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In this work, we consider a typical scenario in a harsh urban propagation environment which is typical for a smart city scenario where multiple reconfigurable intelligent surfaces (RISs) are deployed in different hotspot areas to overcome signal blockage between the base station and users. Our goal is to ensure uninterrupted service availability to users in different hotspot areas regardless of their location. Consistent service availability can be achieved by guaranteeing that each RIS deployed in a hotspot area can support a certain number of users. This plays a critical role in smart city applications in the context of emergency communications and ubiquitous connectivity since the design ensures service availability to as many users as possible in all relevant locations. Taking into consideration the challenges in obtaining channel state information (CSI) given the passive nature of RIS and dynamic environments, we formulate a robust fairness problem to maximize the minimum expected number of served users in proximity to each RIS while considering the available transmit power and the worst-case quality of service (QoS) constraints within the bounded CSI error model framework. The resulting problem is a mixed integer non-convex program which is highly coupled and challenging to solve in polynomial time. Thus, we resort to binary variable relaxation, convex approximation techniques, and alternating optimization to tackle the problem. Additionally, we handle the semi-infinite uncertainty constraints by employing the S-procedure and general sign-definiteness. Simulation results demonstrate the effectiveness of the proposed design in obtaining consistent and reliable service in different hotspot areas compared to the relevant benchmark schemes. In addition, the proposed design shows flexibility in serving users with their target QoS given different channel uncertainty levels.

In this paper, we study the resource allocation problem in multi-RIS-aided outdoor-to-indoor communications. Specifically, we aim to provide geographical fairness to ensure that users in different hotspot areas in a smart city can be served regardless of their location. We consider a scenario where RISs are deployed to extend coverage to indoor users in different buildings where there is limited network accessibility. This design is crucial in smart cities in the context of emergency communication and ubiquitous connectivity since it ensures service availability to as many users as possible independently of the locations. Thus, to achieve geographical fairness, we formulate a max-min fairness problem to maximize the minimum number of users served by each RIS by jointly optimizing the active precoding and RIS-based beamforming subject to power and quality of service constraints. The geographical location of users is directly linked to the RIS which means that users are served by the RIS closest to them. In this case, we ensure that a certain number of users can be supported by each RIS. The formulated problem is a mixed integer nonlinear program, which is challenging to solve directly using methods of convex optimization. Accordingly, we propose an efficient successive convex approximation-based alternating optimization algorithm to tackle the complexity of the formulated problem. The presented results show the performance gain of the proposed design in providing geographical fairness compared to the relevant benchmark schemes.

The third generation partnership project (3GPP) has recently defined two frequency bands for direct access with satellites, which is a concrete step toward realizing the anticipated space-air-ground integrated networks. In addition, given the rapid increase in the numbers of satellites orbiting the Earth and emerging satellites applications, non-terrestrial networks (NTNs) might soon need to operate with integrated access and backhaul (lAB), which has been standardized for terrestrial networks to enable low-cost, flexible and scalable network densification. Therefore, this work investigates the performance of satellite lAB, where the same spectrum resources at a low earth orbit (LEO) satellite are utilized to provide access to a handheld user (UE) and backhaul via inter-satellite links. The UE is assumed to operate with frequency division duplex (FDD) as specified by the 3GPP, while both FDD and time division duplex (TDD) are investigated for backhauling. Our analysis demonstrate that the interference between access and backhaul links can significantly affect the performance under TDD backhauling, especially when the access link comes with high quality-of-service demands.

Reconfigurable intelligent surfaces (RISs) have emerged as a game-changing technology to improve wireless network performance by intelligently manipulating and customizing the physical propagation environment. Such capability is especially important for the application of smart cities as it increases wireless service offers and quality to end-users. In this paper, we aim to maximize the number of served users in a challenging RIS-aided smart city street by jointly optimizing the multislot scheduling, precoding, and passive RIS-based beamforming design under quality of service and power constraints. Multislot scheduling is introduced in order to benefit from additional time diversity and thus better exploit the available degrees of freedom. The formulated problem is a mixed integer nonlinear programming, which is NP-hard. To solve the problem with affordable complexity, we develop an efficient iterative algorithm based on binary variable relaxation, alternating optimization, and successive convex approximation techniques. Simulation results demonstrate the superiority of the proposed design over the design without RIS and the design without scheduling, especially in the presence of a large number of users. In addition, results illustrate that by introducing a quality of service margin, the proposed design can improve its robustness to outdated channel state information in mobility scenarios.

