results in a radial gap between the achieved and the desired focal points. While the gap vanishes when the array size $N$ becomes sufficiently large, we propose a practical algorithm to remove this gap in the non-asymptotic case when $N$ is not very large. Finally, the {\it non-radiating} distance, below which the reactive power dominates active power, has been studied in the literature for single-element antennas. We analytically explore this for phased arrays and show how different excitation phases of the antenna array impact it. We also clarify some misconceptions about the non-radiating and Fresnel distances prevailing in the literature.

In this paper, we study the trade-off between reliability and latency in machine type communication (MTC), which consists of single transmitter and receiver in the presence of Rayleigh fading channel. We assume that the transmitter does not know the channel conditions, therefore it would be transmitting information over a fixed rate. The fixed rate transmission is modeled as a two-state continuous-time Markov process, where the optimum transmission rate is obtained. Moreover, we conduct a performance analysis for different arrival traffic originated from MTC device via effective rate transmission. We consider that the arrival traffic is modeled as a Markovian process namely Discrete-Time Markov process, Fluid Markov process, and Markov Modulated Poisson process, under delay violation constraints. Using effective bandwidth and effective capacity theories, we evaluate the trade-off between reliability-latency and identify QoS (Quality of Service) requirement, and derive lower and upper bounds for the effective capacity subject to channel memory decay rate limits.

This paper studies combined relay selection and opportunistic geographic routing strategies for autonomous wireless sensor networks where transmissions occur over multiple hops. The proposed solution is built upon three constituent parts: (i) relay selection algorithm, (ii) contention resolution mechanism, and (iii) geographic forwarding strategy. Using probability generating function and spatial point process as the theoretic background, we propose an auction-based algorithm for selecting the relay node that relies on the network topology as side-information. Our results show that geographic solutions that iteratively exploit the local knowledge of the topology to ponder the algorithm operation outperforms established random approaches.

Vehicles are among the fastest growing type of connected devices. Therefore, there is a need for Vehicle-to-Everything (V2X) communication i.e. passing of information from a Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) and vice versa. In this paper, the main focus is on the communication between vehicles and road side units (RSUs) commonly referred to as V2I communication in a multi-lane freeway scenario. Moreover, we analyze network related bottlenecks such as the maximum number of vehicles that can be supported when coverage is provided by the Long Term Evolution Advanced (LTE-A) network. The performance evaluation is assessed through extensive system-level simulations. Results show that new resource allocation and interference mitigation techniques are needed in order to achieve the required high reliability requirements, especially when network load is high.