Freezing of Gait (FoG) is a prevalent motor dysfunction experienced by patients with Parkinson’s disease, and while extensive research has focused on detecting FoG episodes in both clinical and home environments over the past decade, predictive approaches that enable preemptive prompting are still scarce. Most existing studies rely on fully supervised learning conditions, which pose significant challenges. This study introduces a semi-supervised learning framework utilizing a prototype network designed to leverage a FoG prediction model based on impaired gait patterns indicative of pre-FoG, simultaneously harnessing information from unlabeled sensor data for real-time FoG prediction. To validate our framework, we establish pre-FoG state labeling across four datasets: DAPHNet, CPGDD, PDTURN, and BXHC. Our developed real-time detection model demonstrates strong performance, achieving up to \(96\%\) sensitivity, \(99\%\) specificity, and \(91\%\) average accuracy on the cross-validation dataset. Furthermore, we compare the accuracy between semi-supervised and fully supervised modes, revealing that the semi-supervised approach yields improvements in average accuracy ranging from 0.01 to 0.02 across three datasets. Thus, our proposed method represents an effective strategy for the accurate prediction of FoG in real-time algorithmic settings.

Data Center-enabled High Altitude Platforms (DCHAPs) show great potential in reducing the energy consumption of terrestrial data centers by leveraging natural cooling and solar power harvesting. This paper advances the field by analyzing multi-platform architectures with multiple terrestrial data centers (TDCs) and HAPs performing heterogeneous task offloading. We formulate this complex problem mathematically and develop a feasibility-preserving transformation approach to ensure optimization stability. To solve this challenging problem, we propose a novel heuristic sequential algorithm specifically designed for multiplatform DC-HAP systems, while also tailoring two established optimization methods—sequential quadratic programming (SQP) and differential evolution (DE)—to address our unique problem constraints. Comprehensive simulations demonstrate that robust results are guaranteed with our proposed heuristic algorithm, while the SQP algorithm with the transformed problem balances performance and runtime better than the DE approach. The increasing number of HAPs leads to capability degradation for the SQP and DE algorithms, while our proposed heuristic algorithm maintains powerful performance with the highest efficiency for complex system architectures.

Sunlight is commonly used for indoor natural illumination in modern buildings, but it also holds potential as an information carrier in indoor optical wireless communication (OWC) systems. This paper introduces a multi-user, sunlightbased modulation downlink multiple-input single-output (MISO) system employing a recently developed dual-cell liquid crystal shutter (DLS) as the modulator, allowing building facades to simultaneously provide communication and illumination based on user needs. We first outline the system framework, detailing the transmitter and receiver structures. To assess channel performance, we derive an end-to-end transmission model by examining the physical mechanisms of the DLS. A preprocessing linearization method is applied to mitigate nonlinear behavior, enabling us to derive a theoretical lower bound for the system's channel capacity, facilitated by time division multiplexing (TDM). Additionally, we establish an illumination model that accounts for the contributions of active and inactive DLS cells. An optimization problem is formulated to maximize total channel capacity across multiple users, subject to minimum illumination requirements and boundary constraints. Simulation results show that the proposed system achieves high communication throughput by dynamically adjusting the ratio of active to inactive DLS cells. This study establishes a basis for high-speed indoor OWC systems powered by natural sunlight.

Sunlight offers not only illumination but also an eco-friendly solution for indoor optical wireless communication (OWC). This article presents a comprehensive investigation of sunlight modulation using the recently introduced dual-cell liquid crystal shutter (DLS), examining both communication and illumination aspects. We propose a system featuring a glass window equipped with a large array of DLS, including configurable active and non-active communication cells. Our study begins by formulating channel models for DLS configurations in downlink communication based on experimental measurements and thorough analysis. We then derive the illumination expression resulting from the generalized contribution of active and non-active communication cells. Building on these models, we analyze the channel capacity bounds of the proposed communication system and formulate an optimization problem to maximize channel capacity while meeting the minimum indoor illumination constraint. Simulation results demonstrate the superior channel capacity of the DLS-based system compared to conventional light-emitting diode (LED) communication systems. Furthermore, by dynamically adjusting the number of active DLS cells in a multiple DLS system, we achieve high communication throughput for receivers at various locations. Our findings reveal that the discrepancy between our theoretical predictions and empirical findings underscores the potential for elevating the data rate from tens of kbps to tens of Mbps.

Accurate measurement of oxygen uptake ( \(\dot{\mathrm{V}}\mathrm{O}_{2}\) ) dynamics and maximal oxygen consumption ( \(\dot{\mathrm{V}}\mathrm{O}_{2}\max\) ), a vital marker of cardiorespiratory fitness and exercise capacity, requires specialized exercise physiology laboratories with costly equipment. This study develops a Temporal Fusion Network (TFN) approach utilizing easily accessible physiological parameters (heart rate, heart rate reserve, tidal volume, and breathing frequency), which can be measured with wearable sensors, anthropometric variables (age, gender, height, and weight), as well as health status to estimate \(\dot{\mathrm{V}}\mathrm{O}_{2}\) dynamics during cardiopulmonary exercise testing (CPET). These input physiological parameters were derived from 140 laboratory CPET of a diverse cohort of adults (90 males, 50 females; 77 healthy, 63 smokers; average age: 26.6 years), to analyze \(\dot{\mathrm{V}}\mathrm{O}_{2}\) dynamics. The TFN model demonstrated high predictive accuracy to estimate \(\dot{\mathrm{V}}\mathrm{O}_{2}\) dynamics, with a Root Mean Square Error (RMSE) of 0.03 L/min and an R-squared ( R 2 ) value of 0.92, indicating robust performance across varied population groups. This TFN model paves the way for practical and cost-effective approach to estimate \(\dot{\mathrm{V}}\mathrm{O}_{2}\) during exercise, with potential integration with consumer health devices to expand accessibility and, enhance its utility for clinical and fitness applications.

Freezing of Gait (FoG) represents a critical and debilitating symptom of Parkinson's disease, posing significant challenges in patient mobility and safety. Numerous research efforts have focused on predicting the onset of FoG using wearable sensors and computational aids. However, the unpredictability and brief nature of FoG episodes complicate the ability to predict their onset in real-time, rendering timely detection a complex goal. In our study, we employed an Autoencoder-Wavenet network architecture designed to effectively utilize data from triaxial accelerometers and tri-axial gyroscopes positioned at the ankle. This approach allowed for a more nuanced analysis of movement patterns associated with FoG. Our findings indicated that this model successfully achieved a high level of accuracy in detecting FoG, with a specificity of 0.81, and sensitivity of 0.87. Furthermore, the model demonstrated the capability to provide real-time warnings of FoG onset, achieving a specificity of 0.68 and a sensitivity of 0.86. And an accuracy of 0.67 within a 1-second timeframe on the test set. Consequently, these results underscore the potential of our model in contributing to the real-time detection and management of FoG in Parkinson's disease patients.

