Reconfigurable Intelligent Surfaces (RIS) concept

Reconfigurable Intelligent Surfaces (RIS) – A Tutorial

For each generation of cellular networks, there is a significant jump in data rates due to the rising demand and novel use cases from emerging applications and associated ecosystems. Some examples in 6G networks are driverless and collaborative transportation, joint communication, localization and sensing, e-health and tactile Internet. Therefore, at the start of each concept-to-deployment cycle, engineers and researchers propose, evaluate and experiment with new ideas, preferably one or two disruptive technologies that can help them meet their targets. For 5G systems, these technologies appeared in the form of a large number of antennas (massive MIMO) and usage of higher

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Electromagnetic spectrum

On TeraHertz (THz) Band for Wireless Communication

Larger bandwidth has been the single most contributing factor in higher data rates throughout the history of wireless communication. In the past decade, this resulted in expansion towards mmWave bands that were adopted in 5G systems. Now the trend is continuing towards Tera Hz (THz) bands where large swathes of bandwidth are available for instantaneous and seamless transfer of huge amounts of information. This is because symbol rate $R_M$ is directly proportional to the bandwidth in digitally modulated signals. \[ R_M=\frac{1}{T_M} \propto B \] This is shown in the figure below where a high data rate implies a short symbol

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A composite channel model consisting of a pulse shaping filter, baseband channel and a matched filter

Channel Estimation in Wireless Communication

Channel estimation is a special case of the system identification problem that has a long history in the field of signal processing. The most common method to estimate a channel at the Rx is based on a training sequence (i.e., a data-aided scenario). The strategies below explain the fundamental idea of channel estimation in single-carrier systems that are still used by most advanced channel estimation techniques (aided by fancy mathematical modifications in subsequent steps). Channel estimation in OFDM systems is a topic of another article. System Parameters In this article, the modulation symbols are denoted by $a[m]$ while the channel

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Linear interpolation between pilot subcarriers

Channel Estimation in OFDM Systems

Channel estimation in single-carrier systems has been described in a previous article. In OFDM systems, each subcarrier acts as an independent channel as long as there is no Inter-Carrier Interference (ICI) left in the synchronized signal. The options of both a training sequence and individual pilots are available for channel estimation and the choice between the two depends on time variation rate of the channel as well as the computational complexity. Many systems acquire the channel through the preamble while employ the pilots for channel tracking. The discussion in this article is mostly based on Ref. [1]. For a simplified

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Pilot contamination problem arises by reusing the same set of pilots in different cells

What is Pilot Contamination in Massive MIMO?

5G NR standard supports both Frequency Division Duplex (FDD) and Time Division Duplex (TDD) modes in massive MIMO systems. For a reasonably pure estimate, it is necessary to make sure that each pilot transmission in a cell occurs in a vacuum, i.e., free from the interference of other pilots in the same time or frequency. This is achieved through orthogonality or separation of training signals in time or frequency slots. As we see now, simple orthogonality is not enough and new problems emerge due to the interaction among different cells in a network. Uplink A set of orthogonal pilots in

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