Offline multi-agent reinforcement learning (MARL) addresses key limitations of online MARL, such as safety concerns, expensive data collection, extended training intervals, and high signaling overhead caused by online interactions with the environment. In this work, we propose an offline MARL algorithm for radio resource management (RRM), focusing on optimizing scheduling policies for multiple access points (APs) to jointly maximize the sum and tail rates of user equipment (UEs). We evaluate three training paradigms: centralized, independent, and centralized training with decentralized execution (CTDE). Our simulation results demonstrate that the proposed offline MARL framework outperforms conventional baseline approaches, achieving over a$15\%$improvement in a weighted combination of sum and tail rates. Additionally, the CTDE framework strikes an effective balance, reducing the computational complexity of centralized methods while addressing the inefficiencies of independent training. These results underscore the potential of offline MARL to deliver scalable, robust, and efficient solutions for resource management in dynamic wireless networks.

This paper introduces the minimum error entropy (MEE) criterion as an advanced information-theoretic loss function tailored for deep learning applications in wireless communications. The MEE criterion leverages higher-order statistical properties, offering robustness in noisy scenarios like Rayleigh fading and impulsive interference. In addition, we propose a less complex, matrix based version of the MEE function to enhance practical usability in wireless communications. The method is evaluated through simulations on two critical applications: over-the-air regression and indoor localization. Results indicate that the MEE criterion outperforms conventional loss functions, such as mean squared error (MSE) and mean absolute error (MAE), achieving significant performance improvements in terms of accuracy, over 20% gain over traditional methods, and convergence speed across various channel conditions. This work establishes MEE as a promising alternative for wireless communication tasks in deep learning models, enabling better resilience and adaptability.

Contemporary wireless communication systems rely on Multi-User Multiple-Input Multiple-Output (MU-MIMO) techniques. In such systems, each Access Point (AP) is equipped with multiple antenna elements and serves multiple devices simultaneously. Notably, traditional systems utilize fixed antennas, i.e., antennas without any movement capabilities, while the idea of movable antennas has recently gained traction among the research community. By moving in a confined region, movable antennas are able to exploit the wireless channel variation in the continuous domain. This additional degree of freedom may enhance the quality of the wireless links, and consequently the communication performance. However, movable antennas for MU-MIMO proposed in the literature are complex, bulky, expensive and present a high power consumption. In this paper, we propose an alternative to such systems that has lower complexity and lower cost. More specifically, we propose the incorporation of rotation capabilities to APs equipped with Uniform Linear Arrays (ULAs) of antennas. We consider the uplink of an indoor scenario where the AP serves multiple devices simultaneously. The optimal rotation of the ULA is computed based on estimates of the positions of the active devices and aiming at maximizing the per-user mean achievable Spectral Efficiency (SE). Adopting a spatially correlated Rician channel model, our numerical results show that the rotation capabilities of the AP can bring substantial improvements in the SE in scenarios where the line-of-sight component of the channel vectors is strong. Moreover, our proposed system is robust against imperfect positioning estimates.

This article introduces a sophisticated and adaptable framework combining extreme value theory with radio maps to spatially model extreme channel conditions accurately. Utilizing existing signal-to-noise ratio (SNR) measurements and leveraging Gaussian processes, our approach predicts the tail of the SNR distribution, which entails estimating the parameters of a generalized Pareto distribution, at unobserved locations. This innovative method offers a versatile solution adaptable to various resource allocation challenges in ultrareliable communications. We evaluate the performance of this method in a rate maximization problem with defined outage constraints and compare it with a benchmark in the literature. Notably, the proposed approach meets the outage demands in a larger percentage of the coverage area and reaches higher transmission rates. Finally, we analyze the impact of the localization error on the system performance, highlighting the need for accurate positioning algorithms to enable efficient resource allocation.

This paper introduces an enhancement to the Long-Range Frequency-Hopping Spread Spectrum (LR-FHSS), a modulation within the LoRaWAN specification, particularly useful in dense networks supported by satellite gateways. LR-FHSS replicates headers and payload fragments across different physical channels, but the distinct transmission strategies for headers and payloads can lead to uneven success rates and degrading performance. To address this, we propose a scheme that allocates orthogonal physical channels for headers and payloads. Through analytical and simulation results, considering factors such as payload size and the number of users, we identify the optimal distribution of channels between headers and payloads to maximize success probability. The proposed scheme significantly improves performance, supporting up to 50% more users at the same reliability level as the standard LR-FHSS.

This paper compares distributed massive Multiple-Input Multiple-Output (mMIMO) and centralized MIMO in the context of over-the-air federated learning, taking into account a detailed wireless channel model that includes spatially uncorrelated Rician fading to represent the Line of Sight (LoS) component. Our simulation results provide valuable insight into how a distributed architecture can maintain a lower Mean Squared Error (MSE) in scenarios that require extensive coverage. Furthermore, our research highlights the importance of considering the impact of the LoS component and power limitations when analyzing over-the-air aggregation, as these characteristics can substantially influence aggregation performance. Adopting a distributed architecture allows devices to conserve energy while simultaneously achieving a lower MSE in the aggregation process compared to the centralized architecture. Specifically, when considering four Access Points (APs) and 200 devices, the average transmit power is reduced by approximately 31% while achieving 16% lower aggregation MSE compared to a centralized approach.

Industry and academia have been working towards the evolution from Centralized massive Multiple-Input Multiple-Output (CmMIMO) to Distributed mMIMO (DmMIMO) architectures. Instead of splitting a coverage area into many cells, each served by a single Base Station equipped with several antennas, the whole coverage area is jointly covered by several Access Points (AP) equipped with few or single antennas. Nevertheless, when choosing between deploying more APs with few or single antennas or fewer APs equipped with many antennas, one observes an inherent trade-off between the beamforming and macro-diversity gains that has not been investigated in the literature. Given a total number of antenna elements and total downlink power, under a channel model that takes into account a probability of Line-of-Sight (LoS) as a function of the distance between the User Equipments (UEs) and APs, our numerical results show that there exists a “sweet spot” on the optimal number of APs and of antenna elements per AP which is a function of the physical dimensions of the coverage area.

Smart Campus is one of the essential use cases in the Internet of Things (acrshort IoT). This work describes a long-range wide area network (LoRaWAN)-based smart campus data set comprising measurements of several sensors in hundreds of acrshort IoT devices. In addition, the data set contains information about the PHY and MAC layers of the LoRaWAN network. Therefore, we first describe the LoRa network, the connection between devices and gateway, and the gateway and network server. As the wireless network is prone to errors, e.g., outages, collisions, and interference, among other factors, the collected data may contain missing transmissions. To alleviate the problem of missing values, we resort to a$k$-nearest neighbor approach. For example, once the data imputation phase is complete, we employ an long short-term memory (LSTM) architecture to predict future sensor readings. Then, we build a deep neural network (DNN) to predict the room occupancy based on the selected sensor’s readings. Our results show that our model achieves an accuracy of 95% in predicting the number of people in a room. Furthermore, the data set is openly available and described in detail, which is an opportunity to explore other features and applications in Smart Campus.

The age of information (AoI) is used to measure the freshness of the data. In IoT networks, the traditional resource management schemes rely on a message exchange between the devices and the base station (BS) before communication which causes high AoI, high energy consumption, and low reliability. Unmanned aerial vehicles (UAVs) as flying BSs have many advantages in minimizing the AoI, energy-saving, and throughput improvement. In this paper, we present a novel learning-based framework that estimates the traffic arrival of IoT devices based on Markovian events. The learning proceeds to optimize the trajectory of multiple UAVs and their scheduling policy. First, the BS predicts the future traffic of the devices. We compare two traffic predictors: 1) the forward algorithm (FA) and 2) the long short-term memory (LSTM). Afterward, we propose a deep reinforcement learning (DRL) approach to optimize the optimal policy of each UAV. Finally, we manipulate the optimum reward function for the proposed DRL approach. Simulation results show that the proposed algorithm outperforms the random-walk (RW) baseline model regarding the AoI, scheduling accuracy, and transmission power.