Integrating environmental and health information in the authentication and authorization processes is challenging in the Internet of things (IoT) systems using only wireless radio communications. Recent research plans for IoT future networks shed light on the need for developing new authentication techniques suitable for health care and environmental use cases. To this end, we propose a novel molecular keys (MKs)-based authentication system that uses molecules as data carriers while considering the receiver's types, concentration, and arrival time. To facilitate our proposal, we built a MK generator and detector. The system achieved a decoding accuracy of 86% for sending bit sequences within a distance of 1m. The proposed method can also accommodate dynamic authentication and authorization changes across time and places, emerging marketing applications, and evolving human activities.

Information technology organizations and companies are seeking greener alternatives to traditional terrestrial data centers to mitigate global warming and reduce carbon emissions. Currently, terrestrial data centers consume a significant amount of energy, estimated at about 1.5% of worldwide electricity use. Furthermore, the increasing demand for data-intensive applications is expected to raise energy consumption, making it crucial to consider sustainable computing paradigms. In this study, we propose a data center-enabled High Altitude Platform (HAP) system, where a flying data center supports the operation of terrestrial data centers. We conduct a detailed analytical study to assess the energy benefits and communication requirements of this approach. Our findings demonstrate that a data center-enabled HAP is more energy-efficient than a traditional terrestrial data center, owing to the naturally low temperature in the stratosphere and the ability to harvest solar energy. Adopting a data center-HAP can save up to 14% of energy requirements while overcoming the offloading outage problem and the associated delay resulting from server distribution. Our study highlights the potential of a data center-enabled HAP system as a sustainable computing solution to meet the growing energy demands and reduce carbon footprint.

Hyperloop or evacuated-tube transportation is a groundbreaking technology that can reach aircraft-like speeds. Its uncommon configuration of a steel-made tube isolates the moving pod from the outside wireless world. In this work, we propose an inner tube network architecture that can provide the moving pod with a seamless and reliable connection. The proposed network consists of successive access points (APs) and intelligent reflecting surfaces (IRS) strategically positioned along the tube and connected to a control station (CS) through wired links to improve the wireless cell coverage. The subsequent entities of the proposed design are intelligently placed along the movement path, steering the transmitted beam towards the receiver, while a soft handover is achieved between consecutive cells. First, we optimize each IRS's positioning and phase shifts to maximize cell coverage thanks to the IRS scanning abilities while keeping a minimum quality of service. Afterward, we exploit the centralized operation at the CS and design a soft handover scheme for the inner-tube wireless network. The numerical results show that the proposed approach provides good cell coverage and spectral efficiency with different IRS scanning ranges.

Industry and academics have shown a strong interest in satellite-terrestrial networks combined with new technologies such as free-space optics (FSO). We propose a downlink satellite-terrestrial network solution combining FSO with site diversity and multi-hop hybrid radio-frequency (RF)/FSO links. Although the proposed network architecture is a cost-effective solution with wide coverage and immunity against different weather conditions, the multi-hop links can degrade its delay performance. Hence, we analyze the network dropping probability and latency to address the hop-count problem for various applications. We validated the derived network performance by Monte-Carlo simulation and showed that this setting could support ultra low latency applications such as medical applications with latency between 3–10 milliseconds, audio/video 2–50 milliseconds, and augmented reality 7–20 milliseconds. Also, the proposed network guarantees 98 % data delivery with 20 nodes.

Terahertz (THz) band spans the frequencies lying between 0.1 and 10 THz and represents the gap between millimeter waves and Infrared bands. THz will play an influential role in mitigating the spectrum resources shortage to meet the exponential growth of services and wireless devices. Standardization activities are being carried out to regulate the THz band's exploitation under the IEEE 802.15 standardization project. Despite the generous bandwidth, THz communications suffer from high pathloss and attenuation due to Molecular Absorption. As such, THz systems need to use extra power, suitable antennas, and enhanced signal processing and communication techniques to compensate for different signal attenuation sources. In this paper, we propose a new operation mode for the IEEE standard 802.15 to save the power transmission requirement while achieving the required data rate. Hence, less battery or antenna size is needed to support the same communication link quality. To this end, we optimize the power, modulation scheme, and channel allocation to minimize the total transmitted power keeping a minimum quality-of-service. Moreover, the design captures the effect of humidity on the system performance.

This paper studies the temporal characteristics of the intelligent reflecting surface (IRS)-based visible light communication (VLC) channel using radiometric concepts. Throughout this study, we account for the delays experienced by the transmitted power along the continuum of paths originating at the source, passing through the IRS, reaching the detector. Then, we derive the impulse response of multi-element phase-tunable metasurface and orientation-tunable mirror array-based reflector setups for a general setting of source, reflector, and detector dimensions and relative positions. In addition, we derive simpler expressions for the two special cases, namely, the point source and the large-source small-reflector. Moreover, we present the exact expression for the delay spread and derive lower, upper bounds and asymptotic expressions when the number of reflecting elements increases for both reflector types. Finally, we study the impact of several system parameters on the temporal characterization of the two IRS-based VLC systems.

The integration between the satellite network and the terrestrial network will play a key role in the upcoming sixth-generation (6G) of mobile cellular networks thanks to the wide coverage and bandwidth offered by satellite networks. To leverage this integration, we propose a proactive traffic offloading scheme in an integrated multi-satellite terrestrial network (IMSTN) that considers the future networks’ heterogeneity and predicts their variability. Our proposed offloading scheme hinges on traffic prediction to answer the stringent requirements of data-rate, latency and reliability imposed by heterogeneous and coexisting services and traffic namely enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable low latency communication (URLLC). However, the fulfilment of these requirements during offloading in dynamic IMSTN comes at the expense of significant energy consumption and introduces inherently supplementary latency. Therefore, our offloading scheme aims to balance the fundamental trade-offs first between energy consumption and the achievable data-rate and second between energy consumption and latency while meeting the respective needs of the present traffic. Our findings prove the importance of the cooperation between the multi-satellite network and the terrestrial network conditioned by traffic prediction to enhance the performance of IMTSN in terms of latency and energy consumption.

