The widespread use of intelligent devices and the emergence of 6G networks have led to a massive amount of edge data being produced in a distributed manner, bringing forth a pressing need for privacy-preserving and communication-efficient learning. Federated learning (FL) enables collaborative model training without sharing raw data, but it suffers from challenges such as high communication overhead, non-IID data distribution, straggling devices, and rigid offloading strategies in wireless communication environments. Recent works have attempted to address these issues using device-to-device (D2D) communication or edge server-based approaches; however, achieving both communication efficiency and high learning performance remains a significant challenge. In this work, a hybrid federated learning architecture for D2D-enabled 6G networks is proposed, integrating adaptive D2D communication and edge server offloading to enhance both learning efficiency and communication performance. The proposed framework incorporates channel awareness and federated averaging to ensure robust global model aggregation under non-IID data conditions. Extensive simulation results on the EMNIST Digits dataset demonstrate that the proposed approach achieves a final accuracy of 97.41% with an average accuracy of 93.98%, while significantly reducing communication cost across federated learning rounds. Furthermore, the results illustrate adaptive hybrid communication behavior with approximately 60% D2D communication and 40% edge offloading. Comparative analysis with existing federated learning methods confirms the effectiveness, scalability, and suitability of the proposed framework for next-generation 6G-enabled intelligent edge networks.

The increased adoption of intelligent devices and the development of 6 G networks have resulted in a tremendous amount of edge data being generated in a distributed fashion, thus giving rise to a clear need for privacy-preserving and communication-efficient learning. Federated learning (FL) enables shared model learning, but it has some limitations, such as high communication overhead due to non-IID data distribution, device straggling, and inflexible offloading strategies in wireless communication networks. The existing research addressed all the above-mentioned issues related to traditional federated learning either through device-to-device communication methods for 6 G networks or through edge server-based strategies for wireless communication networks. However, the existing research could not improve both communication efficiency and learning performance simultaneously. In this research, an adaptive offloading federated learning framework for D2D-enabled 6G networks is proposed that leverages the strengths of both D2D communication methods and edge server-based offloading strategies. The proposed framework integrates channel-state awareness with weighted federated averaging to enable stable global aggregation under non-IID data distributions. By incorporating communication constraints into the learning process, it jointly improves convergence behaviour and transmission efficiency. Experimental validation on the MNIST benchmark demonstrates robust convergence with reduced communication overhead across federated rounds. The adaptive hybrid strategy, combining device-to-device collaboration and edge offloading, enhances scalability and resource efficiency.

Vehicular networks (VNs) are playing a key role in shaping the future of intelligent transportation systems by enabling real-time communication among vehicle-to-vehicle, vehicle-to-infrastructure, and broader entities vehicle-to-everything. These networks offer immense potential for improving road safety, easing traffic congestion, and supporting smart mobility services. At the same time, their open and dynamic nature brings significant challenges related to privacy and security, including risks like unauthorized access, data breaches, location tracking, and denial-of-service attacks. This survey offers a comprehensive overview of the major security and privacy concerns confronted by the contemporary VNs. It introduces a clear taxonomy that organizes various threat categories, defense strategies, and cryptographic techniques that are commonly used in this domain. Recent developments are reviewed and grouped based on various authentication schemes, blockchain-based methods, privacy-preserving protocols, and artificial intelligence-driven intrusion detection systems. These solutions are then evaluated in terms of their cryptographic robustness, privacy safeguards, processing requirements, and their suitability for fitting into real-world applications such as autonomous driving and vehicular edge computing. Beyond the current landscape, the paper also outlines key gaps and promising research directions, including federated learning, lightweight blockchain consensus mechanisms, quantum-resistant cryptography, and intelligent threat detection at the edge. By bringing together both foundational concepts and the latest innovations, this survey aims to support researchers and practitioners in understanding the contemporary security challenges inherent to the VNs and building more secure, scalable, and privacy-aware vehicular communication systems.