In this work, we apply an asynchronous contention resolution-aided ALOHA protocol to the recently proposed long-range frequency hopping spread spectrum (LR-FHSS) scheme, which is an amendment of widespread chirp spread spectrum (CSS)-based long-range (LoRa) modulation patented by Semtech. More specifically, we modify the so-called asynchronous contention resolution diversity ALOHA (ACRDA) protocol to comply with LR-FHSS requirements and improve LR-FHSS performance against collisions. In the proposed scheme, only the gateway requires modifications, while the ACRDA operation is transparent from the end-devices perspective, which follows the regular LR-FHSS operation. Our results, obtained using a discrete event simulator, show that the proposed scheme outperforms regular LR-FHSS in various aspects, such as average network success (up to 60% improvement in the scenarios considered), goodput (2× better) and number of supported devices for a target reliability level (more than 2×). Finally, we also evaluate the impact on the performance of the proposed scheme of parameters such as the window and the step sizes, showing that a relatively small window size (~2× the time-on-air of a single message) is sufficient to approach the asymptotic maximum network success achieved by an infinite window.

This work proposes and assesses Bayesian-based localization methods using time difference of arrival, angle of arrival, and hybrid measurements. First, a Bayesian localization model is constructed, and the Markov chain Monte Carlo method approximates the target’s three-dimensional posterior distribution. Then, the model’s performance is evaluated in a 3GPP indoor factory environment using a distributed antenna system with a centralized controller for synchronizing and merging information. The received signal strength is used to select a subset of available anchors, and the estimation accuracy is measured in terms of horizontal and vertical errors. The results show that the Bayesian framework meets the horizontal and vertical errors requirements of the 3GPP for commercial cases with over 100 measurements. The accuracy can be improved by acquiring more measurements or increasing the number of active remote radio heads. However, when information fusion is applied (hybrid model), increasing the number of active anchors decreases the estimation performance.

In this work, we address a novel replication scheme for Internet-of-Things (IoT) low power wide area networks (LPWAN), considering the ability of a gateway to recover, through successive interference cancellation (SIC), superposed signals in the power domain. We show that the introduced mathematical framework matches the Monte Carlo simulations. The proposed scheme outperforms typical transmission schemes, at least from 1 to 2 orders of magnitude, in terms of outage probability. We also show that the scheme fits well with previous replication schemes, outperforming their versions without non-orthogonal replications. Moreover, we show that the proposed scheme can be robust to cases with high SIC imperfection, presenting slight performance losses even in very harsh scenarios, as long as the constraints are met. Finally, we conclude that the proposed scheme can be more energy efficient than previous replication schemes and achieve better reliability in traffic-loaded networks.

Compressed sensing multi-user detection (CS-MUD) algorithms play a key role in optimizing grant-free (GF) non-orthogonal multiple access (NOMA) for massive machine-type communications (mMTC). However, current CS-MUD algorithms cannot be efficiently parallelized, leading to computationally expensive implementations of joint activity detection and channel estimation (JADCE) as the number of deployed machine-type devices (MTDs) increases. To address this, the present work proposes novel JADCE algorithms that can be applied in parallel for different clusters of MTDs by exploiting the structure of the pilot sequences. These are the approximation error method (AEM)-alternating direction method of multipliers (ADMM), and AEM-sparse Bayesian learning (SBL). Results presented in terms of the normalized mean square error and the probability of miss detection show comparable performance to the conventional algorithms. However, both AEM-ADMM and AEM-SBL algorithms have significantly reduced computational complexity and run times, thus, facilitating network scalability.

Channel state information (CSI) is crucial for achieving ultra-reliable low-latency communication (URLLC) in wireless networks. The main associated problems are the CSI acquisition time, which impacts the latency requirements of time-critical applications, and the estimation accuracy, which degrades the signal-to-interference-plus-noise ratio, thus, reducing communication reliability. In this work, we formulate and solve a minimum-power precoding design problem simultaneously serving multiple URLLC users in the downlink with imperfect CSI. Specifically, we develop an algorithm that exploits state-of-the-art precoding schemes such as maximal ratio transmission and zero-forcing, and adjust the power of the precoders to compensate for the channel estimation error uncertainty based on the extreme value theory framework. Finally, we evaluate the performance of our method and show its superiority with respect to a worst-case robust precoding benchmark.

In many massive IoT communication scenarios, the IoT devices require coverage from dynamic units that can move close to the IoT devices and reduce the uplink energy consumption. A robust solution is to deploy a large number of UAVs (UAV swarm) to provide coverage and a better line of sight (LoS) for the IoT network. However, the study of these massive IoT scenarios with a massive number of serving units leads to high dimensional problems with high complexity. In this paper, we apply multi-agent deep reinforcement learning to address the high-dimensional problem that results from deploying a swarm of UAVs to collect fresh information from IoT devices. The target is to minimize the overall age of information in the IoT network. The results reveal that both cooperative and partially cooperative multi-agent deep reinforcement learning approaches are able to outperform the high-complexity centralized deep reinforcement learning approach, which stands helpless in large-scale networks.

Radio-frequency wireless energy transfer (WET) is a promising technology to realize wireless-powered underground sensor networks (WPUSNs) and enable sustainable underground monitoring. However, due to the severe attenuation in harsh underground soil and the tight energy budget of the underground sensors, traditional WPUSNs relying on the channel state information (CSI) are highly inefficient, especially in massive WET scenarios. To address this challenge, we comparatively assess the feasibility of several state-of-the-art CSI-free multiantenna WET schemes for WPUSNs, under a given power budget. Moreover, to overcome the extremely low WET efficiency in underground channels, we propose a distributed CSI-free system, where multiple power beacons (PBs) simultaneously charge a large set of underground sensors without any CSI. We consider the position-aware$K$-Means and the position-agnostic equally far-from-center (EFFC) approaches for the optimal deployment of the PBs. Our results evince that the performance of the proposed distributed CSI-free system can approach or even surpass that of a traditional full-CSI WET strategy, especially when adopting an appropriate CSI-free scheme, applying the advisable PBs deployment approach, and equipping the PBs with an appropriate number of antennas. Finally, we discuss the impact of underground parameters, i.e., the burial depth of devices and the volumetric water content of soil, on the system’s performance, and identify potential challenges and research opportunities for practical distributed CSI-free WPUSNs deployment.

Ultrareliable low-latency communication (URLLC) constitutes a key service class of the fifth generation (5G) and beyond cellular networks. Notably, designing and supporting URLLC pose a herculean task due to the fundamental need to identify and accurately characterize the underlying statistical models in which the system operates, e.g., interference statistics, channel conditions, and the behavior of protocols. In general, multilayer end-to-end approaches considering all the potential delay and error sources and proper statistical tools and methodologies are inevitably required for providing strong reliability and latency guarantees. This article contributes to the body of knowledge in the latter aspect by providing a tutorial on several statistical tools and methodologies that are useful for designing and analyzing URLLC systems. Specifically, we overview the frameworks related to the following: 1) reliability theory; 2) short packet communications; 3) inequalities, distribution bounds, and tail approximations; 4) rare-events simulation; 5) queuing theory and information freshness; and 6) large-scale tools, such as stochastic geometry, clustering, compressed sensing, and mean-field (MF) games. Moreover, we often refer to prominent data-driven algorithms within the scope of the discussed tools/methodologies. Throughout this article, we briefly review the state-of-the-art works using the addressed tools and methodologies, and their link to URLLC systems. Moreover, we discuss novel application examples focused on physical and medium access control layers. Finally, key research challenges and directions are highlighted to elucidate how URLLC analysis/design research may evolve in the coming years.

