The existing sub-6 GHz band is insufficient to support the bandwidth requirement of emerging data-rate-hungry applications and Internet of Things devices, requiring ultrareliable low latency communication (URLLC), thus making the migration to millimeter-wave (mmWave) bands inevitable. A notable disadvantage of a mmWave band is the significant losses suffered at higher frequencies that may not be overcome by novel optimization algorithms at the transmitter and receiver and thus result in a performance degradation. To address this, Intelligent Reflecting Surface (IRS) is a new technology capable of transforming the wireless channel from a highly probabilistic to a highly deterministic channel and as a result, overcome the significant losses experienced in the mmWave band. This paper aims to survey the design and applications of an IRS, a 2-dimensional (2D) passive metasurface with the ability to control the wireless propagation channel and thus achieve better spectral efficiency (SE) and energy efficiency (EE) to aid the fifth and beyond generation to deliver the required data rate to support current and emerging technologies. It is imperative that the future wireless technology evolves toward an intelligent software paradigm, and the IRS is expected to be a key enabler in achieving this task. This work provides a detailed survey of the IRS technology, limitations in the current research, and the related research opportunities and possible solutions.
Abstract-Providing high data rate wireless transmissions has been difficult in indoor environments, particularly in multi-floor buildings. One way to achieve high data rate wireless transmissions is to reduce the radio transmission distance between the transmitter and the receiver by using distributed antenna systems (DASs) and employing frequency reuse. However, due to the reuse of the limited available spectrum, co-channel interference can severely degrade system capacity. In this paper, the uplink spectral efficiency of an in-building DAS with frequency reuse is studied, where remote antenna units (RAUs) deployed on each floor throughout the building are connected to a central unit (CU) where received signals are processed. The impact of co-channel interference on system performance is investigated by using a propagation channel model derived from multifloor, in-building measurement results. The proposed scheme exploits the penetration loss of the signal through the floors, resulting in frequency reuse in spatially separated floors, which increases system spectral efficiency and also reduces co-channel interference. A comparative analysis with conventional co-located antenna deployment at the floor center is provided. Location based RAU selection and deployment options are investigated. System performance is evaluated in terms of location-specific spectral efficiency for a range of potential mobile terminal (MT) locations and various in-building propagation characteristics.Index Terms-Wireless communications, spectral efficiency, distributed antenna system, frequency reuse, co-channel interference, multi-floor in-building propagation, Nakagami fading.
In this paper, an analytical framework is presented for device detection in an impulse radio (IR) ultra-wide bandwidth (UWB) system and its performance analysis is carried out. The Neyman–Pearson (NP) criteria is employed for this device-free detection. Different from the frequency-based approaches, the proposed detection method utilizes time domain concepts. The characteristic function (CF) is utilized to measure the moments of the presence and absence of the device. Furthermore, this method is easily extendable to existing device-free and device-based techniques. This method can also be applied to different pulse-based UWB systems which use different modulation schemes compared to IR-UWB. In addition, the proposed method does not require training to measure or calibrate the system operating parameters. From the simulation results, it is observed that an optimal threshold can be chosen to improve the ROC for UWB system. It is shown that the probability of false alarm, PFA, has an inverse relationship with the detection threshold and frame length. Particularly, to maintain PFA<10−5 for a frame length of 300 ns, it is required that the threshold should be greater than 2.2. It is also shown that for a fix PFA, the probability of detection PD increases with an increase in interference-to-noise ratio (INR). Furthermore, PD approaches 1 for INR >−2 dB even for a very low PFA i.e., PFA=1×10−7. It is also shown that a 2 times increase in the interference energy results in a 3 dB improvement in INR for a fixed PFA=0.1 and PD=0.5. Finally, the derived performance expressions are corroborated through simulation.
It has been observed that mobile learning (mLearning) in institutions like Museums in the United Kingdom (UK) has been underutilized. mLearning usage could potentially increase productivity by delivering just-in-time technical knowledge to the science museum group (SMG) staff. This study uses the unified theory of acceptance and use of technology (UTAUT) model to determine factors affecting mLearning adoption at the SMG. Two research questions were formulated based on an adaptation of the UTAUT model. 1) What are the determinants of behavior intentions to use mLearning at the SMG? 2) Does gender or age have a moderating effect on the factors that determine behavior intentions to use mLearning at the SMG? 118 respondents were surveyed from the SMG. Data obtained were analyzed using Structured Equation Modelling on IBM SPSS 20 and Amos version 25. Results indicate that the UTAUT constructs, performance expectancy, effort expectancy, social influence and facilitating conditions are all significant determinants of behavioral intention to use mLearning. A newly proposed construct, self-directed learning was not a significant determinant of behaviour intentions. Further examination found age and gender moderate the relationship between the UTAUT constructs. These findings present several useful implications for mLearning research and practice for ICT service desk at the SMG. The research contributes to mLearning technology adoption and strategy.
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