To expedite research progress on terahertz (THz) communications, we analyze the outage performance of THz communication systems by a compound channel model in this paper. Different from existing models, the compound channel model incorporates the effects of spreading loss, molecular absorption loss, shadowing, and multi-path fading via a composite distribution. By using this model, we maintain an equilibrium of the outage performance analysis between mathematical tractability and the fidelity of realistic THz channels. Specifically, by utilizing the compound channel model, outage performance analysis can get rid of sophisticated case-specific channel modeling relying on field measurement and the ray-tracing assessment. To facilitate the application of the proposed channel model, we also design a maximum likelihood estimation (MLE) based channel parameter estimation approach for the compound channel model. The analytical results of outage performance by using the compound channel model are given in closed form and verified by numerical results.

Hardware distortions (HWDs) render drastic effects on the performance of communication systems. They are recently proven to bear asymmetric signatures; and hence can be efficiently mitigated using improper Gaussian signaling (IGS), thanks to its additional design degrees of freedom. Discrete asymmetric signaling (AS) can practically realize the IGS by shaping the signals' geometry or probability. In this paper, we adopt the probabilistic shaping (PS) instead of uniform symbols to mitigate the impact of HWDs and derive the optimal maximum a posterior detector. Then, we design the symbols' probabilities to minimize the error rate performance while accommodating the improper nature of HWD. Although the design problem is a non-convex optimization problem, we simplified it using successive convex programming and propose an iterative algorithm. We further present a hybrid shaping (HS) design to gain the combined benefits of both PS and geometric shaping (GS). Finally, extensive numerical results and Monte Carlo (MC) simulations highlight the superiority of the proposed PS over conventional uniform constellation and GS. Both PS and HS achieve substantial improvements over the traditional uniform constellation and GS with up to one order magnitude in error probability and throughput.

By exploiting fractional-Fourier-transform (FrFT), a novel technique for the QRS complex detection is proposed. The application of the FrFT rotates the Electrocardiograph (ECG) signal in the time-frequency plane. We claim this rotation can give simple and effective QRS complex detection even in the presence of versatile artifacts, such as left-bundle-branch-block, right-bundle-branch-block, and negative polarization. In this work, in the first step, the noise and baseline drifts are removed by applying a wavelet transform on the given ECG signal. While, in the next step, the clean ECG signal is passed through the proposed algorithm, which rotates the ECG signal in the time-frequency plane and detects the QRS complex very easily. The proposed algorithm validated over the 48 signals of the MIT-BIH arrhythmia database, and it yielded 26 false-positive and only five false-negatives compared to the 80 and 42, the best result reported so far.

In this paper, we propose studying the disease spread mechanism in the atmosphere as an engineering problem. Aerosol transmission is the most significant mode among the viral transmission mechanisms that do not include physical contact, where airflows carry virus-laden droplets over long distances. Throughout this work, we study the transport of these droplets as a molecular communication problem, where one has no control over the transmission source, but a robust receiver can be designed using bio-sensors. To this end, we present a complete system model and derive an end-to-end mathematical model for the transmission channel under certain constraints and boundary conditions. We derive the system response for both continuous sources such as breathing and jet or impulsive sources such as coughing and sneezing. In addition to transmitter and channel, we assumed a receiver architecture composed of air sampler and Silicon Nanowire field-effect transistor. Then, we formulate a detection problem to maximize the likelihood decision rule and minimize the corresponding missed detection probability. Finally, we present several numerical results to observe the impact of parameters that affect the performance and justify the feasibility of the proposed setup in related applications.

In this paper, we study the performance of simultaneous lightwave information and power transfer (SLIPT) systems in a multi-cell indoor scenario. We investigate the energy harvesting and data rate performance of multiple users while meeting the lighting constraints. To this end, we develop optimization frameworks that tune the parameters of transmitters or receivers to improve the SLIPT system performance. Firstly, we model the relationship between tunable lens-based optical receivers concentrator gain and their fields of view. Next, we develop algorithms to maximize the spectral efficiency (SE) considering per-user minimum harvested energy requirements and lightning constraints by controlling the average light emitting diode (LED) excitation current or tuning the optical receivers' fields of view. Then, we study the joint system performance limits of SE and total harvested energy. Towards this aim, we formulate multi-objective optimization problems and propose algorithms to find the best SE - energy harvesting tradeoff for both designing strategies. Finally, we present some extensive simulations to explore the benefits of the proposed algorithms comparing with basic benchmarks. Besides, we monitor the effect of changing several system parameters on the two objectives and the behavior of the inherent trade-off between them under both transmitter and receiver sides designing strategies.

The continuous improvement in optical energy harvesting devices motivates the development of visible light communication systems that utilize such available free energy. In this paper, an outdoor visible light communications (VLC) system is considered where a VLC base station sends data to multiple users that are capable of harvesting optical energy. The proposed VLC system serves multiple users using time division multiple access (TDMA) with unequal time and power allocation, which are allocated to achieve the system communications and illumination objectives. In an outdoor setup, the system lighting objective is to maximize the average illumination flux, while the communication design objective is to maximize the spectral efficiency (SE). A multiobjective optimization problem is formulated to obtain the Pareto front of the SE-illumination region. To this end, the marginal optimization problems are solved first using low complexity algorithms. Then, based on the proposed algorithms, a Karush-Kuhn-Tucker-based algorithm is developed to obtain an inner bound of the Pareto front for the SE-illumination tradeoff. The inner bound for the Pareto-front is shown to be close to the optimal Pareto-frontier via several simulation scenarios for different system parameters.

In this paper, we analyze the performance degradation of a multi-hop decode-and-forward full-duplex relaying system caused by the residual self-interference (RSI) and hardware distortions (HWD) imposed by the FDR operation and imperfect hardware, respectively. In addition, we study the benefits of employing improper Gaussian signaling (IGS) in the MH-FDR system. Different from the traditional symmetric signaling scheme, i.e., proper Gaussian signaling (PGS), IGS has non-zero pseudo-variance that can limit the impact of RSI and HWD in the MH-FDR system. To evaluate the system performance gain using IGS, first we express the end-to-end achievable rate of the MH system as the minimum rate supported by all participating links. Then, we optimize the pseudo-variance of all participating transmitters, including source and relays to compensate the interference impact and improve the end-to-end achievable rate. We propose two network optimization schemes based on the system characteristics, i.e., joint optimization framework and distributed optimization scenario. Interestingly, IGS-based scheme outperforms its counterpart PGS-based scheme, especially at higher interference-to-noise ratio. Our findings reveal that using IGS in single-user detection systems that suffer from both RSI and HWD can effectively mitigate the degradation in the achievable rate performance.

