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The media conveys crucial information, speaks out, and spreads awareness of violations of human rights. Pakistan media plays a vital role in human rights especially in violence against women. The present research is based on print media and its role in violence against women. This is a comparative study of popular and Quality press on violence against women in Pakistan. Data is collected from two national newspaper of Pakistan, Daily Dawn (Quality press) and Daily Khabrain (Popular Press) with help of content analysis. Researcher collected data on coding sheet with defined categories. Framing theory used to support the study. The findings of the study revealed that the popular press (Daily khabrian) is given more negative news about violence against women and portrayed negative image of women and also have negative tone as compared to Quality press. Additionally, it has been discovered that the quality press typically devotes very little space in their daily news publications to the problems that women face, which serve as the primary catalyst for violence against women in Pakistan.

Transition metal dichalcogenide monolayers and heterostructures are highly tunable material systems that provide excellent models for physical phenomena at the two-dimensional (2D) limit. While most studies to date have focused on electrons and electron-hole pairs, phonons also play essential roles. Here, we apply ultrafast electron diffraction and diffuse scattering to directly quantify, with time and momentum resolution, electron-phonon coupling (EPC) in monolayer molybdenum disulfide (MoS$_{2}$) and phonon transport from the monolayer to a silicon nitride (Si$_3$N$_4$) substrate. Optically generated hot carriers result in a profoundly anisotropic distribution of phonons in the monolayer on the $\sim$ 5 ps time scale. A quantitative comparison with ab-initio ultrafast dynamics simulations reveals the essential role of dielectric screening in weakening EPC. Thermal transport from the monolayer to the substrate occurs with the phonon system far from equilibrium. While screening in 2D is known to strongly affect equilibrium properties, our findings extend this understanding to the dynamic regime.

Cellular networks are envisioned to be a cornerstone in future industrial IoT (IIoT) wireless connectivity in terms of fulfilling the industrial-grade coverage, capacity, robustness, and ultra-responsiveness requirements. This vision has led to verticals-centric service-based architecture in 5G radio access and core networks, with the capabilities to include 5G-AI-Edge ecosystem for computing, intelligence, and flexible deployment and integration options (e.g., centralized and distributed, physical and virtual) while eliminating the privacy/security concerns of mission-critical systems. In this paper, driven by the industrial interest in enabling large-scale wireless IIoT deployments for operational agility, flexible, and cost-efficient production, we present the state-of-the-art 5G architecture, transformative technologies, and recent design trends, which we also selectively supplemented with new results. We also identify several research challenges in these promising design trends that beyond-5G systems must overcome to support rapidly unfolding transition in creating value-centric industrial wireless networks.

Reconfigurable intelligent surfaces (RISs), with the potential to realize a smart radio environment, have emerged as an energy-efficient and a cost-effective technology to support the services and demands foreseen for coming decades. By leveraging a large number of low-cost passive reflecting elements, RISs introduce a phase-shift in the impinging signal to create a favorable propagation channel between the transmitter and the receiver.~\textcolor{black}{In this article, we provide a tutorial overview of RISs for sixth-generation (6G) wireless networks. Specifically, we present a comprehensive discussion on performance gains that can be achieved by integrating RISs with emerging communication technologies. We address the practical implementation of RIS-assisted networks and expose the crucial challenges, including the RIS reconfiguration, deployment and size optimization, and channel estimation. Furthermore, we explore the integration of RIS and non-orthogonal multiple access (NOMA) under imperfect channel state information (CSI). Our numerical results illustrate the importance of better channel estimation in RIS-assisted networks and indicate the various factors that impact the size of RIS. Finally, we present promising future research directions for realizing RIS-assisted networks in 6G communication., 7 pages, 6 figures, to appear in IEEE IEEE Wireless Communications Magazine

Zero-energy radios in energy-constrained devices are envisioned as key enablers to realizing the next-generation Internet-of-things (NG-IoT) networks for ultra-dense sensing and monitoring. This paper presents analytical modeling and analysis of the energy-efficient uplink transmission of an energyconstrained secondary sensor operating opportunistically among several primary sensors. The considered scenario assumes that all primary sensors transmit in a round-robin, time division multiple access-based schemes, and the secondary sensor is admitted in the time slot of each primary sensor using a nonorthogonal multiple access technique, inspired by cognitive radio. The energy efficiency of the secondary sensor is maximized by exposing it to a deep reinforcement learning-based algorithm, recognized as a deep deterministic policy gradient (DDPG). Our results demonstrate that the DDPG-based transmission scheme outperforms the conventional random and greedy algorithms in terms of energy efficiency at different operating conditions.