This paper proposes a novel transmission scheme that leverages the synergistic benefits of multiple reconfigurable intelligent surfaces (RISs) and cooperative non-orthogonal multi-ple access (NOMA) to improve both spectral and energy efficiency in 5G and beyond wireless systems. The proposed scheme involves selecting one of cell-center users to an access point (AP), which then relays the data to a cell-edge user without a direct connection to the AP with assistance of multiple distributed RISs. In this respect, we propose a cooperative NOMA scheme and develop a user selection strategy for two RIS exploitation scenarios: (i) the RIS selection scheme where the transmission between the selected cell-center and cell-edge users is performed via a selected RIS, and (ii) the distributed RIS scheme where all RISs assist in the end-to-end communications. The system outage performance is statistically characterized and its closed-form expressions are derived. Additionally, simulations are carried out to validate the analytical expressions and compare the performance of these two schemes to a single RIS-assisted network under different numbers of RISs, reflecting elements, and cell-center users. The results show that combining multiple RIS in cooperative NOMA can yield high spectral efficiency gains and improved outage performance, even with a low number of RIS elements.

To allow flexible and cost-efficient network densification and deployment, the integrated access and backhaul (IAB) was recently standardized by the third generation partnership project (3GPP) as part of the fifth-generation new radio (5G-NR) networks. However, the current standardization only defines the IAB for the terrestrial domain, while non-terrestrial networks (NTNs) are yet to be considered for such standardization efforts. In this work, we motivate the use of IAB in NTNs, and we discuss the compatibility issues between the 3GPP specifications on IAB in 5G-NR and the satellite radio regulations. In addition, we identify the required adaptation from the 3GPP and/or satellite operators for realizing an NTN-enabled IAB operation. A case study is provided for a low earth orbit (LEO) satellite-enabled in-band IAB operation with orthogonal and non-orthogonal bandwidth allocation between access and backhauling, and under both time- and frequency-division duplex (TDD/FDD) transmission modes. Numerical results demonstrate the feasibility of IAB through satellites, and illustrate the superiority of FDD over TDD transmission. It is also shown that in the absence of precoding, non-orthogonal bandwidth allocation between the access and the backhaul can largely degrades the network throughput.

In this paper, we tackle the problem of joint symbol level precoding (SLP) and reconfigurable intelligent surface (RIS) phase shift design with constellation rotation in the finite block length regime. We aim to increase energy efficiency by minimizing the total transmit power while satisfying the quality of service constraints. The total power consumption can be significantly minimized through the exploitation of multiuser interference by symbol level precoding and by the intelligent manipulation of the propagation environment using reconfigurable intelligent surfaces. In addition, the constellation rotation per user contributes to energy efficiency by aligning the symbol phases of the users, thus improving the utilization of constructive interference. The formulated power minimization problem is non-convex and correspondingly difficult to solve directly. Hence, we employ an alternating optimization algorithm to tackle the joint optimization of SLP and RIS phase shift design. The optimal phase of each user’s constellation rotation is obtained via an exhaustive search algorithm. Through Monte-Carlo simulation results, we demonstrate that the proposed solution yields substantial power minimization as compared to conventional SLP, zero forcing precoding with RIS as well as the benchmark schemes without RIS.

The key advantage of successive relaying (SR) networks is their ability to mimic the full-duplex (FD) operation with half-duplex (HD) relays. However, the main challenge that comes with such schemes is the associated inter-relay interference (IRI). In this work, we propose a reconfigurable intelligent surface (RIS)-enhanced SR network, where one RIS is deployed near each of the two relay nodes to provide spatial suppression of IRI, and to maximize the gain of desired signals. The resultant max-min optimization problem with joint phase-shift design for both RISs is first tackled via the semidefinite programming (SDP) approach. Then, a lower-complexity solution suitable for real-time implementation is proposed based on particle swarm optimization (PSO). Numerical results demonstrate that even relatively small RISs can provide significant gains in achievable rates of SR networks, and the proposed PSO scheme can achieve a near optimal performance.

In this work, we investigate the decode-and-forward (DF) relay-aided double reconfigurable intelligent surface (RIS)-assisted networks, where the signal is subject to reflections from two RISs before reaching the destination. Different relay-aided network architectures are considered for maximum achievable rate under a total power constraint. Phase optimization for the double-RIS channels is tackled via the alternating optimization and majorization-minimization (MM) schemes. Moreover, closed-form solutions are obtained for each case. Numerical results indicate that the deployment of two relays, one near each RIS, achieves higher rates at low and medium signal-to-noise ratios (SNRs) compared to placing a single relay between the two RISs; while at high SNRs, the latter approach achieves higher rates only if the inter-relay interference for the former case is considerably high.