Visible light communications (VLCs), in general, and resource allocation for VLC networks in particular, have gained lots of attention recently. In this paper, we consider the resource allocation problem of a VLC downlink transmission system employing dynamic time division multiple access, where time and power variables are tuned to maximize the downlink spectral efficiency (SE). As for the operational conditions, we impose constraints on the average optical intensity, the energy budget, and the quality-of-service. To solve this non-convex problem, we transform the objective function into a difference of concave functions by solving a second-order differential inequality. Then, we propose a low-complexity algorithm to solve the resource allocation problem. Finally, we show by simulations the SE performance gains achieved by optimizing time and power allocation over the initial total power minimization solution for the considered system.

Error probability study of hardware impaired (HWI) systems highly depends on the adopted model. Considering the distinct improper Gaussian features of HWI systems, captured by recent models, HWI-aware receivers are designed. An optimal maximum likelihood (ML) receiver serves as a performance benchmark, and a sub-optimal linear minimum mean square error introduces a reduced-complexity implementation. Whereas, the conventional HWI-unaware minimum Euclidean distance receiver, based on the proper noise assumption, exhibits substandard performance. Next, the average error probability of the proposed optimal ML-receiver is analyzed, where several tight bounds and approximations are derived for various HWI systems. Motivated by the benefit of improper Gaussian signaling in mitigating HWI, which is proven in recent studies, asymmetric modulation is adopted and optimized for transmission. The numerical results demonstrate a bit error rate (BER) reduction up to 70% of the proposed HWI-aware receivers over HWI-unaware receivers. Moreover, the asymmetric modulation is shown to reduce the BER by 93%. These results signify the importance of incorporating accurate HWI models, designing appropriate receivers and optimizing signal transmission for the BER performance compensation.

In this paper, we study the performance of simultaneous lightwave information and power transfer (SLIPT) systems of multi-cell indoor scenario. We aim to investigate the energy harvesting and data rate performance of multiple users while meeting the lightning constraints. To this end, we develop optimization frameworks and tune the light emitting diodes average currents to improve the performance of the SLIPT system. Firstly, we propose an algorithm to maximize the spectral efficiency (SE) subject to lighting and minimum harvested energy per user requirements. The proposed algorithm can be implemented in a distributed fashion with a reduced computational burden at each node. Then, we consider the energy harvesting maximization problem to investigate the maximum possible energy gain and its corresponding SE performance. Finally, we present some extensive simulations to explore the benefit of the optimization frameworks with respect to standard equal allocation setting. In addition, we monitor the effect of changing several system parameters on the two objectives and highlight the underlying trade-off between them.

A generalized cooperative spectrum sharing (GCSS) scheme for machine-to-machine (M2M) communications is pro- posed in internet-of-things (IoT) systems. The proposed scheme makes use of the existence of massive connected machines to overcome the challenges of spectrum scarcity while avoiding interference and meeting the green requirements of IoT systems. The cooperative proposed scheme extends the coverage of M2M wireless network as well as increasing the throughput while reducing the energy consumption of the connected low power devices. The performance of the GCSS scheme is evaluated analytically by the outage performance by deriving the outage probability. Furthermore, a numerical simulations are presented to support the theoretical findings.

Aerosol Transmission is one of the major spread mechanism for diseases and is responsible for transmission of virus over long distances. The advancement in nanotechnology has resulted in sensors and systems that allow us to deal with nanosized biological entities such as virus and bacteria. In this work, the idea of viewing virus transmission through aerosols and their transport as a molecular communication problem is introduced. In such problems one has little or no control over transmission, however, a robust receiver can be designed using nano-biosensors for information extraction. Thus, the objective of this work is to treat viral aerosol spread as a blind communication problem and present a mathematical model for it. Specifically, we study the virus transmission from an engineering perspective and derive an end-to-end mathematical model for virus transmission in the atmosphere. The receiver architecture composed of air sampler and Silicon Nanowire field effect transistor is also discussed. Furthermore, a detection problem is formulated and simulation results are reported that justify the feasibility of such setups in bio-monitoring applications.

We study the potential employment of improper Gaussian signaling (IGS) in full-duplex relaying (FDR) with non-negligible residual self-interference (RSI) under Nakagami-m fading. IGS is recently shown to outperform traditional proper Gaussian signaling (PGS) in several interference-limited settings. In this paper, IGS is employed as an attempt to alleviate RSI. We use two performance metrics, namely, the outage probability and the ergodic rate. First, we provide upper and lower bounds for the system performance in terms of the relay transmit power and circularity coefficient, a measure of the signal impropriety. Then, we numerically optimize the relay signal parameters based only on the channel statistics to improve the system performance. Based on the analysis, IGS allows FDR to operate even with high RSI. The results show that IGS can leverage higher power budgets to enhance the performance, meanwhile it relieves RSI impact via tuning the signal impropriety. Interestingly, 1-D optimization of the circularity coefficient, with maximum relay power, offers a similar performance as the joint optimization, which reduces the optimization complexity. From a throughput standpoint, it is shown that IGS-FDR can outperform not only PGS-FDR, but also half-duplex relaying with/without maximum ratio combining over certain regions of the target source rate.

Improper Gaussian signaling (IGS) has proven its ability in improving the performance of underlay and overlay cognitive radio paradigms. In this paper, the interweave cognitive radio paradigm is studied when the cognitive user employs IGS. The instantaneous achievable rate performance of both the primary and secondary users are analyzed for specific secondary user sensing and detection capabilities. Next, the IGS scheme is optimized to maximize the achievable rate secondary user while satisfying a target minimum rate requirement for the primary user. Proper Gaussian signaling (PGS) scheme design is also derived to be used as benchmark of the IGS scheme design. Finally, different numerical results are introduced to show the gain reaped from adopting IGS over PGS under different system parameters. The main advantage of employing IGS is observed at low sensing and detection capabilities of the SU, lower PU direct link and higher SU interference on the PU side.

