Why is MIMO Fading Capacity Higher than AWGN Capacity

From linear algebra we know that to find four unknowns we need four independent equations. There is no way we can find the values of  A, B, C and D from the above equations. To simplify the above equations we have removed AWGN but even in presence of AWGN we will have the same predicament. This shows that in the absence of fading there is no multiplexing gain however high the Signal to Noise Ratio is (in the above example SNR is infinite).

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MIMO, SIMO and MISO Capacity in AWGN and Fading Environment

In a previous post we had discussed MIMO capacity in a fading environment and compared it to AWGN capacity. It sometimes feels unintuitive that fading capacity can be higher than AWGN capacity. If a signal is continuously fluctuating how is it possible that we are able to have reliable communication. But this is the remarkable feature of MIMO systems that they are able to achieve blazing speeds over an unreliable channel, at least theoretically.

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Fundamentals of Direction of Arrival Estimation

Direction of Arrival (DOA) estimation is a fundamental problem in communications and signal processing with application in cellular communications, radar, sonar etc. It has become increasingly important in recent times as 5G communications uses DOA to spatially separate the users resulting in higher capacity and throughput. Direction of Arrival estimation can be thought of as the converse of beamforming.

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Near Field of an Antenna

The Electromagnetic Radiation from an antenna, particularly dipole antenna, has been studied in great detail. The mathematical framework proposed by Maxwell has stood the test of time and theoretical concepts have been verified through physical measurements. But the behavior of Electromagnetic (EM) waves close to the radiating antenna is not that well understood. This region that extends to about a wavelength from the antenna is called Near Field, as opposed to Far Field, which extends further out.

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Reconfigurable Intelligent Surfaces Explained

Transmit or receive beamforming has been around for a long time and it works like magic. But there is a down side to it; antenna arrays require multiple RF chains which can be a power hungry and expensive solution. What if instead of aligning the copies at the transmitter or receiver we do it while the signal is under transmission in the channel; this is achieved through a Reconfigurable Intelligent Surface (RIS).

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Fundamentals of a Circular Array – Mathematical Model and Code

Array Factor and Element Factor In the previous post we discussed the case of a Square Array which is a special case of a Rectangular Array. The code we shared can handle both the cases as well as Uniform Linear Array. We did briefly talk about the response of an element vs the response of an array, but we did not put forward the mathematical relationship. So here it is: Response of an Array = Array Factor x Element Factor In this post as well as previous posts we have assumed the element response to be isotropic (or at least […]

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Fundamentals of a Rectangular Array – Mathematical Model and Code

Background In the previous few posts we discussed the fundamentals of Uniform Linear Arrays (ULAs), Beamforming, Multiuser Detection and Massive MIMO ([1], [2], [3], [4]). Now we turn our attention to more complicated array structures such as rectangular, triangular and circular. We still assume each element of the array to have an isotropic or omni-directional (in the plane of the array) radiation pattern. The mathematical models for more complicated radiation patterns are an extension of  the what is developed here. Square and Rectangular Arrays In this post we consider a square array which is a special case of rectangular array.  […]

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Massive MIMO Fundamentals and Code

Background Just like different frequency bands and time slots can be used to multiplex users, spatial domain can also be exploited to achieve the same result. It is well known that if there are 4 transmit antennas and 4 receive antennas then four simultaneous data streams can be transmitted over the air. This can be scaled up to 8 x 8 or in the extreme case to 128 x 128. When the number of transmit or receive antennas is greater than 100 we typically call it a Massive MIMO scenario and we need specialized signal processing techniques to handle this […]

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