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Power Control for Reliable M2M Communication
Published in Hongjian Sun, Chao Wang, Bashar I. Ahmad, From Internet of Things to Smart Cities, 2017
For the random channel, it is impossible to guarantee 100% packet delivery. We use packet delivery rate or outage rate to measure link reliability. For Rayleigh fading, the distribution is an exponential function as we discussed in the previous section and is easy to analyze. Most analytical models assume that channel fading follows the Rayleigh fading model. We can obtain a closed form of outage probability [21] Oi=1-∏j≠i11+βiGijPjGiiPi $$ O_{i} = 1 - \mathop \prod \limits_{{j \ne i}}^{{}} \frac{1}{{1 + \frac{{\beta _{i} G_{{ij}} P_{j} }}{{G_{{ii}} P_{i} }}}} $$
Introduction to MIMO Systems
Published in Brijesh Kumbhani, Rakhesh Singh Kshetrimayum, MIMO Wireless Communications over Generalized Fading Channels, 2017
Brijesh Kumbhani, Rakhesh Singh Kshetrimayum
where ri is the signal received at the ith receiving antenna, xi is the symbol transmitted through the ith transmitting antenna and hij is the complex channel coefficient of the wireless link between ith receive antenna and jth transmit antenna. Channel modeling is an essential requirement to analyze various performance metrics of wireless communication systems. In most of the cases, it is assumed that the received signal is a collection of many multipath components generated as a result of reflections/diffraction/scattering from various obstacles in the path between transmitter and receiver. As a result, the real and imaginary part of the channel can be modeled as Gaussian distributed. The magnitude of gain of such channels follows Rayleigh distribution. Therefore, such channels are known as Rayleigh fading channels. Various generalized channel models are discussed in the next chapter.
MIMO for WirelessMAN
Published in Yan Zhang, Hsiao-Hwa Chen, Mobile Wimax, 2007
Xiaopeng Fan, Steven Y. Lai, Yuan Zheng, Jiannong Cao
In the above scenario, the channel model can be modeled as the flat Rayleigh fading channel. We assume that the channel coefficients are zero-mean i.i.d. complex Gaussian random variables with variance of 1/2 per dimension (real and imaginary). Hence each channel coefficient has a Rayleigh distributed magnitude and uniformly distributed phase. The expected value of the squared magnitude equals one (i.e., E{|hi,j2|=1}). In all the following sections, channel coefficients are assumed to be known at the receiver, but unknown at the transmitter. Thus the transmitted power per TX antenna is assumed to be identical and equals Ptj=PnT, for j = 1, …, nT.
Probabilistic stability of power control systems with Nakagami-m fading channels
Published in International Journal of Systems Science, 2023
It is noteworthy that many existing works focus on power control systems with fixed channels rather than shadowed fading channels. However, wireless channels experience fast fading due to multi-path propagation in power transfer networks. The channel attenuation can be characterised by Rayleigh fading model if the received signal is composed of scattered and reflected waves from various directions. In Moutsinas et al. (2019), the stability issue of the FM algorithm with Rayleigh fading model was studied and the probability of the system being stable with respect to parameters of Rayleigh distribution was given. One deficiency of the Rayleigh distribution is that it cannot model multi-path fading with a direct line-of-sight (LOS) path in suburban areas. Moreover, it was shown in P. M. Shankar (2017) that Rayleigh fading model can be regarded as a special class of Nakagami-m fading model. The Nakagami-m fading model in power control systems receives few attention, which motivates our work.
Performance analysis of joint transmit antenna selection and user scheduling for massive MIMO systems
Published in Cogent Engineering, 2021
Fikreselam Gared Mengistu, Gizachew Worku
In this work, we consider a multi-user massive MIMO system with total M antennas at the BS and K users which are served at the same time by the available antennas. The transmission channel model from antenna to user is assumed to be Rayleigh fading so that the channel gain is a Rayleigh distributed random variable.
A sleep scheduling based cooperative data transmission for wireless sensor network
Published in International Journal of Electronics, 2022
Yogesh Tripathi, Arun Prakash, Rajeev Tripathi
It is assumed that the wireless channel follows the Rayleigh fading. The fading channel coefficient is |hij| and link between two nodes suffers with the white Gaussian noise (nij). It is also assumed that attenuation follows the exponential path-loss model with an exponent γ (Mansourkiaie & Ahmed, 2015).