The commercial low earth orbiting (LEO) satellite constellations have shown unprecedented growth. Accordingly, the risk of generating harmful interference to the geostationary orbit (GSO) satellite services increases with the number of satellites in such mega-constellations. As the GSO arc encompasses the primary and existing satellite assets providing essential fixed and broadcasting satellite services, the interference avoidance for this area is of the utmost importance. In particular, non-geostationary orbit (NGSO) operators should comply with the regulations set up both by their national regulators and by the International Telecommunications Union (ITU) to minimize the impact of emissions on existing GSO and non-GSO systems. In this paper, we first provide an overview of the most recent radio regulations that dictate the NGSO-GSO spectral co-existence. Next, we analyze the NGSO-GSO radio frequency interference for the downlink scenario, following the so-called time-simulation methodology introduced by ITU. The probability distribution of aggregated power flux-density for NGSO co-channel interference is evaluated and assessed, adopting different degrees of exclusion angle strategy for interference avoidance. We conclude the paper by discussing the resulting implications for the continuity of operation and service provision and we provide remarks for future work.

Through precoding, the spectral efficiency of the system can be improved; thus, more users can benefit from 5G and beyond broadband services. However, complete precoding (using all precoding coefficients) may not be possible in practice due to the high signal processing complexity involved in calculating a large number of precoding coefficients and combining them with symbols for transmission. In this paper, we propose an energy-efficient sparse precoding design, where only a few precoding coefficients are used with lower transmit power consumption depending on the demand. In this context, we formulate an optimization problem that minimizes the number of in-use precoding coefficients and the system power consumption while matching the per beam demand. This problem is non-convex. Hence, we apply Lagrangian relaxation and successive convex approximation to convexify it. The proposed solution outperforms the benchmark schemes in energy efficiency and demand satisfaction with the additional advantage of sparse precoding design.

In this paper, we examine the potential for a reconfigurable intelligent surface (RIS) to be powered by energy harvested from information signals. This feature might be key to reap the benefits of RIS technology’s lower power consumption compared to active relays. We first identify the main RIS power-consuming components and then propose an energy harvesting and power consumption model. Furthermore, we formulate and solve the problem of the optimal RIS placement together with the amplitude and phase response adjustment of its elements in order to maximize the signal-to-noise ratio (SNR) while harvesting sufficient energy for its operation. Finally, numerical results validate the autonomous operation potential and reveal the range of power consumption values that enables it.

Reconfigurable Intelligent Surfaces (RIS) are planar structures connected to electronic circuitry, which can be employed to steer the electromagnetic signals in a controlled manner. Through this, the signal quality and the effective data rate can be substantially improved. While the benefits of RIS-assisted wireless communications have been investigated for various scenarios, some aspects of the network design, such as coverage, optimal placement of RIS, etc., often require complex optimization and numerical simulations, since the achievable effective rate is difficult to predict. This problem becomes even more difficult in the presence of phase estimation errors or location uncertainty, which can lead to substantial performance degradation if neglected. Considering randomly distributed receivers within a ring-shaped RIS-assisted wireless network, this paper mainly investigates the effective rate by taking into account the above-mentioned impairments. Furthermore, exact closed-form expressions for the effective rate are derived in terms of Meijer’s G-function, which (i) reveals that the location and phase estimation uncertainty should be well considered in the deployment of RIS in wireless networks; and (ii) facilitates future network design and performance prediction.

Among a plethora of new wireless communication technologies, reconfigurable intelligent surface (RIS) emerges as one of the revolutionary solutions to provide energy- and cost-efficient signal transmissions. RIS is capable of reflecting electromagnetic signals in a controlled manner. In this paper, we jointly design the active beamforming at the base station and passive beamforming at the RIS to maximize the number of served users in a practical Smart City street scenario, subject to quality of service (QoS) and power constraints. The formulated problem belongs to the difficult class of mixed-integer non-convex programming, which is NP-hard. To arrive at a low-complexity solution, we first decompose the original problem into two subproblems and then propose an alternating optimization algorithm based on successive convex approximation (SCA) to solve them in an iterative manner. Simulation results are provided to verify the performance improvement of the proposed algorithm as compared to baseline schemes.