In this paper, we accurately model the transceiver hardware impairments (HWIs) of multiple-input multiple-output (MIMO) systems considering different HWI stages at transmitter and receiver. The proposed novel statistical model shows that transceiver HWIs transform the transmitted symmetric signal to asymmetric one. Moreover, it shows that the aggregate self-interference has asymmetric characteristics. Therefore, we propose improper Gaussian signaling (IGS) for transmission in order to improve the achievable rate performance. IGS is considered as a general signaling scheme which includes the proper Gaussian signaling (PGS) as a special case. Thus, IGS has additional design parameters which enable it to mitigate the HWI self-interference. As a case study, we analyze the achievable rate performance of single-input multiple-output systems with linear and selection combiner. Furthermore, we optimize the IGS statistical characteristics for interference alignment. This improves the achievable rate performance as compared to the PGS, which is validated through numerical results.

The conventional minimum Euclidean distance (MED) receiver design is based on the assumption of ideal hardware transceivers and proper Gaussian noise in communication systems. Throughout this study, an accurate statistical model of various hardware impairments (HWIs) is presented. Then, an optimal maximum likelihood (ML) receiver is derived considering the distinct characteristics of the HWIs comprised of additive improper Gaussian noise and signal distortion. Next, the average error probability performance of the proposed optimal ML receiver is analyzed and tight bounds are derived. Finally, different numerical and simulation results are presented to support the superiority of the proposed ML receiver over MED receiver and the tightness of the derived bounds.

In this paper, we study the benefits of employing improper Gaussian signaling (IGS) in full duplex relaying (FDR) suffering from in-phase and quadrature imbalance (IQI). Different from the traditional symmetric signaling scheme, proper Gaussian signaling (PGS), that is parametrized by its variance, IGS needs additional statistical-quantity called the pseudo-variance to be fully described. The cooperative system under consideration suffers from two types of interferences, the residual self-interference (RSI) and IQI. To evaluate the system performance gain using IGS, first we express the end-to-end achievable rate for different IQI. Then, we optimize the pseudo-variance to compensate the interferences impact and improve the end-to-end achievable rate. Interestingly, IGS-based scheme outperforms its counterpart PGS-based scheme, especially at higher interference-to-noise ratio. Our findings reveal that using single-user detection with asymmetric signaling can compensate both RSI and IQI and improve the system performance.

In this paper, we accurately model the hardware impairments (HWI) as improper Gaussian signaling (IGS) which can characterize the asymmetric characteristics of different HWI sources. The proposed model encourages us to adopt IGS scheme for transmitted signal that represents a general study compared with the conventional scheme, proper Gaussian signaling (PGS). First, we express the achievable rate of HWI systems when both PGS and IGS schemes are used when the aggregate effect of HWI is modeled as IGS. Moreover, we tune the IGS statistical characteristics to maximize the achievable rate. Then, we analyze the outage probability for both schemes and derive closed form expressions. Finally, we validate the analytic expressions through numerical and simulation results. In addition, we quantify through the numerical results the performance degradation in the absence of ideal transceivers and the gain reaped from adopting IGS scheme compared with PGS scheme.

In this paper, we develop selective retransmission schemes for multiple-channel systems. The proposed schemes are selective automatic repeat request with fixed bandwidth (SARQ-FB), selective chase combining with fixed bandwidth (SCC-FB) and selective automatic repeat request with variable bandwidth (SARQ-VB). The main objective of the proposed schemes is to use the available power and bandwidth budget effectively along with the selective retransmission to deliver the required data successfully within a limited number of transmissions. To investigate the performance of each scheme, we first analyze the average spectral and energy efficiency and derive closed form expressions for each scheme. Then, we compare the EE and SE of each scheme through numerical results.

The use of drone base stations to provide wireless connectivity for ground terminals is becoming a promising part of future technologies. The design of such aerial networks is however different compared to cellular 2D networks, as antennas from the drones are looking down, and the channel model becomes height-dependent. In this paper, we study the effect of antenna patterns and height-dependent shadowing. We consider a random network topology to capture the effect of dynamic changes of the flying base stations. First we characterize the aggregate interference imposed by the co-channel neighboring drones. Then we derive the link coverage probability between a ground user and its associated drone base station. The result is used to obtain the optimum system parameters in terms of drones antenna beamwidth, density and altitude. We also derive the average LoS probability of the associated drone and show that it is a good approximation and simplification of the coverage probability in low altitudes up to 500 m according to the required signal-to-interference-plus-noise ratio (SINR).

Improper Gaussian signaling (IGS) has been recently shown to provide performance improvements in underlay cognitive radio systems as opposed to the conventional proper Gaussian signaling (PGS) scheme. For the first time, this paper implements IGS scheme in overlay cognitive radio system, where the secondary transmitter broadcasts a mixture of two different signals. The first signal is selected from the PGS scheme to support the primary message transmission. On the other hand, the second signal is chosen to be from the IGS scheme in order to reduce the interference effect on the primary receiver. We then optimally design the overlay cognitive radio that employs IGS to maximize the secondary link achievable rate while satisfying the minimum rate requirement of the primary network. In particular, we derive closed form expressions for the circularity coefficient used in the IGS scheme and the power distribution parameters. Simulation results are provided to support our theoretical derivations.

Improper Gaussian signaling (IGS) scheme has been recently shown to provide performance improvements in interference limited networks as opposed to the conventional proper Gaussian signaling (PGS) scheme. In this paper, we implement the IGS scheme in overlay cognitive radio system, where the secondary transmitter broadcasts a mixture of two different signals. The first signal is selected from the PGS scheme to match the primary message transmission. On the other hand, the second signal is chosen to be from the IGS scheme in order to reduce the interference effect on the primary receiver. We then optimally design the overlay cognitive radio to maximize the secondary link achievable rate while satisfying the primary network quality of service requirements. In particular, we consider full and partial channel knowledge scenarios and derive the feasibility conditions of operating the overlay cognitive radio systems. Moreover, we derive the superiority conditions of the IGS schemes over the PGS schemes supported with closed form expressions for the corresponding power distribution and the circularity coefficient and parameters. Simulation results are provided to support our theoretical derivations.

