Quantum key distribution (QKD) has emerged as a promising solution for guaranteeing information-theoretic security. Inspired by this, a great amount of research effort has been recently put on designing and testing QKD systems as well as articulating preliminary application scenarios. However, due to the considerable high-cost of QKD equipment, a lack of QKD communication system design tools, wide deployment of such systems and networks is challenging. Motivated by this, this paper introduces a QKD communication system design tool. First we articulate key operation elements of the QKD, and explain the feasibility and applicability of coherent-one-way (COW) QKD solutions. Next, we focus on documenting the corresponding simulation framework as well as defining the key performance metrics, i.e., quantum bit error rate (QBER), and secrecy key rate. To verify the accuracy of the simulation framework, we design and deploy a real-world QKD setup. We perform extensive experiments for three deployments of diverse transmission distance in the presence or absence of a QKD eavesdropper. The results reveal an acceptable match between simulations and experiments rendering the simulation framework a suitable tool for QKD communication system design.

This work presents a new spiking neural network (SNN)-based approach for user equipment-base station (UE-BS) association in non-terrestrial networks (NTNs). With the introduction of UAV’s in wireless networks, the system architecture becomes heterogeneous, resulting in the need for dynamic and efficient management to avoid congestion and sustain overall performance. The presented framework compares two SNN-based optimization strategies. Specifically, a top-down centralized approach with complete network visibility and a bottom-up distributed approach for individual network nodes. The SNN is based on leak integrate-and-fire neurons with temporal components, which can perform fast and efficient event-driven inference. Realistic ray-tracing simulations are conducted, which showcase that the bottom-up model attains over 90% accuracy, while the top-down model maintains 80-100% accuracy. Both approaches reveal a trade-off between individually optimal solutions and UE-BS association feasibility, thus revealing the effectiveness of both approaches depending on deployment scenarios.

Downlink channel estimation and achievable rate optimization are critical challenges in current RIS-assisted MIMO systems. The two issues are studied separately: deep learning is used for channel estimation, and convex optimization is employed for rate maximization. But the step-by-step execution will lead to an increase in computational complexity and algorithmic complexity due to the separate handling of each task. To overcome these limitations, we propose an end-to-end integrated deep learning framework that jointly executes channel estimation and optimizes phase configuration to improve system performance. Specifically, a two-stage deep neural network architecture is introduced, which is trained using a multi-task optimization strategy. Moreover, the optimal network structure was explored by adjusting the scale of each stage and the training weight ratio. The simulation results show that the proposed algorithm significantly reduces computational overhead while maintaining high system performance.

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

Wireless-powered communications (WPCs) are increasingly crucial for extending the lifespan of low-power Internet of Things (IoT) devices. Furthermore, reconfigurable intelligent surfaces (RISs) can create favorable electromagnetic environments by providing alternative signal paths to counteract blockages. The strategic integration of WPC and RIS technologies can significantly enhance energy transfer and data transmission efficiency. However, passive RISs suffer from double-fading attenuation over RIS-aided cascaded links. In this article, we propose the application of an active RIS within WPC-enabled IoT networks. The enhanced flexibility of the active RIS in terms of energy transfer and information transmission is investigated using adjustable parameters. We derive novel closed-form expressions for the ergodic rate and outage probability by incorporating key parameters, including signal amplification, active noise, power consumption, and phase quantization errors. Additionally, we explore the optimization of WPC scenarios, focusing on the time-switching factor and power consumption of the active RIS. The results validate our analysis, demonstrating that an active RIS significantly enhances WPC performance compared to a passive RIS.

This paper focuses on providing a theoretical framework for the evaluation of the performance of holographic reconfigurable intelligent surface (HRIS) empowered terahertz (THz) wireless systems under fog conditions. In more detail, we present a comprehensive methodology for evaluating the geometric losses of the end-to-end channel. Moreover, the stochastic nature of the end-to-end channel is characterized by novel closed-form probability density and cumulative distribution functions. Building upon them, the outage probability and the throughput of the system are extracted in closed form. These formulas account for the impact of transceiver hardware imperfections and are expected to become useful tools for the design of HRIS empowered THz wireless systems due to its remarkable engineering insights.