Smart parking in vehicular networks is an emerging technology that has been proposed to alleviate congestion in urban areas by employing intelligent systems, secure communication, and real-time decision-making. Smart parking systems enable vehicles to interact seamlessly with the infrastructure, thereby ensuring user convenience with minimal fuel consumption and waiting times. However, ensuring secure and privacy-preserving operation is a major challenge, and this is particularly true when dealing with threats such as unauthorized access and replay attacks. Various solutions such as blockchain-based smart parking systems and privacy-preserving cryptographic protocols have been proposed to overcome these challenges. However, these solutions are associated with high computational overhead and latency. In this paper, we propose an SLQ (Secure Lightweight and Queue Balanced)-based smart parking system that employs Elliptic Curve Cryptography (ECC) for lightweight key generation, Advanced Encryption Standard in Galois/Counter Mode (AES-GCM) for secure and authenticated encryption, and JSON Web Tokens for session validation. A challenge-response mechanism is employed to ensure the authenticity of the vehicle prior to accessing the parking facility, and replay protection strategies are also incorporated to prevent misuse of stale requests. We implement our framework using six candidate methods and compare our results against three well-known baselines (DyPARK, PriParkRec, and PrivRep) under identical Simulation of Urban Mobility (SUMO) settings. Experimental results show that our SLQ-based parking method achieves SPEI = 0.837 and throughput = 811.170 veh/h, exceeding the nearest competitor, DyPARK (SPEI = 0.834, throughput = 809.870 veh/h), while reducing the registration delay from 0.596 ms to 0.240 ms (59.7% improvement).

The present study investigates the static response of homogenized piezoelectric (PE) composites comprising a carbon nanotube (CNT)-doped polydimethylsiloxane (PDMS) matrix reinforced with piezoelectrically active material distributed within a Primitive Triply Periodic Minimal Surface (TPMS) geometry, subjected to electric and mechanical loadings. The solution of each layer is expressed in terms of a cylindrical system of vector functions. The eigenvalue–eigenvector approach is utilized to obtain the layer solutions, while the Dual Variable and Position (DVP) method is employed to handle multilayered configurations efficiently. The results in the high-frequency physical domain are then obtained by applying appropriate boundary and interface conditions. For numerical investigations, CNT-doped PDMS matrices reinforced with a TPMS-based PE phase corresponding to the Primitive geometry are considered. In the absence of any agglomeration, the CNT volume fraction (VF) is set as 0.0%, 0.061%, and 0.602%, and the composites are referred to as NoAg-VF0, NoAg-VF0.061, and NoAg-VF0.602. On the other hand, two different levels of agglomeration, represented as ζ = 0.15 and 0.40, are considered. The CNT VFs at ζ = 0.15 are 0.07 and 0.693, while at ζ = 0.40, the CNT VFs are set as 0.09% and 0.891%. The composites so formed are referred to as Ag-VF0.07, Ag-VF0.693, Ag-VF0.09, and Ag-VF0.891. The TPMS-based PE phase is incorporated at VFs of 10%, 20%, 30%, and 40%. The results highlight the importance of microstructural optimization in designing advanced PE composites, with potential applications in geophysical sensors and large-scale devices.

The smooth and efficient movement of emergency vehicles in congested areas has always been a problem in intelligent transportation systems (ITS). The conventional approach to routing, typically through static shortest path calculations, often fails to respond to dynamically changing traffic conditions, leading to avoidable delays in critical situations. In this article, we propose a new approach to routing, termed RACER (Real-time Adaptive Congestion-aware Emergency Routing), which dynamically responds to changing traffic conditions without requiring additional traffic-signal-control infrastructure, relying instead on congestion information obtained through standard vehicle-to-infrastructure (V2I) telemetry such as roadside units or cellular reporting, which we model in SUMO via its Traffic Control Interface (TraCI). This is implemented through a combination of a proactive multi-edge look-ahead approach, a congestion-aware cost function, and a controlled approach to rerouting, ensuring stability during navigation. The proposed approach is evaluated using the SUMO microscopic traffic simulator on two large-scale real urban road networks (Bhubaneswar and Visakhapatnam), across five source and destination pairs, three congestion levels (light, moderate, severe), and 20 random seeds, for a total of 1,200 controlled runs. Because emergency-vehicle travel times are heavily right-skewed, we report the median as the primary metric alongside the mean. RACER attains the lowest median travel time across all routes and congestion levels, improving on the strongest baseline in every route, and its median travel time remains essentially flat as congestion increases (372/395/385 s for light/moderate/severe), in contrast to static Dijkstra, which degrades sharply (993/1993/3321 s). Paired statistical testing confirms the improvements over all baselines are significant (p< 10-29), and measured wall-clock runtime confirms the method operates in real time. We further make explicit the vehicle-to-infrastructure communication architecture on which the method operates, and show that its bounded, cooldown-gated rerouting keeps the control-plane signaling overhead low (on average fewer than three route updates per trip), making congestion-aware routing feasible over capacity-limited vehicular networks. These results demonstrate the effectiveness of incorporating congestion awareness into routing decisions, leading to faster and more reliable emergency response in such congested areas.