Multi-tier heterogeneous networks have become an essential constituent for next-generation cellular networks. Meanwhile, energy efficiency (EE) has been considered a critical design criterion along with the traditional spectral efficiency (SE) metric. In this context, we study power and spectrum allocation for a two-tier phantom cellular network. The optimization framework includes both EE and SE. We consider densely deployed phantom cellular networks and model the EE optimization problem considering the inevitable interference in this setup and imperfect channel estimation impairments. To this end, we propose three resource allocation strategies aiming at optimizing this network EE performance metric. Furthermore, we investigate the effect of changing some system parameters on the performance of these strategies, such as phantom cells resource units share, number of deployed phantom cells within a macro cell, number of pilots, and the phantom cells transmission power budget. It is found that increasing the number of pilots will deteriorate the EE performance of the whole setup, while increasing phantom cells transmission power budget will not affect the EE of the whole setup significantly. In addition, we observed that it is always useful to allocate most of the network resource units to the phantom cells tier.

Sharing the spectrum with in-band full-duplex (FD) primary users (PUs) is a challenging and interesting problem in the underlay cognitive radio systems. The self-interference introduced at the primary network may dramatically impede the secondary user (SU) opportunity to access the spectrum. To tackle this problem, we use the so-called improper Gaussian signaling. Particularly, we assume the downlink transmission of a SU that uses improper Gaussian signaling while the FD PU pair implements the regular proper Gaussian signaling. First, we derive a closed-form expression and an upper bound for the SU and PUs outage probabilities, respectively. Second, we optimize the SU signal parameters to minimize its outage probability while maintaining the required PUs quality-of-service based on the average channel state information (CSI). Moreover, we provide the conditions to reap merits from employing improper Gaussian signaling at the SU. Third, we design the SU signal parameters based on the perfect knowledge of its direct link instantaneous CSI and investigate all benefits that can be achieved at both the SU and PUs. Finally, we provide some numerical results that demonstrate the advantages of using improper Gaussian signaling to access the spectrum of the FD PUs.

Multi-tier heterogeneous networks have become an essential constituent for next generation cellular networks. Mean-while, energy efficiency (EE) has been considered a critical design criterion along with the traditional spectral efficiency (SE) metric. In this context, we study power and spectrum allocation for the recently proposed two-tier network architecture known as phantom cellular networks. The optimization framework includes both EE and SE, where we propose an algorithm that finds the SE and EE resource allocation strategies for phantom cellular networks. Then, we compare the performance of both design strategies versus the number of users, and phantom cells share of the total number of available resource blocks. We aim to investigate the effect of some system parameters to achieve improved SE performance at a non-significant loss in EE performance, or vice versa. It was found that increasing phantom cells share of resource blocks decreases the SE performance loss due to EE optimization when compared with the optimized SE performance.

We study the potential employment of improper Gaussian signaling (IGS) in full-duplex cooperative settings with residual self-interference (RSI). IGS is recently shown to outperform traditional proper Gaussian signaling (PGS) in several interference-limited channel settings. In this work, IGS is employed in an attempt to alleviate the RSI adverse effect in full-duplex relaying (FDR). To this end, we derive a tight upper bound expression for the end-to-end outage probability in terms of the relay signal parameters represented in its power and circularity coefficient. We further show that the derived upper bound is either monotonic or unimodal in the relay's circularity coefficient. This result allows for easily locating the global optimal point using known numerical methods. Based on the analysis, IGS allows FDR systems to operate even with high RSI. It is shown that, while the communication totally fails with PGS as the RSI increases, the IGS outage probability approaches a fixed value that depends on the channel statistics and target rate. The obtained results show that IGS can leverage higher relay power budgets than PGS to improve the performance, meanwhile it relieves its RSI impact via tuning the signal impropriety.

The advent of smartphones and tablets over the past several years has resulted in a drastic increase of global carbon footprint, due to the explosive growth of data traffic. Improving energy efficiency (EE) becomes, therefore, a crucial design metric in next generation wireless systems (5G). Cloud radio access network (C-RAN), a promising 5G network architecture, provides an efficient framework for improving the EE performance, by means of coordinating the transmission across the network. This paper considers a C-RAN system formed by several clusters of remote radio heads (RRHs), each serving a predetermined set of mobile users (MUs), and assumes imperfect channel state information (CSI). The network performance becomes therefore a function of the intra-cluster and inter-cluster interference, as well as the channel estimation error. The paper optimizes the transmit power of each RRH in order to maximize the network global EE subject to MU service rate requirements and RRHs maximum power constraints. The paper proposes solving the optimization problem using a heuristic algorithm based on techniques from optimization theory via a two-stage iterative solution. Simulation results show that the proposed power allocation algorithm provides an appreciable performance improvement as compared to the conventional systems with maximum power transmission strategy. They further highlight the convergence of the proposed algorithm for different networks scenarios.

In-band full-duplex (FD) communications have been optimistically promoted to improve the spectrum utilization and efficiency. However, the penetration of FD communications to the cellular networks domain is challenging due to the imposed uplink/downlink interference. This paper presents a tractable framework, based on stochastic geometry, to study FD communications in cellular networks. Particularly, we assess the FD communications effect on the network performance and quantify the associated gains. This paper proves the vulnerability of the uplink to the downlink interference and shows that FD rate gains harvested in the downlink (up to 97%) come at the expense of a significant degradation in the uplink rate (up to 94%). Therefore, we propose a novel fine-grained duplexing scheme, denoted as the α-duplex scheme, which allows a partial overlap between the uplink and the downlink frequency bands. We derive the required conditions to harvest rate gains from the α-duplex scheme and show its superiority to both the FD and half-duplex (HD) schemes. In particular, we show that the α-duplex scheme provides a simultaneous improvement of 28% for the downlink rate and 56% for the uplink rate. Finally, we show that the amount of the overlap can be optimized based on the network design objective.

Improper Gaussian signaling has the ability over proper (conventional) Gaussian signaling to improve the achievable rate of systems that suffer from interference. In this paper, we study the impact of using improper Gaussian signaling on the performance limits of the underlay cognitive radio system by analyzing the achievable outage probability of both the primary user (PU) and secondary user (SU). We derive the exact outage probability expression of the SU and construct upper and lower bounds of the PU outage probability which results in formulating an approximate expression of the PU outage probability. This allows us to design the SU signal by adjusting its transmitted power and the circularity coefficient to minimize the SU outage probability while maintaining a certain PU quality-of-service. Finally, we evaluate the derived expressions for both the SU and the PU and the corresponding adaptive algorithms by numerical results.