Cellular networks that are traditionally designed for human-type communication (HTC) have the potential to provide cost effective connectivity to machine-type communication (MTC). However, MTC is characterized by unprecedented traffic in cellular networks, thus posing a challenge to its successful incorporation. In this work, we propose a unified framework for amicable coexistence of MTC and HTC. We consider a heterogeneous network where machine-type devices coexist with enhanced mobile broadband (eMBB) devices and propose transceiver techniques that promote efficient signal recovery from these devices. For this, we present an eMBB pilot and MTC data generation strategy that facilitates joint coherent decoding of eMBB data and non-coherent decoding of MTC data. Furthermore, we assess the feasibility of coexistence using receiver operating characteristics, outage probability, and normalized mean square error (NMSE). Our numerical results reveal that a harmonious coexistence of the heterogeneous services can be achieved with properly configured average signal-to-noise ratios and pilot length.

Two of the main problems to achieve ultra-reliable low-latency communications (URLLC) are related to instantaneous channel state information (I-CSI) acquisition and the coexistence with other service modes such as enhanced mobile broadband (eMBB). The former comes from the non-negligible time required for accurate I-CSI acquisition, while the latter, from the heterogeneous and conflicting requirements of different nodes sharing the same network resources. In this paper, we leverage the I-CSI of multiple eMBB links and the channel measurement’s history of a URLLC user for multi-antenna beamforming design. Specifically, we propose a precoding design that minimizes the transmit power of a base station (BS) providing eMBB and URLLC services with signal-to-interference-plus-noise ratio (SINR) and outage constraints, respectively, by modifying existing I-CSI-based precoding schemes to account for URLLC channel history information. Moreover, we illustrate and validate the proposed method by adopting zero-forcing (ZF) and the transmit power minimization (TPM) precoding with SINR constraints. We show that the ZF implementation outperforms TPM in adverse channel conditions as in Rayleigh fading, while the situation is rapidly reversed as the channel experiences some line-of-sight (LOS). Finally, we determine the confidence levels at which the target outage probabilities are reached. For instance, we show that outage probabilities below 10-3are achievable with more than 99% confidence for both precoding schemes under favorable LOS conditions with 16 transmit antennas and 500 samples of URLLC channel history.

This paper presents new analytical results for evaluating the ALOHA-like multi-class random access wireless network’s performance. The proposed model is motivated by the growth of low-power wireless networks that employ random access protocols. In particular, we compare our analytical formulation with system-level simulations of Long Range (LoRa) technology. We show that the proposed formulation provides an accurate approximation of LoRaWAN performance capturing its main trade-offs. The main contributions are (i) an extensive analysis of the impact of different LoRa spreading factors (SFs) allocation strategies, including area intersection among SFs, which is little explored in the literature and represents the optimal approach under some conditions; and (ii) the optimal proportion of users that maximizes the network throughput for each class and for each allocation strategy considered in the paper.

The current random access (RA) allocation techniques suffer from congestion and high signaling overhead while serving massive machine-type communication (mMTC) applications. To this end, third-generation partnership project introduced the need to use fast uplink grant (FUG) allocation in order to reduce latency and increase reliability for smart Internet of Things (IoT) applications with strict Quality-of-Service constraints. We propose a novel FUG allocation based on support vector machine (SVM). First, machine-type communication (MTC) devices are prioritized using an SVM classifier. Second, a long short-term memory architecture is used for traffic prediction and correction techniques to overcome prediction errors. Both results are used to achieve an efficient resource scheduler in terms of the average latency and total throughput. A coupled Markov modulated Poisson process (CMMPP) traffic model with mixed alarm and regular traffic is applied to compare the proposed FUG allocation to other existing allocation techniques. In addition, an extended traffic model-based CMMPP is used to evaluate the proposed algorithm in a more dense network. We test the proposed scheme using real-time measurement data collected from the Numenta anomaly benchmark (NAB) database. Our simulation results show the proposed model outperforms the existing RA allocation schemes by achieving the highest throughput and the lowest access delay of the order of 1 ms by achieving prediction accuracy of 98 % when serving the target massive and critical MTC applications with a limited number of resources.

In this work, we present a novel traffic prediction and fast uplink (FU) framework for IoT networks controlled by binary Markovian events. First, we apply the forward algorithm with hidden Markov models (HMMs) in order to schedule the available resources to the devices with maximum likelihood activation probabilities via the FU grant. In addition, we evaluate the regret metric as the number of wasted transmission slots to evaluate the performance of the prediction. Next, we formulate a fairness optimization problem to minimize the Age of Information (AoI) while keeping the regret as minimum as possible. Finally, we propose an iterative algorithm to estimate the model hyperparameters (activation probabilities) in a real-time application and apply an online-learning version of the proposed traffic prediction scheme. Simulation results show that the proposed algorithms outperform baseline models, such as time-division multiple access (TDMA) and grant-free (GF) random-access in terms of regret, the efficiency of system usage, and AoI.

Radio-frequency (RF) wireless energy transfer (WET) is a key technology that may allow seamlessly powering future massive low-energy Internet of Things (IoT) networks. To enable efficient massive WET, channel state information (CSI)-limited/free multiantenna transmit schemes have been recently proposed in the literature. The idea is to reduce/null the energy costs to be paid by energy harvesting (EH) IoT nodes from participating in large-scale time/power-consuming CSI training, but still enable some transmit spatial gains. In this article, we take another step forward by proposing a novel CSI-free rotary antenna beamforming (RAB) WET scheme that outperforms all state-of-the-art CSI-free schemes in a scenario, where a power beacon (PB) equipped with a uniform linear array (ULA) powers a large set of surrounding EH IoT devices. RAB uses a properly designed CSI-free beamformer combined with a continuous or periodic rotation of the ULA at the PB to provide average EH gains that scale as$0.85\sqrt {M}$, where$M$is the number of PB’s antenna elements. Moreover, a rotation-specific power control mechanism was proposed to: 1) fairly optimize the WET process if devices’ positioning information is available and/or 2) avoid hazards to human health in terms of specific absorption rate (SAR), which is an RF exposure metric that quantifies the absorbed power in a unit mass of human tissue. We show that RAB performance even approaches quickly (or surpasses, for scenarios with a sufficiently large number of EH devices, or when using the proposed power control) the performance of a traditional full-CSI-based transmit scheme, and it is also less sensitive to SAR constraints. Finally, we discuss important practicalities related to RAB such as its robustness against non line-of-sight (LOS) conditions compared to other CSI-free WET schemes, and its generalizability to scenarios where the PB uses other than a ULA topology.

In this article, we evaluate the effective energy efficiency (EEE) and propose delay-outage aware resource allocation strategies for energy-limited Internet of Things (IoT) devices under the finite blocklength (FBL) regime. The EEE is a cross-layer model, measured by the ratio of effective capacity to the total consumed power. To maximize the EEE, there is a need to optimize transmission parameters, such as transmission power and rate efficiently. Whereas it is quite complex to study the impact of transmission power, or rate alone, the complexity is aggravated by the simultaneous consideration of both variables. Hence, we formulate power allocation (PA) and rate allocation (RA) optimization problems individually and jointly to maximize EEE. Furthermore, we investigate the performance of the EEE under constant and random arrivals, where statistical QoS constraints are imposed on buffer overflow probability. Using effective bandwidth and effective capacity theories, we determine the arrival rate and the required service rate that satisfy the QoS constraints. After that, we compare the performance of different iterative algorithms, such as Dinkelbach’s and cross entropy, which guarantee the convergence for the optimal solution. By numerical analysis, the influence of source characteristics, fixed transmission rate, error probability, coding blocklength, and QoS constraints on the throughput are identified. Our analysis reveals that the joint PA and RA is the optimal resources allocation strategy for maximizing the EEE in the presence of constant and random data arrivals. Finally, the results illustrate that modified Dinkelbach’s algorithm has high performance and low complexity compared to others.