In this paper, we consider a novel hybrid reconfigurable intelligent surface (HRIS) consisting of active as well as passive reflecting elements mounted on unmanned aerial vehicle (UAV). The aim is to improve air-to-ground communication by assisting multiple users, while detecting several low mobility targets. We formulate a sum-rate optimization problem that accounts for statistical channel estimation errors (SCEEs) to concurrently fine-tune both active and passive phase-shift matrices, UAV trajectory, and transmit beamformer for integrated sensing and communication (ISAC). Subsequently, we introduce a transfer learning based approach combining with deep deterministic policy gradient (DDPG) to enhance the overall data rate while minimizing the time it takes for users to transmit data. Additionally, we present an alternating optimization (AO) algorithm that employs a repetitive method to address the combinatorial nonconvex optimization problem and offers a solution that is very close to optimal. Finally, we showcase the superiority of the proposed scheme through Monte Carlo simulations. Also, we have compared the performance with perfect channel state information (CSI) counterpart. The outcomes of simulations confirm the theoretical analysis and demonstrate the efficiency of the proposed framework. Additionally, the results reveal the advantages of incorporating HRIS aided UAV assisted ISAC in improving the quality of both communication and sensing performance.

This paper presents an American sign language (ASL) semantic communications scheme. The scheme consists of a semantic encoder that leverages a convolutional neural network to effectively utilize the ASL alphabet. The encoded information is transmitted with the 24-QAM quadrature amplitude modulation (QAM). Additionally, this paper introduces a dataset that involves the overlaying of red-green-blue landmarks and key-points onto the acquired images, thereby augmenting the depiction of hand posture. The quantification of the proposed system’s training, testing, and communication performance is accomplished through numerical results, which serve to emphasize the attainable benefits and stimulate meaningful discussions.

In this paper, we consider a novel hybrid reconfigurable intelligent surface (HRIS) consisting of active as well as passive reflecting elements mounted on unmanned aerial vehicle (UAV). The aim is to improve air-to-ground communication by assisting multiple users, while detecting several moving targets. We formulate an optimization model that account for statistical channel estimation errors (SCEEs) to concurrently fine-tune both active and passive phase-shift matrices, UAV trajectory and transmit beamformer for integrated sensing and communication (ISAC), while aiming to maximize the overall achievable sum-rate. Subsequently, we introduce an alternating optimization algorithm that employs a repetitive method to address the combinatorial nonconvex optimization problem and ultimately yielding a solution that is nearly optimal. Using Monte Carlo simulations, we showcase the superiority of the suggested approach in comparison to baseline schemes.

It is true that medical diagnosis and treatment have always been at the top of research activities. Recently, a very promising area that combines the operation of human body with the principles of wireless communications has arisen and given birth to the field of bio-photonics. Although the deployment of nano-networks inside the human body seems to be an achievable goal, the implementation of optical wireless nano-networks remains a very challenging field due to the lack of accurate channel modeling. Therefore, in this paper, we comprehensively investigate the optical wireless nano-communication channels by considering the deterministic geometric losses (e.g., optical absorption and scattering in human tissues), and modeling the impact of nano-node disorientation and misalignment in in-body nano-networks. To this end, we analytically study the stochastic nature of disorientation and misalignment coefficient in terms of the position and orientation of the nano-node with reference to the out-of-body optical receiver. In particular, we study the statistics of the joint disorientation and misalignment coefficient in terms of the probability density function, which is given in closed-form expression, whereas its cumulative distribution function is introduced as a semi-infinite series expression. Building upon them, we extract novel analytical expressions for the average signal-to-noise-ratio and the outage probability, and a thorough study on the impact of the human tissues on the above performance metrics is conducted. The analytical results are corroborated and cross-compared by means of Monte Carlo simulations, which reveal the importance of accounting for the joint impact of disorientation and misalignment of nano-node when designing optical wireless nano-networks.