The satellite component is recognized as a promising solution to complement and extend the coverage of future Internet of Things (IoT) terrestrial networks (TNs). In this context, a study item to integrate satellites into narrowband-IoT (NB-IoT) systems has been approved within the 3rd Generation Partnership Project (3GPP) standardization body. However, as NB-IoT systems were initially conceived for TNs, their basic design principles and operation might require some key modifications when incorporating the satellite component. These changes in NB-IoT systems, therefore, need to be carefully implemented in order to guarantee a seamless integration of both TN and nonterrestrial network (NTN) for a global coverage. This article addresses this adaptation for the random access (RA) step in NB-IoT systems, which is in fact the most challenging aspect in the NTN context, for it deals with multiuser time-frequency synchronization and timing advance for data scheduling. In particular, we propose an RA technique which is robust to typical satellite channel impairments, including long delays, significant Doppler effects, and wide beams, without requiring any modification to the current NB-IoT RA waveform. Performance evaluations demonstrate the proposal’s capability of addressing different NTN configurations recently defined by 3GPP for the 5G new radio system.

Smart radio resource allocation combined with the recent advances of digital payloads will allow to control the transmit power and bandwidth of the satellites depending on the demand and the channel conditions of users. The system flexibility is important not only to handle divergent demand requirements but also to efficiently utilize the limited and expensive satellite resources. In this paper, we propose a demand-aware smart radio resource allocation technique, where the transmit power and the bandwidth of the GEO satellite are minimized while satisfying the user demand. The formulated optimization problem is non-convex mixed-integer nonlinear program which is difficult to solve. Hence, we apply a quadratic transform to solve the problem iteratively. The numerical results showed that the proposed scheme outperforms the benchmark schemes in terms of bandwidth utilization while accurately providing capacity-on-demand.

Satellite Communications offer complementary benefits to terrestrial 5G/6G infrastructure, covering a wide range of use cases in need of ubiquitous coverage and reliability. However, to be as competitive as the terrestrial counterpart in terms of supplied throughput, satellite communications require a highly efficient use of the limited available spectrum. Linear precoding has demonstrated the ability to boost the spectral efficiency in the satellite domain, but raising a new issue: the bandwidth requirements of the feeder link. Deployment of several gateways, each of which precoding an independent cluster of beams causes performance degradation. Therefore, in this paper, we investigate the centralized gateway concept, where all digital baseband processes (including precoding) are implemented in a remote server connected via high speed fibers to the distributed remote gateways responsible for the downlink and uplink of the satellite radio frequency signals. In particular, we highlight the main technical challenges and provide a preliminary vision of potential solutions.

In this paper, the limits in terms of offered capacity for a non-precoded geostationary (GEO) satellite communication system is investigated. In particular, we focus on the smart radio resource assignment as a technique to manage the interference across the multi-beam pattern of the GEO system. In this context, a joint power and carrier allocation problem is formulated to maximize the capacity of the system. The formulated optimization problem is non-convex and difficult to solve. Hence, we propose to address the frequency allocation first by assuming an equal power distribution, followed by the optimal power assignment to maximize the sum-capacity. Numerical evaluations are presented comparing the proposed method with a precoded-based system and with benchmark resource allocation schemes, showing the benefits of the proposed technique and identifying the limits of a non-precoded GEO satellite communications system.

The narrowband Internet-of-Things (NB-IoT) standard is a new cellular wireless technology, which has been introduced by the 3rd generation partnership project (3GPP) with the goal to connect massive low-cost, low-complexity and long-life IoT devices with extended coverage. In order to improve power efficiency, 3GPP proposed a new random access (RA) waveform for NB-IoT based on a single-tone frequency-hopping scheme. RA handles the first connection between user equipments (UEs) and the base station (BS). Through this, UEs can be identified and synchronized with the BS. In this context, receiver methods for the detection of the new waveform should satisfy the requirements on the successful user detection as well as the timing synchronization accuracy. This is not a trivial task, especially in the presence of radio impairments like carrier frequency offset (CFO) which constitutes one of the main radio impairments besides the noise. In order to tackle this problem, we propose a new receiver method for NB-IoT physical RA channel (NPRACH). The method is designed to eliminate perfectly the CFO without any additional computational complexity and supports all NPRACH preamble formats. The associated performance has been evaluated under 3GPP conditions. We observe a very high performance compared both to 3GPP requirements and to the existing state-of-the-art methods in terms of detection accuracy and complexity.

Conventional GEO satellite communication systems rely on a multibeam foot-print with a uniform resource allocation to provide connectivity to users. However, applying uniform resource allocation is inefficient in presence of non-uniform demand distribution. To overcome this limitation, the next generation of broadband GEO satellite systems will enable flexibility in terms of power and bandwidth assignment, enabling on-demand resource allocation. In this paper, we propose a novel satellite resource assignment design whose goal is to satisfy the beam traffic demand by making use of the minimum transmit power and utilized bandwidth. The motivation behind the proposed design is to maximize the satellite spectrum utilization by pushing the spectrum reuse to affordable limits in terms of tolerable interference. The proposed problem formulation results in a non-convex optimization structure, for which we propose an efficient tractable solution. We validate the proposed method with extensive numerical results, which demonstrate the efficiency of the proposed approach with respect to benchmark schemes.