The spread of the novel coronavirus (COVID-19) has caused trillions of dollars of damages to the governments and health authorities by affecting the global economies. It is essential to identify, track and trace COVID-19 spread at its earliest detection. Timely action can not only reduce further spread but also help in providing an efficient medical response. Existing schemes rely on volunteer participation, and/or mobile traceability, which leads to delays in containing the spread. There is a need for an autonomous, connected, and centralized paradigm that can identify, trace and inform connected personals. We propose a novel connected Internet of Things (IoT) based paradigm using convolution neural networks (CNN), smart wearable, and connected E-Health to help governments resume normal life again. Our autonomous scheme provides three-level detection: Inter-object distance for social distancing violations using CNN, area-based monitoring of active smartphone users and their current state of illness using connected E-Health, and smart wearable. Our exhaustive performance evaluation identifies that the proposed scheme with CNN YOLOv3 achieves up to 90% mean average precision in detecting social distancing violations, as compared to existing YOLOv2 achieving 70% and faster R-CNN with 76%. Our evaluation also identifies that wearing protective gear reduces spread by 50%, and timely actions in the first hour can help avoid three times COVID-19 exposure. © 2021 Turkiye Klinikleri. All rights reserved.

Pakistan and China enjoy close bilateral relations, owing to the ‘China-Pakistan Economic Corridor (CPEC) as China is developing a special economic zone across Pakistan under CPEC. SEZs are a new phenomenon for Pakistan and it’s the need of the hour for Pakistan to study the working mechanism of China’s SEZs to work on those challenges that Pakistan is facing due to non-expertise in this sector. China has a deep-rooted history as far as SEZs are concerned. With China’s experience and progress, a comparative analysis of Chinese SEZs could provide benefits for Pakistan. This study aims to give a comparative analysis of SEZs in both countries. A comparative analysis between China and Pakistan’s SEZ policies will help Pakistan in making better economic policies about Special economic zones.

Devices with full-duplex (FD) radios are able to transmit and receive at the same time without requiring orthogonal resources, thereby creating strong self-interference (SI) that results from their own transmissions. Spatial modulation (SM), on the other hand, is a multi-antenna scheme that activates only one transmit antenna to send a data symbol, where the index of the activated antenna depends on the input bits. In this manner, additional data can be conveyed implicitly via the selection of the active transmit antenna. Quadrature spatial modulation (QSM) is a variant of SM, in which the quadrature components of the constellation symbol are separately modulated on the indices of the transmit antenna, leading to an increase in the data rate by implicitly encoding on the spatial domain. This article provides an overview of the research activity on FD-SM and proposes a novel FD-QSM scheme that exploits multiple antennas to achieve antenna cancellation at the receiving side to mitigate the SI signal. Assuming active cancellation mechanisms are also in place, the performance of FD-QSM is studied in the presence of residual SI (RSI). The results reveal that FD-QSM can provide more than 40 percent capacity gain over its half-duplex (HD) counterpart in the presence of strong RSI and roughly the same gain over HD spatial multiplexing (SMX)-based multiple-input-multiple-output (MIMO) systems with moderate RSI. When applied to the downlink of a cellular network, FD-QSM provides 2dB gain over FD-SM and 5dB gain over FD-MIMO, operating at the same spectral efficiency, while huge gains are observed when FD-QSM is applied to non-orthogonal multiple access (NOMA)-aided FD relay network.

In this article, we investigate the downlink performance of a three-tier heterogeneous network (HetNet). The objective is to enhance the edge capacity of a macro cell by deploying unmanned aerial vehicles (UAVs) as flying base stations and small cells (SCs) for improving the capacity of indoor users in scenarios such as temporary hotspot regions or during disaster situations where the terrestrial network is either insufficient or out of service. UAVs are energy-constrained devices with a limited flight time, therefore, we formulate a two layer optimization scheme, where we first optimize the power consumption of each tier for enhancing the system energy efficiency (EE) under a minimum quality-of-service (QoS) requirement, which is followed by optimizing the average hover time of UAVs. We obtain the solution to these nonlinear constrained optimization problems by first utilizing the Lagrange multipliers method and then implementing a sub-gradient approach for obtaining convergence. The results show that through optimal power allocation, the system EE improves significantly in comparison to when maximum power is allocated to users (ground cellular users or connected vehicles). The hover time optimization results in increased flight time of UAVs thus providing service for longer durations.