Virtual reality (VR) is one of the applications with the most strict requirements in the performance of next generation networks, since it requires both high throughput, low delay, and packet loss. As the performance of networks, and the level of congestion varies over time, a need for adaptation in the stream’s data rate, in order to maintain reasonable packet loss, while using the available bandwidth, emerges. Motivated by this, in this contribution, we present an adaptation algorithm for VR applications, that exploits fuzzy logic, and transmission control protocol (TCP) transport in order to maintain the optimal data rate of the VR stream. In this direction, we perform a performance assessment of VR networks in the network simulator 3, that reveals that adaptation of the data rate is indeed necessary to provide the best possible VR data at the client. Moreover, it becomes evident that TCP transport, in combination with a data rate adaptation algorithm significantly reduces both the packet loss and the network delay, while maintaining high throughput. Finally, the proposed fuzzy algorithm outperforms well known adaptation algorithms in terms of throughput, delay and fairness.

A core vision of the future sixth-generation wireless networks is to serve, in the most spectral and energy efficient way, a massive number of devices and/or Internet-of-Things (IoT) sensors, plenty of which are expected to be low-powered or even battery-free. In this direction, non-orthogonal multiple access (NOMA) and ambient backscatter communications (AmBC) are considered as two key promising technologies. In this work, we present a novel analytical framework for studying the performance of uplink NOMA-based AmBC systems. Specifically, analytical expressions for both NOMA-users’ and IoT backscatter device’s (BD) outage probabilities (OPs) are derived under both perfect and the more realistic case of imperfect successive interference cancellation (SIC). Likewise, system’s performance at the high signal-to-noise ratio (SNR) regime is investigated as well as expressions for the system’s overall average throughput are also derived. A performance comparison between the proposed setup and a conventional orthogonal multiple access (OMA)-based AmBC system is provided as well as it is proved that the proposed system’s average throughput is equal or even greater compared to a conventional uplink NOMA system with two NOMA-users and no BDs.

This paper presents a quantified assessment of the physical layer security capabilities of reconfigurable intelligent surface (RIS)-aided wireless systems under eavesdropping. Specifically, we derive a closed-form expression for the ergodic secrecy capacity (ESC) that is adaptable to different types of fading and RIS size. The channels between the transmitter (TX) and RIS, the RIS and legitimate receiver as well as the TX and eavesdropper are assumed to follow independent mixture Gamma (MG) distributions. Note that MG is capable of modeling a large variety of well-known distributions, including Gaussian, Rayleigh, Nakagami-m, Rice, and others. The results reveal that as the RIS size increases, although the legitimate links diversity order increases, the ESC gain decreases.

Reconfigurable intelligent surface (RIS)-assisted unmanned areal vehicles (UAV) communications have been identified as a key enabler of a number of next-generation applications. However, to the best of our knowledge, there is no generalized framework for the quantification of the throughput performance of RIS-assisted UAV systems. Motivated by this, in this paper, we present a comprehensive system model that accounts the impact of multi-path fading, which is modeled by means of mixture gamma, transceiver hardware imperfections, and stochastic beam disorientation and misalignment in order to examine the throughput performance of a RIS-assisted UAV wireless system. In this direction, we present a novel closed-form expression for the systems throughput for two scenarios: i) in the presence and ii) in the absence of disorientation and misalignment. Interestingly, our results reveal the importance of accurate modeling the aforementioned phenomena as well as the existence of an optimal transmission spectral efficiency.

In this paper, we analyze the performance of holographic surface assisted wireless systems that employ non-orthogonal multiple access (NOMA) approaches. In this direction, we present a comprehensive system model that captures the particularities of holographic surfaces as well as the impact of multipath fading. Building upon the system model, we quantify the outage performance of the downlink scenario, by providing novel closed-form expressions for its outage probability. Simulation results validate the authenticity of the presented analysis, illustrate the performance gain of the proposed NOMA scheme over conventional orthogonal multiple access schemes and reveal the feasibility and efficiency of the proposed approach.