Indoor localization using WiFi fingerprinting has gained significant a ttentiond ue toi tss calability a nd costeffectiveness; however, achieving high accuracy, robustness, and computational efficiency r emains c hallenging. T his paper presents a set of targeted enhancements to the FasterKAN frame-work for indoor localization, aimed at improving both predictive performance and deployment efficiency. The proposed improvements include refined access-point filtering strategies, an optimized activation function design, GPU-accelerated training, and a more stable regression modeling approach. The enhanced FasterKAN framework employs a single unified model to simultaneously perform three tasks: coordinate regression for estimating user latitude, longitude, and altitude; floor and building classification; and space-ID classification within predefined indoor regions. Experimental results demonstrate consistent performance gains over the original FasterKAN implementation, achieving a mean absolute localization error of 3.40 m compared to 3.56 m. Classification accuracy is improved from 99% to 99.3% for the floor/building task and from 71% to 72.46% for the space ID task. Furthermore, the proposed implementation achieves substantial computational advantages, delivering inference speeds that are 2.7 times faster than convolutional neural networks on CPUs and 7-8 times faster on GPUs. These results confirm that the optimized FasterKAN framework offers a robust, accurate, and computationally efficient alternative to convolution-based models, making it well suited for large-scale and real-time indoor localization deployments.

This study aims to analyse the effect of different shapes of pores on the damping properties of spherical particle-filled composites. A two-step stochastic homogenization approach is developed to evaluate the effective elastic properties within the micromechanical framework. Modified Mori-Tanaka homogenization approach considering the differential scheme for iteratively addition of pores as well as fillers is incorporated in the present model. From the results, the saturated ratio of the stiffness of fillers to the matrix is found to exist beyond which further increase in the stiffness of the fillers does not add in the increase in the effective elastic properties. It is further noted that the presence of voids decreases the elastic properties significantly. The results obtained from the proposed model are found to be in good agreement with that obtained from the computational study.

The explosive growth of connected mobile devices and data-heavy applications running on these devices is pushing wireless networks to their limits, posing a critical challenge: achieving high spectral efficiency (SE) while minimizing energy consumption. Conventional systems transmit every bit of raw data, even when much of it is irrelevant to the actual task, wasting precious spectrum, power, and time - problems that will only intensify in 6G networks. This paper proposes a effective solution by integrating semantic communication with device-to-device (D2D) links. Semantic communication extracts and transmits only the task-critical meaning of the data, dramatically reducing redundant bits and overall traffic. D2D enables nearby devices to communicate directly, shortening transmission distances, cutting path loss, latency, and energy use even further. Extensive simulations across a wide range of signal-to-noise ratios (SNR) reveal that both energy efficiency (EE) and SE improve markedly with increasing SNR, primarily because of fewer errors and retransmissions. However, beyond a SNR threshold, gains taper off and saturated, reflecting the classic EE-SE tradeoff. By intelligently combining semantic transmission and D2D, proposed approach achieve greener, more efficient networks without compromising reliability which makes this approach highly promising for sustainable 6G ecosystems where spectrum and energy will be severely constrained.

In underlay cellular networks supporting Device-to-Device (D2D) communication, efficient r esource allocation remains a key challenge due to spectrum sharing and crosstier interference. This work proposes a Deep Reinforcement Learning (DRL)-based framework for joint spectrum and power allocation aimed at maximizing system throughput while limiting interference to cellular users (CUs). The network environment is modeled as a Markov Decision Process (MDP) that captures essential wireless characteristics, including path loss, interference, and signal-to-interference-plus-noise ratio (SINR). Two DRL algorithms, namely Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), are implemented using the Stable Baselines3 framework to dynamically assign resource blocks and transmission power levels to D2D pairs in a shared spectrum setting. Simulation results demonstrate that the PPO-based agent consistently outperforms DQN and baseline allocation schemes in terms of average throughput, SINR distribution, energy efficiency, and convergence stability. Overall, the proposed DRL-based approach achieves an effective trade-off between throughput maximization and interference mitigation under dynamic network conditions. While federated learning is discussed as a promising direction for distributed and privacy-preserving optimization, this study focuses on centralized DRL-based resource allocation and leaves federated extensions for future investigation.