The Internet of Things (IoT) brings connectivity to a massive number of devices that demand energy-efficient solutions to deal with limited battery capacities, uplink-dominant traffic, and channel impairments. In this work, we explore the use of unmanned aerial vehicles (UAVs) equipped with configurable antennas as a flexible solution for serving low-power IoT networks. We formulate an optimization problem to set the position and antenna beamwidth of the UAV, and the transmit power of the IoT devices subject to average-signal-to-average-interference-plus-noise ratio ($\bar {\text {S}}\overline {\text {IN}}\text {R}$) Quality-of-Service (QoS) constraints. We minimize the worst case average energy consumption of the latter, thus targeting the fairest allocation of the energy resources. The problem is nonconvex and highly nonlinear; therefore, we reformulate it as a series of three geometric programs that can be solved iteratively. Results reveal the benefits of planning the network compared to a random deployment in terms of reducing the worst case average energy consumption. Furthermore, we show that the target$\bar {\text {S}}\overline {\text {IN}}\text {R}$is limited by the number of IoT devices, and highlight the dominant impact of the UAV hovering height when serving wider areas. Our proposed algorithm outperforms other optimization benchmarks in terms of minimizing the average energy consumption at the most energy-demanding IoT device, and convergence time.

Reducing energy consumption is a pressing issue in low-power machine-type communication (MTC) networks. In this regard, the Wake-up Signal (WuS) technology, which aims to minimize the energy consumed by the radio interface of the machine-type devices (MTDs), stands as a promising solution. However, state-of-the-art WuS mechanisms use static operational parameters, so they cannot efficiently adapt to the system dynamics. To overcome this, we design a simple but efficient neural network to predict MTC traffic patterns and configure WuS accordingly. Our proposed forecasting WuS (FWuS) leverages an accurate long short-term memory (LSTM)-based traffic prediction that allows extending the sleep time of MTDs by avoiding frequent page monitoring occasions in the idle state. Simulation results show the effectiveness of our approach. The traffic prediction errors are shown to be below 4%, being a false-alarm and miss-detection probabilities, respectively, below 8.8% and 1.3%. In terms of energy consumption reduction, FWuS can outperform the best benchmark mechanism by up to 32%. Finally, we certify the ability of FWuS to dynamically adapt to traffic density changes, promoting low-power MTC scalability.

Various industrial Internet of Things applications demand execution periods throughout which no communication failure is tolerated. However, the classical understanding of reliability in the context of ultra-reliable low-latency communication (URLLC) does not reflect on the time-varying characteristics of the wireless channel. In this article, we introduce a novel mission reliability and mission effective capacity metric that takes these phenomena medium into account, while specifically studying multiconnectivity (MC)-enabled industrial radio systems. We assume uplink short packet transmission with no channel state information at URLLC user (the transmitter) and sporadic traffic arrival. Moreover, we leverage the existing framework of dependability theory and provide closed-form expressions (CFEs) for the mission reliability of the MC system using the maximal-ratio combining scheme. We do so by utilizing the mean time to first failure, which is the expected time of failure occurring for the first time. Moreover, we also derive exact CFEs for second-order statistics, such as level crossing rate and average fade duration, showing how fades are distributed in fading channels with respect to time. Furthermore, the design throughput maximization problem under the mission reliability constraint is solved numerically through the cross-entropy method.

The offshore wind farms are gaining momentum due to their promise to offer sustainable energy with low pollution and greenhouse gas emission. However, despite all the immense technological progress of recent years, the operation in a harsh and hard-to-reach environment remains challenging. According to the reports, each offshore wind turbine requires five maintenance visits a year on average, and the cumulative repair costs constitute around 30% of the turbine’s life-cycle expenditure. Motivated by the advancement of massive machine-type connectivity (mMTC) and satellite technologies, in this study, we investigate the potential of these to enable remote monitoring of the offshore wind farms. Specifically, the two alternative architectures are considered. The indirect architecture relies on using a local mMTC gateway (GW) with a backbone over a reliable communication channel (e.g., satellite or wire-based). The direct approach implies the transmission of the data by sensors on the wind turbines directly to the mMTC GW on the low-Earth-orbit satellite. The details of the system design, the alternative implementation strategies, and relevant pros, cons, and tradeoffs are pin-pointed. Finally, we employ simulations using realistic deployment and traffic and advanced propagation and collision models to characterize these two approaches’ feasibility and packet delivery probability numerically when implemented over LoRaWAN mMTC technology.

Data aggregation is an efficient approach to handle the congestion introduced by a massive number of machine type devices (MTDs). The aggregators not only collect data but also implement scheduling mechanisms to cope with scarce network resources. We use the concept of meta distribution (MD) of the signal-to-interference ratio (SIR) to gain a complete understanding of the per-link reliability and describe the performance of two scheduling methods for data aggregation of machine type communication (MTC): random resource scheduling (RRS) and channel-aware resource scheduling (CRS). The results show the fraction of users in the network that achieves a target reliability, which is an important aspect to consider when designing wireless systems with stringent service requirements.

In this work, we study the coexistence in the same Radio Access Network (RAN) of two use cases present in the Fifth Generation (5G) of wireless communication systems: enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC). eMBB services are requested for applications that demand extremely high data rates and moderate requirements on latency and reliability, whereas mMTC enables applications for connecting a massive number of low-power and low-complexity devices. The coexistence of both services is enabled by means of network slicing and Non-Orthogonal Multiple Access (NOMA) with Successive Interference Cancellation (SIC) decoding. Under the orthogonal slicing, the radio resources are exclusively allocated to each service, while in the non-orthogonal slicing the traffics from both services overlap in the same radio resources. We evaluate the uplink performance of both services in a scenario with a multi-antenna Base Station (BS). Our simulation results show that the performance gains obtained through multiple receive antennas are more accentuated for the non-orthogonal slicing than for the orthogonal allocation of resources, such that the non-orthogonal slicing outperforms its orthogonal counterpart in terms of achievable data rates or number of connected devices as the number of receive antennas increases.

The 5G systems feature three generic services: enhanced Mobile BroadBand (eMBB), massive Machine-Type Communications (mMTC) and Ultra-Reliable and Low-Latency Communications (URLLC). The diverse requirements of these services in terms of data-rates, number of connected devices, latency and reliability can lead to a sub-optimal use of the 5G network, thus network slicing is proposed as a solution that creates customized slices of the network specifically designed to meet the requirements of each service. Under the network slicing, the radio resources can be shared in orthogonal and non-orthogonal schemes. Motivated by Industrial Internet of Things (IIoT) scenarios where a large number of sensors may require connectivity with stringent requirements of latency and reliability, we propose the use of Non-Orthogonal Multiple Access (NOMA) to improve the number of URLLC devices that are connected in the uplink to the same base station (BS), for both orthogonal and non-orthogonal network slicing with eMBB devices. The multiple URLLC devices transmit simultaneously and across multiple frequency channels. We set the reliability requirements for the two services and evaluate the pairs of achievable sum rates. We show that, even with overlapping transmissions from multiple eMBB and URLLC devices, the use of NOMA techniques allows us to guarantee the reliability requirements for both services.

Recent advances on wireless energy transfer (WET) make it a promising solution for powering future Internet-of-Things (IoT) devices enabled by the upcoming sixth-generation (6G) era. The main architectures, challenges and techniques for efficient and scalable wireless powering are overviewed in this article. Candidates enablers, such as energy beamforming (EB), distributed antenna systems (DASs), advances on devices' hardware and programmable medium, new spectrum opportunities, resource scheduling, and distributed ledger technology are outlined. Special emphasis is placed on discussing the suitability of channel state information (CSI)-limited/free strategies when powering simultaneously a massive number of devices. The benefits from combining DAS and EB, and from using average CSI whenever available, are numerically illustrated. The pros and cons of the state-of-the-art CSI-free WET techniques in ultralow power setups are thoroughly revised, and some possible future enhancements are outlined. Finally, key research directions toward realizing WET-enabled massive IoT networks in the 6G era are identified and discussed in detail.