In this work, we propose a time- and a unit cell-splitting protocol for supplying the energy needs of reconfigurable intelligent surfaces (RISs) through wireless energy harvesting (EH) from information signals. We first compute the RIS energy consumption per frame that is common for both protocols and incorporates the energy burden for channel estimation. Based on it, we subsequently formulate an optimization problem that maximizes the average rate under the constraint of meeting the RIS long-term energy consumption demands. In addition, closed-form solutions regarding the optimal allocation of resources are provided for both protocols in the case of deterministic channel gains for the transmitter-RIS links and a methodology to obtain such a solution in the general case of random channels. Finally, for the optimal resource allocation for both protocols numerical results based on Monte-Carlo simulations reveal that the unit cell-splitting protocol exhibits a superior performance compared to its time-splitting counterpart.

The transformation of the conventional electrical grid into a digital ecosystem brings significant benefits, such as two-way communication between energy consumers and utilities, self-monitoring and pervasive controls. However, the advent of the smart electrical grid raises severe cybersecurity and privacy concerns, given the presence of legacy systems and communications protocols. This paper focuses on False Data Injection (FDI) cyberattacks against a low-voltage distribution system, taking full advantage of Man In The Middle (MITM) actions. The first cyberattack targets the communication between a smart meter and an Active Distribution Management System (ADMS), while the second FDI cyberattack targets the communication between a smart inverter and ADMS. In both cases, the cyberattacks affect the operation of the distribution transformer, thus resulting in devastating consequences. Moreover, this paper provides an Artificial Intelligence (AI)-based Intrusion Detection System (IDS), detecting and mitigating the above cyberattacks in a timely manner. The evaluation results demonstrate the efficiency of the proposed IDS.

In this paper, we introduce a theoretical framework for analyzing the performance of multi-reconfigurable intelligence surface (RIS) empowered terahertz (THz) wireless systems subject to turbulence and stochastic beam misalignment. In more detail, we extract a closed-form expression for the outage probability that quantifies the joint impact of turbulence and misalignment as well as the effect of transceivers’ hardware imperfections. Our results highlight the importance of accurately modeling both turbulence and misalignment when assessing the performance of multi-RIS-empowered THz wireless systems.

Long range wide area network (LoRaWAN) represents a promising low power wide area network (LPWAN) technology in the context of internet-of-things (IoT) that has recently attracted intense research interest. Due to the limited energy resources available on LoRaWAN constituent elements and intermittent power supply of gateways in harsh environments, an energy-efficient communication protocol is constituted of utmost importance in order to prolong network lifetime. Motivated by the aforementioned, this work presents a green, robust, and resilient communication protocol, namely GreenLoRaWAN, which increases energy efficiency, scalability and robustness of the LoRaWAN. The proposed protocol is evaluated by means of Monte Carlo simulations; Performance evaluation results acquired are very promising, revealing an important reduction in energy consumption and increase the duration of network lifetime.

This contribution aims at experimentally validating the suitability of Gaussian mixture (GM) distributions to capture the stochastic characteristics of outdoor terahertz (THz) wireless channels. In this direction, we employ a machine learning enabled approach, based on the expectation maximization algorithm, in order to identify the suitable number of Gaussian distributions as well as their corresponding parameters that result to an acceptable fit. The fitting accuracy of the GMs to the empirical distributions is evaluated by means of the Kolmogorov-Smirnov (KS), Kullback-Leibler (KL), root-mean-square-error (RMSE) and R2fitting accuracy tests. These tests verify the suitability of GMs to model the small-scale fading channel amplitude of outdoor THz wireless links. In addition, the fitting accuracy results indicate that as the number of mixtures increases the resulting GMs achieve a better fit to the empirical data.

Motivated by the emergence of next-generation internet-of-things applications, this paper introduces a novel non-orthogonal semi-grant-free multiple access protocol that enables reliable massive connectivity of scheduled and random access devices with varying data rate requirements. Specifically, the protocol divides devices into two classes, namely primary and secondary devices. Primary devices (PDs) have high data rate requirements, while secondary devices (SDs) transmit data at a lower frequency compared to primary devices, and have a lower data rate requirement. The base-station (BS) enables a single PD and two SDs to access the same radio resource block (RRB). The PD uses grant-based (GB) access to the RRB, while the secondary devices use grant-free (GF) access. The BS first decodes the PD signal by treating the SDs signals as interference. Then, it decodes the signal of the SD with the higher channel gain. To assess the performance of the proposed protocol, we carried out simulations and measured the achieved throughput of each device under various parameters, such as power-to-noise gain, signal-to-noise threshold, and transmission probability.