This study introduces an innovative MIMO antenna tailored for 5G millimeter-wave applications. By integrating a rectangular closed loop (RCL) and split-ring resonators (SRRs) with a monopole structure, the design achieves notable enhancement in impedance performance around the 26.5 GHz frequency. The MIMO configuration comprises four radiating elements positioned on a common PCB with a space-efficient footprint of 30 × 30mm2. The developed four-port antenna system offers a wide impedance bandwidth of 2 GHz (25.7-27.7 GHz) centred at 26.5 GHz, achieving a total efficiency of 85-95% over the operating band. Additionally, the antenna exhibits envelope correlation coefficient (ECC) values within acceptable limits, ensuring excellent isolation between the ports. The antenna achieves an average gain of 7 dBi, confirming its effectiveness for deployment in millimeter-wave 5G New Radio (NR) bands n257, n258, and n261.

In modern wireless networks, knowing the precise location of devices isn't just a luxury it's essential, especially for areas like vehicle navigation, mobile robotics, and emergency services. Traditionally, systems have depended on a fixed set of reference points (or anchors), but in many challenging environments, these anchors aren't enough. With the rise of Device to Device (D2D) communication, there's now a chance to improve localization accuracy by letting devices help each other out using their own distance measurements. This study explores two methods of pinpointing a node's location. One method uses only the traditional anchor based measurements, while the other takes advantage of D2D communication, combining both anchor and peer to peer range information. Using MATLAB for simulations and applying convex optimization (via the CVX toolbox) along with a fallback gradient descent algorithm, our analysis shows that the D2D method generally cuts localization errors by 20-30%. These findings underscore the potential value of D2D based approaches in situations where anchors are sparse or signals are noisy.

Large Language Models (LLMs) have revolutionized business communication with automated email writing, improved efficiency, and personalization. This paper presents a comparative study of four widely used AI-powered LLMs in business email writing considering the following parameters: formality, readability, grammatical correctness, and word limit. This research compares models such as OpenAI-GPT-4o-mini, Google-Gemini-2.0-Flash, Claude-3.7-Sonnet, and Meta-Llama3-70b in producing professional business emails that fit various scenarios. This work quantitatively compares AI-produced emails by adopting automated scoring. Based on our findings, Google-Gemini-2.0-Flash leads its peers in producing professional, polished, and grammatically correct business communication. This research aims to help professionals choose the most appropriate AI LLM to write effective and contextually relevant emails.

Crime prediction is revolutionizing the public safety by equipping law enforcement with data-driven insights. With the recent development in machine learning and deep learning techniques, recently developed tools analyze crime trends and predict the high-risk areas with greater precision. Methods such as spatio-temporal analysis and hybrid models are essential for improving the accuracy and dependability of predictions. Nonetheless, guaranteeing high-quality and uniform data continues to be a vital concern, since incomplete or biased datasets can distort predictions. Moreover, issues of privacy and fairness demand immediate consideration, while the complexity of these models frequently restricts their practical applications. To address these issues, systems that are transparent and comprehensible are essential. For enhancing the system’s reliability, it is also necessary to integrate socio-economic and environmental data. Additionally, crime prediction systems must also be designed to scale efficiently and process the real-time data, and to prevent the misuse and maintaining public trust, designing the ethical frameworks are essential. This paper provides the current state of crime prediction methods, their applications, and challenges. It also identifies gaps and proposes strategies for developing more reliable and ethical systems.