We propose a novel technique for total transmit power minimization and optimal precoder design in wireless multi-group (MG) multicasting (MC) systems. The considered framework consists of three different systems capable of handling heterogeneous user types viz., information decoding (ID) specific users with conventional receiver architectures, energy harvesting (EH) only users with non-linear EH module, and users with joint ID and EH capabilities having separate units for the two operations, respectively. Each user is categorized under unique group(s), which can be of MC type specifically meant for ID users, and/or an energy group consisting of EH explicit users. The joint ID and EH users are a part of the (last) EH group as well as any one of the MC groups distinctly. In this regard, we formulate an optimization problem to minimize the total transmit power with optimal precoder designs for the three aforementioned scenarios, under constraints on minimum signal-to-interference-plus-noise ratio and harvested energy by the users with respective demands. The problem may be adapted to the well-known semi-definite program, which can be typically solved via relaxation of rank-l constraint. However, the relaxation of this constraint may in some cases lead to performance degradation, which increases with the rank of the solution obtained from the relaxed problem. Hence, we develop a novel technique motivated by the feasible-point pursuit and successive convex approximation method in order to address the rank-related issue. The benefits of the proposed method are illustrated under various operating conditions and parameter values, with comparison between the three above-mentioned scenarios.

Current multi-beam GEO satellite systems operate under a limited frequency reuse configuration and considering uniform power assignment across beams. The latter has been shown to be inefficient in matching the geographic distribution of the traffic demand. In this context, next generation of broadband GEO satellite systems will be equipped with more flexible and reconfigurable payloads, facilitating on-demand resource allocation. In this paper, we consider both carrier and power assignment to match the requested beam demands while minimizing the total transmit power and the total utilized bandwidth. A novel optimization problem is formulated and, given its non-convex structure, we divide the problem into two tractable sub-problems. First, we estimate the number of adjacent frequency carriers required for each beam to satisfy its demand and, subsequently, we optimize the power allocation based on the previously assigned carriers. We validate the proposed method with extensive numerical results, which demonstrate its efficiency with respect to benchmark strategies.

Ultrareliable uplink communication based on random access poses novel research challenges for the receiver design. Here, the uncertainty imposed by the random access and a large amount of interfering transmissions is the limiting factor for the system performance. Recently, this type of communication has been addressed in context of simultaneous wireless information and power transfer (SWIPT). The need to adapt the power splitting to the signal states according to the underlying random access has been tackled by introducing a predictor, which determines the valid states of the received signal based on the long-term observation. Hence, the power splitting factor is scaled accordingly in order to guarantee ultrareliable communication and maximized harvested energy.In this work, we extend the considered SWIPT scenario by introducing multiple antennas at the receiver side. Through this, the received energy can be substantially increased, if the energy harvesting parameters and the spatial filter coefficients are jointly optimized. Hence, we propose an optimization procedure, which aims at maximizing the harvested energy under the ultrareliability constraint. The mentioned prediction method is then combined with the optimization solution and the resulting system performance is numerically evaluated.

Low Power Wide Area (LPWA) networks are known to be highly vulnerable to external in-band interference in terms of packet collisions which may substantially degrade the system performance. In order to enhance the performance in such cases, the telegram splitting (TS) method has been proposed recently. This approach exploits the typical burstiness of the interference via forward error correction (FEC) and offers a substantial performance improvement compared to other methods for packet transmissions in LPWA networks. While it has been already demonstrated that the TS method benefits from knowledge on the current interference state at the receiver side, corresponding practical receiver algorithms of high performance are still missing. The modeling of the bursty interference via Markov chains leads to the optimal detector in terms of a-posteriori symbol error probability. However, this solution requires a high computational complexity, assumes an a-priori knowledge on the interference characteristics and lacks flexibility. We propose a further developed scheme with increased flexibility and introduce an approach to reduce its complexity while maintaining a close-to-optimum performance. In particular, the proposed low-complexity solution substantially outperforms existing practical methods in terms of packet error rate and therefore is highly beneficial for practical LPWA network scenarios.