Motivated by the need for unmanned aerial vehicle (UAV) communications in the modern era of wireless communication, this chapter focuses on a case study of millimeter‐Wave (mmWave) and teraHertz (THz) communication and the technical challenges in applying mmWave and THz frequency band for communication with UAVs. Specifically, this chapter focuses on the placement of UAVs to replace the terrestrial backhaul network with an aerial network. In addition, we address the performance of UAV‐enabled hybrid heterogeneous network (HetNet) by considering stringent communication‐related constraints, e.g. bandwidth, data rate, and signal‐to‐noise ratio (SNR). Also, the association of terrestrial small‐cell base stations (SCBs) with UAVs is addressed such that the sum rate of the overall system is maximized. To this end, an association algorithm is presented, which deals not only with the association of SCBs but also with backhaul link capacity. A detailed analysis of simulation‐based results shows a favorable performance of UAV‐assisted wireless network.

The use of intelligent reflecting surfaces (IRS) in next‐generation mobile networks is currently a hot topic. At the same time, next‐generation mobile networks are going airborne as heavily discussed in this book. Since the base stations and users both are meant to be mobile in this setting, this chapter explores the possibility of using IRS in airborne networks (ANs) for localization of users and base stations. Positioning is an important aspect in present and future wireless networks, where it augments the network operations and assists in multiple localization‐based applications. This chapter outlines the use of IRS in AN in different settings and gives an overview of potential positioning using pilot symbols in beyond‐5G (B5G) mobile networks employing IRSs.

Age of Information (AoI) is a critical metric in status update systems as these systems require the fresh updates. This letter investigates the uplink of an Internet-of-Thing (IoT) network where ${L}$ nodes transmit their information packets to a base station. The effects of the arrival rate of packets at the nodes, the number of nodes in the system, and queue length of each node have been studied by devising a discrete time Markov chain (MC) model. This model helps in predicting the values of AoI and probability of packet drops in such systems. The notion of first-in first-out is used for queuing, which transmits the oldest packet first, resulting in decreasing the overall AoI of the system. The results show that AoI increases with the increase in queue length, number of nodes and arrival rate and we quantify the aforementioned metrics using the MC model. The results found using the MC model are also validated using extensive simulations.

Backscatter communication (BackCom) has been emerging as a prospective candidate in tackling lifetime management problems for massively deployed Internet-of-things (IoT) devices, which suffer from battery related issues, i.e., replacements, charging, and recycling. This passive sensing approach allows a backscatter sensor node (BSN) to transmit information by reflecting the incident signal from a carrier emitter without initiating its transmission. To multiplex multiple BSNs, power-domain non-orthogonal multiple access (NOMA) is fully exploited in this work. In this paper, we present the design and analysis of a NOMA enhanced bistatic BackCom system for a battery-less smart communication paradigm. Specifically, we derive the closed-form bit error rate (BER) expressions for a cluster of two devices in a bistatic BackCom system employing NOMA with imperfect successive interference cancellation under Nakagami- ${m}$ fading channel. The obtained expressions are utilized to evaluate the reflection coefficients of devices needed for the most favorable system performance along with the performance comparison with orthogonal multiple access-time domain multiple access scheme (OMA-TDMA).

Transition-metal dichalcogenide monolayers and heterostructures are highly tunable material systems that provide excellent models for physical phenomena at the two-dimensional (2D) limit. While most studies to date have focused on electrons and electron-hole pairs, phonons also play essential roles. Here, we apply ultrafast electron diffraction and diffuse scattering to directly quantify, with time and momentum resolution, electron-phonon coupling (EPC) in monolayer molybdenum disulfide and phonon transport from the monolayer to a silicon nitride substrate. Optically generated hot carriers result in a profoundly anisotropic distribution of phonons in the monolayer within ∼5 ps. A quantitative comparison with

In this paper, we propose a channel estimation scheme for spatial modulation (SM) systems. In general, SM systems require each transmit antenna to separately send pilot symbols for channel estimation. This is a lengthy process, which may incur significant channel estimation errors in a time varying channel. Thus, we propose correlation-based channel estimation (CBCE) scheme, which exploits the correlation between transmit antennas to estimate channels of inactive antennas using the pilot-based estimate of the active antenna. The change in the active channel from the last pilot-based estimate is calculated, and a time-proportionate amount of that change is scaled according to the channel correlation coefficients to estimate the channel state of the inactive channels. With pilot slots designed to spread out in a data frame, the estimation process for every channel is carried out $N_t$ times in a data frame with $N_t$ denoting the number of transmit antennas. We observe that in a high signal-to-noise ratio (SNR) regime, our proposed scheme provides about 2 dB and 5 dB gains compared to conventional channel estimation (CCE) method for moderately correlated and highly correlated antennas, respectively. Through Monte Carlo simulations with different correlation $\rho$ and user speeds, we validate our analysis and show that the proposed scheme outperforms CCE when $\rho \geq 0.3$ , while it provides comparable performance for small $\rho$ .