In-band full-duplex (FD) communications have been optimistically promoted to improve the spectrum utilization in cellular networks. However, the explicit impact of spatial interference, imposed by FD communications, on uplink and downlink transmissions has been overlooked in the literature. This paper presents an extensive study of the explicit effect of FD communications on the uplink and downlink performances. For the sake of rigorous analysis, we develop a tractable framework based on stochastic geometry toolset. The developed model accounts for uplink truncated channel inversion power control in FD cellular networks. The study shows that FD communications improve the downlink throughput at the expense of significant degradation in the uplink throughput. Therefore, we propose a novel fine-grained duplexing scheme, denoted as α-duplex scheme, which allows a partial overlap between uplink and downlink frequency bands. To this end, we show that the amount of the overlap can be optimized via adjusting α to achieve a certain design objective.

Sharing the spectrum with in-band full-duplex (FD) primary users (PU) is a challenging and interesting problem in the underlay cognitive radio (CR) systems. The self-interference introduced at the primary network may dramatically impede the secondary user (SU) opportunity to access the spectrum. In this work, we attempt to tackle this problem through the use of the so- called improper Gaussian signaling. Such a signaling technique has demonstrated its superiority in improving the overall performance in interference limited networks. Particularly, we assume a system with a SU pair working in half- duplex mode that uses improper Gaussian signaling while the FD PU pair implements the regular proper Gaussian signaling techniques. First, we derive a closed form expression for the SU outage probability and an upper bound for the PU outage probability. Then, we optimize the SU signal parameters to minimize its outage probability while maintaining the required PU quality-of- service based on the average channel state information. Finally, we provide some numerical results that validate the tightness of the PU outage probability bound and demonstrate the advantage of employing the improper Gaussian signaling to the SU in order to access the spectrum of the FD PU.

Improper Gaussian signaling has proved its ability to improve the achievable rate of the systems that suffer from interference compared with proper Gaussian signaling. In this paper, we first study impact of improper Gaussian signaling on the performance of the cognitive radio system by analyzing the outage probability of both the primary user (PU) and the secondary user (SU). We derive exact expression of the SU outage probability and upper and lower bounds for the PU outage probability. Then, we design the SU signal by adjusting its transmitted power and the circularity coefficient to minimize the SU outage probability while maintaining a certain PU quality-of-service. Finally, we evaluate the proposed bounds and adaptive algorithms by numerical results.

Sharing the licensed spectrum of full-duplex (FD) primary users (PU) brings strict limitations on the underlay cognitive radio operation. Particularly, the self interference may overwhelm the PU receiver and limit the opportunity of secondary users (SU) to access the spectrum. Improper Gaussian signaling (IGS) has demonstrated its superiority in improving the performance of interference channel systems. Throughout this paper, we assume a FD PU pair that uses proper Gaussian signaling (PGS), and a half-duplex SU pair that uses IGS. The objective is to maximize the SU instantaneous achievable rate while meeting the PU quality-of-service. To this end, we propose a simplified algorithm that optimizes the SU signal parameters, i.e, the transmit power and the circularity coefficient, which is a measure of the degree of impropriety of the SU signal, to achieve the design objective. Numerical results show the merits of adopting IGS compared with PGS for the SU especially with the existence of week PU direct channels and/or strong SU interference channels.

In this paper, we adopt an energy-efficiency (EE) metric, namedworst-EE, that is suitable for EE fairness optimization in the uplink transmission of amplify-and-forward (AF) cooperative orthogonal frequency division multiple access (OFDMA) networks. More specifically, we assign subcarriers and allocate powers for mobile and relay stations in order to maximize the worst-EE, i.e., to maximize the EE of the mobile station (MS) with the lowest EE value, subject to MSs transmit power, relay station (RS) transmit power, and MSs quality-of-service (QoS) constraints. The formulated primal max–min optimization problem is nonconvex fractional mixed integer nonlinear program, i.e., NP-hard to solve. We provide a novel optimization framework that studies the structure of the primal problem and prove that the dual min–max optimization problem attains the same optimal solution of the primal problem. Additionally, we propose a modified Dinkelbach algorithm, named dual Dinkelbach, to achieve the optimal solution of the dual problem in a polynomial time complexity. We further exploit the structure of the obtained optimal solution and develop a low complexity suboptimal heuristic. Numerical results show the effectiveness of the proposed algorithm to improve the network performance in terms of fairness between MSs, worst-EE, and average network transmission rate when compared to traditional schemes that maximize the EE of the whole network. Presented results also show that the suboptimal heuristic balances the achieved performance and the computational complexity.

In this paper, the power loading problem for orthogonal frequency division multiplexing (OFDM) with imperfect channel estimation is investigated considering the trade-off between energy efficiency (EE) and spectral efficiency (SE). Unlike traditional research that uses the EE as an objective function and imposes constraints either on the SE or the achievable rate, we propound a multiobjective optimization approach that can flexibly switch between the EE and the SE functions or change the priority level of each function using a trade-off parameter. Our dynamic approach is more tractable than conventional approaches and more convenient to realistic communication applications and scenarios. The system model considers the path loss and shadowing effect in modeling the EE and SE metrics, in addition to taking the channel estimation error into account. We first solve the marginal problems of maximizing the EE and the SE individuality, and then prove that the multiobjective optimization of the EE and the SE is equivalent to a simple problem that maximizes the capacity and minimizes the total power consumption. Finally, we use numerical results to discuss the choice of the trade-off parameter and study the effect of the estimation error, transmission power budget and channel-to-noise ratio on the multiobjective optimization.

A high spectral efficiency cooperative communication system is proposed in this paper. The concept of signal space diversity (SSD) is utilized to double the spectral efficiency of opportunistic cooperative communication networks. SSD rotates the transmitted symbols by a certain angle at the source, before transmission, such that each in-phase and quadrature component of the rotated symbol contains enough information to retrieve the entire original data symbol. As such, parts of the rotated symbols are transmitted by the source and the other parts are transmitted by a relay. In opportunistic relaying, the best relay among all relays that decode the source signals correctly participates in forwarding a message to the destination. Optimum receiver along with detailed performance analysis are presented in this paper. Also, asymptotic analysis and optimum power allocations at the source and the active relay are given. It is shown that the proposed technique enhances the performance by about 2dB over the conventional opportunistic relaying. As well, optimum power allocation is shown to further enhance the error probability performance.