Large communication networks, e.g. Internet of Things (IoT), are known to be vulnerable to co-channel interference. One possibility to address this issue is the use of orthogonal multiple access (OMA) techniques. However, due to a potentially very long duty cycle, OMA is not well suited for such schemes. Instead, random medium access (RMA) appears more promising. An RMA scheme is based on transmission of short data packets with random scheduling, which is typically unknown to the receiver. The received signal, which consists of the overlapping packets, can be used for energy harvesting and powering of a relay device. Such an energy harvesting relay may utilize the energy for further information processing and uplink transmission. In this paper, we address the design of a simultaneous information and power transfer scheme based on randomly scheduled packet transmissions and reliable symbol detection. We formulate a prediction problem with the goal to maximize the harvested power for an RMA scenario. In order to solve this problem, we propose a new prediction method, which shows a significant performance improvement compared to the straightforward baseline scheme. Furthermore, we investigate the complexity of the proposed method and its vulnerability to imperfect channel state information.

Low PowerWide Area (LPWA) networks are known to be very vulnerable to external in-band interference in terms of packet collisions, which may substantially degrade the system performance. In order to improve the performance under collisions, the so-called telegram splitting (TS) method has been proposed recently. This approach is based on the assumption of a bursty behavior of the interference, which can be efficiently accounted for by forward error correction. In this paper, we assume that all interferers have the same structure in terms of packet duration and signal variance, which are assumed to be known to the receiver. However, the number of simultaneously active interfering transmissions and their scheduling are unknown. Hence, the amount of interference observed in each symbol interval can vary which needs to be accounted for by the receiver in order to increase the reliability of transmission. For this scenario, we develop the optimal signal detection strategy. We also show that the proposed method substantially outperforms a baseline scheme in terms of packet error rate.

Underground communication systems present a variety of new research challenges. Here, the goal is to establish an efficient wireless connection between the transceivers in the challenging underground medium. Typical applications for this type of communication systems include soil condition monitoring, earthquake prediction, communication in mines/tunnels, etc. These applications require a gathering of relevant information from multiple locations, which suggests the use of multiple sensor nodes that would be organized in wireless underground sensor networks (WUSNs). Due to the harsh propagation conditions in the soil medium (including rock, sand, and water sheds), traditional wireless signal propagation techniques using electromagnetic waves can only be applied for very short transmission ranges. In recent years, magnetic induction (MI)-based transmission has been proposed to overcome these issues. In this approach, induction coils are utilized as antennas in the transceivers in order to reduce the vulnerability of signal propagation to the soil properties, in particular the soil conductivity. Correspondingly, the design rules for optimum MI-WUSNs have been shown to substantially differ from the design rules for the traditional wireless communication systems due to unique properties of the transmission channel. In this survey paper, the recent advances in the area of MI-WUSNs are discussed, which range from signal transmission techniques and network design to wireless power transfer and localization. Also, new research challenges in this area are provided.

Wireless underground sensor networks (WUSNs) based on magnetic induction (MI) have been recently proposed as a promising candidate for underground networking. The benefit of the MI-WUSNs compared to other solutions (e.g. so-called Through-The-Earth communication) is related to the substantially lower path loss and lower vulnerability to the changes of the soil properties. In the past, some efforts have been made to characterize the signal transmission in MI-WUSNs. Those investigations, however, refer mostly to the information transmission. One of the target applications of the WUSNs is the object localization in the underground medium, which remains an open issue due to the complicated characteristics of the MI channels corrupted by the influence of soil. In this work, we propose a machine learning based solution for localization. In addition, a novel passive localization technique is introduced, which requires no signal from the target node and thus proves useful for rescue operations, where the battery of the node to be localized is either empty or damaged.

Magnetic induction (MI)-based communication systems have gained increased attention in recent years. Typical applications for these systems lie in the area of wireless power transfer, near-field communication (NFC), and wireless sensor networks in challenging environments. In this paper, a system for simultaneous wireless information and power transfer (SWIPT) using MI-based signal transmission is designed for supporting one data stream and multiple parallel power streams. One of the possible applications for this scheme is an NFC-based access point. The overall system is optimized to guarantee a certain quality-of-service for the data stream as well as a maximum sum receive power for all power receivers (max-sum problem) or a maximum receive power for the worst power receiver (max-min problem), respectively. Both optimization problems turn out to be non-convex, such that the optimum solution cannot be found with limited computational complexity. Hence, we provide efficient suboptimal solutions. In this context, a convex approximation of the transmit power constraint in MI-based multiple-input multiple-output systems turns out to be very useful. A very high achievable power efficiency renders the proposed MI-based SWIPT system very promising.