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.

This paper presents an analytical pathloss model for reconfigurable intelligent surface (RIS) assisted terahertz (THz) wireless systems. Specifically, the model accommodates both the THz link and the RIS particularities. Finally, we derive a closed-form expression that returns the optimal phase shifting of each RIS reflection unit. The derived pathloss model is validated through extensive electromagnetic simulations and is expected to play a key role in the design of RIS-assisted THz wireless systems.

This work investigates the suitability of α–µ distribution to model line-of-sight (LoS) and non-line-of-sight (NLoS) multi-path fading in terahertz (THz) wireless systems. The goodness of fit of α–µ to the small-scale fading of the measured channels is evaluated in terms of the Kolmogorov-Smirnov (KS) test. The KS test revealed the capability of α–µ distribution to capture the fading characteristics of THz wireless channels. To highlight the importance of this study and the applicability to the theoretical analysis of THz wireless systems, for indicative values of α and µ, the ergodic capacity of THz wireless systems is assessed.

Ultra-low latency connections for a massive number of devices are one of the main requirements of the next-generation tactile Internet-of-Things (TIoT). Grant-free non-orthogonal multiple access (GF-NOMA) is a novel paradigm that leverages the advantages of grant-free access and non-orthogonal transmissions, to deliver ultra-low latency connectivity. In this work, we present a joint channel assignment and power allocation solution for semi-GF-NOMA systems, which provides access to both grant-based (GB) and grant-free (GF) devices, maximizes the network throughput, and is capable of ensuring each device’s throughput requirements. In this direction, we provide the mathematical formulation of the aforementioned problem. After explaining that it is not convex, we propose a solution strategy based on the Lagrange multipliers and subgradient method. To evaluate the performance of our solution, we carry out system-level Monte Carlo simulations. The simulation results indicate that the proposed solution can optimize the total system throughput and achieve a high association rate, while taking into account the minimum throughput requirements of both GB and GF devices.

This paper focuses on quantifying the outage performance of terahertz (THz) relaying systems. In this direction, novel closed-form expressions for the outage probability of a dual-hop relaying systems, in which both the source-relay and relay-destination links suffer from fading and stochastic beam misalignment, are extracted. Our results reveal the importance of taking into account the impact of beam misalignment when characterizing the outage performance of the system as well as when selecting the transmission frequencies.

This paper presents the theoretical framework for the performance evaluation of terahertz (THz) wireless fiber extender in the presence of misalignment and multipath fading. In more detail, after providing the appropriate system model that incorporates the different operation, design, and environmental parameters, such as the operation frequency, transceivers antenna gains, the level of misalignment as well as the stochastic behavior of the channel, we extract novel closed-form expressions for the ergodic capacity. These expressions are expected to be used as useful tools for the analysis and design of such systems. Moreover, several insightful scenarios are simulated. Their results highlight the importance of taking into account the impact of misalignment fading when analyzing the performance of the THz wireless fiber extender.

In this paper, we provide the mathematical framework to evaluate and quantify the performance of wireless systems, which employ beamforming, in the presence of hardware imperfections at both the basestation and the user equipment. In more detail, by taking a macroscopic view of the joint impact of hardware imperfections, we introduce a general model that accounts for transceiver impairments in beamforming transmissions. In order to evaluate their impact, we present novel closed form expressions for the outage probability and upper bounds for the characterization of the system's capacity. Our analysis reveals that the level of imperfection can significantly constraint the ergodic capacity. Therefore, it is important to take them into account when evaluating and designing beamforming systems.