Radio-frequency wireless energy transfer (RF-WET) is emerging as a potential green enabler for massive Internet of Things (IoT). Herein, we analyze channel state information (CSI)free multiantenna strategies for powering wirelessly a large set of single-antenna IoT devices. The CSI-free schemes are AASS (AA-IS), where all antennas transmit the same (independent) signal(s), and SA, where just one antenna transmits at a time such that all antennas are utilized during the coherence block. We characterize the distribution of the provided energy under correlated Rician fading for each scheme and find out that while AA-IS and SA cannot take advantage of the multiple antennas to improve the average provided energy, its dispersion can be significantly reduced. Meanwhile, AA-SS provides the greatest average energy, but also the greatest energy dispersion, and the gains depend critically on the mean phase shifts between the antenna elements. We find that consecutive antennas must be π-phase shifted for optimum average energy performance under AA-SS. Our numerical results evidence that correlation is beneficial under AA-SS, while a greater line of sight (LOS) and/or the number of antennas is not always beneficial under such a scheme. Meanwhile, both AA-IS and SA schemes benefit from small correlation, large LOS, and/or a large number of antennas. Finally, AA-SS (SA and AA-IS) is (are) preferable when devices are (are not) clustered in specific spatial directions.

Wireless energy transfer (WET) is emerging as an enabling green technology for Internet-of-Things (IoT) networks. WET allows the IoT devices to wirelessly recharge their batteries with energy from external sources such as dedicated radio-frequency transmitters called power beacons (PBs). In this article, we investigate the optimal deployment of PBs that guarantees a network-wide energy outage constraint. Optimal positions for the PBs are determined by maximizing the average incident power for the worst location in the service area since no information about the sensor deployment is provided. Such network planning guarantees the fairest harvesting performance for all the IoT devices. Numerical simulations evidence that our proposed optimization framework improves the energy supply reliability compared to benchmark schemes. Additionally, we show that although both, the number of deployed PBs and the number of antennas per PB, introduce performance improvements, the former has a dominant role. Finally, our proposal allows to extend the coverage area while keeping the total power budget fixed, which additionally reduces the level of electromagnetic radiation in the vicinity of PBs.

Effective capacity (EC) defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between EC and power consumption. We analyze the EEE of ultrareliable networks operating in the finite-blocklength regime. We obtain a closed-form approximation for the EEE in quasistatic Nakagami- m (and Rayleigh as subcase) fading channels as a function of power, error probability, and latency. Furthermore, we characterize the quality-of-service constrained EEE maximization problem for different power consumption models, which shows a significant difference between finite and infinite-blocklength coding with respect to EEE and optimal power allocation strategy. As asserted in the literature, achieving ultrareliability using one transmission consumes a huge amount of power, which is not applicable for energy limited Internet-of-Things devices. In this context, accounting for empty buffer probability in machine-type communication (MTC) and extending the maximum delay tolerance jointly enhances the EEE and allows for adaptive retransmission of faulty packets. Our analysis reveals that obtaining the optimum error probability for each transmission by minimizing the nonempty buffer probability approaches EEE optimality, while being analytically tractable via Dinkelbach's algorithm. Furthermore, the results illustrate the power saving and the significant EEE gain attained by applying adaptive retransmission protocols, while sacrificing a limited increase in latency.

Wireless Energy Transfer (WET) is a promising solution for powering massive Internet of Things deployments. An important question is whether the costly Channel State Information (CSI) acquisition procedure is necessary for optimum performance. In this paper, we shed some light into this matter by evaluating CSI-based and CSI-free multi-antenna WET schemes in a setup with WET in the downlink, and periodic or Poisson-traffic Wireless Information Transfer (WIT) in the uplink. When CSI is available, we show that a maximum ratio transmission beamformer is close to optimum whenever the farthest node experiences at least 3 dB of power attenuation more than the remaining devices. On the other hand, although the adopted CSI-free mechanism is not capable of providing average harvesting gains, it does provide greater WET/WIT diversity with lower energy requirements when compared with the CSI-based scheme. Our numerical results evidence that the CSI-free scheme performs favorably under periodic traffic conditions, but it may be deficient in case of Poisson traffic, specially if the setup is not optimally configured. Finally, we show the prominent performance results when the uplink transmissions are periodic, while highlighting the need of a minimum mean square error equalizer rather than zero-forcing for information decoding.

This paper presents a novel framework for traffic prediction of IoT devices activated by binary Markovian events. First, we consider a massive set of IoT devices whose activation events are modeled by an On-Off Markov process with known transition probabilities. Next, we exploit the temporal correlation of the traffic events and apply the forward algorithm in the context of hidden Markov models (HMM) in order to predict the activation likelihood of each IoT device. Finally, we apply the fast uplink grant scheme in order to allocate resources to the IoT devices that have the maximal likelihood for transmission. In order to evaluate the performance of the proposed scheme, we define the regret metric as the number of missed resource allocation opportunities. The proposed fast uplink scheme based on traffic prediction outperforms both conventional random access and time division duplex in terms of regret and efficiency of system usage, while it maintains its superiority over random access in terms of average age of information for massive deployments.

We propose and evaluate an iterative localization mechanism employing Bayesian inference to estimate the position of a target using received signal strength measurements. The probability density functions of the target's coordinates are estimated through a Bayesian network. Herein, we consider an iterative procedure whereby our predictor (posterior distribution) is updated in a sequential order whenever new measurements are made available. The performance of the mechanism is assessed in terms of the respective root mean square error and kernel density estimation of the target coordinates. Our numerical results showed the proposed iterative mechanism achieves increasingly better estimation of the target node position each updating round of the Bayesian network with new input measurements.

Recently, three useful secrecy metrics based on the partial secrecy regime were proposed to analyze secure transmissions on wireless systems over quasi-static fading channels, namely: generalized secrecy outage probability, average fractional equivocation, and average information leakage. These metrics were devised from the concept of fractional equivocation, which is related to the decoding error probability at the eavesdropper, so as to provide a comprehensive insight on the practical implementation of wireless systems with different levels of secrecy requirements. Considering the partial secrecy regime, in this paper we examine the secrecy performance of an amplify-and-forward relaying network with an untrusted relay node, where a destination-based jamming is employed to enable secure transmissions. In this regard, a closed-form approximation is derived for the generalized secrecy outage probability, and integral-form expressions are obtained for the average fractional equivocation and the average information leakage rate. Additionally, equal and optimal power allocation schemes are investigated and compared for the three metrics. From this analysis, we show that different power allocation approaches lead to different system design criteria. The obtained expressions are validated via Monte Carlo simulations.

Low-power wide-area networks (LPWANs) are wireless connectivity solutions for Internet-of-Things applications, including industrial automation. Among the several LPWAN technologies, LoRaWAN has been extensively addressed by the research community and the industry. However, the reliability and scalability of LoRaWAN are still uncertain. One of the techniques to increase the reliability of LoRaWAN is message replication, which exploits time diversity. This article proposes a novel hybrid coded message replication scheme that interleaves simple repetition and a recently proposed coded replication method. We analyze the optimization of the proposed scheme under minimum reliability requirements and show that it enhances the network performance without requiring additional transmit power compared to the competing replication techniques.