Body Area Sensor Networks (BASNs) are a challenging research area with applications in healthcare and entertainment. Due to the importance of the target applications in the daily life, BASNs are a promising candidate for being included into the future Internet of Things (IoT). In particular, the data gathering of the human activity may help customizing the services provided by the IoT and thus dramatically improve the IoT user experience. Magnetic Induction (MI) based communication is known in the context of wireless power transfer (WPT), near-field communication (NFC), and wireless sensor networks (WSNs) in challenging environments. In this approach, induction coils are utilized as antennas in the sensor nodes. Distributed beamforming is a well-known technique, that has been thoroughly investigated in the past. Here, the basic idea is to align the phases of signals from different sensor nodes in such a way that a virtual multiple-input multiple-output (MIMO) system with favorable properties is created. For example, this strategy may lead to an improved directionality of the transmitted signals and increase the achievable data rate. In this work, we analyze the potential of the distributed beamforming based MI-BASNs. We observe a significant increase of the achievable data rate for the proposed distributed beamforming compared to our selected baseline scheme.

Wireless underground sensor networks (WUSNs) present a variety of new research challenges. Magnetic induction (MI) based transmission has been proposed to overcome the very harsh propagation conditions in underground communications in recent years. In this approach, induction coils are utilized as antennas in the sensor nodes. This solution achieves larger transmission ranges compared to the traditional electromagnetic (EM) waves based approach. In the past, some efforts have been made to characterize the signal transmission in MI-WUSNs. Those investigations, however, refer mostly to the information transmission. One of the open issues, that may constrain the system design in some of the applications, is the powering of the individual sensor nodes. Due to the low accessibility of the nodes, a new method of wireless power transfer (WPT) for MI-WUSNs is proposed in this work. This method is mainly based on simultaneous signal transmissions from multiple sensor nodes with optimized signal constellations. Furthermore, the optimal scheduling for power transmission and reception is provided, which maximizes the energy efficiency of the network charging procedure. The proposed method is compared with the naive approach and shows a significant improvement of the system performance in terms of energy efficiency.

With the emergence of numerous novel data-intensive applications, the demand on fast wireless access for huge data file transfer and fast mobile data access is growing rapidly. Following this trend, the “Terabit era” is expected to become a reality in the near future. Terahertz (THz) technology is promising as an enabler due to its unique features, among others such as extremely high bandwidth, resistance to eavesdropping and minimal risk to human health. However, a number of technical hurdles need to be overcome to achieve such ultra-fast data rate of Terabit-per-second (Tbps) in the THz spectrum. In this work, a fundamental insight into the modulation scheme design is aimed to be established for THz communication systems. A strategy for transmission scheme selection and a corresponding efficient power allocation algorithm are proposed. Bounds on the maximum distance are determined for which data rates in the order of Tbps can be achieved. The results provide a fundamental insight into the selection of THz transmission schemes for given performance requirements.

Wireless underground sensor networks (WUSNs) present a variety of new research challenges. Magnetic induction (MI)-based transmission has been proposed to overcome the very harsh propagation conditions in underground communications in recent years. In this approach, induction coils are utilized as antennas in the sensor nodes. This solution achieves larger transmission ranges compared with the traditional electromagnetic wave-based approach. In the past, some efforts have been made to characterize the signal transmission in MI-WUSNs. Those investigations, however, mostly refer to the information transmission. One of the open issues that may constrain the system design in some of the applications is the powering of the individual sensor nodes. Due to the low accessibility of the nodes, a new method of wireless power transfer for MI-WUSNs is proposed in this paper. This method is mainly based on simultaneous signal transmissions from multiple sensor nodes with optimized signal constellations. Furthermore, the optimal scheduling for power transmission and reception is provided, which maximizes the energy efficiency of the network charging procedure. The proposed method is compared with the naive approaches and shows a significant improvement of the system performance in terms of energy efficiency.

Magnetic induction (MI) based communication and power transfer systems have gained an increased attention in the recent years. Typical applications for these systems lie in the area of wireless charging, near-field communication, and wireless sensor networks. For an optimal system performance, the power efficiency needs to be maximized. Typically, this optimization refers to the impedance matching and tracking of the split-frequencies. However, an important role of magnitude and phase of the input signal has been mostly overlooked. Especially for the wireless power transfer systems with multiple transmitter coils, the optimization of the transmit signals can dramatically improve the power efficiency. In this work, we propose an iterative algorithm for the optimization of the transmit signals for a transmitter with three orthogonal coils and multiple single coil receivers. The proposed scheme significantly outperforms the traditional baseline algorithms in terms of power efficiency.

Wireless underground sensor networks (WUSNs) present a variety of new research challenges. Magnetic induction (MI) based transmission has been proposed to overcome the very harsh propagation conditions in underground communications in recent years. In this approach, induction coils are utilized as antennas in the sensor nodes. This solution achieves longer transmission ranges compared to the traditional electromagnetic (EM) waves based approach. Furthermore, a passive relaying technique has been proposed in the literature where additional resonant circuits are deployed between the nodes. However, this solution is shown to provide only a limited performance improvement under practical system design contraints. In this work, the potential of an active relay device is investigated which may improve the performance of the system by combining the benefits of the traditional wireless relaying and the MI based signal transmission.