The proliferation of X and Ku band applications in satellite communications, remote sensing, radar systems, and in ever-increasing wireless networks, impels to design of an ultra-wideband reflection-based linear polarization converter. An efficiently designed converter enhances the signal quality in addition to minimizing the interferences in wireless links. Seeing these upcoming myriad number of applications, in this paper, we design and fabricate a lightweight ultra-wideband converter structure by utilizing two 0.15 mm thin FR-4 sheets and a Teflon air-spacer having a thickness of 5.25 mm. Specifically, the top side of the unit cell consists of a diagonally arranged parallel metallic strip printed on thin FR-4 substrate material which is separated by a Teflon spacer with complete metal on the bottom side. To show the efficacy, numerical simulations are performed and the obtained results are validated by fabricating the device in the lab. The experimental and simulation results show that the proposed structure works as a cross-polarizer with a polarization conversion ratio of more than 90% in the C, X, and Ku bands with an operating range of 6.2-16.6 GHz. The measured co-reflection coefficient of the fabricated device completely matched with the simulated reflection coefficient which corroborates with the obtained results. The proposed structure features a sub-wavelength-sized unit cell ((Formula presented.)), which significantly enhances the angular stability of the design. Additionally, it achieves an impressive fractional bandwidth of 91.2% and is characterized by its lightweight structure, making it highly efficient for various applications, such as radiometers and RCS reduction.

Owing to the continuous growth of THz absorber, it finds applications in multidimensional fields such as wireless communication, biomedical, military, and defense. These absorbers play a crucial role in RF imaging system design, mutual coupling reduction in antenna array systems, and RCS reduction applications. In this chapter, we begin with defining the THz band, EM wave absorbers, FSS, metamaterial, and impedance matching. Then, we showcase how FSS and metamaterials play a vital role in the design of absorbers. Further, we classify THz absorbers as FSS, resistive sheets, flexible, and graphene-based absorbers showcasing their working principles. Then, we differentiate THz absorber fabrication techniques, including conventional UV lithography and advanced nano-fabrication technology. The chapter concludes by highlighting different types of absorbance measurement techniques such as THz time-domain spectroscopy, waveguide setup, and anechoic chambers in detail, along with their advantages and limitations.

The rapid growth of multimedia content has created a demand for effective communication systems. Traditional systems treat all pixel values the same while applying compression techniques, eventually significantly losing important information from the image. To address this problem, this paper proposed a saliency-guided semantic communication, where the salient object from each image is separated as Region of interest and Non-Region of Interest, and to make the transmission efficient different compression rates are applied to these regions. The salient and non-salient regions of the image are transmitted and reconstructed at the receiver side. To show the efficacy of the proposed system, numerical simulations are conducted on VOC2012 dataset. The obtained results show the effectiveness of the proposed method in terms of better high Structural Similarity Index Measure, Peak Signal-to-Noise Ratio, lower Mean Square Error and Contrastive Language–Image Pretraining over LTE and 5G channels. Proposed saliency-guided ROI approach delivers a 40% improvement in energy efficiency, and a 37% reduction in average transmitted bits per image over the JPEG baseline transmission.

Seeing the recent rise in applications of RFID tags in industrial internet of things (IIoT), it become very evident to design an efficient and compact antenna which is able to satisfy the uprising IoT demands. This work presents a new 3D quasi-isotropic electrically small antenna (ESA) for RFID applications in the 2.4 GHz band. The proposed antenna is designed on a single perfect electric conductor (PEC) sheet by loading an inverted L-shaped slot. An opened aperture is excited to realize magnetic dipole characteristics to achieve a quasi-isotropic radiation pattern in 3D spatial coverage. The overall volume of the prototype is O.18Ax0.07Ax0.0096A nm3; here, λ is the free space wavelength that corresponds to operating frequencies. The proposed antenna offers a 30MHz (2.42-2.45 GHz) impedance bandwidth centered at 2.43 GHz. The antenna exhibits a quasi-isotropic radiation pattern with a maximum efficiency of 85%, which makes it suitable for RFID applications.

We address the problem of distributed resource allocation for multicast communication in device-to-device (D2D) enabled underlay cellular networks. The optimal resource allocation is crucial for maximizing the performance of such networks, which are limited by the severe co-channel interference between cellular users (CU) and D2D multicast groups. However, finding such optimal allocation for networks with a large number of CUs and D2D users is challenging. Therefore, we propose a pragmatic scheme that allocates resources distributively, reducing signaling overhead and improving network scalability. Numerical simulations establish the efficacy of the proposed solution in improving the overall system throughout, compared to various existing schemes.

