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Wireless Signal Propagation
Published in Mahbub Hassan, Wireless and Mobile Networking, 2022
In free space without any absorbing or reflecting objects, the path loss depends on the distance as well as on the frequency (or wavelength) according to the following Frii’s law: PR=PTGTGR(λ4πd)2=PTGTGR(c4πfd)2 where PR and PT are the received and transmitted powers (in Watts), respectively, while GT and GR are transmitter and receiver antenna gains in linear scale, respectively. We see that, for a given frequency, path loss increases as inverse square of distance, which is sometimes referred to as the d−2 law (path loss exponent = 2). It is also observed that path loss increases as inverse square of the frequency, which means that the signal power attenuates more rapidly for higher frequency signals, and vice versa.
MIMO Antenna Designs with Diversity Techniques for LTE Applications
Published in Leeladhar Malviya, Rajib Kumar Panigrahi, M.V. Kartikeyan, MIMO Antennas for Wireless Communication, 2020
Leeladhar Malviya, Rajib Kumar Panigrahi, M. V. Kartikeyan
Multipath fading, path loss, and shadowing are experienced in wireless and mobile devices. Fading and path loss are caused due to multiple reflection of signals from obstacles. Better spectral efficiency, solution of fading, gain, channel capacity, data rate, better QOS in NLOS environments are offered in modern MIMO antennas as compared to SISO antennas [241]. Current extension of MIMO technology can be seen in the form of stacking of MIMO antennas i.e. Massive MIMO. Massive MIMO is an amazing application and great solution to the exponential growth of portable and wireless devices [242]. Wireless communication requires perfect channel models/coding, and fast signal processing approaches [243]. Base stations and mobile stations all are modernized with latest MIMO antennas for the better services to mobile and wireless users [244, 245, 246].
Smart Antenna System Architecture and Hardware Implementation
Published in Lal Chand Godara, Handbook of Antennas in Wireless Communications, 2018
At first glance, the simplest way to increase the capacity of a cellular mobile network is to increase the spectral bandwidth, but this is not always the most feasible or economical solution, because of strict governments regulations and the high cost of acquiring new spectral bandwidths. One must also consider the higher path loss associated with higher frequency bands. Another possible solution is to further subdivide the cells, but a reduction in cell size is also accompanied by an increase in infrastructure cost, which may not be justifiable. Alternatively, one can optimize the overall receiver design by using more advanced signal processing techniques, including more complex adaptive time-domain equalizers, but it appears that only limited improvement may be expected in the near future. Other solutions include, among other things, adopting lower rate codec. However, a very low rate codec may not function well in the harsh mobile environment, with relatively high bit-error rates, especially when high mobility is also required. Thus, the search for the optimum solution goes on.
Distributed non-fragile set-membership filtering for nonlinear systems under fading channels and bias injection attacks
Published in International Journal of Systems Science, 2021
Lei Liu, Lifeng Ma, Jie Zhang, Yuming Bo
Wireless communication is based on the propagation of electromagnetic waves in space to achieve information transmission. On the one hand, during the process of electromagnetic wave propagation from the transmitting point to the receiving point, the energy diffusion of electromagnetic wave will cause the path loss. On the other hand, the electromagnetic wave may encounter obstacles (e.g. buildings and trees) in the process of propagation, and the shadow effect of electromagnetic field will be produced behind these obstacles. Due to the influence of path loss and obstacles, the received signal is weaker than the transmitted signal, which is called channel fading (Dong et al., 2015; Niu, 2020; Sun et al., 2020). Moreover, the signal transmitted in wireless sensor networks is exposed and vulnerable to attack from adversary (L. Ma, Wang, Han, et al., 2017; Z. Wang et al., 2018). Therefore, in this study, the fading phenomenon and a typical network attack behaviour (i.e. bias injection attack) are considered simultaneously, which makes the research more practical.
On the Application of IoT: Meteorological Information Display System Based on LoRa Wireless Communication
Published in IETE Technical Review, 2018
Haftu Tasew Reda, Philip Tobianto Daely, Jeevan Kharel, Soo Young Shin
Describing the link budget analysis between the transmitter and receiver as well as different propagation conditions such as the time-invariant nature of channels in mobile nodes is out of scope of this study. However, we try to demonstrate path loss through both theoretical and measurement results. In wireless channels, path loss refers to the power loss along the path between transmitter and receiver. Different propagation models for different situations considering different parameters have been discussed in the literature to predict the path loss [25–30]. We measure the path loss between transmitter and receiver using the following equation:where Pt is the transmitted power, Pr is the received power, Gt is the transmitter gain, and Gr is the receiver gain. The value of both Gt and Gr is 4.5 dBi.
LeHE-MRP: leveraging health monitoring by enhancing throughput of multi-hop routeing protocol in WBANs
Published in Journal of Medical Engineering & Technology, 2021
Subba Reddy Chavva, Ravi Sankar Sangam
We calculate path loss using distance and frequency functions. To calculate path loss take distance to sink from sensor node in the network and (standard deviation), k = 3.38 (path loss coefficient) as parameters. Figure 7 shows the different sensor nodes of path loss. LeHE-MRP, very less path loss due to the use of multi-hop transmission for construction of WBANs. Reduces distance between sink node and different sensor nodes in a network, so we get minimum path loss. LeHE-MRP, initially performed well compared to SIMPLE and M-ATTEMPT up to average 2500 rounds.