The objective of Wireless Underground Sensor Networks (WUSNs) is to establish an efficient wireless communication in the underground medium. A magnetic induction (MI)-based signal transmission scheme has been proposed to overcome the very harsh propagation conditions in WUSNs. Due to a much lower vulnerability to the environmental changes, the MI technique has been shown to improve the system performance in terms of achievable data rates and coverage compared to the traditional EM wave based transmission. Two different approaches are known from the literature: direct MI transmission and MI waveguides, where many resonant relay circuits are deployed in the latter between the two nodes to be connected. In this work, digital transmission schemes are investigated for MI-WUSNs employing these two approaches. The influence of transmission parameters like symbol duration and modulation scheme are studied and new methods for their optimization are proposed. In this context, significant gains can be achieved compared to the naive straightforward approaches.

Wireless underground sensor networks (WUSNs) are an emerging and promising research area. The aim of WUSNs is to establish an efficient wireless communication in the underground medium. A magnetic induction (MI)-based waveguide technique has been proposed to overcome the very harsh propagation conditions in WUSNs. In this approach, several resonant relay circuits are deployed between the two nodes to be connected. This technique allows for an extension of the transmission range. In this work, we investigate digital transmission schemes for MI-WUSNs. We analyze the influence of transmission parameters like symbol duration and modulation scheme and propose methods for their optimization.

Wireless underground sensor networks (WUSNs) present a variety of new research challenges. Recently, a magnetoinductive (MI) waveguide technique has been proposed to cope with the very harsh propagation conditions in WUSNs. This relay-based approach allows for an extension of the transmission range, which can be quite limited if relays are not deployed. In this paper, tree-based WUSNs are considered. The objective of our work is to determine the optimal system parameters, topology, and deployment strategy in order to avoid bottlenecks in the system and achieve optimal network throughput. We compare two different deployment schemes: MI waveguides and direct MI transmission (no relays deployed) based connections between sensors. The two schemes are different in nature and propagation characteristics. Therefore, different optimization techniques are utilized. The optimal set of system parameters is chosen to maximize the channel capacity of the worst link and therefore optimize the available data rate. The bottleneck throughput of the direct MI transmission based network can be then compared with the respective results of the MI waveguides based network. In several cases, we observe a better performance of the direct MI transmission based networks.

Wireless Underground Sensor Networks (WUSNs) present a variety of new research challenges. Recently, a magneto-inductive (MI) waveguide technique has been proposed to cope with the very harsh propagation conditions in WUSNs. This approach allows for an extension of the transmission range, which can be quite limited if relays are not deployed. In this paper, tree-based WUSNs are considered with sensors connected via MI-waveguides. The objective of our work is to determine the optimal system parameters and topology in order to avoid bottlenecks in the system and achieve optimal network throughput.

Wireless Underground Sensor Networks (WUSNs) present a variety of new research challenges. Recently a magneto-inductive (MI) waveguide technique has been proposed to overcome the very harsh propagation conditions in WUSNs. In this approach, several resonant relay circuits are deployed between the two nodes to be connected. This technique allows for an extension of the transmission range, which can be quite limited, if relays are not deployed. In this paper, channel and noise models for MI-WUSNs using MI-waveguides are developed. Results of a numerical evaluation of the channel capacity under practical constraints are provided and the influence of the system parameters on the performance is discussed.

Magnetic Induction (MI) techniques enable efficient wireless communications in dense media with high material absorptions, such as underground soil medium and oil reservoirs. A wide range of novel and important applications in such RF-challenged environments can be realized based on the MI communication mechanism. Despite the potential advantages, the major bottleneck of the MI communication is the limited channel capacity due to the low MI bandwidth. In this paper, the Spread Resonance (RS) strategy is developed for the MI communication in RF-challenged environments which greatly increases the MI channel capacity. Specifically, instead of using the same resonant frequency for all the MI coils, the spread resonance strategy allocates different resonant frequencies for different MI relay and transceiver coils. An optimization solution for the resonant frequency allocation is formulated to maximize the MI channel capacity which captures multiple unique MI effects, including the parasitic capacitor in each MI coil, the Eddy currents in various transmission media with limited conductivities, and the random direction of each coil. Numerical evaluations are provided to validate the significant channel capacity improvements by the proposed SR strategy for MI communication systems.