The exponential increase in video data demands over the wireless network has created the interest among the researchers to come up with a potential solution to efficiently distribute the video content over 5G and beyond networks. Therefore, in this paper, an energy-efficient scheme for video content dissemination by utilizing the device-to-device (D2D) communication has been proposed. The work aims to address the twin issues of energy efficiency and distortion minimization simultaneously. In the current cellular networks, every user downloads the video independently which often leads to low quality as the distance between the base station (BS) and mobile device increases. In the proposed scheme, proximate mobile nodes are grouped into clusters and among them a cluster head is chosen which forwards the data to cluster members using D2D communication. To make the system more spectral efficient, performance of the proposed scheme is evaluated in underlay mode where D2D links are sharing the channels with the primary cellular users. To show the efficacy of the proposed scheme, numerical analysis is conducted, and results show that significant energy saving can be achieved with the proposed scheme as compared to the conventional multicasting scheme, and it also provides improved video quality with lesser distortion and delay.

Device-to-device (D2D) communication that allows direct communication between nearby mobile devices without traversing through the base station is a potential solution to solve the problem of safer and faster data rate communication. This review paper comprehensively explores the security aspects of D2D communication, focusing on the vulnerabilities, threats, and existing security mechanisms. In addition, it provides an in-depth analysis of the security challenges in D2D communication, including eavesdropping, data integrity, authentication, and privacy concerns. Furthermore, it also delves into the potential security risks associated with different D2D communication scenarios, such as public safety, proximity-based services, and ad-hoc networking. Toward the end, this survey discusses the open research challenges and future directions in securing D2D communication, highlighting the need for robust security protocols to mitigate the evolving threats in this dynamic communication paradigm. The findings presented in this survey aim to provide researchers, practitioners, and policymakers with a comprehensive understanding of the security landscape in D2D communication, thereby contributing to the successful deployment of secure and reliable D2D communication systems.

Seeing the rising applications of metamaterial in sensing and imaging, satellite communications, it becomes evident to design a high-gain microstrip patch antenna. To support these applications, this paper proposes a 7 x 7 array of planar novel metamaterial unit cells used as a superstrate to enhance the gain of microstrip patch antenna operating at 11.2 GHz. This proposed metamaterial structure yields a very low (near zero) value of effective refractive index at 11.2 GHz. Hence, the superstrate behaves as a near zero-indexed-medium (NZIM) around this frequency. NZIM superstrate are very popular because of their ability to focus the radiation and by utilizing this property, a significant gain enhancement has been achieved in the usage of patch antennas. Numerical simulations have been conducted using the CST Microwave studio, and obtained results corroborate that NZIM superstrate when suspended over a microstrip patch antennas significantly improves the gain around the value of 7.5 dB at 11.2 GHz, and efficiency is also improved.

This paper considers a device-to-device (D2D) communication-enabled multi-cell massive multi-input multi-output (MIMO) system, where multiple D2D pairs reuse the pilot signal allocated to the cellular users. The closed-form expressions for the spectral efficiency and its lower-bounds are derived using maximal ratio combining. To closely model the channel in the practical environment, a combination of line-of-sight (LoS) path and a stochastic non-line-of-sight (NLoS) component describing a spatially correlated multipath environment is considered. To characterize the achieved spectral efficiency for considered channel modeling, simulations are performed. The obtained results show that the system performance achieved with the Rician correlated fading is higher than the spectral efficiency achieved with Rayleigh-fading.

In this paper, a graph and matrix theory-based network selection scheme is proposed for overlapping wireless networks which comprises of WiFi/WiMAX/LTE technologies. The parameters data rate, service cost, delay, and power consumption have been taken into account. A graph and corresponding matrix is constructed by considering the above parameters and their relative importance for a particular application. Then, a 'network satisfaction value' is determined by computing the permanent of matrix. This value is used to select the optimal access point. In comparison to conventional received signal strength indicator (RSSI) based schemes, improved results have been obtained owing to the proposed graph-based selection mechanism. The results are also compared with those of other existing schemes like TOPSIS (techniques for order preference by similarity to ideal solution), result shows that the proposed scheme is able to select most suitable network according to user preferences, and also reduce the number of handoffs.