The scale of wireless technologies penetration in our daily lives, primarily triggered by the Internet-of-things (IoT)-based smart cities, is beaconing the possibilities of novel localization and tracking techniques. Recently, low-power wide-area network (LPWAN) technologies have emerged as a solution to offer scalable wireless connectivity for smart city applications. LoRa is one such technology that provides energy efficiency and wide-area coverage. This article explores the use of intelligent machine learning techniques, such as support vector machines, spline models, decision trees, and ensemble learning, for received signal strength indicator (RSSI)-based ranging in LoRa networks, on a training dataset collected in two different environments: indoors and outdoors. The suitable ranging model is then used to experimentally evaluate the accuracy of localization and tracking using trilateration in the studied environments. Later, we present the accuracy of LoRa-based positioning system (LPS) and compare it with the existing ZigBee, WiFi, and Bluetooth-based solutions. In the end, we discuss the challenges of satellite-independent tracking systems and propose future directions to improve accuracy and provide deployment feasibility., Comment: 7 pages, 5 figures, 1 table

A new wireless era beckons, giving rise to novel communication techniques to support the services and demands foreseen for the coming decades. One such revolutionizing technique intended to enable the Internet of Things (IoT) is backscatter communication. Simply employing backscatter communication may not be enough to efficiently connect the massive number of devices in the IoT network. To achieve this feat, non-orthogonal multiple access (NOMA) techniques have been merged with backscatter communications. Although NOMA-enabled backscatter communication is expected to significantly improve the low-powered IoT system, the benefits come with several challenges. In this article, we show that NOMA-enabled backscatter communication has the potential to connect a large number of IoT devices in a battery-free manner. To begin, this article provides the basics on backscatter communication and NOMA techniques. Next, a taxonomy and gap analysis of the studies on backscatter communication is provided. Then novel use cases for NOMA-enabled backscatter communication are detailed, and a case study for smart farming using the overall data rate of backscatter sensor devices employing power-domain NOMA is presented. Finally, we discuss some interesting and potential research challenges to the realization of massive IoT networks using NOMA-enabled backscatter communication.

Underwater sensor networks (UWSNs) play a significant role in ocean exploration and monitoring by gathering information and routing it to the ground stations. Therefore, the design of efficient routing protocols is necessary as it guarantees robust and reliable data delivery from the source nodes to the destination nodes. However, the effects of underwater environment, dynamic nature of the channel for acoustic, radio and optical waves, and harsh underwater noise conditions, make the routing protocol design a challenging task. This article overviews various issues which are faced in designing the routing protocols and summarizes the popular routing protocols for UWSNs alongside their pros and cons. More specifically, the routing protocols are characterized into three main categories; localization-based, localization-free and cooperative schemes. The performance of these protocols are discussed with respect to various network parameters such as energy consumption, network lifetime, delay, reliability, and communication overheads. Towards the end, new directions for potential research in improving routing protocols performance in UWSNs are highlighted.

본 논문은 YOLO(You Only Look Once)와 깊이 카메라를 이용한 차량과 보행자 간 사고방지 시스템을 제안한다. 딥러닝 기반의 YOLOv2-tiny를 이용한 차선과 보행자의 인식정보를 기반으로 보행자가 차도로 들어오는 상황에서 사용자에게 사전에 위기 상황을 알려준다. 기존의 관련 연구에서 사용한 이미지 처리 방식은 알고리즘이 복잡하므로 제안하는 시스템은 딥러닝을 이용하여 시스템 복잡성을 개선하였다. 또한, 딥러닝을 이용하여 실시간으로 물체 인식을 이용해 차선과 보행자를 동시에 인식했다. 그리고 점선을 이용하여 현재 주행 중인 차선을 확인하였다. 보행자와 사용자 간의 거리를 계산하기 위해 깊이 카메라를 사용하였으며 사고 상황 인식 알고리즘을 개발하였다. 본 시스템은 2차선을 기준으로 자동차 주행 시 보행자 충돌 사고를 중점으로 구현하였다. 마지막으로 실험 결과를 통해 제안한 시스템의 인식 정확도를 확인할 수 있으며 향후 연구 방향에 대해 논의한다.