In this paper, the secrecy outage performance of a three-node amplify-and-forward relay network with an untrustworthy relay is investigated. To enable a secure transmission, we consider a destination-based jamming technique to prevent the relay from successfully decoding the secret messages between the source and the destination. In addition, the relay is assumed to be an energy-constrained device, thus being first energized by the source in order to be able to retransmit the information to the destination. In doing so, a time switching-based simultaneous wireless information and power transfer scheme is used. A closed-form asymptotic expression for the secrecy outage probability is derived in order to obtain a tight approximation at medium-to-high signal-to-noise ratio. The accuracy of the performed analysis is corroborated by Monte Carlo simulations through different illustrative cases. Numerical results show the impact of key system parameters on the secrecy performance, such as the time allocation factor between the energy harvesting and information transmission phases, the power allocation factor between source and destination for cooperative jamming, and the relay position, so as to provide insights on the design criteria for energy efficient and secure networks.

In URLLC the performance of short message communications highly depends on the training sequence length due to the stringent latency and reliability requirements. In this paper, we study the performance of cooperative and non-cooperative transmissions under imperfect channel estimation and Rayleigh fading for URLLC. We assume a peak power constraint on pilot symbols in addition to the average power constraint which is used for comparison purposes. We obtain the optimal training length as a function of blocklength and power constraint factor to meet the URLLC requirements. Moreover, the simulation results show the impact of pilot overhead on reliability, latency, and goodput of cooperative communications compared to point-to-point transmission.

In this paper, we present a finite-block-length comparison between the orthogonal multiple access (OMA) scheme and the non-orthogonal multiple access (NOMA) for the uplink channel. First, we consider the Gaussian channel, and derive the closed form expressions for the rate and outage probability. Then, we extend our results to the quasi-static Rayleigh fading channel. Our analysis is based on the recent results on the characterization of the maximum coding rate at finite block-length and finite block-error probability. The overall system throughput is evaluated as a function of the number of information bits, channel uses and power. We find what would be the respective values of these different parameters that would enable throughput maximization. Furthermore, we analyze the system performance in terms of reliability and throughput when applying the type-I ARQ protocol with limited number of retransmissions. The throughput and outage probability are evaluated for different blocklengths and number of information bits. Our analysis reveals that there is a trade-off between reliability and throughput in the ARQ. While increasing the number of retransmissions boosts reliability by minimizing the probability of reception error, it results in more delay which decreases the throughput. Nevertheless, the results show that NOMA always outperforms OMA in terms of throughput, reliability and latency regardless of the users priority or the number of retransmissions in both Gaussian and fading channels.

Coming cellular systems are envisioned to open up to new services with stringent reliability and energy efficiency requirements. In this paper, we focus on the joint power control and rate allocation problem in single-input multiple-output (SIMO) wireless systems with Rayleigh fading and stringent reliability constraints. We propose an allocation scheme that maximizes the energy efficiency of the system while making use only of average statistics of the signal and interference, and the number of antennas M that are available at the receiver side. We show the superiority of the maximum ratio combining (MRC) scheme over selection combining (SC) in terms of energy efficiency, and prove that the gap between the optimum allocated resources converges to (M!)1/(2M)as the reliability constraint becomes more stringent. Meanwhile, in most cases, MRC was also shown to be more energy efficient than the switch and stay combining (SSC) scheme, although this does not hold only when operating with extremely large M, extremely high/small average signal/interference power and/or highly power consuming receiving circuitry. Numerical results show the feasibility of the ultra-reliable operation when M increases, while greater the fixed power consumption and/or drain efficiency of the transmit amplifier is, the greater the optimum transmit power and rate.

Wireless energy transfer (WET) is emerging as a potential solution for powering small energy-efficient devices. We propose strategies that use multiple antennas at a power station, which wirelessly charges a large set of single-antenna devices. The proposed strategies operate without channel state information (CSI), and we attain the distribution and main statistics of the harvested energy under Rician fading channels with sensitivity and saturation energy-harvesting (EH) impairments. A switching antenna strategy, where a single antenna with full power transmits at a time, provides the most predictable energy source, and it is particularly suitable for powering sensor nodes with highly sensitive EH hardware operating under non-LOS (NLOS) conditions while other WET schemes perform alike or better in terms of the average harvested energy. While switching antennas is the best under NLOS, transmitting simultaneously with equal power in all antennas is the most beneficial as LOS increases. Moreover, spatial correlation is not beneficial unless the power station transmits simultaneously through all antennas, raising a tradeoff between average and variance of the harvested energy since both metrics increase with the spatial correlation. Moreover, the performance gap between CSI-free and CSI-based strategies decreases quickly as the number of devices increases.

Non-orthogonal multiple access (NOMA) has been identified as a promising technology for future wireless systems due to its performance gains in spectral efficiency when compared to conventional orthogonal schemes (OMA). This gain can be easily translated to an increasing number of served users, but imposes a challenge in the system reliability which is of vital importance for new services and applications of coming cellular systems. To cope with these issues we propose a NOMA rate control strategy that makes use only of topological characteristics of the scenario and the reliability constraint. We attain the necessary conditions so that NOMA overcomes the OMA alternative, while we discuss the optimum allocation strategies for the 2-user NOMA setup when operating with equal rate or maximum sum-rate goals. In such scenario we show that the user with the largest target error probability times the ratio between the average receive signal power and the average interference power, should be scheduled to be decoded first for optimum performance. We compare numerically the performance of our allocation scheme with its ideal counterpart requiring full CSI at the BSs and infinitely long blocklength, and show how the gap increases as the reliability constraint becomes more stringent. Results also evidence the benefits of NOMA when the co-interference can be efficiently canceled, specially when the goal is to maximize the sum-rate.

We consider a two-phase Wireless-Powered Communication Network under Nakagami-m fading, where a wireless energy transfer process first powers a sensor node that then uses such energy to transmit its data in the wireless information transmission phase. We explore a fixed transmit rate scheme designed to cope with the reliability and delay constraints of the system while attaining closed-form approximations for the optimum wireless energy transfer and wireless information transmission blocklength. Then, a more-elaborate rate control strategy exploiting the readily available battery charge information is proposed and the results evidence its outstanding performance when compared with the fixed transmit rate, for which no battery charge information is available. It even reaches an average rate performance close to that of an ideal scheme requiring full Channel State Information at transmitter side. Numerical results show the positive impact of a greater number of antennas at the destination, and evidence that the greater the reliability constraints, the smaller the message sizes on average, and the smaller the optimum information blocklengths. Finally, we corroborate the appropriateness of using the asymptotic blocklength formulation as an approximation of the non-asymptotic finite blocklength results.

We propose a destination-based jamming scheme for cooperative networks with multiple untrusted amplify-and-forward relays, in which the destination operates in full-duplex mode so as to enable simultaneous reception of the information coming directly from the source and the transmission of jamming signals to the untrusted relay. An approximate analytical expression for the secrecy outage probability, under the effect of residual self-interference at the destination, is presented. The developed analysis is verified through Monte Carlo simulations.

This work assesses the performance of secure machine-type communication networks composed of a legitimate pair of devices communicating in the presence of an eavesdropper. We evaluate the impact of legitimate source's arrival traffic in the design of secure communication protocol. Our approach is based on the secrecy outage probability framework, which identifies the security level of transmissions. We then characterize the secrecy transmission rate that includes the arrival traffic, evaluating its impact on the secrecy performance of the network. We introduce ON-OFF adaptive and non-adaptive transmission schemes that maximizes both the secure effective capacity and the maximum average arrival rate at the source node. Our numerical results provide insights on the interplay between the different traffic originated from MTC devices and security in the system.

Wireless networks are becoming extremely pervasive while traffic demand is ever increasing. In order to cope with the forecast increase in traffic volume for the upcoming years, as well as the number of connected devices, new technologies, practices and spectrum rearrangements are required. In this context, a key question arises: how to provide extensive backhaul connectivity and capacity for pervasive ultra dense networks? The answer is rather complex, if feasible. To shed some light into this issue we overview potential technologies, either wired or wireless, and identify technical challenges. Moreover, we evaluate an illustrative scenario of a ultra-dense network that operates with hybrid wired-wireless backhaul. We assume multiple radio access technologies at small and macro base stations (BSs), and we discuss optimal traffic splitting and routing solutions for different topologies and traffic profiles.