In this paper, a novel cooperation-aided localization and tracking approach, suitable for terahertz (THz) wireless systems is presented. It combines an angle of arrival (AoA) tracking algorithm with the two-way time of arrival method, in order to accurately track the user equipments (UE) position and reduce the deafness caused by the estimation errors of the tracking algorithms. This algorithm can be used by one base station (BS) to estimate the UEs position, or by multiple BSs, that cooperate with each other to increase the accuracy of the estimations, as well as the probability of successful estimations and guarantee low-estimation overhead. The efficiency of the algorithm is evaluated in terms of deafness and probability of successful AoA estimation and is compared with the corresponding performance of the fast channel tracking algorithm.

In this paper, we present a novel two-path channel model for wireless terahertz (THz) systems operating in the range of 275 to 400 GHz, which accommodates both the channel particularities and the transceivers parameters. The channel particularities include the frequency selectivity, path-loss, as well as the atmospheric conditions, namely temperature, relative humidity and pressure, while the transceiver parameters, which are taken into account, are the antenna gains as well as the power of the transmitted signal. Finally, we evaluated the THz system performance in terms of average signal to noise ratio and ergodic capacity.

In this paper, we establish an appropriate system model for the terahertz (THz) wireless links in the range of 275 to 400 GHz, which accommodates the channel particularities and transceivers parameters. The former includes the frequency selectivity, pathloss, as well as the atmospheric conditions, namely temperature and pressure, whereas the latter are assumed to be consisted of the antenna gains as well as the power allocation of the transmitted signal. Moreover, we present analytical expressions of low computational complexity, for the evaluation of the average SNR, and capacity of the line of sight wireless THz links. These expressions are expected to be the key tools for the design of the THz link.

Direct-conversion radio transceivers can offer reprogrammable and low-cost hardware solutions for full-duplex (FD) cognitive radio (CR) devices. However, they are susceptible to radio frequency (RF) impairments, such as in-phase (I) and quadrature (Q) imbalance (IQI), which can significantly constrain the spectrum sensing capabilities. This paper is devoted to quantify and evaluate the effects of IQI in the context of spectrum sensing in FD CR systems, in which self-interference suppression (SIS) techniques are employed. Specifically, closed form expressions are derived for the false alarm and detection probabilities, under three different scenarios: imperfect SIS with joint transmitter (TX) and receiver (RX) IQI, imperfect SIS and ideal TX/RX RF front-end, and perfect SIS and ideal TX/RX RF front-end. The derived analytical results are validated through extensive simulations, which reveal that IQI has a detrimental impact on the spectrum sensing performance of the FD CR transceiver, which brings significant losses in the spectrum utilization.

A direct-conversion architecture can offer highly integrated low-cost hardware solutions to communication transceivers. However, it has been demonstrated that radio frequency impairments, such as amplifier nonlinearities, phase noise, and in-phase/quadrature-phase imbalances (IQI), can lead to a severe degradation in the performance and fairness. To this end, we study the power allocation (PA) problem in an orthogonal frequency-division multiple access system, when the served user equipment (UE) suffers from different levels of IQI. Additionally, we present a novel low-complexity solution with directly calculated PA policies, given the Lagrange multiplier, which mitigates the impact of IQI and achieves fairness in terms of capacity for the served UE, by maximizing the minimum achievable capacity of the UE. The effectiveness of the offered solution is validated through simulation results, which reveal that it can drastically increase the minimum achievable UE's capacity.

Direct-conversion radio (DCR) receivers can offer highly integrated low-cost hardware solutions for spectrum sensing in cognitive radio (CR) systems. However, the DCR receivers are susceptible to radio frequency (RF) impairments, such as in-phase and quadrature-phase imbalance, low-noise amplifier nonlinearities, and phase noise, which limit the spectrum sensing capabilities. In this paper, we investigate the joint effects of RF impairments on energy detection-based spectrum sensing for CR systems in multi-channel environments. In particular, we provide the novel closed-form expressions for the evaluation of the detection and false alarm probabilities, assuming Rayleigh fading. Furthermore, we extend the analysis to the case of CR networks with cooperative sensing, where the secondary users suffer from different levels of RF imperfections, considering both scenarios of error free and imperfect reporting channel. Numerical and simulation results demonstrate the accuracy of the analysis as well as the detrimental effects of RF imperfections on the spectrum sensing performance, which bring significant losses in the spectrum utilization.