In this paper, we consider a unmanned aerial vehicle (UAV)-enabled multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system for a smart city environment, where a multi-antenna UAV acts as an amplify-and-forward (AF) relay to extend the coverage to several disconnected users aided by NOMA. We present an optimization problem that jointly determines the location of the UAV, optimal beamforming at the UAV and power allocation at the base station (BS), in order to maximize the system sum-rate. Due to the severe non-convexity of the problem, we decouple the problem into three sub-problems and solve them sequentially. First, the optimal location of the UAV is determined by minimizing the total path loss. Next, the UAV beamforming is solved by transforming the problem into second-order cone programming (SOCP) and finally the power allocation at the BS is determined using linear-fractional programming (LFP). The simulation results demonstrate that the proposed scheme performs better than the baseline schemes and orthogonal multiple access (OMA) scheme.

With the increase of connected Internet-of-things (IoT) devices, the need for low-power wide-area networks (LP-WANs) is imminent, and LoRaWAN is one such technology that offers an elegant solution to the problem of long-range communication and battery consumption. A parameter of special interest in LoRaWANis the spreading factor (SF), and it is often assumed that communication between different SFs is independent of each other. However, this claim has been practically debunked by many works, proving that SFs have imperfect orthogonality. To maximize connectivity and throughput, several techniques have been introduced, such as non-orthogonal-multiple-access (NOMA) and dynamic resource allocation. NOMA is getting a lot of attention recently, especially for IoT networks, because it embraces interference and tries to obtain desired information packets from corrupted ones. Furthermore, NOMA can be easily implemented on the base-station side by using the principle of successive interference cancellation (SIC). In this paper, we investigate how SIC, under the assumption of imperfect orthogonality of SF channels, can be used to increase the performance of the system. We find the expressions for success and coverage probability considering various SF allocation schemes and found the most efficient scheme for different scenarios.

As the number of mobile devices grows exponentially, it is critical to design a robust access scheme that can handle a large number of devices with low latency. Non-orthogonal multiple access (NOMA) is an effective approach to meet such requirements that can provide higher spectral efficiency. As an extended version, cooperative NOMA has been introduced, where the users with higher channel gains forward signals for users with weak channel gains as cooperators. The conventional cooperative NOMA (CCN) technique can provide diversity gains, which can be used to improve reliability. However, in CCN, the overhead caused by successive interference cancellations (SIC) at each user may become significant, when there exist a massive number of users, which eventually degrades the performance of NOMA considering the power and computational resource limitations of mobile devices. As an alternative, the space-time block-coded NOMA (STBC-NOMA) has been proposed to achieve diversity gain with lower SIC overhead compared to CCN. To better evaluate it, in this paper, we investigate the impact of imperfect channel state information (ipCSI), which is more realistic in real-time processing. We dervie the closed-form expression of the outage probability of STBC-NOMA with ipCSI and compare its performance with CCN and conventional NOMA.

Device-to-device (D2D) communications is the integral part of public safety (PS) communications. However, the most important requirement is the users’ rapid discovery to timely communicate the emergency information. The existing half-duplex (HD) results in large time delays because it cannot transceive during same time slot. In-band full duplex (IB-FD) communications is an enabler to quickly discover the user because of simultaneous transmission and reception during same time-frequency block. In this paper, we proposed the frame structure for IB-FD system with prioritized PS users in resource allocation. Moreover, to improve spectral efficiency, time-efficient device discovery resource allocation (TE-DDRA) scheme has been proposed as well, where a user can switch the transmission mode from HD to IB-FD when the demand exceeds the available resources in HD mode. The proposal is validated by system-level simulations in terms of discovery time with conventional random access mode. The simulation results shows that with PS priority mode around 37% discovery time as compared with random access mode.

Receiver complexity is an important performance metric in space time block coding (STBC). After comprehensive study and analysis of STBCs over complex domain, quaternion orthogonal designs (QODs) have been explored that can provide less decoder complexity. However, some researchers argue that existing maximum likelihood (ML) decoding rule for QODs does not yield optimal performance for quasi-orthogonal STBC-based QODs and, therefore, the design of optimal decoder for such designs remains a big challenge of this field. To counter this observation, we modify the system model slightly. In addition to this, we present a new generalized QOD construction that can generate QODs for any number of transmit antennas. These codes are quasi-orthogonal in complex domain and provide code rates higher than their respective complex orthogonal designs (CODs). We show that the proposed ML decoder provides optimal linear decoupled decoding solution for these codes because of their distinctive construction. Simulation results verify the optimal performance of the ML decoder for the proposed codes and also show that the diversity of the proposed quaternion-based codes is same as CODs, however, they provide better code rates.