This paper considers a cooperative cognitive radio network with two primary users (PUs), and two secondary users (SUs) that enables two-way communications of primary, and secondary systems in conjunction with non-linear energy harvesting based simultaneous wireless information, and power transfer (SWIPT). With the considered network, SUs are able to realize their communications over the licensed spectrum while extending relay assistance to the PUs. The overall bidirectional end-to-end transmission takes place in four phases, which include both energy harvesting (EH), and information transfer. A non-linear energy harvester with a hybrid SWIPT scheme is adopted in which both power-splitting, and time-switching EH techniques are used. The SUs aid in relay cooperation by performing an amplify-and-forward operation, whereas selection combining technique is adopted at the PUs to extract the intended signal from multiple received signals broadcasted by the SUs. Accurate outage probability expressions for the primary, and secondary links are derived under the Nakagami-m fading environment. Further, the system behavior is analyzed with respect to achievable system throughput, and energy efficiency. Since the performance of the considered system is strongly affected by the spectrum sharing factor, and hybrid SWIPT parameters, particle swarm optimization is implemented to optimize the system parameters so as to maximize the system throughput, and energy efficiency. Simulation results are provided to corroborate the performance analysis, and give useful insights into the system behavior concerning various system/channel parameters.

In this letter, we investigate the performance of wireless powered cognitive radio sensor networks (CRSNs) in the presence of hardware impairments (HIs). Wireless powered CRSN can be a potential solution to address spectrum scarcity and power shortage in the wireless sensor networks. Herein, the spectrum sharing is exploited to compensate for the spectrum scarcity, whereas the radio frequency energy harvesting technique is utilized to prolong the lifetime. Specifically, we consider a CRSN scenario with two primary nodes and a pair of sensor nodes (SNs). The SNs are assumed to be low-cost devices in view of the Internet of Things infrastructure. Consequently, they are more prone to suffer from different HIs. For evaluating the system performance, we obtain accurate expressions of the outage probability and the system throughput over Nakagami-m fading in the presence of transceiver HIs.

Supporting ever-increasing number of mobile users with data-hungry applications, running on battery-limited devices, is a daunting challenge for the telecommunication community. Device-to-device (D2D) communication, which allows physically proximate mobile users to directly communicate with each other by reusing the spectrum, without going through the base station, holds promise to help us tackle this challenge. In a cellular network, D2D communication offers opportunities for spectrum reuse and spatial diversity that may lead to enhanced coverage, higher throughput, and robust communication in the network. Further, for applications such as weather forecasting and live streaming, which may require the same chunks of data distributed to geographically proximate users, D2D multicasting may provide better utilization of network resources compared to D2D unicast or the base station-based multicast, such as LTE eMBMS. However, extensive deployment of D2D multicast in a network may introduce various issues, such as severe co-channel interference due to spectrum reuse, underutilization of spectrum, and rapid battery depletion of the multicasting D2D nodes due to higher transmit power to mitigate co-channel interference and facilitating data-relaying. Therefore, this article discusses some of the challenges in supporting D2D multicast communication in cellular networks. It then surveys various existing approaches to address these challenges, which along with some future research directions may help us develop practical D2D multicast protocols for cellular networks.

Underlay in-band device-To-device (D2D) multicast communication, where same content is disseminated via direct links in a group, has potential to improve the spectral and energy efficiencies of cellular networks. However, existing resource allocation techniques may not work well for multicast in next generation wireless networks with many simultaneously connected devices. To address this problem, we focus on channel allocation algorithms where multiple D2D multicast groups (MGs) share the channel with a cellular user (CU). The objective is to maximize the sum throughput of CUs and D2D multicast groups, while ensuring a certain level of quality of service (QoS) to CUs and D2D MGs. Our main contributions are the exact calculation of outage probability experienced by a D2D receiver in the multicast group and a scheme to share channels among D2D MGs and CUs by minimizing these probabilities. Numerical results demonstrate the impact on the sum throughput of the number of MGs sharing the channel with a CU, geographical spread of MGs, and the maximum transmit power of cellular users.

Underlay in-band device-to-device (D2D) multicast communication, where the same content is disseminated via direct links in a group, has the potential to improve the spectral and energy efficiencies of cellular networks. However, most of the existing approaches for this problem only address either spectral efficiency (SE) or energy efficiency (EE). We study the tradeoff between SE and EE in a single cell D2D integrated cellular network, where multiple D2D multicast groups (MGs) may share the uplink channel with multiple cellular users. We explore SE-EE tradeoff for this problem by formulating the EE maximization problem with constraint on SE and maximum available transmission power. A power allocation algorithm is proposed to solve this problem and its efficacy is demonstrated via extensive numerical simulations. The tradeoff between SE and EE as a function of density of D2D MGs, and maximum transmission power of an MG is characterized.