In this paper, we present a novel low-complexity technique, which improves the performance of single-antenna multi-carrier communication systems, suffering from in-phase and quadrature (I/Q)-imbalance (IQI) at the receiver. We refer to the proposed scheme as I/Q-imbalance self-interference coordination (IQSC). This technique not only mitigates the detrimental effects of IQI, but, through appropriate signal processing, also coordinates the self-interference terms produced by IQI in order to achieve second-order frequency diversity. However, these benefits come at the expense of a reduction in transmission rate. More specifically, IQSC is a simple transmit diversity scheme that improves the signal quality at the receiver by elementary signal processing operations across symmetric (mirror) pairs of subcarriers. Thereby, the proposed transmission protocol has a similar complexity as Alamouti's space-time block coding scheme and does not require extra transmit power nor any feedback. To evaluate the performance of IQSC, we derive closed-form expressions for the resulting outage probability and symbol-error rate. Interestingly, IQSC outperforms not only existing IQI compensation schemes but also the ideal system without IQI for the same spectral efficiency and practical target error rates, while it achieves almost the same performance as ideal (i.e., IQI-free) equal-rate repetition coding. Our findings reveal that IQSC is a promising low-complexity technique for significantly increasing the reliability of low-cost devices that suffer from high levels of IQI.

Radio frequency (RF) front-ends constitute a fundamental part of both conventional and emerging wireless communication systems. However, in spite of their importance they are often assumed ideal, although they are practically subject to certain detrimental impairments, such as amplifier nonlinearities, phase noise and in phase and quadrature (I/Q) imbalance (IQI). The present work is devoted to the quantification and evaluation of the RF IQI effects in the context of realistic wireless vehicle-to-vehicle (V2V) communications over double-Nakagami-m fading channels. Novel closed form expressions are derived for the corresponding outage probability for the case of ideal transmitter (TX) and receiver (RX), ideal TX and I/Q imbalanced RX, I/Q imbalanced TX and ideal RX, and joint I/Q imbalanced TX/RX. The offered analytic results have a relatively convenient algebraic representation and their validity is extensively justified through comparisons with respective results from computer simulations. Based on these, it is shown that cascaded fading results to considerable degradations in the system performance and that assuming ideal RF front-ends at the TX and RX induces non-negligible errors in the outage probability evaluation that can exceed 20% in several V2V communication scenarios.

We study the outage performance of orthogonal frequency-division multiplexing dual-hop opportunistic amplify-and-forward relaying in the presence of I/Q imbalance (IQI) in all nodes. We derive a closed-form expression for the end-to-end outage probability for the general case where each node suffers from a different IQI level and assuming that the distance between source and relay is not the same for all relays. Furthermore, we consider the more general case where there is a direct link between the source and the destination and we derive a closed-form expression for the end-to-end outage probability for both maximum-ratio and selection combining at the destination. To gain more insights, we analyze a special case where all relays lie on the perpendicular line midway between source and destination and all nodes experience the same IQI level. Our simulations show excellent match to the analytical results and both demonstrate that uncompensated IQI can be detrimental but it can also be effectively mitigated using a few opportunistic relays. In summary, the main contributions of this paper are analyzing the effect of IQI on the outage probability of an opportunistic relaying system and determining the number of relays needed to effectively mitigate uncompensated IQI.

We study the outage performance of orthogonal frequency-division multiplexing dual-hop opportunistic amplify-and-forward relaying in the presence of I/Q imbalance in all of the nodes' transceivers. We derive a closed-form expression for the end-to-end outage probability for the general case, where each node suffers from a different I/Q imbalance level and under the assumption that the distance between source and relay is not the same for all relays. Our simulation results show excellent match to our analysis and both demonstrate that uncompensated I/Q imbalance can be detrimental but it can also be effectively mitigated using a few relays. This result has a significant impact on relay deployment in future wireless communication networks.