Performance analysis of multi‐hop wireless networks with non‐regenerative relays over Nakagami‐ m fading channels in the presence of co‐channel interference is presented in this paper. In the analysis, an arbitrary number of independent and identically distributed Nakagami‐ m interferers will be considered. Closed‐form expression for the cumulative distribution function (CDF) of the upper bounded end‐to‐end signal‐to‐interference‐plus‐noise ratio (SINR) at the destination is given. The obtained CDF is then considered to study the average bit error probability (ABEP). Furthermore, an approximate expression for the CDF of the instantaneous end‐to‐end SINR is derived, based on which simple and general asymptotic expression for the error probability is presented and discussed. The derived asymptotic equation is shown to reduce to the already published equation in Rayleigh fading, which corroborate the exactness of the derived analysis. Besides, analytical comparison between amplify‐and‐forward and decode‐and‐forward multi‐hop systems in terms of ABEP is provided. Optimisation of the power allocation at the network's transmit nodes and the positioning of the relays are studied as well. It is shown that optimum power allocation achieves only coding gain, whereas optimising relay locations yields diversity gains as well as coding gain.

In this paper, performance and optimization study of multi-hop wireless system with amplify and forward (AF) relays over Nakagami-m fading channels and in the presence of co-channel interference is considered. Arbitrary number of independent and identically distributed Nakagami-m interferers is assumed in the analysis. The cumulative distribution function (CDF) of the upper bounded end-to-end signal-to-interference-plus-noise ratio (SINR) at the destination is calculated in closed-form. The obtained CDF is then used to study the end-to-end average bit error probability (ABEP). As well, approximate expression for the CDF of the instantaneous end-to-end SINR is derived. Based on this, simple and general asymptotic expression for the error probability is presented and discussed. Finally, optimizing power allocation at the transmit nodes and the positioning of the relays are studied. Obtained results revealed that optimum power allocation slightly enhances the performance, whereas optimizing relay locations yields significant performance enhancement. Further improvement is shown to be achieved through joint power and positions optimization.

In this study, the authors introduce an adaptive bit loading scheme for an amplify-and-forward orthogonal frequency division multiple cooperative system with relay selection. The proposed bit loading scheme maximises the throughput for a target error rate taking into account the quality of imperfect channel estimation. It relies on estimated channel state information (CSI) at the destination node and partial CSI (which involves the number of loaded bits) at the source node. The authors provide extensive Monte Carlo simulation results to demonstrate the throughput and the no-transmission probability performance of the proposed scheme and discuss the effect of various system and channel parameters on the performance.

In this paper, we propose an adaptive power loading algorithm for an OFDM (orthogonal frequency division multiplexing)-based multi-relay regenerative network with relay selection. The proposed scheme is based on selecting the "best" relay(s) according to different strategies and distributing the power adaptively among the subcarriers of source and selected relay(s) as to minimize an upper bound on the bit error rate (BER). Our Monte Carlo simulation results demonstrate superior performance gains through power loading with respect to conventional equal power loading schemes. The effects of imperfect channel estimation and relay location on the BER performance are further discussed.

In this paper, we investigate bit and power allocation strategies for an orthogonal frequency division multiplexing (OFDM) cooperative network over frequency-selective fading channels. We assume amplify-and-forward relaying and consider the bit error rate (BER) performance as our performance measure. Aiming to optimize the BER under total power constraint and for a given average data rate, we propose three adaptive algorithms; optimal power loading (OPL), optimal bit loading (OBL), and optimal joint bit and power loading (OBPL). Our Monte Carlo simulation results demonstrate performance gains through adaptive bit and power loading over conventional non-adaptive systems as well as currently available adaptive cooperative scheme in the literature. The impact of practical issues on the performance of proposed adaptive schemes such as imperfect channel estimation and limited feedback is further discussed.

In this paper, we investigate the effect of channel estimation error on the error rate performance of a multi-relay system with amplify-and-forward relaying. We consider orthogonal relaying and study both conventional cooperative systems (i.e., all relays participate in the relaying phase) and opportunistic cooperative systems (i.e., only the best relay participates in the relaying phase). Based on the derivation of effective signal-to-noise ratio at the receiver taking into account channel estimation error, we obtain closed-form expressions for bit error rate. Monte-Carlo simulations are further provided to confirm the analytical results.

In this paper, we investigate the performance of constant-power adaptive L-ary quadrature modulation (L-QAM) for an opportunistic decode-and-forward (DF) relaying scheme. Specifically, we adopt adaptive five-mode L-QAM with fixed and optimum switching and derive expressions for the error rate, outage probability and spectral efficiency over both independent and identical (i.i.d.) and independent and non-identical (i.n.d.) Rayleigh fading channels.

The authors investigate the performance of amplify-and-forward relaying with two different pilot-symbol-assisted channel estimation methods. In the first estimation method, the cascaded channel consisting of source-to-relay and relay-to-destination links is estimated at the destination terminal. No channel estimator is required at the relay terminal. In the second estimation method, the estimation of cascaded channel is disintegrated into separate estimations of source-to-relay and relay-to-destination links which are carried out at the relay and the destination terminals, respectively. The latter method involves feed-forwarding a quantised version of the source-to-relay channel estimate to the destination terminal. Through the derivation of mean squared error (MSE) diversity gain analysis and Monte-Carlo simulations, the authors investigate the performance of amplify-and-forward relaying with these channel estimation methods. Our results demonstrate that full diversity can be achieved in the presence of channel estimation. The authors further show that cascaded channel estimator outperforms its competitor with a small number of quantisation bits. As the number of employed quantisation bits increase, disintegrated channel estimator approaches to its competitor eventually outperforming it.

Cooperative communication techniques promise the advantages of multi-input multi-output (MIMO) communications for wireless scenarios with single-antenna terminals. A main assumption in majority of the research work on cooperative communications is the availability of channel state information at the receiver. In practice, knowledge of the channel is obtained by sending known training (pilot) symbols to the receiver. In this paper, we study the effect of training on the performance of an amplify-and-forward cooperative relaying system with pilot-assisted channel estimator over quasi-static Rayleigh fading channels. We consider average received signal-to-noise ratio at the destination node as the objective function and formulate optimization problems for a single-relay scenario under total network power (TNP) and individual node power (INP) constraints. We aim to answer the following fundamental questions: Q1) How should overall transmit power be shared between training and data transmission periods?; Q2) How should training power be allocated to broadcasting and relaying phases?; Q3) How should data power be allocated to broadcasting and relaying phases? Our simulation results demonstrate that optimized schemes significantly outperform the original schemes with equal power allocation. Depending on the relay location, performance gains up to 5.5 dB and 2.8 dB are observed, respectively, under TNP and INP constraints.