Device-to-device (D2D) multicast communication is considered as a potential solution to improve the spectral efficiency of cellular networks. This paper focuses on resource allocation in underlay D2D multicast networks where multiple D2D multicast groups (MGs) share the uplink frequency channels with multiple cellular users (CUs). An optimization problem that maximizes the system throughput while fulfilling the maximum power constraint of every mobile user and ensuring a certain level of quality of service (QoS) to every CU and D2D multicast group is formulated. This formulation leads to mixed integer non-linear programming (MINLP), which is computationally intractable for large scale networks. Therefore, to find a feasible solution, we propose a channel sharing algorithm which determines how many MGs can share a channel with certain QoS guarantees to CU and D2D MGs. Then, we propose a power allocation algorithm that maximizes the system throughput while satisfying the various constraints. The impact of geographical spread of MGs, number of MGs, maximum available transmission power, and QoS requirements of every CU on achievable system throughput is analyzed. Numerical results show the efficacy of proposed model in terms of D2D MG's throughput and spectrum efficiency.

Device-to-Device (D2D) multicast communication is emerging as a practical solution to alleviate severe capacity crunch in data-centric wireless networks and to encourage backhaul-free communication directly among devices with similar content requirement. Resource allocation in multicast networks is a critical issue that impacts both, the network throughput and spectrum efficiency. We devise an uplink resource reuse strategy for multiple multicast D2D groups and multiple cellular users (CUs), with the objective of maximizing the sum throughput, while guaranteeing a certain level of quality of service (QoS) to CUs and D2D users. We establish the efficacy of the proposed scheme for variable group sizes and geographical spread.

Device-To-Device (D2D) multicast communication is emerging as a practical solution for alleviating severe capacity crunch in data-centric wireless networks and encourage backhaul-free communication directly among devices with similar content requirement. To exploit D2D communication for achieving higher throughput, less delay and efficient spectrum usage, a careful resource allocation is required. In this study, we analyze the resource sharing between cellular users (CUs) and D2D users when one or more than one D2D multicast groups share the resources with a CU, with the objective of maximizing the system throughput and spectrum efficiency, while guaranteeing a certain level of quality of service to CUs and D2D users.

The potential of device-to-device (D2D) technology to support multicast services in LTE-Advanced networks has been recently realized. D2D communication brings great benefits to cellular networks in terms of enhanced spectral efficiency and larger coverage by enabling the devices to communicate directly with each other. D2D communication may lead to improved network capacity by sharing resources with cellular users (CUs). However, the resulting mutual interference may decrease or even outweigh the gain of D2D communication. In this paper, we propose a scheme to minimize the interference among D2D and CUs through a resource allocation scheme. We formulate the uplink resource allocation problem where a D2D multicast group can reuse resources of CUs under the constraint that the signal to interference plus noise ratio (SINR) requirements of CUs and D2D users are satisfied. We analyze a joint power and channel allocation scheme to maximize the total throughput of CUs and D2D users. The performance of D2D communication depends on maximum power constraint for the D2D users. Simulation results establish the efficacy of the proposed scheme.

In relay-assisted cellular networks, relay nodes are usually deployed in a cellular cell without taking the information about the place where it needs to be deployed. So sometime it will ultimately leads to wastage of resources. In this paper, we have focused on this problem and proposed a fuzzy-based methodology to find the optimum quantity and requirement of these relay nodes in cellular networks. Proposed methodology tackles with two problems, which are “where to deploy,” “how many relay nodes to deploy.” In cellular cell, users residing near the base station get higher data services and users residing near the boundary of cellular cell get lower data services. So this introduces unfairness for far users in terms of data rate. Relay-assisted networks are introduced to solve this problem. As the number of users is increasing day by day, so it is necessary to provide adaptive positioning of relay nodes for getting optimal services within limited infrastructure cost. In other words, relay-assisted cellular networks should be adaptive for traffic offered by specific area. In this paper, we have taken three parameters that strongly affect the position of relay nodes. These three parameters include user density in specific area, amount of high-speed data requirement from a certain area on regular basis and signal strength to tackle with dead zones over the entire cellular cell.