This paper analysis the energy efficiency of an unlicensed wireless network in which retransmission is possible if the transmitted message is decoded in outage. A wireless sensor network is considered in which the sensor nodes are unlicensed users of a wireless network which transmit its data in the uplink channel used by the licensed users. Poisson point process is used to model the distributions of the nodes and the interference caused by the licensed users for the sensor nodes. After finding the optimal throughput in the presence of retransmissions, we focus on analyzing the total power consumption and energy efficiency of the network and how retransmissions, network density and outage threshold affects the energy efficiency of the network.

This paper analyzes the throughput of an unlicensed wireless network where messages decoded in outage may be retransmitted. We assume that some wireless devices such as sensors are the unlicensed users, which communicate in the licensed uplink channel. In this case, the licensed users that interfere with the unlicensed transmissions devices are mobile devices whose spatial distribution are assumed to follow a Poisson point process with respect to a reference unlicensed link. We investigate how the number of allowed retransmissions and the spectrum efficiency jointly affect the throughput in [bits/s/Hz] of a reference unlicensed link for different licensed network densities, constrained by a given required error rate. The optimal throughput is derived for this case as a function of the network density. We also prove that the optimal constrained throughput can always reach the unconstrained optimal value. Our numerical results corroborate those of the analytical findings, also illustrating how the number of allowed retransmissions that leads to the optimal throughput changes with the error rate requirements.

This paper provides an in-depth analysis of the event-based metering strategy proposed by Simonov et al. This strategy is an alternative to the traditional periodic (time-based) metering where the power demand is average in fixed time periods (e.g. every 15 minutes). The event-based approach considers two thresholds that trigger an event, one related to the (instantaneous) power demanded, other to the accumulated energy consumed. The original work assumed these thresholds fixed for the measurements. Our present contribution relaxes this assumption by proposing a method to set the thresholds from the percentage of the peak power consumption over the period under analysis. This approach, in contrast to the time-based and the fixed thresholds, better captures the actual power demanded when different households with diverse power demand profiles are studied.In this sense, our method provides a more efficient way to store electricity demand data while maintaining the estimation error (in relation to the real-time power demand) under acceptable values. Numerical examples presented to illustrate the advantage and possible drawbacks of the proposed method.

This paper proposes a novel analytical framework based on the Fox H-function distribution. To test its efficacy we focus on a study case where we derive closed form expressions for the distribution of the end-to-end signal to noise ratio, and corresponding outage probability for the dual-hop amplify-and-forward configuration over Weibull-fading channels with channel state information. An extensive simulation campaign was carried out to corroborate the proposed approach.

This paper presents a novel opportunistic spectrum sharing scheme that applies ARQ protocol to achieve ultra reliability in the finite blocklength regime. A primary user shares its licensed spectrum to a secondary user, where both communicate to the same base station. The base station applies ARQ with the secondary user, which possess a limited number of trials to transmit each packet. We resort to the interweave model in which the secondary user senses the primary user activity and accesses the channel with access probabilities which depend on the primary user arrival rate and the number of available trials. We characterize the secondary user access probabilities and transmit power in order to achieve target error constraints for both users. Furthermore, we analyze the primary user performance in terms of outage probability and delay. The results show that our proposed scheme outperforms the open loop and non-opportunistic scenarios in terms of secondary user transmit power saving and primary user reliability.

We analyze a wireless communication system with finite block length and finite battery energy, under quasi-static Nakagami-m fading. Wireless energy transfer is carried out in the downlink while information transfer occurs in the uplink. Transmission strategies for scenarios with/without energy accumulation between transmission rounds are characterized in terms of error probability and energy consumption. A power control protocol for the energy accumulation scenario is proposed and results show the enormous impact on improving the system performance, in terms of error probability and energy consumption. The numerical results corroborate the existence and uniqueness of an optimum target error probability, while showing that a relatively small battery could be a limiting factor for some setups, especially when using the energy accumulation strategy.

Data aggregation is a promising approach to enable massive machine-type communication. This paper focuses on the aggregation phase where a massive number of machine-type devices (MTDs) transmit to aggregators. By using non-orthogonal multiple access (NOMA) principles, we allow several MTDs to share the same orthogonal channel in our proposed hybrid access scheme. We develop an analytical framework based on stochastic geometry to investigate the system performance in terms of average success probability and average number of simultaneously served MTDs, under imperfect successive interference cancellation (SIC) at the aggregators, for two scheduling schemes: random resource scheduling and channel-aware resource scheduling (CRS). We identify the power constraints on the MTDs sharing the same channel to attain a fair coexistence with purely orthogonal multiple access (OMA) setups. Then, power control coefficients are found, so that these MTDs perform with similar reliability. We show that under high access demand, the hybrid scheme with CRS outperforms the OMA setup by simultaneously serving more MTDs with reduced power consumption.

The challenges with full-duplex transceiver (FD) implementation and transmission in small area radio communication systems are introduced. The main challenge in the FD transceiver design is the self-interference (SI). A three-stage SI cancellation is used for SI mitigation. Analog SI isolation is performed at radio frequency (RF) by utilizing an antenna design based on the characteristic modes theory and using active cancellation principle. Phase and attenuation values of the active cancellation signal path are tuned while transmitting a data signal to a distant node. After the tuning the SI isolation at RF is 90 dB. The remaining SI is then cancelled at baseband. For the baseband SI cancellation an estimate of the SI channel is needed. SI channel estimation is done in full-duplex mode and since the receiver has full knowledge of the transmitted signal no extra pilots are used for the SI cancellation.

We analyze the performance of the type-I automatic repeat request (ARQ) protocol with ultra-reliability constraints. First, we show that achieving a very low packet outage probability by using an open loop setup is a difficult task. Thus, we introduce the ARQ protocol as a solution for achieving the required low outage probabilities for ultra reliable communication. For this protocol, we present an optimal power allocation scheme that would allow us to reach any outage probability target in the finite block-length regime. We formulate the power allocation problem as minimization of the average transmitted power under a given outage probability and maximum transmit power constraint. By utilizing the Karush-Kuhn-Tucker (KKT) conditions, we solve the optimal power allocation problem and provide a closed form solution. Next, we analyze the effect of implementing the ARQ protocol on the throughput. We show that by using the proposed power allocation scheme we can minimize the loss of throughput that is caused from the retransmissions. Furthermore, we analyze the effect of the feedback delay length in our scheme.

We consider a dual-hop wireless network where an energy constrained relay node first harvests energy through the received radio-frequency signal from the source, and then uses the harvested energy to forward the source's information to the destination node. The throughput and delay metrics are investigated for a decode-and-forward relaying mechanism at finite blocklength regime and delay-limited transmission mode. We consider ultra-reliable communication scenarios under discussion for the next fifth-generation of wireless systems, with error and latency constraints. The impact on these metrics of the blocklength, information bits, and relay position is investigated.

We analyze and optimize a wireless system with energy transfer in the downlink and information transfer in the uplink, under quasi-static Nakagami-m fading. We consider ultrareliable communication scenarios representative of the fifth generation of wireless systems, with strict error and latency requirements. The error probability and delay are investigated, and an approximation for the former is given and validated through simulations. The numerical results demonstrate that there are optimum numbers of channels uses for both energy and information transfer for a given message length.

In this paper, an adaptive scheme for amplify-and-forward relaying networks is proposed, which selects a certain transmission mode for each communication process. Depending on the instantaneous channel conditions, one of the following modes is selected: direct transmission with no cooperation, cooperative transmission with half-duplex relaying and maximal-ratio combining at the destination, or cooperative transmission with full-duplex relaying and maximal-ratio combining at the destination. A three-node network is considered, containing a single-antenna source, a two-antenna relay that is able to implement full-duplex communication, and a single-antenna destination. Energy normalization per block is assumed, so that in those modes using cooperation, the system’s transmission power is shared between source and relay. The performance analysis is provided in terms of outage probability and energy efficiency. We derive a tight approximate expression in closed form for the outage probability and an approximate expression in integral form for the mean energy consumption. The results show that our scheme outperforms all of transmission modes separately in terms of outage probability, while being more energy efficient than the cooperative transmission modes. In addition, the asymptotic analysis proves that the proposed scheme achieves full diversity order equal to 2, thus outperforming those schemes with direct transmission or full-duplex cooperation only.

We propose an adaptive transmission scheme for cognitive decode-and-forward relaying networks, whereby, before each communication process, one out of three transmission modes is dynamically selected in order to maximize the instantaneous capacity of the system, namely, half-duplex (HD) relaying, full-duplex (FD) relaying, or direct transmission with no cooperation. The following key issues, relevant to underlay spectrum sharing and cooperative relaying, are considered: 1) the overall transmit power at the secondary network is constrained by both the maximum tolerable interference at the primary receiver and the maximum transmit power available at the secondary nodes; 2) under FD operation, the secondary relay is subject to residual self-interference, which is modeled as a fading channel; and 3) the signals coming from the secondary source and relay are handled at the secondary destination via maximal-ratio combining, in the HD relaying mode, and via a joint-decoding technique, in the FD relaying mode. We derive an exact analytical expression for the outage probability of the proposed scheme. Then, an approximate closed-form expression is proposed, and a corresponding asymptotic expression is derived. Monte Carlo simulations are run to validate the accuracy of the presented mathematical analysis and to showcase the tightness of the proposed approximation.

This paper investigates the performance of dual-hop full-duplex relaying schemes subject to co-channel interference (CCI) and noise. In our analysis, two main scenarios are considered: in the first scenario, the link between the source and destination is seen as interference and, in the second one, it is seen as useful information. In both schemes, the effect of self-interference at the relay is taken into account. Accurate, closed- form expressions for the outage probability are derived for the general case, in which CCI and noise are assumed at both the relay and destination. The derived expressions allow for independent non-identically distributed Nakagami-m fading. Based on these expressions, special cases (but, yet new) assuming CCI only at the relay and assuming CCI only at the destination are examined. It is shown that CCI at the relay is more harmful for the system performance than CCI at the destination.

This paper investigates a hybrid network configuration in which full-duplex base stations serve half-duplex users on both Uplink and Downlink simultaneously. Users are modeled as a homogeneous Poisson point process while the channel is modeled as a composite fading with correlated Log-normal shadowing and Nakagami-m fading. We characterize the signal-to-interference-ratio at an user of interest, and then evaluate how the network performs in terms of outage probability. We account for the cross-correlation between the user of interest and a random co-site interferer within range. Thus, we provide a valuable insight on on how the hybrid network performs under the assumption of correlated shadowing. We show that when the aforesaid correlation is low, the user of interest can achieve higher data rate at expense of high outage; however, if the distance to the serving base station is short and the cross correlation is high, a satisfactory data rate can be sustained at low outage.

We assume a full-duplex (FD) cooperative network subject to hostile attacks and undergoing composite fading channels. We focus on two scenarios: a) the transmitter has full CSI, for which we derive closed-form expressions for the average secrecy rate; and b) the transmitter only knows the CSI of the legitimate nodes, for which we obtain closed-form expressions for the secrecy outage probability. We show that secure FD relaying is feasible, even under strong self-interference and in the presence of sophisticated multiple antenna eavesdropper.

This letter deals with the secrecy capacity of the radio channel in interference-limited regime. We assume that interferers are uniformly scattered over the network area according to a Point Poisson Process and the channel model consists of path-loss, log-normal shadowing and Nakagami-m fading. Both the probability of non-zero secrecy capacity and the secrecy outage probability are then derived in closed-form expressions using tools of stochastic geometry and higher-order statistics. Our numerical results show how the secrecy metrics are affected by the disposition of the desired receiver, the eavesdropper and the legitimate transmitter.

This letter investigates the outage probability of a variable-gain amplify-and-forward full-duplex relaying network in which not only the relaying link but also the direct link is used to convey information, thus improving the system reliability. We consider a basic three-node relaying system composed of a source, a destination, and a two-antenna relay—one antenna for transmission, one antenna for reception. Also, we consider that the full-duplex relay undergoes some residual self interference, modeled as a fading channel, and that maximal-ratio combining is employed at the destination to merge the signals from the source and relay. We derive an exact integral-form expression for the outage probability and validate this via Monte Carlo simulation. In addition, we propose a highly-accurate closed-form approximation to the outage probability, as well as a simple asymptotic expression at high signal-to-noise ratio. The use of the direct link is shown to overcome the zero diversity order inherent to full-duplex relaying.

In this paper we investigate the physical layer security in a cooperative scenario where the source and the eavesdropper are equipped with multiple antennas, while the relay and the destination are single-antenna devices. We consider that no channel state information (CSI) is available at the transmitter, besides the index of the selected transmit antenna. Closed-form expressions for the secrecy outage probability of the selective decode-and-forward (SDF) and incremental decode-and-forward (IDF) schemes are derived. In addition, we also conduct an asymptotic analysis to get further insights on the performance of each scheme, which predicts a performance floor inherent to the scenario without CSI at the transmitter. Moreover, we also analyze the schemes in terms of the secrecy throughput, which shows that the performance floor that appear in the outage analysis only affects the secrecy throughput if the floor is too high, as is the case of the non-cooperative transmission. Our analysis show that IDF can considerably outperform the SDF and the non-cooperative transmission both in terms of secrecy outage probability and secrecy throughput.

In this paper we investigate the performance of a cooperative network in the presence of an eavesdropper (Eve). Alice and Bob communicate with the help of a relay, which can operate either in half-duplex (HD) or full-duplex (FD) mode. We account for the self interference at the relay when operating under FD mode. Our analysis focus in the case that the CSI of Eve is not available at Alice. Thus, we derive closed-form expressions for secrecy outage probability. Our results allow us to compare the performance of FD and HD cooperative scenarios under secrecy constraints and, despite the additional interference at the relay, show the advantages of FD relaying over HD. In addition, we also show that a cooperative network is more vulnerable if Eve is closer to Alice than to the relay.

In this work we compare full-duplex (FD) and half-duplex (HD) relaying in terms of outage probability and throughput. We consider a practical FD relay model where the loop interference between transmitted and received signals is taken into account. We analyze two modes for FD transmission, block Markov encoding and multi-hop transmission without interference cancellation, and two different modes for HD transmission, which are based on selective and incremental decode-and-forward protocols. Results show that there is a tradeoff between SNR and information rate in which each scheme becomes more suitable.

We analyze the physical layer (PHY) security of a communication scheme consisting of a multiple antenna transmitter with a single radio frequency (RF) chain using transmit antenna selection (TAS) and a single antenna receiver, in the presence of a sophisticated multiple antenna eavesdropper. We develop closed-form expressions for the analysis of the secrecy outage probability, and we show that the PHY security can be considerably enhanced when multiple antennas are available at the legitimate transmitter. Moreover, a single RF chain multiple antenna transmitter reduces cost, complexity, size and power consumption at the expense of a slight loss in performance with respect to a multiple RF chain transmitter.

Truncated Type-I and Type-II HARQ schemes are compared, in terms of throughput, when incremental decode and forward relaying is used. The comparison is based on the outage analysis of both schemes. Two different scenarios are considered: ad-hoc relaying, where all users are at ground level, and infra-structured relaying, where the relay and the destination antennas are at a higher height than the user. Moreover, we also consider the effect of rate adaptation. Results show that Type-II (incremental redundancy) only significantly outperforms Type-I schemes (selection and Chase combining) in the case of ad-hoc relaying without rate adaptation, and in the low SNR region.